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Title: A deep neural network to de-noise single-cell RNA sequencing data | Body:
I.
Introduction
Recent single-cell RNA sequencing (scRNA-seq) technologies provide invaluable insight into gene expression at individual cell levels. However, they often result in many genes displaying zero expression levels. These zero values can arise from two distinct events: (i) biological events, referred to as structural zeros [1], when a gene expresses no RNA at the time of the experiment, and (ii) non-biological events, also known as dropout events [1]–[3], stemming from the insufficiencies in library preparation and amplification processes [4], [5]. Non-biological zeros occur due to various factors in the employed technology. These factors can include mRNA degradation after cell lysis, variability in converting mRNA to cDNA, inconsistencies in amplification efficiency, cell library concentration or sequencing depth, and other batch effects [3].
Unique Molecular Identifiers (UMIs) are a valuable tool in rectifying amplification biases in non-zero gene expression measurements by identifying and eliminating reads from cDNA duplicates generated during amplification [6]. Despite their utility, UMIs cannot rectify sampling zeros and the corresponding cDNA copy numbers for these zeros remain unidentified [7].
In the context of scRNA-seq data, imputation involves inferring missing expression values or non-biological zeros. The aim is to estimate the actual expression values that were not detected due to experimental limitations. Imputation methods are often developed based on the principle that similar cells will likely have similar gene expression profiles [8], [9].
Noise in scRNA-seq data can arise from various sources, including the aforementioned technical variabilities and the inherent stochasticity of gene expression. Denoising methods strive to eliminate or reduce noise in the data by identifying and adjusting for these sources of variability, making the signal more pronounced. For instance, a common denoising approach adjusts the expression values for each gene based on the total amount of RNA detected in each cell, thereby correcting for differences in sequencing depth [10]. Statistical learning and deep learning models, such as autoencoders, are among the most powerful tools for processing complex biological data like scRNA-seq.
Imputation methods for scRNA-seq data are generally built upon probabilistic and mathematical models. They aim to replace missing values, often zeros, with estimates derived from the existing data points and their interrelationships. To infer the missing values, these methods often exploit the structure and characteristics of the data, such as the correlation patterns in the scRNA-seq data, i.e., the similarities among cells and genes.
One example is the Markov affinity-based graph imputation of cells [11] (MAGIC), which is among the most renowned statistical methods tailored for denoising and imputation of scRNA-seq data. This computational approach denoises the cell count matrix and imputes missing gene expression values. MAGIC constructs a graph where nodes represent cells and edges reflect similarities between cells, facilitating information sharing across similar cells via a diffusion process. Another notable method, SAVER [12], uses an empirical Bayes-like approach to estimate the true expression levels, leveraging expression similarities between cells to impute non-biological zeros. scImpute [8] is another method that finds and imputes highly probable dropout values using a clustering approach. This method draws on information from the same gene in similar cells to the imputed cell. A similar approach DrImpute [13], employs k-means clustering [14], [15] to identify similar cells and then imputes dropouts based on the average expression values in those cells.
Deep learning models have emerged as revolutionary tools in the last two decades due to their unique ability to solve complex problems. With the exponential growth of biological data, these models help determine patterns and draw inferences from large, complex datasets. Deep models leverage artificial neural networks with multiple hidden layers to perform downstream tasks, including feature extraction, dimension reduction, prediction, and classification. They offer novel approaches to tackle and interpret the complexity of this information, providing new insights into various areas such as genomics, proteomics, and cell biology [16]–[18]. Applications vary from predicting protein structure and detecting genetic variants to understanding gene regulatory networks and interpreting scRNA-seq data. As these models evolve, their application in biological research is expected to drive significant breakthroughs and help reveal the complex underlying mechanisms.
Notably, self-supervised models like autoencoders play a prominent role in deep learning. Deep autoencoders are explicitly designed to provide efficient data representations. First, the encoder compresses the input into a lower-dimensional representation. Next, the decoder reconstructs the original data from the compressed representation. Training an autoencoder involves minimizing the difference between the input and the output. Incorporating multiple layers in the encoder and the decoder results in deep autoencoders that can model complex data with a high-dimensional feature space. However, a challenge known as the “vanishing gradients” emerges during the backpropagation stage of training, particularly in deeper models. Because of the chain rule and multiplication of the gradients, there is a possibility that when some of the gradients are small, the multiplication would be numerically too small, leading to no significant learning in back-propagation. Techniques such as batch normalization [19] and residual connections [20] fix the vanishing gradients problem [21]. Deep autoencoders are valuable in different applications, including feature extraction, dimension reduction, and denoising.
Regardless of the denoising approach, discrete statistical distributions are needed to model the count data in scRNA-seq. Various count distributions such as negative binomial (NB), zero-inflated NB (ZINB), Poisson, and zero-inflated Poisson (ZIP) have been widely employed for modeling UMIs or gene reads in scRNA-seq [4]. For instance, the deep count autoencoder (DCA) [22], is used to denoise the scRNA-seq data using an NB model for noise. This approach characterizes the reconstruction error by evaluating the probability distribution of the noise model rather than directly reconstructing the input data. The ZINB distribution introduces a zero-inflation parameter that signifies the proportion of additional zeros, distinct from those originating from the NB component. Similarly, the ZIP distribution also incorporates a zero-inflation parameter, specifically targeted at the zeros not accounted for by the Poisson distribution. Employing a zero-inflated model versus a non-zero-inflated model does not depend on the prevalence of zeros in the data. Zero inflation is a statistical consideration that takes into account the inevitable nonbiological zeros. Zero inflation and its biological and technical validity for different sequencing methods are among the highly debated topics.
Here, we introduce ZiPo, a deep model that takes scRNA-seq data as its input and performs two tasks: (a) imputing the missing data using zero-inflated distributions, and (b) providing an embedding of the single-cell gene expression data in a low-dimensional space, offering a nonlinear transformation akin to PCA’s dimensionality reduction capabilities that can be used in downstream tasks like cell-type clustering. We introduce a regularization parameter that not only adjusts the zero-inflation level but also offers a more refined, adaptable, and biologically accurate approach for handling the intricacies of sequencing data, setting our work apart in this domain. This model can also be used for single-nucleus data, but we only use single-cell terminology in this manuscript for simplicity.
II.
Methods
In ZiPo, we first encode the high-dimensional gene expression data of the cells into a low-dimensional space (e.g., with 32 latent variables) using a deep model. Next, we consider these latent variables as unknown covariates of the scRNA-seq data and recover the expression levels using these unknown covariates together with other possibly known covariates that may influence the gene expression patterns observed in individual cells. Such covariates might include tissue or cell types, sequencing depth, spatial location, age, sex, etc. [23].
We assume a zero-inflated distribution for gene expression levels. The two most common distributions for scRNA-seq data are zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB). The results in this manuscript are based on ZIP, but they can readily apply to ZINB with minimal adjustments. We prioritized ZIP because the straightforward Poisson distribution aligns well with UMI-based sequencing data. This approach eliminates the need for ZINB’s additional parameter that might account for potentially greater dispersion in the model.
The model takes each cell’s measured gene expression vector as its input. The encoder consists of dense linear layers for reducing the dimension followed by sigmoid activation. The model is then followed by parallel layers from the encoded variables (unknown covariates) to predict:
the library size,
the zero-inflation rate, and
the parameters of the desired distribution.
A schematic representation of the model is provided in Fig. 1.
A.
Advantages of predicting the library size
We separate the library size (i.e., the sum of all gene expressions for each cell) from the other predictions because what we want to model in the zero-inflated distribution is the relative expression levels of genes in a cell, not the absolute values, which depend on less relevant variables like the sequencing quality, assay efficiency, etc. To normalize gene expression data, we can adjust raw count data using count per million (CPM) [24] to account for differences in library size or sequencing depth between samples, allowing for fair comparisons of gene expression levels across samples. In this method, the raw counts for each gene in a sample are divided by the total number of reads (or counts) in that sample. Predicting the library size offers at least two distinct advantages over calculating it via adding all gene expressions for each cell: (1) it can help reduce technical noise in the data, and (2) it retains potentially valuable information about the library size in the latent variables that can be used in subsequent analyses. Notably, the library size could convey information relevant to the cell size [25].
B.
Sparsifying of the weights
We expect each feature in the second layer to represent a pathway or an eigengene, incorporating only a small subset of the genes [26], [27]. Therefore, most of the weights of the first encoding layer should be zero or near zero, forming a sparse weight matrix. The sparsity of this layer can be enforced by adding a term to the loss function that acts similarly to an L1 penalty. Not all activation functions can produce the intended sparsity as discussed in detail in section II-D.
C.
The model structure in mathematical notation
Throughout the manuscript, we use the subscript c for any variable to indicate that it is calculated for the cell c. The input matrix is denoted as X = {xcg}, where xcg is the expression of gene g in cell c. Xc denotes the full expression vector of cell c over all available genes. For t encoding layers, the cellular embedding for cell c is formulated as:
Uc=Gt⊕…⊕G2⊕G1(Xc),
where Uc is the embedding for cell c. Each Gi is composed of a linear layer, an activation function (e.g., sigmoid), and a batch normalization layer, which helps resolve the vanishing gradients problem and accelerates the training [19], [28]. We add known covariates of the cells, Uc′, to the embedding vector Uc, yielding the complete encoded representation of the cell,
Vc=concat(Uc,Uc′).
Subsequently, optional layers are used to predict the following variables:
log(lc)=liblog(Vc),
logit(zc)=N(Vc),
log(μc)=Mlog(Vc),
θc(j)=Θj(Vc);1≤j≤p,
where l is the library size, μ is the mean parameter for the distribution, z is the zero-inflation probability, θ represents additional relevant parameters of the distribution, and p is the number of these parameters. In the case of the Poisson distribution, we only have the mean parameter, μ. For the NB distribution, we need an extra parameter representing the difference between the mean and the dispersion of gene expressions. For N, we use a dense linear layer. For Mlog, we use a dense linear layer followed by logSoftmax activation, forcing the sum of the average gene expressions for each cell to be equal to 1.
Given this configuration, the following linear relationships are established:
zc=sig(AzVc+Bz)
μ≈exp(AμVc+Bμ).
To model the library size, we use a series of dense linear layers followed by batch normalization and an activation function. We can readily employ deep models to predict distribution parameters, resulting in nonlinear rather than linear Vc functions in the above equations.
D.
The loss function
The zero-inflated distribution is given by:
Pr(x∣z,l,μ,θ)=zδ0(x)+(1−z)expf(x;m,θ),m=lμ,
where f represents the logarithm of the distribution for non-zero-inflated expression levels with mean m and possibly other parameters θ and δ0 is the atomic distribution on zero. Note that m = lμ is the expected expression level according to our definitions.
We denote the rate of zero inflation by r, i.e., r = logit(z) and the likelihood of our samples, given the distribution parameters by ℒ. The first component of the loss function is the log-likelihood of the zero-inflated distribution:
logℒ=logPr(X∣z,μ,θ)=log∑xcgPr(xcg∣z,μ,θ)=∑xcg=0log(z+(1−z)expf(0;lμ,θ))+∑xcq>0(log(1−z)+f(x;lμ,θ))=∑log(1−z)+∑xcg=0log(z1−z+expf(0;lμ,θ))+∑xcg>0f(x;lμ,θ)=∑logsig(−r)+∑xcg=0log(expr+expf(0;lμ,θ))+∑xcg>0f(x;lμ,θ)=∑logsig(−r)+∑xcg=0r−logsig(r−f(0;lμ,θ))+∑xcg>0f(x;lμ,θ).
A typical approach employed in the DCA method involves using truncated log functions by adding a tiny offset quantity to the argument of the log function to avoid infinity (or very large) numeric values when the argument is zero (or near zero). Although this approach might be appropriate in specific conditions, it is inaccurate when we have no a priori knowledge of the input argument to decide on a proper value for the offset. To prioritize precision in our calculations, instead of relying on truncated log functions, we use the composite function log sig [29], which stabilizes the numerical schema without truncation.
The ZiPo model is regularized in two ways. We apply L2 regularization to the predicted zero probabilities and other distribution parameters. The regularization hyperparameter in ZiPo, αz, controls the level of zero inflation. An excessively large αz prevents the model from using significant zero inflation. Setting αz to zero will yield a model with strong zero inflation that overestimates the expression rates. We also impose a regularization term on the weights of the first layer to sparsify them. We use this regularization term to shrink weights appearing to be less important. We then prune those relatively small weights after ensuring that removing them does not affect the model performance.
We use NLL for “negative log-likelihood” and MSE for “mean square error” throughout the manuscript.
E.
Which activation function and weight regularization approach should be used?
In deep neural networks, activation functions such as ReLU [30] or GELU [31] are commonly employed to tackle the vanishing gradients issue. However, for the encoding segment of ZiPo, we favor the sigmoid activation function. This choice is influenced by the specific type of regularization applied to the weights (parameters) of the first encoding layer.
A limitation of ReLU arises from its linear properties. That is, the model can decrease the absolute weights in the initial layer while compensating for this by increasing the weights of the second layer, without affecting the final output of the second layer and the final performance of the model. Therefore, simple L1 or L2 regularization penalties would not significantly change the model’s performance or output. Some variations of the above activation functions like ReLU6, defined as min(max(0,x),6), could potentially resolve this problem. However, we prefer the nonlinearity of the sigmoid function over the piecewise linearity of ReLU6.
Similarly, the optimizer can lower the weights of the first encoding layer but compensate for them by increasing the batch normalization weights in the following layer. To protect the first layer weights, we use a scale-invariant loss function defined by dividing L1 by L2:
−E[logℒc]+αzE[zcg2]+αwmeanimeang|Wig|(meangWig2)12+E[αθ⋅θ2],
where Wigs are the weights of the first linear layer and α’s are regularization hyperparameters determining the strength of the corresponding penalty terms (i.e., αz controls the zeroinflation rate and αw controls the weights in the first layer and αθ controls the parameters of the distribution).
If the features of the initial layer are sufficiently interpretable, the model should perform well because those features are directly related to the genes. Employing similar regularization across all layers is possible. However, introducing an additional hyperparameter for each layer complicates the model and the training process.
F.
Datasets
We have used three datasets to illustrate ZiPo performance (Table I). The ROSMAP Alzheimer’s disease single-nucleus RNA-seq (snRNA-seq) dataset was generated by Mathys et al. [32], and is available from https://www.synapse.org/ with synapse ID: syn18485175. This dataset includes 80,660 single nuclei derived from the dorsolateral prefrontal cortex of 48 postmortem human brains. The liver [33] and myeloma [34] scRNA-seq datasets are accessible through GEO with accession numbers GSE125188 and GSE117156, respectively. In the liver dataset, 70,706 human CD45+ cells from the paired liver perfusion, spleen, and human peripheral blood mononuclear cells (PBMC) were profiled. The myeloma dataset includes 51,840 cells from 29 diagnosed patients and 11 control donors.
While the distributions of library sizes (i.e., the sum of gene expression per cell) were similar across these three datasets, the total gene expression (i.e., the sum of expression levels per gene) varied (Fig. 2). We used the liver dataset to conduct hyperparameter analysis. Then, we assessed the performance of the optimized architecture in all three datasets.
G.
Model parameters
Here, we systematically investigate the hyperparameter space to identify reasonable values and provide insight for ranges that can be extensively explored using an automatic hyperparameter optimization software such as optuna package [35]. Specifically, we use two middle layers with 16 and 4 neurons to predict the library size. The Adam optimizer [36] is used with the default linear layer initialization of weights in pytorch [37]. The desired learning rate is 0.001. However, due to the relatively large library sizes, the optimizer needs large learning rates to converge quickly. Therefore, to accelerate the training process, we set the initial learning rate at 0.0212 and decrease it by a factor of 0.9 to reach 0.001 at epoch 30. If the learning process halts, i.e., no improvement of at least 0.1% in validation loss, the learning rate will be reduced automatically after 15 epochs using the class ReduceLROnPlateau of pytorch-ignite [38]. The learning process stops early if the validation loss does not improve over 30 epochs.
In our implementation, users can either manually specify the model hyperparameters or allow our program to search for the optimum settings in a predefined sampling space using the Optuna package.
H.
Model comparison
We compared the trained models based on the average loss they obtained using 12 random seeds. The error bars in the figures in the Results section represent λ times of standard error of the mean, where λ=Φ−1(0.99)/2≈1.64, where Φ is the cumulative distribution function for the normal distribution. With this choice, assuming a one-sided t test (i.e., normality of distributions and same variance), non-overlapping bars indicate that we can reject the null hypothesis with 99% confidence.
A.
Advantages of predicting the library size
We separate the library size (i.e., the sum of all gene expressions for each cell) from the other predictions because what we want to model in the zero-inflated distribution is the relative expression levels of genes in a cell, not the absolute values, which depend on less relevant variables like the sequencing quality, assay efficiency, etc. To normalize gene expression data, we can adjust raw count data using count per million (CPM) [24] to account for differences in library size or sequencing depth between samples, allowing for fair comparisons of gene expression levels across samples. In this method, the raw counts for each gene in a sample are divided by the total number of reads (or counts) in that sample. Predicting the library size offers at least two distinct advantages over calculating it via adding all gene expressions for each cell: (1) it can help reduce technical noise in the data, and (2) it retains potentially valuable information about the library size in the latent variables that can be used in subsequent analyses. Notably, the library size could convey information relevant to the cell size [25].
B.
Sparsifying of the weights
We expect each feature in the second layer to represent a pathway or an eigengene, incorporating only a small subset of the genes [26], [27]. Therefore, most of the weights of the first encoding layer should be zero or near zero, forming a sparse weight matrix. The sparsity of this layer can be enforced by adding a term to the loss function that acts similarly to an L1 penalty. Not all activation functions can produce the intended sparsity as discussed in detail in section II-D.
C.
The model structure in mathematical notation
Throughout the manuscript, we use the subscript c for any variable to indicate that it is calculated for the cell c. The input matrix is denoted as X = {xcg}, where xcg is the expression of gene g in cell c. Xc denotes the full expression vector of cell c over all available genes. For t encoding layers, the cellular embedding for cell c is formulated as:
Uc=Gt⊕…⊕G2⊕G1(Xc),
where Uc is the embedding for cell c. Each Gi is composed of a linear layer, an activation function (e.g., sigmoid), and a batch normalization layer, which helps resolve the vanishing gradients problem and accelerates the training [19], [28]. We add known covariates of the cells, Uc′, to the embedding vector Uc, yielding the complete encoded representation of the cell,
Vc=concat(Uc,Uc′).
Subsequently, optional layers are used to predict the following variables:
log(lc)=liblog(Vc),
logit(zc)=N(Vc),
log(μc)=Mlog(Vc),
θc(j)=Θj(Vc);1≤j≤p,
where l is the library size, μ is the mean parameter for the distribution, z is the zero-inflation probability, θ represents additional relevant parameters of the distribution, and p is the number of these parameters. In the case of the Poisson distribution, we only have the mean parameter, μ. For the NB distribution, we need an extra parameter representing the difference between the mean and the dispersion of gene expressions. For N, we use a dense linear layer. For Mlog, we use a dense linear layer followed by logSoftmax activation, forcing the sum of the average gene expressions for each cell to be equal to 1.
Given this configuration, the following linear relationships are established:
zc=sig(AzVc+Bz)
μ≈exp(AμVc+Bμ).
To model the library size, we use a series of dense linear layers followed by batch normalization and an activation function. We can readily employ deep models to predict distribution parameters, resulting in nonlinear rather than linear Vc functions in the above equations.
D.
The loss function
The zero-inflated distribution is given by:
Pr(x∣z,l,μ,θ)=zδ0(x)+(1−z)expf(x;m,θ),m=lμ,
where f represents the logarithm of the distribution for non-zero-inflated expression levels with mean m and possibly other parameters θ and δ0 is the atomic distribution on zero. Note that m = lμ is the expected expression level according to our definitions.
We denote the rate of zero inflation by r, i.e., r = logit(z) and the likelihood of our samples, given the distribution parameters by ℒ. The first component of the loss function is the log-likelihood of the zero-inflated distribution:
logℒ=logPr(X∣z,μ,θ)=log∑xcgPr(xcg∣z,μ,θ)=∑xcg=0log(z+(1−z)expf(0;lμ,θ))+∑xcq>0(log(1−z)+f(x;lμ,θ))=∑log(1−z)+∑xcg=0log(z1−z+expf(0;lμ,θ))+∑xcg>0f(x;lμ,θ)=∑logsig(−r)+∑xcg=0log(expr+expf(0;lμ,θ))+∑xcg>0f(x;lμ,θ)=∑logsig(−r)+∑xcg=0r−logsig(r−f(0;lμ,θ))+∑xcg>0f(x;lμ,θ).
A typical approach employed in the DCA method involves using truncated log functions by adding a tiny offset quantity to the argument of the log function to avoid infinity (or very large) numeric values when the argument is zero (or near zero). Although this approach might be appropriate in specific conditions, it is inaccurate when we have no a priori knowledge of the input argument to decide on a proper value for the offset. To prioritize precision in our calculations, instead of relying on truncated log functions, we use the composite function log sig [29], which stabilizes the numerical schema without truncation.
The ZiPo model is regularized in two ways. We apply L2 regularization to the predicted zero probabilities and other distribution parameters. The regularization hyperparameter in ZiPo, αz, controls the level of zero inflation. An excessively large αz prevents the model from using significant zero inflation. Setting αz to zero will yield a model with strong zero inflation that overestimates the expression rates. We also impose a regularization term on the weights of the first layer to sparsify them. We use this regularization term to shrink weights appearing to be less important. We then prune those relatively small weights after ensuring that removing them does not affect the model performance.
We use NLL for “negative log-likelihood” and MSE for “mean square error” throughout the manuscript.
E.
Which activation function and weight regularization approach should be used?
In deep neural networks, activation functions such as ReLU [30] or GELU [31] are commonly employed to tackle the vanishing gradients issue. However, for the encoding segment of ZiPo, we favor the sigmoid activation function. This choice is influenced by the specific type of regularization applied to the weights (parameters) of the first encoding layer.
A limitation of ReLU arises from its linear properties. That is, the model can decrease the absolute weights in the initial layer while compensating for this by increasing the weights of the second layer, without affecting the final output of the second layer and the final performance of the model. Therefore, simple L1 or L2 regularization penalties would not significantly change the model’s performance or output. Some variations of the above activation functions like ReLU6, defined as min(max(0,x),6), could potentially resolve this problem. However, we prefer the nonlinearity of the sigmoid function over the piecewise linearity of ReLU6.
Similarly, the optimizer can lower the weights of the first encoding layer but compensate for them by increasing the batch normalization weights in the following layer. To protect the first layer weights, we use a scale-invariant loss function defined by dividing L1 by L2:
−E[logℒc]+αzE[zcg2]+αwmeanimeang|Wig|(meangWig2)12+E[αθ⋅θ2],
where Wigs are the weights of the first linear layer and α’s are regularization hyperparameters determining the strength of the corresponding penalty terms (i.e., αz controls the zeroinflation rate and αw controls the weights in the first layer and αθ controls the parameters of the distribution).
If the features of the initial layer are sufficiently interpretable, the model should perform well because those features are directly related to the genes. Employing similar regularization across all layers is possible. However, introducing an additional hyperparameter for each layer complicates the model and the training process.
F.
Datasets
We have used three datasets to illustrate ZiPo performance (Table I). The ROSMAP Alzheimer’s disease single-nucleus RNA-seq (snRNA-seq) dataset was generated by Mathys et al. [32], and is available from https://www.synapse.org/ with synapse ID: syn18485175. This dataset includes 80,660 single nuclei derived from the dorsolateral prefrontal cortex of 48 postmortem human brains. The liver [33] and myeloma [34] scRNA-seq datasets are accessible through GEO with accession numbers GSE125188 and GSE117156, respectively. In the liver dataset, 70,706 human CD45+ cells from the paired liver perfusion, spleen, and human peripheral blood mononuclear cells (PBMC) were profiled. The myeloma dataset includes 51,840 cells from 29 diagnosed patients and 11 control donors.
While the distributions of library sizes (i.e., the sum of gene expression per cell) were similar across these three datasets, the total gene expression (i.e., the sum of expression levels per gene) varied (Fig. 2). We used the liver dataset to conduct hyperparameter analysis. Then, we assessed the performance of the optimized architecture in all three datasets.
G.
Model parameters
Here, we systematically investigate the hyperparameter space to identify reasonable values and provide insight for ranges that can be extensively explored using an automatic hyperparameter optimization software such as optuna package [35]. Specifically, we use two middle layers with 16 and 4 neurons to predict the library size. The Adam optimizer [36] is used with the default linear layer initialization of weights in pytorch [37]. The desired learning rate is 0.001. However, due to the relatively large library sizes, the optimizer needs large learning rates to converge quickly. Therefore, to accelerate the training process, we set the initial learning rate at 0.0212 and decrease it by a factor of 0.9 to reach 0.001 at epoch 30. If the learning process halts, i.e., no improvement of at least 0.1% in validation loss, the learning rate will be reduced automatically after 15 epochs using the class ReduceLROnPlateau of pytorch-ignite [38]. The learning process stops early if the validation loss does not improve over 30 epochs.
In our implementation, users can either manually specify the model hyperparameters or allow our program to search for the optimum settings in a predefined sampling space using the Optuna package.
H.
Model comparison
We compared the trained models based on the average loss they obtained using 12 random seeds. The error bars in the figures in the Results section represent λ times of standard error of the mean, where λ=Φ−1(0.99)/2≈1.64, where Φ is the cumulative distribution function for the normal distribution. With this choice, assuming a one-sided t test (i.e., normality of distributions and same variance), non-overlapping bars indicate that we can reject the null hypothesis with 99% confidence.
III.
Results
Our systematic investigation of the hyperparameter space starts with seeking an optimum design for the encoder and decoder, using the ROSMAP dataset.
A.
A deep enough encoder with residuals leads to better performance.
Almost half of the model variables are embedded in the first layer of the encoder. The first layer also influences the size of the other layers. Therefore, it is important to optimize the first layer. To focus on the encoder, we used a simple linear structure for the decoder, as discussed in II-C.
1)
The first layer’s size:
In a 3-layer model with 64 latent variables, we assessed the effect of the first layer size, s1, on model training time and performance (Fig. 3). The middle layer size was set to be the geometric mean of the first layer size and the number of the latent variables (i.e., s1*64). In Fig. 3 and throughout this paper, we use the s1–s2–···–sp convention to describe the number of neurons used in the structure of a model with p layers, e.g., 8192–512–64 is a model with 8192, 512, and 64 neurons in the first, second, and third layers, respectively.
With increasing s1, the average loss was reduced until s1 = 4096. The model became relatively unstable, and loss obtained a wider range with s1 = 8096 (Fig. 3a). The s1 = 4096 model had the lowest average loss, and according to error bars, was significantly better than the models with a smaller first layer, except the models with s1 = 2048. Because we are interested in smaller networks, for the rest of our experiments, we chose s1 = 2048, a smaller model than s1 = 4096, but not significantly worse in terms of the likelihood. The rationale was that smaller models require less memory and computation for training and have a better chance of interpretability.
2)
The number of latent variables:
In a 3-layer architecture with a first layer of 2048 neurons and the size of the middle layer determined by the geometric mean as described, the likelihood improved as we increased the number of latent variables on the third layer up to 128 (Fig. 4a). Subsequent increases in the number of latent variables caused no significant improvement. As a smaller quantity of latent variables is computationally beneficial (Fig. 4b), we chose 128 latent variables for our model.
3)
Deeper encoders with residuals perform better:
We investigated the effect of the encoder depth and the incorporation of residual connections from the encoder layers to the latent layer on models featuring 2048 neurons in the first layer.
Adding residual connections to the models with 3 or more layers lowered their NLL loss (Fig. 5). The deepest model with the full residual connections was the optimal model. Because adding the middle layers did not significantly affect the model size (i.e., most weights are in the first layer), we continued to use this configuration for the rest of our experiments.
B.
A simple decoder is better than a deep one.
Using the same optimal encoder structure, (i.e., 2048 neurons in the first layer, 128 latents, 1024, 512, and 256 neurons in the middle layers, respectively, equipped with full residual connections from all encoder layers to the latent layer), we trained the model with different decoder structures (Fig. 6). More complex decoders needed more epochs for training and resulted in larger NLL losses. Therefore, we chose the simple linear dense decoder described in the Methods section.
C.
Batch normalization is essential for convergence.
The effect of batch normalization on training depended on the activation function. With the GELU activation function, removing the batch-normalization layer destabilized the training process, and the optimizer did not converge. With the sigmoid activation function, removing the batch-normalization layer did not destabilize the training process, and the optimizer converged, but got stuck in local minima (Fig. 7). Either way, batch normalization was essential for training.
D.
Controlling the zero-inflation rate improves library size estimation and MSE of reconstruction.
With no regularization on zero inflation, the model overestimated their rates, leading to a much larger MSE in 3 out of 8 tests with different seeds (Fig. 8c). Also, the mean of zero probabilities was very large (Fig. 8d). With a very large zero-inflation regularization parameter (αz = 108 in Fig. 8d), the model had almost no zero inflation, the training needed more epochs, and the NLL loss was larger than the models with smaller regularization. Apart from these extreme cases, the models had stable reconstruction regarding NLL loss and MSE rates for a vast range of regularization parameters (i.e., 0.01 ≤ αz ≤ 10, 000). However, smaller regularization parameters generally led to smaller NLL losses and larger mean zero probabilities. In the following experiments, we set αz = 0.1, which appeared to be a reasonable value for the mean of zero-inflation probabilities.
E.
Sparsifying the weights through weight regularization
With αz = 0.1, we investigated the effect of sparsifying the weights in the first layer using the weight regularization parameter αw. Note that the relative weights are important for each neuron on the first layer. One can multiply all the weights of a neuron by a constant and compensate for it by changing the batch normalization coefficient.
We normalized the weights of each neuron in the first layer by dividing all weights by the maximum weight. Then, we prune the first layer weights by setting those smaller than a threshold δ to zero. We investigated the effect of this sparsification on the model performance.
With no regularization (i.e., αw = 0), more than 90% of the weights were above 0.01 (Fig. 10a). With a large regularization parameter, say αw ≥ 1, many weights were very small, making the model sensitive to pruning (Fig. 10, c and d). In the middle range (i.e., 0.01 ≤ αw ≤ 0.1), the sensitivity of MSE of rates to the pruning seemed reasonable (Fig. 10c), we had even better NLL losses compared to no regularization (Fig. 9a), and the weight loss stayed almost the same (Fig. 9d).
F.
ZINB compared to ZIP
Our experiments showed that due to the greater complexity of the NB distribution compared to the Poisson distribution, we would need to apply another regularization (although very small) on the predicted rates of the model to train it. Therefore, we added 0.1*E[(lcμc−∑gxcg)2] to the loss function. In models with ZINB, training stopped earlier than in models with ZIP. In terms of the MSE of the rates, the models with ZIP and ZINB were not significantly different (Fig. 11). However, the models with ZIP performed significantly better regarding NLL, and their first layer weights were much sparser (Fig. 12) than those with ZINB.
G.
ZiPo compared to DCA
Due to technical difficulties, we could not use the DCA package to compare the corresponding method with ZiPo. Instead, we leveraged our implementation, which is in a sense a generalization of DCA, to simulate DCA using three approaches: (1) the recommended parameters by DCA authors in our settings, (2) the Poisson distribution (zipDCA), and (3) the settings that are similar to our optimized ZiPo model (tunedDCA). For the base settings, we used a 128–64–128 structure for the number of neurons in the model, applied logarithmic transformation, and normalized the input matrix by dividing the expression of each gene by the library size of each cell.
In terms of NLL, ZiPo, and DCA performed similarly and better than other models (Fig. 13a). The simplest model, i.e., DCA, was trained faster than the others but had the largest MSE for the rates (Fig. 13b). Poisson distribution would decrease the rates’ MSE, but the NLL was not as low as ZiPo.
H.
Automatic hyperparameter optimization
We used the “Optuna” package to optimize ZiPo’s hyperparameters. Specifically, we searched five different encoder structures, 2048–1024–512–256–128, 1024–512–256–128, 1024–512–256–128–64, 512–256–128, and 512–256–128–64 with full residual connections. For the decoder, we used a simple linear model For the regularization parameter of zero inflation and the weights, we searched the range 0.01–1 in the logarithmic space. After 96 iterations, the hyper-optimizer chose the deepest model, which resulted in a relatively reasonable value for the MSE of expression rates (Fig. 14). For the best model, the regularization parameter of zero inflation was 0.10594359 and the regularization parameter of weights was 0.14182903, both of which were near our choice of 0.1.
I.
Three additional datasets
Using the same hyperparameters optimized on the liver dataset, we trained the ZiPo model on the three datasets mentioned in II-F. The ZINB distribution generally seemed more powerful due to its larger number of parameters (Fig. 15). ZINB requires fewer epochs, which means faster convergence of the optimizer, most probably because it has more parameters and can easily fit the input distribution. However, ZIP acted even better regarding NLL in cases like the Liver dataset.
A.
A deep enough encoder with residuals leads to better performance.
Almost half of the model variables are embedded in the first layer of the encoder. The first layer also influences the size of the other layers. Therefore, it is important to optimize the first layer. To focus on the encoder, we used a simple linear structure for the decoder, as discussed in II-C.
1)
The first layer’s size:
In a 3-layer model with 64 latent variables, we assessed the effect of the first layer size, s1, on model training time and performance (Fig. 3). The middle layer size was set to be the geometric mean of the first layer size and the number of the latent variables (i.e., s1*64). In Fig. 3 and throughout this paper, we use the s1–s2–···–sp convention to describe the number of neurons used in the structure of a model with p layers, e.g., 8192–512–64 is a model with 8192, 512, and 64 neurons in the first, second, and third layers, respectively.
With increasing s1, the average loss was reduced until s1 = 4096. The model became relatively unstable, and loss obtained a wider range with s1 = 8096 (Fig. 3a). The s1 = 4096 model had the lowest average loss, and according to error bars, was significantly better than the models with a smaller first layer, except the models with s1 = 2048. Because we are interested in smaller networks, for the rest of our experiments, we chose s1 = 2048, a smaller model than s1 = 4096, but not significantly worse in terms of the likelihood. The rationale was that smaller models require less memory and computation for training and have a better chance of interpretability.
2)
The number of latent variables:
In a 3-layer architecture with a first layer of 2048 neurons and the size of the middle layer determined by the geometric mean as described, the likelihood improved as we increased the number of latent variables on the third layer up to 128 (Fig. 4a). Subsequent increases in the number of latent variables caused no significant improvement. As a smaller quantity of latent variables is computationally beneficial (Fig. 4b), we chose 128 latent variables for our model.
3)
Deeper encoders with residuals perform better:
We investigated the effect of the encoder depth and the incorporation of residual connections from the encoder layers to the latent layer on models featuring 2048 neurons in the first layer.
Adding residual connections to the models with 3 or more layers lowered their NLL loss (Fig. 5). The deepest model with the full residual connections was the optimal model. Because adding the middle layers did not significantly affect the model size (i.e., most weights are in the first layer), we continued to use this configuration for the rest of our experiments.
1)
The first layer’s size:
In a 3-layer model with 64 latent variables, we assessed the effect of the first layer size, s1, on model training time and performance (Fig. 3). The middle layer size was set to be the geometric mean of the first layer size and the number of the latent variables (i.e., s1*64). In Fig. 3 and throughout this paper, we use the s1–s2–···–sp convention to describe the number of neurons used in the structure of a model with p layers, e.g., 8192–512–64 is a model with 8192, 512, and 64 neurons in the first, second, and third layers, respectively.
With increasing s1, the average loss was reduced until s1 = 4096. The model became relatively unstable, and loss obtained a wider range with s1 = 8096 (Fig. 3a). The s1 = 4096 model had the lowest average loss, and according to error bars, was significantly better than the models with a smaller first layer, except the models with s1 = 2048. Because we are interested in smaller networks, for the rest of our experiments, we chose s1 = 2048, a smaller model than s1 = 4096, but not significantly worse in terms of the likelihood. The rationale was that smaller models require less memory and computation for training and have a better chance of interpretability.
2)
The number of latent variables:
In a 3-layer architecture with a first layer of 2048 neurons and the size of the middle layer determined by the geometric mean as described, the likelihood improved as we increased the number of latent variables on the third layer up to 128 (Fig. 4a). Subsequent increases in the number of latent variables caused no significant improvement. As a smaller quantity of latent variables is computationally beneficial (Fig. 4b), we chose 128 latent variables for our model.
3)
Deeper encoders with residuals perform better:
We investigated the effect of the encoder depth and the incorporation of residual connections from the encoder layers to the latent layer on models featuring 2048 neurons in the first layer.
Adding residual connections to the models with 3 or more layers lowered their NLL loss (Fig. 5). The deepest model with the full residual connections was the optimal model. Because adding the middle layers did not significantly affect the model size (i.e., most weights are in the first layer), we continued to use this configuration for the rest of our experiments.
B.
A simple decoder is better than a deep one.
Using the same optimal encoder structure, (i.e., 2048 neurons in the first layer, 128 latents, 1024, 512, and 256 neurons in the middle layers, respectively, equipped with full residual connections from all encoder layers to the latent layer), we trained the model with different decoder structures (Fig. 6). More complex decoders needed more epochs for training and resulted in larger NLL losses. Therefore, we chose the simple linear dense decoder described in the Methods section.
C.
Batch normalization is essential for convergence.
The effect of batch normalization on training depended on the activation function. With the GELU activation function, removing the batch-normalization layer destabilized the training process, and the optimizer did not converge. With the sigmoid activation function, removing the batch-normalization layer did not destabilize the training process, and the optimizer converged, but got stuck in local minima (Fig. 7). Either way, batch normalization was essential for training.
D.
Controlling the zero-inflation rate improves library size estimation and MSE of reconstruction.
With no regularization on zero inflation, the model overestimated their rates, leading to a much larger MSE in 3 out of 8 tests with different seeds (Fig. 8c). Also, the mean of zero probabilities was very large (Fig. 8d). With a very large zero-inflation regularization parameter (αz = 108 in Fig. 8d), the model had almost no zero inflation, the training needed more epochs, and the NLL loss was larger than the models with smaller regularization. Apart from these extreme cases, the models had stable reconstruction regarding NLL loss and MSE rates for a vast range of regularization parameters (i.e., 0.01 ≤ αz ≤ 10, 000). However, smaller regularization parameters generally led to smaller NLL losses and larger mean zero probabilities. In the following experiments, we set αz = 0.1, which appeared to be a reasonable value for the mean of zero-inflation probabilities.
E.
Sparsifying the weights through weight regularization
With αz = 0.1, we investigated the effect of sparsifying the weights in the first layer using the weight regularization parameter αw. Note that the relative weights are important for each neuron on the first layer. One can multiply all the weights of a neuron by a constant and compensate for it by changing the batch normalization coefficient.
We normalized the weights of each neuron in the first layer by dividing all weights by the maximum weight. Then, we prune the first layer weights by setting those smaller than a threshold δ to zero. We investigated the effect of this sparsification on the model performance.
With no regularization (i.e., αw = 0), more than 90% of the weights were above 0.01 (Fig. 10a). With a large regularization parameter, say αw ≥ 1, many weights were very small, making the model sensitive to pruning (Fig. 10, c and d). In the middle range (i.e., 0.01 ≤ αw ≤ 0.1), the sensitivity of MSE of rates to the pruning seemed reasonable (Fig. 10c), we had even better NLL losses compared to no regularization (Fig. 9a), and the weight loss stayed almost the same (Fig. 9d).
F.
ZINB compared to ZIP
Our experiments showed that due to the greater complexity of the NB distribution compared to the Poisson distribution, we would need to apply another regularization (although very small) on the predicted rates of the model to train it. Therefore, we added 0.1*E[(lcμc−∑gxcg)2] to the loss function. In models with ZINB, training stopped earlier than in models with ZIP. In terms of the MSE of the rates, the models with ZIP and ZINB were not significantly different (Fig. 11). However, the models with ZIP performed significantly better regarding NLL, and their first layer weights were much sparser (Fig. 12) than those with ZINB.
G.
ZiPo compared to DCA
Due to technical difficulties, we could not use the DCA package to compare the corresponding method with ZiPo. Instead, we leveraged our implementation, which is in a sense a generalization of DCA, to simulate DCA using three approaches: (1) the recommended parameters by DCA authors in our settings, (2) the Poisson distribution (zipDCA), and (3) the settings that are similar to our optimized ZiPo model (tunedDCA). For the base settings, we used a 128–64–128 structure for the number of neurons in the model, applied logarithmic transformation, and normalized the input matrix by dividing the expression of each gene by the library size of each cell.
In terms of NLL, ZiPo, and DCA performed similarly and better than other models (Fig. 13a). The simplest model, i.e., DCA, was trained faster than the others but had the largest MSE for the rates (Fig. 13b). Poisson distribution would decrease the rates’ MSE, but the NLL was not as low as ZiPo.
H.
Automatic hyperparameter optimization
We used the “Optuna” package to optimize ZiPo’s hyperparameters. Specifically, we searched five different encoder structures, 2048–1024–512–256–128, 1024–512–256–128, 1024–512–256–128–64, 512–256–128, and 512–256–128–64 with full residual connections. For the decoder, we used a simple linear model For the regularization parameter of zero inflation and the weights, we searched the range 0.01–1 in the logarithmic space. After 96 iterations, the hyper-optimizer chose the deepest model, which resulted in a relatively reasonable value for the MSE of expression rates (Fig. 14). For the best model, the regularization parameter of zero inflation was 0.10594359 and the regularization parameter of weights was 0.14182903, both of which were near our choice of 0.1.
I.
Three additional datasets
Using the same hyperparameters optimized on the liver dataset, we trained the ZiPo model on the three datasets mentioned in II-F. The ZINB distribution generally seemed more powerful due to its larger number of parameters (Fig. 15). ZINB requires fewer epochs, which means faster convergence of the optimizer, most probably because it has more parameters and can easily fit the input distribution. However, ZIP acted even better regarding NLL in cases like the Liver dataset.
IV.
Discussion
We have described a novel deep model, ZiPo, for rate estimation and library size prediction in scRNA-seq data. ZiPo presents several unique features described below. Most prominently, the present model features an adjustable zero-inflation rate.
a)
Handling big data with deep models and batches:
A significant advantage of using deep models is their capability to handle massive datasets in batches. In contrast, other methods would be impractical as they would need to load the whole dataset in the memory. Also, similar to other deep models, ZiPo can be partially trained with one dataset and then fine-tuned using other datasets. This usually cannot be done easily with other methods.
b)
Software quality:
To ensure the longevity of our code and its maintenance in the long term, we have used popular software packages and modern programming techniques in implementing ZiPo. Using features like configuration files, we have provided a flexible platform for software developers and end-users.
c)
ZIP overall surpasses ZINB:
With an appropriate architecture, zero-inflation models using ZIP distribution perform well. As shown here, the models with ZINB distribution can achieve smaller losses, hence better performances than those using ZIP. However, due to the need for extra regularization in the models with ZINB, they are relatively larger with more trainable weights, their training is more complicated and time-consuming, and they are harder to interpret than those with ZIP.
d)
Latents for downstream analysis:
To assess the validity of the latent variables, we tested ZiPo for clustering the cells in the ROSMAP dataset. We compared the results with the predefined cell types. The tSNE plots [39] based on the latent variables from the two ZIP and ZINB models showed that these models generally performed well except for a few outliers (Fig. 16).
e)
Future work:
The latent variables can be investigated more thoroughly and used in downstream analyses including regressing eigengenes [40] or classifying senescent cells [41].
a)
Handling big data with deep models and batches:
A significant advantage of using deep models is their capability to handle massive datasets in batches. In contrast, other methods would be impractical as they would need to load the whole dataset in the memory. Also, similar to other deep models, ZiPo can be partially trained with one dataset and then fine-tuned using other datasets. This usually cannot be done easily with other methods.
b)
Software quality:
To ensure the longevity of our code and its maintenance in the long term, we have used popular software packages and modern programming techniques in implementing ZiPo. Using features like configuration files, we have provided a flexible platform for software developers and end-users.
c)
ZIP overall surpasses ZINB:
With an appropriate architecture, zero-inflation models using ZIP distribution perform well. As shown here, the models with ZINB distribution can achieve smaller losses, hence better performances than those using ZIP. However, due to the need for extra regularization in the models with ZINB, they are relatively larger with more trainable weights, their training is more complicated and time-consuming, and they are harder to interpret than those with ZIP.
d)
Latents for downstream analysis:
To assess the validity of the latent variables, we tested ZiPo for clustering the cells in the ROSMAP dataset. We compared the results with the predefined cell types. The tSNE plots [39] based on the latent variables from the two ZIP and ZINB models showed that these models generally performed well except for a few outliers (Fig. 16).
e)
Future work:
The latent variables can be investigated more thoroughly and used in downstream analyses including regressing eigengenes [40] or classifying senescent cells [41]. |
Title: Tamoxifen metabolites treatment promotes ERα+ transition to triple negative phenotype | Body:
Introduction
Breast cancer (BC) is the most common malignancy in women worldwide. In 2020, BC had an approximate incidence of 2 million cases and generated an estimated 685,000 deaths (Global Cancer Observatory, 2022) associated with metastatic tumors in vital organs and tissues. Breast tumors can be classified by molecular subtypes, depending on the hormonal receptor expression [1], the main subtypes are Luminal A with Estrogen Receptor alpha (ERα)/Progesterone Receptor (PR) expression and no expression of human epidermal growth factor receptor type 2 (HER2); Luminal B with ERα/PR and HER2 expression; HER2 positive with HER2 overexpression and no expression of ERα/PR, and triple-negative (TNBC) with no hormonal receptors expression [2].
Approximately 80% of breast cancer tumors are ERα+, and for these cases, the neoadjuvant election treatment for premenopausal women is the selective estrogen receptor modulators (SERMS), mainly tamoxifen. Tamoxifen is a prodrug whose plasmatic primary active metabolites, 4-hydroxytamoxifen (4OH-Tam) and 4-hydroxy-N-desmethyl-tamoxifen (endoxifen), are generated by cytochrome P450 isoenzymes families such as CYP3A4 and CYP2D6 [3]. 4OH-Tam and Endoxifen have similar potency, possibly associated with the hydroxyl group at the C4 position in their structure [4]; although, endoxifen is the most abundant plasmatic metabolite in tamoxifen-treated patients [3]. Tamoxifen and its metabolites function as ERα antagonists in breast tissue, interfering with growth and proliferation pathways in cancer cells [4].
Tamoxifen treatment has been associated with a decrease in recurrence rate by 41% and a mortality rate of up to 31% [5]; however, nearly one-third of the patients relapse due to chemoresistance [6]. Several mechanisms responsible for tamoxifen resistance have been proposed in chemoresistant tumoral ERα+ cells; one of them corresponds to overexpression of sulfotransferase 1A1 (SULT1A), a phase II metabolism enzyme that inactivates 4OH-Tam and endoxifen by sulfation mechanism [7]. Other chemoresistance mechanisms are related to the ERα; for instance, loss or down-regulation of ERα expression by mutations, epigenetic modifications (ESR1), or post-translational alterations [8]. Changes in other hormonal receptors expression could occur as chemoresistant mechanism and/or associated with disease progression [9].
Changes in hormonal receptors expression among primary and metastatic tumors are frequently reported in clinical studies [10,11]. A meta-analysis that included 39 studies described a 19.3% change in ERα status, a 30.9% change in PR status, and a 10.3% change in HER2 status in metastatic tumors; the change from positive to negative status was the most frequent in this study [12]. Other meta-analysis evaluating hormonal status in BC brain metastasis in comparison with primary tumors reported a 42.6% discordance in any receptor status and individually a 17% in ERα status, 23% in PR status, and a 12% in HER2 status [13]. Although the causes of this phenomenon must be multifactorial, the effect of dyslipidemia in patients that undergo this complication has not been studied in detail.
High low-density lipoproteins (LDL) serum levels and low high density lipoprotein (HDL) serum levels or a high c-LDL/c-HDL ratio, commonly associated to an increased cardiovascular risk [14], has been also widely associated with an increased risk of developing BC cancer and BC cancer progression [15–19]. Focusing in LDL, it has been demonstrated in vitro that LDL cholesterol promotes proliferation and migration in ERα negative cell lines, while its effect on ERα+ cell lines remains debatable [19,20]. In animal models, it has been described a relationship between high LDL and total cholesterol levels and cancer progression [19]. In vivo, plasma LDL-C levels above 117 mg/dL have been positively associated with higher histological grade, higher proliferative rate and more advanced clinical stage [21]; on the other hand, tumors overexpressing LDLR, which allow them to capture more cholesterol, correlates to a more aggressive behavior and show a greater metastasis potential [22]. Although, the molecular mechanisms triggered by LDL high concentrations have not been described in detail in several contexts.
Metabolic adaptations are commonly related to cancer progression. It is common for tumoral cells to have increased free radical synthesis and, in consequence, augmented oxidative stress due to their accelerated metabolic activity [23]. Nuclear factor erythroid-2-related factor 2 (Nrf2), a leucine zipper transcription factor described as the redox homeostasis master regulator, plays an essential role in managing oxidative stress in tumoral cells [23,24] in addition to increasing metastatic capacity [25] and potentially chemoresistance. In turn, this phenotype could be triggered by high LDL concentration.
Therefore, this study aimed to evaluate the effect of LDL on tamoxifen resistance generation in a cellular ERα+ breast cancer model. For the first time, the induced chemoresistance was reached through treatment with primary plasmatic tamoxifen-derived metabolites (4-OH Tam and endoxifen), promoting the transition to a TNBC phenotype-like. However, contrary to what was expected, this phenotype was not observed under co-incubation of metabolites with a high LDL concentration (75 µg/ml). Elucidating LDL role in ERα BC tamoxifen chemoresistance acquisition and the molecular mechanisms involved is critical to stablish the most suitable disease management and to propose new therapeutic targets.
Materials and methods
Materials
Cell culture reagents were purchased from Thermo-Fisher (Carlsbad, CA, U.S.A.), tissue culture plates and other plastic materials were obtained from Corning Inc. Tamoxifen metabolites 4OH-Tam and endoxifen were obtained from Santa Cruz Biotechnology with a purity greater than 98%. Tunicamycin (Tum) was obtained from Sigma-Aldrich. Buffers and Dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide (MTT) were obtained from Merck (Darmstadt, Germany). Monoclonal antibodies anti-Fas (1:250), anti-LDH-A (1:400), anti-PCNA (1:2500), anti-Nrf2 (1:300), anti-SNAI-1 (1:250) anti-β-actin (1:300), anti-FAK (1:300), Anti-p-FAK (1:400), anti-Ki67 (1:900), anti-HER2 (Neu) (1:200), anti-ER alpha (1:250), anti-PR (1:400), anti-HO-1 (1:500), anti-SULT1A1 (1:400), and anti-NQO1 (1:500) were obtained from Santa Cruz Biotechnology. LDLR (1:800) from Abcam. Anti-mouse secondary antibodies coupled to Horseradish peroxidase from Thermo-Fisher. Chemiluminescence detection was performed using Immobilon Western kit (Millipore, MA, U.S.A.) and X-ray film for some blots, for others Bio-Rad ChemiDoc XRS+ was used and a digital image was obtained.
Cell culture
MCF-7 cells obtained from American Type Culture Collection (ATCC), were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 50 U/ml penicillin and 50 µg/ml streptomycin and insulin (0.57 UI/ml). Cells were maintained at 37°C with 90% humidity and 5% CO2.
Chemoresistance generation
Through the development of this protocol, two chemoresistant variants were generated. MCF-7Var-H: the treatment was performed with equimolar 1 µM concentration of tamoxifen metabolites 4OH-Tam and endoxifen in culture media for 30 days. Later, the concentration was increased to 2 µM for both metabolites in culture media for 15 days. Cells were maintained under 50 nM concentration of both metabolites during cellular expansion and recovery period. MCF-7Var-I: treatment was followed as referred to above for MCF-7Var-H plus 75 µg/ml of LDL for 45 days. Cells were maintained in 50 nM concentration of both metabolites and 7.5 µg/ml of LDL for expansion. Chemoresistance acquisition in both variants was confirmed through MTT assay. In each experimental panel, parental MCF-7 cells were used as a control.
LDL isolation and fluorescent labeling
Human plasma samples were obtained from a healthy donor who signed an informed consent. The protocol was designed and carried out according to the Declaration of Helsinki and registered in the Research Ethics Committee of Facultad de Medicina Mexicali (FMM/CEI-FMM/006/2022-1). Briefly, plasma density was initially adjusted to density 1.019 g/ml by adding KBr and then centrifuged at 345,000 g for 160 min at 4°C. Later, fraction <1.019 g/ml was discarded, and the remaining plasma was adjusted to 1.053 g/mL density. It was centrifuged at 377,000 g for 200 min. Finally, <1.053 g/ml fraction was recovered, dialyzed against 150 mM NaCl/EDTA 0.024 mM, and filtered through 0.45 μm. In the LDL fraction, protein concentration was measured with the bicinchoninic acid assay (BCA), and the determination of LDL-cholesterol was carried out (Spinreact). A defined volume of buffer (NaCl 150 mM, EDTA 0.024 mM) corresponding to a specific dose was evaluated as a control for the experimentation. The quality of the isolates was evaluated by characterization of apolipoproteins apoB-100 and apoA1 (Supplementary Figure S1).
Labeling of LDL fraction was carried out with the dilC18 probe (D3911), which is incorporated into the phospholipid monolayer, through incubation of 10 µl of the probe (2 mg/ml) for each 1 mg of protein-LDL for 18 h at 37 °C, obtaining dil-LDL. The solution density was adjusted to a 1.053 g/ml and centrifuged at 377,000 g for 200 min to recover fluorescent LDL. The fraction was recovered and dialyzed against PBS. The experiments evaluated a range of 0–75 µg/ml.
dil-LDL cytometer assays
Before internalization experiments, MCF-7 and cell variant cultures at 90% confluence were incubated in a FBS-free culture medium. Two hours fasting later, cells were treated under diferent dil-LDL concentrations (0–75 µg/ml) for 24 h. Later, cells were washed twice with PBS and recovered in a 200 µl volume. Cellular characterization was performed in a Beckman-Coulter cytometer Cytoflex. Employing the PC7-A channel to record the dil-LDL fluorescence based on a previous work, 30,000 events were registered [26].
Cell viability assay
Cell viability was assessed using an MTT reduction assay. Cells were seeded into 96-well plates with a density of 20,000 cells/well and allowed to grow to 75% confluence. The DMEM-supplemented medium was replaced with Opti-MEM and incubated at 37°C for 2 h; later, several treatments were added (0.125–16 µM of 4-OH Tam or Endoxifen) and incubated for 72 h. The next step was the addition of 30 µl MTT (2.1 g/ml) in Opti-MEM and incubation at 37°C for 4 h. Formazan crystals generated by mitochondrial activity were dissolved with a lysis buffer (20% SDS, 50% N, N-dimethyl-formamide, pH 3.7). A lecture on absorbances at 590 nm was performed 12 h later. The IC50 of each metabolite was calculated with GraphPad Prism 8.
Wound-healing assay
Cells were placed in a 6-well plate under a density of 400, 000 cells/well and allowed to grow to 90–95% confluence, forming a cellular monolayer. The DMEM-supplemented medium was replaced with the DMEM experimental medium, and cells were incubated at 37°C for 2 h. Later, cells were treated with mitomycin C for 2 h to prevent cell proliferation. The monolayer was scratched with a pipette tip, washed with PBS to remove floating cells, and treatments were added. Cells were allowed to migrate for 72 h at 37°C. Each well was fixed with paraformaldehyde, stained with Blue Coomassie, and photographed using a Motic Images Plus 3.0 camera coupled to an optical inverted microscope. Results were processed using ImageJ software, and statistical analysis was calculated using GraphPad Prism 8.
Cellular lysates and Western blot analysis
Cells were seeded into 6-well plates with a density of 400,000 cells/well and allowed to reach 90–95% confluence. Next, cells were incubated under the specific treatments. Cells were washed with PBS and lysed with a protein lysis buffer supplemented with protease and phosphatase inhibitors. Sample proteins were quantified by the BCA assay. Samples (25 µg/lane) were analyzed by SDS-PAGE on 8–12% gels and further transferred to PVDF membranes. Later, membranes were blocked in TBS/0.1% tween (TBST)/5% low-fat milk at 37°C for 1 h and incubated at 4°C overnight with the corresponding primary antibody. Next, membranes were washed with TBST and incubated for 120 min at 37°C with the corresponding horseradish peroxidase-conjugated (HRP) secondary antibodies. Membranes were washed with TBST, and the HRP activity was detected with the Immobilon Western Kit (Millipore, MA, U.S.A.). Analysis of immunoblot films was made with the ImageJ software (NIH, Maryland, DC, U.S.A.).
Optical microscopy
Cells were photographed using a Motic Images Plus 3.0 camera coupled to an optical inverted microscope.
Zymography analysis
Variants and MCF-7 cells were seeded at a density of 200,000 cells/ml. Cellular cultures were incubated under 90% confluency under the specific schemes, and the extracellular medium was collected. Volumes of 40 µl non-heated conditioned medium samples were mixed with 5× sample buffer (0.313 M Tris pH 6.8, 10% SDS, 50% glycerol, and 0.05% bromophenol blue) and loaded on 8% polyacrylamide gels copolymerized with gelatin (1% w/v). Gels were rinsed twice with 2.5% Triton X-100 and then incubated in development buffer (50 mM Tris-HCl pH 7.4, 10 mM CaCl2, and 0.02% NaN3) for 40 h at 37 °C. Gels were fixed and stained with 0.25% Coomassie Brilliant Blue G-250 in 10% acetic acid and 30% methanol. Proteolytic activity was detected as clear bands against the background stain. In these experiments, β-actin as a reference for the amount of protein was used as a loading control.
Nrf2 localization
We performed the analysis of Nrf2 cellular localization. Briefly, cells were seeded on slides placed in untreated 6-well dishes (400,000 cells/well) and allowed to reach 60–70% confluence. Cells were incubated under several treatments for 24 h at 37°C. Later, cells were washed with filtered PBS and fixed with 2% paraformaldehyde/PBS. Next, cells were treated with a 0.01% triton solution for 5 min and blocked with 2% bovine albumin/PBS for 30 min. Cells were incubated with anti-Nrf2 antibody overnight at 4°C. Later, cells were washed with PBS and incubated with a FITC-coupled secondary antibody. Subsequently, counterstaining was carried out with Propidium Iodide. Finally, the cells were observed using the green and red filter in an EPI-fluorescence microscope (Axio VertA.1, Zeiss, Göttingen, Germany).
GEPIA analysis
We used the GEPIA database (http://gepia.cancer-pku.cn/about.html) to evaluate the Nrf2 role in breast cancer. We analyzed the mRNA expression levels of NFE2L2 (Nrf2 coding gene, Gene ID: 4780) and their relationship with overall survival in BC patients using GEPIA which collects information from the TCGA (The Cancer Genome Atlas) [27] and GTEx (Genotype-Tissue-Expression) [28].
Molecular docking
For molecular docking experimentation, the atomic coordinates of protein SULT1A1 (PDB ID: 4GRA) with a resolution of 2.56 Å were evaluated. The structures of tamoxifen (CID:10540-29-1), 4OH-Tam (CID: 112093-28-4) and endoxifen (CID:11002528-0) were obtained from the PubChem Database. The protein structure was prepared, waters and small molecules were removed. The ligands and protein were 3D-protonated and energy minimization was carried out by employing Molecular Operating Environment software (MOE) using default parameters (Placement: Triangle Matcher, Rescoring 1: London G, AMBER99 forcefield). Each ligand was generated up to different conformations and protein was visualized with ligand interactions implemented in MOE. Estradiol was used as a natural ligand (CHEBI: 23965), obtained from ChEBI Database.
Statistical analysis
Data are expressed as mean± SD. The IC50 and ANOVA probes were calculated in GraphPad Prism 8.
Materials
Cell culture reagents were purchased from Thermo-Fisher (Carlsbad, CA, U.S.A.), tissue culture plates and other plastic materials were obtained from Corning Inc. Tamoxifen metabolites 4OH-Tam and endoxifen were obtained from Santa Cruz Biotechnology with a purity greater than 98%. Tunicamycin (Tum) was obtained from Sigma-Aldrich. Buffers and Dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide (MTT) were obtained from Merck (Darmstadt, Germany). Monoclonal antibodies anti-Fas (1:250), anti-LDH-A (1:400), anti-PCNA (1:2500), anti-Nrf2 (1:300), anti-SNAI-1 (1:250) anti-β-actin (1:300), anti-FAK (1:300), Anti-p-FAK (1:400), anti-Ki67 (1:900), anti-HER2 (Neu) (1:200), anti-ER alpha (1:250), anti-PR (1:400), anti-HO-1 (1:500), anti-SULT1A1 (1:400), and anti-NQO1 (1:500) were obtained from Santa Cruz Biotechnology. LDLR (1:800) from Abcam. Anti-mouse secondary antibodies coupled to Horseradish peroxidase from Thermo-Fisher. Chemiluminescence detection was performed using Immobilon Western kit (Millipore, MA, U.S.A.) and X-ray film for some blots, for others Bio-Rad ChemiDoc XRS+ was used and a digital image was obtained.
Cell culture
MCF-7 cells obtained from American Type Culture Collection (ATCC), were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 50 U/ml penicillin and 50 µg/ml streptomycin and insulin (0.57 UI/ml). Cells were maintained at 37°C with 90% humidity and 5% CO2.
Chemoresistance generation
Through the development of this protocol, two chemoresistant variants were generated. MCF-7Var-H: the treatment was performed with equimolar 1 µM concentration of tamoxifen metabolites 4OH-Tam and endoxifen in culture media for 30 days. Later, the concentration was increased to 2 µM for both metabolites in culture media for 15 days. Cells were maintained under 50 nM concentration of both metabolites during cellular expansion and recovery period. MCF-7Var-I: treatment was followed as referred to above for MCF-7Var-H plus 75 µg/ml of LDL for 45 days. Cells were maintained in 50 nM concentration of both metabolites and 7.5 µg/ml of LDL for expansion. Chemoresistance acquisition in both variants was confirmed through MTT assay. In each experimental panel, parental MCF-7 cells were used as a control.
LDL isolation and fluorescent labeling
Human plasma samples were obtained from a healthy donor who signed an informed consent. The protocol was designed and carried out according to the Declaration of Helsinki and registered in the Research Ethics Committee of Facultad de Medicina Mexicali (FMM/CEI-FMM/006/2022-1). Briefly, plasma density was initially adjusted to density 1.019 g/ml by adding KBr and then centrifuged at 345,000 g for 160 min at 4°C. Later, fraction <1.019 g/ml was discarded, and the remaining plasma was adjusted to 1.053 g/mL density. It was centrifuged at 377,000 g for 200 min. Finally, <1.053 g/ml fraction was recovered, dialyzed against 150 mM NaCl/EDTA 0.024 mM, and filtered through 0.45 μm. In the LDL fraction, protein concentration was measured with the bicinchoninic acid assay (BCA), and the determination of LDL-cholesterol was carried out (Spinreact). A defined volume of buffer (NaCl 150 mM, EDTA 0.024 mM) corresponding to a specific dose was evaluated as a control for the experimentation. The quality of the isolates was evaluated by characterization of apolipoproteins apoB-100 and apoA1 (Supplementary Figure S1).
Labeling of LDL fraction was carried out with the dilC18 probe (D3911), which is incorporated into the phospholipid monolayer, through incubation of 10 µl of the probe (2 mg/ml) for each 1 mg of protein-LDL for 18 h at 37 °C, obtaining dil-LDL. The solution density was adjusted to a 1.053 g/ml and centrifuged at 377,000 g for 200 min to recover fluorescent LDL. The fraction was recovered and dialyzed against PBS. The experiments evaluated a range of 0–75 µg/ml.
dil-LDL cytometer assays
Before internalization experiments, MCF-7 and cell variant cultures at 90% confluence were incubated in a FBS-free culture medium. Two hours fasting later, cells were treated under diferent dil-LDL concentrations (0–75 µg/ml) for 24 h. Later, cells were washed twice with PBS and recovered in a 200 µl volume. Cellular characterization was performed in a Beckman-Coulter cytometer Cytoflex. Employing the PC7-A channel to record the dil-LDL fluorescence based on a previous work, 30,000 events were registered [26].
Cell viability assay
Cell viability was assessed using an MTT reduction assay. Cells were seeded into 96-well plates with a density of 20,000 cells/well and allowed to grow to 75% confluence. The DMEM-supplemented medium was replaced with Opti-MEM and incubated at 37°C for 2 h; later, several treatments were added (0.125–16 µM of 4-OH Tam or Endoxifen) and incubated for 72 h. The next step was the addition of 30 µl MTT (2.1 g/ml) in Opti-MEM and incubation at 37°C for 4 h. Formazan crystals generated by mitochondrial activity were dissolved with a lysis buffer (20% SDS, 50% N, N-dimethyl-formamide, pH 3.7). A lecture on absorbances at 590 nm was performed 12 h later. The IC50 of each metabolite was calculated with GraphPad Prism 8.
Wound-healing assay
Cells were placed in a 6-well plate under a density of 400, 000 cells/well and allowed to grow to 90–95% confluence, forming a cellular monolayer. The DMEM-supplemented medium was replaced with the DMEM experimental medium, and cells were incubated at 37°C for 2 h. Later, cells were treated with mitomycin C for 2 h to prevent cell proliferation. The monolayer was scratched with a pipette tip, washed with PBS to remove floating cells, and treatments were added. Cells were allowed to migrate for 72 h at 37°C. Each well was fixed with paraformaldehyde, stained with Blue Coomassie, and photographed using a Motic Images Plus 3.0 camera coupled to an optical inverted microscope. Results were processed using ImageJ software, and statistical analysis was calculated using GraphPad Prism 8.
Cellular lysates and Western blot analysis
Cells were seeded into 6-well plates with a density of 400,000 cells/well and allowed to reach 90–95% confluence. Next, cells were incubated under the specific treatments. Cells were washed with PBS and lysed with a protein lysis buffer supplemented with protease and phosphatase inhibitors. Sample proteins were quantified by the BCA assay. Samples (25 µg/lane) were analyzed by SDS-PAGE on 8–12% gels and further transferred to PVDF membranes. Later, membranes were blocked in TBS/0.1% tween (TBST)/5% low-fat milk at 37°C for 1 h and incubated at 4°C overnight with the corresponding primary antibody. Next, membranes were washed with TBST and incubated for 120 min at 37°C with the corresponding horseradish peroxidase-conjugated (HRP) secondary antibodies. Membranes were washed with TBST, and the HRP activity was detected with the Immobilon Western Kit (Millipore, MA, U.S.A.). Analysis of immunoblot films was made with the ImageJ software (NIH, Maryland, DC, U.S.A.).
Optical microscopy
Cells were photographed using a Motic Images Plus 3.0 camera coupled to an optical inverted microscope.
Zymography analysis
Variants and MCF-7 cells were seeded at a density of 200,000 cells/ml. Cellular cultures were incubated under 90% confluency under the specific schemes, and the extracellular medium was collected. Volumes of 40 µl non-heated conditioned medium samples were mixed with 5× sample buffer (0.313 M Tris pH 6.8, 10% SDS, 50% glycerol, and 0.05% bromophenol blue) and loaded on 8% polyacrylamide gels copolymerized with gelatin (1% w/v). Gels were rinsed twice with 2.5% Triton X-100 and then incubated in development buffer (50 mM Tris-HCl pH 7.4, 10 mM CaCl2, and 0.02% NaN3) for 40 h at 37 °C. Gels were fixed and stained with 0.25% Coomassie Brilliant Blue G-250 in 10% acetic acid and 30% methanol. Proteolytic activity was detected as clear bands against the background stain. In these experiments, β-actin as a reference for the amount of protein was used as a loading control.
Nrf2 localization
We performed the analysis of Nrf2 cellular localization. Briefly, cells were seeded on slides placed in untreated 6-well dishes (400,000 cells/well) and allowed to reach 60–70% confluence. Cells were incubated under several treatments for 24 h at 37°C. Later, cells were washed with filtered PBS and fixed with 2% paraformaldehyde/PBS. Next, cells were treated with a 0.01% triton solution for 5 min and blocked with 2% bovine albumin/PBS for 30 min. Cells were incubated with anti-Nrf2 antibody overnight at 4°C. Later, cells were washed with PBS and incubated with a FITC-coupled secondary antibody. Subsequently, counterstaining was carried out with Propidium Iodide. Finally, the cells were observed using the green and red filter in an EPI-fluorescence microscope (Axio VertA.1, Zeiss, Göttingen, Germany).
GEPIA analysis
We used the GEPIA database (http://gepia.cancer-pku.cn/about.html) to evaluate the Nrf2 role in breast cancer. We analyzed the mRNA expression levels of NFE2L2 (Nrf2 coding gene, Gene ID: 4780) and their relationship with overall survival in BC patients using GEPIA which collects information from the TCGA (The Cancer Genome Atlas) [27] and GTEx (Genotype-Tissue-Expression) [28].
Molecular docking
For molecular docking experimentation, the atomic coordinates of protein SULT1A1 (PDB ID: 4GRA) with a resolution of 2.56 Å were evaluated. The structures of tamoxifen (CID:10540-29-1), 4OH-Tam (CID: 112093-28-4) and endoxifen (CID:11002528-0) were obtained from the PubChem Database. The protein structure was prepared, waters and small molecules were removed. The ligands and protein were 3D-protonated and energy minimization was carried out by employing Molecular Operating Environment software (MOE) using default parameters (Placement: Triangle Matcher, Rescoring 1: London G, AMBER99 forcefield). Each ligand was generated up to different conformations and protein was visualized with ligand interactions implemented in MOE. Estradiol was used as a natural ligand (CHEBI: 23965), obtained from ChEBI Database.
Statistical analysis
Data are expressed as mean± SD. The IC50 and ANOVA probes were calculated in GraphPad Prism 8.
Results
ERα+ cells internalize LDL
We focused on the LDL endocytosis capability of the ERα+ cell model (MCF-7 cells). LDL isolation was performed by the KBr ultracentrifugation method (Figure 1A) and validated through the evaluation of protein targets apoA-1 and apoB-100 on LDL enriched fraction, but also in VLDL, HDL enriched fractions and in plasma-free fraction. Results verified the appropriate enrichment of LDL in the desired fraction (Supplementary Figure S1). The yields in the LDL fraction were characterized by protein determination (0.76 ± 0.13 µg/µl) and LDL-cholesterol quantification (1.190 ± 0.39 mg/dL). LDL was labeled with a fluorescent dil-C18 probe, obtaining dil-LDL particles based on previous reports [29,30] (Section 2.4). Then, MCF-7 cells were treated for 24 h with dil-LDL (0–75 µg/ml) and evaluated by flow cytometry, with results suggesting MCF-7 cell internalization (Figure 1B).
Figure 1
LDL internalization and chemoresistance induction in MCF-7 cells
(A) Scheme corresponding to LDL isolation by KBr method. (B) Evaluation of dil-LDL internalization on parental MCF-7 cells (0, 7.5, 75 µg/ml) through flow-cytometry. (C) Scheme of chemoresistance protocol for generation of MCF-7Var-H and MCF-7Var-I cell variants. (A) and (C) were created in BioRender.com.
For chemoresistance induction, for the first time, MCF-7 cultures were incubated under an equimolar ratio of 4OH-Tam and endoxifen, the primary plasmatic tamoxifen-derived metabolites. In this regard, we generated two cellular variants (Figure 1C); for the variant denominated MCF-7Var-H, we treated MCF7 cells with 1 µM of 4OH-Tam and 1 µM Endoxifen for a 30-day period; the metabolites concentration was increased to 2 µM and treatment was continued for 15 days. Later, cell cultures remained in a proliferation and recovery period for 45 days with 50 nM of metabolites (Figure 1C). For MCF-7Var-I, cells were treated with 1 µM of 4-OH Tam and 1 µM Endoxifen for a 30-day period and concomitant 75 µg/ml concentration of LDL. Metabolite concentration was increased to 2 µM, and treatment was continued for 15 days; LDL concentration remained unchanged. After treatment was finished, cells remained in a proliferation and recovery period for 45 days with 50 nM of metabolites plus 7.5 µg/ml of LDL (Figure 1C).
Chemoresistance induction by tamoxifen’s metabolites
Later, we determined the chemoresistance through half-inhibitory concentration (IC50) for 4OH-Tam and endoxifen on cellular variants regarding parental cells, using the MTT assay. A range of 0.125–8 μM metabolite concentration was employed and treatments were maintained for 72 h (Figure 2B,C). The IC50 values of 4OH-Tam (7.64 µM) and endoxifen (7.85 µM) were higher for MCF-7Var-H than for the parental cells, whose values were 6.16 and 5.33 µM, respectively (Figure 2B,C).
Figure 2
Chemoresistance evaluation in MCF-7 derived cellular variants
(A) Tamoxifen metabolites chemical structures and in vivo synthesis representation by CYP isoenzymes, structures were generated with ChemDraw. Cell viability for 4OH-Tam (B) and Endoxifen (C) evaluated by MTT assay in a metabolite range of 0.25–8 µM. Treatments were performed during 72 h. Inserts showed values of inhibitory concentration 50 (IC50) in cellular variants.
On the other hand, MCF-7Var-I showed IC50 values of 12.36 and 5.7 µM for 4OH-Tam and endoxifen, respectively (Figure 2B,C), a different behavior from the observed one in MCF-7Var-H. For the first time, we reported the chemoresistance acquisition under equimolar concentrations of the main pharmacological active tamoxifen-derived metabolites and the influence of high LDL concentrations (75 µg/ml).
Important hormonal receptor down-regulation associated with chemoresistance in MCF7VarH cells
Several clinical studies have described the association between chemoresistance acquisition and the down-regulation of hormonal receptors in breast tumors [11,31]. Then, hormonal receptor status in chemoresistant cellular variants was evaluated. We analyzed the expression of ERα, HER2, and progesterone receptor (PR). A significant down-regulation in the three hormonal receptor expression levels in the MCF7VarH was registered (Figure 3A). However, MCF7VarI, obtained by co-incubation of MCF-7 cells with tamoxifen’s metabolites and LDL, did not display these drastic changes (Figure 3A). Considering that the 4-OH Tam and endoxifen major pharmacological effects are related to ERα inhibition, we decided to evaluate the receptor levels in cellular variants under treatment with tamoxifen metabolites (8 µM), a standard concentration according to IC50 values. Interestingly, down-regulation of ERα remains in MCF7VarH cells even under 4OH-Tam and endoxifen treatment, and its effect on MCF-7 and MCF7VarI appears to be not statistically significant (Figure 3B,C).
Figure 3
Chemoresistance is associated with a transition of ER+ to TNBC like phenotype
(A) Evaluation of hormonal receptor expression, ERα, PR and HER2 by western blot. β-actin was used as a loading control in parental, MCF-7Var-H, and MCF-7Var-I cells. (B) ERα expression under 4OH-Tam and endoxifen treatment (8 µM) in three cellular variants. (C) Densitometry analysis of ERα; results are reported as mean ± SD (n = 3); ***P<0.001 regarding respective control. (D) Expression of PCNA in variants MCF-7, MCF-7Var-H, and MCF-7Var-I cells. (E) Semiquantitative analysis of PCNA in three cellular variants. Optical microscopy images corresponding to MCF-7 (F), MCF-7Var-H (G), and MCF-7Var-I cells (H), images were taken at low cellular density with 40× objective.
As a complementary characterization, proliferation cell markers were evaluated. Expression levels of PCNA related to cell division were higher in MCF-7Var-H and MCF-7Var-I cells with regard to MCF-7 parental (Figure 3D,E). Likewise, we observed an important morphological change in MCF-7Var-H, these cells acquired a mesenchymal-like morphology distinct to epithelial morphology of MCF-7 and MCF-7Var-I cells (Figure 3F–H). The down-regulation of hormonal receptors, PCNA levels, and the mesenchymal morphology led us to suggest the induction of TNBC-like phenotype in MCF-7Var-H variant.
MCF-7Var-H cells showed enhanced migratory capability in vitro and augmented metalloproteinase secretion
TNBC tumors are associated with aggressive behavior and a poorer prognosis than other molecular subtypes. Then, we decided to evaluate the metastatic potential in resistant cellular variants, mainly in MCF-7Var-H, to determine the potential acquisition of TNBC-like features. First, we assessed migration capability through wound healing assay (Figure 4A), and we found that MCF-7Var-H showed high migratory capability to the wound area under FBS 10% stimuli. Meanwhile, neither MCF-7 nor MCF-7Var-I showed migratory capacity under the evaluated conditions (Figure 4A,B). We identified in wound healing assay a mesenchymal morphology in MCF-7Var-H migrated cells, mainly on the migration fronts (Figure 4A), probably associated with rearrangements in their cytoskeleton.
Figure 4
Characterization of cell migration and invasion capability in chemoresistant cellular variants
(A) Representative images of the wound-healing assay in MCF-7, MCF-7Var-H, and MCF-7Var-I cells under FBS stimuli (10%) and respective controls. In an amplified image of cells, lamellipodia and filopodia were observed in MCF-7VarH migrating cells, indicated by a red arrow. (B) Wound-healing results expressed as percentage respect to controls in parental, MCF-7Var-H, and MCF-7Var-I cells. Mean values are presented (n=3, mean ± SD); **P<0.005 respect to MCF-7 control. (C) Under tamoxifen-derived metabolites treatment (8 µM), evaluation of FAK activation (p-FAKY397) by Western blot. β-Actin was used as a loading control. (D) Representative image of MMP-9 activity in extracellular media of cellular variants, under 4-OHTam and endoxifen treatment (8 µM). (E) Statistic evaluation of MMP-9 activity in extracellular media of three biological replicates, ***P<0.0001 with respect to controls.
In aggressive tumors, the Focal Adhesion Kinase (FAK) pathway is often hyperactivated, promoting stromal remodeling and inducing tissue stiffness, cell proliferation, survival, and chemoresistance [32]. Then, we evaluated FAK expression and activation (p-FAKY397); data suggest FAK activation (p-FAKY397) in MCF-7Var-H (Figure 4C), wherein the data corroborate a potential greater malignancy in MCF-7Var-H compared to MCF-7Var-I and parental cells. Even more, our previous evidence showed that FAK pathway activation was associated with chemoresistance in TNBC cells [26]
In a complementary way, we evaluated the extracellular matrix degradation capability of the chemoresistant variants and parental cells, a phenomenon associated with metastasis processes [33]. In the first focus, matrix metallopeptidase 9 (MMP-9) activity was evaluated in the extracellular media of cellular variants. In an important way, we observed a high activity of MMP-9-mediated collagen degradation only in MCF-7Var-H cells (Figure 4D,E); this phenomenon was not affected by 4OH-Tam or endoxifen treatments (8 µM) (Figure 4D), a concentration close to the IC50 for both metabolites. This phenomenon suggests a high invasive capability in the MCF-7Var-H despite pharmacological treatment. Even more, it has been described that metabolic adaptations can occur, for instance, in targets involved in lipoprotein metabolism, associated with an increase in tumor aggressiveness [34].
Metabolic evaluation and the LDL role in chemoresistance acquisition
We evaluated LDL internalization in cellular variants after 7.5 µg/ml dil-LDL treatment (24 h). Results suggest a slightly higher capability for LDL endocytosis in MCF-7Var-H (97.12%) cells in comparison with MCF-7 (80.46%) (Figure 5A). Indeed, the lowest levels of LDL internalization were recorded in variant MCF-7Var-I (7.82%) (Figure 5B). In an attempt to explain this behavior, we determined the LDLR expression. We observed a correspondence of LDL internalization capability and LDLR expression in chemoresistant variants (Figure 5C,D).
Figure 5
Characterization of lipid metabolism in chemoresistant variants
LDL internalization in MCF-7Var-H (A) and MCF-7Var-I (B) regarding MCF-7 parental cells. Cell cultures were dil-LDL (7.5 µg/l) treated for 24 h. C) Expression of the protein targets LDLR and FAS in MCF-7, MCF-7Var-H and MCF-7Var-I cells. Densitometry analysis of LDLR (D) and FAS (E), mean values are presented (n=3, mean ± SD) and expressed as % of control, *P<0.01. β-Actin was used as a loading control.
A down-regulation trend in Fatty Acid Synthase (FAS) was registered in MCF-7Var-H as well as an increase in the MCF-7Var-I, suggesting a potential different rearrangement in lipid metabolism in both variants (Figure 5C,E). Other metabolic changes related to chemoresistance could take place; it is common for chemoresistant tumoral cells to have an enhanced antioxidant and detoxification capacity [23]; this response may be related to the activation of specific transcriptional factors.
Overexpression of transcription factor Nrf2 could be associated with phenotypic changes
Nrf2, a transcription factor involved in the management of cellular redox conditions [35], has been associated with chemoresistance and metastasis [36,37], and could be related to changes in hormone receptor status (personal communication). Critically, we found a Nrf2 overexpression in MCF-7Var-H cells (Figure 6A) and this behavior was not influenced by tamoxifen-derived metabolites treatment (Figure 6A). Likewise, our data suggest an overexpression of a lower molecular weight (45 kDa) Nrf2 isoform in MCF-7Var-H, and this phenomenon is also not influenced by metabolites treatment (Figure 6A). Lower molecular weight Nrf2 bands have been previously reported in vitro [38–40] and in vivo in pulmonary tumors [39]; however, in breast cancer cells have not been reported.
Figure 6
Overexpression of Nrf2 could be associated with chemoresistance induced through treatment with tamoxifen derived metabolites in MCF-7Var-H
(A) Western blot of Nrf2 expression in variants MCF-7, MCF-7Var-H, and MCF-7Var-I under tamoxifen derived metabolites treatment (8 μM). β-Actin was used as a loading control. (B) Localization of Nrf2 in MCF-7, MCF-7Var-H, and MCF-7Var-I cells. Images showed nuclei stained with propidium iodide (PI) (red), FITC-Nrf2 (green) and the merge. Scale bar corresponds to 50 µm. (C) Characterization of the expression of HO-1, NQO1 and SULT1A1 in MCF-7, MCF-7Var-H, and MCF-7Var-I cells under the tamoxifen metabolites treatment (8 µM). β-Actin was used as a loading control. (D) Overall survival plot for low and high expression of NFE2L2 (Nrf2 coding gene) in BC patients from GEPIA.
In a complementary way, we characterized the Nrf2 cellular localization; we employed the monoclonal Nrf2 antibody and FITC coupled secondary antibody, and nuclear colocalization was determined by the use of propidium iodide. Results suggest nuclear and cytoplasmic Nrf2 localization in MCF-7Var-H cells, compared with MCF-7Var-I and parental cells (Figure 6B) wherein its localization is mostly in cytoplasm. Several controls were used in these experiments (Section 2.11). Therefore, results correspond with data of protein expression (Figure 6A).
In order to broaden the impact of Nrf2 on chemoresistance development, we characterized protein targets dependent on Nrf2 activity. In this case, we found a differential expression of Nrf2 most representative targets such as heme oxygenase-1 (HO-1) and NAD(P)H dehydrogenase [quinone] 1 (NQO1) (Figure 6C). In MCF-7Var-H cells, HO-1 was overexpressed while NQO1 was down-regulated compared with MCF-7 cells. For its part, in MCF-7Var-I HO-1 and NQO1 expression levels remain similar to those in MCF-7, despite tamoxifen-derived metabolites treatment (8 µM). This phenomenon suggests the presence of a specific antioxidant profile adapted to cell survival in chemoresistant MCF-7Var-H cells (Figure 6C).
Subsequently, we evaluated the expression of SULT1A1, an enzyme whose expression is influenced by Nrf2 activity and has been related to 4OH-Tam and endoxifen inactivation by a sulfate group addition reaction [41]. Contrary to what was expected, a SULT1A1 down-regulation was observed in MCF-7Var-H cells (Figure 6C; Supplementary Figure S3), this response is not influenced by metabolites treatment (8 µM) in any of the variants. In complementary way, we performed a molecular docking characterization to evaluate SULT1A1 affinity for tamoxifen metabolites in comparison with estradiol, one of its known target molecules, founding a lower affinity (Supplementary Figure S4). Data suggest Nrf2 regulates the expression of necessary genes in the MCF-7Var-H cells based on a specific gene regulation condition, suggesting a specific adaptive cellular response.
Considering the potential implications of Nrf2 overexpression, we assessed the role of Nrf2 in breast cancer patient’s prognosis databases. Therefore, we evaluated the role of Nrf2 expression levels in breast cancer patients using the GEPIA database (http://gepia.cancer-pku.cn/about.html). The data showed a marked relationship between high expression of Nrf2 and a lower overall survival (Figure 6D).
The activation of the Nrf2 pathway may be related to chemoresistance development and augmented metastasis-related processes in the MCF-7Var-H cells, even more so with the transition toward a TNBC phenotype. In this regard, we registered an increment in Nrf2 expression dependent on the concentration of Tunicamycin (Tuni) (0–4 µg/ml) in the cellular variants (Supplementary Figure S5A,C), a compound that inhibits protein N-glycosylation in the endoplasmic reticulum (ER) [42], triggering the unfolded protein response (UPR) and activating Nrf2 [43]. Tuni concentrations as low as 0.25 μg/ml could upregulate Nrf2. Subsequently, in the evaluation of hormonal receptors ERα and HER2 expression in the cellular variants, we observed an inverse relationship among Tuni dose and hormonal receptor expression (Supplementary Figure S5B,D).
ERα+ cells internalize LDL
We focused on the LDL endocytosis capability of the ERα+ cell model (MCF-7 cells). LDL isolation was performed by the KBr ultracentrifugation method (Figure 1A) and validated through the evaluation of protein targets apoA-1 and apoB-100 on LDL enriched fraction, but also in VLDL, HDL enriched fractions and in plasma-free fraction. Results verified the appropriate enrichment of LDL in the desired fraction (Supplementary Figure S1). The yields in the LDL fraction were characterized by protein determination (0.76 ± 0.13 µg/µl) and LDL-cholesterol quantification (1.190 ± 0.39 mg/dL). LDL was labeled with a fluorescent dil-C18 probe, obtaining dil-LDL particles based on previous reports [29,30] (Section 2.4). Then, MCF-7 cells were treated for 24 h with dil-LDL (0–75 µg/ml) and evaluated by flow cytometry, with results suggesting MCF-7 cell internalization (Figure 1B).
Figure 1
LDL internalization and chemoresistance induction in MCF-7 cells
(A) Scheme corresponding to LDL isolation by KBr method. (B) Evaluation of dil-LDL internalization on parental MCF-7 cells (0, 7.5, 75 µg/ml) through flow-cytometry. (C) Scheme of chemoresistance protocol for generation of MCF-7Var-H and MCF-7Var-I cell variants. (A) and (C) were created in BioRender.com.
For chemoresistance induction, for the first time, MCF-7 cultures were incubated under an equimolar ratio of 4OH-Tam and endoxifen, the primary plasmatic tamoxifen-derived metabolites. In this regard, we generated two cellular variants (Figure 1C); for the variant denominated MCF-7Var-H, we treated MCF7 cells with 1 µM of 4OH-Tam and 1 µM Endoxifen for a 30-day period; the metabolites concentration was increased to 2 µM and treatment was continued for 15 days. Later, cell cultures remained in a proliferation and recovery period for 45 days with 50 nM of metabolites (Figure 1C). For MCF-7Var-I, cells were treated with 1 µM of 4-OH Tam and 1 µM Endoxifen for a 30-day period and concomitant 75 µg/ml concentration of LDL. Metabolite concentration was increased to 2 µM, and treatment was continued for 15 days; LDL concentration remained unchanged. After treatment was finished, cells remained in a proliferation and recovery period for 45 days with 50 nM of metabolites plus 7.5 µg/ml of LDL (Figure 1C).
Chemoresistance induction by tamoxifen’s metabolites
Later, we determined the chemoresistance through half-inhibitory concentration (IC50) for 4OH-Tam and endoxifen on cellular variants regarding parental cells, using the MTT assay. A range of 0.125–8 μM metabolite concentration was employed and treatments were maintained for 72 h (Figure 2B,C). The IC50 values of 4OH-Tam (7.64 µM) and endoxifen (7.85 µM) were higher for MCF-7Var-H than for the parental cells, whose values were 6.16 and 5.33 µM, respectively (Figure 2B,C).
Figure 2
Chemoresistance evaluation in MCF-7 derived cellular variants
(A) Tamoxifen metabolites chemical structures and in vivo synthesis representation by CYP isoenzymes, structures were generated with ChemDraw. Cell viability for 4OH-Tam (B) and Endoxifen (C) evaluated by MTT assay in a metabolite range of 0.25–8 µM. Treatments were performed during 72 h. Inserts showed values of inhibitory concentration 50 (IC50) in cellular variants.
On the other hand, MCF-7Var-I showed IC50 values of 12.36 and 5.7 µM for 4OH-Tam and endoxifen, respectively (Figure 2B,C), a different behavior from the observed one in MCF-7Var-H. For the first time, we reported the chemoresistance acquisition under equimolar concentrations of the main pharmacological active tamoxifen-derived metabolites and the influence of high LDL concentrations (75 µg/ml).
Important hormonal receptor down-regulation associated with chemoresistance in MCF7VarH cells
Several clinical studies have described the association between chemoresistance acquisition and the down-regulation of hormonal receptors in breast tumors [11,31]. Then, hormonal receptor status in chemoresistant cellular variants was evaluated. We analyzed the expression of ERα, HER2, and progesterone receptor (PR). A significant down-regulation in the three hormonal receptor expression levels in the MCF7VarH was registered (Figure 3A). However, MCF7VarI, obtained by co-incubation of MCF-7 cells with tamoxifen’s metabolites and LDL, did not display these drastic changes (Figure 3A). Considering that the 4-OH Tam and endoxifen major pharmacological effects are related to ERα inhibition, we decided to evaluate the receptor levels in cellular variants under treatment with tamoxifen metabolites (8 µM), a standard concentration according to IC50 values. Interestingly, down-regulation of ERα remains in MCF7VarH cells even under 4OH-Tam and endoxifen treatment, and its effect on MCF-7 and MCF7VarI appears to be not statistically significant (Figure 3B,C).
Figure 3
Chemoresistance is associated with a transition of ER+ to TNBC like phenotype
(A) Evaluation of hormonal receptor expression, ERα, PR and HER2 by western blot. β-actin was used as a loading control in parental, MCF-7Var-H, and MCF-7Var-I cells. (B) ERα expression under 4OH-Tam and endoxifen treatment (8 µM) in three cellular variants. (C) Densitometry analysis of ERα; results are reported as mean ± SD (n = 3); ***P<0.001 regarding respective control. (D) Expression of PCNA in variants MCF-7, MCF-7Var-H, and MCF-7Var-I cells. (E) Semiquantitative analysis of PCNA in three cellular variants. Optical microscopy images corresponding to MCF-7 (F), MCF-7Var-H (G), and MCF-7Var-I cells (H), images were taken at low cellular density with 40× objective.
As a complementary characterization, proliferation cell markers were evaluated. Expression levels of PCNA related to cell division were higher in MCF-7Var-H and MCF-7Var-I cells with regard to MCF-7 parental (Figure 3D,E). Likewise, we observed an important morphological change in MCF-7Var-H, these cells acquired a mesenchymal-like morphology distinct to epithelial morphology of MCF-7 and MCF-7Var-I cells (Figure 3F–H). The down-regulation of hormonal receptors, PCNA levels, and the mesenchymal morphology led us to suggest the induction of TNBC-like phenotype in MCF-7Var-H variant.
MCF-7Var-H cells showed enhanced migratory capability in vitro and augmented metalloproteinase secretion
TNBC tumors are associated with aggressive behavior and a poorer prognosis than other molecular subtypes. Then, we decided to evaluate the metastatic potential in resistant cellular variants, mainly in MCF-7Var-H, to determine the potential acquisition of TNBC-like features. First, we assessed migration capability through wound healing assay (Figure 4A), and we found that MCF-7Var-H showed high migratory capability to the wound area under FBS 10% stimuli. Meanwhile, neither MCF-7 nor MCF-7Var-I showed migratory capacity under the evaluated conditions (Figure 4A,B). We identified in wound healing assay a mesenchymal morphology in MCF-7Var-H migrated cells, mainly on the migration fronts (Figure 4A), probably associated with rearrangements in their cytoskeleton.
Figure 4
Characterization of cell migration and invasion capability in chemoresistant cellular variants
(A) Representative images of the wound-healing assay in MCF-7, MCF-7Var-H, and MCF-7Var-I cells under FBS stimuli (10%) and respective controls. In an amplified image of cells, lamellipodia and filopodia were observed in MCF-7VarH migrating cells, indicated by a red arrow. (B) Wound-healing results expressed as percentage respect to controls in parental, MCF-7Var-H, and MCF-7Var-I cells. Mean values are presented (n=3, mean ± SD); **P<0.005 respect to MCF-7 control. (C) Under tamoxifen-derived metabolites treatment (8 µM), evaluation of FAK activation (p-FAKY397) by Western blot. β-Actin was used as a loading control. (D) Representative image of MMP-9 activity in extracellular media of cellular variants, under 4-OHTam and endoxifen treatment (8 µM). (E) Statistic evaluation of MMP-9 activity in extracellular media of three biological replicates, ***P<0.0001 with respect to controls.
In aggressive tumors, the Focal Adhesion Kinase (FAK) pathway is often hyperactivated, promoting stromal remodeling and inducing tissue stiffness, cell proliferation, survival, and chemoresistance [32]. Then, we evaluated FAK expression and activation (p-FAKY397); data suggest FAK activation (p-FAKY397) in MCF-7Var-H (Figure 4C), wherein the data corroborate a potential greater malignancy in MCF-7Var-H compared to MCF-7Var-I and parental cells. Even more, our previous evidence showed that FAK pathway activation was associated with chemoresistance in TNBC cells [26]
In a complementary way, we evaluated the extracellular matrix degradation capability of the chemoresistant variants and parental cells, a phenomenon associated with metastasis processes [33]. In the first focus, matrix metallopeptidase 9 (MMP-9) activity was evaluated in the extracellular media of cellular variants. In an important way, we observed a high activity of MMP-9-mediated collagen degradation only in MCF-7Var-H cells (Figure 4D,E); this phenomenon was not affected by 4OH-Tam or endoxifen treatments (8 µM) (Figure 4D), a concentration close to the IC50 for both metabolites. This phenomenon suggests a high invasive capability in the MCF-7Var-H despite pharmacological treatment. Even more, it has been described that metabolic adaptations can occur, for instance, in targets involved in lipoprotein metabolism, associated with an increase in tumor aggressiveness [34].
Metabolic evaluation and the LDL role in chemoresistance acquisition
We evaluated LDL internalization in cellular variants after 7.5 µg/ml dil-LDL treatment (24 h). Results suggest a slightly higher capability for LDL endocytosis in MCF-7Var-H (97.12%) cells in comparison with MCF-7 (80.46%) (Figure 5A). Indeed, the lowest levels of LDL internalization were recorded in variant MCF-7Var-I (7.82%) (Figure 5B). In an attempt to explain this behavior, we determined the LDLR expression. We observed a correspondence of LDL internalization capability and LDLR expression in chemoresistant variants (Figure 5C,D).
Figure 5
Characterization of lipid metabolism in chemoresistant variants
LDL internalization in MCF-7Var-H (A) and MCF-7Var-I (B) regarding MCF-7 parental cells. Cell cultures were dil-LDL (7.5 µg/l) treated for 24 h. C) Expression of the protein targets LDLR and FAS in MCF-7, MCF-7Var-H and MCF-7Var-I cells. Densitometry analysis of LDLR (D) and FAS (E), mean values are presented (n=3, mean ± SD) and expressed as % of control, *P<0.01. β-Actin was used as a loading control.
A down-regulation trend in Fatty Acid Synthase (FAS) was registered in MCF-7Var-H as well as an increase in the MCF-7Var-I, suggesting a potential different rearrangement in lipid metabolism in both variants (Figure 5C,E). Other metabolic changes related to chemoresistance could take place; it is common for chemoresistant tumoral cells to have an enhanced antioxidant and detoxification capacity [23]; this response may be related to the activation of specific transcriptional factors.
Overexpression of transcription factor Nrf2 could be associated with phenotypic changes
Nrf2, a transcription factor involved in the management of cellular redox conditions [35], has been associated with chemoresistance and metastasis [36,37], and could be related to changes in hormone receptor status (personal communication). Critically, we found a Nrf2 overexpression in MCF-7Var-H cells (Figure 6A) and this behavior was not influenced by tamoxifen-derived metabolites treatment (Figure 6A). Likewise, our data suggest an overexpression of a lower molecular weight (45 kDa) Nrf2 isoform in MCF-7Var-H, and this phenomenon is also not influenced by metabolites treatment (Figure 6A). Lower molecular weight Nrf2 bands have been previously reported in vitro [38–40] and in vivo in pulmonary tumors [39]; however, in breast cancer cells have not been reported.
Figure 6
Overexpression of Nrf2 could be associated with chemoresistance induced through treatment with tamoxifen derived metabolites in MCF-7Var-H
(A) Western blot of Nrf2 expression in variants MCF-7, MCF-7Var-H, and MCF-7Var-I under tamoxifen derived metabolites treatment (8 μM). β-Actin was used as a loading control. (B) Localization of Nrf2 in MCF-7, MCF-7Var-H, and MCF-7Var-I cells. Images showed nuclei stained with propidium iodide (PI) (red), FITC-Nrf2 (green) and the merge. Scale bar corresponds to 50 µm. (C) Characterization of the expression of HO-1, NQO1 and SULT1A1 in MCF-7, MCF-7Var-H, and MCF-7Var-I cells under the tamoxifen metabolites treatment (8 µM). β-Actin was used as a loading control. (D) Overall survival plot for low and high expression of NFE2L2 (Nrf2 coding gene) in BC patients from GEPIA.
In a complementary way, we characterized the Nrf2 cellular localization; we employed the monoclonal Nrf2 antibody and FITC coupled secondary antibody, and nuclear colocalization was determined by the use of propidium iodide. Results suggest nuclear and cytoplasmic Nrf2 localization in MCF-7Var-H cells, compared with MCF-7Var-I and parental cells (Figure 6B) wherein its localization is mostly in cytoplasm. Several controls were used in these experiments (Section 2.11). Therefore, results correspond with data of protein expression (Figure 6A).
In order to broaden the impact of Nrf2 on chemoresistance development, we characterized protein targets dependent on Nrf2 activity. In this case, we found a differential expression of Nrf2 most representative targets such as heme oxygenase-1 (HO-1) and NAD(P)H dehydrogenase [quinone] 1 (NQO1) (Figure 6C). In MCF-7Var-H cells, HO-1 was overexpressed while NQO1 was down-regulated compared with MCF-7 cells. For its part, in MCF-7Var-I HO-1 and NQO1 expression levels remain similar to those in MCF-7, despite tamoxifen-derived metabolites treatment (8 µM). This phenomenon suggests the presence of a specific antioxidant profile adapted to cell survival in chemoresistant MCF-7Var-H cells (Figure 6C).
Subsequently, we evaluated the expression of SULT1A1, an enzyme whose expression is influenced by Nrf2 activity and has been related to 4OH-Tam and endoxifen inactivation by a sulfate group addition reaction [41]. Contrary to what was expected, a SULT1A1 down-regulation was observed in MCF-7Var-H cells (Figure 6C; Supplementary Figure S3), this response is not influenced by metabolites treatment (8 µM) in any of the variants. In complementary way, we performed a molecular docking characterization to evaluate SULT1A1 affinity for tamoxifen metabolites in comparison with estradiol, one of its known target molecules, founding a lower affinity (Supplementary Figure S4). Data suggest Nrf2 regulates the expression of necessary genes in the MCF-7Var-H cells based on a specific gene regulation condition, suggesting a specific adaptive cellular response.
Considering the potential implications of Nrf2 overexpression, we assessed the role of Nrf2 in breast cancer patient’s prognosis databases. Therefore, we evaluated the role of Nrf2 expression levels in breast cancer patients using the GEPIA database (http://gepia.cancer-pku.cn/about.html). The data showed a marked relationship between high expression of Nrf2 and a lower overall survival (Figure 6D).
The activation of the Nrf2 pathway may be related to chemoresistance development and augmented metastasis-related processes in the MCF-7Var-H cells, even more so with the transition toward a TNBC phenotype. In this regard, we registered an increment in Nrf2 expression dependent on the concentration of Tunicamycin (Tuni) (0–4 µg/ml) in the cellular variants (Supplementary Figure S5A,C), a compound that inhibits protein N-glycosylation in the endoplasmic reticulum (ER) [42], triggering the unfolded protein response (UPR) and activating Nrf2 [43]. Tuni concentrations as low as 0.25 μg/ml could upregulate Nrf2. Subsequently, in the evaluation of hormonal receptors ERα and HER2 expression in the cellular variants, we observed an inverse relationship among Tuni dose and hormonal receptor expression (Supplementary Figure S5B,D).
Discussion
High LDL levels have been associated with BC development and progression in many in vitro [18], in vivo [19], and clinical studies [44]; however, the potential LDL impact on chemoresistance in ER+ BC cells has not been described clearly. We developed two chemoresistant cellular variants by co-incubation of ER+ MCF7 cells with endoxifen and 4-OH tam. Although several protocols have been described for tamoxifen resistance generation in MCF-7 cells [45] it is the first time that chemoresistance is achieved under an equimolar treatment scheme of the two primary plasmatic tamoxifen’s active metabolites. MCF-7Var-I was obtained by maintaining high LDL concentrations (75 µg/ml) in culture media during chemoresistance acquisition period, and MCF-7Var-H was obtained with metabolites treatment only (Figure 1C, Section 2.3). This strategy was designed to evaluate the LDL effect on chemoresistance development, simulating the mechanisms triggered in ER+ breast cancer patients with high LDL plasma levels during tamoxifen treatment.
We determined chemoresistance acquisition by MTT cell viability assays in MCF-7Var-H and MCF-7Var-I by comparing their IC50 metabolites values with those of MCF-7 parental cells (Figure 2B,C). Among differences observed in cellular variants, one of the most noticeable was that MCF-7Var-H showed a significant knockdown in the hormonal receptors ERα and HER2. In contrast, although not significant, MCF-7Var-I showed a slight increase in the expression of ERα and HER2 compared to MCF-7 cells (Figure 3A–C).
MCF-7Var-H apparently showed diminished estradiol (E2)-ERα mediated signaling. Furthermore, another target of E2 is the G protein-coupled receptor 30 (GPR30) [46]; our data suggest a knockdown in GPR30 expression on MCF-7Var-H (personal communication), providing evidence that in these chemoresistance cells the response mediated by E2 is down-regulated.
Moreover, MCF-7Var-H showed enhanced proliferation corroborated by PCNA (Figure 3D,E) and Ki67 (Supplementary Figure S2) by Western blot compared with MCF-7Var-I and MCF-7. Another visible difference was the morphology, MCF-7Var-H acquired a mesenchymal-like phenotype, while MCF-7Var-I conserved an epithelial one (Figure 3F–H). Those results led us to propose the similarity of MCF-7Var-H variant with TNBC cells, which are commonly related to a basal phenotype and have a greater metastatic capability compared with ER+ BC cells [47]. To corroborate this proposal, we evaluated some processes associated with metastasis in vitro, such as migration capability and MMP-9 activity in extracellular media. As expected, MCF-7Var-H had an increased migratory capability compared with MCF-7Var-I and MCF-7 (Figure 4A,B). Indeed, the mesenchymal morphology of MCF-7Var-H cells at the wound edge was evident. In addition, only MCF-7Var-H metalloproteinase secretion was detected in zymography, which was not affected by treatment with Tamoxifen metabolites (Figure 4D,E). Therefore, MCF-7Var-H acquired a TNBC like phenotype, while MCF-7VarI maintained its luminal cell behavior and characteristics (Figure 7).
Figure 7
Phenotypic differences between MCF-7Var-H and MCF-7Var-I
MCF-7Var-H (left side) generated by treating the MCF-7 cell line with equimolar concentrations of 4-OH Tam and Endoxifen showed decreased hormonal receptors, increased migration capacity, and increased MMP-9 secretion, features similar to those observed in TNBC. Our proposed mechanism involves an increased Nrf2 expression and activity favored by metabolites ROS generation. We suggest that Nrf2 activity promotes proteins related to migration and invasion processes transcription. MCF-7Var-I (right side) generated by treating the MCF-7 cell line with equimolar concentrations of 4-OH Tam and Endoxifen in addition to 75 mg/dl of LDL cholesterol maintained its luminal ER+ phenotype related to an epithelial morphology and a low migration capability. We proposed that high intracellular concentrations of cholesterol, generated by LDL treatment and the effect of tamoxifen metabolites in LDL intracellular traffic, favor Nrf2 interaction with cav-1 in lipid rafts, preventing its nuclear accumulation and transcriptional activity. Dashed lines refer to proposed mechanisms; solid lines refer to observed phenomena in variants. Triple negative breast cancer (TNBC), low-density lipoprotein receptor (LDL), human epidermal growth factor receptor type 2 (HER2), estrogen receptor α (ERα), cholesterol (CHOL), fatty acid synthase (FAS), metalloproteinase-9 (MMP-9), reactive oxygen species (ROS), antioxidant response element (ARE). Created with BioRender.com
In another instance, we registered significant changes in lipid metabolism targets in both variants. MCF-7VarI showed an augmented expression of FAS which catalyzes palmitic acid syntheses using acetyl-CoA, malonyl-CoA and NADPH as cofactor [48], suggesting active lipogenic metabolism in this variant. MCF-7VarI also exhibited a significant low dil-LDL internalization of only 7.82%, that could be related to a negative feedback mechanism due to exposure to high LDL concentrations during chemoresistance acquisition. For its part, MCF-7VarH expressed statistically significant higher LDLR expression levels than MCF-7 and showed a corresponding enhanced LDL internalization after treatment with 7.5µg/ml dil-LDL (Figure 5). Previously, increased LDL internalization and neutral lipid storage were described in the TNBC cell line MDA-MB-231 compared with MCF-7 [49]. This evidence suggests again an ER+-TNBC transition in MCF-7VarH cells.
Furthermore, it has been described that SERMS such as tamoxifen and its metabolites can alter lipidic metabolism in cells independently of their action on ERα [50]. SERMS can favor the activation of SREBP2 and, in turn, inhibit the intracellular traffic of LDL-cholesterol, increasing cellular cholesterol accumulation [51]. Cholesterol accumulation facilitates the generation of oxysterols such as 27-hydroxy-cholesterol [34], metabolite related to ERα cell proliferation in low E2 environments [34,52]. E2-deprived MCF-7 cells exert an increased response to oxysterols in contrast with MCF-7 native cells, which favors REα mediated transcription [53]. To explain the phenotypic differences observed in MCF-7Var-H and MCF-7Var-I, mainly in relation to hormonal receptors expression, a proposal generated so far is that LDL overload achieved in MCF-7VarI may have favored 27-hydroxy-cholesterol intracellular generation and ERα activation, allowing latency of this receptor.
Other significant changes were described in both variants, such as increased expression of the Nrf2 master regulator of redox homeostasis, but in a significant way in MCF-7VarH (Figure 6A). We also observed increased nuclear localization in this cell variant (Figure 6B). It has been widely described that Nrf2 overexpression in different tumor types contributes to pro-oncogenic processes and chemoresistance [54] by promoting the transcription of antioxidant (NQO1), detoxifying (SULT1A1), and anti-apoptotic (BCL-2) enzymes with ARE element [55]. It has been described that Tamoxifen treatment can promote ROS generation, favoring Nrf2 activation and chemoresistance acquisition to therapy and other therapies in a concomitant way [56]. Interestingly, although both variants were generated by tamoxifen metabolites treatment, an increased expression of Nrf2 and augmented nuclear localization was observed predominately in MCF-7VarH. Inactive Nrf2 is mainly localized in cell membranes interacting with caveolin-1 (Cav-1) in lipid rafts. Lipid raft formation favored by high cholesterol concentrations, such as in MCF-7Var-I, could hinder Nrf2 nuclear translocation [57] and explain the observed behavior among variants (Figure 7).
Furthermore, differential expression of Nrf2-dependent genes was observed in both variants. Although the canonical Nrf2 pathway involves Nrf2-sMaf complex formation, other transcription factors could be influencing Nrf2 differential downstream genes expression among variants. It has been described that Nrf2 interacts physically and forms a complex with Activating Transcription Factor 4 (ATF4) promoting specifically HO-1 transcription, while Nrf2-ATF3 can act as a repressor of Glutathione S-transferase (GST) expression [58]. Even more, observed metabolic adaptation in variants could be related to Nrf2 action as well. It has been described that increased Nrf2 signaling is associated with suppression of lipogenesis related genes like FAS to avoid NADPH expenditure, necessary for detoxification reactions [59], which corresponds with what we observed in MCF-7Var-H.
Finally, Nrf2 could be implicated in chemoresistance adaptations in other ways that have not yet been described. Notably, under the subtle induction of ER stress by Tuni and FBS, which leads to an indirect Nrf2 activation, data suggest an inverse relationship between Tuni dose and hormonal receptor expression. This relationship has to be verified with more analysis.
Elucidating the role of LDL in tamoxifen chemoresistance development and the mechanism involved bring us closer to propose new strategies and/or pharmacological targets to prevent or counteract chemoresistance, avoiding complications in ER+ tumors treatment. Nrf2 inhibition is a promising therapeutic approach for Nrf2-dependent cancers and Nrf2-inhibitors are actively being developed in our group (personal communication).
Conclusions
We evaluated the LDL involvement in chemoresistance acquisition to the two mainly active tamoxifen metabolites, endoxifen and 4-OHTam, by the development of two cellular variants derived from an ERα+ (MCF-7) cell line. We observed a completely different behavior when comparing variant MCF-7VarH generated by tamoxifen metabolites treatment and variant MCF-7VarI generated by co-incubation of tamoxifen metabolites and high LDL concentrations (75 µg/ml). Variants showed differences in the IC50 to metabolites, morphology, expression of hormone receptors, metabolic characteristics, and processes associated with metastasis. MCF-7VarH showed similar features to those presented by triple-negative cells and increased antioxidant response, while MCF-7VarI only showed subtle changes in its phenotype compared with MCF-7 cells. A better understanding of the metabolic changes displayed in these cellular variants allows us to propose new pharmacological targets, such as the master factor of the antioxidant response Nrf2, which can be modulated with current pharmacological treatments and potentially allow recurrence-free recovery in patients.
Supplementary Material
Supplementary Figures S1-S9 |
Title: Anticancer Potential of Tocopherols-Containing Plants and Semi-Synthetic Tocopherols | Body:
1. Introduction
Bioactive compounds are substances with biological activity that directly affect living organisms. The effects of bioactive chemicals can differ based on the substance, dosage, and bioavailability [1]. However, bioactive compounds consumed at sufficient levels can provide medicinal advantages, including avoiding and curing several illnesses [2]. Recently, the increase in illnesses, including heart attack, obesity, and carcinoma, has led to the development of research on herbal plants as drugs in the pharmaceutical industry sector for cancer prevention. Bioactive compounds are found in fruits, vegetables, and grains [3,4]. They belong to a very heterogeneous class of compounds (polyphenolic compounds, carotenoids, phytosterols, and tocopherols) with different chemical structures (hydrophilic or lipophilic) [3,5].
Tocopherols are various forms of vitamin E, which is a group of bioactive, fat-soluble compounds composed of tocopherols and tocotrienols as two main classes, essential for human nutrition [6]. Alpha-tocopherol is the most biologically active among beta-, gamma- and delta-tocopherol in human. According to epidemiological studies, vitamin E could potentially reduce cancer and heart attacks, increase the immune system’s efficiency, and protect against various age-degenerative conditions (cataracts, spinal disorders, and arthritis) [7]. The antioxidant vitamin E supplements, have been used for its antioxidant qualities to reduce the risk of developing cancer [7]. Tocopherols have different variants, namely four tocopherol groups: α, β, γ, and δ. The difference in tocopherol groups lies in their function and ability to inhibit cells [8]. Tocopherol derivatives can be synthesized through various chemical modifications, which have been shown to enhance their anti-cancer properties, making them promising candidates for further pharmacological research [9]. Plants can be the main source of the body’s antioxidant vitamin E since tocopherols can be exclusively generated by photosynthesis in organisms like plants.
Exploring medicinal plants for anticancer agents is important as these plants have been used in traditional medicine for centuries and may offer novel compounds that could enhance cancer treatment [10]. Plants may produce a range of chemical compounds with biochemical and medicinal properties and are ideal for research as basic therapeutic ingredients, including anticancer [11]. The potential of plants being employed as therapeutic agents is partly because of their accumulation of bioactive chemicals [12]. Some herbal medicinal plants that can be used as inhibitors of cancer cell growth, are Typhonium flagelliforme [13,14], Moringa oleifera [15,16], Zingiber officinale [17,18,19], Curcuma longa [20,21], Withania somnifera [22,23], and Pisum fulsum [24,25].
In some herbal medicinal plants, tocopherol content is found in all plant parts, including leaves, tubers, stems, roots, and seeds [26]. Plants that have tocopherol content, as showcased in Table 1, include Bulbine anguistifolia Poelln (Asphodelaceae family), Moringa oleifera (Moringaceae family), Kigelia pinnata (Bignoniaceae family), Cissus assamica (Vitaceae family), Asclepias curassavica L. (Apocynaceae family), and Typhonium flagelliforme (Araceae family) [27,28,29,30,31,32,33].
Despite the promising potential of tocopherols as an anticancer agent, research on tocopherol-containing plants has been relatively limited compared to other bioactive compounds such as polyphenols and carotenoids [34,35,36]. This research gap can be attributed to several factors, including the complexity of isolating and studying fat-soluble compounds like tocopherol, the historical focus on synthetic vitamin E in medical research, and challenges in standardizing tocopherol content across different plant sources. However, the unique properties of plant-based tocopherols, including their natural co-occurrence with other beneficial plant compounds and potential synergistic effects, make them worthy of increased research attention. Furthermore, the growing interest in natural product-based pharmaceuticals underscores the timely nature of focused research on tocopherol-containing plants for anticancer applications.
This review discusses herbal medicinal plants that contain potential anticancer tocopherols and may be developed as anticancer drugs in further research. When testing anticancer activity using unpurified extracts, it is important to consider that the observed effects may not be solely due to the compound present in the highest concentration. The interactions between various components in the extract, including potential synergistic or antagonistic effects, can significantly influence the overall bioactivity. This review selects medicinal plants from Scopus publications from 2016–2024 because Scopus provides a comprehensive collection of multidisciplinary research, making it suitable for our focus on medicinal plants and tocopherols. Scopus was chosen for its extensive coverage of peer-reviewed literature and its robust content curation process overseen by the Content Selection and Advisory Board [37,38,39]. It appears that reviews on herbal medicinal plants containing tocopherol for anticancer purpose are not available. Furthermore, it explores the emerging field of semi-synthetic tocopherol derivatives, potentially leading to improved therapeutic outcomes in cancer treatment. The objectives of this review are as follows: (1) To select publications containing tocopherols with anticancer activities. (2) To analyze the various forms and concentrations of tocopherols in the selected medicinal plants or extracts and their contribution to anticancer activities. (3) To identify factors influencing the anticancer activities of tocopherols in the selected plants, including common traits among species, plant parts utilized, tocopherol forms and concentrations, the presence of other phytochemicals, stability, degradation, and analytical methods employed.
2. Tocopherol Amounts in Selected Herbal Medicinal Plant Species
Anticancer tocopherol variants in herbal medicinal plants and their biological activity are shown in Table 1, with a total of 15 publications derived from the literature search (see Section 5 for literature search and plant selection method). Table 1 presents the results of diverse studies aimed at extracting tocopherol variants from various medicinal plant species, focusing on inhibiting cancer cells. The chemical structure forms of tocopherol compounds presented in Table 1 are illustrated in Figure 1. This figure showcases the structural variants of tocopherols, which are essential for understanding their biochemical properties and potential anticancer activities. By highlighting the different forms of tocopherols, this visual representation emphasizes the diversity of these bioactive compounds and their implications for medicinal applications.
plants-13-02994-t001_Table 1
Table 1
Compilation of existing studies on anticancer tocopherol variants in herbal medicinal plants.
No.
Plant Species
Plant Source
Biological Activity
Total Tocopherol Content
Structure Number
IC50 & Cancer Cell Lines
Reference
1.
Bulbine anguistifolia Poelln (Asphodelaceae family)
Roots extracts
Exhibit significant cytotoxic effects against cancer cells, indicating potential as an anticancer agent.
α-tocopherol succinate *: 4.16%
10
- IC50 of acetone extract = 28.21 μg/mL, COX-2- LC50 of dichloromethane extract (50% lethal concentration) = 22.46 μg/mL- MCF-7 cells and Caco-2 cell line
[28]
2.
Punica granatum L. (Punicaceae family)
Fruits extracts
Red pomegranate exhibited the highest cytotoxic activity among the three varieties, followed closely by easy red and white pomegranates, which displayed comparable effects.
Total tocols (α-, γ-, δ-tocopherol, and β-, γ-tocotrienol):- 32.1 ± 0.2 μg (white pomegranate) - 69.4 ± 1.0 μg (pink pomegranate) - 110.0 ± 0.2 μg (red pomegranate)
α-tocopherol: 1γ-tocopherol: 2δ- tocopherol: 3β-tocotrienol: 4γ- tocotrienol: 5
- IC50 > 200 μg/mL - MCF-7 cells (breast), HCT116 cells (colorectal), PC-3 cells (prostate), A549 cells (lung)
[40]
3.
Calligonum comosum (Polygonaceae family)
Leaves extracts
Significant anticancer for MCF-7 and MDA 231 cancerous breast cells. Has a specific apoptotic effect on cancer cells and shows minimal toxicity against non-cancerous breast cells (MCF-12).
DL-α-tocopherol: 14.75%
6
- IC50 = 269 μg/mL, MCF-7 cells- IC50 = 258 μg/mL- MDA 231 cells, MCF-7 cells
[41]
4.
Moringa oleifera (Moringaceae family)
Seed extract
Significant in vitro anticancer effects have been observed, demonstrating potent inhibitory activity against target proteins associated with breast and prostate cancer.
α-tocopherol: - 13.76 ± 0.89 mg/100 mL (crude oil)- 12.32 ± 0.43 mg/100 mL (degummed oil)
1
- No IC50 value mentioned- Breast and prostate cancer cells based on molecular docking
[29]
5.
Kigelia pinnata (Bignoniaceae family)
Fruits extracts
Significant cytotoxic effects against liver cancer (Huh-7), pancreatic cancer (PANC-1), colorectal cancer (Colo-205 and HT-29), gastric carcinoma (SNU-16), colorectal adenocarcinoma (SW620), and colon carcinoma (HCT116) cells. It has high free radical scavenging activity and iron ion reduction ability.
Not mentioned
-
- IC50 = 6.79 μg/mL, SW620 (colorectal adenocarcinoma)- IC50 = 8.69 μg/mL, SNU-16 (gastric carcinoma)- IC50 = 10.34 μg/mL, PANC-1 (pancreatic cancer)- IC50 = 65.55 μg/mL, Huh-7 (liver cancer)- IC50 = 52.92 μg/mL, Colo-205 (colorectal cancer)- IC50 = 91.32 μg/mL, HT-29 (colorectal cancer)
[33]
6.
Haloxylon salicornicum (Amaranthaceae family)
Whole of plant bodies extracts
Anticancer (liver cancer) activity through apoptosis (AO/EB staining) and cell cycle arrest (flow cytommetry)
Not mentioned
-
- Antioxidant IC50 = 4120 μg/mL- Liver cancer cells (HCAM)
[42]
7.
Prunus persica Linn. (Rosaceae family) & Malpighia glabra Linn. (Malpighiaceae family)
Leaves extracts
Anticancer activities on colon or HCT-116 and breast or MCF-7 cancer cells
α-tocopherol: 0.139 mg/100 g
1
- IC50 = P. persica cv. Desert red: 249.5 μg/mL, Florida prince: >1000 μg/mL, Swelling: 617 μg/mL, M.glabra: 490 μg/mL, breast cancer cells (MCF-7) - IC50 = P. persica cv. Desert red: 617 μg/mL, Florida prince: 617 μg/mL, Swelling: 490 μg/mL, M. glabra: 302 μg/mL- Colon cancer cells (HCT-116)
[43]
8.
Petroselium crispum L. & Anethum graveolens L. (Apiaceae family)
Shoot tissues extracts
Anticancer activities in HepG2 (carcinoma), Colo205 (colon carcinoma), 293 (embryonic kidney adenocarcinoma), and T24P (urinary bladder carcinoma) cell lines.
- P. crispum L. = 0.211 ± 0.09 μmole CO2/mole air, eCO2
- A. graveolens L. = 0.338 ± 0.06 μmole CO2/mole air, eCO2
1
- No IC50 value mentioned- MCF-7 (breast), HepG2 (liver), Colo205 (colorectal), and T24P (bladder) cancer cells
[44]
9.
Cissus assamica (Vitaceae family)
Stems purified extracts
Anticancer activity with cytotoxic effects on non-small cell lung carcinoma cells (NCI-H226) and colon cancer cells (HCT-116)
α-tocopherol: 200 mg tocopherol trimer IVa: 24.3 mg tocopherol trimer IVb: 28.1 mg 1,2-bis-(5- γ- tocopheril)- ethane:15.4 mg α-tocospiro B: 6.1 mg
α-tocopherol: 1tocopherol trimer IVa: 7tocopherol trimer IVb: 8α-tocospiro B: 9
Betulinic acid:- IC50 = 2.0 μM, non-small cell lung carcinoma cancer cells (NCI-H226) - IC50 = 1.6 μM, colorectal cancer cells (HCT-116) Epi-glut-5(6)- en-ol compound:- IC50 = 9.1 μM, non-small cell lung carcinoma cancer cells (NCI-H226) - IC50 = 6.0 μM, colorectal cancer cells (HCT-116)
[30]
10.
Asclepias curassavica L. (Apocynaceae family)
Whole of plant bodies extracts
Exhibits anticancer activities.
- vitamin E (tocopherol): 3.95%- γ -tocopherol: 0.79%
γ-tocopherol: 2
No IC50 value and cancer cell specificity mentioned
[31]
11.
Rubus fairholmianus (Rosaceae family)
Root methanolic column subfraction (RFM) extract
Promotes apoptosis in MCF-7 cancer cells, characterized by decreased ATP, increased LDH, increased apoptosis, and Caspase 3/7 activity
Not mentioned
-
- No IC50 value mentioned- MCF-7 (breast) cancer cells
[45]
12.
Michelia nilagirica (Magnoliaceae family)
Bark extract
α -glucosidase inhibitory activity associated with blood sugar management as well as anticancer activity in HepG2 cells
2.43%
1
- IC50 = 303.26 ± 2.30 mg/mL- HepG2 cancer cells
[46]
13.
Typhonium flagelliforme Lodd. (Araceae family)
Shoots and tubers extracts
Not investigated
Vitamin E (α-tocopherol): 0.46%
1
Not investigated
[32]
14.
Epilobium Species (E. hirsutum L., E. parviflorum Schreb., E. palustre L., E. dodonaei Vill., and E. angustifolium L.) (Onagraceae family)
Aerial parts and leaves extracts
Anticancer activities on LNCap cell line through decreasing cellular viability.
- α-tocopherol: 9435.35 ± 398.51 ng/g d.w.- δ-tocopherol: 572.76 ± 33.14 ng/g d.w.
α-tocopherol: 1δ-tocopherol: 3
- E. hirsutum L. leaves (EtOH 50%), IC50 = 6.10 μg/mL- E. hirsutum L. leaves (EtOH 30%), IC50 = 6.69 μg/mL- E. hirsutum L. aerial parts (EtOH 30%), IC50 = 9.10 μg/mL- E. parviflorum aerial parts (EtOH 30%), IC50 = 8.49 μg/mL- E. palustre aerial parts (EtOH 30%), IC50 = 5.84 μg/mL- E. dodonaei aerial parts (EtOH 30%), IC50 = 6.85 μg/mL- E. angustifolium leaves (EtOH 30%), IC50 = 7.05 μg/mL- E. angustifolium aerial parts (EtOH 30%), IC50 = 10.56 μg/mL- LNCap cancer cells
[47]
15.
Rubus sp. (Rosaceae family)
Fruits purified extracts
Anticancer against HeLa cells with an increase in apoptosis reaching 67%
430 μg/g FW
1
- IC50 = 35 μg/mL- HeLa cells
[48]
Note: This table summarizes key findings from selected studies (Scopus 2016–2024) on tocopherol-containing herbal medicinal plants and their potential anticancer properties. Compounds marked (*) are semi-synthetic. Structure numbers in the “Structure Number” column refer to Figure 1.
Table 1 also include studies employing gas chromatography-mass spectrometry (GC-MS), liquid chromatographic-mass spectrometry (LC-MS), and high-performance liquid chromatography (HPLC) analysis to determine the amount/content of tocopherol, and using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay (MTT assay) to analyze the toxicity of test samples toward cancer cells.
In their study, Raletsena and Mongalo [28] identified 56 compounds in the crude acetone extract of Bulbine anguistifolia Poelln. Among these, the semi-synthetic derivative α-tocopheryl succinate was quantified at 4.16%. The study used gas chromatography time-of-flight mass spectrometry (GC-ToF-MS) analysis of the acetone extract to quantify the compound content. GC-ToF-MS is an analytical technique that combines the separation capabilities of gas chromatography (GC) with the susceptible and high-resolution detection of time-of-flight mass spectrometry (ToF-MS). Another case was with Kumar et al. [41], who used the GC-MS analysis method to obtain a more significant number of tocopherol variants. They managed to identify DL-α-Tocopherol as much as 14.75% in Calligonum comosum leaves. Many studies have used GC-MS analysis to measure high tocopherol content (Table 1).
Cortez-Trejo et al. [40] detailed the total content of tocols from the fruit of Punica granatum L. Tocols, including α-, δ-, γ-tocopherol, and β-, γ-tocotrienol, had a total amount of 32.1 ± 0.2 μg in white pomegranate, 69.4 ± 1.0 μg in pink pomegranate, and 110.0 ± 0.2 μg in red pomegranate. Red pomegranate has a higher nutrient and phytochemical content than pink and white pomegranate. Moringa oleifera, the seed part, has α-tocopherol content with α-tocopherol type of 13.76 ± 0.89 mg/100 mL in crude oil and 12.32 ± 0.43 mg/100 mL in oil that has undergone a degumming process [29]. Prunus persica Linn. from the Malpighiaceae family and Malpighia glabra Linn. from the Rosaceae family have high amounts of vitamin E (tocopherol) and vitamin C based on GC-MS analysis [43]. Vitamin E content (α-tocopherol) in P. persica cv. Florida prince reached 0.139 mg/100 g, and vitamin C in acerola (M. glabra) reached 34 mg/100 g.
As mentioned, most research in obtaining tocopherol variants is through GC-MS analysis. After knowing the variation of tocopherol content in various medicinal plant species through GC-MS analysis, it is essential to understand the factors that can affect the increase of this compound content. One method proven effective in increasing tocopherol content in plants is elevated CO2 (eCO2) treatment, as reported by Saleh et al. [44]. Elevated CO2 can stimulate metabolic pathways that produce secondary compounds, including antioxidants such as tocopherols. The eCO2 treatment can increase the accumulation of phenolic compounds and flavonoids, often associated with increased tocopherol content [44]. The total tocopherol content in Petroselium crispum L. before eCO2 treatment was 0.211 ± 0.09 μmole CO2/mole air, and after eCO2 treatment increased to 0.496 ± 0.02 μmole CO2/mole air, eCO2. The same treatment was also given to Anethum graveolens L. with total tocopherol content from 0.338 ± 0.06 μmole CO2/mole air to 0.421 ± 0.02 CO2/mole air, eCO2 [44]. The eCO2 treatment in the study could produce a significant increase in β- and γ-tocopherol levels in P. crispum yet not for A. graveolens, and δ-and α-tocopherol only increased for A. graveolens. However, total tocopherol levels increased in both plants. Other studies have also shown that increasing atmospheric CO2 concentrations can stimulate the production of (tocopherol) compounds in soybean (Glycine max) plants by increasing photosynthetic activity and protecting cell membranes from oxidative stress [49].
In addition to eCO2, other studies have explored various plant species for their unique tocopherol content and potential as bioactive agents. For example, Chan et al. [30] reported five types of tocopherols isolated from Cissus assamica species which showed significant potential in health studies. The five types of tocopherols had a total amount of tocopherols of 273.9 mg [30]. α-tocopherol was 200 mg, tocopherol trimer IVa was 24.3 mg, tocopherol trimer IVb was 28.1 mg, 1,2-bis-(5-γ-tocopheril)-ethane was 15.4 mg, and α-tocospiro B was 6.1 mg [42]. Among the five types of tocopherols, the first natural isolation of 1,2-bis-(5-γ-tocopheril)-ethane from C. assamica was identified. In addition, GC-MS analysis of Asclepias curassavica L. plants revealed that the vitamin E content, in the form of γ-tocopherol, reached 3.95% [31].
Furthermore, analytical techniques such as LC-MS and GC-MS not only help identify tocopherol content but also other compounds related to bioactive properties. In plants such as Rubus fairholmianus root methanolic column subfraction (RFM), α-tocopherol content and other chemicals lead to its biological function [45]. LC-MS analysis of RFM (at dominant LC-MS peaks at 469.71, 779, and 893) showed mass equivalents of α-tocopherol and flavonol glycosidic compounds. GC-MS analysis and Fourier transform infra-red (FT-IR) spectroscopy stated that 28 compounds were successfully detected from the extract of ethyl acetate of Michelia nilagirica stem bark, one of which was vitamin E (tocopherols) as much as 2.43% [46]. Likewise, the plant Typhonium flagelliforme Lodd. (family: Araceae) has an amount of vitamin E (α-tocopherol) as much as 0.46% in the tuber [32]. The α-tocopherol content is a new type of tocopherol detected in rodent tubers.
LC-MS analysis revealed that Epilobium species (E. parviflorum Schreb., E. hirsutum L., E. dodonaei Vill., E. angustifolium L., and E. palustre L.) contained α-tocopherol as much as 9435.35 ± 398.51 ng/g d.w and δ-tocopherol as much as 572.76 ± 33.14 ng/g d.w [47]. These amounts are the highest concentrations of α-tocopherol and δ-tocopherol. In addition to the species discussed, several other herbal medicinal plants exhibit high tocopherol content, although the total amount has not been specifically reported. These examples include Kigelia pinnata (fruit parts), Haloxylon salicornicum (throughout the plant), Rubus sp. (fruit parts), and Typhonium flagelliforme Lodd. (leaf and tuber parts), which are essential to explore further [32,42,48,50].
It is important to note that not all tocopherols have the same structure and function. Each type of tocopherol, such as α-, β-, γ-, and δ-tocopherol, has a unique role in plants and different anticancer activities [51]. In plants, α-tocopherol protects chloroplasts from damage caused by reactive oxygen species (ROS) during photosynthesis [52]. Meanwhile, β-tocopherol is less common in nature and is usually present in lower plant concentrations. γ-tocopherol plays a role in protecting plant cells from environmental stress, and δ-tocopherol is very effective in detoxifying harmful substances and protecting lipids in cell membranes from oxidation [53].
Apart from tocopherols, there are derived compounds such as tocopherol trimers IVa, IVb, 1,2-bis-(5-γ-tocopheril)-ethane, and α-tocospiro B, each with specific roles in responding to stress and maintaining cell membrane stability. Recent research has increasingly focused on understanding how these compounds are formed and accumulated under different environmental conditions, such as stress and elevated CO2 levels, to boost tocopherol content for health benefits and medical uses [54,55]. Tocopherols have beneficial biological effects by influencing gene expression, signal transmission, and altering cell function through protein-membrane interactions [51,56]. The variants of tocopherols in different medicinal plants can significantly impact their therapeutic properties, especially anticancer activity.
3. Herbal Medicinal Plants Containing Tocopherol with Anticancer Potential
Through a comprehensive understanding of tocopherol levels across a variety of plants and the factors that impact their biosynthesis, we can evaluate the potential therapeutic value of these compounds, particularly in the context of cancer treatment. Tocopherol have been shown to have potent anticancer activity (Table 1). All compounds presented in Table 1 are natural, except for those marked with an asterisk, which are semi-synthetic. For example, Raletsena and Mongalo [28] reported that the semi-synthetic tocopherol content of B. anguistifolia Poelln roots has anticancer potential against soybean lipoxygenase enzyme 15-LOX in addition to Cyclooxygenase-1 (COX-1) and COX-2. The acetone extract of this plant had a minimum inhibitory concentration (MIC) of 0.05 mg/mL for several pathogens. It showed antiproliferative effects against MCF-7 cells with LC50 concentrations of 25.33 and 22.46 μg/mL for acetone and dichloromethane extracts, respectively. The IC50 concentration of dichloromethane extract for 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) was 12.52 μg/mL, while the IC50 concentration of acetone extract against 2,2-Diphenyl-1-picrylhydrazyl (DPPH) was 2.88 μg/mL.
Kumar et al. [41] observed that the ethanol extract from C. comosum (EECC) demonstrates significant antiproliferative effects against MCF-7 (IC50 approximately 269 μg/mL) and MDA-MB-231 (IC50 approximately 258 μg/mL) breast cancer cells. Treatment with EECC induced notable apoptosis in both types of cancer cells, suggesting its potential as an effective anticancer agent. Additionally, Cortez-Trejo et al. [40] reported that pomegranate extract exhibits anticancer potential by inhibiting cancer cell growth. Moreover, the presence of tocopherol and phenolic compounds in pomegranate contributes to maintaining cell integrity, preventing genetic mutations, and reducing inflammation, which is a known risk factor for various cancer types [40]. Furthermore, molecular docking studies have shown that M. oleifera seed oil displays promising anticancer potential compared to degummed oil [29].
The methanolic concentrate of another species, the ripe fruit of K. penata, had significantly greater cytotoxic activities compared to the ethyl acetate extract and even compared to the drug doxorubicin, a commonly used chemotherapeutic agent [33]. Interestingly, the study showed that this extract exhibited substantial cytotoxic activity on numerous types of cancer cells, suchHuh-7 (liver cancer), PANC-1 (pancreatic cancer), Colo-205, HT-29, SW620, and HCT116 (all colorectal cancers), as well as SNU-16 (gastric carcinoma). Another study also showed that P. crispum L. and A. graveolens L. plants have anticancer potential in cell lines HepG2 (carcinoma), Colo205 (colon carcinoma), 293 (embryonic kidney adenocarcinoma), and T24P (urinary bladder carcinoma) [44]. This aligns with the results obtained from M. glabra plant extract, which showed anticancer activity against colon cancer cells with effectiveness equivalent to standard doxorubicin (0.1 μg/mL) [43]. These findings underscore the importance of further exploration of the M. glabra plant as a potential cancer drug.
However, not all plant extracts show similar anticancer activity. For example, H. salicornicum only has cytotoxic effects on liver cancer cells (HCAM), using microwave-assisted extraction (MAE) with an ethanol solvent [42]. The study showed significant toxicity activity at a dosage of 1000 μg/mL, with a cell death rate of 42.35%. This suggests that the anticancer activity of H. salicornicum may be more limited compared to other plants tested. On the other hand, tocopherols in C. assamica plants showed promising cytotoxicity potential against non-small cell lung carcinoma (NCI-H226) and colon cancer (HCT-116) with IC50 values in the range of 1.6 to 9.1 μM [30]. The significant decrease in cell viability at specific concentrations suggests that tocopherol can induce apoptosis, adding to the evidence that tocopherol plays a role in inhibiting cancer cell proliferation. For example, the plant R. fairholmianus showed a 67.73% decrease in cell viability at a concentration of 20 μg/mL, which induced apoptosis [45]. In addition, Rubus sp. has anticancer activity against HeLa cells, with an increase in apoptosis reaching 67% [48]. The PI3K/PTEN/AKT/mTOR pathway is a key signaling route involved in regulating cell growth, proliferation, and metabolism. Similar findings were also found in M. nilagirica and Epilobium [46,47].
Extraction of T. flagelliforme Lodd. has exhibited significant potential in mitigating breast cancer cell proliferation, attributed to its substantial tocopherol content [13,14,57]. Tocopherol, recognized for its potent anticancer properties, mitigates oxidative stress in cancer cells, thereby precipitating apoptosis and impeding cancer cell proliferation by safeguarding cellular membrane structures against oxidative harm [58]. These properties hold significance as oxidative stress contributes to the onset and progression of cancer. The foregoing studies affirm the antioxidative function of tocopherols in herbal medicinal plants, effectively curtailing cancer cell proliferation and instigating apoptosis.
Accordingly, these findings underscore the promising potential of plants containing tocopherol and methanolic extracts as prospective anticancer agents. Therefore, the utilization of tocopherol-containing plants as anticancer agents offers a potential natural approach to cancer therapy and could be the basis for developing plant-based anticancer drugs in the future.
4. Semi-Synthetic Tocopherols with Anticancer Potential
4.1. Semi-Synthetic Tocopherol Derivatives
Recent studies have significantly expanded our understanding of tocopherols and their derivatives in cancer research. A comprehensive review by Baj et al. [9] highlighted several promising semi-synthetic tocopherol derivatives. α-tocopherol succinate (α-TOS) has shown remarkable potential by inducing apoptosis in breast, prostate, and colon cancer cell lines through the disruption of mitochondrial function and generation of reactive oxygen species [9,59]. Another noteworthy derivative is tocopheryl polyethylene glycol succinate (TPGS), which enhances the bioavailability and efficacy of anticancer drugs. TPGS acts as a P-glycoprotein inhibitor, potentially overcoming multidrug resistance in cancer cells [9,60]. The development of redox-silent vitamin E analogs, such as α-tocopheryl ether-linked acetic acid (α-TEA) and α-tocopheroloxybutyric acid (α-TOS), represents another significant advancement. These compounds induce apoptosis independent of their antioxidant properties and have demonstrated effectiveness against breast, prostate, and ovarian cancer cells [9,61]. This research highlights the potential of modified tocopherols to enhance anticancer efficacy beyond the capabilities of their natural counterparts.
Synthetic derivatives of tocopherols have also shown promising results in cancer research. For instance, α-tocopherol succinate (α-TOS), a semi-synthetic derivative of α-tocopherol, has demonstrated potent anticancer effects across various cancer types. α-TOS has been shown to selectively induce apoptosis in malignant cells while sparing normal cells, making it an attractive candidate for cancer therapy [62,63]. Another synthetic analog, α-tocopherol phosphate (α-TP), has exhibited enhanced bioavailability and stability compared to natural tocopherols, potentially leading to improved anticancer efficacy [64]. These synthetic models provide valuable insights into structure-activity relationships and may guide the development of more potent tocopherol-based anticancer agents.
Moreover, tocopherols have shown potential as adjuvants in chemotherapy, enhancing the efficacy of conventional anticancer drugs while potentially reducing their side effects. For example, α-tocopherol has been found to increase the sensitivity of multidrug-resistant cancer cells to chemotherapeutic agents like doxorubicin [65]. Additionally, γ-tocopherol has demonstrated synergistic effects with cisplatin in lung cancer cells, enhancing the drug’s cytotoxicity while protecting normal cells from oxidative damage [66]. These findings suggest that combining tocopherols with standard chemotherapy regimens could improve treatment outcomes and reduce toxicity, opening new avenues for cancer treatment strategies.
4.2. Mechanisms of Action
Jiang et al. [67] elaborated on several mechanisms through which tocopherols exert their anticancer effects. A key mechanism involves the regulation of Peroxisome Proliferator-Activated Receptors (PPARs), particularly PPARγ, leading to decreased cell proliferation and increased apoptosis in various cancer types [67,68]. Additionally, tocopherols, especially γ-tocopherol, inhibit HMG-CoA reductase, which can lead to decreased cholesterol synthesis crucial for rapidly dividing cancer cells [67]. Beyond their antioxidant properties, tocopherols exhibit anticancer effects through various antioxidant-independent mechanisms. These include the inhibition of COX-2 and 5-LOX enzymes, reducing inflammation-related cancer progression [67,69], and the modulation of sphingolipid metabolism, which affects cell survival and death pathways [67]. Tocopherols also influence crucial cell signaling pathways such as NF-κB, STAT3, and MAPK, which play vital roles in regulating cell survival, proliferation, and apoptosis [67,70].
4.3. Tocopherols as Adjuvants in Chemotherapy
The potential of tocopherols as adjuvants in chemotherapy has been highlighted in several studies. Prasad et al. [71] demonstrated synergistic effects when γ-tocotrienol was combined with erlotinib in pancreatic cancer cells, while Pereira-Silva et al. [72] showed that α-tocopheryl succinate enhanced the efficacy of gemcitabine in pancreatic cancer models. Moreover, tocopherols have shown promise in reducing the side effects of chemotherapy. Vitamin E supplementation has been found to reduce cisplatin-induced cytotoxicity in cancer patients [73], and tocopherols have mitigated doxorubicin-induced cardiotoxicity in preclinical models [74]. This dual action of enhancing efficacy while reducing toxicity makes tocopherols particularly interesting as chemotherapy adjuvants.
4.4. Drug Delivery and Overcoming Resistance
In the realm of drug delivery, TPGS-based nanocarriers have improved the delivery and efficacy of paclitaxel in various cancer models [75]. D-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) has enhanced the oral bioavailability of several anticancer drugs [60]. Furthermore, tocopherols and their derivatives have shown potential in overcoming drug resistance, a major challenge in cancer treatment. α-TOS has been observed to sensitize resistant leukemia cells to TRAIL-induced apoptosis [76], while TPGS inhibits P-glycoprotein, potentially reversing multidrug resistance in cancer cells [77,78]. These findings underscore the multifaceted potential of tocopherols and their derivatives in cancer treatment, both as standalone agents and as adjuvants to conventional therapies. The ongoing research in this field promises to open new avenues for more effective and less toxic cancer treatments, leveraging the benefits of both natural compounds and their synthetic modifications.
4.5. Future Perspective
The growing body of research on tocopherols and their derivatives in cancer treatment opens up several promising avenues for future investigation. One key area is the continued development of more potent semi-synthetic derivatives with enhanced anticancer properties. These could potentially combine the beneficial effects of natural tocopherols with improved bioavailability, targeting, or efficacy. For instance, recent work by Niculescu et al. [79] has shown promising results with a novel tocopherol-based nanoparticle that demonstrates enhanced tumor-targeting capabilities [79]. Future research could focus on optimizing these derivatives for specific cancer types or combining them with other anticancer agents for synergistic effects.
Additionally, further exploration of the molecular mechanisms underlying the anticancer effects of tocopherols is crucial. This could lead to the identification of new cellular targets or pathways that could be exploited for cancer therapy. Recent studies have begun to unravel the complex interplay between tocopherols and cancer cell metabolism. For example, Zhou et al. [80] discovered a novel interaction between α-tocopherol and the mTOR signaling pathway in breast cancer cells [80]. Expanding on these findings could potentially reveal new therapeutic targets.
Clinical trials represent another critical area for future research. While preclinical studies have shown promising results, more extensive clinical trials are needed to evaluate the efficacy and safety of tocopherols as adjuvants in various cancer types and treatment regimens. These trials could help establish optimal dosing strategies and identify specific cancer types that are most responsive to tocopherol-based interventions. A recent phase II trial by Kunnumakkara et al. [81] investigating the use of γ-tocotrienol in combination with gemcitabine for pancreatic cancer showed encouraging results [81]. Future large-scale, multi-center trials will be crucial in translating these findings into clinical practice.
Investigation of potential synergies between tocopherols and other natural compounds or conventional cancer therapies is another exciting area for future research. Combination therapies that leverage the unique properties of tocopherols could potentially lead to more effective treatment strategies with reduced side effects. For instance, Nesaretnam and Selvaduray [82] demonstrated a synergistic effect between δ-tocotrienol and curcumin in inhibiting prostate cancer growth in vitro and in vivo [82]. Exploring such combinations could open new avenues for cancer treatment.
Furthermore, the role of tocopherols in cancer prevention, particularly in high-risk populations, warrants further investigation. Long-term epidemiological studies and intervention trials are needed to establish the optimal intake of tocopherols for cancer prevention. A recent 10-year follow-up study by Donovan et al. [83] suggested that higher dietary intake of mixed tocopherols was associated with reduced risk of colorectal cancer in a Mediterranean population [83]. Expanding on such studies could help develop evidence-based prevention strategies.
Advancements in drug delivery systems, such as nanoformulations incorporating tocopherols, present opportunities for improving the targeted delivery of anticancer agents. This could enhance the efficacy of treatments while minimizing systemic toxicity. Recent work by Mehata et al. [75] and Gao et al. [84] on TPGS-based nanocarriers for the co-delivery of paclitaxel and siRNA has shown promising results in overcoming multidrug resistance in breast cancer [84]. Further research in this area could revolutionize cancer drug delivery.
As our understanding of the complex interactions between tocopherols and cancer biology deepens, we may uncover new paradigms in cancer therapy that could significantly impact patient outcomes. The multifaceted nature of tocopherols—from their antioxidant properties to their role in cell signaling and their potential as drug delivery vehicles—positions them as versatile tools in the fight against cancer. Future research directions should aim to leverage these diverse properties, potentially leading to novel, more effective, and less toxic approaches to cancer prevention and treatment.
4.1. Semi-Synthetic Tocopherol Derivatives
Recent studies have significantly expanded our understanding of tocopherols and their derivatives in cancer research. A comprehensive review by Baj et al. [9] highlighted several promising semi-synthetic tocopherol derivatives. α-tocopherol succinate (α-TOS) has shown remarkable potential by inducing apoptosis in breast, prostate, and colon cancer cell lines through the disruption of mitochondrial function and generation of reactive oxygen species [9,59]. Another noteworthy derivative is tocopheryl polyethylene glycol succinate (TPGS), which enhances the bioavailability and efficacy of anticancer drugs. TPGS acts as a P-glycoprotein inhibitor, potentially overcoming multidrug resistance in cancer cells [9,60]. The development of redox-silent vitamin E analogs, such as α-tocopheryl ether-linked acetic acid (α-TEA) and α-tocopheroloxybutyric acid (α-TOS), represents another significant advancement. These compounds induce apoptosis independent of their antioxidant properties and have demonstrated effectiveness against breast, prostate, and ovarian cancer cells [9,61]. This research highlights the potential of modified tocopherols to enhance anticancer efficacy beyond the capabilities of their natural counterparts.
Synthetic derivatives of tocopherols have also shown promising results in cancer research. For instance, α-tocopherol succinate (α-TOS), a semi-synthetic derivative of α-tocopherol, has demonstrated potent anticancer effects across various cancer types. α-TOS has been shown to selectively induce apoptosis in malignant cells while sparing normal cells, making it an attractive candidate for cancer therapy [62,63]. Another synthetic analog, α-tocopherol phosphate (α-TP), has exhibited enhanced bioavailability and stability compared to natural tocopherols, potentially leading to improved anticancer efficacy [64]. These synthetic models provide valuable insights into structure-activity relationships and may guide the development of more potent tocopherol-based anticancer agents.
Moreover, tocopherols have shown potential as adjuvants in chemotherapy, enhancing the efficacy of conventional anticancer drugs while potentially reducing their side effects. For example, α-tocopherol has been found to increase the sensitivity of multidrug-resistant cancer cells to chemotherapeutic agents like doxorubicin [65]. Additionally, γ-tocopherol has demonstrated synergistic effects with cisplatin in lung cancer cells, enhancing the drug’s cytotoxicity while protecting normal cells from oxidative damage [66]. These findings suggest that combining tocopherols with standard chemotherapy regimens could improve treatment outcomes and reduce toxicity, opening new avenues for cancer treatment strategies.
4.2. Mechanisms of Action
Jiang et al. [67] elaborated on several mechanisms through which tocopherols exert their anticancer effects. A key mechanism involves the regulation of Peroxisome Proliferator-Activated Receptors (PPARs), particularly PPARγ, leading to decreased cell proliferation and increased apoptosis in various cancer types [67,68]. Additionally, tocopherols, especially γ-tocopherol, inhibit HMG-CoA reductase, which can lead to decreased cholesterol synthesis crucial for rapidly dividing cancer cells [67]. Beyond their antioxidant properties, tocopherols exhibit anticancer effects through various antioxidant-independent mechanisms. These include the inhibition of COX-2 and 5-LOX enzymes, reducing inflammation-related cancer progression [67,69], and the modulation of sphingolipid metabolism, which affects cell survival and death pathways [67]. Tocopherols also influence crucial cell signaling pathways such as NF-κB, STAT3, and MAPK, which play vital roles in regulating cell survival, proliferation, and apoptosis [67,70].
4.3. Tocopherols as Adjuvants in Chemotherapy
The potential of tocopherols as adjuvants in chemotherapy has been highlighted in several studies. Prasad et al. [71] demonstrated synergistic effects when γ-tocotrienol was combined with erlotinib in pancreatic cancer cells, while Pereira-Silva et al. [72] showed that α-tocopheryl succinate enhanced the efficacy of gemcitabine in pancreatic cancer models. Moreover, tocopherols have shown promise in reducing the side effects of chemotherapy. Vitamin E supplementation has been found to reduce cisplatin-induced cytotoxicity in cancer patients [73], and tocopherols have mitigated doxorubicin-induced cardiotoxicity in preclinical models [74]. This dual action of enhancing efficacy while reducing toxicity makes tocopherols particularly interesting as chemotherapy adjuvants.
4.4. Drug Delivery and Overcoming Resistance
In the realm of drug delivery, TPGS-based nanocarriers have improved the delivery and efficacy of paclitaxel in various cancer models [75]. D-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) has enhanced the oral bioavailability of several anticancer drugs [60]. Furthermore, tocopherols and their derivatives have shown potential in overcoming drug resistance, a major challenge in cancer treatment. α-TOS has been observed to sensitize resistant leukemia cells to TRAIL-induced apoptosis [76], while TPGS inhibits P-glycoprotein, potentially reversing multidrug resistance in cancer cells [77,78]. These findings underscore the multifaceted potential of tocopherols and their derivatives in cancer treatment, both as standalone agents and as adjuvants to conventional therapies. The ongoing research in this field promises to open new avenues for more effective and less toxic cancer treatments, leveraging the benefits of both natural compounds and their synthetic modifications.
4.5. Future Perspective
The growing body of research on tocopherols and their derivatives in cancer treatment opens up several promising avenues for future investigation. One key area is the continued development of more potent semi-synthetic derivatives with enhanced anticancer properties. These could potentially combine the beneficial effects of natural tocopherols with improved bioavailability, targeting, or efficacy. For instance, recent work by Niculescu et al. [79] has shown promising results with a novel tocopherol-based nanoparticle that demonstrates enhanced tumor-targeting capabilities [79]. Future research could focus on optimizing these derivatives for specific cancer types or combining them with other anticancer agents for synergistic effects.
Additionally, further exploration of the molecular mechanisms underlying the anticancer effects of tocopherols is crucial. This could lead to the identification of new cellular targets or pathways that could be exploited for cancer therapy. Recent studies have begun to unravel the complex interplay between tocopherols and cancer cell metabolism. For example, Zhou et al. [80] discovered a novel interaction between α-tocopherol and the mTOR signaling pathway in breast cancer cells [80]. Expanding on these findings could potentially reveal new therapeutic targets.
Clinical trials represent another critical area for future research. While preclinical studies have shown promising results, more extensive clinical trials are needed to evaluate the efficacy and safety of tocopherols as adjuvants in various cancer types and treatment regimens. These trials could help establish optimal dosing strategies and identify specific cancer types that are most responsive to tocopherol-based interventions. A recent phase II trial by Kunnumakkara et al. [81] investigating the use of γ-tocotrienol in combination with gemcitabine for pancreatic cancer showed encouraging results [81]. Future large-scale, multi-center trials will be crucial in translating these findings into clinical practice.
Investigation of potential synergies between tocopherols and other natural compounds or conventional cancer therapies is another exciting area for future research. Combination therapies that leverage the unique properties of tocopherols could potentially lead to more effective treatment strategies with reduced side effects. For instance, Nesaretnam and Selvaduray [82] demonstrated a synergistic effect between δ-tocotrienol and curcumin in inhibiting prostate cancer growth in vitro and in vivo [82]. Exploring such combinations could open new avenues for cancer treatment.
Furthermore, the role of tocopherols in cancer prevention, particularly in high-risk populations, warrants further investigation. Long-term epidemiological studies and intervention trials are needed to establish the optimal intake of tocopherols for cancer prevention. A recent 10-year follow-up study by Donovan et al. [83] suggested that higher dietary intake of mixed tocopherols was associated with reduced risk of colorectal cancer in a Mediterranean population [83]. Expanding on such studies could help develop evidence-based prevention strategies.
Advancements in drug delivery systems, such as nanoformulations incorporating tocopherols, present opportunities for improving the targeted delivery of anticancer agents. This could enhance the efficacy of treatments while minimizing systemic toxicity. Recent work by Mehata et al. [75] and Gao et al. [84] on TPGS-based nanocarriers for the co-delivery of paclitaxel and siRNA has shown promising results in overcoming multidrug resistance in breast cancer [84]. Further research in this area could revolutionize cancer drug delivery.
As our understanding of the complex interactions between tocopherols and cancer biology deepens, we may uncover new paradigms in cancer therapy that could significantly impact patient outcomes. The multifaceted nature of tocopherols—from their antioxidant properties to their role in cell signaling and their potential as drug delivery vehicles—positions them as versatile tools in the fight against cancer. Future research directions should aim to leverage these diverse properties, potentially leading to novel, more effective, and less toxic approaches to cancer prevention and treatment.
5. Materials and Methods
This review explores the role of tocopherol-containing herbal medicinal plants as potential anticancer agents. We aim to identify current research trends, highlight promising plant sources of tocopherol for anticancer applications, and suggests a potential mechanisms underlying the anticancer properties of plant-derived tocopherols and semi-synthetic derivatives. To gather relevant literature, we conducted a comprehensive search of the Scopus database (https://scopus.com, accessed on 3 July 2024).
Scopus was chosen for its extensive coverage of peer-reviewed literature in the field of plant sciences and pharmacology. Scopus’s robust content curation process is overseen by the Content Selection and Advisory Board [37,38,39]. Our search strategy employed combinations of keywords including “tocopherol”, “medicinal plants”, “herbal plants”, and “bioactive anticancer compound” for tocopherol-containing plants. To specifically gather information on semi-synthetic tocopherols, we conducted an additional search using related terms, ensuring we included relevant studies on this topic.
We focused on original research articles published between 2016 and 2024, a period that reflects growing interest in the therapeutic potential of tocopherols in cancer research. Studies were selected based on their relevance to tocopherol-containing herbal medicinal plants and semi-synthetic tocopherols related to anticancer properties. We prioritized peer-reviewed articles that presented clear methodologies, sufficient data reporting, and specific results relating tocopherol to anticancer effects. From the collected literature, we extracted and analyzed information on plant species and parts used, biological activities, total tocopherol content, anticancer mechanisms and effects, as well as insights into semi-synthetic tocopherols. This approach allowed us to synthesize current knowledge and identify promising directions for future research in this field.
6. Conclusions
This review analyzed Scopus publications from 2016 to 2024 on tocopherol-containing plants and their anticancer potential. Fifteen herbal medicinal plants were identified as promising candidates, including Bulbine anguistifolia Poelln, Punica granatum L., Moringa oleifera, Kigelia pinnata, and Typhonium flagelliforme Lodd. The review explored tocopherols’ anticancer mechanisms, particularly apoptosis induction and cell cycle arrest. Key factors influencing the anticancer effects of tocopherols were examined, including their forms (α, β, γ, δ), concentrations, plant parts utilized, and their stability in various plants. The emerging research on semi-synthetic tocopherol derivatives highlighted their potential as adjuvants in chemotherapy, their role in enhancing drug delivery, and reducing side effects. This comprehensive analysis provides a foundation for future research in plant-based anticancer therapies, emphasizing the need for further investigation to develop novel, more effective, and less toxic cancer treatments. The findings contribute to a nuanced understanding of how tocopherol-containing plants can be leveraged in the development of future anticancer drugs. Future experimental studied, including in vitro and in vivo models, are essential to validate the anticancer potential of these tocopherol-rich plants and their derivatives. |
Title: YIPF2 regulates genome integrity | Body:
Introduction
The nuclear genome is constantly exposed to a variety of endogenous and exogenous factors, leading to the initiation and accumulation of diverse DNA damage and lesions within cells [1, 2]. DNA damage has been identified as the primary factor that contributes to the aging process [3–5]. Early studies indicated a relationship between excision-repair capability and longevity in a healthy population [6]. The repair capacity for double-strand breaks (DSB) is positively correlated with the maximum healthy lifespan in long-lived species [7].
Deficiencies in DNA repair enzymes, such as ERCC1 or XPG, in mice have been shown to lead to significant premature aging phenotypes [8–10]. Mutated DNA repair proteins have been associated with manifestations of accelerated aging. A cluster of proteins, such as BLM, WRN, and RECQL4, participated in the repair of DNA damage and the maintenance of genome stability. Mutations in these proteins was linked to human progeroid diseases, namely Bloom syndrome (BS), Werner syndrome (WS), and Rothmund-Thomson syndrome (RTS), respectively [11].
The molecular mechanisms responsible for DNA damage-induced senescence involved in the DNA damage response (DDR) activating ATR, ATM, and p53. These mechanisms inhibit cell growth by activating cyclin-dependent kinase inhibitors such as p16, p21, and p27, and by inducing hyperphosphorylation of the retinoblastoma protein [12].
Prior studies have demonstrated that genome instability can induce an inflammatory response through the cGAS-STING pathway [13, 14]. Senescent cells typically exhibit impaired genome integrity, characterized by the presence of micronuclei or cytoplasmic chromatin fragments (CCF) [15, 16]. The chronic inflammatory response is primarily thought to be caused by the activation of the cGAS-STING pathway, which is primarily mediated by the presence of cytoplasmic chromatin fragments (CCF) [15–19]. CCF is defined by a heterochromatin structure that contains H3K9me3 histone markers [15, 16]. Moreover, CCF also exhibits positivity for γH2A.X [15, 16], suggesting the involvement of double-strand breaks in the genesis of CCF [20]. Based on these observations, it is hypothesized that the presence of chromosomal DNA fragmentation (CCF) could serve as an indicator of the integrity of the nuclear genome.
Organisms have evolved multiple mechanisms to protect genome integrity by recognizing and repairing different forms of DNA damages [21]. The maintenance of genome integrity primarily relies on three crucial pathways: DNA damage repair systems, DNA replication, and chromosome separation during the process of mitosis [22, 23]. As mentioned above, dysregulation of these biological pathways can lead to a range of severe diseases, such as cancer, degenerative diseases, premature aging, and other abnormalities [24, 25]. Convergent researches have shown that genome integrity is compromised as individuals age, resulting in the accumulation of DNA lesions [26–28].In aged tissues, DNA damage can be triggered by inflammation associated with senescence-associated secretory phenotype (SASP) [29]. Numerous studies have revealed a decrease in both the expression levels and activity of DNA damage repair enzymes with aging. Considering these observations, it is reasonable to infer that enhancing DNA repair capacity may improve genome stability and delay the aging process. Several rejuvenation strategies, such as NAD + supplementation and caloric restriction, have been recognized for their capacity to enhance DNA repair [30, 31]. The overexpression of Sirt6 also restored DNA repair capacity and improved genome stability [32]. However, the upregulation of particular DNA double-strand break (DSB) repair enzymes often leads to genomic instability or hinders the efficiency of homologous recombination (HR) [33, 34]. DNA damage repair pathways consist of a variety of enzymes or factors whose activities are tightly controlled in response to different forms of damage. Hence, delaying the aging process by enhancing DNA repair solely through the upregulation of the activity of one or multiple genes poses a considerable challenge [5]. Based on these observations, it is worthwhile to explore new regulators of DNA repair systems to enhance our understanding of genome stability maintenance. In this study, we opted to utilize CCF counts as the readout to discover novel regulators for genome integrity by employing a whole-genome RNAi library. Through the screening process, the Golgi-resident protein YIPF2 was discovered to play a crucial role in maintaining genome integrity.
Results
Identification of novel factors regulating genome integrity by RNAi screening
A genome-wide siRNA screening was conducted in IMR90 cells using CCF as a DNA damage indicator to identify potential regulators maintaining genome integrity. After a 72 h delivery of the siRNA library, DNA damage was assessed by quantifying CCF number, which are γH2A.X and H3K9me3 double positive [17]. A total of 1206 genes were identified to be involved in the regulation of genome integrity, as indicated by an increase in CCF counts following siRNA transduction (Fig. 1A, B). The genes were enriched in pathways related to insulin signaling pathway, Wnt signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Alzheimer disease, Huntington disease, as determined by Kyoto Encyclopedia of Genes and Genomes (KEGG) (Fig. 1C). To validate the candidate hits from the initial round of screening, a second round was conducted using four individual siRNAs targeting these 1206 genes, following the same procedure and protocol (Fig. 1A). The data indicated that approximately 100 genes were validated and they were enriched in pathways related to DNA damage, such as the cell cycle, spliceosome, proteasome, Parkinson’s disease, mRNA surveillance pathway, and mitophagy (Fig. 1D, E). Remarkably, our screening identified new targets like LSM2, ARCN1, and YIPF2, not previously linked to DNA damage in the literature (Fig. 1F). YIPF2 belongs to the Yip domain family (YIPF), which comprises seven Golgi-resident proteins, YIPF1 to YIPF7. The protein is believed to have five transmembrane segments, with an N-terminal segment facing the cytoplasm and a short C-terminal segment facing the Golgi lumen to facilitate protein transport [35]. However, only YIPF2 depletion caused CCF foci increase (Fig. S1A). Therefore, we selected YIPF2 for further investigation to determine how Golgi-localized YIPF2 regulates genome integrity.Fig. 1Genome-wide siRNAs screening identifies YIPF2 as a novel protein involved in genome integrity. A Workflow of the genome-wide siRNAs screening. IMR90 cells were transfected with siNC or a mixed four siRNAs. 1206 genes were screened out in the first-round screening. The second-round screening for these 1206 genes was carried out using the same procedure. B Volcano plot of CCF fold changes (Log2FC) in the first-round screening. C KEGG analysis of 1206 candidate genes identified in the first-round screening. D Volcano plot of CCF Log2FC in the second-round screening. E KEGG analysis of candidate genes in the second screening. F List of genes in the siRNA screening that are associated with genome integrity
YIPF2 deficiency impairs genome integrity
To validate the screening results, we utilized short hairpin RNAs (shRNAs) to interfere with YIPF2 expression (Fig. S2A). The levels of H3K9me3 and γH2A.X-positive CCF and nuclear γH2A.X intensity were significantly elevated in YIPF2-depleted cells (Fig. 2A and B). Genome integrity in these cells was compromised, as indicated by the results of the neutral comet assay (Fig. 2C and D). To confirm the phenotypes of YIPF2-depleted cells, we complemented shRNA-knockdown cells with full-length and different truncated YIPF2 (Fig. S2B). The different constructions were confirmed by Western blotting (Fig. S2C). Only full-length YIPF2 could rescued the double-strand breaks (DSBs) and genome integrity phenotypes, as indicated by γH2A.X immunostaining and neutral comet assay (Fig. 2E and F), which indicated that both N-terminal and C terminal were important for YIPF2 functions. These results suggest that YIPF2 is a novel protein involved in maintaining genome integrity.Fig. 2YIPF2 depletion impaired genomic integrity. A Detection of CCF foci in control and YIPF2-depleted IMR90 cells. The red arrow marks CCF. Scale bars, 20 μm. B Quantification of (A): the number of CCF foci per cell and nuclear γH2A.X signaling intensity (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (**p < 0.01, ****p < 0.0001). C Detection of neutral comet assay in the control and YIPF2-depleted IMR90 cells. Scale bars, 100 μm. D Quantification of (C): the tail moment of each cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001). E Detection of γH2A.X foci in the control and full-length or truncated YIPF2 construction in YIPF2-depleted IMR90 cells. Scale bars, 20 μm. F. Quantification of the tail moment of each cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001). G Immunoblotting analysis of the DDR markers in control and YIPF2-depleted IMR90 cells. H Evaluation of the effect of YIPF2 on DNA damage repair using HDR-GFP reporter. Diagram of the U2OS HDR-GFP reporter (left). HDR activity in the control and YIPF2-depleted U2OS HDR-GFP cells were examined, knockdown of BRCA1 was chose as a positive control (middle). The BRCA1 mRNA level was confirmed by RT-PCR (right). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (*p < 0.05, **p < 0.01). I Detection of γH2A.X foci in control and YIPF2-depleted IMR90 cells, HDF cells and U2OS cells. Scale bars, 20 μm. J Quantification of (I): the number of γH2A.X foci per cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05). K Detection of SA-β-gal activity in control and YIPF2-depleted IMR90 cells. Scale bars, 100 μm. L Quantification of (K): the percentage of SA-β-gal staining positive cells (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (*p < 0.05). M Immunoblotting analysis of the cellular senescent markers in control and YIPF2-depleted IMR90 cells
A significant increase in double-strand breaks (DSBs) was observed in the absence of YIPF2, therefore, we examined whether YIPF2 regulates DNA damage repair. In mammalian cells, there are two major pathways for double-strand break (DSB) repair – non-homologous end joining (NHEJ) and homologous recombination (HR). The choice between the two pathways depends on the phase of the cell cycle and DSB ends [36]. Previous studies have shown that BRCA1 and 53BP1 are involved in determining whether non-homologous end joining (NHEJ) repair or homologous recombination (HR) repair is utilized to repair DSB sites [37–39]. We detected the levels of core proteins in the repair complex after YIPF2 knockdown. It was observed that 53BP1, phosphorylated DNA-PKcs, Ku80, and XRCC4 remained unchanged in the YIPF2 knockdown cells. However, phosphorylated BRCA1, BACH1, and RAD51 were apparently decreased (Fig. 2G). Consistently, DNA double-strand break sensors, the phosphorylated ATM and CHK2, were upregulated in these cells (Fig. 2G). Subsequently, we used a well-established homology-directed repair GFP (HDR-GFP) reporter [40], to measure HR repair activity. The YIPF2 knockdown reduced the number of GFP-positive cells by 40% compared to the control group, while knockdown of BRCA1 almost completely blocked HR repair (Fig. 2H). These data suggested that HR repair was inhibited in the absence of YIPF2.
Furthermore, we checked the effects of YIPF2 on DSB repair in zeocin treated cells, which mimic radiation-induced DSB damage [41]. It was found that YIPF2 did not affect the co-localization of 53BP1 with γH2A.X (Fig. S2D, S2E); however, the recruitment of BRCA1 to chromatin was apparently reduced in YIPF2-depleted cells (Fig. S2F, S2G).
We also knocked down the protein in two other cell lines, including human dermal fibroblast (HDF) and U2OS cells. The data showed that YIPF2 depletion resulted in a significant increase in γH2A.X foci in the two cell lines (Fig. 2I and J), suggesting that the effect is independent of cell type. Furthermore, we found that only depletion of YIPF2 among the YIPF family proteins resulted in a significant accumulation of nuclear γH2A.X foci (Fig. S2H, S2I). These results demonstrated that YIPF2 was involved in maintaining genome integrity by regulating HR repair.
Because DNA damage has been shown to induce senescence [42–44], we performed several assays to evaluate the senescence status of YIPF2-depleted cells. Around 8 days after shRNAs transduction, cells ceased proliferation, as indicated by EdU staining (Fig. S2J and S2K). These YIPF2-depleted cells gradually entered senescence, as evidenced by flat and enlarged nuclei, and positive staining for senescence-associated β-galactosidase (SA-β-gal) activity (Fig. 2K and L). Moreover, senescence-associated markers, including EZH2 [45, 46], Lamin B1 [47, 48] and phosphorylated Rb, decreased, while p16 [49] increased in YIPF2-depleted cells (Fig. 2M). These results demonstrated that YIPF2 deficiency drove cells toward senescence mediated by DNA damage.
YIPF2 overexpression promotes DNA damage repair
In light of the DNA damage accumulation resulting from YIPF2 deficiency, we aimed to investigate whether YIPF2 overexpression can enhance genome integrity using a replication senescence model and a zeocin-induced DNA damage model. Indeed, we found that the level of γH2A.X in senescent IMR90 cells significantly increased because DNA damage repair capacity decreased along with senescence, as shown in previous studies [50] (Fig. 3A). Overexpression of YIPF2 could reduce senescence-associated γH2A.X formation (Fig. 3A). Zeocin treatment induced DNA damage can be repaired by the endogenous DNA damage repair system as indicated by γH2A.X staining and comet assay (Fig. 3B–H). Our study showed that YIPF2 depletion disrupted the repair capacity (Fig. 3C and D); but overexpression of YIPF2 promoted DNA damage repair (Fig. 3E–H).Fig. 3Overexpression of YIPF2 promotes genomic integrity and DDR. A Immunoblotting analysis of the γH2A.X level and YIPF2 level in proliferating or senescent cells and YIPF2 overexpression in senescent cells (P. represents proliferating cells; Sen. represents senescent cells). B Schematic of Zeocin treatment experimental workflow was shown. Cells were treated with 100 μg/mL Zeocin for 2 h and continued cultivation for different times and γH2A.X foci were examined. C Detection of γH2A.X foci after 0, 2, 4, 8, 12 h withdrawal of Zeocin in control and YIPF2-depleted IMR90 cells. Scale bars, 20 μm. D Quantification of (C): the number of γH2A.X foci per cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student's t-test (****p < 0.0001, ***p < 0.001, *p < 0.05). E Detection of γH2A.X foci after 0, 2, 4, 8, 12 h withdrawal of Zeocin in control and YIPF2 overexpressing IMR90 cells. Scale bars, 20 μm. F Quantification of (E): the number of γH2A.X foci per cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student's t-test (****p < 0.0001, ***p < 0.001, *p < 0.05). G Detection of neutral comet assay after 0, 4, 12 h withdrawal of Zeocin in control and YIPF2 overexpressing IMR90 cells. Scale bars, 100 μm. H Quantification of (G): the tail moment of each cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001). I Schematic of serum starvation experimental workflow. Cells were cultured without serum for 18 h. Then the cells were treated with 100 μg/mL Zeocin for 1 h under conditions of 10% FBS or no FBS and continued cultivation with 10% FBS or no FBS for another 4 h. J Detection of γH2A.X foci under conditions of no FBS or 10% FBS in control and YIPF2 overexpressing IMR90 cells. Scale bars, 20 μm. K Quantification of γH2A.X foci per cell in Fig. 3 J (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (*p < 0.05). L Senescent markers in control and YIPF2 overexpression IMR90 cells were detected using western blotting. M Immunoblotting analysis of the DNA damage and cellular senescence markers in control and YIPF2 overexpressing IMR90 cells with ETO treatment
HR repair is intimately related with S phase; to determine whether the cell cycle also influences YIPF2-mediated DNA repair, we evaluate repair efficiency in G1 and S phases. Cell cycle was synchronized by removing serum from the culture media for 18 h to induce cell in the G1 phase, subsequently,10% serum was used to promote cell to S phase [51], simultaneously, zeocin was employed to induce DNA damage (Fig. 3I). Around 5 h, EdU staining revealed that around 10% of the cells were in the S phase (Fig. S3A). YIPF2 was observed to enhance DNA damage repair only in the S phase, not in the G1 phase (Fig. 3J and K). The data suggests that YIPF2 is critical for genomic integrity.
Based on the observed effect of YIPF2 overexpression on DNA damage repair, we evaluated the impact of YIPF2 overexpression on cell senescence. YIPF2 overexpression significantly reduced the number of SA-β-gal-positive cells in both the replication senescence model and the zeocin-induced senescence model. Similarly, the protein levels of cellular senescence markers and DNA damage response were reduced in YIPF2-overexpressing cells in these two models (Fig. 3L and M, S3B and S3C). These results demonstrate that the overexpression of YIPF2 promotes DNA damage repair and delays cell senescence.
YIPF2 regulates DNA replication relevant genes
To understand how YIPF2 regulates DNA damage repair, we investigated differentially regulated expression genes (DEGs) in response to YIPF2 depletion or overexpression using RNA-Seq data. KEGG analysis of DEG genes revealed that several pathways, including cell cycle, cellular senescence, DNA replication, and mismatch repair, were commonly enriched in the two RNA-Seq datasets (Fig. 4A, left and right panels). The transcriptome analysis results were consistent with DNA damage phenotypes in response to YIPF2 expression.Fig. 4YIPF2 regulated DNA replication genes. A KEGG analysis of differentially expressed genes (DEG) for YIPF2-depleted cells (left panel) and YIPF2 overexpressing cells (right panel). B Venn diagram analysis of DEGs including cell cycle and DNA replication relevant genes in YIPF2-depletion cells and YIPF2 overexpressing cells. C Heatmap analysis of the co-regulated DEGs in YIPF2-depletion cells (left panel) and YIPF2 overexpressing cells (right panel). D Detection of pRPA2 S33 foci in control and YIPF2-depleted IMR90 cells treated with DMSO or Zeocin. Scale bars, 20 μm. E The number of pRPA2 S33 foci per cell was examined (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student's t-test (**p < 0.01, *p < 0.05). F Detection of Golgi apparatus morphology in control and shYIPF2 cells. Immunostaining of GM130, TGN46 and GRASP65. G The area of Golgi was quantified by ZEN (n ≥ 100). The relative ratio is shown. Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001, **p < 0.01, *p < 0.05)
We observed that BRCA1, long-range end-resection factor exonuclease 1 (EXO1), the DNA crosslinking helicase FA complementation group D2 (FANCD2), and DNA ligase 1 (LIG1), which are involved in HR repair, decreased in YIPF2 depletion cells (Fig. S4A). This consistents with the findings of impaired DNA damage repair capacity due to YIPF2 knockdown (Fig. 3C).
Moreover, it was found that cell cycle and DNA replication-related genes were closely regulated by the level of YIPF2, as demonstrated by gene interference and gene overexpression (Fig. 4A). We overlapped these gene sets and found that a variety of proteins (52 proteins) were common targets, including MCM family proteins, CDC family proteins, and centromere proteins (Fig. 4B and C). Specifically, MCM proteins, which are involved in replication by forming replicative DNA helicase motor [52], were commonly regulated by YIPF2 deficiency or overexpression (Fig. 4C). The analysis implies that YIPF2 may be essential for DNA replication. Indeed, we found that depletion of YIPF2 caused a significant increase in pRPA2 S33 foci, which is an indicator of DNA replication stress [53, 54] (Fig. 4D, E and S4B). Additionally, the overexpression of YIPF2 reduced the level of pRPA2 S33 induced by zeocin or replicative senescence (Fig.S4B). It was believed that R-loops are usually formed in the presence of DNA replication stress [55, 56]. Fanconi Anemia proteins, which protect genome integrity by removing R-loops [57], were also enriched in the list (Fig.S4A). Therefore, we examined whether the R-loop level changed following alterations in YIPF2 expression levels. The S9.6 level significantly increased in YIPF2-depleted cells but decreased in YIPF2-overexpressing cells, as detected by dot blotting (Fig. S4C). As expected, the S9.6 signal was eliminated when samples were exposed to RNase H, a nuclease specific for R-loop [58] (Fig. S4C). These data suggest that YIPF2 regulates genome integrity through HR repair and DNA replication.
YIPF2 is known to localize in the Golgi apparatus. Our data also showed that YIPF2 doesn’t appear in the nucleus (Fig. S4D and S4E). Therefore, we aimed to investigate whether YIPF2-depletion induced DNA damage associate with alterations in the Golgi structure. The results showed that knocking down YIPF2 led to an increase in cells with dispersed Golgi, as confirmed by TGN46, GM130, and GRASP65 staining (Fig. 4F and G). As previously reported [59], DNA damage triggers Golgi dispersal. YIPF1-depletion also led to an increase in cells with dispersed Golgi, however, there was no DNA damage occurred as indicated by γH2A.X staining (Fig.S4F and S4G). The data suggested that Yip family were crucial for maintaining intact Golgi structure, but it is unclear if YIPF2 performs any unique tasks including genome integrity maintenance in the Golgi structure that are not shared by other YIPF proteins. On the other hand, Golgi resided YIPF2 is necessary for full function of YIPF2 mediated DNA damage repair (Fig. 2E, F).
Taken together, our results demonstrate that YIPF2 is crucial for genome stability through DNA replication and HR repair by regulating the transcription of relevant genes including DNA damage repair and DNA replication genes (Fig. S4H).
Identification of novel factors regulating genome integrity by RNAi screening
A genome-wide siRNA screening was conducted in IMR90 cells using CCF as a DNA damage indicator to identify potential regulators maintaining genome integrity. After a 72 h delivery of the siRNA library, DNA damage was assessed by quantifying CCF number, which are γH2A.X and H3K9me3 double positive [17]. A total of 1206 genes were identified to be involved in the regulation of genome integrity, as indicated by an increase in CCF counts following siRNA transduction (Fig. 1A, B). The genes were enriched in pathways related to insulin signaling pathway, Wnt signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Alzheimer disease, Huntington disease, as determined by Kyoto Encyclopedia of Genes and Genomes (KEGG) (Fig. 1C). To validate the candidate hits from the initial round of screening, a second round was conducted using four individual siRNAs targeting these 1206 genes, following the same procedure and protocol (Fig. 1A). The data indicated that approximately 100 genes were validated and they were enriched in pathways related to DNA damage, such as the cell cycle, spliceosome, proteasome, Parkinson’s disease, mRNA surveillance pathway, and mitophagy (Fig. 1D, E). Remarkably, our screening identified new targets like LSM2, ARCN1, and YIPF2, not previously linked to DNA damage in the literature (Fig. 1F). YIPF2 belongs to the Yip domain family (YIPF), which comprises seven Golgi-resident proteins, YIPF1 to YIPF7. The protein is believed to have five transmembrane segments, with an N-terminal segment facing the cytoplasm and a short C-terminal segment facing the Golgi lumen to facilitate protein transport [35]. However, only YIPF2 depletion caused CCF foci increase (Fig. S1A). Therefore, we selected YIPF2 for further investigation to determine how Golgi-localized YIPF2 regulates genome integrity.Fig. 1Genome-wide siRNAs screening identifies YIPF2 as a novel protein involved in genome integrity. A Workflow of the genome-wide siRNAs screening. IMR90 cells were transfected with siNC or a mixed four siRNAs. 1206 genes were screened out in the first-round screening. The second-round screening for these 1206 genes was carried out using the same procedure. B Volcano plot of CCF fold changes (Log2FC) in the first-round screening. C KEGG analysis of 1206 candidate genes identified in the first-round screening. D Volcano plot of CCF Log2FC in the second-round screening. E KEGG analysis of candidate genes in the second screening. F List of genes in the siRNA screening that are associated with genome integrity
YIPF2 deficiency impairs genome integrity
To validate the screening results, we utilized short hairpin RNAs (shRNAs) to interfere with YIPF2 expression (Fig. S2A). The levels of H3K9me3 and γH2A.X-positive CCF and nuclear γH2A.X intensity were significantly elevated in YIPF2-depleted cells (Fig. 2A and B). Genome integrity in these cells was compromised, as indicated by the results of the neutral comet assay (Fig. 2C and D). To confirm the phenotypes of YIPF2-depleted cells, we complemented shRNA-knockdown cells with full-length and different truncated YIPF2 (Fig. S2B). The different constructions were confirmed by Western blotting (Fig. S2C). Only full-length YIPF2 could rescued the double-strand breaks (DSBs) and genome integrity phenotypes, as indicated by γH2A.X immunostaining and neutral comet assay (Fig. 2E and F), which indicated that both N-terminal and C terminal were important for YIPF2 functions. These results suggest that YIPF2 is a novel protein involved in maintaining genome integrity.Fig. 2YIPF2 depletion impaired genomic integrity. A Detection of CCF foci in control and YIPF2-depleted IMR90 cells. The red arrow marks CCF. Scale bars, 20 μm. B Quantification of (A): the number of CCF foci per cell and nuclear γH2A.X signaling intensity (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (**p < 0.01, ****p < 0.0001). C Detection of neutral comet assay in the control and YIPF2-depleted IMR90 cells. Scale bars, 100 μm. D Quantification of (C): the tail moment of each cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001). E Detection of γH2A.X foci in the control and full-length or truncated YIPF2 construction in YIPF2-depleted IMR90 cells. Scale bars, 20 μm. F. Quantification of the tail moment of each cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001). G Immunoblotting analysis of the DDR markers in control and YIPF2-depleted IMR90 cells. H Evaluation of the effect of YIPF2 on DNA damage repair using HDR-GFP reporter. Diagram of the U2OS HDR-GFP reporter (left). HDR activity in the control and YIPF2-depleted U2OS HDR-GFP cells were examined, knockdown of BRCA1 was chose as a positive control (middle). The BRCA1 mRNA level was confirmed by RT-PCR (right). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (*p < 0.05, **p < 0.01). I Detection of γH2A.X foci in control and YIPF2-depleted IMR90 cells, HDF cells and U2OS cells. Scale bars, 20 μm. J Quantification of (I): the number of γH2A.X foci per cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05). K Detection of SA-β-gal activity in control and YIPF2-depleted IMR90 cells. Scale bars, 100 μm. L Quantification of (K): the percentage of SA-β-gal staining positive cells (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (*p < 0.05). M Immunoblotting analysis of the cellular senescent markers in control and YIPF2-depleted IMR90 cells
A significant increase in double-strand breaks (DSBs) was observed in the absence of YIPF2, therefore, we examined whether YIPF2 regulates DNA damage repair. In mammalian cells, there are two major pathways for double-strand break (DSB) repair – non-homologous end joining (NHEJ) and homologous recombination (HR). The choice between the two pathways depends on the phase of the cell cycle and DSB ends [36]. Previous studies have shown that BRCA1 and 53BP1 are involved in determining whether non-homologous end joining (NHEJ) repair or homologous recombination (HR) repair is utilized to repair DSB sites [37–39]. We detected the levels of core proteins in the repair complex after YIPF2 knockdown. It was observed that 53BP1, phosphorylated DNA-PKcs, Ku80, and XRCC4 remained unchanged in the YIPF2 knockdown cells. However, phosphorylated BRCA1, BACH1, and RAD51 were apparently decreased (Fig. 2G). Consistently, DNA double-strand break sensors, the phosphorylated ATM and CHK2, were upregulated in these cells (Fig. 2G). Subsequently, we used a well-established homology-directed repair GFP (HDR-GFP) reporter [40], to measure HR repair activity. The YIPF2 knockdown reduced the number of GFP-positive cells by 40% compared to the control group, while knockdown of BRCA1 almost completely blocked HR repair (Fig. 2H). These data suggested that HR repair was inhibited in the absence of YIPF2.
Furthermore, we checked the effects of YIPF2 on DSB repair in zeocin treated cells, which mimic radiation-induced DSB damage [41]. It was found that YIPF2 did not affect the co-localization of 53BP1 with γH2A.X (Fig. S2D, S2E); however, the recruitment of BRCA1 to chromatin was apparently reduced in YIPF2-depleted cells (Fig. S2F, S2G).
We also knocked down the protein in two other cell lines, including human dermal fibroblast (HDF) and U2OS cells. The data showed that YIPF2 depletion resulted in a significant increase in γH2A.X foci in the two cell lines (Fig. 2I and J), suggesting that the effect is independent of cell type. Furthermore, we found that only depletion of YIPF2 among the YIPF family proteins resulted in a significant accumulation of nuclear γH2A.X foci (Fig. S2H, S2I). These results demonstrated that YIPF2 was involved in maintaining genome integrity by regulating HR repair.
Because DNA damage has been shown to induce senescence [42–44], we performed several assays to evaluate the senescence status of YIPF2-depleted cells. Around 8 days after shRNAs transduction, cells ceased proliferation, as indicated by EdU staining (Fig. S2J and S2K). These YIPF2-depleted cells gradually entered senescence, as evidenced by flat and enlarged nuclei, and positive staining for senescence-associated β-galactosidase (SA-β-gal) activity (Fig. 2K and L). Moreover, senescence-associated markers, including EZH2 [45, 46], Lamin B1 [47, 48] and phosphorylated Rb, decreased, while p16 [49] increased in YIPF2-depleted cells (Fig. 2M). These results demonstrated that YIPF2 deficiency drove cells toward senescence mediated by DNA damage.
YIPF2 overexpression promotes DNA damage repair
In light of the DNA damage accumulation resulting from YIPF2 deficiency, we aimed to investigate whether YIPF2 overexpression can enhance genome integrity using a replication senescence model and a zeocin-induced DNA damage model. Indeed, we found that the level of γH2A.X in senescent IMR90 cells significantly increased because DNA damage repair capacity decreased along with senescence, as shown in previous studies [50] (Fig. 3A). Overexpression of YIPF2 could reduce senescence-associated γH2A.X formation (Fig. 3A). Zeocin treatment induced DNA damage can be repaired by the endogenous DNA damage repair system as indicated by γH2A.X staining and comet assay (Fig. 3B–H). Our study showed that YIPF2 depletion disrupted the repair capacity (Fig. 3C and D); but overexpression of YIPF2 promoted DNA damage repair (Fig. 3E–H).Fig. 3Overexpression of YIPF2 promotes genomic integrity and DDR. A Immunoblotting analysis of the γH2A.X level and YIPF2 level in proliferating or senescent cells and YIPF2 overexpression in senescent cells (P. represents proliferating cells; Sen. represents senescent cells). B Schematic of Zeocin treatment experimental workflow was shown. Cells were treated with 100 μg/mL Zeocin for 2 h and continued cultivation for different times and γH2A.X foci were examined. C Detection of γH2A.X foci after 0, 2, 4, 8, 12 h withdrawal of Zeocin in control and YIPF2-depleted IMR90 cells. Scale bars, 20 μm. D Quantification of (C): the number of γH2A.X foci per cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student's t-test (****p < 0.0001, ***p < 0.001, *p < 0.05). E Detection of γH2A.X foci after 0, 2, 4, 8, 12 h withdrawal of Zeocin in control and YIPF2 overexpressing IMR90 cells. Scale bars, 20 μm. F Quantification of (E): the number of γH2A.X foci per cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student's t-test (****p < 0.0001, ***p < 0.001, *p < 0.05). G Detection of neutral comet assay after 0, 4, 12 h withdrawal of Zeocin in control and YIPF2 overexpressing IMR90 cells. Scale bars, 100 μm. H Quantification of (G): the tail moment of each cell (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001). I Schematic of serum starvation experimental workflow. Cells were cultured without serum for 18 h. Then the cells were treated with 100 μg/mL Zeocin for 1 h under conditions of 10% FBS or no FBS and continued cultivation with 10% FBS or no FBS for another 4 h. J Detection of γH2A.X foci under conditions of no FBS or 10% FBS in control and YIPF2 overexpressing IMR90 cells. Scale bars, 20 μm. K Quantification of γH2A.X foci per cell in Fig. 3 J (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (*p < 0.05). L Senescent markers in control and YIPF2 overexpression IMR90 cells were detected using western blotting. M Immunoblotting analysis of the DNA damage and cellular senescence markers in control and YIPF2 overexpressing IMR90 cells with ETO treatment
HR repair is intimately related with S phase; to determine whether the cell cycle also influences YIPF2-mediated DNA repair, we evaluate repair efficiency in G1 and S phases. Cell cycle was synchronized by removing serum from the culture media for 18 h to induce cell in the G1 phase, subsequently,10% serum was used to promote cell to S phase [51], simultaneously, zeocin was employed to induce DNA damage (Fig. 3I). Around 5 h, EdU staining revealed that around 10% of the cells were in the S phase (Fig. S3A). YIPF2 was observed to enhance DNA damage repair only in the S phase, not in the G1 phase (Fig. 3J and K). The data suggests that YIPF2 is critical for genomic integrity.
Based on the observed effect of YIPF2 overexpression on DNA damage repair, we evaluated the impact of YIPF2 overexpression on cell senescence. YIPF2 overexpression significantly reduced the number of SA-β-gal-positive cells in both the replication senescence model and the zeocin-induced senescence model. Similarly, the protein levels of cellular senescence markers and DNA damage response were reduced in YIPF2-overexpressing cells in these two models (Fig. 3L and M, S3B and S3C). These results demonstrate that the overexpression of YIPF2 promotes DNA damage repair and delays cell senescence.
YIPF2 regulates DNA replication relevant genes
To understand how YIPF2 regulates DNA damage repair, we investigated differentially regulated expression genes (DEGs) in response to YIPF2 depletion or overexpression using RNA-Seq data. KEGG analysis of DEG genes revealed that several pathways, including cell cycle, cellular senescence, DNA replication, and mismatch repair, were commonly enriched in the two RNA-Seq datasets (Fig. 4A, left and right panels). The transcriptome analysis results were consistent with DNA damage phenotypes in response to YIPF2 expression.Fig. 4YIPF2 regulated DNA replication genes. A KEGG analysis of differentially expressed genes (DEG) for YIPF2-depleted cells (left panel) and YIPF2 overexpressing cells (right panel). B Venn diagram analysis of DEGs including cell cycle and DNA replication relevant genes in YIPF2-depletion cells and YIPF2 overexpressing cells. C Heatmap analysis of the co-regulated DEGs in YIPF2-depletion cells (left panel) and YIPF2 overexpressing cells (right panel). D Detection of pRPA2 S33 foci in control and YIPF2-depleted IMR90 cells treated with DMSO or Zeocin. Scale bars, 20 μm. E The number of pRPA2 S33 foci per cell was examined (n ≥ 100). Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student's t-test (**p < 0.01, *p < 0.05). F Detection of Golgi apparatus morphology in control and shYIPF2 cells. Immunostaining of GM130, TGN46 and GRASP65. G The area of Golgi was quantified by ZEN (n ≥ 100). The relative ratio is shown. Error bars indicate mean ± SEM of three independent experiments. P values were calculated using a one-tailed Student’s t-test (****p < 0.0001, **p < 0.01, *p < 0.05)
We observed that BRCA1, long-range end-resection factor exonuclease 1 (EXO1), the DNA crosslinking helicase FA complementation group D2 (FANCD2), and DNA ligase 1 (LIG1), which are involved in HR repair, decreased in YIPF2 depletion cells (Fig. S4A). This consistents with the findings of impaired DNA damage repair capacity due to YIPF2 knockdown (Fig. 3C).
Moreover, it was found that cell cycle and DNA replication-related genes were closely regulated by the level of YIPF2, as demonstrated by gene interference and gene overexpression (Fig. 4A). We overlapped these gene sets and found that a variety of proteins (52 proteins) were common targets, including MCM family proteins, CDC family proteins, and centromere proteins (Fig. 4B and C). Specifically, MCM proteins, which are involved in replication by forming replicative DNA helicase motor [52], were commonly regulated by YIPF2 deficiency or overexpression (Fig. 4C). The analysis implies that YIPF2 may be essential for DNA replication. Indeed, we found that depletion of YIPF2 caused a significant increase in pRPA2 S33 foci, which is an indicator of DNA replication stress [53, 54] (Fig. 4D, E and S4B). Additionally, the overexpression of YIPF2 reduced the level of pRPA2 S33 induced by zeocin or replicative senescence (Fig.S4B). It was believed that R-loops are usually formed in the presence of DNA replication stress [55, 56]. Fanconi Anemia proteins, which protect genome integrity by removing R-loops [57], were also enriched in the list (Fig.S4A). Therefore, we examined whether the R-loop level changed following alterations in YIPF2 expression levels. The S9.6 level significantly increased in YIPF2-depleted cells but decreased in YIPF2-overexpressing cells, as detected by dot blotting (Fig. S4C). As expected, the S9.6 signal was eliminated when samples were exposed to RNase H, a nuclease specific for R-loop [58] (Fig. S4C). These data suggest that YIPF2 regulates genome integrity through HR repair and DNA replication.
YIPF2 is known to localize in the Golgi apparatus. Our data also showed that YIPF2 doesn’t appear in the nucleus (Fig. S4D and S4E). Therefore, we aimed to investigate whether YIPF2-depletion induced DNA damage associate with alterations in the Golgi structure. The results showed that knocking down YIPF2 led to an increase in cells with dispersed Golgi, as confirmed by TGN46, GM130, and GRASP65 staining (Fig. 4F and G). As previously reported [59], DNA damage triggers Golgi dispersal. YIPF1-depletion also led to an increase in cells with dispersed Golgi, however, there was no DNA damage occurred as indicated by γH2A.X staining (Fig.S4F and S4G). The data suggested that Yip family were crucial for maintaining intact Golgi structure, but it is unclear if YIPF2 performs any unique tasks including genome integrity maintenance in the Golgi structure that are not shared by other YIPF proteins. On the other hand, Golgi resided YIPF2 is necessary for full function of YIPF2 mediated DNA damage repair (Fig. 2E, F).
Taken together, our results demonstrate that YIPF2 is crucial for genome stability through DNA replication and HR repair by regulating the transcription of relevant genes including DNA damage repair and DNA replication genes (Fig. S4H).
Discussion
Maintenance of genome integrity is crucial for preventing premature senescence. Understanding the mechanisms of DNA damage repair could contribute to interventions for aging and age-associated diseases.
In this study, we used CCF as a readout to screen factors regulating genome stability. Unexpectedly, the Golgi-localized protein YIPF2 was identified as a novel regulator that maintains genome integrity.
YIPF2 belongs to the YIP family, which consists of seven proteins. YIPF family proteins have five transmembrane domains. The N-terminal regions face the cytoplasm, and a short C-terminal region resides in the Golgi lumen [60]. The family of proteins plays vital roles in intracellular vesicular transport [61–64]. Previous studies have shown that YIPF6 forms complexes with YIPF1 and YIPF2 to regulate glycan synthesis [35]. However, in the study, we found that only YIPF2 depletion caused DNA damage and genome instability (Fig. S2H), suggesting that the regulatory role of YIPF2 in DNA damage is specific to the protein.
Mammalian cells use non-homologous end joining (NHEJ) and homologous recombination (HR) to repair DSB [36, 65, 66], Depletion of YIPF2 resulted in a decrease in BRCA1 protein levels, impairing HR repair (Fig. 2G and S2F). Conversely, overexpression of YIPF2 promoted DNA damage repair and genomic integrity (Fig. 3A–H). Previous research has shown that DNA damage triggers Golgi dispersal to regulate cell survival [59]. Thus, it is possible that the Golgi apparatus regulates nuclear genome stability. The influence of cytoplasmic Golgi on DNA damage repair and genome integrity remains unknown in details. However, the YIP family member YIPF1 depletion didn’t induce DNA damage (Fig. S4F and S4G). Understanding the regulatory role of YIPF2 in DNA damage repair may help elucidate the crosstalk between the cytoplasm and the nucleus in response to DNA damage.
Materials and methods
Cell culture and siRNA screening
The cells were cultured in DMEM media with 10%FBS and 1% penicillin/streptomycin supplementation. IMR90 cells and HDF cells were cultured in an incubator at 37 ℃, 5% CO2, 3% oxygen. And other cells were cultured at 37 ℃, 5% CO2. The siRNA screening was performed as previously described methods at Chemical Biology Core Facility in CEMCS, CAS [67]. The whole genome human ON-TARGETplus siRNA Library (Horizon) was used to perform the screening. The first siRNA screening was performed with four replicates in 384-well plates. Around 18,000 siRNA pools (four siRNAs targeted one gene) were transfected into IMR90 cells using Lipofectamine™ RNAiMAX (Thermo Fisher) according to the provided protocol. After 72 h of transfection, cells were fixed to perform immunostaining with indicated antibodies, and were analyzed by High Content Screening (GE IN Cell Analyser 6500HS). Cells with three or more cytoplasmic chromatin fragments which are H3K9me3 and γH2A.X double positive was defined as CCF positive cells. 1206 genes were screened out in the first screening. The second screening was performed using individual siRNA with same procedure. Scramble siRNA controls were setup in each plate during screening. The number of CCF for siRNA controls in each plate were used to normalize data.
Plasmids and lentivirus production
For the expression of YIPF2 protein, YIPF2 ORF cDNA and truncated YIPF2 mutants were amplified from cDNA. The PCR product was ligated into HpaI and BamHI site of pLVX expression vector via ClonExpress II One Step Cloning Kit (Vazyme). ShCtrl and YIPF2-specific 21 nt shRNA sequences were cloned into AgeI and EcorI site of pLKO plasmid. PLKO-shCtrl (Target Sequence: CCTAAGGTTAAGTCGCCCTCG), PLKO-YIPF2-shRNA1 (Target Sequence: AGCTACTATCAGAGCTTCTTT), PLKO-YIPF2-shRNA2 (Target Sequence: CATGGGCTGTAAGTTGTACTT), PLKO-YIPF2-shRNA3 (Target Sequence: CTTCAGCTACTATCAGAGCTT), PLKO-shBRCA1 (Target Sequence: GAGTATGCAAACAGCTATAAT), PLKO-YIPF1-shRNA1 (Target Sequence: CGTACCATTATGTGCCCGAAT), PLKO-YIPF1-shRNA2 (Target Sequence: GTGACAATTGTGTTGCTCCAT). All shRNAs were synthetized in Genewiz. Lentivirus were prepared with second generation packing system. Briefly, psPAX2, pMD2.G, and transfer vectors were co-transfected into 293 T cells with polyethylenimine (PEI, linear MW 40 000, Yeasen). Lentivirus was collected 48 h after transfection.
DNA damage induction
IMR90 cell lines were treated with Zeocin (100 μg/mL, Thermo Fisher) for 2 h; IMR90 cell lines were treated with ETO (0.5 μg/mL, MedChemExpress) for 5–7 days to induce DNA damage or cell senescence.
Immunostaining
Cells cultured on coverslips were fixed with 4% paraformaldehyde at room temperature (RT) for 15 min followed by permeabilized with 0.3% Triton X-100 at RT for 30 min. Cells were blocked with 10% goat serum. Primary antibody was incubated at 4 ℃ overnight followed by incubation with fluorescence-labeled secondary antibodies (ThermoFisher) and DAPI for 1 h at RT and visualized by fluorescence microscopy. The following antibodies for immunostaining: γH2A.X (1:200, Santa Cruz, sc-517348), H3K9me3 (1:200, Abcam, ab8898), γH2A.X (1:500, CST, 9718), BRCA1 (1:200, Santa Cruz, sc-6954), 53BP1 (1:500, CST, 4937), p-RPA2 S33 (1:200, Bethyl, A300-246A).The images were acquired Zeiss Axion Observer 7 and Zeiss LSM 800 microscope. For quantification of the number of foci, over 100 cells were counted in every experiment and data from three independent experiments are shown.
Neutral comet assay
The assay was performed as described method in detail [68]. Briefly, 1 × 105 cells were added to molten low-melting agarose 37 ℃at a ratio of 1:10 and lysis at room temperature for 30 min. Then cells were incubated in pre-chilled neutral electrophoresis buffer at 4 ℃ for 1 h and 21 V electrophoresis for 30 min. Washed the slides with ddH2O twice and 70% ethanol for 5 min at room temperature and air-dry the slides. Add diluted SYBR® Gold solution and incubated for 30 min. Image were taken with fluorescence microscope and analyzed with CaspLab software. For quantification of the tail moment, over 100 cells were counted in every experiment and data from three independent experiments are shown.
Western blotting
Protein was extracted with RIPA Lysis Buffer System (Beyotime, P0013C) for SDS-PAGE electrophoresis. After blocking with 5% milk or BSA buffer for 1 h, the membranes were incubated with primary antibody at 1:1000 dilution overnight at 4 °C. After washing with PBS containing 0.1% Tween-20, membranes were incubated with secondary antibody targeting either anti-rabbit (Abcam, ab6721) or anti-mouse (Abcam, ab6708) at 1:3000 dilution for 1 h and signals were detected with BeyoECL western blotting substrate (Beyotime, P0018FM) and signal was detected by chemiluminescence. The following antibodies for western blot: p-ATM (CST, 5883), p-CHK2 (CST, 2197), p-BRCA1 (CST, 9009), p-DNA-PKcs (Abcam, ab18192), Rad51(Novus, NB100-148), Ku80 (Abclonal, A12338), XRCC4 (Abclonal, A1677), BACH1 (Abclonal, A5393), P53 (Proteintech, KHC0079), P21 (Proteintech, 10355-1-AP), p-RB1 (CST, 8516), Lamin B1 (Proteintech, 12987-1-AP), YIPF2 (ThermoFisher, PA5-54112), EZH2 (CST, 5246), β-actin (Abclonal, AC026), p-RPA2 S33 (Bethyl, A300-246A).
RT-qPCR and data analysis
Total RNA was prepared using RNAiso Plus (TaKaRa, 9109) and cDNA was sunthesized from 1 μg total RNA using HiScript II Q Select RT SuperMix for qPCR (Vazyme, R233-01). YIPF1 qPCR-U: TCCAGATCTCTATGGCCCCTT, YIPF1 qPCR-L: GGAAACCCCAGAGTGCAAGA, BRCA1 qPCR-U: GAAACCGTGCCAAAAGACTTC, BRCA1 qPCR-L: CCAAGGTTAGAGAGTTGGACAC, β-actin qPCR-U: AGAAAATCTGGCACCACACC, β-actin qPCR-L: AGAGGCGTACAGGGATAGCA. Accumulation of PCR products was monitored in real time by measuring the level of fluorescence. Results were analysed by the ΔΔCt method and normalized to β-actin to determine relative fold changes in gene expression,
SA-β-galactosidase staining
Cell senescence evaluation for IMR90 was performed with a Senescence β-Galactosidase Staining Kit (Beyotime, C0602) according to the provided protocol. Images were taken with microscopy. For quantification, over 100 cells were counted in every experiment and three independent experiments were shown.
EdU assay
Cells grown on coverslips were treated with 10 μM EdU for 4 h and performed with an Edu assay kit (Beyotime, C0071L) according to the provided protocol. Images were taken with fluorescence microscopy and analyzed with Image J software. For quantification, over 100 cells were counted in every experiment and three independent experiments were shown.
Quantification of Golgi structure
The Golgi structure stains were quantified the area occupy by ZEN software.
S9.6 dot blot
Cells were lysed using 10% SDS and in 25 mM Tris–HCl (pH8.0) and 5 mM EDTA at 37 ℃ overnight with proteinase K. The DNA-RNA hybrids were extracted with phenol:chloroform:isoamyl alcohol (25:24:1, pH8.0). 200 ng samples were loaded to the nylon membranes with 1/10 volume Ammonium oxalate. Before blocking, the membranes were crosslinked using UV light (1200 μJ × 100) and stained with methyl blue. After blocking with 5% milk for 1 h, the membranes were incubated with S9.6 antibody (Millipore, MABE1095) at 1:1000 dilution overnight at 4 °C. After washing with PBS containing 0.1% Tween-20, membranes were incubated with secondary antibody anti-mouse (Abcam, ab6708)1:3000 dilution for 1 h and signals were detected with BeyoECL western blotting substrate (Beyotime, P0018FM) and signal was detected by chemiluminescence.
Cell culture and siRNA screening
The cells were cultured in DMEM media with 10%FBS and 1% penicillin/streptomycin supplementation. IMR90 cells and HDF cells were cultured in an incubator at 37 ℃, 5% CO2, 3% oxygen. And other cells were cultured at 37 ℃, 5% CO2. The siRNA screening was performed as previously described methods at Chemical Biology Core Facility in CEMCS, CAS [67]. The whole genome human ON-TARGETplus siRNA Library (Horizon) was used to perform the screening. The first siRNA screening was performed with four replicates in 384-well plates. Around 18,000 siRNA pools (four siRNAs targeted one gene) were transfected into IMR90 cells using Lipofectamine™ RNAiMAX (Thermo Fisher) according to the provided protocol. After 72 h of transfection, cells were fixed to perform immunostaining with indicated antibodies, and were analyzed by High Content Screening (GE IN Cell Analyser 6500HS). Cells with three or more cytoplasmic chromatin fragments which are H3K9me3 and γH2A.X double positive was defined as CCF positive cells. 1206 genes were screened out in the first screening. The second screening was performed using individual siRNA with same procedure. Scramble siRNA controls were setup in each plate during screening. The number of CCF for siRNA controls in each plate were used to normalize data.
Plasmids and lentivirus production
For the expression of YIPF2 protein, YIPF2 ORF cDNA and truncated YIPF2 mutants were amplified from cDNA. The PCR product was ligated into HpaI and BamHI site of pLVX expression vector via ClonExpress II One Step Cloning Kit (Vazyme). ShCtrl and YIPF2-specific 21 nt shRNA sequences were cloned into AgeI and EcorI site of pLKO plasmid. PLKO-shCtrl (Target Sequence: CCTAAGGTTAAGTCGCCCTCG), PLKO-YIPF2-shRNA1 (Target Sequence: AGCTACTATCAGAGCTTCTTT), PLKO-YIPF2-shRNA2 (Target Sequence: CATGGGCTGTAAGTTGTACTT), PLKO-YIPF2-shRNA3 (Target Sequence: CTTCAGCTACTATCAGAGCTT), PLKO-shBRCA1 (Target Sequence: GAGTATGCAAACAGCTATAAT), PLKO-YIPF1-shRNA1 (Target Sequence: CGTACCATTATGTGCCCGAAT), PLKO-YIPF1-shRNA2 (Target Sequence: GTGACAATTGTGTTGCTCCAT). All shRNAs were synthetized in Genewiz. Lentivirus were prepared with second generation packing system. Briefly, psPAX2, pMD2.G, and transfer vectors were co-transfected into 293 T cells with polyethylenimine (PEI, linear MW 40 000, Yeasen). Lentivirus was collected 48 h after transfection.
DNA damage induction
IMR90 cell lines were treated with Zeocin (100 μg/mL, Thermo Fisher) for 2 h; IMR90 cell lines were treated with ETO (0.5 μg/mL, MedChemExpress) for 5–7 days to induce DNA damage or cell senescence.
Immunostaining
Cells cultured on coverslips were fixed with 4% paraformaldehyde at room temperature (RT) for 15 min followed by permeabilized with 0.3% Triton X-100 at RT for 30 min. Cells were blocked with 10% goat serum. Primary antibody was incubated at 4 ℃ overnight followed by incubation with fluorescence-labeled secondary antibodies (ThermoFisher) and DAPI for 1 h at RT and visualized by fluorescence microscopy. The following antibodies for immunostaining: γH2A.X (1:200, Santa Cruz, sc-517348), H3K9me3 (1:200, Abcam, ab8898), γH2A.X (1:500, CST, 9718), BRCA1 (1:200, Santa Cruz, sc-6954), 53BP1 (1:500, CST, 4937), p-RPA2 S33 (1:200, Bethyl, A300-246A).The images were acquired Zeiss Axion Observer 7 and Zeiss LSM 800 microscope. For quantification of the number of foci, over 100 cells were counted in every experiment and data from three independent experiments are shown.
Neutral comet assay
The assay was performed as described method in detail [68]. Briefly, 1 × 105 cells were added to molten low-melting agarose 37 ℃at a ratio of 1:10 and lysis at room temperature for 30 min. Then cells were incubated in pre-chilled neutral electrophoresis buffer at 4 ℃ for 1 h and 21 V electrophoresis for 30 min. Washed the slides with ddH2O twice and 70% ethanol for 5 min at room temperature and air-dry the slides. Add diluted SYBR® Gold solution and incubated for 30 min. Image were taken with fluorescence microscope and analyzed with CaspLab software. For quantification of the tail moment, over 100 cells were counted in every experiment and data from three independent experiments are shown.
Western blotting
Protein was extracted with RIPA Lysis Buffer System (Beyotime, P0013C) for SDS-PAGE electrophoresis. After blocking with 5% milk or BSA buffer for 1 h, the membranes were incubated with primary antibody at 1:1000 dilution overnight at 4 °C. After washing with PBS containing 0.1% Tween-20, membranes were incubated with secondary antibody targeting either anti-rabbit (Abcam, ab6721) or anti-mouse (Abcam, ab6708) at 1:3000 dilution for 1 h and signals were detected with BeyoECL western blotting substrate (Beyotime, P0018FM) and signal was detected by chemiluminescence. The following antibodies for western blot: p-ATM (CST, 5883), p-CHK2 (CST, 2197), p-BRCA1 (CST, 9009), p-DNA-PKcs (Abcam, ab18192), Rad51(Novus, NB100-148), Ku80 (Abclonal, A12338), XRCC4 (Abclonal, A1677), BACH1 (Abclonal, A5393), P53 (Proteintech, KHC0079), P21 (Proteintech, 10355-1-AP), p-RB1 (CST, 8516), Lamin B1 (Proteintech, 12987-1-AP), YIPF2 (ThermoFisher, PA5-54112), EZH2 (CST, 5246), β-actin (Abclonal, AC026), p-RPA2 S33 (Bethyl, A300-246A).
RT-qPCR and data analysis
Total RNA was prepared using RNAiso Plus (TaKaRa, 9109) and cDNA was sunthesized from 1 μg total RNA using HiScript II Q Select RT SuperMix for qPCR (Vazyme, R233-01). YIPF1 qPCR-U: TCCAGATCTCTATGGCCCCTT, YIPF1 qPCR-L: GGAAACCCCAGAGTGCAAGA, BRCA1 qPCR-U: GAAACCGTGCCAAAAGACTTC, BRCA1 qPCR-L: CCAAGGTTAGAGAGTTGGACAC, β-actin qPCR-U: AGAAAATCTGGCACCACACC, β-actin qPCR-L: AGAGGCGTACAGGGATAGCA. Accumulation of PCR products was monitored in real time by measuring the level of fluorescence. Results were analysed by the ΔΔCt method and normalized to β-actin to determine relative fold changes in gene expression,
SA-β-galactosidase staining
Cell senescence evaluation for IMR90 was performed with a Senescence β-Galactosidase Staining Kit (Beyotime, C0602) according to the provided protocol. Images were taken with microscopy. For quantification, over 100 cells were counted in every experiment and three independent experiments were shown.
EdU assay
Cells grown on coverslips were treated with 10 μM EdU for 4 h and performed with an Edu assay kit (Beyotime, C0071L) according to the provided protocol. Images were taken with fluorescence microscopy and analyzed with Image J software. For quantification, over 100 cells were counted in every experiment and three independent experiments were shown.
Quantification of Golgi structure
The Golgi structure stains were quantified the area occupy by ZEN software.
S9.6 dot blot
Cells were lysed using 10% SDS and in 25 mM Tris–HCl (pH8.0) and 5 mM EDTA at 37 ℃ overnight with proteinase K. The DNA-RNA hybrids were extracted with phenol:chloroform:isoamyl alcohol (25:24:1, pH8.0). 200 ng samples were loaded to the nylon membranes with 1/10 volume Ammonium oxalate. Before blocking, the membranes were crosslinked using UV light (1200 μJ × 100) and stained with methyl blue. After blocking with 5% milk for 1 h, the membranes were incubated with S9.6 antibody (Millipore, MABE1095) at 1:1000 dilution overnight at 4 °C. After washing with PBS containing 0.1% Tween-20, membranes were incubated with secondary antibody anti-mouse (Abcam, ab6708)1:3000 dilution for 1 h and signals were detected with BeyoECL western blotting substrate (Beyotime, P0018FM) and signal was detected by chemiluminescence.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary file1 (XLSX 8812 KB) |
Title: Effect of Coptidis Rhizoma on gastrointestinal system before and after processing with wine based on gut microbiota and short chain fatty acids | Body:
1 Introduction
Traditional Chinese medicines (TCM) have been widely used for the clinical treatment of various diseases in countries such as China, South Korea and Japan, and their effectiveness has been confirmed for thousands of years. Coptidis Rhizoma is the dried rhizome and root of Coptis chinensis Franch., Coptis deltoidea C.Y. Cheng et Hsiao., and Coptis teeta Wall., is known for its ability to clear heat, remove fire and detoxify. It can be used to treat dampness and heat, vomiting, diarrhea, jaundice, high fever, dizziness and toothache (China Pharmacopeia Commission., 2020). It has demonstrated a wide range of pharmacological effects that primarily include hypoglycemic, antibacterial, antioxidant, anti-inflammatory, and antitumor properties (Song et al., 2020; Wang et al., 2019). Coptidis Rhizoma has the characteristic of bitter taste and cold nature, and traditional Chinese medicine theory suggests that the bitter and cold of TCM can injure the stomach.
It is important to note that the term “stomach,” as referenced in TCM theory and the clinical manifestations associated with “injury to the stomach,” extends beyond its anatomical definition. It encompasses not only the stomach itself but also includes both the large and small intestines, along with other components of the digestive system. This concept is supported by a passage from Ling Shu-Ben Shu, which states: “The large and small intestines all belong to the stomach” (Bai et al., 2023). In addition, experimental studies have shown that large doses of bitter cold Chinese medicines can caused gastrointestinal tissue damage in animals, some of which can lead to death (Li., 2005). Surprisingly, previous literature has shown that processing with wine can reduce the bitter and cold nature of Coptidis Rhizoma and relieve the damage to intestinal tissue (Zhou et al., 2023).
TCM were typically processed to enhance its efficacy, mitigate or eliminate toxicity and side effects, and ensure clinical applicability (Pei et al., 2022; Zhong et al., 2019). The methods of processing TCM with wine had been documented since the Han Dynasty, and Coptidis Rhizoma processed with wine was first recorded in Qian Jin Yi Fang. Until the 2020 edition of the Chinese Pharmacopoeia, stir-frying with wine was recognized as one of the primary methods for moderating the bitter-cold properties of Coptidis Rhizoma. Existing studies have confirmed that high doses of Coptidis Rhizoma can adversely affect the gastrointestinal tract, leading to issues such as constipation. However, the mechanisms by which processing Coptidis Rhizoma with rice wine mitigates its coldness and its specific effects on the gastrointestinal system remain unclear. Therefore, further studies are needed.
Gut microbiota (GM) was considered to be a “forgotten organ” and helpful for the body’s nutrient absorption, drug metabolism, immune regulation, maintains the structural integrity of the intestinal mucosal barrier and inhibits pathogens (Flint et al., 2012; Jandhyala et al., 2015; Zhang Q. et al., 2023). Under normal physiological conditions, the types and proportions of GM were in homeostatic equilibrium, and play the functions of immunity, metabolism, and maintenance of intestinal barrier homeostasis (Yue et al., 2019). Under pathological conditions, the micro-ecosystems within the intestinal tract are significantly compromised, resulting in an increase in the population of conditionally pathogenic bacteria and a decrease in probiotic bacteria. This imbalance leads to GM dysbiosis, which is closely associated with the development of various gastrointestinal diseases, including diarrhea, inflammatory bowel disease, metabolic disorders (such as obesity and diabetes mellitus), and cardiovascular diseases (Afzaal et al., 2022; Nishida et al., 2018).
Short-chain fatty acids (SCFAs) are the primary metabolites of GM. They played a crucial role in promoting the growth of beneficial bacteria, improving GM composition, and regulating host immune balance (Liu et al., 2021; Tan et al., 2014). Current research has demonstrated that acetic, propionic, and butyric acids constitute over 95% of SCFAs present in the human gut. Furthermore, Firmicutes is identified as the predominant species within the gut microbiota responsible for SCFA production (Louis et al., 2014). Moreover, the occurrence of gastrointestinal diseases was closely related to GM changes (Dong et al., 2019; Li et al., 2020). For instance, the relative abundance of Bifidobacteria and Lactobacillus were reduced and Bacteroides were increased in adults with constipation compared to controls (Chassard et al., 2012; Khalif et al., 2005). Meanwhile, constipated patients had lower SCFAs than healthy controls. Furthermore, the intake of probiotics can alleviate constipation by regulating the composition of the GM and the production of SCFAs (Jeong et al., 2023; Lai et al., 2023). Fascinatingly, the regulatory effect of probiotics on intestinal motility were also related to neurotransmitters (Dimidi et al., 2017). Thus, more attention to GM may help to reveal the pathogenesis of gastrointestinal diseases.
In this study, the components change of Coptidis Rhizoma before and after processing with wine were studied, as well as effects on GM and SCFAs were studied, simultaneously, the pathological effects of the gastrointestinal tract were observed, the levels of inflammatory factors and the expression of NF-κB p65 were analyzed, aiming to explore the potential mechanism of Coptidis Rhizoma processing with wine in alleviating side effects on the intestines, and provide scientific evidence for its rational clinical application.
2 Materials and methods
2.1 Chemicals and reagents
4% paraformaldehyde solution was purchased from Shaanxi Zhonghui Hecai Biomedical Technology Co., Ltd. (Shaanxi, China). 75% ethanol was bought from Tianjin Kemio Chemical Reagent Co., Ltd. (Tianjin, China). Carmine dye was obtained from Sigma Aldrich.
2.2 Plant material
Coptis deltoidea C.Y. Cheng et Hsiao (CD) were purchased from Shaanxi Shangluo Panlong Botanical Pharmaceutical Co., Ltd. (Shaanxi, China), and identified by Yonggang Yan (the professor of Shaanxi University of Chinese Medicine). The voucher specimens (No. 20220301) were deposited in Shaanxi University of Chinese Medicine. The preparation of CD processed with wine (PCD) was as follows: PCD was made up of CD slices mixed with yellow rice wine (100:12.5), slightly moistened, waiting for yellow rice wine to be absorbed, and then placed in a container (150 ± 10) °C until slightly scorched spots appeared on the surface of CD, then, PCD was obtained (China Pharmacopeia Commission., 2020). Take the appropriate amount of CD and PCD add 10 times the amount of water, soak for 30 min at room temperature and then decoct, keep slightly boiling for 30 min, filtered while hot; the remaining dregs of the drug add 8 times the amount of water and decoct, keep slightly boiling for 30 min, filtered while hot, and then combine the two decoctions of the resulting filtrate, concentrated, cooled to room temperature, and then placed in a freeze dryer machine freeze-dried to a powder, stored in −20°C refrigerator for backup.
2.3 UPLC-ESI-MS/MS analysis
CD or PCD powder (50 mg) was accurately weighed and add 1,200 μL of - 20°C pre-cooled 70% methanolic aqueous internal standard extract (less than 50 mg added at the rate of 1,200 μL extractant per 50 mg sample). Vortex once every 30 min for 30 s, for a total of 6 times. After centrifugation (rotation speed 12,000 rpm, 3 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 μm pore size). The sample extracts were analyzed using an UPLC-ESI-MS/MS system and Tandem mass spectrometry system. The analytical conditions were as follows, UPLC: column, Agilent SB-C18 (1.8 µm, 2.1 mm * 100 mm); The mobile phase was consisted of solvent A, pure water with 0.1% formic acid, and solvent B, acetonitrile with 0.1% formic acid. Sample measurements were performed with a gradient program that employed the starting conditions of 95% A, 5% B. Within 9 min, a linear gradient to 5% A, 95% B was programmed, and a composition of 5% A, 95% B was kept for 1 min. Subsequently, a composition of 95% A, 5.0% B was adjusted within 1.1 min and kept for 2.9 min. The flow velocity was set as 0.35 mL per minute; The column oven was set to 40°C; The injection volume was 2 μL. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS. The ESI source operation parameters were as follows: source temperature 500°C; ion spray voltage (IS) 5,500 V (positive ion mode)/- 4,500 V (negative ion mode); ion source gas I (GSI), gas II (GSII) and curtain gas (CUR) were set at 50, 60, and 25 psi, respectively; the collision-activated dissociation (CAD) was high. QQQ scans were acquired as multiple reaction mode (MRM) experiments with collision gas (nitrogen) set to medium. DP (declustering potential) and CE (collision energy) for individual MRM transitions was done with further DP and CE optimization. A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period.
2.4 Animal experiments
Sixty male C57BL/6 mice (18–22 g) were purchased from Xi’an Keaoke Biotechnology Co., Ltd. (Xi’an, China. License No: SCXK 2021-0006). All animal experiments were approved by the Animal Experimental Ethical Committee of Shaanxi University of Chinese Medicine (Approval number: SUCMDL20210310005). The mice were housed at 21°C, 55%–60% ambient humidity under 12 h light (08:00 to 20:00):12 h dark conditions. Mice were randomly divided normal control (NC), CD, high-dose CD (CD-H), PCD and high-dose PCD (PCD-H) groups after adaptively feeding for 1 week, with 12 mice in each group. Mice in CD and CD-H groups were treated with CD (i.g., 0.65 and 2.6 g/kg), respectively, and mice in PCD and PCD-H groups were treated with PCD (i.g., 0.65 and 2.6 g/kg), respectively, and NC group was treated with an equal volume of physiological saline, 6 consecutive weeks, once a day.
2.5 Determination of body weight, food intake, fecal water content and intestinal transit time in each group of mice
The body weight, food intake, fecal water content, and intestinal transit time of mice were measured in the first, third and sixth weeks. The intestinal transit time was measured in the following ways: Each mouse was transferred to a clean empty cage and adapted for 1 h, and treated with 150 μL of Sigma-Aldrich solution (i.g.), then, the production of red fecal particles was monitored every 10 min starting 120 min after administration. The collected stools were weighed and dried at 95°C, and the fecal water content was calculated as the following formula:
Fecal water content %=wet weight−dry weight/wet weight×100%.
2.6 Histopathological examination
As the previous study description (Zhang Q. et al., 2021), stomach, small intestine and colon tissues were fixed in 4% paraformaldehyde solution, and stained with hematoxylin-eosin (HE). The histopathological changes were observed by a light microscope (Nikon eclipsets 2, Japan).
2.7 Detection of interleukin-6 (IL-6), interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α)
The concentrations of total protein were determined using a bicinchoninic acid (BCA, No. BC0524) protein assay kit (Shaanxi Zhonghui, Xi’an, China), and the levels of IL-6, IFN-γ, and TNF-α (No. 20220620, 20220618, 20220724) in serum were measured using enzyme-linked immunosorbent assay (ELISA) kits (Meimian, Yancheng, China).
2.8 IHC analysis
As described in the previous literature (Zhang Q. et al., 2021), Paraffin-embedded small intestine sections were processed and added with primary antibody (NF-κB p65 1:200 dilution, No: AF5006, Affinity, China; TLR4 1:100 dilution, No: GB11519, SERVICEBIO, China) overnight at 4°C, and washed three times with PBS and incubated with Goat anti-Rabbit IgG (Dilution: 1:500, No: BL732A, BIOSS, China) for 40 min at 37°C, the slices were stained with DAB and subsequently washed to visualize the target signals. The measurement of mean density was performed on Image-Pro Plus 6.0 software (Media Cybernetics, Silver Spring, MD, United States).
2.9 WB analysis
As described in the previous literature (Zhang Q. et al., 2021), the sample was processed and 50 μg protein sample was separated by 10% SDS-PAGE gel and the electrophoresis was transferred to the PVDF membrane. TBST was sealed with 5% protein sealing dry powder for 1.5 h, the closure was completed, and TBST was washed three times. Antibodies against β-actin (Dilution: 1:2000; No: BST17353873, BOSTER, China), NF-κB p65 (Dilution: 1:1,000; No: 8242S; CST, United States) and NF-κB p-p65 (Dilution: 1:1,000; No: 3033S; CST, United States) were added and incubated overnight at 4°C. After completion, Goat anti-Rabbit IgG (Dilution: 1:5000, No: BOSTER, BST18J20B18K54, China) was added and incubated for 1 h. The antigen-antibody complex was detected by ECL reagent. Then the gray values of each protein band were determined and analyzed by ImageJ image analysis software.
2.10 Analysis of SCFAs in feces
The feces were collected after final administration, and stored at −80°C for further research. An ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS) system (Vanquish™ Flex UHPLC-TSQ Altis™, Thermo Scientific Corp., Germany) was used to quantitate SCFA in Novogene Co., Ltd. (Beijing, China). Separation was performed on a Waters ACQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm) which was maintained at 40°C. The mobile phase, consisting of 10 mM ammonium acetate in water (solvent A) and acetonitrile: isopropanol (1:1) (solvent B), was delivered at a flow rate of 0.30 mL/min. The mass spectrometer was operated in negative MRM. Parameters were as follows: IonSpray Voltage (−4500 V), Sheath Gas (35 psi), Ion Source Temp (550°C), Auxiliary Gas (50psi), Collision Gas (55psi).
2.11 16 S rRNA gene sequencing
DNA from the fecal samples was extracted, and sequenced by Majorbio biotechnology platform. The extracted DNA was detected by 1% agarose gel electrophoresis. PCR products were detected and quantified using the QuantiFluor-ST™ Blue Fluor Quantitative System. The gene sequencing was performed by Shanghai Meiji Biomedical Technology Co., Ltd. (Contract No: MJ20220916022) and analyzed by Majorbio biotechnology platform.
2.12 Statistical analysis
Statistical analysis was performed by GraphPad Prism 8.3.0 and the data were presented as mean ± standard deviations (SD). Differences were considered statistically significant for p < 0.05, p < 0.01, and P < 0.001.
2.1 Chemicals and reagents
4% paraformaldehyde solution was purchased from Shaanxi Zhonghui Hecai Biomedical Technology Co., Ltd. (Shaanxi, China). 75% ethanol was bought from Tianjin Kemio Chemical Reagent Co., Ltd. (Tianjin, China). Carmine dye was obtained from Sigma Aldrich.
2.2 Plant material
Coptis deltoidea C.Y. Cheng et Hsiao (CD) were purchased from Shaanxi Shangluo Panlong Botanical Pharmaceutical Co., Ltd. (Shaanxi, China), and identified by Yonggang Yan (the professor of Shaanxi University of Chinese Medicine). The voucher specimens (No. 20220301) were deposited in Shaanxi University of Chinese Medicine. The preparation of CD processed with wine (PCD) was as follows: PCD was made up of CD slices mixed with yellow rice wine (100:12.5), slightly moistened, waiting for yellow rice wine to be absorbed, and then placed in a container (150 ± 10) °C until slightly scorched spots appeared on the surface of CD, then, PCD was obtained (China Pharmacopeia Commission., 2020). Take the appropriate amount of CD and PCD add 10 times the amount of water, soak for 30 min at room temperature and then decoct, keep slightly boiling for 30 min, filtered while hot; the remaining dregs of the drug add 8 times the amount of water and decoct, keep slightly boiling for 30 min, filtered while hot, and then combine the two decoctions of the resulting filtrate, concentrated, cooled to room temperature, and then placed in a freeze dryer machine freeze-dried to a powder, stored in −20°C refrigerator for backup.
2.3 UPLC-ESI-MS/MS analysis
CD or PCD powder (50 mg) was accurately weighed and add 1,200 μL of - 20°C pre-cooled 70% methanolic aqueous internal standard extract (less than 50 mg added at the rate of 1,200 μL extractant per 50 mg sample). Vortex once every 30 min for 30 s, for a total of 6 times. After centrifugation (rotation speed 12,000 rpm, 3 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 μm pore size). The sample extracts were analyzed using an UPLC-ESI-MS/MS system and Tandem mass spectrometry system. The analytical conditions were as follows, UPLC: column, Agilent SB-C18 (1.8 µm, 2.1 mm * 100 mm); The mobile phase was consisted of solvent A, pure water with 0.1% formic acid, and solvent B, acetonitrile with 0.1% formic acid. Sample measurements were performed with a gradient program that employed the starting conditions of 95% A, 5% B. Within 9 min, a linear gradient to 5% A, 95% B was programmed, and a composition of 5% A, 95% B was kept for 1 min. Subsequently, a composition of 95% A, 5.0% B was adjusted within 1.1 min and kept for 2.9 min. The flow velocity was set as 0.35 mL per minute; The column oven was set to 40°C; The injection volume was 2 μL. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS. The ESI source operation parameters were as follows: source temperature 500°C; ion spray voltage (IS) 5,500 V (positive ion mode)/- 4,500 V (negative ion mode); ion source gas I (GSI), gas II (GSII) and curtain gas (CUR) were set at 50, 60, and 25 psi, respectively; the collision-activated dissociation (CAD) was high. QQQ scans were acquired as multiple reaction mode (MRM) experiments with collision gas (nitrogen) set to medium. DP (declustering potential) and CE (collision energy) for individual MRM transitions was done with further DP and CE optimization. A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period.
2.4 Animal experiments
Sixty male C57BL/6 mice (18–22 g) were purchased from Xi’an Keaoke Biotechnology Co., Ltd. (Xi’an, China. License No: SCXK 2021-0006). All animal experiments were approved by the Animal Experimental Ethical Committee of Shaanxi University of Chinese Medicine (Approval number: SUCMDL20210310005). The mice were housed at 21°C, 55%–60% ambient humidity under 12 h light (08:00 to 20:00):12 h dark conditions. Mice were randomly divided normal control (NC), CD, high-dose CD (CD-H), PCD and high-dose PCD (PCD-H) groups after adaptively feeding for 1 week, with 12 mice in each group. Mice in CD and CD-H groups were treated with CD (i.g., 0.65 and 2.6 g/kg), respectively, and mice in PCD and PCD-H groups were treated with PCD (i.g., 0.65 and 2.6 g/kg), respectively, and NC group was treated with an equal volume of physiological saline, 6 consecutive weeks, once a day.
2.5 Determination of body weight, food intake, fecal water content and intestinal transit time in each group of mice
The body weight, food intake, fecal water content, and intestinal transit time of mice were measured in the first, third and sixth weeks. The intestinal transit time was measured in the following ways: Each mouse was transferred to a clean empty cage and adapted for 1 h, and treated with 150 μL of Sigma-Aldrich solution (i.g.), then, the production of red fecal particles was monitored every 10 min starting 120 min after administration. The collected stools were weighed and dried at 95°C, and the fecal water content was calculated as the following formula:
Fecal water content %=wet weight−dry weight/wet weight×100%.
2.6 Histopathological examination
As the previous study description (Zhang Q. et al., 2021), stomach, small intestine and colon tissues were fixed in 4% paraformaldehyde solution, and stained with hematoxylin-eosin (HE). The histopathological changes were observed by a light microscope (Nikon eclipsets 2, Japan).
2.7 Detection of interleukin-6 (IL-6), interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α)
The concentrations of total protein were determined using a bicinchoninic acid (BCA, No. BC0524) protein assay kit (Shaanxi Zhonghui, Xi’an, China), and the levels of IL-6, IFN-γ, and TNF-α (No. 20220620, 20220618, 20220724) in serum were measured using enzyme-linked immunosorbent assay (ELISA) kits (Meimian, Yancheng, China).
2.8 IHC analysis
As described in the previous literature (Zhang Q. et al., 2021), Paraffin-embedded small intestine sections were processed and added with primary antibody (NF-κB p65 1:200 dilution, No: AF5006, Affinity, China; TLR4 1:100 dilution, No: GB11519, SERVICEBIO, China) overnight at 4°C, and washed three times with PBS and incubated with Goat anti-Rabbit IgG (Dilution: 1:500, No: BL732A, BIOSS, China) for 40 min at 37°C, the slices were stained with DAB and subsequently washed to visualize the target signals. The measurement of mean density was performed on Image-Pro Plus 6.0 software (Media Cybernetics, Silver Spring, MD, United States).
2.9 WB analysis
As described in the previous literature (Zhang Q. et al., 2021), the sample was processed and 50 μg protein sample was separated by 10% SDS-PAGE gel and the electrophoresis was transferred to the PVDF membrane. TBST was sealed with 5% protein sealing dry powder for 1.5 h, the closure was completed, and TBST was washed three times. Antibodies against β-actin (Dilution: 1:2000; No: BST17353873, BOSTER, China), NF-κB p65 (Dilution: 1:1,000; No: 8242S; CST, United States) and NF-κB p-p65 (Dilution: 1:1,000; No: 3033S; CST, United States) were added and incubated overnight at 4°C. After completion, Goat anti-Rabbit IgG (Dilution: 1:5000, No: BOSTER, BST18J20B18K54, China) was added and incubated for 1 h. The antigen-antibody complex was detected by ECL reagent. Then the gray values of each protein band were determined and analyzed by ImageJ image analysis software.
2.10 Analysis of SCFAs in feces
The feces were collected after final administration, and stored at −80°C for further research. An ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS) system (Vanquish™ Flex UHPLC-TSQ Altis™, Thermo Scientific Corp., Germany) was used to quantitate SCFA in Novogene Co., Ltd. (Beijing, China). Separation was performed on a Waters ACQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm) which was maintained at 40°C. The mobile phase, consisting of 10 mM ammonium acetate in water (solvent A) and acetonitrile: isopropanol (1:1) (solvent B), was delivered at a flow rate of 0.30 mL/min. The mass spectrometer was operated in negative MRM. Parameters were as follows: IonSpray Voltage (−4500 V), Sheath Gas (35 psi), Ion Source Temp (550°C), Auxiliary Gas (50psi), Collision Gas (55psi).
2.11 16 S rRNA gene sequencing
DNA from the fecal samples was extracted, and sequenced by Majorbio biotechnology platform. The extracted DNA was detected by 1% agarose gel electrophoresis. PCR products were detected and quantified using the QuantiFluor-ST™ Blue Fluor Quantitative System. The gene sequencing was performed by Shanghai Meiji Biomedical Technology Co., Ltd. (Contract No: MJ20220916022) and analyzed by Majorbio biotechnology platform.
2.12 Statistical analysis
Statistical analysis was performed by GraphPad Prism 8.3.0 and the data were presented as mean ± standard deviations (SD). Differences were considered statistically significant for p < 0.05, p < 0.01, and P < 0.001.
3 Results
3.1 UPLC-ESI-MS/MS analysis of CD and PCD
The metabolites of CD and PCD were analyzed by UPLC-ESI-MS/MS (Supplementary Figure S1). As showed in Table 1, the results declared that total of 22 metabolites were identified, and the alkaloids, flavonoids, phenylpropanoid compounds were the main metabolites in CD and PCD. Among them, metabolite 17 were not detected in CD, the relative peak area of metabolites 2, 6, 7, 8, 16, 18 and 20 were decreased in PCD, and the others were increased.
TABLE 1
Mass spectral data of the characterized metabolites of CD and PCD by UPLC-ESI-MS/MS.
No.
Compound name
Remark
Molecular formula
M/Z
MS2
Source
Relative peak area
Reference
CD
PCD
1
Epiberberine
[M]+
C20H18NO4
+
336.123
320, 292
+
+
up
Cheng et al. (2023)
2
(S)-Canadine
[M + H]+
C20H21NO4
339.1471
310, 325, 340, 224
+
+
down
Wang X. L et al. (2023)
3
Dehydrocorydaline
[M]+
C22H24NO4+
366.1701
350, 336, 322, 308
+
+
up
Ma et al. (2024)
4
Dihydrosanguinarine
[M + H]+
C20H15NO4
334.1071
306, 304, 290
+
+
up
Ma et al. (2024)
5
Columbamine
[M]+
C20H20NO4
+
338.1387
338, 323, 322, 308, 294, 279
+
+
up
Ma et al. (2024)
6
Wogonin
[M + H]+
C16H12O5
284.0685
285, 211, 179, 167
+
+
down
Hao Y et al. (2020)
7
Ferulic acid
[M + H]+
C10H10O4
194.0579
177, 149, 131, 121, 117, 103
+
+
down
Hao Y. M et al. (2020)
8
Vanillic acid
[M-H]−
C8H8O4
168.0423
167, 152, 108
+
+
down
Hao Y et al. (2020)
9
Berberine
[M + H]+
C20H18NO4
336.1209
321, 320, 318, 306, 304, 292, 278, 275
+
+
up
Hao Y. M et al. (2020)
10
Palmatine
[M-H]+
C21H22NO4
+
352.1544
337, 336, 322, 320, 308, 294, 292, 291
+
+
up
Hao Y et al. (2020)
11
Coptisine
[M]+
C19H14NO4
320.0924
292, 277, 262, 249
+
+
up
Hao Y. M et al. (2020)
12
Beberrubine
[M]+
C19H16NO4
322.1071
307, 279, 250
+
+
up
Hao Y et al. (2020)
13
Jatrorrhizine
[M]+
C20H20NO4
338.1381
322, 308, 294, 280
+
+
up
Hao Y.M et al. (2020)
14
Acacetin
[M + H]+
C16H12O5
284.0685
242, 153
+
+
up
Zhang Y et al. (2021)
15
Luteolin
[M-H]−
C15H10O6
286.0477
285, 267, 200
+
+
up
Hao Y et al. (2020)
16
Salidroside
[M-H]−
C14H20O7
300.1209
299, 179, 119
+
+
down
Hao Y. M et al. (2020)
17
Cryptochlorogenic acid
[M-H]−
C16H18O9
354.0951
353, 309, 191
-
+
up
Wang et al. (2022)
18
Caffeic acid
[M-H]−
C9H8O4
180.0423
181, 163, 135, 117
+
+
down
Wang et al. (2022)
19
Chlorogenic acid
[M-H]−
C16H18O9
354.0951
355, 163, 145
+
+
up
Wang et al. (2022)
20
Protocatechualdehyde
[M-H]−
C7H6O3
138.0317
136, 135, 108
+
+
down
Wang X. L. et al. (2023)
21
Magnoflorine
[M]+
C20H24NO4
+
342.1718
299, 282, 265, 237, 222, 207, 191
+
+
up
Wang K. et al. (2023)
22
8-Oxyberberine
[M + H]+
C20H17NO5
351.1107
337, 336, 319, 308, 292, 291, 278
+
+
up
Wang et al. (2020)
3.2 Effects of CD and PCD on physiological parameters in mice
As shown in Figure 1A, compared to NC group, CD and PCD could significantly reduce the weight gain, and the effect of CD was slightly stronger than that of PCD. Similarly, the food intake of mice in CD group at week 6 was significantly lower than that of NC group (P < 0.05). And the effect of PCD group was not significant (Figure 1B). Food intake between the first and third weeks among different group shown no obvious change. As shown in Figure 1C, in the first week, administration of CD and PCD could increase the fecal water content compared with the NC group, especially in the high dose group. However, with the prolongation of the administration time, the fecal water content showed a decreasing trend. This indicated that diarrhea occurs after the administration of the drug, and then with the prolongation of the administration time, the fecal water content gradually decreases and then constipation occurs, in which the PCD can alleviate the effect of CD. In Figure 1D, administration of CD and PCD could decrease the intestinal transit time at the first week compared with the NC group. However, the intestinal transit time of healthy mice increased to varying degrees with prolongation of medication.
FIGURE 1
Effects of CD and PCD on physiological parameters in mice. (A) weight. (B) food intake. (C) fecal moisture content. (D) intestinal transit of mice. Data were represented as the mean ± SD (n = 6). *p < 0.05, **p < 0.01.
3.3 Effect of CD and PCD on pathological changes in the mouse stomach
In Figure 2, the mucosal, submucosal, muscularis and serous layer of the gastric tissue of the NC group were structurally intact. After 3 weeks of administration there was a small amount of epithelial cell detachment from the gastric tissue in the CD and PCD groups compared to the NC group. After 6 weeks of administration, epithelial cells were shed in the administered group, and a small infiltration of inflammatory cells was seen in the submucosa of the gastric mucosa in the CD-H group.
FIGURE 2
Effects of CD and PCD on pathological changes in the stomach of mice. (Magnification: ×100) (n = 6).
3.4 Effects of CD and PCD on small intestine of pathological changes
It can be seen from the HE stained sections in Figure 3, that the duodenal villi of the NC group were showed irregular leaf-like or columnar shapes, the ileal villi were showed tongue-like or leaf-shaped, and the villi of the jejunum were shorter and narrower, resembling finger-like projections. After 3 weeks of administration, it was observed that the villi in the small intestine were disrupted, with disorganized arrangement of the mucosal cells. Edema was present, along with partial cell necrosis and shedding. In addition, inflammatory molecules infiltrated the tissue. After 6 weeks of administration, the epithelial cells of the small intestinal villi showed swelling, necrosis, and shedding. The arrangement of the villi became disordered and their length and width were shortened. Additionally, a small amount of inflammatory cell infiltration was observed.
FIGURE 3
Effects of CD and PCD on pathological changes in the small intestine in mice. (A) Duodenum. (B) Jejunum. (C) Ileum. (Magnification: ×100) (n = 6).
3.5 Effect of CD and PCD on the length of colon and cecum in mice
In this study, the effects of CD and PCD on cecal and colon length in mice were measured (Supplementary Figure S3). As shown in Figure 4A, the total length of the cecum and colon did not change significantly with increasing administration time and dose. In Figure 4B, the length of the colon in the dosed group decreased to varying degrees after 3 weeks of dosing compared to the NC group. However, there was no significant changes occurred after 6 weeks of administration.
FIGURE 4
Effects of CD and PCD on cecal and colon lengths in mice (A) Length of the cecum and colon. (B) colon length. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05.
3.6 Effect of CD and PCD on pathologic changes in the mouse colon
In Figure 5A, the colonic epithelial muscular layer and mucosal structure of the NC group were intact, with no edema or inflammatory infiltration in the field of view, and the glands and cupped cells were closely and neatly arranged. After 3 weeks of administration, the goblet cells of the colon in mice were partially ruptured and inflammatory infiltrates were appeared, and the basal crypts were partially damaged. After 6 weeks of administration, the goblet cells of the colon in mice were atrophied and inflammatory infiltrates decreased. As shown in Figure 5B, compared with the PCD group, the number of goblet cells in the colon gland part of the mouse in the CD-H group and PCD-H group was significantly reduced after 3 weeks of administration (p < 0.05). Compared with the NC group, the number of goblet cells in the CD-H group was significantly reduced after 6 weeks of administration (p < 0.05).
FIGURE 5
Effects of CD and PCD on pathological changes. (A) Colon. (Magnification: ×100) (B) Number of goblet cells. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05, **p < 0.01.
3.7 Effects of CD and PCD on the levels of IL-6, IFN-γ, and TNF-α in mice
As shown in Figure 6, compared to the NC group, the levels of inflammatory factors in serum of mice were increased by CD. The levels of IL-6, IFN-γ, and TNF-α were increased by CD and PCD. The high-dose group exhibited a more significant increase compared to the low-dose group. Compared with the CD group, PCD alleviated the upward trend of IL-6, IFN-γ, and TNF-α.
FIGURE 6
Effects of CD and PCD on Inflammatory factors in healthy mice. (A) IL-6. (B) TNF-α (C) IFN-γ. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05, **p < 0.01.
3.8 Effects of CD and PCD on IHC and WB of small intestine in mice
As shown in Figure 7, compared with NC group, the expression of NF-κB p65 in CD-H group and PCD-H group was significantly increased, and the expression of NF-κB p65 in CD-H group was more obvious than that in PCD-H group.
FIGURE 7
Effects of CD and PCD on IHC and WB of small intestine in mice. (A) IHC analysis. (Magnification: ×400) (B) Expression of NF-κB p-p65, NF-κB p65 and β-actin of the small intestine in each group tested by WB analysis. Data were represented as the mean ± standard deviation (n = 3). *p < 0.05, **p < 0.01.
3.9 Effects of CD and PCD on SCFAs in mice
The linear regression equations and correlation coefficients were shown in Supplementary Table S1. As shown in Figure 8, compared to the NC group, the content of SCFAs in feces of mice was reduced by CD. The contents of acetic acid, propionic acid, butyric acid, valeric acid, isobutyric acid, 2-methylpenteric acid and isovaleric acid in feces were reduced by CD and PCD. The high-dose group exhibited a more significant decrease compared to the low-dose group. Compared with the CD group, PCD alleviated the downward trend of acetic acid, butyric acid, and valeric acid.
FIGURE 8
Effects of CD and PCD on SCFAs in healthy mice. (A) acetic acid. (B) Propionic acid. (C) Valeric acid. (D) Butyric acid. (E) 2-Methylbutyrate. (F) Isobutyric acid. (G) Isovaleric acid. (H) Total SCFAs. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05, **p < 0.01.
3.10 CD and PCD regulated GM in mice
3.10.1 Effects on GM diversity and richness
Rank-Abundance curves were used to demonstrate species richness and community uniformity. As shown in Figure 9A, in the horizontal direction, the NC group had a wider curve width compared to the administered group, and the PCD group had a wider curve width compared to the CD group, i.e., CD decreases the abundance of the GM, whereas PCD can alleviate the degree of its decrease; the NC group had a more gradual curve compared to the administered group, and the PCD group had a more gradual curve compared to the CD group, which shows that CD decreases the homogeneity of the GM, and PCD alleviates this change. In Figure 9B, the rarefaction curves of all groups tended to be flat, indicating that the amount of data sequenced is sufficient to basically reflect most of the information about the microbial diversity in the samples. In Figure 9C, there was a significant difference in richness and diversity between groups in Alpha diversity analysis, and there was an overall decreasing trend in species diversity of the bacterial flora in the administered group compared to the NC group, which was mitigated by PCD compared to CD. GM species richness and diversity also tended to decrease with increasing administered dose. In Figure 9D, PCoA analysis showed that the sample distances between the same groups were small, which indicated that the structural distribution of the GM was compact and similar. The composition of the intestinal bacteria in both the CD and PCD groups was different from that of the NC group, with the difference in the low-dose group being smaller than that in the high-dose group; and the PCD group was closer to the normal group than the CD group, and its difference was smaller than that of the CD.
FIGURE 9
Effect of CF and CP on the diversity of intestinal microorganisms in mice. (A) Rank-Abundance curves. (B) Rarefaction curves. (C) Alpha. (D) PCoA. Data were expressed as means ± SD (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001.
3.10.2 Effects of CD and PCD on GM structure and composition
As shown in Figure 10A, the species Venn diagram, reflecting the similarity and overlap of species composition between groups, showed that the bacterial colony structure varied greatly between groups. Compared with the NC group, the total number of AVS of the bacterial colonies decreased after administration of the drug, with the decrease in the CD group being more pronounced than that in the PCD group.
FIGURE 10
Effect of CD and PCD on the relative abundance of GM in mice. (A) venn. (B) phylum. (C) genus. (D) Multi-group species variability analysis. (E) Week 3 genus level LEfSe analysis. (F) Week 6 genus level LEfSe analysis. Data were expressed as means ± SD (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001.
As shown in Figure 10B, the dominant species in each group at the phylum level were Firmicutes, Verrucomicrobia, and Bacteroidetes, with a relative abundance of more than 85%, followed by Proteobacteria and Actinobacteria, among others. The experimental study found that CD reduced the proportion of Firmicutes, and the decreasing trend increased with the prolongation of the administration time, in which the decreasing trend was obvious in the CD group compared with the PCD group; the proportion of Verrucomicrobia in the administration group increased, and the degree of elevation in the CD group was greater than that in the PCD group; the proportion of Bacteroidetes in the administration group firstly decreased, and then gradually increased.
As shown in Figure 10C, at the genus level the groups were mainly present in the genera Akkermansia, Lactobacillus, Allobaculum and Bacteroides. Compared with the NC group, the proportion of Akkermansia was elevated by CD and the upward trend was more significant in the CD group than in the PCD group; Lactobacillus was reduced by CD and PCD mitigated this reduction; and the proportion of Allobaculum was decreased in the administered group, and the reduction in the CD group was progressively greater than that in the PCD group with the extension of time.
The analysis of species differentiation at the genus level in multiple groups was shown in Figure 10D, which showed that the abundance of Akkermansia was increased in the administered group compared to the NC group; and the abundance of norank_f_S24-7 was decreased, with a more pronounced effect in the CD group than in the PCD group; the abundance of Lactobacillus and Allobaculum was reduced; and the abundance of Bacteroides was increased; the abundance of norank_o_Clostridiales was decreased, in which the decreasing trend of CD was more obvious than that of PCD; and the abundance of Blautia, Dorea, and Prevotella was increased.
LEfSe analyses, shown in Figures 10E, F, were used to mine for species that differed significantly between groups, and linear discriminant analysis (LDA) scores >3 were selected as the screening criterion used to identify microorganisms with higher relative abundance in each group.
In the third week, the genus level dominant strains in the NC group included norank_f_S24-7, norank_o_Clostridiales, norank_o_Coriobacteriaceae, Desulfovibrio, etc.; genus level dominant strains in the CD group included Akkermansia, unclassified_o_Clostridiales, Christensenella and Oscillospira. CD-H group genus level dominant species include norank_f_Enterobacteriaceae, Enterococcus, Dorea etc.; PCD group genus level dominant species include Lactobacillus; PCD-H group genus level dominant species include Butyricimonas, Bilophila, Parabacteroides, norank_f_Lachnospiraceae and Bacteroides_f_Bacteroidacea. In the sixth week, the genus level dominant strains in the NC group included Allobaculum, norank_f_Coriobacteriaceae, norank_f_S24-7, norank_o_Clostridiales, etc.; in the CD group the genus level dominant strains included Prevotella, norank_f_Lachnospiraceae, Oscillospira, etc.; CD-H group genus level dominant species include Bilophila, Bacteroides_f_Bacteroidacea, Butyricimonas, etc.; PCD group genus level dominant species include norank_f_Christensenellaceae; PCD-H group genus level dominant species include Akkermansia, Sutterella and Streptococcus.
In summary, the result of CD affecting the GM composition of healthy mice was demonstrated by community composition analysis. At the phylum level, CD decreased the proportion of Firmicutes and increased the proportion of Verrucomicrobia, at the genus level elevated the proportion of Akkermansia in the administered group and decreased the proportions of Lactobacillus and Allobaculum, however, PCD mitigated this decreasing trend in comparison to CD. During the administration period, the dominant genera were different in the NC and dosing groups, and the composition of the dominant genera species was altered by CD and PCD, and the dominant genera of each group changed over time.
3.11 Correlation analysis between SCFAs and GM
The correlation analysis of GM and SCFAs suggested that there was a significant positive correlation between GM (Allobaculum and norank_f_S24-7) and SCAFs (Isobutyric acid, 2-Methylbutyrate, Butyric acid, Isovaleric acid and Valeric acid) at the genus level, and there was a significant negative correlation between GM (Akkermansia and Bacteroides) and SCAFs (Isobutyric acid, 2-Methylbutyrate, Isovaleric acid and Valeric acid) at the genus level. In addition, there was a negative correlation between Lactobacillus and SCFAs without a significant difference (Figure 11).
FIGURE 11
Correlation analysis between SCFAs and GM. *p < 0.05, **p < 0.01, ***p < 0.001.
3.1 UPLC-ESI-MS/MS analysis of CD and PCD
The metabolites of CD and PCD were analyzed by UPLC-ESI-MS/MS (Supplementary Figure S1). As showed in Table 1, the results declared that total of 22 metabolites were identified, and the alkaloids, flavonoids, phenylpropanoid compounds were the main metabolites in CD and PCD. Among them, metabolite 17 were not detected in CD, the relative peak area of metabolites 2, 6, 7, 8, 16, 18 and 20 were decreased in PCD, and the others were increased.
TABLE 1
Mass spectral data of the characterized metabolites of CD and PCD by UPLC-ESI-MS/MS.
No.
Compound name
Remark
Molecular formula
M/Z
MS2
Source
Relative peak area
Reference
CD
PCD
1
Epiberberine
[M]+
C20H18NO4
+
336.123
320, 292
+
+
up
Cheng et al. (2023)
2
(S)-Canadine
[M + H]+
C20H21NO4
339.1471
310, 325, 340, 224
+
+
down
Wang X. L et al. (2023)
3
Dehydrocorydaline
[M]+
C22H24NO4+
366.1701
350, 336, 322, 308
+
+
up
Ma et al. (2024)
4
Dihydrosanguinarine
[M + H]+
C20H15NO4
334.1071
306, 304, 290
+
+
up
Ma et al. (2024)
5
Columbamine
[M]+
C20H20NO4
+
338.1387
338, 323, 322, 308, 294, 279
+
+
up
Ma et al. (2024)
6
Wogonin
[M + H]+
C16H12O5
284.0685
285, 211, 179, 167
+
+
down
Hao Y et al. (2020)
7
Ferulic acid
[M + H]+
C10H10O4
194.0579
177, 149, 131, 121, 117, 103
+
+
down
Hao Y. M et al. (2020)
8
Vanillic acid
[M-H]−
C8H8O4
168.0423
167, 152, 108
+
+
down
Hao Y et al. (2020)
9
Berberine
[M + H]+
C20H18NO4
336.1209
321, 320, 318, 306, 304, 292, 278, 275
+
+
up
Hao Y. M et al. (2020)
10
Palmatine
[M-H]+
C21H22NO4
+
352.1544
337, 336, 322, 320, 308, 294, 292, 291
+
+
up
Hao Y et al. (2020)
11
Coptisine
[M]+
C19H14NO4
320.0924
292, 277, 262, 249
+
+
up
Hao Y. M et al. (2020)
12
Beberrubine
[M]+
C19H16NO4
322.1071
307, 279, 250
+
+
up
Hao Y et al. (2020)
13
Jatrorrhizine
[M]+
C20H20NO4
338.1381
322, 308, 294, 280
+
+
up
Hao Y.M et al. (2020)
14
Acacetin
[M + H]+
C16H12O5
284.0685
242, 153
+
+
up
Zhang Y et al. (2021)
15
Luteolin
[M-H]−
C15H10O6
286.0477
285, 267, 200
+
+
up
Hao Y et al. (2020)
16
Salidroside
[M-H]−
C14H20O7
300.1209
299, 179, 119
+
+
down
Hao Y. M et al. (2020)
17
Cryptochlorogenic acid
[M-H]−
C16H18O9
354.0951
353, 309, 191
-
+
up
Wang et al. (2022)
18
Caffeic acid
[M-H]−
C9H8O4
180.0423
181, 163, 135, 117
+
+
down
Wang et al. (2022)
19
Chlorogenic acid
[M-H]−
C16H18O9
354.0951
355, 163, 145
+
+
up
Wang et al. (2022)
20
Protocatechualdehyde
[M-H]−
C7H6O3
138.0317
136, 135, 108
+
+
down
Wang X. L. et al. (2023)
21
Magnoflorine
[M]+
C20H24NO4
+
342.1718
299, 282, 265, 237, 222, 207, 191
+
+
up
Wang K. et al. (2023)
22
8-Oxyberberine
[M + H]+
C20H17NO5
351.1107
337, 336, 319, 308, 292, 291, 278
+
+
up
Wang et al. (2020)
3.2 Effects of CD and PCD on physiological parameters in mice
As shown in Figure 1A, compared to NC group, CD and PCD could significantly reduce the weight gain, and the effect of CD was slightly stronger than that of PCD. Similarly, the food intake of mice in CD group at week 6 was significantly lower than that of NC group (P < 0.05). And the effect of PCD group was not significant (Figure 1B). Food intake between the first and third weeks among different group shown no obvious change. As shown in Figure 1C, in the first week, administration of CD and PCD could increase the fecal water content compared with the NC group, especially in the high dose group. However, with the prolongation of the administration time, the fecal water content showed a decreasing trend. This indicated that diarrhea occurs after the administration of the drug, and then with the prolongation of the administration time, the fecal water content gradually decreases and then constipation occurs, in which the PCD can alleviate the effect of CD. In Figure 1D, administration of CD and PCD could decrease the intestinal transit time at the first week compared with the NC group. However, the intestinal transit time of healthy mice increased to varying degrees with prolongation of medication.
FIGURE 1
Effects of CD and PCD on physiological parameters in mice. (A) weight. (B) food intake. (C) fecal moisture content. (D) intestinal transit of mice. Data were represented as the mean ± SD (n = 6). *p < 0.05, **p < 0.01.
3.3 Effect of CD and PCD on pathological changes in the mouse stomach
In Figure 2, the mucosal, submucosal, muscularis and serous layer of the gastric tissue of the NC group were structurally intact. After 3 weeks of administration there was a small amount of epithelial cell detachment from the gastric tissue in the CD and PCD groups compared to the NC group. After 6 weeks of administration, epithelial cells were shed in the administered group, and a small infiltration of inflammatory cells was seen in the submucosa of the gastric mucosa in the CD-H group.
FIGURE 2
Effects of CD and PCD on pathological changes in the stomach of mice. (Magnification: ×100) (n = 6).
3.4 Effects of CD and PCD on small intestine of pathological changes
It can be seen from the HE stained sections in Figure 3, that the duodenal villi of the NC group were showed irregular leaf-like or columnar shapes, the ileal villi were showed tongue-like or leaf-shaped, and the villi of the jejunum were shorter and narrower, resembling finger-like projections. After 3 weeks of administration, it was observed that the villi in the small intestine were disrupted, with disorganized arrangement of the mucosal cells. Edema was present, along with partial cell necrosis and shedding. In addition, inflammatory molecules infiltrated the tissue. After 6 weeks of administration, the epithelial cells of the small intestinal villi showed swelling, necrosis, and shedding. The arrangement of the villi became disordered and their length and width were shortened. Additionally, a small amount of inflammatory cell infiltration was observed.
FIGURE 3
Effects of CD and PCD on pathological changes in the small intestine in mice. (A) Duodenum. (B) Jejunum. (C) Ileum. (Magnification: ×100) (n = 6).
3.5 Effect of CD and PCD on the length of colon and cecum in mice
In this study, the effects of CD and PCD on cecal and colon length in mice were measured (Supplementary Figure S3). As shown in Figure 4A, the total length of the cecum and colon did not change significantly with increasing administration time and dose. In Figure 4B, the length of the colon in the dosed group decreased to varying degrees after 3 weeks of dosing compared to the NC group. However, there was no significant changes occurred after 6 weeks of administration.
FIGURE 4
Effects of CD and PCD on cecal and colon lengths in mice (A) Length of the cecum and colon. (B) colon length. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05.
3.6 Effect of CD and PCD on pathologic changes in the mouse colon
In Figure 5A, the colonic epithelial muscular layer and mucosal structure of the NC group were intact, with no edema or inflammatory infiltration in the field of view, and the glands and cupped cells were closely and neatly arranged. After 3 weeks of administration, the goblet cells of the colon in mice were partially ruptured and inflammatory infiltrates were appeared, and the basal crypts were partially damaged. After 6 weeks of administration, the goblet cells of the colon in mice were atrophied and inflammatory infiltrates decreased. As shown in Figure 5B, compared with the PCD group, the number of goblet cells in the colon gland part of the mouse in the CD-H group and PCD-H group was significantly reduced after 3 weeks of administration (p < 0.05). Compared with the NC group, the number of goblet cells in the CD-H group was significantly reduced after 6 weeks of administration (p < 0.05).
FIGURE 5
Effects of CD and PCD on pathological changes. (A) Colon. (Magnification: ×100) (B) Number of goblet cells. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05, **p < 0.01.
3.7 Effects of CD and PCD on the levels of IL-6, IFN-γ, and TNF-α in mice
As shown in Figure 6, compared to the NC group, the levels of inflammatory factors in serum of mice were increased by CD. The levels of IL-6, IFN-γ, and TNF-α were increased by CD and PCD. The high-dose group exhibited a more significant increase compared to the low-dose group. Compared with the CD group, PCD alleviated the upward trend of IL-6, IFN-γ, and TNF-α.
FIGURE 6
Effects of CD and PCD on Inflammatory factors in healthy mice. (A) IL-6. (B) TNF-α (C) IFN-γ. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05, **p < 0.01.
3.8 Effects of CD and PCD on IHC and WB of small intestine in mice
As shown in Figure 7, compared with NC group, the expression of NF-κB p65 in CD-H group and PCD-H group was significantly increased, and the expression of NF-κB p65 in CD-H group was more obvious than that in PCD-H group.
FIGURE 7
Effects of CD and PCD on IHC and WB of small intestine in mice. (A) IHC analysis. (Magnification: ×400) (B) Expression of NF-κB p-p65, NF-κB p65 and β-actin of the small intestine in each group tested by WB analysis. Data were represented as the mean ± standard deviation (n = 3). *p < 0.05, **p < 0.01.
3.9 Effects of CD and PCD on SCFAs in mice
The linear regression equations and correlation coefficients were shown in Supplementary Table S1. As shown in Figure 8, compared to the NC group, the content of SCFAs in feces of mice was reduced by CD. The contents of acetic acid, propionic acid, butyric acid, valeric acid, isobutyric acid, 2-methylpenteric acid and isovaleric acid in feces were reduced by CD and PCD. The high-dose group exhibited a more significant decrease compared to the low-dose group. Compared with the CD group, PCD alleviated the downward trend of acetic acid, butyric acid, and valeric acid.
FIGURE 8
Effects of CD and PCD on SCFAs in healthy mice. (A) acetic acid. (B) Propionic acid. (C) Valeric acid. (D) Butyric acid. (E) 2-Methylbutyrate. (F) Isobutyric acid. (G) Isovaleric acid. (H) Total SCFAs. Data were represented as the mean ± standard deviation (n = 6). *p < 0.05, **p < 0.01.
3.10 CD and PCD regulated GM in mice
3.10.1 Effects on GM diversity and richness
Rank-Abundance curves were used to demonstrate species richness and community uniformity. As shown in Figure 9A, in the horizontal direction, the NC group had a wider curve width compared to the administered group, and the PCD group had a wider curve width compared to the CD group, i.e., CD decreases the abundance of the GM, whereas PCD can alleviate the degree of its decrease; the NC group had a more gradual curve compared to the administered group, and the PCD group had a more gradual curve compared to the CD group, which shows that CD decreases the homogeneity of the GM, and PCD alleviates this change. In Figure 9B, the rarefaction curves of all groups tended to be flat, indicating that the amount of data sequenced is sufficient to basically reflect most of the information about the microbial diversity in the samples. In Figure 9C, there was a significant difference in richness and diversity between groups in Alpha diversity analysis, and there was an overall decreasing trend in species diversity of the bacterial flora in the administered group compared to the NC group, which was mitigated by PCD compared to CD. GM species richness and diversity also tended to decrease with increasing administered dose. In Figure 9D, PCoA analysis showed that the sample distances between the same groups were small, which indicated that the structural distribution of the GM was compact and similar. The composition of the intestinal bacteria in both the CD and PCD groups was different from that of the NC group, with the difference in the low-dose group being smaller than that in the high-dose group; and the PCD group was closer to the normal group than the CD group, and its difference was smaller than that of the CD.
FIGURE 9
Effect of CF and CP on the diversity of intestinal microorganisms in mice. (A) Rank-Abundance curves. (B) Rarefaction curves. (C) Alpha. (D) PCoA. Data were expressed as means ± SD (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001.
3.10.2 Effects of CD and PCD on GM structure and composition
As shown in Figure 10A, the species Venn diagram, reflecting the similarity and overlap of species composition between groups, showed that the bacterial colony structure varied greatly between groups. Compared with the NC group, the total number of AVS of the bacterial colonies decreased after administration of the drug, with the decrease in the CD group being more pronounced than that in the PCD group.
FIGURE 10
Effect of CD and PCD on the relative abundance of GM in mice. (A) venn. (B) phylum. (C) genus. (D) Multi-group species variability analysis. (E) Week 3 genus level LEfSe analysis. (F) Week 6 genus level LEfSe analysis. Data were expressed as means ± SD (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001.
As shown in Figure 10B, the dominant species in each group at the phylum level were Firmicutes, Verrucomicrobia, and Bacteroidetes, with a relative abundance of more than 85%, followed by Proteobacteria and Actinobacteria, among others. The experimental study found that CD reduced the proportion of Firmicutes, and the decreasing trend increased with the prolongation of the administration time, in which the decreasing trend was obvious in the CD group compared with the PCD group; the proportion of Verrucomicrobia in the administration group increased, and the degree of elevation in the CD group was greater than that in the PCD group; the proportion of Bacteroidetes in the administration group firstly decreased, and then gradually increased.
As shown in Figure 10C, at the genus level the groups were mainly present in the genera Akkermansia, Lactobacillus, Allobaculum and Bacteroides. Compared with the NC group, the proportion of Akkermansia was elevated by CD and the upward trend was more significant in the CD group than in the PCD group; Lactobacillus was reduced by CD and PCD mitigated this reduction; and the proportion of Allobaculum was decreased in the administered group, and the reduction in the CD group was progressively greater than that in the PCD group with the extension of time.
The analysis of species differentiation at the genus level in multiple groups was shown in Figure 10D, which showed that the abundance of Akkermansia was increased in the administered group compared to the NC group; and the abundance of norank_f_S24-7 was decreased, with a more pronounced effect in the CD group than in the PCD group; the abundance of Lactobacillus and Allobaculum was reduced; and the abundance of Bacteroides was increased; the abundance of norank_o_Clostridiales was decreased, in which the decreasing trend of CD was more obvious than that of PCD; and the abundance of Blautia, Dorea, and Prevotella was increased.
LEfSe analyses, shown in Figures 10E, F, were used to mine for species that differed significantly between groups, and linear discriminant analysis (LDA) scores >3 were selected as the screening criterion used to identify microorganisms with higher relative abundance in each group.
In the third week, the genus level dominant strains in the NC group included norank_f_S24-7, norank_o_Clostridiales, norank_o_Coriobacteriaceae, Desulfovibrio, etc.; genus level dominant strains in the CD group included Akkermansia, unclassified_o_Clostridiales, Christensenella and Oscillospira. CD-H group genus level dominant species include norank_f_Enterobacteriaceae, Enterococcus, Dorea etc.; PCD group genus level dominant species include Lactobacillus; PCD-H group genus level dominant species include Butyricimonas, Bilophila, Parabacteroides, norank_f_Lachnospiraceae and Bacteroides_f_Bacteroidacea. In the sixth week, the genus level dominant strains in the NC group included Allobaculum, norank_f_Coriobacteriaceae, norank_f_S24-7, norank_o_Clostridiales, etc.; in the CD group the genus level dominant strains included Prevotella, norank_f_Lachnospiraceae, Oscillospira, etc.; CD-H group genus level dominant species include Bilophila, Bacteroides_f_Bacteroidacea, Butyricimonas, etc.; PCD group genus level dominant species include norank_f_Christensenellaceae; PCD-H group genus level dominant species include Akkermansia, Sutterella and Streptococcus.
In summary, the result of CD affecting the GM composition of healthy mice was demonstrated by community composition analysis. At the phylum level, CD decreased the proportion of Firmicutes and increased the proportion of Verrucomicrobia, at the genus level elevated the proportion of Akkermansia in the administered group and decreased the proportions of Lactobacillus and Allobaculum, however, PCD mitigated this decreasing trend in comparison to CD. During the administration period, the dominant genera were different in the NC and dosing groups, and the composition of the dominant genera species was altered by CD and PCD, and the dominant genera of each group changed over time.
3.10.1 Effects on GM diversity and richness
Rank-Abundance curves were used to demonstrate species richness and community uniformity. As shown in Figure 9A, in the horizontal direction, the NC group had a wider curve width compared to the administered group, and the PCD group had a wider curve width compared to the CD group, i.e., CD decreases the abundance of the GM, whereas PCD can alleviate the degree of its decrease; the NC group had a more gradual curve compared to the administered group, and the PCD group had a more gradual curve compared to the CD group, which shows that CD decreases the homogeneity of the GM, and PCD alleviates this change. In Figure 9B, the rarefaction curves of all groups tended to be flat, indicating that the amount of data sequenced is sufficient to basically reflect most of the information about the microbial diversity in the samples. In Figure 9C, there was a significant difference in richness and diversity between groups in Alpha diversity analysis, and there was an overall decreasing trend in species diversity of the bacterial flora in the administered group compared to the NC group, which was mitigated by PCD compared to CD. GM species richness and diversity also tended to decrease with increasing administered dose. In Figure 9D, PCoA analysis showed that the sample distances between the same groups were small, which indicated that the structural distribution of the GM was compact and similar. The composition of the intestinal bacteria in both the CD and PCD groups was different from that of the NC group, with the difference in the low-dose group being smaller than that in the high-dose group; and the PCD group was closer to the normal group than the CD group, and its difference was smaller than that of the CD.
FIGURE 9
Effect of CF and CP on the diversity of intestinal microorganisms in mice. (A) Rank-Abundance curves. (B) Rarefaction curves. (C) Alpha. (D) PCoA. Data were expressed as means ± SD (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001.
3.10.2 Effects of CD and PCD on GM structure and composition
As shown in Figure 10A, the species Venn diagram, reflecting the similarity and overlap of species composition between groups, showed that the bacterial colony structure varied greatly between groups. Compared with the NC group, the total number of AVS of the bacterial colonies decreased after administration of the drug, with the decrease in the CD group being more pronounced than that in the PCD group.
FIGURE 10
Effect of CD and PCD on the relative abundance of GM in mice. (A) venn. (B) phylum. (C) genus. (D) Multi-group species variability analysis. (E) Week 3 genus level LEfSe analysis. (F) Week 6 genus level LEfSe analysis. Data were expressed as means ± SD (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001.
As shown in Figure 10B, the dominant species in each group at the phylum level were Firmicutes, Verrucomicrobia, and Bacteroidetes, with a relative abundance of more than 85%, followed by Proteobacteria and Actinobacteria, among others. The experimental study found that CD reduced the proportion of Firmicutes, and the decreasing trend increased with the prolongation of the administration time, in which the decreasing trend was obvious in the CD group compared with the PCD group; the proportion of Verrucomicrobia in the administration group increased, and the degree of elevation in the CD group was greater than that in the PCD group; the proportion of Bacteroidetes in the administration group firstly decreased, and then gradually increased.
As shown in Figure 10C, at the genus level the groups were mainly present in the genera Akkermansia, Lactobacillus, Allobaculum and Bacteroides. Compared with the NC group, the proportion of Akkermansia was elevated by CD and the upward trend was more significant in the CD group than in the PCD group; Lactobacillus was reduced by CD and PCD mitigated this reduction; and the proportion of Allobaculum was decreased in the administered group, and the reduction in the CD group was progressively greater than that in the PCD group with the extension of time.
The analysis of species differentiation at the genus level in multiple groups was shown in Figure 10D, which showed that the abundance of Akkermansia was increased in the administered group compared to the NC group; and the abundance of norank_f_S24-7 was decreased, with a more pronounced effect in the CD group than in the PCD group; the abundance of Lactobacillus and Allobaculum was reduced; and the abundance of Bacteroides was increased; the abundance of norank_o_Clostridiales was decreased, in which the decreasing trend of CD was more obvious than that of PCD; and the abundance of Blautia, Dorea, and Prevotella was increased.
LEfSe analyses, shown in Figures 10E, F, were used to mine for species that differed significantly between groups, and linear discriminant analysis (LDA) scores >3 were selected as the screening criterion used to identify microorganisms with higher relative abundance in each group.
In the third week, the genus level dominant strains in the NC group included norank_f_S24-7, norank_o_Clostridiales, norank_o_Coriobacteriaceae, Desulfovibrio, etc.; genus level dominant strains in the CD group included Akkermansia, unclassified_o_Clostridiales, Christensenella and Oscillospira. CD-H group genus level dominant species include norank_f_Enterobacteriaceae, Enterococcus, Dorea etc.; PCD group genus level dominant species include Lactobacillus; PCD-H group genus level dominant species include Butyricimonas, Bilophila, Parabacteroides, norank_f_Lachnospiraceae and Bacteroides_f_Bacteroidacea. In the sixth week, the genus level dominant strains in the NC group included Allobaculum, norank_f_Coriobacteriaceae, norank_f_S24-7, norank_o_Clostridiales, etc.; in the CD group the genus level dominant strains included Prevotella, norank_f_Lachnospiraceae, Oscillospira, etc.; CD-H group genus level dominant species include Bilophila, Bacteroides_f_Bacteroidacea, Butyricimonas, etc.; PCD group genus level dominant species include norank_f_Christensenellaceae; PCD-H group genus level dominant species include Akkermansia, Sutterella and Streptococcus.
In summary, the result of CD affecting the GM composition of healthy mice was demonstrated by community composition analysis. At the phylum level, CD decreased the proportion of Firmicutes and increased the proportion of Verrucomicrobia, at the genus level elevated the proportion of Akkermansia in the administered group and decreased the proportions of Lactobacillus and Allobaculum, however, PCD mitigated this decreasing trend in comparison to CD. During the administration period, the dominant genera were different in the NC and dosing groups, and the composition of the dominant genera species was altered by CD and PCD, and the dominant genera of each group changed over time.
3.11 Correlation analysis between SCFAs and GM
The correlation analysis of GM and SCFAs suggested that there was a significant positive correlation between GM (Allobaculum and norank_f_S24-7) and SCAFs (Isobutyric acid, 2-Methylbutyrate, Butyric acid, Isovaleric acid and Valeric acid) at the genus level, and there was a significant negative correlation between GM (Akkermansia and Bacteroides) and SCAFs (Isobutyric acid, 2-Methylbutyrate, Isovaleric acid and Valeric acid) at the genus level. In addition, there was a negative correlation between Lactobacillus and SCFAs without a significant difference (Figure 11).
FIGURE 11
Correlation analysis between SCFAs and GM. *p < 0.05, **p < 0.01, ***p < 0.001.
4 Discussion
In this study, CD was found to affect the composition of the GM in healthy mice, reduced the diversity of GM and the content of SCFAs. In addition, the bidirectional modulating effect of CD was confirmed, which had both laxative and astringent effects, and PCD produced a milder effect relative to CD.
Symptoms of diarrhea were that the feces pass through the gastrointestinal tract for a shorter period of time, the stools become soft or the feces are watery; the primary symptom of constipation is prolonged defecation time, less water in feces, dry and hard stools (Marques et al., 2023; Wen et al., 2023). Diarrhea and constipation were accompanied by damage to the gastrointestinal tract such as destruction of gastrointestinal villi and inflammatory infiltrates, and the causes of gastrointestinal damage are related to the release of inflammatory factors such as IL-6 and TNF-α (Seyedmirzaee et al., 2016; Yao et al., 2021). Meanwhile, studies have shown that the occurrence of intestinal inflammation is related to the regulation of PI3K/Akt/NF-κB signaling pathway (Yan et al., 2022). Biological factors such as cytokines or viruses can lead to phosphorylation of PI3K to activate Akt, and the activated Akt can then activate NF-κB to regulate the expression of IFN-γ, TNF-α or other inflammatory factors (Rahmani et al., 2020; Choi et al., 2022). Our study showed that the administration of CD initially induced diarrhea in mice, which later progressed to constipation with prolonged administration, with significant changes in fecal water content and intestinal transit time. The HE stained sections of the gastrointestinal tract show that CD causes damage to the gastrointestinal tract, and the study found that the expression of NF-κB and inflammatory factors were increased after producing mild constipation in the sixth week. However, PCD produced weaker effects than CD.
SCFAs were the end products of fermentation of dietary fiber by GM, mainly including acetic acid, propionic acid and butyric acid (Tan et al., 2023). They served several essential functions, including the maintenance of intestinal mucosal integrity, enhancement of immune system responses, promotion of intestinal electrolyte balance, regulation of gut microbiota, and resistance to inflammation (Rauf et al., 2022). Impaired intestinal epithelial integrity can lead to chronic low-grade inflammation. However, acetic and butyric acids were considered to enhance epithelial integrity (Furusawa et al., 2013; Tong et al., 2016), and acetic acid could promote intestinal motility (Niwa et al., 2002). It has been found that the levels of butyric acid reduced lead to increased oxygenation thereby promoting microecological dysregulation, and enhancing the expansion of aerobic pathogens in the gut (Handa et al., 2023). In the present study, it was found that CD reduced the contents of acetic acid, propionic acid, butyric acid, valeric acid, isobutyric acid, 2-methylpentanoic acid and isovaleric acid in feces of mice.
The growing evidence suggested that the balance of GM played an important role in the development of human health (Adak and Khan, 2019; Kataoka, 2016; Yang Y. et al., 2023). Both Coptidis Rhizoma and its formulation had been shown to regulate the composition of GM (Chen et al., 2018; Yang F. et al., 2023). The results of this study showed that the abundance of Firmicutes decreased, the proportion of Verrucomicrobia increased, and Bacteroidetes first decreased and then gradually increased in the administered group compared to the normal group. These changes may further lead to some bacterial imbalances. For example, we found that at the genus level Akkermansia, Dorea, Bacteroides, and Blautia were upregulated in the CD group compared to the NC group, while the levels of Lactobacillus, Allobaculum, Ruminococcus, and norank_f_S24-7 were reduced. It is noteworthy that PCD can alleviate the GM dysbiosis induced by CD. The increased abundance of Akkermansia can exacerbate the occurrence and development of intestinal inflammatory reactions (Castro-Mejía et al., 2016). Excess Bacteroides increases mucus degradation, thereby reducing intestinal inflammation, especially the colonic mucus barrier, by reducing bacterial interactions with intestinal epithelial cells (Desai et al., 2016). Lactobacillus is the main probiotic in the gut and plays a crucial role in maintaining the microecological balance of the gastrointestinal tract (Huang et al., 2022). The relative abundance of Allobaculum had been reported to be positively correlated with the levels of RORγt in the ileum (Cox et al., 2014).
CD regulates short-chain fatty acid metabolism by affecting the composition of GM in healthy mice, reducing the abundance and homogeneity of GM. These had been partially validated in correlation analyses between gut flora and SCFAs. For example, there was a significant positive correlation between Allobaculum and SCFAs (isobutyric, 2-methylbutyric, butyric, isovaleric, isovaleric, and valeric acids) and a significant negative correlation between GM (Akkermansia and Bacteroides) and SCFAs (isobutyric, 2-methylbutyric, isovaleric, and valeric acids). Additionally, there was a negative correlation between Lactobacillus and SCAFs with no significant difference.
Numerous studies have shown that GM affects intestinal immune homeostasis, with SCFAs being the primary mediators (Zhang S. et al., 2023). GM was found to activate T cells and induce the production of inflammatory factors through its metabolite SCFAs. In addition, SCFAs inhibited IL-6 and TNF-α production by monocytes, myeloid dendritic cells, and plasmacytoid dendritic cells (Porbahaie et al., 2023). Butyrate reduces the development of interferon-gamma (IFN-γ) generating cells while promoting the development of regulatory T cells (Duan et al., 2023). Decrease in Lactobacillus can lead to the production of TNF-α and IFN-γ and induce IL-6 and IL-23 infiltration into helper T cells 17 (Th17), thus inducing an inflammatory response (Gerassy-Vainberg et al., 2018). It was shown that CD and PCD decreased the levels of SCFAs and elevated levels of TNF-α, IL-6 and IFN-γ, and the effect of CD was more obvious. In summary, PCD alleviated the gastrointestinal injury and abnormal levels of inflammatory factors caused by CD, which are related to PCD’s regulation of GM disorders and GM mediated SCFA changes.
PCD is a processed product of CD. According to the theory of TCM, CD may cause gastrointestinal damage due to side effects if used for a long period of time or in large quantities, so it is necessary to process it so as to mitigate the effects and alleviate the side effects. This study also showed that PCD was weaker than CD in causing damage to the gastrointestinal tract of healthy mice. For example, PCD ameliorates CD-induced weight loss as well as histopathological damage to the gastrointestinal tract and levels of inflammatory factors. Moreover, CD-H showed a tendency to exacerbate the inflammatory damage in the gastrointestinal tract induced by CD. These findings suggest that PCD can reduce the side effects of CD and increase the range of safe therapeutic doses for CD. This is consistent with the theory that processing of Chinese herbal medicines can reduce their toxicity and maintain their efficacy. These findings suggested that processing of CD can reduce its side effects.
In general, the results of the present study indicated that CD can affect the balance of GM in healthy mice by modulating the abundance of Akkermansia, Allobaculum and Bacteroides. However, only correlation analyses of SCFAs and GM were performed in this study, and further causal link has not been established. Therefore, follow-up studies will screen differential strains based on macro-genome sequencing technology, and then compare and analyze their effects on mouse GM under the intervention of the differential strains, so as to elucidate the causal relationship between the side effects induced by CD and the alteration of intestinal flora.
5 Conclusion
The current study demonstrated that CD can cause mild diarrhea and constipation, which may be associated with GM disorders and GM-mediated changes in SCFAs, as well as the anomalous expression of NF-κB and the release of inflammatory factors, and induce pathological intestinal damage, and the effects of PCD were weaker than that of CD. Furthermore, the alleviating effect of PCD on gastrointestinal injury may be due to the differences in the types and contents of ingredients in PCD. And this study contributes to our understanding of the different effects of CD and PCD on the intestine, and provides a reference for safe and rational use of CD in clinics. |
Title: DDR1 Drives Malignant Progression of Gastric Cancer by Suppressing HIF‐1α Ubiquitination and Degradation | Body:
1
Introduction
Gastric cancer (GC) is the second leading cause of cancer‐related deaths worldwide, causing 769 000 deaths.[
1
] GC can be curatively managed by surgical resection at early stages, with a 5‐year survival rate of more than 90%.[
2
,
3
] However, the molecular and clinical characteristics of GC are highly heterogeneous, leading to the poor prognosis of late‐stage GC. After spread to distant organs, the survival rate of patients with GC is only <10%.[
4
] Recently, targeted therapy, which targets specific genes or proteins with improved efficacy and largely reduces the side effects, has emerged as a promising therapeutic approach for malignancies.[
5
] However, the lack of specific treatment targets for GC limits the application of targeted therapy.[
6
] Thus, it is highly urgent to explore new treatment targets for GC.
The extracellular matrix (ECM), an intricate network of extracellular macromolecules and minerals, is crucial for physical tissue maintenance as well as diverse cellular processes, including proliferation, adhesion, migration, polarity, differentiation, and apoptosis.[
7
] Compelling evidence suggests that collagen, one of the most important components of the ECM,[
8
] can regulate almost every biological characteristic of both tumor cells and the stroma predominantly through ECM‐cells interactions.[
9
] Discoidin domain receptor 1 (DDR1) is a member of the transmembrane receptor tyrosine kinase (RTK) family and plays vital roles in ECM‐cells interactions as a collagen receptor.[
10
,
11
] Mechanistically, DDR1 mediates collagen‐induced signal transduction[
10
] and triggers chemoresistance and immune evasion in tumors.[
12
,
13
,
14
,
15
] In GC, elevated DDR1 expression is correlated with worse survival,[
16
] and inhibition of DDR1 is found to retard metastasis, indicating the crucial roles of DDR1 in GC.[
16
,
17
] However, the in‐depth mechanism underlying the role of DDR1 in GC remains largely unknown.
During malignant progression, angiogenesis is a crucial process facilitating ecosystem maintenance and tumor metastasis.[
18
] The ECM plays critical roles in angiogenesis by secreting proangiogenic growth factors and stabilizing vascular tissues.[
19
] Hypoxia‐inducible factor‐1α (HIF‐1α), a transcription factor, promotes the transcription of proangiogenic genes, such as vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptors (VEGFR). In addition to angiogenesis, many other cellular processes in tumors, including metastasis, aerobic glycolysis and inflammation, also depend on HIF‐1α.[
20
] To date, the effects of DDR1, as a major mediator of ECM signaling, on angiogenesis and HIF‐1α are not well investigated.
Here, we revealed the mechanism by which DDR1 binds to HIF‐1α to inhibit its ubiquitin‐mediated degradation and promote angiogenesis in GC. In addition, our study suggested that HIF‐1α contributes to the activation of Ras Homolog Family Member A (RhoA)/Rho‐associated protein kinase 1 (ROCK1) signaling induced by DDR1 and consequently results in cytoskeleton reorganization and GC metastasis. Furthermore, our data showed that DDR1 inhibitors suppressed progression and angiogenesis in patient‐derived xenograft (PDX) and organoid models, highlighting the translational value of targeting the DDR1‐HIF1α axis for treating GC. Taken together, our studies first elucidated the intracellular mechanism of DDR1 in GC and its great therapeutic potential in translational medicine.
2
Results
2.1
High DDR1 Expression Positively Correlates with Malignant Progression in Human GC
The ECM is the major component of the tumor microenvironment (TME), exerting great influence on cellular phenotypes and signal transduction via ECM‐cell interactions.[
21
] As collagen is the most abundant component of the ECM, collagen receptors in tumor cells have been proven to greatly affect the characteristics of tumors. In our work, we analyzed the levels of collagen receptors in GC and adjacent normal tissues, based on data from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA‐STAD) database. The results revealed that DDR1 was the only upregulated collagen‐binding receptor with kinase activity in GC (Figure
1a). Data from GSE51575 and GSE13861 also confirmed the elevated expression of DDR1 in GC tissues (Figure S1a, Supporting Information). In addition, 8 paired nontumor and tumor tissues from GC patients in the First Affiliated Hospital of Sun Yat‐sen University (FAHS) were analyzed, and we found that DDR1 expression was also higher in the tumor tissues in this cohort (Figure 1b; Figure S1b, Supporting Information). The immunohistochemistry (IHC) results showed that DDR1 expression positively correlated with tumor grade in GC (Figure 1c,d), indicating the vital role of DDR1 in GC progression. Since DDR1 mutation has also been demonstrated to be positively correlated with poor prognosis in breast cancer,[
22
] we analyzed DDR1 mutations based on cBioPortal and a GC cohort from our center.[
23
,
24
] However, only 14 mutations were found in 534 patients in cBioPortal (Figure 1e), and we found 13 S495S mutations, suggesting that mutation might not be the primary contributor to GC progression (Figure 1f). Finally, the high DDR1 group showed an inferior prognosis compared to the low DDR1 group (Figure 1g; Figure S1c, Supporting Information). Multivariate survival analyses suggested that DDR1 expression was a significant prognostic factor, indicating the critical role of DDR1 in GC (Tables S1–S3, Supporting Information).
Figure 1
High DDR1 expression positively correlates with malignant progression in GC. a) Comparative gene expression analysis of collagen receptors (DDR1, DDR2, integrin β1, integrin α1, integrin α2, integrin α10, PLA2R, Endo180) between GC and adjacent normal tissues based on data from the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA‐STAD) database (Cancer Genome Atlas Research Network, 2008). b) The DDR1 protein levels in GC tissues were higher than those in the matched stomach tissues, as measured by western blot analysis. The 8 paired tumor tissues and normal stomach tissues were from the FAHS cohort. c,d) DDR1 expression was significantly correlated with the aggressiveness of GC, as determined by IHC staining in normal gastric mucosa tissues (n = 58) and GC tissues (n = 182) from the FAHS cohort. Representative IHC staining images c) and quantification d) of DDR1 expression in normal gastric mucosa tissues (Normal) and in well, moderately and poorly differentiated GC tissues. Scale bar of upper panel: 100 µm. Scale bar of lower panel: 50 µm. e) DDR1 alterations in GC from the indicated studies are summarized based on data from cBioPortal. f) The S495S (13/131) and A495S (0/131) mutations of DDR1 were detected by sequencing based on a GC cohort from the FAHS (n = 131). g) GC patients with high DDR1 expression showed worse overall survival. Kaplan‒Meier analysis was performed according to DDR1 expression evaluated by IHC staining (log rank test; n = 182). h) Gene Ontology enrichment analysis of biological processes for up‐ and down‐regulated genes between HGC27 cells transfected with DDR1‐expressing versus control vector. i) Representative images of DDR1 (green) and CD31 (red) co‐staining in GC tissues, as revealed by IF staining (n = 32). Scale bar: 50 µm. j) Correlation matrix showing the correlations between DDR1 expression and VEGF‐A, VEGF‐B, and VEGF‐C expression in GC tissues based on data from the TCGA‐STAD cohort (Spearman test). k) Representative images of IHC staining for DDR1 and VEGF‐A in GC tissues. Scale bar: 25 µm. All data are presented as the mean ± SEM from three independent experiments. The P values in panels (a) were calculated by Student's t‐test. *P < 0.05, ***P < 0.001, ns: not significant.
However, the biological significance of DDR1 in GC remains largely unclear. Thus, we performed whole transcriptome analysis to investigate the biological function of DDR1. GO enrichment analysis revealed that blood vessel morphogenesis and morphogenesis of branching structures were significantly enriched in DDR1‐overexpressed GC cells (Figure 1h). During IHC staining, we noticed that the blood perfusion in the high DDR1 group seemed to be better than that in the low DDR1 group, indicating the proangiogenic effect of DDR1. Immunofluorescence (IF) staining of CD31, a marker of blood vessels, also revealed positive correlations between the DDR1 and CD31 levels (Figure 1i; Figure S1d, Supporting Information). To understand the mechanism underlying the angiogenic effect of DDR1, we analyzed the correlations between the expression of DDR1 and VEGFs (VEGF‐A, VEGF‐B and VEGF‐C), which are crucial regulators of angiogenesis, and a significant correlation between DDR1 and VEGF‐A expression was found (Figure 1j; Figure S1e, Supporting Information). Moreover, we confirmed that DDR1 expression was positively correlated with VEGF‐A expression by IHC (Figure 1k; Figure S1f, Supporting Information), further indicating that VEGF‐A might be one of the main mediators of the proangiogenic effects of DDR1.
2.2
DDR1 Induces Angiogenesis through HIF‐1α in GC Cells
As a hallmark of cancer, angiogenesis is indispensable for tumor growth and metastasis.[
18
] To further elucidate the influence of DDR1 on angiogenesis in vitro, HGC27 and AGS cells with ectopic DDR1 expression were constructed (Figure S2a,b, Supporting Information). Endothelial cell recruitment and tube formation assays were conducted to explore the impact of DDR1 on endothelial cells in vitro. Treatment with conditioned medium from DDR1‐overexpressing GC cells significantly promoted the migration of human umbilical vein endothelial cells (HUVECs) (Figure S2c,d, Supporting Information) and obviously increased the formation of capillary‐like structures and branches (Figure S2e,f, Supporting Information). Meanwhile, DDR1 knockout (KO) in MKN74 cells suppressed the migration of HUVECs and the formation of capillary‐like structures and branches (Figure S2g,h, Supporting Information). To further explore the mechanism of the proangiogenic effect of DDR1, we performed GSEA analysis and showed that the Vascular Endothelial Growth Factor Signaling Pathway was significantly upregulated in DDR1‐overexpressed HGC27 cells (Figure
2a). We then sought to elucidate the influence of DDR1 on VEGFs expression in GC cell lines. RT‐PCR analyses showed that DDR1 overexpression increased the mRNA level of VEGF‐A but not VEGF‐B or VEGF‐C (Figure 2b). The western blot and ELISA results further confirmed the changes in VEGF‐A protein expression induced by DDR1 (Figure 2c,d; Figure S2k,l, Supporting Information), indicating that DDR1 might promote capillary formation by increasing the VEGF‐A level.
Figure 2
DDR1 induces angiogenesis through HIF‐1α in GC cells. a) GSEA of Vascular Endothelial Growth Factor Signaling in HGC27 cells with DDR1‐overexpressing vector control vector. b) DDR1 overexpression upregulated VEGF‐A expression in GC cells. Real‐time PCR analysis was performed to measure the mRNA levels of VEGF‐A, VEGF‐B, and VEGF‐C. c) Ectopic DDR1 expression increased the VEGF‐A protein level in GC cells. The VEGF‐A protein level was measured by western blot analysis in DDR1‐overexpressing and control GC cells. d) DDR1 increased the VEGF‐A level in CM from GC cells. The level of secreted VEGF‐A protein in CM derived from HGC27 or AGS cells stably expressing DDR1, or control vector was evaluated by ELISA. e) Western‐blot analysis was performed to evaluate the expression of VEGF‐A regulators, including HIF‐1α, PDGF and FGF2. f,g) Silencing of HIF1A in DDR1‐overexpressing GC cells inhibited the migration f) and tube formation g) of HUVECs. HUVECs were incubated with CM from HGC27 or AGS cells transduced with the indicated vectors. h) Knockdown of HIF1A suppressed VEGF‐A expression in DDR1‐overexpressing GC cells. Western blot analysis was performed to measure the protein level of VEGF‐A in HGC27 or AGS cells transfected with the indicated vectors. i) Silencing of HIF1A inhibited VEGF‐A secretion into the CM from DDR1‐overexpressing GC cells. ELISA was performed to measure the level of secreted VEGF‐A in the CM from GC cells transfected with the indicated vectors. All data are presented as the mean ± SEM from three independent experiments. The P values in panels (b) and (d) were calculated by Student's t‐test. The P values in panels (i) were calculated by one‐way ANOVA. **P < 0.01, ***P < 0.001, ns: not significant.
Previous research has indicated that HIF‐1α, PDGF, and FGF2 are the primary regulators of VEGF‐A.[
25
] The western blot showed that only HIF‐1α was upregulated in DDR1‐overexpressed GC cells, but not PDGF or FGF2 (Figure 2e). HIF‐1α is one of the major drivers of angiogenesis and shows crucial influence on VEGF‐A expression as a transcription factor.[
20
] To investigate whether HIF‐1α is involved in DDR1‐induced angiogenesis, we silenced HIF1A and found that the migration and tube formation of HUVECs caused by treatment with conditioned medium from GC cells overexpressing DDR1 were dramatically suppressed (Figure 2f,g; Figure S2n,o, Supporting Information). RT‐PCR analysis revealed that HIF1A knockdown significantly decreased the mRNA levels of VEGF‐A in cells with ectopic DDR1 expression (Figure S2p, Supporting Information), and this decrease was further verified by western blot and ELISA (Figure 2h,i). Furthermore, we intended to investigate the effect of HIF1A restoration in DDR1 KO GC cells. Endothelial cell recruitment and tube formation assays showed that HIF1A overexpression in DDR1 KO GC cells rescued the migration and capillary tube formation of HUVECs (Figure S3a,b, Supporting Information). Westen blot analyses suggested that HIF1A overexpression induced VEGF‐A expression in DDR1 KO GC cells (Figure S3c, Supporting Information). These data together suggested that DDR1‐induced angiogenesis in GC is mediated by HIF‐1α.
2.3
DDR1 Stabilizes the HIF‐1α Protein by Suppressing its Ubiquitination
We then aimed to elucidate the influence of DDR1 on HIF‐1α expression in GC cells. Western blot analyses suggested that DDR1 overexpression increased HIF‐1α protein levels under both normoxic and hypoxic conditions, while the mRNA level of HIF1A remained unchanged (Figure
3a,b). Consistent with this finding, IF analysis showed that HIF‐1α was upregulated by DDR1 overexpression in GC cells (Figure 3c,d). These data suggested that the increase in the HIF‐1α protein level might be attributed to posttranslational regulation in DDR1‐overexpressing GC cells.
Figure 3
DDR1 stabilizes the HIF‐1α protein by suppressing its ubiquitination. a) DDR1 overexpression did not change the mRNA level of HIF1A in HGC27 and AGS cells under normoxic or hypoxic conditions. b) DDR1 overexpression upregulated HIF‐1α protein expression in GC cells. Western blot analysis was performed to measure the HIF‐1α protein level in HGC27 and AGS cells transfected with the indicated vectors and cultured under normoxic or hypoxic conditions for 24 h. c,d) Representative IF images c) and quantification d) of HIF‐1α expression in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector and cultured under normoxic or hypoxic conditions. Nuclei were stained with DAPI. Scale bar: 20 µm. e) DDR1 overexpression inhibited HIF‐1α degradation in GC cells. HGC27 and AGS cells were treated with 10 µg mL−1 cycloheximide (CHX) for the indicated times, and the HIF‐1α level was measured by western blot analysis. f) Treatment with the proteasome inhibitor MG132 suppressed the HIF‐1α protein degradation induced by DDR1 KO in GC cells. MG132 (10 µM) was used to treat GC cells transfected with the indicated vectors. g) DDR1 knockout increased the colocalization of ubiquitin and HIF‐1α in GC cells. Representative images of the colocalization of ubiquitin (green) and HIF‐1α (red), as determined by IF analysis. Scale bar: 10 µm. h) Silencing of DDR1 increased the ubiquitination level of HIF‐1α in AGS cells. MG132 (10 µM) was used to treat AGS cells transfected with Control sgRNA and DDR1‐sgRNA. All data are presented as the mean ± SEM of three independent experiments. The P values in panels (a), (d), (g) were calculated by Student's t‐test. The P values in panels (e) were calculated by two‐way ANOVA. **P < 0.01, ***P < 0.001, ns: not significant.
The ubiquitin‐proteasome system is a main mechanism contributing to HIF‐1α degradation in tumors.[
26
] To investigate whether DDR1 modulates HIF‐1α by influencing its protein stability, DDR1‐overexpressing GC cells were treated with the protein synthesis inhibitor cycloheximide (CHX). The results showed that DDR1 overexpression significantly attenuated the degradation of HIF‐1α protein in GC cells (Figure 3e). To determine whether ubiquitin‐proteasome‐mediated degradation was responsible for the increase in the HIF‐1α protein level caused by DDR1, the proteasome inhibitor MG132 was utilized in DDR1 KO GC cells. The results showed that the suppression of ubiquitin‐proteasome‐mediated degradation by MG132 reversed the decrease in the HIF‐1α level in DDR1 KO GC cells (Figure 3f). IF staining and western blot analyses both suggested that GC cells with DDR1 KO exhibited increased ubiquitination of HIF‐1α (Figure 3g,h). In summary, these data suggested that DDR1 stabilizes the HIF‐1α protein by suppressing its ubiquitination.
2.4
DDR1 Directly Interacts with the PAS Domain of HIF‐1α
To clarify the mechanism by which DDR1 regulates ubiquitin‐proteasome‐mediated degradation of HIF‐1α, IF staining was performed to investigate the intracellular distributions of DDR1 and HIF‐1α in AGS and HGC27 cells. Interestingly, marked colocalization of DDR1 and HIF‐1α was observed which demonstrates the interaction between DDR1 and HIF‐1α in situ (Figure
4a). Then, we performed co‐IP assays to further confirm the interactions between DDR1 and HIF‐1α. The results showed that HIF‐1α and DDR1 coprecipitated with each other, further revealing that DDR1 binds with HIF‐1α in GC cell (Figure 4b,c).
Figure 4
DDR1 directly interacts with the PAS domain of HIF‐1α. a) IF staining showed that DDR1 (green) and HIF‐1α (red) were colocalized in DDR1‐overexpressing GC cells. Scale bar: 10 µm. b,c) DDR1 interacted with HIF‐1α in GC cells. HGC27 and AGS cells transfected with the DDR1‐expressing or control vector were utilized for co‐IP assays using the anti‐DDR1 (b) or anti‐HIF‐1α c) antibody. d) Schematic representation of full‐length and different flag‐tagged deletion mutants of HIF‐1α. e) The lysates of HEK293T cells transfected with the indicated vectors were subjected to co‐IP with the anti‐HA (left) or anti‐Flag (right) antibody, and the precipitates were analysed by western blotting. f,g,h) The PAS domain was indispensable for the interaction of HIF‐1α with DDR1. HEK293T cells were transfected with vectors expressing the H1‐Flag f), H2‐Flag g) or H3‐Flag h) deletion mutants of HIF‐1α together with the DDR1‐HA vector for co‐IP assays. i) The PAS domain mediated the DDR1‐induced migration and tube formation of HUVECs. Representative images of migration (upper panel) and tube formation (lower panel) of HUVECs cultured with CM derived from cells co‐transfected with the indicated vectors. Scale bar for migration images (upper panel): 100 µm. Scale bar for tube formation images (lower panel): 200 µm.
To explore the domain of HIF‐1α involved in its binding to DDR1, plasmids expressing flag‐tagged HIF‐1α core domain deletion mutants were constructed and transfected into HEK293T cells (Figure 4d,e). The Per/ARNT/Sim (PAS) domain of HIF‐1α was the key domain in its interaction with DDR1 (Figure 4f–h). Furthermore, the PAS domain of HIF‐1α was also indispensable for mediating DDR1‐induced VEGF‐A upregulation as well as HUVEC migration and tube formation (Figure 4i; Figure S4a–c, Supporting Information). Correlation analysis based on TCGA‐STAD data revealed strong correlations between the expression of DDR1 and HIF‐1α target genes, including GLUT1, ALDOA, PGK1 and TFRC, as further confirmed in AGS and HGC27 cell lines (Figure S4d,e, Supporting Information). Consistent with this finding, the correlation between DDR1 and HIF‐1α expression was also detected by IHC in 182 GC tissues (Figure S4f,g, Supporting Information). These data suggested that the PAS domain of HIF‐1α is responsible for its direct interaction with DDR1 and is vital for DDR1‐induced angiogenesis.
2.5
DDR1 Regulates Actin Cytoskeleton Reorganization via RhoA/ROCK1 Signaling
As the impact of DDR1 on HUVECs was elucidated, we made further efforts to clarify its impacts on GC cells. Accumulating evidence implicates the actin cytoskeleton as a critical regulator of many cellular processes.[
27
] Recent studies suggest that DDR1 exerts diverse effects on actin cytoskeleton reorganization in different cell types.[
28
,
29
,
30
] Phalloidin staining was performed to visualize the actin cytoskeleton and showed that ectopic DDR1 expression in GC cells induced increased actin stress fiber formation. In addition, the formation of some specialized membrane structures, such as pseudopodia, was also enhanced by DDR1 overexpression (Figure
5a; Figure S5a, Supporting Information), suggesting the increased migratory capacity of GC cell lines. RhoA, a small GTPase protein, is one of the main regulators of actin cytoskeleton reorganization.[
31
] Activation of RhoA was found to induce actin cytoskeleton remodeling in vascular smooth muscle cells.[
30
] However, the effects of DDR1 on RhoA activation have seldom been reported previously. Thus, we investigated whether DDR1 affected the reorganization of actin filaments by influencing RhoA activity in GC cells. The results showed that DDR1 overexpression significantly enhanced RhoA activity (Figure 5b). ROCK1 is the main downstream effector of RhoA, which subsequently phosphorylates LIM kinase (LIMK) and myosin light chain (MLC) to regulate actin filament organization.[
32
] Western blot analyses suggested that the phosphorylation levels of LIMK and MLC were obviously higher in HGC27 and AGS cells overexpressing DDR1 (Figure 5c). Additionally, we observed that DDR1 KO had a profound impact on RhoA/ROCK1 signaling in GC cells (Figure S5b,c, Supporting Information).
Figure 5
DDR1 regulates actin cytoskeleton reorganization via RhoA/ROCK1 signaling in GC cells. a) DDR1 overexpression triggered actin cytoskeleton reorganization in GC cells. IF analysis of F‐actin (white) using phalloidin staining and DDR1 (red) in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector. Scale bar: 20 µm. b) DDR1 significantly enhanced RhoA activity in GC cells. RhoA activity in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector was determined by a GTP‐RhoA pulldown assay using GST‐Rhotekin‐RBD. c) DDR1 promoted ROCK1 expression and the phosphorylation of LIMK and MLC in GC cells. Western blot analysis was performed to measure ROCK1, p‐LIMK, LIMK1, p‐MLC and MLC levels in HGC27 and AGS cells transfected with the DDR1‐expressing and control vectors. d,e) DDR1 overexpression induced EMT in GC cells. E‐cadherin (CDH1), N‐cadherin (CDH2) and vimentin (VIM) expression were measured by RT‐PCR d) and western blotting e) in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector. f) DDR1 suppressed E‐cadherin and ZO‐1 expression in GC cells. IF staining of E‐Cadherin (purple) and ZO‐1 (green) was performed in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector. Nuclei were stained with DAPI. Scale bar: 25 µm. g,h) DDR1 expression was correlated with E‐Cadherin and Vimentin expression in primary human GC tissues. Representative IHC images g) and quantification h) of E‐Cadherin and Vimentin expression in DDR1‐high and DDR1‐low GC tissues based on the FAHS cohort (n = 182). Scale bar: 50 µm. i) ROCK1 inhibition attenuated the downregulation of E‐cadherin and ZO‐1 in DDR1‐overexpressing GC cells. Y‐27632 (10 µM) was used to treat HGC27 and AGS cells transfected with the indicated vectors. IF staining of E‐cadherin (purple) and ZO‐1 (green). Nuclei were stained with DAPI. Scale bar: 25 µm. All data are presented as the mean ± SEM from three independent experiments. The P values in panels (d) were calculated by Student's t‐test. The P values in panels (h) were calculated by χ2 test. *P < 0.05, **P < 0.01, ***P < 0.001.
While actin cytoskeleton reorganization has profound influences on cell migration, morphogenesis and tumor metastasis,[
33
] we intended to elucidate the effect of DDR1 on the metastatic capacity of GC cells. Transwell and scratch wound healing assays were conducted to investigate how DDR1 influences the migratory and invasive abilities of GC cells. We demonstrated that DDR1 overexpression enhanced the migratory and invasive abilities of GC cells (Figure S6a,b, Supporting Information). Epithelial‐to‐mesenchymal transition (EMT) is a crucial process in tumor metastasis defined by the conversion of epithelial tumor cells to cells with mesenchymal properties.[
34
] Here, we demonstrated that the expression of E‐cadherin, an epithelial marker, was reduced in HGC27 and AGS cell lines overexpressing DDR1, while the expression of mesenchymal markers, including N‐cadherin and vimentin, was increased (Figure 5d,e). In contrast, elimination of DDR1 suppressed the EMT process in HGC27 and AGS cells, as shown by RT‐PCR analysis in Figure S5d (Supporting Information). IF analysis showed similar results (Figure 5f; Figure S6c, Supporting Information). Moreover, IHC analysis was conducted to explore the correlations between DDR1 and EMT markers in clinical GC samples. The results revealed that GC samples with high DDR1 expression showed upregulated Vimentin expression compared to those with low DDR1 expression (62/93 versus 39/89; P = 0.003). In addition, a negative correlation between DDR1 and E‐cadherin expression was observed in patient GC samples (Figure 5g,h). Next, we utilized the ROCK inhibitor Y‐27632 to explore whether ROCK signaling blockade would decrease the DDR1‐promoted metastatic ability. Y‐27632 was found to effectively suppress ROCK and its signaling pathway molecules that were induced in DDR1‐overexpressing GC cells (Figure S7a, Supporting Information) and to reverse the effect of DDR1 overexpression on EMT marker expression and metastatic capacity (Figure S7b–e, Supporting Information). Similarly, the IF results also suggested that Y‐27632 attenuated the DDR1‐induced changes in E‐cadherin and ZO‐1 expression (Figure 5i; Figure S7f, Supporting Information). These data revealed that DDR1 regulates actin cytoskeleton reorganization through RhoA/ROCK1 signaling in GC.
2.6
HIF‐1α is Required for the Actin Cytoskeleton Reorganization Induced by DDR1
Recent studies have indicated that HIF‐1α is involved in actin cytoskeleton reorganization in several kinds of cells and tissues.[
35
] Hence, we speculated that HIF‐1α might also contribute to the actin cytoskeleton reorganization regulated by DDR1. Since RhoA/ROCK1 signaling has been reported to correlate with the actin cytoskeleton reorganization caused by DDR1, we tried to show whether the effect of DDR1 on actin cytoskeleton reorganization is dependent on HIF‐1α/RhoA/ROCK1 signaling in GC cells. In DDR1‐overexpressing GC cell lines, silencing HIF1A significantly suppressed the activity of RhoA and its downstream effectors, including phosphorylated LIMK and phosphorylated MLC (Figure
6a,b). Furthermore, phalloidin staining revealed that the formation of actin stress fibers and pseudopodia was decreased after HIF1A knockdown (Figure 6c,d). Because RhoA/ROCK1 signaling is also closely related to tumor EMT and metastasis,[
36
] we further investigated the role of HIF‐1α in DDR1‐induced EMT and metastasis in GC. The results showed that HIF1A knockdown could retard the EMT caused by DDR1 overexpression in GC cells (Figure 6e–g; Figure S8a,b, Supporting Information). Similarly, HIF1A knockdown also reduced the metastatic ability of GC cells with DDR1 overexpression (Figure S8c–e, Supporting Information). In summary, these results demonstrated that HIF‐1α contributed to actin cytoskeleton reorganization by mediating the activation of RhoA/ROCK1 signaling in GC cells.
Figure 6
HIF‐1α is required for the actin cytoskeleton reorganization induced by DDR1. a) HIF1A silencing blocked RhoA activation triggered by DDR1 in GC cells. RhoA activity in HGC27 and AGS cells transduced with the indicated vectors was measured by a GTP‐RhoA pulldown assay using GST‐Rhotekin‐RBD. b) HIF1A knockdown suppressed DDR1‐induced expression of ROCK and phosphorylation of LIMK and MLC. Western blot analysis was performed to measure ROCK1, p‐LIMK, LIMK1, p‐MLC and MLC protein levels. c,d) Knockdown of HIF1A suppressed actin cytoskeleton reorganization in DDR1‐overexpressing GC cells. Representative images c) and quantification d) of F‐actin (white) by phalloidin staining with confocal fluorescence microscopy. Nuclei were stained with DAPI. Scale bar: 20 µm. e) Silencing of HIF1A inhibited DDR1‐induced EMT in GC cells. Western blot analysis was performed to evaluate N‐cadherin, E‐cadherin and Vimentin expression in HGC27 and AGS cells transduced with the indicated vectors. f,g) HIF‐1α mediated DDR1‐induced EMT in GC cells. The expression levels of E‐Cadherin (purple) and ZO‐1 (green) were assessed by IF staining. Scale bar: 25 µm. All data are presented as the means ± SEMs from three independent experiments. The P values in panels (d), (g) were calculated using one‐way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001.
2.7
DDR1 Promotes GC Progression via HIF‐1α In Vivo
To further explore the influence of DDR1 on tumor progression in vivo, mouse xenograft models were applied. AGS cells with and without DDR1 overexpression (referred to as AGS‐DDR1 and AGS‐Vector, respectively) were subcutaneously injected to establish the xenograft model (n = 6 mice per group). The tumors were harvested 4 weeks later (Figure
7a). Tumors formed by AGS‐DDR1 cells exhibited an increased volume and weight (Figure S9a,b, Supporting Information). IF staining of CD31 suggested that tumors with DDR1 overexpression showed an increased microvessel density (Figure 7b,c). To clarify the role of DDR1 in tumor angiogenesis in vivo, HIF‐1α and VEGF‐A expression were measured in xenograft tumor samples. Western blot analysis suggested that the HIF‐1α and VEGF‐A levels were higher in AGS‐DDR1 tumor xenografts, consistent with the in vitro findings (Figure 7d). Furthermore, the western blot analyses showed that the RhoA signaling was also upregulated in AGS‐DDR1 tumor xenografts (Figure S9c,d, Supporting Information), and DDR1 overexpression promoted EMT process in tumor xenografts (Figure S9e, Supporting Information). To explore the impact of DDR1 on the metastatic capacity, a lung metastasis model was established by injecting GC cells into the tail vein. H&E staining showed that DDR1 overexpression increased the number of lung micrometastatic nodules (Figure 7e).
Figure 7
DDR1 promotes GC progression via HIF‐1α in vivo. a) Representative images of subcutaneous tumors established by AGS‐DDR1 and AGS‐Vector cells. b,c) DDR1 overexpression increased the microvessel density in subcutaneous tumors. Representative micrographs b) and quantification c) of CD31 (red) and DDR1 (green) expression in xenograft tumors from nude mice via IF staining. Nuclei were stained with DAPI. Scale bar: 50 µm. d) DDR1 overexpression promoted HIF‐1α and VEGF‐A expression in vivo. Western blot analysis was performed to measure DDR1, HIF‐1α and VEGF‐A protein levels in the subcutaneous tumors from the 2 groups. e) DDR1 overexpression significantly increased the number of lung metastatic nodules in mouse models. Representative images (left) and quantification (right) of lung metastases established by tail vein injection, as determined by H&E staining. Scale bar: 25 µm. f) HIF1A silencing suppressed DDR1‐induced tumor growth. The growth of tumors formed by AGS‐DDR1 and AGS‐Vector cells was measured every three days (n = 6/group). g) HIF1A knockdown inhibited the VEGF‐A and CD31 expression induced by DDR1. Representative micrographs of IHC staining for DDR1, HIF‐1α, VEGF‐A and CD31 in subcutaneous tumors. Scale bar: 50 µm. h) HIF1A silencing attenuated DDR1‐induced EMT in vivo. Representative micrographs of IF staining for E‐cadherin (purple) and ZO‐1 (green) in tumors from nude mice. Nuclei were stained with DAPI. Scale bar: 50 µm. All data are presented as the means ± SEMs from three independent experiments. The P values in panels (c) and (e) were calculated using Student's t‐test. The P values in panels (f) were calculated using two‐way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant.
Furthermore, HIF1A expression was silenced to elucidate its functional role in DDR1‐induced angiogenesis and metastasis in vivo. Silencing of HIF1A significantly attenuated DDR1‐increased tumor growth in nude mice (Figure 7f; Figure S9f,g, Supporting Information). The CD31 and VEGF‐A levels in AGS‐DDR1 xenografts were also decreased after HIF1A knockdown (Figure 7g; Figure S9h,i, Supporting Information), suggesting that HIF‐1α is correlated with the in vivo angiogenic effect of DDR1. In addition, HIF1A silencing also reversed the EMT caused by DDR1 overexpression (Figure 7h; Figure S9j, Supporting Information), and lung metastasis of AGS‐DDR1 in vivo was inhibited by HIF1A knockdown (Figure S9k,l, Supporting Information). Taken together, these data indicated that HIF‐1α is the key mediator of DDR1‐induced angiogenesis and metastasis in GC.
2.8
Pharmacological Inhibition of DDR1 Suppresses Malignant Progression of GC in PDX and Organoid Models
In recent years, PDX and organoid models, both of which directly originate from human tumor specimens, have emerged as promising approaches for translational research in cancer treatment.[
37
] To investigate the therapeutic potential of DDR1 as a treatment target for GC, a highly selective inhibitor of DDR1, 7rh benzamide, was used to block DDR1 signaling in PDX and organoid models. In our study, PDX models (PDX1 and PDX2) were constructed with passage three tumors derived from two patients’ GC samples (Figure S10a, Supporting Information). When the xenograft tumors were palpable (≈200 mm3), mice with similar tumor volumes were randomly administered 7rh benzamide or vehicle for 4 weeks (Figure
8a). Treatment with 7rh benzamide had no obvious influence on body weight (Figure S10b, Supporting Information) but effectively inhibited tumor growth in both the PDX1 and PDX2 models (Figure 8b,c; Figure S10c, Supporting Information). The IHC results demonstrated that DDR1 inhibition with 7rh benzamide blocked HIF‐1α and VEGF‐A expression and upregulated E‐cadherin expression in the PDX models (Figure 8d,e). Then, we established an organoid model from primary GC specimens to assess the efficacy of 7rh benzamide (Figure 8f). The results of IF staining demonstrated that in the organoid model, pharmacological inhibition of DDR1 suppressed cytoskeletal reorganization and simultaneously enhanced E‐cadherin and ZO‐1 expression (Figure 8g; Figure S10d, Supporting Information). Collectively, these results in PDX and organoid models elucidated the significant role of DDR1 in promoting GC malignancy and pointed out the potential of DDR1 as a therapeutic target for GC in translational medicine. In summary, our results suggest that DDR1 directly interacts with HIF‐1α to inhibit its ubiquitin‐mediated degradation, which in turn promotes the malignant progression of GC.
Figure 8
Pharmacological inhibition of DDR1 suppresses malignant progression in PDX and organoid models. a) Experimental protocol of the PDX models. b) Representative images of tumors harvested from the 2 PDX models. c) Pharmacological inhibition of DDR1 suppressed tumor growth in the PDX models. The DDR1 inhibitor 7rh benzamide was administered at a dose of 25 mg kg−1 by oral gavage every day. d,e) Treatment with 7rh benzamide decreased the expression of HIF‐1α, VEGF‐A, and E‐Cadherin in the PDX models. Representative images d) and quantification e) of DDR1, HIF‐1α, VEGF‐A, and E‐Cadherin expression determined by IHC staining in tumors from PDX models treated with 7rh benzamide or vehicle. Scale bar: 50 µm. f) Schematic illustration of organoid models derived from dissected human GC tissues. g) IF images of E‐Cadherin, ZO‐1, and F‐actin in GC organoids treated with 7rh benzamide or vehicle. Scale bar: 50 µm. h) Schematic illustration of the model that DDR1 interacts with HIF‐1α to suppress its degradation, and consequently promote malignant progression of GC. All data are presented as the means ± SEMs from three independent experiments. The P values in panels (c), (e) were calculated using Student's t‐test. *P < 0.05, **P < 0.01, ***P < 0.001, ns: not significant.
2.1
High DDR1 Expression Positively Correlates with Malignant Progression in Human GC
The ECM is the major component of the tumor microenvironment (TME), exerting great influence on cellular phenotypes and signal transduction via ECM‐cell interactions.[
21
] As collagen is the most abundant component of the ECM, collagen receptors in tumor cells have been proven to greatly affect the characteristics of tumors. In our work, we analyzed the levels of collagen receptors in GC and adjacent normal tissues, based on data from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA‐STAD) database. The results revealed that DDR1 was the only upregulated collagen‐binding receptor with kinase activity in GC (Figure
1a). Data from GSE51575 and GSE13861 also confirmed the elevated expression of DDR1 in GC tissues (Figure S1a, Supporting Information). In addition, 8 paired nontumor and tumor tissues from GC patients in the First Affiliated Hospital of Sun Yat‐sen University (FAHS) were analyzed, and we found that DDR1 expression was also higher in the tumor tissues in this cohort (Figure 1b; Figure S1b, Supporting Information). The immunohistochemistry (IHC) results showed that DDR1 expression positively correlated with tumor grade in GC (Figure 1c,d), indicating the vital role of DDR1 in GC progression. Since DDR1 mutation has also been demonstrated to be positively correlated with poor prognosis in breast cancer,[
22
] we analyzed DDR1 mutations based on cBioPortal and a GC cohort from our center.[
23
,
24
] However, only 14 mutations were found in 534 patients in cBioPortal (Figure 1e), and we found 13 S495S mutations, suggesting that mutation might not be the primary contributor to GC progression (Figure 1f). Finally, the high DDR1 group showed an inferior prognosis compared to the low DDR1 group (Figure 1g; Figure S1c, Supporting Information). Multivariate survival analyses suggested that DDR1 expression was a significant prognostic factor, indicating the critical role of DDR1 in GC (Tables S1–S3, Supporting Information).
Figure 1
High DDR1 expression positively correlates with malignant progression in GC. a) Comparative gene expression analysis of collagen receptors (DDR1, DDR2, integrin β1, integrin α1, integrin α2, integrin α10, PLA2R, Endo180) between GC and adjacent normal tissues based on data from the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA‐STAD) database (Cancer Genome Atlas Research Network, 2008). b) The DDR1 protein levels in GC tissues were higher than those in the matched stomach tissues, as measured by western blot analysis. The 8 paired tumor tissues and normal stomach tissues were from the FAHS cohort. c,d) DDR1 expression was significantly correlated with the aggressiveness of GC, as determined by IHC staining in normal gastric mucosa tissues (n = 58) and GC tissues (n = 182) from the FAHS cohort. Representative IHC staining images c) and quantification d) of DDR1 expression in normal gastric mucosa tissues (Normal) and in well, moderately and poorly differentiated GC tissues. Scale bar of upper panel: 100 µm. Scale bar of lower panel: 50 µm. e) DDR1 alterations in GC from the indicated studies are summarized based on data from cBioPortal. f) The S495S (13/131) and A495S (0/131) mutations of DDR1 were detected by sequencing based on a GC cohort from the FAHS (n = 131). g) GC patients with high DDR1 expression showed worse overall survival. Kaplan‒Meier analysis was performed according to DDR1 expression evaluated by IHC staining (log rank test; n = 182). h) Gene Ontology enrichment analysis of biological processes for up‐ and down‐regulated genes between HGC27 cells transfected with DDR1‐expressing versus control vector. i) Representative images of DDR1 (green) and CD31 (red) co‐staining in GC tissues, as revealed by IF staining (n = 32). Scale bar: 50 µm. j) Correlation matrix showing the correlations between DDR1 expression and VEGF‐A, VEGF‐B, and VEGF‐C expression in GC tissues based on data from the TCGA‐STAD cohort (Spearman test). k) Representative images of IHC staining for DDR1 and VEGF‐A in GC tissues. Scale bar: 25 µm. All data are presented as the mean ± SEM from three independent experiments. The P values in panels (a) were calculated by Student's t‐test. *P < 0.05, ***P < 0.001, ns: not significant.
However, the biological significance of DDR1 in GC remains largely unclear. Thus, we performed whole transcriptome analysis to investigate the biological function of DDR1. GO enrichment analysis revealed that blood vessel morphogenesis and morphogenesis of branching structures were significantly enriched in DDR1‐overexpressed GC cells (Figure 1h). During IHC staining, we noticed that the blood perfusion in the high DDR1 group seemed to be better than that in the low DDR1 group, indicating the proangiogenic effect of DDR1. Immunofluorescence (IF) staining of CD31, a marker of blood vessels, also revealed positive correlations between the DDR1 and CD31 levels (Figure 1i; Figure S1d, Supporting Information). To understand the mechanism underlying the angiogenic effect of DDR1, we analyzed the correlations between the expression of DDR1 and VEGFs (VEGF‐A, VEGF‐B and VEGF‐C), which are crucial regulators of angiogenesis, and a significant correlation between DDR1 and VEGF‐A expression was found (Figure 1j; Figure S1e, Supporting Information). Moreover, we confirmed that DDR1 expression was positively correlated with VEGF‐A expression by IHC (Figure 1k; Figure S1f, Supporting Information), further indicating that VEGF‐A might be one of the main mediators of the proangiogenic effects of DDR1.
2.2
DDR1 Induces Angiogenesis through HIF‐1α in GC Cells
As a hallmark of cancer, angiogenesis is indispensable for tumor growth and metastasis.[
18
] To further elucidate the influence of DDR1 on angiogenesis in vitro, HGC27 and AGS cells with ectopic DDR1 expression were constructed (Figure S2a,b, Supporting Information). Endothelial cell recruitment and tube formation assays were conducted to explore the impact of DDR1 on endothelial cells in vitro. Treatment with conditioned medium from DDR1‐overexpressing GC cells significantly promoted the migration of human umbilical vein endothelial cells (HUVECs) (Figure S2c,d, Supporting Information) and obviously increased the formation of capillary‐like structures and branches (Figure S2e,f, Supporting Information). Meanwhile, DDR1 knockout (KO) in MKN74 cells suppressed the migration of HUVECs and the formation of capillary‐like structures and branches (Figure S2g,h, Supporting Information). To further explore the mechanism of the proangiogenic effect of DDR1, we performed GSEA analysis and showed that the Vascular Endothelial Growth Factor Signaling Pathway was significantly upregulated in DDR1‐overexpressed HGC27 cells (Figure
2a). We then sought to elucidate the influence of DDR1 on VEGFs expression in GC cell lines. RT‐PCR analyses showed that DDR1 overexpression increased the mRNA level of VEGF‐A but not VEGF‐B or VEGF‐C (Figure 2b). The western blot and ELISA results further confirmed the changes in VEGF‐A protein expression induced by DDR1 (Figure 2c,d; Figure S2k,l, Supporting Information), indicating that DDR1 might promote capillary formation by increasing the VEGF‐A level.
Figure 2
DDR1 induces angiogenesis through HIF‐1α in GC cells. a) GSEA of Vascular Endothelial Growth Factor Signaling in HGC27 cells with DDR1‐overexpressing vector control vector. b) DDR1 overexpression upregulated VEGF‐A expression in GC cells. Real‐time PCR analysis was performed to measure the mRNA levels of VEGF‐A, VEGF‐B, and VEGF‐C. c) Ectopic DDR1 expression increased the VEGF‐A protein level in GC cells. The VEGF‐A protein level was measured by western blot analysis in DDR1‐overexpressing and control GC cells. d) DDR1 increased the VEGF‐A level in CM from GC cells. The level of secreted VEGF‐A protein in CM derived from HGC27 or AGS cells stably expressing DDR1, or control vector was evaluated by ELISA. e) Western‐blot analysis was performed to evaluate the expression of VEGF‐A regulators, including HIF‐1α, PDGF and FGF2. f,g) Silencing of HIF1A in DDR1‐overexpressing GC cells inhibited the migration f) and tube formation g) of HUVECs. HUVECs were incubated with CM from HGC27 or AGS cells transduced with the indicated vectors. h) Knockdown of HIF1A suppressed VEGF‐A expression in DDR1‐overexpressing GC cells. Western blot analysis was performed to measure the protein level of VEGF‐A in HGC27 or AGS cells transfected with the indicated vectors. i) Silencing of HIF1A inhibited VEGF‐A secretion into the CM from DDR1‐overexpressing GC cells. ELISA was performed to measure the level of secreted VEGF‐A in the CM from GC cells transfected with the indicated vectors. All data are presented as the mean ± SEM from three independent experiments. The P values in panels (b) and (d) were calculated by Student's t‐test. The P values in panels (i) were calculated by one‐way ANOVA. **P < 0.01, ***P < 0.001, ns: not significant.
Previous research has indicated that HIF‐1α, PDGF, and FGF2 are the primary regulators of VEGF‐A.[
25
] The western blot showed that only HIF‐1α was upregulated in DDR1‐overexpressed GC cells, but not PDGF or FGF2 (Figure 2e). HIF‐1α is one of the major drivers of angiogenesis and shows crucial influence on VEGF‐A expression as a transcription factor.[
20
] To investigate whether HIF‐1α is involved in DDR1‐induced angiogenesis, we silenced HIF1A and found that the migration and tube formation of HUVECs caused by treatment with conditioned medium from GC cells overexpressing DDR1 were dramatically suppressed (Figure 2f,g; Figure S2n,o, Supporting Information). RT‐PCR analysis revealed that HIF1A knockdown significantly decreased the mRNA levels of VEGF‐A in cells with ectopic DDR1 expression (Figure S2p, Supporting Information), and this decrease was further verified by western blot and ELISA (Figure 2h,i). Furthermore, we intended to investigate the effect of HIF1A restoration in DDR1 KO GC cells. Endothelial cell recruitment and tube formation assays showed that HIF1A overexpression in DDR1 KO GC cells rescued the migration and capillary tube formation of HUVECs (Figure S3a,b, Supporting Information). Westen blot analyses suggested that HIF1A overexpression induced VEGF‐A expression in DDR1 KO GC cells (Figure S3c, Supporting Information). These data together suggested that DDR1‐induced angiogenesis in GC is mediated by HIF‐1α.
2.3
DDR1 Stabilizes the HIF‐1α Protein by Suppressing its Ubiquitination
We then aimed to elucidate the influence of DDR1 on HIF‐1α expression in GC cells. Western blot analyses suggested that DDR1 overexpression increased HIF‐1α protein levels under both normoxic and hypoxic conditions, while the mRNA level of HIF1A remained unchanged (Figure
3a,b). Consistent with this finding, IF analysis showed that HIF‐1α was upregulated by DDR1 overexpression in GC cells (Figure 3c,d). These data suggested that the increase in the HIF‐1α protein level might be attributed to posttranslational regulation in DDR1‐overexpressing GC cells.
Figure 3
DDR1 stabilizes the HIF‐1α protein by suppressing its ubiquitination. a) DDR1 overexpression did not change the mRNA level of HIF1A in HGC27 and AGS cells under normoxic or hypoxic conditions. b) DDR1 overexpression upregulated HIF‐1α protein expression in GC cells. Western blot analysis was performed to measure the HIF‐1α protein level in HGC27 and AGS cells transfected with the indicated vectors and cultured under normoxic or hypoxic conditions for 24 h. c,d) Representative IF images c) and quantification d) of HIF‐1α expression in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector and cultured under normoxic or hypoxic conditions. Nuclei were stained with DAPI. Scale bar: 20 µm. e) DDR1 overexpression inhibited HIF‐1α degradation in GC cells. HGC27 and AGS cells were treated with 10 µg mL−1 cycloheximide (CHX) for the indicated times, and the HIF‐1α level was measured by western blot analysis. f) Treatment with the proteasome inhibitor MG132 suppressed the HIF‐1α protein degradation induced by DDR1 KO in GC cells. MG132 (10 µM) was used to treat GC cells transfected with the indicated vectors. g) DDR1 knockout increased the colocalization of ubiquitin and HIF‐1α in GC cells. Representative images of the colocalization of ubiquitin (green) and HIF‐1α (red), as determined by IF analysis. Scale bar: 10 µm. h) Silencing of DDR1 increased the ubiquitination level of HIF‐1α in AGS cells. MG132 (10 µM) was used to treat AGS cells transfected with Control sgRNA and DDR1‐sgRNA. All data are presented as the mean ± SEM of three independent experiments. The P values in panels (a), (d), (g) were calculated by Student's t‐test. The P values in panels (e) were calculated by two‐way ANOVA. **P < 0.01, ***P < 0.001, ns: not significant.
The ubiquitin‐proteasome system is a main mechanism contributing to HIF‐1α degradation in tumors.[
26
] To investigate whether DDR1 modulates HIF‐1α by influencing its protein stability, DDR1‐overexpressing GC cells were treated with the protein synthesis inhibitor cycloheximide (CHX). The results showed that DDR1 overexpression significantly attenuated the degradation of HIF‐1α protein in GC cells (Figure 3e). To determine whether ubiquitin‐proteasome‐mediated degradation was responsible for the increase in the HIF‐1α protein level caused by DDR1, the proteasome inhibitor MG132 was utilized in DDR1 KO GC cells. The results showed that the suppression of ubiquitin‐proteasome‐mediated degradation by MG132 reversed the decrease in the HIF‐1α level in DDR1 KO GC cells (Figure 3f). IF staining and western blot analyses both suggested that GC cells with DDR1 KO exhibited increased ubiquitination of HIF‐1α (Figure 3g,h). In summary, these data suggested that DDR1 stabilizes the HIF‐1α protein by suppressing its ubiquitination.
2.4
DDR1 Directly Interacts with the PAS Domain of HIF‐1α
To clarify the mechanism by which DDR1 regulates ubiquitin‐proteasome‐mediated degradation of HIF‐1α, IF staining was performed to investigate the intracellular distributions of DDR1 and HIF‐1α in AGS and HGC27 cells. Interestingly, marked colocalization of DDR1 and HIF‐1α was observed which demonstrates the interaction between DDR1 and HIF‐1α in situ (Figure
4a). Then, we performed co‐IP assays to further confirm the interactions between DDR1 and HIF‐1α. The results showed that HIF‐1α and DDR1 coprecipitated with each other, further revealing that DDR1 binds with HIF‐1α in GC cell (Figure 4b,c).
Figure 4
DDR1 directly interacts with the PAS domain of HIF‐1α. a) IF staining showed that DDR1 (green) and HIF‐1α (red) were colocalized in DDR1‐overexpressing GC cells. Scale bar: 10 µm. b,c) DDR1 interacted with HIF‐1α in GC cells. HGC27 and AGS cells transfected with the DDR1‐expressing or control vector were utilized for co‐IP assays using the anti‐DDR1 (b) or anti‐HIF‐1α c) antibody. d) Schematic representation of full‐length and different flag‐tagged deletion mutants of HIF‐1α. e) The lysates of HEK293T cells transfected with the indicated vectors were subjected to co‐IP with the anti‐HA (left) or anti‐Flag (right) antibody, and the precipitates were analysed by western blotting. f,g,h) The PAS domain was indispensable for the interaction of HIF‐1α with DDR1. HEK293T cells were transfected with vectors expressing the H1‐Flag f), H2‐Flag g) or H3‐Flag h) deletion mutants of HIF‐1α together with the DDR1‐HA vector for co‐IP assays. i) The PAS domain mediated the DDR1‐induced migration and tube formation of HUVECs. Representative images of migration (upper panel) and tube formation (lower panel) of HUVECs cultured with CM derived from cells co‐transfected with the indicated vectors. Scale bar for migration images (upper panel): 100 µm. Scale bar for tube formation images (lower panel): 200 µm.
To explore the domain of HIF‐1α involved in its binding to DDR1, plasmids expressing flag‐tagged HIF‐1α core domain deletion mutants were constructed and transfected into HEK293T cells (Figure 4d,e). The Per/ARNT/Sim (PAS) domain of HIF‐1α was the key domain in its interaction with DDR1 (Figure 4f–h). Furthermore, the PAS domain of HIF‐1α was also indispensable for mediating DDR1‐induced VEGF‐A upregulation as well as HUVEC migration and tube formation (Figure 4i; Figure S4a–c, Supporting Information). Correlation analysis based on TCGA‐STAD data revealed strong correlations between the expression of DDR1 and HIF‐1α target genes, including GLUT1, ALDOA, PGK1 and TFRC, as further confirmed in AGS and HGC27 cell lines (Figure S4d,e, Supporting Information). Consistent with this finding, the correlation between DDR1 and HIF‐1α expression was also detected by IHC in 182 GC tissues (Figure S4f,g, Supporting Information). These data suggested that the PAS domain of HIF‐1α is responsible for its direct interaction with DDR1 and is vital for DDR1‐induced angiogenesis.
2.5
DDR1 Regulates Actin Cytoskeleton Reorganization via RhoA/ROCK1 Signaling
As the impact of DDR1 on HUVECs was elucidated, we made further efforts to clarify its impacts on GC cells. Accumulating evidence implicates the actin cytoskeleton as a critical regulator of many cellular processes.[
27
] Recent studies suggest that DDR1 exerts diverse effects on actin cytoskeleton reorganization in different cell types.[
28
,
29
,
30
] Phalloidin staining was performed to visualize the actin cytoskeleton and showed that ectopic DDR1 expression in GC cells induced increased actin stress fiber formation. In addition, the formation of some specialized membrane structures, such as pseudopodia, was also enhanced by DDR1 overexpression (Figure
5a; Figure S5a, Supporting Information), suggesting the increased migratory capacity of GC cell lines. RhoA, a small GTPase protein, is one of the main regulators of actin cytoskeleton reorganization.[
31
] Activation of RhoA was found to induce actin cytoskeleton remodeling in vascular smooth muscle cells.[
30
] However, the effects of DDR1 on RhoA activation have seldom been reported previously. Thus, we investigated whether DDR1 affected the reorganization of actin filaments by influencing RhoA activity in GC cells. The results showed that DDR1 overexpression significantly enhanced RhoA activity (Figure 5b). ROCK1 is the main downstream effector of RhoA, which subsequently phosphorylates LIM kinase (LIMK) and myosin light chain (MLC) to regulate actin filament organization.[
32
] Western blot analyses suggested that the phosphorylation levels of LIMK and MLC were obviously higher in HGC27 and AGS cells overexpressing DDR1 (Figure 5c). Additionally, we observed that DDR1 KO had a profound impact on RhoA/ROCK1 signaling in GC cells (Figure S5b,c, Supporting Information).
Figure 5
DDR1 regulates actin cytoskeleton reorganization via RhoA/ROCK1 signaling in GC cells. a) DDR1 overexpression triggered actin cytoskeleton reorganization in GC cells. IF analysis of F‐actin (white) using phalloidin staining and DDR1 (red) in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector. Scale bar: 20 µm. b) DDR1 significantly enhanced RhoA activity in GC cells. RhoA activity in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector was determined by a GTP‐RhoA pulldown assay using GST‐Rhotekin‐RBD. c) DDR1 promoted ROCK1 expression and the phosphorylation of LIMK and MLC in GC cells. Western blot analysis was performed to measure ROCK1, p‐LIMK, LIMK1, p‐MLC and MLC levels in HGC27 and AGS cells transfected with the DDR1‐expressing and control vectors. d,e) DDR1 overexpression induced EMT in GC cells. E‐cadherin (CDH1), N‐cadherin (CDH2) and vimentin (VIM) expression were measured by RT‐PCR d) and western blotting e) in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector. f) DDR1 suppressed E‐cadherin and ZO‐1 expression in GC cells. IF staining of E‐Cadherin (purple) and ZO‐1 (green) was performed in HGC27 and AGS cells transfected with the DDR1‐expressing or control vector. Nuclei were stained with DAPI. Scale bar: 25 µm. g,h) DDR1 expression was correlated with E‐Cadherin and Vimentin expression in primary human GC tissues. Representative IHC images g) and quantification h) of E‐Cadherin and Vimentin expression in DDR1‐high and DDR1‐low GC tissues based on the FAHS cohort (n = 182). Scale bar: 50 µm. i) ROCK1 inhibition attenuated the downregulation of E‐cadherin and ZO‐1 in DDR1‐overexpressing GC cells. Y‐27632 (10 µM) was used to treat HGC27 and AGS cells transfected with the indicated vectors. IF staining of E‐cadherin (purple) and ZO‐1 (green). Nuclei were stained with DAPI. Scale bar: 25 µm. All data are presented as the mean ± SEM from three independent experiments. The P values in panels (d) were calculated by Student's t‐test. The P values in panels (h) were calculated by χ2 test. *P < 0.05, **P < 0.01, ***P < 0.001.
While actin cytoskeleton reorganization has profound influences on cell migration, morphogenesis and tumor metastasis,[
33
] we intended to elucidate the effect of DDR1 on the metastatic capacity of GC cells. Transwell and scratch wound healing assays were conducted to investigate how DDR1 influences the migratory and invasive abilities of GC cells. We demonstrated that DDR1 overexpression enhanced the migratory and invasive abilities of GC cells (Figure S6a,b, Supporting Information). Epithelial‐to‐mesenchymal transition (EMT) is a crucial process in tumor metastasis defined by the conversion of epithelial tumor cells to cells with mesenchymal properties.[
34
] Here, we demonstrated that the expression of E‐cadherin, an epithelial marker, was reduced in HGC27 and AGS cell lines overexpressing DDR1, while the expression of mesenchymal markers, including N‐cadherin and vimentin, was increased (Figure 5d,e). In contrast, elimination of DDR1 suppressed the EMT process in HGC27 and AGS cells, as shown by RT‐PCR analysis in Figure S5d (Supporting Information). IF analysis showed similar results (Figure 5f; Figure S6c, Supporting Information). Moreover, IHC analysis was conducted to explore the correlations between DDR1 and EMT markers in clinical GC samples. The results revealed that GC samples with high DDR1 expression showed upregulated Vimentin expression compared to those with low DDR1 expression (62/93 versus 39/89; P = 0.003). In addition, a negative correlation between DDR1 and E‐cadherin expression was observed in patient GC samples (Figure 5g,h). Next, we utilized the ROCK inhibitor Y‐27632 to explore whether ROCK signaling blockade would decrease the DDR1‐promoted metastatic ability. Y‐27632 was found to effectively suppress ROCK and its signaling pathway molecules that were induced in DDR1‐overexpressing GC cells (Figure S7a, Supporting Information) and to reverse the effect of DDR1 overexpression on EMT marker expression and metastatic capacity (Figure S7b–e, Supporting Information). Similarly, the IF results also suggested that Y‐27632 attenuated the DDR1‐induced changes in E‐cadherin and ZO‐1 expression (Figure 5i; Figure S7f, Supporting Information). These data revealed that DDR1 regulates actin cytoskeleton reorganization through RhoA/ROCK1 signaling in GC.
2.6
HIF‐1α is Required for the Actin Cytoskeleton Reorganization Induced by DDR1
Recent studies have indicated that HIF‐1α is involved in actin cytoskeleton reorganization in several kinds of cells and tissues.[
35
] Hence, we speculated that HIF‐1α might also contribute to the actin cytoskeleton reorganization regulated by DDR1. Since RhoA/ROCK1 signaling has been reported to correlate with the actin cytoskeleton reorganization caused by DDR1, we tried to show whether the effect of DDR1 on actin cytoskeleton reorganization is dependent on HIF‐1α/RhoA/ROCK1 signaling in GC cells. In DDR1‐overexpressing GC cell lines, silencing HIF1A significantly suppressed the activity of RhoA and its downstream effectors, including phosphorylated LIMK and phosphorylated MLC (Figure
6a,b). Furthermore, phalloidin staining revealed that the formation of actin stress fibers and pseudopodia was decreased after HIF1A knockdown (Figure 6c,d). Because RhoA/ROCK1 signaling is also closely related to tumor EMT and metastasis,[
36
] we further investigated the role of HIF‐1α in DDR1‐induced EMT and metastasis in GC. The results showed that HIF1A knockdown could retard the EMT caused by DDR1 overexpression in GC cells (Figure 6e–g; Figure S8a,b, Supporting Information). Similarly, HIF1A knockdown also reduced the metastatic ability of GC cells with DDR1 overexpression (Figure S8c–e, Supporting Information). In summary, these results demonstrated that HIF‐1α contributed to actin cytoskeleton reorganization by mediating the activation of RhoA/ROCK1 signaling in GC cells.
Figure 6
HIF‐1α is required for the actin cytoskeleton reorganization induced by DDR1. a) HIF1A silencing blocked RhoA activation triggered by DDR1 in GC cells. RhoA activity in HGC27 and AGS cells transduced with the indicated vectors was measured by a GTP‐RhoA pulldown assay using GST‐Rhotekin‐RBD. b) HIF1A knockdown suppressed DDR1‐induced expression of ROCK and phosphorylation of LIMK and MLC. Western blot analysis was performed to measure ROCK1, p‐LIMK, LIMK1, p‐MLC and MLC protein levels. c,d) Knockdown of HIF1A suppressed actin cytoskeleton reorganization in DDR1‐overexpressing GC cells. Representative images c) and quantification d) of F‐actin (white) by phalloidin staining with confocal fluorescence microscopy. Nuclei were stained with DAPI. Scale bar: 20 µm. e) Silencing of HIF1A inhibited DDR1‐induced EMT in GC cells. Western blot analysis was performed to evaluate N‐cadherin, E‐cadherin and Vimentin expression in HGC27 and AGS cells transduced with the indicated vectors. f,g) HIF‐1α mediated DDR1‐induced EMT in GC cells. The expression levels of E‐Cadherin (purple) and ZO‐1 (green) were assessed by IF staining. Scale bar: 25 µm. All data are presented as the means ± SEMs from three independent experiments. The P values in panels (d), (g) were calculated using one‐way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001.
2.7
DDR1 Promotes GC Progression via HIF‐1α In Vivo
To further explore the influence of DDR1 on tumor progression in vivo, mouse xenograft models were applied. AGS cells with and without DDR1 overexpression (referred to as AGS‐DDR1 and AGS‐Vector, respectively) were subcutaneously injected to establish the xenograft model (n = 6 mice per group). The tumors were harvested 4 weeks later (Figure
7a). Tumors formed by AGS‐DDR1 cells exhibited an increased volume and weight (Figure S9a,b, Supporting Information). IF staining of CD31 suggested that tumors with DDR1 overexpression showed an increased microvessel density (Figure 7b,c). To clarify the role of DDR1 in tumor angiogenesis in vivo, HIF‐1α and VEGF‐A expression were measured in xenograft tumor samples. Western blot analysis suggested that the HIF‐1α and VEGF‐A levels were higher in AGS‐DDR1 tumor xenografts, consistent with the in vitro findings (Figure 7d). Furthermore, the western blot analyses showed that the RhoA signaling was also upregulated in AGS‐DDR1 tumor xenografts (Figure S9c,d, Supporting Information), and DDR1 overexpression promoted EMT process in tumor xenografts (Figure S9e, Supporting Information). To explore the impact of DDR1 on the metastatic capacity, a lung metastasis model was established by injecting GC cells into the tail vein. H&E staining showed that DDR1 overexpression increased the number of lung micrometastatic nodules (Figure 7e).
Figure 7
DDR1 promotes GC progression via HIF‐1α in vivo. a) Representative images of subcutaneous tumors established by AGS‐DDR1 and AGS‐Vector cells. b,c) DDR1 overexpression increased the microvessel density in subcutaneous tumors. Representative micrographs b) and quantification c) of CD31 (red) and DDR1 (green) expression in xenograft tumors from nude mice via IF staining. Nuclei were stained with DAPI. Scale bar: 50 µm. d) DDR1 overexpression promoted HIF‐1α and VEGF‐A expression in vivo. Western blot analysis was performed to measure DDR1, HIF‐1α and VEGF‐A protein levels in the subcutaneous tumors from the 2 groups. e) DDR1 overexpression significantly increased the number of lung metastatic nodules in mouse models. Representative images (left) and quantification (right) of lung metastases established by tail vein injection, as determined by H&E staining. Scale bar: 25 µm. f) HIF1A silencing suppressed DDR1‐induced tumor growth. The growth of tumors formed by AGS‐DDR1 and AGS‐Vector cells was measured every three days (n = 6/group). g) HIF1A knockdown inhibited the VEGF‐A and CD31 expression induced by DDR1. Representative micrographs of IHC staining for DDR1, HIF‐1α, VEGF‐A and CD31 in subcutaneous tumors. Scale bar: 50 µm. h) HIF1A silencing attenuated DDR1‐induced EMT in vivo. Representative micrographs of IF staining for E‐cadherin (purple) and ZO‐1 (green) in tumors from nude mice. Nuclei were stained with DAPI. Scale bar: 50 µm. All data are presented as the means ± SEMs from three independent experiments. The P values in panels (c) and (e) were calculated using Student's t‐test. The P values in panels (f) were calculated using two‐way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant.
Furthermore, HIF1A expression was silenced to elucidate its functional role in DDR1‐induced angiogenesis and metastasis in vivo. Silencing of HIF1A significantly attenuated DDR1‐increased tumor growth in nude mice (Figure 7f; Figure S9f,g, Supporting Information). The CD31 and VEGF‐A levels in AGS‐DDR1 xenografts were also decreased after HIF1A knockdown (Figure 7g; Figure S9h,i, Supporting Information), suggesting that HIF‐1α is correlated with the in vivo angiogenic effect of DDR1. In addition, HIF1A silencing also reversed the EMT caused by DDR1 overexpression (Figure 7h; Figure S9j, Supporting Information), and lung metastasis of AGS‐DDR1 in vivo was inhibited by HIF1A knockdown (Figure S9k,l, Supporting Information). Taken together, these data indicated that HIF‐1α is the key mediator of DDR1‐induced angiogenesis and metastasis in GC.
2.8
Pharmacological Inhibition of DDR1 Suppresses Malignant Progression of GC in PDX and Organoid Models
In recent years, PDX and organoid models, both of which directly originate from human tumor specimens, have emerged as promising approaches for translational research in cancer treatment.[
37
] To investigate the therapeutic potential of DDR1 as a treatment target for GC, a highly selective inhibitor of DDR1, 7rh benzamide, was used to block DDR1 signaling in PDX and organoid models. In our study, PDX models (PDX1 and PDX2) were constructed with passage three tumors derived from two patients’ GC samples (Figure S10a, Supporting Information). When the xenograft tumors were palpable (≈200 mm3), mice with similar tumor volumes were randomly administered 7rh benzamide or vehicle for 4 weeks (Figure
8a). Treatment with 7rh benzamide had no obvious influence on body weight (Figure S10b, Supporting Information) but effectively inhibited tumor growth in both the PDX1 and PDX2 models (Figure 8b,c; Figure S10c, Supporting Information). The IHC results demonstrated that DDR1 inhibition with 7rh benzamide blocked HIF‐1α and VEGF‐A expression and upregulated E‐cadherin expression in the PDX models (Figure 8d,e). Then, we established an organoid model from primary GC specimens to assess the efficacy of 7rh benzamide (Figure 8f). The results of IF staining demonstrated that in the organoid model, pharmacological inhibition of DDR1 suppressed cytoskeletal reorganization and simultaneously enhanced E‐cadherin and ZO‐1 expression (Figure 8g; Figure S10d, Supporting Information). Collectively, these results in PDX and organoid models elucidated the significant role of DDR1 in promoting GC malignancy and pointed out the potential of DDR1 as a therapeutic target for GC in translational medicine. In summary, our results suggest that DDR1 directly interacts with HIF‐1α to inhibit its ubiquitin‐mediated degradation, which in turn promotes the malignant progression of GC.
Figure 8
Pharmacological inhibition of DDR1 suppresses malignant progression in PDX and organoid models. a) Experimental protocol of the PDX models. b) Representative images of tumors harvested from the 2 PDX models. c) Pharmacological inhibition of DDR1 suppressed tumor growth in the PDX models. The DDR1 inhibitor 7rh benzamide was administered at a dose of 25 mg kg−1 by oral gavage every day. d,e) Treatment with 7rh benzamide decreased the expression of HIF‐1α, VEGF‐A, and E‐Cadherin in the PDX models. Representative images d) and quantification e) of DDR1, HIF‐1α, VEGF‐A, and E‐Cadherin expression determined by IHC staining in tumors from PDX models treated with 7rh benzamide or vehicle. Scale bar: 50 µm. f) Schematic illustration of organoid models derived from dissected human GC tissues. g) IF images of E‐Cadherin, ZO‐1, and F‐actin in GC organoids treated with 7rh benzamide or vehicle. Scale bar: 50 µm. h) Schematic illustration of the model that DDR1 interacts with HIF‐1α to suppress its degradation, and consequently promote malignant progression of GC. All data are presented as the means ± SEMs from three independent experiments. The P values in panels (c), (e) were calculated using Student's t‐test. *P < 0.05, **P < 0.01, ***P < 0.001, ns: not significant.
3
Discussion
Previous studies have well demonstrated the significant role of ECM‐cells interactions in regulating the TME,[
38
,
39
] while the precise mechanism by which the ECM affects tumor progression remains largely unclear. Interactions between collagen, a fundamental component of the ECM, and its receptors are a main mechanism by which the ECM exerts its influence on tumor progression.[
40
] In the present study, we demonstrated that DDR1 was the only collagen‐binding receptor with kinase activity overexpressed in GC. Both in vivo and in vitro results suggested that DDR1 was involved in angiogenesis and metastasis, probably by inhibiting the ubiquitin‐mediated degradation of HIF‐1α and activating the HIF‐1α/RhoA/ROCK1 signaling pathway. By studies in PDX mouse and organoid models, as well as treatment with 7rh benzamide, our group further confirmed the potential of DDR1 as a novel therapeutic target in GC.
Emerging evidence reveals aberrant DDR1 expression in various kinds of malignancies, such as glioblastoma, lung adenocarcinoma, and head and neck squamous cell carcinoma.[
12
,
14
,
41
] Some studies have demonstrated that somatic mutations may contribute to elevated DDR1 expression and correlate with patient survival in several types of cancers, including breast cancer[
22
] and endometrial cancer.[
42
] In contrast, our results demonstrated that mutations of DDR1 are relatively rare in GC based on the data from cBioPortal and a cohort from our medical center, indicating that mutation might not be the main factor contributing to DDR1 upregulation in GC.
Accumulating studies have suggested that GC exhibits a highly angiogenic phenotype, revealing angiogenesis as a promising target.[
18
,
43
] Recent evidence demonstrated that ECM components are critical to tumor angiogenesis, though the in‐depth mechanism were yet to unravel. Interestingly, our results showed a positive correlation between DDR1 expression and microvessel density in clinical GC samples. Further in vivo and in vitro experiments confirmed the proangiogenic effect of DDR1. Thus, our study provided evidence that DDR1 as a collagen receptor mediated ECM‐induced angiogenesis in GC.
Currently, the mechanism underlying DDR1's function in tumor angiogenesis has not been reported yet. HIF‐1α, PDGF, and FGF2 are the primary regulators of tumor angiogenesis.[
25
] Our data showed that HIF‐1α was upregulated by DDR1, but not PDGF or FGF2. In addition, our results suggested that DDR1 expression was positively correlated with HIF‐1α expression in clinical GC samples. HIF‐1α functions as a transcription factor and exerts a strong influence on angiogenesis by regulating the expression of angiogenic growth factors during embryonic development and disease pathogenesis.[
19
] However, the relationship between DDR1 and HIF‐1α is largely unclear. HIF‐1α mRNA is constantly translated into protein in cells, while due to persistent degradation by the ubiquitin‒proteasome system, the protein level of HIF‐1α remains extremely low under normal oxygen tension.[
26
] Our results demonstrated that DDR1 upregulation could delay the degradation of HIF‐1α and thus raise its protein level. These data suggest that posttranscriptional modifications may contribute to DDR1‐induced HIF‐1α protein expression.
In this study, we demonstrated that DDR1 inhibited the ubiquitination and degradation of HIF‐1α in GC cells, probably via direct binding between DDR1 and HIF‐1α, proposing a novel mechanism by which DDR1 regulates the dynamic equilibrium of HIF‐1α protein expression. Furthermore, our data showed that the PAS domain of HIF‐1α directly interacted with DDR1. Indeed, the PAS domain is involved in a variety of regulatory and sensory functions, such as the stability, protein interactions, and nuclear localization of HIF‐1α.[
44
] Further experiments demonstrated that the PAS domain was vital for DDR1‐induced angiogenesis. Collectively, these results provide insights into a novel mechanism by which DDR1 affects the cellular HIF‐1α level.
In addition to angiogenesis, some studies have demonstrated that DDR1 enhances metastatic capacity, but the mechanism is not well understood.[
45
] Actin cytoskeleton reorganization is closely related to tumor cell migration, and DDR1 is suggested to be correlated with actin cytoskeleton remodeling, although it plays distinct roles in different cell types.[
28
,
29
,
46
] Some studies have demonstrated that DDR1 stimulates the formation of linear invadosomes and actin stress fibers in breast cancer cells,[
28
,
30
] while Yeh et al. reported that DDR1 inhibits Rac1/Cdc42 activity to decrease the F‐actin content in Madin‐Darby canine kidney (MDCK) cells.[
47
] Our study first demonstrated that DDR1 mediated cytoskeletal reorganization by increasing the accumulation of actin stress fibers and further enhancing the metastatic capacity of GC cells. Actin cytoskeleton reorganization is commonly coupled with EMT, which is an initial step for tumor metastasis cascades.[
48
] We also found that DDR1 induced EMT in GC cells. However, the mechanism by which DDR1 regulates actin cytoskeleton reorganization and EMT is still unclear. RhoA and its downstream kinase, ROCK1, are known to be important regulators extensively involved in actin cytoskeleton reorganization.[
49
] Gilkes et al. suggested that HIF‐1α was an activator of RhoA/ROCK1 signaling.[
50
] Our study further provides evidence that DDR1 promotes actin cytoskeleton reorganization through HIF‐1α/RhoA/ROCK1 signaling in GC cells and clarifies a novel mechanism for tumor metastasis in GC.
The past decade has seen little progress in anticancer drugs for GC, leading to limited survival improvements lagging those in other malignancies.[
51
] At present, trastuzumab is the only effective targeted therapeutic drug for GC in clinical practice and can improve the prognosis of HER2‐positive GC patients.[
52
] However, only 10%–20% of GC cases show HER2 overexpression or amplification.[
53
] Thus, there is an urgent need to explore novel therapeutic targets for GC. PDX and organoid models reproduce the genetic and phenotypic heterogeneity of the original tumors and mimic the biological characteristics much better than in vitro culture models.[
54
] Therefore, we established PDX and organoid models of GC, as we have previously described, and tested the effect of pharmacological inhibition of DDR1 with 7rh benzamide.[
55
] Treatment with 7rh benzamide efficiently inhibited tumor angiogenesis and growth in the PDX models. In addition, our organoid model results showed that 7rh benzamide attenuated the reorganization of the actin cytoskeleton, consistent with the in vitro results. Further clinical trials should be performed to validate the therapeutic effect of 7rh benzamide in GC. Collectively, these results provide solid evidence for DDR1 as a treatment target for GC.
Regarding the limitations, this work does not include a pre‐clinical trial using a DDR1 inhibitor or DDR1 monoclonal antibody to block the interaction between DDR1 and HIF‐1α. Incorporating such a trial would provide highly meaningful and convincing evidence of the therapeutic effect in GC patients through the proposed molecular mechanism. Additionally, many E3 ligases are reported to interact with HIF‐1α and participate in its degradation, including VHL, RACK1, FBW7, MDM2, SIAH1/3, and CHIP, among others.[
56
] The binding of DDR1 with HIF‐1α could potentially act as an antagonist, preventing E3 ligase‐mediated HIF‐1α degradation and stabilizing HIF‐1α. Further studies should investigate which E3 ligase can be antagonized and define the extent of this antagonization. This will lead to a deeper understanding of DDR1's role in regulating GC malignancy.
In summary, our results demonstrated that DDR1 triggered GC progression by directly binding to HIF‐1α, which subsequently suppressed its ubiquitin‐mediated degradation. Through HIF‐1α/RhoA/ROCK1 signaling, DDR1 promoted cytoskeleton reorganization to induce tumor metastasis. Pharmacological inhibition of DDR1 retarded tumor progression in PDX and organoid models. This work revealed the intracellular mechanism of DDR1 in driving GC progression and identified the DDR1 antagonist 7rh benzamide as a promising therapeutic option for GC patients.
4
Experimental Section
Patients and Tissue Specimens
Paraffin‐embedded GC specimens were obtained from 182 GC patients diagnosed with histologically proven GC between 2012 and 2014 in this study. The GC patient information and follow‐up data were obtained from the gastric cancer database at the First Affiliated Hospital of Sun Yat‐sen University (FAHS). In addition, fresh tumor tissues and adjacent normal tissues were collected from 40 GC patients who received radical gastrectomy in the FAHS.
All specimens were collected with written informed consent. This study was approved by the Ethical Review Committee of the FAHS (approval numbers: [2021]−497) and the Seventh Affiliated Hospital of Sun Yat‐sen university (approval numbers: KY‐2020‐042‐01). The 8th edition of the tumor‐node‐metastasis (TNM) Classification of Malignant Tumors was used for the staging of GC cases.
Cell Culture
HEK293T cells were grown in Dulbecco's modified Eagle's medium (DMEM) containing 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin‒streptomycin (Invitrogen). HGC27, AGS and MKN74 cells were cultured in RPMI‐1640 medium supplemented with 10% FBS (Gibco) and 1% penicillin‐streptomycin (Invitrogen). HUVECs were isolated and cultured in gelatine‐coated cell culture dishes in M199 medium (Invitrogen) containing 20% FBS, 10 µg L−1 human basic fibroblast growth factor (bFGF) (R&D Systems) and 1% penicillin‒streptomycin (Invitrogen). The cell lines used in this study were validated by short tandem repeat profiling. All the cells were regularly screened for mycoplasma contamination and used only when negative.
Transfection and Vectors
Plasmids expressing HA‐tagged DDR1 (pEZ‐M07‐HA‐DDR1), Flag‐tagged HIF‐1α (pEZ‐M14‐Flag‐HIF‐1α) and the corresponding deletion mutants were purchased from GeneCopoeia (Guangzhou, China). Flag‐tagged HIF‐1α deletion mutants were generated by site‐directed mutagenesis using a QuikChange II XL Site‐Directed Mutagenesis Kit (Agilent Technologies). H1, H2 and H3 deletion mutants were generated by individually deleting the PAS (85‐298), NODD (401‐531), and ID (575‐786) motifs, respectively, from the HIF‐1α sequence. Plasmids were transfected into cells at 70%−80% confluence cultured in 6‐well plates with Lipofectamine 3000 reagent (Invitrogen, Carlsbad, California, USA). Full‐length DDR1 cDNA was inserted into the lentiviral vector pEZ‐Lv201 (GeneCopoeia). Lentiviral particles expressing pEZ‐Lv201‐DDR1 and empty pEZ‐Lv201 were used for transduction of HGC27 and AGS cells according to the manufacturer's instructions. HIF‐1α silencing was performed by inserting the sequences 5′‐UGCUCUUUGUGGUUGGAUCUA‐3′ (HIF‐1α‐shRNA‐1) and 5′‐CCGCUGGAGACACAAUCAUAU‐3′ (HIF‐1α‐shRNA‐2) into the psi‐LVRU6H lentiviral vector (GeneCopoeia, China) vector. Cells were incubated with puromycin (2 µg ml−1) or hygromycin (200 µg ml−1) for selection of stable cells expressing DDR1 or HIF‐1α‐shRNA.
CRISPR/Cas9‐Mediated Knockout of the DDR1 Gene
CRISPR/Cas9 gene editing was adopted to knock out DDR1 expression in human HGC27 and AGS cells. The single‐guide RNA (sgRNA) targeting DDR1 (sgRNA sequence: ATCAGGAGCTATGGGACCAG) was inserted into the lentiviral vector pCRISPR‐LvSG06 (GeneCopoeia, China). Then, lentiviral particles were used for transduction in the presence of 10 µg ml−1 polybrene. Stable cell lines were selected by incubation with puromycin (3 µg ml−1) for 2 weeks.
Immunohistochemistry
IHC analysis was conducted on paraffin‐embedded human GC tissues and subcutaneous tumor tissues from mice as previously described.[
57
] The primary antibodies used for IHC were listed in Table S4 (Supporting Information). Visualization was carried out with DAB (DAKO, #K5007) and counterstained with hematoxylin. Images were captured with an Olympus BX63 microscope (Olympus, Japan). The IHC scores were determined by the proportion and intensity of positive staining in tumor cells. Samples with scores ≥ 8 were deemed to have high expression, and those with a score of 0–8 were considered to have low expression. Two pathologists blinded to the clinicopathological characteristics independently selected 5 high‐power fields in the stained slides and assessed the proportions and intensities.
Western Blotting
Standard western blot analysis was performed as previously described.[
57
] The primary antibodies for immunoblotting in Table S5 (Supporting Information) was listed. Finally, the membranes were visualized using enhanced chemiluminescence reagent (Millipore, #WBKLS0100) with an imaging system (Tanon 5200 Multi, China).
Tumor Cell Migration and Invasion Assays
Cell migration and invasion assays were conducted in 24‐well plates using modified Boyden chambers (8‐mm pore size; BD Biosciences). Cells (5×104) were suspended in serum‐free medium and seeded into the upper chambers with (invasion assay) or without (migration assay) a Matrigel coating on the membrane. The lower chambers were filled with medium containing 10% FBS. After incubation for 24 h, cells remaining in the upper chambers were removed. The migrated cells were fixed with 4% formaldehyde and then stained with 0.5% crystal violet. The migrated and invaded cells were counted in five random fields at 100× magnification.
Wound Healing Assay
AGS and HGC27 cells were cultured in 6‐well plates (5 × 105 cells well−1) and grown to 80% confluence. A scratch was made in the cell layer with a sterile pipette tip, and the detached cells were removed by replacement of the medium with RPMI‐1640 medium containing 1% FBS. Cell movement into the wound area was evaluated with a phase‐contrast microscope (Nikon, Japan) 48 h after the scratch was made.
Co‐IP Assay
Cells were cultured in 10 cm dishes and lysed with IP lysis buffer (Thermo Fisher). The supernatants obtained from cell lysates after centrifugation were incubated with primary antibodies overnight. Then, the protein samples were incubated with Pierce Protein A/G Magnetic Beads (Thermo Fisher) at 4 °C for 2 h and washed with IP lysis buffer three times. Proteins were collected using elution buffer (Thermo Fisher). Finally, the protein samples were analyzed by SDS‒PAGE and immunoblotted with the corresponding antibodies. The primary antibodies used for the co‐IP assay are listed in Table S6 (Supporting Information).
RhoA Activity Assay
After washing with ice‐cold PBS 2 times, cells were lysed with cell lysis buffer. Then, the cell lysates were centrifuged at 14 000 rpm for 5 min at 4 °C. The proteins in the supernatants were collected and incubated with agarose‐bound rhotekin‐RBD beads at 4 °C for 45 min by using the Rho Activation Assay Biochem Kit (#BK036, Cytoskeleton). The beads were pelleted by centrifugation and washed three times in ice‐cold Mg2+ lysis buffer (MLB). The level of active RhoA was measured by immunoblotting using an anti‐RhoA antibody.
Tube Formation and HUVEC Migration Assays
For tube formation assays, Matrigel matrix (Corning, #356 234) was utilized to coat the wells of 24‐well plates and incubated at 37 °C for 30 min. HUVECs (1×105) were seeded in Matrigel‐coated 24‐well plates and treated for 6 h at 37 °C with conditioned medium from GC cells transfected with different vectors. Images of the capillary tube structure were captured by phase contrast microscopy. The tubes were counted to evaluate the tube formation ability. For HUVEC migration assays, cells (3×104) were seeded into Transwell chambers (8‐mm pore size; BD Biosciences) in a 24‐well plate. After treatment with conditioned medium for 24 h, the migrated HUVECs were stained with 0.5% crystal violet and counted. HUVECs at passages 2–6 were utilized in the study.
Enzyme‐Linked Immunosorbent Assay
A human VEGF‐A enzyme‐linked immunosorbent assay (ELISA) kit (Elabscience, #E‐EL‐H0111) was used to measure the secreted VEGF‐A protein level according to the manufacturer's instructions.
Cell Immunofluorescence
Cells seeded on coverslips were fixed with 4% paraformaldehyde for 15 min at room temperature and permeabilized in 0.2% Triton X‐100 for 5 min. Then, after blocking with blocking buffer (PBS with 5% BSA) for 30 min at RT, the cells were incubated with antibodies overnight at 4 °C followed by secondary antibodies for 1 h at RT. The primary antibodies for cell IF were listed in Table S7 (Supporting Information). Thereafter, samples were counterstained with DAPI for 5 min to stain the nuclei. Images were captured with an LSM780 confocal microscope (Zeiss).
RNA Extraction, Quantitative Real‐Time PCR and Sequencing Analysis
Total RNA was extracted with RNAiso Plus (Takara, Japan) as it was previously described.[
57
] cDNA synthesis was conducted with a reverse transcription kit (TAKARA, Japan). RT‐PCR analysis was conducted on an HT 7900 instrument (Applied Biosystems) in 10 µl reaction mixtures with SYBRVR Green qPCR SuperMix (Invitrogen). The primer sequences used are listed in Table S8 (Supporting Information). Relative mRNA expression levels were calculated as fold changes using the 2−ΔΔCt method.
Another 131 GC samples from FAHS were utilized for sequencing analysis. The sequences of the primers used to amplify regions containing previously reported DDR1 mutations were as follows: forwards primer GAGCTGACGGTTCACCTCTC, reverse primer AATGTCAGCCTCGGCATAAT. Then, the polymerase chain reaction products were purified and sequenced.
Animal Studies
The protocols of animal studies were in accordance with the guidelines of the First Affiliated Hospital of Sun Yat‐sen University. The animal studies were approved by the Ethical Review Committee of the FAHS (approval numbers: [2021]−165). Nude mice were obtained from GemPharmatech Co., Ltd. (Guangdong, China). For the tumor xenograft model, AGS cells (5×106) transduced with the indicated vectors were utilized to establish subcutaneous tumors in the right axillae of nude mice (female, 5–6 weeks old) (n = 6 per group). Tumor growth was monitored with callipers every three days, and tumor volume was quantified as follows: V (mm3) = [Length×Width2] ×0.5. After 4 weeks, mice were euthanized, and tumors were collected, weighed, and preserved for further analysis.
Female BALB/c nude mice were used to establish lung metastasis models. AGS cells (1×106) transduced with the indicated vectors were injected into the tail vein. After 8 weeks, the lungs of mice from different groups (n = 6 per group) were collected and fixed in 4% paraformaldehyde. Then, serial lung sections stained with hematoxylin and eosin (H&E) were used to measure the metastatic nodules. Experiments were blinded to the people conducting marker procedure.
Establishment of PDX Models
To establish PDX models, fresh tumor tissues were obtained immediately after removal from GC patients and cut into 3×3×3 mm3 pieces, which were further subcutaneously implanted into the flanks of nude mice. The successfully established PDXs were regarded as passage 1 (P1). Then, the mice were sacrificed, and the xenografts were collected and transplanted into other nude mice to obtain the next passage of tumors. Nude mice with P3 tumors were used to evaluate the efficacy of 7rh benzamide (Sigma–Aldrich, #SML1832). When the tumor volume reached ≈200 mm3, mice were randomly assigned into two groups receiving 7‐rh benzamide (25 mg kg−1, every day, oral gavage) or vehicle. Tumor growth was measured with callipers every three days. All mice were sacrificed four weeks later, and the xenografts were harvested, weighed and processed for immunohistochemical and immunoblot analyses.
Organoid Culture
The establishment and culture of GC organoids were performed as we previously described.[
55
] Human GC tissues for organoid establishment were collected immediately after removal during surgery. All patients signed informed consent forms. GC specimens were preserved in Advanced DMEM/F12 supplemented with 1% penicillin‒streptomycin (Invitrogen) and cut into pieces of 3 mm3. The tumor pieces were digested into solutions by incubation with Advanced DMEM/F12 medium and 1 mg ml−1 type IV collagenase (Sigma–Aldrich) at 37 °C for 1 h. After digestion, samples were strained through a 70‐µm filter (Falcon; #352 350) and resuspended in medium mixed with Matrigel at a ratio of 2:1. Organoids were passed every 2–3 days. At the approximate size and confluence, organoids were collected and fixed with 4% methanol‐free paraformaldehyde (PFA) for IF analysis.
RNA‐Seq Analysis and Bioinformatics
The TRIzol reagent was used to lyse cell, then the total RNA was extracted for high‐throughput sequencing. The Beijing Genomics Institute was responsible for library construction and high‐throughput RNA sequencing. The limma package was utilized to conduct differential gene expression analysis between experimental conditions. Each condition was represented by three independent biological replicates. DEGs (differentially expressed genes) were determined by > 1‐fold change in gene expression with adjusted P< 0.05 and were visualized using volcano plot. The upregulated differentially expressed genes within DDR1 overexpressed cells were selected for GO enrichment analysis. Gene set enrichment analysis (GSEA) was also performed for RNA‐seq data, against signatures in the Molecular Signatures Database.
Gene Expression Analysis in Public Databases
We extracted the RNA‐Seq expression data (HTSeq‐FPKM), clinicopathological features, and prognostic data of GC patients from the TCGA‐STAD database. To compare collagen receptor expression, a log2 (TPM+1) transformation was applied to standardize the mRNA expression levels. The GSE51575 and GSE13861 datasets downloaded from the PubMed GEO database were also obtained to evaluate DDR1 expression in gastric cancer and adjacent normal tissues. For correlation analysis between DDR1 and VEGF gene expression in the cohort of GC patients, the data for the gene expression matrix were extracted from TCGA‐STAD datasets, and correlations were analyzed in R using two‐tailed Spearman correlation analysis. To investigate the correlations between the expression of DDR1 and VEGFs in GC, it was extracted data from the TCGA‐STAD dataset and used two‐tailed Spearman correlation analysis. The visualization of the correlation matrix was conducted with the R packages “ggplot2” and “ggcorrplot”.
Statistical Analysis
The chi‐square test was used to compare differences between 2 groups of categorical variables. For continuous variables, parametric or nonparametric tests were adopted. One‐way analysis of variance was utilized for comparisons among three or more groups. The statistical analyses used are described in figure legend. Data are presented as the mean ± SEM of at least three independent experiments. GraphPad Prism 8 (GraphPad Software Inc., USA), SPSS 22.0 (SPSS Inc., USA) or R software were used for statistical analyses. Statistical significance was deemed to be indicated by P< 0.05.
Patients and Tissue Specimens
Paraffin‐embedded GC specimens were obtained from 182 GC patients diagnosed with histologically proven GC between 2012 and 2014 in this study. The GC patient information and follow‐up data were obtained from the gastric cancer database at the First Affiliated Hospital of Sun Yat‐sen University (FAHS). In addition, fresh tumor tissues and adjacent normal tissues were collected from 40 GC patients who received radical gastrectomy in the FAHS.
All specimens were collected with written informed consent. This study was approved by the Ethical Review Committee of the FAHS (approval numbers: [2021]−497) and the Seventh Affiliated Hospital of Sun Yat‐sen university (approval numbers: KY‐2020‐042‐01). The 8th edition of the tumor‐node‐metastasis (TNM) Classification of Malignant Tumors was used for the staging of GC cases.
Cell Culture
HEK293T cells were grown in Dulbecco's modified Eagle's medium (DMEM) containing 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin‒streptomycin (Invitrogen). HGC27, AGS and MKN74 cells were cultured in RPMI‐1640 medium supplemented with 10% FBS (Gibco) and 1% penicillin‐streptomycin (Invitrogen). HUVECs were isolated and cultured in gelatine‐coated cell culture dishes in M199 medium (Invitrogen) containing 20% FBS, 10 µg L−1 human basic fibroblast growth factor (bFGF) (R&D Systems) and 1% penicillin‒streptomycin (Invitrogen). The cell lines used in this study were validated by short tandem repeat profiling. All the cells were regularly screened for mycoplasma contamination and used only when negative.
Transfection and Vectors
Plasmids expressing HA‐tagged DDR1 (pEZ‐M07‐HA‐DDR1), Flag‐tagged HIF‐1α (pEZ‐M14‐Flag‐HIF‐1α) and the corresponding deletion mutants were purchased from GeneCopoeia (Guangzhou, China). Flag‐tagged HIF‐1α deletion mutants were generated by site‐directed mutagenesis using a QuikChange II XL Site‐Directed Mutagenesis Kit (Agilent Technologies). H1, H2 and H3 deletion mutants were generated by individually deleting the PAS (85‐298), NODD (401‐531), and ID (575‐786) motifs, respectively, from the HIF‐1α sequence. Plasmids were transfected into cells at 70%−80% confluence cultured in 6‐well plates with Lipofectamine 3000 reagent (Invitrogen, Carlsbad, California, USA). Full‐length DDR1 cDNA was inserted into the lentiviral vector pEZ‐Lv201 (GeneCopoeia). Lentiviral particles expressing pEZ‐Lv201‐DDR1 and empty pEZ‐Lv201 were used for transduction of HGC27 and AGS cells according to the manufacturer's instructions. HIF‐1α silencing was performed by inserting the sequences 5′‐UGCUCUUUGUGGUUGGAUCUA‐3′ (HIF‐1α‐shRNA‐1) and 5′‐CCGCUGGAGACACAAUCAUAU‐3′ (HIF‐1α‐shRNA‐2) into the psi‐LVRU6H lentiviral vector (GeneCopoeia, China) vector. Cells were incubated with puromycin (2 µg ml−1) or hygromycin (200 µg ml−1) for selection of stable cells expressing DDR1 or HIF‐1α‐shRNA.
CRISPR/Cas9‐Mediated Knockout of the DDR1 Gene
CRISPR/Cas9 gene editing was adopted to knock out DDR1 expression in human HGC27 and AGS cells. The single‐guide RNA (sgRNA) targeting DDR1 (sgRNA sequence: ATCAGGAGCTATGGGACCAG) was inserted into the lentiviral vector pCRISPR‐LvSG06 (GeneCopoeia, China). Then, lentiviral particles were used for transduction in the presence of 10 µg ml−1 polybrene. Stable cell lines were selected by incubation with puromycin (3 µg ml−1) for 2 weeks.
Immunohistochemistry
IHC analysis was conducted on paraffin‐embedded human GC tissues and subcutaneous tumor tissues from mice as previously described.[
57
] The primary antibodies used for IHC were listed in Table S4 (Supporting Information). Visualization was carried out with DAB (DAKO, #K5007) and counterstained with hematoxylin. Images were captured with an Olympus BX63 microscope (Olympus, Japan). The IHC scores were determined by the proportion and intensity of positive staining in tumor cells. Samples with scores ≥ 8 were deemed to have high expression, and those with a score of 0–8 were considered to have low expression. Two pathologists blinded to the clinicopathological characteristics independently selected 5 high‐power fields in the stained slides and assessed the proportions and intensities.
Western Blotting
Standard western blot analysis was performed as previously described.[
57
] The primary antibodies for immunoblotting in Table S5 (Supporting Information) was listed. Finally, the membranes were visualized using enhanced chemiluminescence reagent (Millipore, #WBKLS0100) with an imaging system (Tanon 5200 Multi, China).
Tumor Cell Migration and Invasion Assays
Cell migration and invasion assays were conducted in 24‐well plates using modified Boyden chambers (8‐mm pore size; BD Biosciences). Cells (5×104) were suspended in serum‐free medium and seeded into the upper chambers with (invasion assay) or without (migration assay) a Matrigel coating on the membrane. The lower chambers were filled with medium containing 10% FBS. After incubation for 24 h, cells remaining in the upper chambers were removed. The migrated cells were fixed with 4% formaldehyde and then stained with 0.5% crystal violet. The migrated and invaded cells were counted in five random fields at 100× magnification.
Wound Healing Assay
AGS and HGC27 cells were cultured in 6‐well plates (5 × 105 cells well−1) and grown to 80% confluence. A scratch was made in the cell layer with a sterile pipette tip, and the detached cells were removed by replacement of the medium with RPMI‐1640 medium containing 1% FBS. Cell movement into the wound area was evaluated with a phase‐contrast microscope (Nikon, Japan) 48 h after the scratch was made.
Co‐IP Assay
Cells were cultured in 10 cm dishes and lysed with IP lysis buffer (Thermo Fisher). The supernatants obtained from cell lysates after centrifugation were incubated with primary antibodies overnight. Then, the protein samples were incubated with Pierce Protein A/G Magnetic Beads (Thermo Fisher) at 4 °C for 2 h and washed with IP lysis buffer three times. Proteins were collected using elution buffer (Thermo Fisher). Finally, the protein samples were analyzed by SDS‒PAGE and immunoblotted with the corresponding antibodies. The primary antibodies used for the co‐IP assay are listed in Table S6 (Supporting Information).
RhoA Activity Assay
After washing with ice‐cold PBS 2 times, cells were lysed with cell lysis buffer. Then, the cell lysates were centrifuged at 14 000 rpm for 5 min at 4 °C. The proteins in the supernatants were collected and incubated with agarose‐bound rhotekin‐RBD beads at 4 °C for 45 min by using the Rho Activation Assay Biochem Kit (#BK036, Cytoskeleton). The beads were pelleted by centrifugation and washed three times in ice‐cold Mg2+ lysis buffer (MLB). The level of active RhoA was measured by immunoblotting using an anti‐RhoA antibody.
Tube Formation and HUVEC Migration Assays
For tube formation assays, Matrigel matrix (Corning, #356 234) was utilized to coat the wells of 24‐well plates and incubated at 37 °C for 30 min. HUVECs (1×105) were seeded in Matrigel‐coated 24‐well plates and treated for 6 h at 37 °C with conditioned medium from GC cells transfected with different vectors. Images of the capillary tube structure were captured by phase contrast microscopy. The tubes were counted to evaluate the tube formation ability. For HUVEC migration assays, cells (3×104) were seeded into Transwell chambers (8‐mm pore size; BD Biosciences) in a 24‐well plate. After treatment with conditioned medium for 24 h, the migrated HUVECs were stained with 0.5% crystal violet and counted. HUVECs at passages 2–6 were utilized in the study.
Enzyme‐Linked Immunosorbent Assay
A human VEGF‐A enzyme‐linked immunosorbent assay (ELISA) kit (Elabscience, #E‐EL‐H0111) was used to measure the secreted VEGF‐A protein level according to the manufacturer's instructions.
Cell Immunofluorescence
Cells seeded on coverslips were fixed with 4% paraformaldehyde for 15 min at room temperature and permeabilized in 0.2% Triton X‐100 for 5 min. Then, after blocking with blocking buffer (PBS with 5% BSA) for 30 min at RT, the cells were incubated with antibodies overnight at 4 °C followed by secondary antibodies for 1 h at RT. The primary antibodies for cell IF were listed in Table S7 (Supporting Information). Thereafter, samples were counterstained with DAPI for 5 min to stain the nuclei. Images were captured with an LSM780 confocal microscope (Zeiss).
RNA Extraction, Quantitative Real‐Time PCR and Sequencing Analysis
Total RNA was extracted with RNAiso Plus (Takara, Japan) as it was previously described.[
57
] cDNA synthesis was conducted with a reverse transcription kit (TAKARA, Japan). RT‐PCR analysis was conducted on an HT 7900 instrument (Applied Biosystems) in 10 µl reaction mixtures with SYBRVR Green qPCR SuperMix (Invitrogen). The primer sequences used are listed in Table S8 (Supporting Information). Relative mRNA expression levels were calculated as fold changes using the 2−ΔΔCt method.
Another 131 GC samples from FAHS were utilized for sequencing analysis. The sequences of the primers used to amplify regions containing previously reported DDR1 mutations were as follows: forwards primer GAGCTGACGGTTCACCTCTC, reverse primer AATGTCAGCCTCGGCATAAT. Then, the polymerase chain reaction products were purified and sequenced.
Animal Studies
The protocols of animal studies were in accordance with the guidelines of the First Affiliated Hospital of Sun Yat‐sen University. The animal studies were approved by the Ethical Review Committee of the FAHS (approval numbers: [2021]−165). Nude mice were obtained from GemPharmatech Co., Ltd. (Guangdong, China). For the tumor xenograft model, AGS cells (5×106) transduced with the indicated vectors were utilized to establish subcutaneous tumors in the right axillae of nude mice (female, 5–6 weeks old) (n = 6 per group). Tumor growth was monitored with callipers every three days, and tumor volume was quantified as follows: V (mm3) = [Length×Width2] ×0.5. After 4 weeks, mice were euthanized, and tumors were collected, weighed, and preserved for further analysis.
Female BALB/c nude mice were used to establish lung metastasis models. AGS cells (1×106) transduced with the indicated vectors were injected into the tail vein. After 8 weeks, the lungs of mice from different groups (n = 6 per group) were collected and fixed in 4% paraformaldehyde. Then, serial lung sections stained with hematoxylin and eosin (H&E) were used to measure the metastatic nodules. Experiments were blinded to the people conducting marker procedure.
Establishment of PDX Models
To establish PDX models, fresh tumor tissues were obtained immediately after removal from GC patients and cut into 3×3×3 mm3 pieces, which were further subcutaneously implanted into the flanks of nude mice. The successfully established PDXs were regarded as passage 1 (P1). Then, the mice were sacrificed, and the xenografts were collected and transplanted into other nude mice to obtain the next passage of tumors. Nude mice with P3 tumors were used to evaluate the efficacy of 7rh benzamide (Sigma–Aldrich, #SML1832). When the tumor volume reached ≈200 mm3, mice were randomly assigned into two groups receiving 7‐rh benzamide (25 mg kg−1, every day, oral gavage) or vehicle. Tumor growth was measured with callipers every three days. All mice were sacrificed four weeks later, and the xenografts were harvested, weighed and processed for immunohistochemical and immunoblot analyses.
Organoid Culture
The establishment and culture of GC organoids were performed as we previously described.[
55
] Human GC tissues for organoid establishment were collected immediately after removal during surgery. All patients signed informed consent forms. GC specimens were preserved in Advanced DMEM/F12 supplemented with 1% penicillin‒streptomycin (Invitrogen) and cut into pieces of 3 mm3. The tumor pieces were digested into solutions by incubation with Advanced DMEM/F12 medium and 1 mg ml−1 type IV collagenase (Sigma–Aldrich) at 37 °C for 1 h. After digestion, samples were strained through a 70‐µm filter (Falcon; #352 350) and resuspended in medium mixed with Matrigel at a ratio of 2:1. Organoids were passed every 2–3 days. At the approximate size and confluence, organoids were collected and fixed with 4% methanol‐free paraformaldehyde (PFA) for IF analysis.
RNA‐Seq Analysis and Bioinformatics
The TRIzol reagent was used to lyse cell, then the total RNA was extracted for high‐throughput sequencing. The Beijing Genomics Institute was responsible for library construction and high‐throughput RNA sequencing. The limma package was utilized to conduct differential gene expression analysis between experimental conditions. Each condition was represented by three independent biological replicates. DEGs (differentially expressed genes) were determined by > 1‐fold change in gene expression with adjusted P< 0.05 and were visualized using volcano plot. The upregulated differentially expressed genes within DDR1 overexpressed cells were selected for GO enrichment analysis. Gene set enrichment analysis (GSEA) was also performed for RNA‐seq data, against signatures in the Molecular Signatures Database.
Gene Expression Analysis in Public Databases
We extracted the RNA‐Seq expression data (HTSeq‐FPKM), clinicopathological features, and prognostic data of GC patients from the TCGA‐STAD database. To compare collagen receptor expression, a log2 (TPM+1) transformation was applied to standardize the mRNA expression levels. The GSE51575 and GSE13861 datasets downloaded from the PubMed GEO database were also obtained to evaluate DDR1 expression in gastric cancer and adjacent normal tissues. For correlation analysis between DDR1 and VEGF gene expression in the cohort of GC patients, the data for the gene expression matrix were extracted from TCGA‐STAD datasets, and correlations were analyzed in R using two‐tailed Spearman correlation analysis. To investigate the correlations between the expression of DDR1 and VEGFs in GC, it was extracted data from the TCGA‐STAD dataset and used two‐tailed Spearman correlation analysis. The visualization of the correlation matrix was conducted with the R packages “ggplot2” and “ggcorrplot”.
Statistical Analysis
The chi‐square test was used to compare differences between 2 groups of categorical variables. For continuous variables, parametric or nonparametric tests were adopted. One‐way analysis of variance was utilized for comparisons among three or more groups. The statistical analyses used are described in figure legend. Data are presented as the mean ± SEM of at least three independent experiments. GraphPad Prism 8 (GraphPad Software Inc., USA), SPSS 22.0 (SPSS Inc., USA) or R software were used for statistical analyses. Statistical significance was deemed to be indicated by P< 0.05.
Conflict of Interest
The authors declare no conflict of interest.
Author Contributions
Z.W.W., J.L., L.Z., and D.J.Y. contributed equally to this work. L.Z., Y.H., W.S., and B.W. participated in the study design. Z.W., L.Z., J.L., and B.W. performed the in vitro experiments. Z.W., W.C., and D.Y. conducted the data analyses. Z.W., L.Z., and W.L. performed the PDX and nude mice experiments. W.C., L.Z., and H.Z. performed the organoid experiments. L.Z., Z.W., L.Z., and D.Y. wrote the manuscript, Z.W. revised the manuscript. W.S. and Z.W. interpreted the data and revised the manuscript. All authors read and approved the final manuscript. Funding for the research was provided by L.Z., Y.H., and W.S.. The order of the co–first authors was assigned based on the relative contributions of these individuals.
Ethics
All patients in this study signed an informed consent form. This study was approved by the Ethical Review Committee of the First Affiliated Hospital of Sun Yat‐sen University involving Human Subjects and the use of mice.
Supporting information
Supporting Information |
Title: Review of pharmacogenetics of antiseizure medications: focusing on genetic variants of mechanistic targets | Body:
Introduction
Antiseizure medications (ASMs), previously called antiepileptic drugs (AEDs), are commonly used to manage seizures in individuals with epilepsy (McCorry et al., 2004). The ultimate goal of seizure treatment is to achieve seizure freedom, defined by the International League Against Epilepsy (ILAE) consensus as being free from seizures for a minimum of three times the longest preintervention inter-seizure interval or 12 months, whichever is longer (Kwan et al., 2010). To achieve this goal, physicians select ASMs based on factors such as age, gender, epilepsy syndrome, co-medications, and comorbidities. However, even an ASM carefully chosen by an experienced epileptologist can have a different response in different patients with similar epilepsy syndrome and/or underlying clinical status. Although more than 30 ASMs are currently available, approximately one-third of patients still have seizures after being treated with more than two ASMs (Kwan and Sander, 2004). Such patients are considered to have drug-resistant epilepsy (DRE), defined by the ILAE consensus as the failure of adequate trials of two tolerated and appropriately chosen ASMs to achieve sustained seizure freedom (Kwan et al., 2010). The effectiveness of controlling seizures or developing adverse drug reactions (ADRs) is unpredictable. Therefore, patients have to endure the consequences of inadequately controlled seizures or ADRs before DRE is confirmed, delaying the initiation of other management strategies to alleviate the seizures, such as a ketogenetic diet (Martin et al., 2016), epilepsy surgery (Rugg-Gunn et al., 2020), vagus nerve stimulation (Elliott et al., 2011), deep brain stimulation (Vetkas et al., 2022), or the use of cannabidiol (Gaston and Szaflarski, 2018). The use of ASMs relies on a trial-and-error approach, with careful adjustments of the dosage to achieve a balance between response and ADRs. This approach relies heavily on the clinicians’ expertise, and only a few objective biomarkers currently exist to aid in ASM selection. One notable success in this regard is the prediction of Stevens-Johnson syndrome/toxic epidermal necrolysis in Taiwanese and Southeast Asian populations harboring the human leucocyte antigen HLA-B*1502 allele (Hung et al., 2010). This discovery has influenced clinical practice, prompting genetic tests before prescribing relevant ASMs to prevent deadly skin reactions (Franciotta et al., 2009).
ASMs exert their therapeutic effects by interacting with various targets in the central nervous system (CNS) (Das et al., 2012). Variations in genes encoding these targets theoretically have the potential to alter the response to ASMs. This can occur through changes in the coding region, resulting in amino acid sequence alterations that modify protein structures and their interaction with ASMs (Kimchi-Sarfaty et al., 2007) (Figure 1). Alternatively, variants in the non-coding region may interfere with protein synthesis, thereby affecting protein production and leading to varied responses to ASMs (Tate et al., 2005) (Figure 2). Pharmacogenetic studies offer a viable approach to predict the outcome of ASM treatment. However, despite extensive investigations, no biomarkers have yet demonstrated a predictive power comparable to that for dermatological side effects. This review focuses on pharmacogenetic studies of ASMs and their relevant mechanistic targets, and in particular on predicting the response to ASM treatment.
FIGURE 1
Coding Variants’ Impact on Antiseizure Medication. The schematic illustrates the impact of coding variants on the target, transportation, and metabolism of antiseizure medication (ASM). Typically, following oral administration of ASMs, the drug undergoes absorption in the gut, metabolism in the liver, and passage across the blood-brain barrier to reach its mechanistic target and exert its therapeutic effect. Genetic variants of efflux transporters in the gut, such as glycoprotein-P, can actively pump the drug back into the intestinal lumen, reducing its absorption. Similarly, variants of glycoprotein-P at the blood-brain barrier may facilitate the drug’s return to the bloodstream, decreasing its concentration in the brain. Genetic variants can also induce changes in metabolizing enzymes, such as cytochrome P450, in the liver. This can affect the rate of drug metabolism, leading to variable concentrations in the blood, differences in excretion, and variations in availability at its target site. Consequently, this may alter the effectiveness of ASMs. Additionally, genetic variants within the brain may modify neuronal receptors, compromising ASM’s ability to bind effectively and consequently diminishing its efficacy. *The figures were created with BioRender.com.
FIGURE 2
Impact of Non-Coding Genetic Variants on Antiseizure Medication Response. Genetic variants in non-coding regions, particularly introns, have the potential to disrupt typical splicing patterns. This disruption can lead to changes in protein expression levels and alter receptor functionality, consequently influencing responses to ASMs. The example provided in this Figure 2 focuses on SCN1A rs3812718. (A) The typical expression of SCN1A includes exon 5A and 5N. While 5N is more sensitive to sodium channel-blocking ASMs. (B) Conversely, within SCN1A carrying the rs3812718 variant, the expression of exon 5N is diminished, consequently reducing the abundance of receptors with heightened sensitivity to sodium channel-blocking ASMs. This decrease in receptors results in fewer channels being blocked, ultimately diminishing the antiseizure efficacy. (The ratio of 5A to 5N depicted in the figure does not accurately reflect the true conditions.). *The figures were created with BioRender.com.
Mechanistic targets of antiseizure medications
ASMs abolish aberrant neuronal excitability during epileptic discharge by altering the function of voltage-gated and ligand-gated ion channels (Meldrum and Rogawski, 2007). Voltage-gated ion channels are one of the main targets of ASMs including sodium channel (SCN) blockers, calcium current inhibitors, and potassium channel openers (Das et al., 2012). SCNs are responsible for the generation and propagation of action potential in neurons, and thus many ASMs act by reducing the high-frequency firing of voltage-dependent SCNs that occurs during a seizure (Abou-Khalil, 2016). Phenytoin (PHT), carbamazepine (CBZ), lamotrigine (LTG), oxcarbazepine (OXC), and lacosamide (LCM) are all SCN blockers (Brodie, 2017). Calcium channels contribute to the burst firing of neurons (Cain and Snutch, 2010) and control the release of presynaptic transmitters (Imbrici et al., 2004). Of the currently available ASMs, the main action of ethosuximide (Coulter et al., 1989), gabapentin (GBP) (Gee et al., 1996), and pregabalin (PGB) (Belliotti et al., 2005) is considered to be as blockers of calcium channels. The antiseizure effect of other ASMs may also in part be through the blockade of calcium channels, including PHT, phenobarbital, CBZ, OXC, zonisamide (ZNS), LTG, felbamate, topiramate (TPM), and valproate (VPA) (Abou-Khalil, 2019). Potassium channels are responsible for repolarizing the cell membrane and regulating the balance of excitability and resting of the neuron (Benarroch, 2009). Retigabine is currently the only ASM with the main action of enhancing resting membrane potential by opening potassium channels, however, it is not widely available due to discoloration of skin and conjunctiva (Gunthorpe et al., 2012). Other ASMs which may also modify potassium currents include ethosuximide (Leresche et al., 1998), LTG (Huang et al., 2004), and levetiracetam (LEV) (Madeja et al., 2003).
Several ligand-gated ion channels are also targets for ASMs, including gamma-aminobutyric acid (GABA) receptors and ionotropic glutamate receptors. GABA is the main fast inhibitory neurotransmitter, and it acts on GABAA and GABAB receptors in the CNS to terminate the bursting neuronal firing during seizures (Belelli et al., 2009). ASMs in this category, such as barbiturates and benzodiazepines can be used to treat status epilepticus (Czuczwar and Patsalos, 2001). Some ASMs modulate the disposition of GABA by inhibiting its catabolism, such as vigabatrin (Jung et al., 1977), or re-uptake, such as tiagabine (Krogsgaard-Larsen et al., 1987). Ionotropic glutamate receptors consist of three major forms, namely α-amino-3-hydroxy-5-methyl- 4-isoxazole propionic acid (AMPA), N-methyl-D-aspartate (NMDA), and kainate receptors, all of which they mediate the excitatory neurotransmission of glutamate (Reiner and Levitz, 2018). Perampanel (PER), a non-competitive AMPA receptor antagonist (Plosker, 2012), is currently the only ASM in this category.
Carbonic anhydrase (CA) regulates the flux of bicarbonate to maintain ion and pH homeostasis in neurons (Krishnamurthy et al., 2008). Inhibition of CA has been postulated to contribute to some of the antiseizure effects of acetazolamide, TPM, and ZNS (Das et al., 2012). The exact mechanism of how these CA inhibitors halt seizures is still unknown and no pharmacogenetic studies of variants in CA genes have been conducted to date.
LEV and brivaracetam share the same target, synaptic vesicle protein 2A (SV2A) (Rossi et al., 2022), and they exert their antiseizure effects by regulating exocytosis from synaptic vesicles and inhibiting the release of presynaptic neurotransmitters.
There are also broad-spectrum ASMs with multiple targets, such as ZNS (SCN blockade, calcium blocker, and CA inhibitor) (Brodie et al., 2012) and TPM (SCN blockade, GABA enhancer, CA inhibitor, and AMPA blockade) (Gryder and Rogawski, 2003), and ASMs with still unclear mechanisms, such as VPA (Romoli et al., 2019). Even for ASMs with known major targets, additional antiseizure mechanisms could arise from interactions with other minor targets (Sills and Rogawski, 2020). As a result, genetic variations of multiple receptors could result in the varied responses to ASMs, especially in patients taking polytherapy for seizure control.
With advances in the understanding of epileptogenesis, researchers are exploring innovative medications that target the pathogenic pathways implicated in seizure development or progression, beyond ion channels. These promising treatments show potential in effectively controlling seizures, and two are either undergoing human studies or are already in use. Cholesterol is metabolized to 24-hydroxycholesterol in the CNS by the cholesterol 24-hydroxylase (CH24H) enzyme (Popiolek et al., 2020). Lowering the cholesterol level in glial cells can result in an increased extracellular glutamate level (Tian et al., 2010) that may contribute to the genesis of seizures (Dejakaisaya et al., 2021). Soticlestat (TAK-935), a selective inhibitor of CH24H, demonstrated efficacy in decreasing seizure frequency among patients with Dravet syndrome during a phase 2 clinical trial (Hahn et al., 2022). Another example is the inhibition of mammalian target of rapamycin (mTOR). The mTOR pathway participates in cellular signaling and plays a crucial role in neuronal development (Lipton and Sahin, 2014). Tuberous sclerosis complex and focal cortical dysplasia are associated with hyperactivation of the mTOR pathway (Crino, 2015). Everolimus, a TOR inhibitor, can reduce seizure frequency in patients with tuberous sclerosis complex (Samueli et al., 2016). Numerous compounds are currently undergoing rigorous cellular and animal studies. For an in-depth review of the latest advances in epilepsy treatment targeting novel pathways, refer to Belete (2023).
Advances in computational bioinformatics have opened a new avenue for discovering novel targets for ASMs. In a recent study, data from genome-wide association studies for epilepsy were combined with data from proteome-wide association studies, transcriptome-wide association studies, and chemical-related gene set enrichment analysis, with the aim of identifying promising new targets for seizure treatment (Lu et al., 2023). Five genes were found to be significantly associated with epilepsy, and consequently may be new candidate epilepsy genes. This study showcased the potential of using bioinformatics techniques to identify new mechanisms potentially relevant to biological processes.
Association studies of genetic variants among mechanistic targets
We conducted a systematic search of PubMed to find relevant articles regarding the response to ASMs and the mechanistic targets previously discussed. We also included data from our recent studies, which not only enhanced the breadth of the review but also contributed original insights to enhance the overall understanding of the subject. Initially, we searched for keywords including “genetic polymorphisms” and “drug resistance epilepsy” in studies published from 2005 to 2023. Studies from any geographic location and including subjects of any ethnicity, gender, and age were included. Case reports were excluded from this review. Non-English articles or abstracts without full text were also excluded. Titles and abstracts of the articles retrieved from the initial search were screened for relevance, and full texts of potentially relevant articles were reviewed. We also extracted and examined the reference lists of these articles for additional relevant papers based on their titles and abstracts. We then searched for studies involving the mechanistic targets of ASMs, including sodium channels, calcium channels, potassium channels, GABA receptors, and ionotropic glutamate receptors. Subsequently, all available full-text articles were evaluated, and those not relevant to the response to ASMs or the mechanistic targets were excluded. The collected data were synthesized and compared to provide an overview of the progress in ASM pharmacogenetics, including conclusions and discussions regarding the limitations and future prospects.
The search returned 223 results. Thirty-five articles were excluded because they were either not relevant to the response to ASMs, focused on ribonucleic acid rather than genes, or were not human studies. The remaining articles contained 38 reviews or meta-analysis articles and 150 articles related to genetic polymorphisms and drug response. After reviewing these 188 articles and their references, 26 were selected, with 13 related to SCNs, two to calcium channels, four to potassium channels, five to GABA receptors, one to synaptic vesicle proteins, and one to ionotropic glutamate receptors. The remaining articles that mentioned the association between drug response and transporter or metabolic genes, although not included in the scope of our review, are also briefly discussed in this article.
Sodium channel gene variations
Since the first introduction of PHT in 1936 (Merritt and Putnam, 1984), SCN blockers have been one of the most popular treatments for both focal and generalized seizures. As a result, the relationship between SCN gene variants and response to ASMs has received much attention.
Most previous studies have focused on single-nucleotide polymorphisms (SNPs) of SCN genes and the dose of SCN blockers. The intronic SCN1A rs3812718 variant is one of the most extensively studied variants. In 2005, Tate et al. (2005) reported that this variant was associated with the maximum dose of PHT and CBZ required to control seizures in European patients. rs3812718 is located between exon 5 and 6 of the SCN1A gene and affects the splicing of exon 5, which results in decreasing the percentage of transcripts containing exon 5N (neonatal form) of SCN1A (Copley, 2004) (Figure 2). By decreasing the amount of the neonatal form of SCN1A, which is more sensitive to SCN blockers (Thompson et al., 2011), the authors suggested that patients with this SNP may need a higher dose or become more resistant to SCN blockers. This association was replicated in one Taiwanese study, where rs3812718 was also associated with a higher maintenance dose of CBZ monotherapy (Hung et al., 2012).
Later reports focused on the association between SNPs in SCN genes and DRE instead of the dose of ASMs. One report regarding Japanese patients with epilepsy found that SCN1A rs3812718 was associated with resistance to CBZ but not the dose of CBZ (Abe et al., 2008). In Chinese patients receiving CBZ or OXC, SCN1A rs3812718 was associated with DRE (Ma et al., 2014). In addition, a Greek study found that SCN1A rs3812718 was associated with resistance to SCN blocker monotherapy (PHT, CBZ, OXC, or LTG) and increased dosing of SCN blockers in patients responsive to SCN blockers (Angelopoulou et al., 2017), however, they did not find an association between SCN1A rs3812718 and the responsive to ASM polytherapy that contained SCN blockers. Other studies have reported conflicting results about the effect of SCN1A rs3812718 on the responsive to SCN blockers. An Italian study found that SCN1A rs3812718 was not associated with the responsive to CBZ/OXC, the dose of CBZ/OXC, or DRE (Manna et al., 2011). In the study, DRE was defined as having at least one seizure per month within 2 years of follow-up under three or more ASMs. A study from northern India also reported that SCN1A rs3812718 was not associated with the response to CBZ/OXC or DRE (Kumari et al., 2013), which they defined as at least four seizures over 1 year under three or more ASMs. In addition, a Spanish study did not find an association between SCN1A rs3812718 and DRE, defined as having at least four seizures under more than three ASMs 1 year before recruitment (Sanchez et al., 2010). However, this study did not specifically focus on the use of SCN blockers.
Besides SCN1A rs3812718, other SNPs of SCN receptor genes have also been associated with drug responsiveness. SCN2A rs17183814 was associated with DRE in Indian patients, defined as at least four seizures in 1 year under three or more ASMs (Lakhan et al., 2009), and an Australian study found that SCN1A rs10188577 was moderately associated with DRE, albeit without statistical significance (Yip et al., 2014).
These studies generally focused on less than or around a dozen SNPs, however, more genetic variants in SCNs are likely to contribute to the response to SCN-blocking ASMs. A study on Hong Kong Chinese patients investigated 27 SNPs in SCN1A, SCN2A, and SCN3A genes, and found that SCN2A rs2304016 was associated with both DRE, defined as one or more seizures per month for 1 year under ASM treatment, and resistance to SCN blockers, defined as patients with DRE when their most recent ASM regimen consisted of SCN-blocking ASMs (Kwan et al., 2008). Another cohort study evaluated 39 polymorphisms in the SCN1A, SCN2A, and SCN3A genes from patients in Malaysia and Hong Kong, and found no association between gene polymorphisms and responsive to CBZ or VPA monotherapy (Haerian et al., 2013). One study involving Taiwanese patients identified rs55742440 in SCN1B, among 21 coding SNPs in SCN1A, SCN1B, SCN2A, and SCN9A, as being associated with resistance to SCN blockers (Lin et al., 2021). This resistance was defined as the failure of the most recently administered SCN blocker to achieve seizure freedom for at least 1 year.
The methodologies in these studies were heterogeneous (Table 1), including the definition of DRE, the specific ASM under investigation, and the inclusion of different genetic variants. These differences make it challenging to compare the results and provide meaningful clinical predictions. However, most of these studies do imply that genetic variants in the SCN genes have an impact on the responsive to SCN-blocking ASMs.
TABLE 1
Studies focusing on the genetic variants of the sodium channel genes.
Author date.
Genes studied
Ethnicity and number of patients
Result of ASM use
Observation
Tate et al. (2005)
SCN1A rs590478
SCN1A rs8191987
SCN1A rs3812718
SCN1A rs2126152
ABCB1 rs1045642
CYP2C9 rs1799853
CYP2C9 rs1057910
European patients with 448 patients using PHT and 425 patients using CBZ
Maximum dose of CBZ and PHT taken by the patients.
SCN1A rs3812718 is associated with the maximum dose of CBZ and PHT.The CYP2C9*3 allele is associated maximum dose of PHT.
Abe et al. (2008)
SCN1A rs3812718
228 Japanese patients
CBZ-responsive epilepsy was defined as not experiencing seizures of any type for a minimum of 1 year under a stable dose of CBZ.DRE is defined as uncontrolled seizures over a year with three or more different ASMs.
SCN1A rs3812718 is associated with CBZ-resistant epilepsy but not the maximum or maintenance dose of CBZ.
Kwan et al. (2008)
27 SNPs in SCN1A, SCN2A, and SCN3A.
272 Chinese patients
Enrolled patients taking ASMs of any type.DRE is defined as one seizure or more per month within 12 months, despite treatment with two or more ASMs.
SCN2A rs2304016 is associated with DRE
Lakhan et al. (2009)
SCN1A rs2298771
SCN2A rs17183814
336 North Indian patients
Enrolled patients taking ASMs of any type.DRE is defined as the occurrence of at least four seizures for 1 year with three appropriate ASMs at maximum tolerated doses
SCN2A rs17183814 is associated with DRE.
Sanchez et al. (2010)
SCN1A rs2298771
SCN1A rs3812718
CYP2C9 rs1799853
CYP2C9 rs1057910
CYP2C19 rs4244285
CYP2C19 rs4986893
UGT1A1 rs35350960
UGT2B7 rs28365063
UGT2B7 rs7668258
ABCB1 rs1045642
ABCB1 rs2032582
289 Caucasian patients
Enrolled patients taking ASMs of all types.DRE is defined as the occurrence of at least four seizures over the year with trials of more than three appropriate ASMs at maximal tolerated doses.
SCN1A polymorphisms are not associated with DRE.
ABCB1 rs1045642 and ABCB1 rs2032582 are associated with DRE.
Manna et al. (2011)
SCN1A rs3812718
883 Italian patients
Enrolled patients taking CBZ or OXC.DRE is defined as the persistence of seizures during the previous 2 years with a frequency of at least one seizure/month, despite current or previous treatment with three or more appropriate ASMs, either alone or in combination, and at the highest tolerated dose.
No association between SCN1A rs3812718 and DRE.
Hung et al. (2012)
SCN1A rs3812718
SCN1A rs2298771
SCN2A rs17183814
EPHX1 rs1051740
EPHX1 rs2234922
UGT2B7 rs7668258
UGT2B7 rs7438135
UGT2B7 rs28365062
UGT2B7 rs7439366
ABCB1 rs1128503
ABCB1 rs2032582
ABCB1 rs1045642
ABCC2 rs717620
ABCC2 rs2273697
234 Taiwanese patients
Enrolled patients taking CBZ monotherapy with seizure free for 1 year.
SCN1A rs3812718 and EPHX1 rs1051740 are associated with higher doses of CBZ.
Kumari et al. (2013)
SCN1A rs3812718
Patients from north India.231 patients using CBZ or OXC monotherapy.272 patients using multiple ASMs.
Two patient groups include CBZ/OXZ monotherapy and a combination of various ASMs.DRE is defined as the occurrence of at least four seizures for 1 year with three or more appropriate ASMs.
SCN1A rs3812718 was not associated with drug responsiveness either in CBZ/OXZ treated monotherapy or multi-drug polytherapy
Haerian et al. (2013)
39 SNPs in the SCN1A, SCN2A, and SCN3A.
1,504 patients from Hong Kong and Malaysia
DRE is defined as the occurrence of seizures at any time during 1 year while undergoing treatment with CBZ or VPA monotherapy at maximally tolerated therapeutic dosages.
None of the polymorphisms in SCN1A, SCN2A, and SCN3A are associated with DRE
Ma et al. (2014)
SCN1A rs2298771
SCN1A rs3812718
SCN2A rs17183814
SCN2A rs2304016
ABCC2 rs3740066
ABCC2 rs2273697
453 Chinese patients.
All patients received CBZ or OXC (either monotherapy or polytherapy).DRE is defined as the failure of adequate trials following two tolerated, appropriately chosen, and administered ASM schedules (whether as monotherapies or in combination) to achieve seizure-free for 1 year.
SCN1A rs3812718 and ABCC2 rs2273697 are associated with CBZ/OXC-resistant epilepsy.
Yip et al. (2014)
SCN1A rs1813502
SCN1A rs1461195
SCN1A rs1020853
SCN1A rs6432860
SCN1A rs1972445
SCN1A rs10188577
SCN1A rs7607543
SCN1A rs11686142
SCN1A rs1461197
519 Caucasian patients
Enrolled patients taking PHT, CBZ, OXC, LTG, VPA, and TPMDRE is defined as having at least four seizures over the year before recruitment with trials of two or more SCN-blocking ASMs at maximal tolerated doses.
SCN1A rs10188577 has moderate association with DRE.
Angelopoulou et al. (2017)
SCN1A rs3812718
200 Italian patients
Patients taking monotherapy with PHT, CBZ, OXC, or LTG.The paper mentioned the definitions of drug responsiveness and drug resistance are those proposed by the Task Force of the ILAE Commission on Therapeutic Strategies (2009) without further description.
SCN1A rs3812718 is associated with monotherapy-resistant epilepsy and with maximum doses of ASMs effectiveness in monotherapy.
Lin et al. (2021)
21 SNPs in SCN1A, SCN1B, SCN2A, and SCN9A
200 Taiwanese patients
Enrolled patients taking various ASMs.DRE is defined as not achieving seizure freedom lasting for ≥12 months after taking the last SCN-blocking ASM.
rs55742440 in SCN1B is associated with DRE.
Abbreviations: ASM, antiseizure medication; CBZ, carbamazepine; DRE, drug-resistant epilepsy; LTG, lamotrigine; OXC, oxcarbazepine; PHT, phenytoin; SCN, sodium channel; SNP, single-nucleotide polymorphisms; TPM, topiramate; VPA, valproic acid.
Calcium channel gene variations
A Chinese study focusing on the association of DRE and with polymorphisms of calcium channel genes, including 15 SNPs in CACNA1A, CACNA1C, and CACNA1H, found that no SNPs but the TAGAA haplotype of CACNA1A was associated with DRE (Lv et al., 2015). Another study involving Jordanian patients found that CACNG5 rs740805 and GABRA1 rs2279020 were associated with DRE (Al-Eitan et al., 2020). Both of these studies included patients taking various combinations of ASMs, although many ASMs can inhibit calcium channels (Abou-Khalil, 2019). These studies only suggested a relationship between genetic variants of calcium channels and DRE.
Potassium channel gene variations
A Chinese study identified that KCNJ10 rs12402969, among seven other SNPs in KCNJ10, was associated with DRE, defined as experiencing four seizures under at least three ASMs during the previous year (Guo et al., 2015). However, another Chinese study, using the same DRE definition found no association between eight SNPs in KCNA1, KCNA2, and KCNV2 with DRE (Qu et al., 2017). Similarly, a separate Chinese study reported no association between nine SNPs in KCNJ10 and DRE (Zhu et al., 2020). In addition, a Jordanian-Arab study focusing on patients initiating treatment with VPA and CBZ as their first ASM revealed no association between seven SNPs in KCNA1, KCNA2, and KCNV2 and the response to these ASMs (Al-Eitan et al., 2018). Notably, while KCNJ10 rs12402969 was included in the studies by Guo et al. and Zhu et al., it was not considered in the studies by Qu et al. and Al-Eitan et al., leaving the association between this SNP and DRE inconclusive.
With various ASMs included, these studies suggest that genetic variants in potassium channels may have a minimal, if any, effect on DRE. This may not be surprising given that most ASMs do not target potassium channels. Therefore, associations between the response to potassium channel-targeted ASMs and genetic variants in potassium channel genes remain to be fully elucidated. Potassium channel enhancer represents a potential avenue for future ASM development.
Gamma-aminobutyric acid receptor gene variations
The GABRG2 rs211037 variant has been extensively studied across various ethnicities. In an Egyptian study, GABRG2 rs211037 was linked to DRE, defined as the inability to achieve 1 year of seizure freedom while using two ASMs (Abou El Ella et al., 2018). In Chinese patients, resistance to VPA, defined as having seizures with at least 12 months of VPA use, was associated with the heterozygous CT genotype of the GABRG2 rs211037 polymorphism (Lu et al., 2021). In addition, an Indian study found that GABRA1 rs2279020 but not GABRG2 rs211037 was associated with DRE, defined as at least four seizures within 1 year under three ASMs (Kumari et al., 2010). However, a Pakistan study failed to find an association between GABRG2 rs211037 and DRE, defined as no change in seizure frequency under two ASMs (Saleem et al., 2022). Another study from southern India that included four GABAA receptor subunit SNPs also found no association with DRE, defined as having a seizure frequency of 12 per year under at least two ASMs for at least 2 years (Balan et al., 2013). In contrast, a meta-analysis showed that GABRG2 rs211037 was related to DRE in the Asian population (Hu et al., 2023).
These studies did not specifically analyze the types of ASMs taken, except for one study which was limited to VPA, a medication known to enhance the effect of GABA (Loscher, 2002). GABA-enhancing ASMs such as barbiturates and benzodiazepines are predominantly used in patients with status epilepticus (Czuczwar and Patsalos, 2001). Nonetheless, there is currently a lack of studies specifically addressing this aspect.
Synaptic vesicle protein gene variations
A European study evaluated 86 common variants in SV2A, SV2B, and SV2C, but found no associations between these variants and the response to LEV (Lynch et al., 2009). Another study investigated genetic susceptibility to the neuropsychiatric side effects of LEV, and despite conducting genome-wide association analysis and rare-variant analysis, no significant associations were identified (Campbell et al., 2022). However, intriguingly, this study revealed that patients experiencing neuropsychiatric side effects had an elevated polygenic risk score for schizophrenia (Campbell et al., 2022). Taken together, these findings suggest that neither the response to LEV nor the occurrence of psychiatric adverse effects was linked to variability in synaptic vesicle genes, but rather that it was influenced by underlying genetic predispositions.
Ionotropic glutamate receptors
PER, an AMPA receptor antagonist, is currently the only ASM in this category. Lin et al. investigated the association between rare coding genetic variants in glutamate receptor genes and ADR, as well as responsive to PER, in Taiwanese patients undergoing PER treatment (Lin et al., 2023). Resistance to PER was defined as not achieving seizure freedom for 1 year under PER treatment. Although Lin et al. did not discover a direct association between individual genetic variants and drug response, they observed that enrichment of genetic variants within the glutamate receptor gene group was statistically associated with the occurrence of ADRs. Upon further analysis, they found that rare variant enrichment in the glutamate ionotropic receptor delta subunit was nominally associated with ADR. In addition, gene burden analysis revealed a nominal association between GRID1 and ADRs. These findings suggest that studies focusing on genetic variants in mechanistic targets hold the potential not only to predict responsiveness but also to predict the occurrence of ADRs.
In summary, genetic variants in ASM targets beyond SCN (as outlined in Table 2) present conflicting findings. Many of the previous investigations did not specifically target the mechanistic action of ASMs, making it challenging to ascertain whether the response to ASMs was correlated with genetic variants in their mechanistic targets. As relatively little research has investigated the correlation between the response to LEV or PER and their respective targets, further investigations are warranted.
TABLE 2
Studies focus on genetic variants other than the sodium channel genes.
Author date.
Gene studied
Ethnicity and number of patients
Result of ASM use
Observation
Calcium channel
Lv et al. (2015)
CACNA1A rs2074880
CACNA1A rs10416717
CACNA1A rs7254351
CACNA1A rs16030
CACNA1A rs2248069
CACNA1C rs2239128
CACNA1C rs215976
CACNA1C rs7316246
CACNA1C rs216008
CACNA1C rs12813888
CACNA1H rs1054645
CACNA1H rs3794619
CACNA1H rs7191246
CACNA1H rs11640796
CACNA1H rs3751664
480 Chinese patients
Enrolled patients taking various ASMs.DRE is defined as failure to achieve seizure-free for at least three times the longest inter-seizure interval before starting a new intervention and not less than 12 months after an adequate trial of two appropriate tolerated ASMs.
No SNPs are associated with DRE but the TAGAA haplotype of CACNA1A was associated with DRE.
Al-Eitan et al. (2020)
CACNG5 rs740805
GABRG2 rs209337
GABRA1 rs2279020
SLC1A1 rs10815018
SLC6A1 rs10510403
FAM131B rs4236482
GPLD1 rs1126617
GPLD1 rs2076317
F2 rs1799963
296 Jordanian patients
Enrolled patients taking various ASMs.DRE is defined as failure to achieve seizure-free for at least three times the longest inter-seizure interval before starting a new intervention and not less than 12 months after an adequate trial of two appropriate tolerated ASMs.
CACNG5 rs740805, FAM131B rs4236482, and GABRA1 rs2279020 are associated with DRE.
Potassium channel
Guo et al. (2015)
KCNJ10 rs1053074
KCNJ10 rs1130183
KCNJ10 rs12729701
KCNJ10 rs12402969
KCNJ10 rs1186688
KCNJ10 rs1186685
KCNJ10 rs6690889
KCNJ10 rs1890532
483 Chinese patients
Enrolled patients taking various ASMs.DRE is defined as having at least four seizures during the previous year with at least three ASMs at maximal tolerated doses.
KCNJ10 rs12402969 is associated with DRE.
Qu et al. (2017)
KCNA1 rs2227910
KCNA1 rs112561866
KCNA1 rs7974459
KCNA2 rs3887820
KCNV2 rs7029012
KCNV2 rs10967705
KCNV2 rs13285989
KCNV2 rs10967728″
483 Chinese patients
DRE is defined as at least four seizures during the previous year while trying at least three ASMs at maximally tolerated doses.
No association between DRE and the studied SNPs.
Al-Eitan et al. (2018)
KCNA1 rs2227910
KCNA1 rs112561866
KCNA1 rs7974459
KCNA2 rs3887820
KCNV2 rs7029012
KCNV2 rs10967705
KCNV2 rs10967728
296 Jordanian-Arab patients
Enrolled patients taking VPA for generalized epilepsy and CBZ for focal epilepsy as the first ASM.Good responder was defined as patients who require the lowest ASM doses or have taken only one drug without relapse in the past 6 months.Poor responders are patients requiring the highest ASM doses or who have taken more than one drug.
No SNPs are associated with the response of ASMs.
Zhu et al. (2020)
KCNJ10 rs12122979
KCNJ10 rs1186685
KCNJ10 rs6690889
KCNJ10 rs2486253
KCNJ10 rs1186675
KCNJ10 rs12402969,
KCNJ10 rs12729701
KCNJ10 rs1890532
KCNJ10 rs3795339
AQP4 rs1058424
AQP4 rs3763043
AQP4 rs35931
510 Chinese patients
Enrolled patients taking various ASMs.DRE is defined as at least four seizures during the previous year while trying at least two appropriate chosen ASMs.
No SNPs are associated with the response of ASMs.
Gamma-Aminobutyric Acid Receptor
Kumari et al. (2010)
GABRA1 rs2279020
GABRG2 rs211037
395 North Indian patients
Enrolled patients taking various ASMs.DRE is defined as the occurrence of at least four seizures for 1 year with three appropriate ASMs at maximum tolerated doses.
GABRA1 rs2279020 is associated with DRE but not GABRG2 rs211037
Balan et al. (2013)
GABRA1 rs2279020
GABRB2 rs2229944
GABRB2 rs211037
GABRA6 rs3219151
441 South Indian patients
Enrolled patients taking various ASMs.DRE is defined as unresponsive to at least two monotherapy trials and one duo therapy trial, each of at least 6 months duration, and had seizure frequency of at least 12 per year for at least 2 years.
None of the SNPs are associated with DRE
Abou El Ella et al. (2018)
GABARG2 rs211037
100 Egyptian patients
Enrolled patients taking various ASMs.DRE is defined as failure to achieve sustained seizure-free for 1 year under two tolerated and appropriately chosen ASMs.
GABARG2 rs211037 is associated with DRE
Lu et al. (2021)
GABRG2 rs211037
96 Chinese patients
Enrolled patients taking VPA alone or in combination with other ASMs for at least 12 months.Not responding to VAP is defined as recurrent seizure with a sufficient dose of VPA achieving therapeutic blood concentration (50–100 μg/mL).
GABRG2 rs211037 was associated with recurrent seizures under VPA use.
Saleem et al. (2022)
GABRG2 rs211037
142 Pakistani patients
Enrolled patients taking various ASMs.DRE is defined as consistent seizure frequency despite treatment with a maximum tolerated dose of two established ASMs for at least a couple of years
Only GABRG2 rs211037 is associated with DRE.
Synaptic vesicle protein
Lynch et al. (2009)
Eighty-six SNPs in SV2A, SV2B, and SV2C
247 patients from the United Kingdom and 290 Irish patients
Enrolled patient using LEV after failure to respond to at least two ASMs.Not responding to LEV is defined as seizure-free for at least 6 months under LEV use.
No SNPs are associated with the response of LEV.
Ionotropic glutamate receptors
Lin et al. (2023)
Rare variants in the 26 glutamate receptor genes, GRIA1-4, GRID1-2, GRIK1-5, GRIN1, GRIN2A-D, GRIN3A-B, and GRM1-8
83 Taiwanese patients
Enrolled patients received PER in the regiment of their ASMs.Resistance to PER is defined as failure to achieve seizure-free for a least 1 year while the PER was the patient’s last ASM.
None of the rare genetic variants in the glutamate receptor genes are associated with responsiveness to PER.
Abbreviations: ASM, antiseizure medication; CBZ, carbamazepine; DRE, drug-resistant epilepsy; SNP, single-nucleotide polymorphisms; LEV, levetiracetam; PER, perampanel; VPA, valproic acid.
Other factors related to drug-resistant epilepsy
Besides the mechanistic targets of ASMs, other factors also contribute to DRE, including alterations in the drug delivery system across the brain, and pharmacodynamic modifications that change the absorption or metabolism of ASMs (Loscher et al., 2020). In addition, Mendelian genetic factors may also determine the disease severity and increase refractoriness to ASM treatment, such as developmental epileptic encephalopathy.
Efflux transporters (Figure 1) in the endothelial cells of the blood-brain barrier (BBB) can hamper the ability of ASMs to enter the CNS and decrease their concentration in epileptogenic tissues (Kwan and Brodie, 2005). The ATP binding cassette (ABC) transporter superfamily is the major efflux transporter on the BBB that limits the access of ASMs to the target sites (Qosa et al., 2015). Glycoprotein-P (P-gp), one of these efflux transporters, has been shown to actively remove ASMs from the BBB back into the bloodstream (Miller et al., 2008). Moreover, it has been shown to be over-expressed in epileptogenic foci of the brain (Marchi et al., 2004), further preventing the seizure-suppression effect of ASMs. Studies on the genetic polymorphism of ABC transporter genes and the relationship between the response to ASMs are conflicting (Seo et al., 2006; Ozgon et al., 2008; Meng et al., 2011). For a comprehensive review of the impact of genetic polymorphisms of ABC transporter and the responsive to ASMs, see Leandro et al. (2019).
Drug-metabolizing genes are polymorphic and can influence the biotransformation of many drugs (Meyer and Zanger, 1997). These polymorphisms may result in a higher plasma drug level under standard dosage that leads to the occurrence of ADRs or increases enzyme activity to make the ASMs less effective (Eadie, 1991) (Figure 1). Drug metabolism occurs in two phases. Phase 1 reactions involve the addition of a functional group, mostly hydroxylation, by the cytochrome P450 (CYP) family followed by a phase 2 metabolism of various conjugating reactions that increase hydrophilicity and facilitate renal excretion of the drug. The enzymes included in this process are CYP, UDP-glucuronyl transferase (UGT), epoxide hydroxylase (EPHX), glutathione S-transferase (GST), microsomal GST, N-acetyltransferase (NAT), and sulfatase (SULF) (Ferraro and Buono, 2005). Various studies including polymorphisms in CYP (van der Weide et al., 2001; Lopez-Garcia et al., 2017; Makowska et al., 2021), EPHX (Hung et al., 2012), and UGT (Ma et al., 2015) have been conducted, with conflicting results.
Recently, the EpiPGX Consortium investigated the complex genetic factors associated with responsive to ASMs (Wolking et al., 2020). The study included 1,622 patients and 808,583 genetic variants, which is by far the largest cohort of pharmacogenetic studies regarding ASM. They enrolled patients taking LEV, VPA, and LTG for seizure control and studied rare genetic variants presumed to have functional consequences. A non-presupposed gene-based enrichment analysis did not find associations between any of the coding variants and response to ASM. The gene-set-based enrichment analysis, focusing on ASM target genes and ADME (absorption, distribution, metabolism, and excretion) gene sets, found that patients with resistance to VPA had enrichment of rare genetic variants in the ADME gene set. No associations were found between the response to ASMs for the target genes in all three studied ASMs and ADME gene sets of LEV and LTG. The results suggest that resistance to ASMs may not be the consequence of a single or a few genetic variants, but rather determined by the cumulative effect of various genetic variants that eventually merge to alter the collective effect of drug responsiveness.
Discussion
One of the weaknesses observed in these association studies is the lack of unified definitions of DRE and responsiveness. The ILAE introduced a new definition of DRE in 2010, and studies conducted before this may not have used this definition. It is anticipated that later association studies will adhere to a unified definition until any subsequent changes are made.
In addition to the varied definitions of DRE, the studies were also diverse in their approach. One approach focused on investigating the response to ASMs concerning the genetic variants of their mechanistic targets. Another approach involved examining the association between the response to various combinations of ASMs as a whole and the target of interest. Studies regarding variants in SCNs usually adopted the former approach, concentrating on ASMs that block SCNs, with a minority involving multiple ASMs. These studies could identify potential associations between genetic variants in SCNs and responses to SCN-blocking ASMs. Conversely, studies on calcium channel blockers, potassium channel openers, and GABA receptors typically included ASMs with diverse mechanisms. Consequently, their findings may only have revealed associations between genetic variants of the targets of interest and DRE, which may not necessarily disclose the association between the response to a particular ASM and its target. The identification of pharmacogenetic markers capable of predicting the response to an ASM prior to prescription would be more beneficial than predicting DRE to various ASMs. A more refined study approach that addresses the primary target and its corresponding ASMs is necessary before these findings can inform clinical practice in selecting the appropriate ASM. Given that SCN-blocking ASMs are the most frequently prescribed, studies on targets beyond SCNs may lack sufficient patient numbers to achieve statistical power. This could result in the inclusion of patients taking multiple ASMs for non-SCN blocker investigations. International collaboration in pharmacogenetic association studies could involve more participants and clarify the connection between ASM effectiveness and the genetic variants of their targets.
The aforementioned association studies primarily focused on common variants of the target genes, except for one study which examined rare variants of the glutamate receptor and their impact on response to PER (Lin et al., 2023). Common genetic variants are typically associated with quantitative phenotypes, with each locus contributing partially to various traits (Speliotes et al., 2010). Common variants often do not directly cause functional changes but rather indicate the presence of nearby functional variants due to linkage disequilibrium with observed phenotypes (Bodmer and Bonilla, 2008). In contrast, rare variants are more likely to have direct functional effects (Bodmer and Bonilla, 2008), although they are not as rare as deleterious mutations and have extremely low frequency in the population. These rare variants are evaluated based on computational algorithms suggesting potential functional consequences, particularly in genes relevant to the phenotypic pathophysiology (qualified variants) (Petrovski et al., 2013). Studies investigating the impact of rare variants on response to ASMs remain limited; however, they represent a promising avenue to augment our understanding of the functional consequences at a genetic level. These two types of variants are not mutually exclusive, and they have both been proposed to contribute to specific traits, with common variants modulating the effects of rare variants (Felix et al., 2006) and rare variants directly influencing trait development through functional consequences (Dickson et al., 2010).
Data regarding the functional consequences of these genetic variants are lacking. Therefore, the effect of the genetic variant and response to the drug cannot be determined. Variants found in studies of SNPs may not be the causal variants, since these common variants typically encompass a large number of variants in linkage disequilibrium, only some of which contribute to the phenotype (Weissbrod et al., 2020). For studies on rare variants, although the variants are predicted by computational algorithms, the true effects of these variants on protein structure and function are unknown (Livesey and Marsh, 2020). Understanding the consequences of these genetic variants can enhance our understanding of the reason for different drug responses. As previously discussed, the intronic variant SCN1A rs3812718 is associated with a reduced expression of the neonatal form of SCN1A, which is more sensitive to SCN blockers. This effect may be mitigated by escalating the dosage of SCN blockers (Thompson et al., 2011). The findings from genetic studies along with subsequent functional analyses can offer valuable insights into ASM prescription. This includes not only identifying the most effective medication but also anticipating individual responses to ASMs and adjusting treatment regimens accordingly.
Besides exploring the association of genetic variants and DRE, polygenetic risk score (PRS) offers an additional method for predicting the response to ASs. PRS hypothesizes that the genetic loading of a set of risk variants contributes to the development of a disease or particular trait with the need for little or no knowledge about the underlying mechanism (Lewis and Vassos, 2020). It is calculated using statistical algorithms and quantifies an individual’s genetic predisposition to a specific trait or disease by integrating data from multiple genetic markers across the genome. The PRS has been demonstrated to be a feasible method to predict the psychiatric side effects of LEV treatment for schizophrenia (Campbell et al., 2022). It may also be a reasonable approach to predict responsiveness, since many ASMs may have more than one mechanistic target, or in some cases an unknown mechanism.
By integrating genetic testing into clinical practice, personalized medicine facilitates the more precise selection and dosing of ASMs. This approach not only enhances the efficacy of ASMs but also minimizes the risk of ADRs, thereby improving patient outcomes and overall quality of life. With genetic testing-guided ASM selection, patients may achieve seizure freedom more rapidly. Moreover, this approach enables the identification of patients at risk of developing DRE earlier in their treatment journey, allowing for timely adjustments in treatment strategies or consideration of surgical options.
Conclusion
This review analyzes pharmacogenetic studies, with a specific focus on the mechanistic targets of ASMs. Our findings show that it is currently challenging to definitively conclude that specific genetic variants in drug targets influence the response to ASMs. The existing pharmacogenetic studies often concentrate on a limited number of genetic variations, sometimes without a direct focus on the target of the studied ASMs, or involving a small number of participants. Studies exploring the association between DRE and the pharmacogenetics of ASMs are still in their infancy. Collaborative efforts among multiple centers are imperative to attain sufficient statistical power to draw conclusions regarding the relationship between the efficacy of ASMs and the genetic variants of their targets. In addition, tools such as PRS can be used to predict potential ADRs and integrate them into prediction methodologies. With further research into the pharmacogenetics of ASMs, it may soon be possible to predict the outcomes of specific ASMs by analyzing the genomic data of the patients, thereby eliminating the need for a trial-and-error approach. |
Title: Biovalorization of whey waste as economic nutriment for mycogenic production of single cell oils with promising antibiofilm and anticancer potentiality | Body:
Introduction
The industrial revolution is the fundamental engine behind numerous advanced technologies, which introduces perceptible leaps in the provision of multiple services, society and economics. Nonetheless, the production of huge amounts of contaminants that released continuously in our biosphere considers being the main obstacle of such technological progress. The pollution of natural resources (i.e., water, soil, air) with anthropogenic waste products undoubtedly harms human health and its surrounding environment, in particular with overpopulation and uncontrolled urbanization. Notably, cancer and biofilm causing illness are the most common diseases caused by environmental pollution and one of the most representative etiological agents for morbidity and mortality as revealed by world health organization (WHO) and National Institutes of Health (NIH) [1–3].
However, the remedy of biofilm infections and firm tumors confronts a similar complication. Wherein, the notable conventional medications face certain difficulties in reaching and damaging the inner cells of cancer and biofilm-embedded bacteria owing to the heterogeneities of local micro-environment [4, 5]. Additionally, the alteration in immune reactions and cytotoxicity of applied chemotherapy against healthy cells were also documented in both diseases. The microbiota dysbiosis, neuromuscular blockade, nephrotoxicity and ototoxicity are the frequent side effects of antimicrobial agents. However, the lower counts of neutrophil, platelet and erythrocyte are the common complications of cancer therapeutic [4–6].
Accordingly, the necessity for design new, biosafe and effective antibiofilm/ anticancer drugs, for coping with such upsetting difficulties, is urgent. In the recent era, the biologically-based technology (i.e., metabolomics and biotechnology) gained colossal attention from scientists and technologists by the dint of its safety, biocompatibility, eco-friendliness and sustainability. Intriguingly, such biologically-based products are derived from plants, animals and microorganisms, which provide prototypes for several pharmacologically active compounds in treating cancer and biofilm-based infections [7]. Single cell oils (SCOs) or Fatty acids (FAs) are categorized among distinct and potential bioactive compounds, which pervasive in nature and have an indispensable role in biological, nutritional and clinical viewpoints [8]. It is worth recalling that consumption of certain FAs has been powerfully associated with several health profits, especially when interchanging saturated fatty acids (SFAs) with mono-un saturated fatty acids (MUFAs) or poly unsaturated fatty acids (PUFAs). They play a crucial role in reducing cholesterol, which in turn reduce the danger of cardiovascular disease and have been reported to reduce the risk of inflammatory conditions such as arthritis, Crohn’s disease and asthma. Besides, MUFAs and PUFAs are recognized as apoptosis-dependent anticancer dietary components, which causing selective cytotoxicity towards cancer cells with little or no toxicity on normal cells [9, 10]. The carcinogenic process is believed to involve MMP-2 and MMP-9 in order to angiogenesis and metastasis occurring in various cancer cell lines. Previous studies demonstrated that matrix metalloproteinase (MMP) activities can be inhibited by PUFAs [11–13]. Unfortunately, human bodies cannot synthesis PUFAs and therefore, it is essential to be obtained them from only dietary sources in our life style and this essentiality is also spirited for cancer cells [14].
Collectively, these overall features seem being advantageous upon employing industrial waste products as raw materials for the manufacturing process; taking into account the green production in an economic way. Food industries, in particular dairy industry, are continuously developed in an incredible dynamic process of transformation to meet market requirements. Due to the economic and nutritional significance of dairy products, their industry deemed a substantial societal asset globally, generating hereby large quantities of whey as co-product or feedstock [15]. Noteworthy mentioning that whey is organic effluent rich in easy fermentable sugar (i.e., lactose), fats, proteins, non-protein nitrogenous compounds, lactic acid, vitamins and minerals with biochemical oxygen demand (BOD) and chemical oxygen demand (COD) oscillated from 40 to 60 and 50 to 80 g/L, respectively. Thus, its arbitrary discharge in water bodies, without proper management and sustainable practices, represents a real environmental risk [16, 17]. Let alone the economic losses due to squander of nutrients and energy. Therefore, for maintaining the nutritional value of whey and simultaneously alleviating its detrimental environmental peril, various studies utilized it as cost-effective and sustainable substrate for resource recovery, microbial growth and yielding advanced valuable biotechnological products [18, 19].
Based on the previous background, our study focused on the economic production of mycogenic SCOs, under the umbrella of reduced operational costs, using cheese whey as sole and chief nutritional media. The extracted SCOs were characterized and subsequently evaluated as antibiofilm and anticancer agent. Hitherto, no study till our knowledge, scrutinized the antibiofilm and anticancer potentiality of SCOs generated by both fungal strains under study.
Materials and methods
Fungal stains, growth conditions, extraction, transesterification and characterization of lipids samples
The two fugal strains of this study Alternaria sp. and Drechslera sp. were isolated, examined as oleaginous filamentous fungi and identified molecularly with accession numbers of MH348917.1 and MG582185.1, respectively [20, 21]. Strains were maintained on potato dextrose agar slants (PDA) at 4 °C and refreshed every 3–4 months. Cheese whey, as waste stream, was procured from dairy processing factories in New Borg El-Arab city and utilized as main cultivation media; replacing entirely, by such way, the microbiological media for fungal propagation and lipid accumulation. By using 1.0 M (HCl or NaOH), its pH was adjusted to 6 followed by sterilization. In 250 ml flasks contained about 70 ml of whey, the fungal lawns were inoculated and incubated at 28oC in an orbital shaking 150 rpm for 4 days, in triplicate. Thereafter, dry biomass, lipid yield and content were determined as described in details by El-shall et al. [9]. Extraction and transesterification of lipids were performed as briefly described in [22]. The transesterified lipids from both fungal strains were characterized by gas chromatography–mass spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FTIR). Initially, the samples were analyzed by Agilent 6890 Gas Chromatograph equipped with a straight deactivated 2 mm direct injector liner and a 15 m Alltech EC-5 column (250 µ I.D., 0.25 µ film thickness), to detect and quantify saturated and unsaturated contents of fatty acid. The operating conditions were held at 250 °C for inlet temperature, 280 °C for detector temperature and 35 °C initial oven temperature, which was held for 2 min then elevated to 300 °C for 23 min. The injection volume was 2 mL, with a split ratio of 10:1. Helium served as a carrier gas at a constant flow rate of 1 ml/min. The FAs profile was identified via comparison of its chromatographic peaks and retention times with those of WILEY 09 and NIST 11 mass spectral database. Notably, each individual peak was quantified by means of standards and their corresponding calibration curves. Regarding FTIR, KBr was used as a matrix in a disc method through a scanning spectrum ranged from 3500 to 500 cm−1 by Shimadzu FTIR-8400 S, Japan spectrophotometer at a resolution of 4 cm−1 [23].
Antibiofilm activity of lipid samples
The microtiter plate assay was applied to determine the antibiofilm activity of the SCOs qualitatively. Herein, Candida albicans (ATCC 10231), Staphylococcus aureus (ATCC 25923) and Pseudomonas aeruginosa (ATCC 27853) were used as representative strains for yeast forming biofilm, Gram-positive and Gram-negative bacteria, respectively. In brief, about 10 µL (1 × 105 CFU/mL) of overnight microbial culture was dispensed in U-bottom microtiter plate contained sterile Trypticase Soy Broth (TSB) supplemented with 1% w/v glucose (TSBG). Meanwhile, different concentrations of both SCOs (1–100 µg/mL) were pipetted in each inoculated well. Remarkably, wells contained TSBG without microbial inoculum containing 0.1% DMSO and inoculated wells lacking SCOs -treatments were run simultaneously as negative controls and positive controls, respectively. The plates were statically incubated for 24 h at 37 °C to empower the microbial propagation and biofilm maturation. Following incubation, the spent medium containing free-floating cells were decanted gently from each well. Then, 200 µl of sterile saline (0.9% NaCl) was used thrice to wash each well. Subsequently, the adherent cells were stained using Hucker’s crystal violet (0.1%, w/v) for 20 min at 37 °C. The excess dye was removed after incubation and the stained biofilms were washed off gently with deionized distilled water. To elute the attached cells, 200 µL of 95% ethanol was pipetted in each well and the absorbance was measured at 595 nm spectrophotometrically by microtiter ELISA reader (Tecan Infinite M200, Switzerland) to quantify biofilms [24]. The percent of biofilm suppression was calculated as described in the following formula:1\documentclass[12pt]{minimal}
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\begin{document}$$\text{Biofilm inhibition}\,\%=\left(\text{A}-\text{A}_{\mathrm o}\right)/\text{A}\ast 100$$\end{document}Biofilm inhibition%=A-Ao/A*100
Where A and A0 pointed out to the absorbance of the positive control and the treated wells, respectively.
The effect of SCOs samples on biofilm metabolic activity
The viability of biofilm cells and their respiratory activity was quantified using (MTT) assay. The assay based on the capability of surviving and metabolically active cells to reduce the yellow tetrazolium salt a (3-[4,5-dimethyl-2-thiazolyl]-2, 5-diphenyl-2 H-tetrazolium-bromide) to a purple formazan. The setting up of the experiment was previously described. Once the biofilm formed after 24 h incubation and washed, about 200 µL of 0.25 mg/mL MTT solution was added and gently pipetted with the content of each well. The 96-well tissue culture plate was incubated under dark and static condition for 2–3 h at 37 °C. The solution was withdrawn and replaced with 2% of dimethyl sulphoxide (DMSO) for solubilizing the insoluble formazan crystals. The absorbance was recorded at 570 nm using microtiter ELISA reader [23]. The higher absorbance reading reveals higher number of surviving cells in the biofilm. The effect of both oils on biofilm cells was calculated as the previous equation (Eq. 1).
The effect of SCOs samples on carbohydrate and protein contents of the biofilms
The biochemical components of the isolated biofilm in terms of total carbohydrate/ePS and total protein were detected before and after SCOs treatment. The phenol–sulfuric acid protocol was employed to quantify the carbohydrate content using glucose as a standard; while Bradford assay was utilized for determination protein concentration and BSA used as a standard [25].
The effect of SCOs samples on biofilm hydrophobicity
The hydrophobicity of untreated or control and SCOs-treated biofilm cells were measured through microbial adhesion to hydrocarbon (MATH) assay as described by [26]. Via such biphasic hydrocarbon/aqueous assay, the cell surface hydrophobicity (CSH) was expressed based on the change in optical density of the aqueous phase relative to the control. Initially, the biofilms were allowed to grow followed by decanting the planktonic cells, washing and suspension in phosphate buffer saline (PBS) (200 µL). Through scraping by a pipette tip, the biofilms became detached and resuspended in 3 mL of PBS buffer, then homogenously disruption by vortexing for 3 min; 0.4 mL of xylene (hydrocarbon) was added to 2 mL of biofilm suspensions and vortexed vigorously for 3 min. The overall suspensions were allowed to stand at room temperature for 15 min till the separation of hydrocarbon/ aqueous phase. Ultimately, the absorbance of biofilm cells that remained in aqueous phase (i.e., hydrophobicity %) was determined spectrophotometrically at 600 nm by the following equation (Eq. 2).2\documentclass[12pt]{minimal}
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\begin{document}$$\begin{aligned} &\text{Hydrophobicity inhibition}\,\%\\ & =1-\left(\text{OD600 nm positive control}\right)\\ &\quad -\left(\text{OD600 nm treated}\right)]/\\ &\qquad (\text{OD600 nm positive control})\times 100 \end{aligned}$$\end{document}Hydrophobicity inhibition%=1-OD600 nm positive control-OD600 nm treated]/(OD600 nm positive control)×100
The cells described as strongly hydrophobic, moderately hydrophobic and hydrophilic when the obtained values recorded > 50%, 20–50% and < 20%, respectively [23, 27].
Anticancer activity of lipid samples
Cell lines
Wi-38 cell line (Human normal fibroblast lung cells), CaCo-2 cell line (Human Colorectal adenocarcinoma, epithelial cells) and A549 cell line (Human Lung adenocarcinoma, epithelial cells) cell lines were purchased from ATCC.
Safety assay and anticancer activity
An In-vitro viability test was used to determine the safety patterns and the anticancer effects of the tested SCOs samples on non-cancerous (Wi-38) and cancerous cell lines (A549 and Caco-2) using 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2 H-tetrazolium (MTS) (Promega) assay according to the manual instructions. Briefly, 100 µL of (6 × 104 cells/mL) of the each overnight cell line culture were inoculated into 96 well plates. The inoculated plates were incubated overnight till semiconfluency. After incubation, 100 µL of oil samples at different concentrations (prepared in DMEM media) were added to the plates and the plates were incubated for further 2 days. The cellular viability was determined by quantifying the solubilized formazan in DMSO at 570 nm. The inhibition concentration at 50% (IC50) was quantified from the cytotoxicity % curve using GraphPad prism 9.
Selectivity index of oil samples
Cancer cell selectivity index of the examined SCOs-treated samples was calculated according to the method explained by [28] with a minor modification; SI = IC50nc/IC50cc, where IC50nc refers to the value of IC50 of the oil samples effects on normal cells, while IC50cc refers to the IC50 of the oil samples effects on cancer cell line.
Determination of caspase 3 activation
In brief, the untreated and treated cell lines were lysed in the supplemented lysis buffer and centrifuged. Supernatants were incubated with the kit reaction buffer and substrate at 37 °C, as illustrated in caspase-3 assay colorimetric kit (Abcam, US). After 2 h, the absorbance of all samples were assessed at 405 nm using microplate reader.
Determination of MMP2 and MMP9 inhibition
Following kit instructions (Abcam, US), serial concentrations of both SCOs were incubated with assay buffer and enzyme (MMP2 or MMP9) for 45 min at 37 °C. After adding substrate, absorbances were measured at 412 nm.
Statistical analysis
In the present study, all experiments were performed trice and the results were averaged and represented as means ± SEM (Standard Error of Mean). All results were analyzed by one way ANOVA followed by Tukeys test. The statistical significance of collected data was accounted when P- value was ≤ 0.05, as per Graphpad Prism 5.03 software (Graph Pad Software Inc., La Jolla, CA, USA) [23].
Fungal stains, growth conditions, extraction, transesterification and characterization of lipids samples
The two fugal strains of this study Alternaria sp. and Drechslera sp. were isolated, examined as oleaginous filamentous fungi and identified molecularly with accession numbers of MH348917.1 and MG582185.1, respectively [20, 21]. Strains were maintained on potato dextrose agar slants (PDA) at 4 °C and refreshed every 3–4 months. Cheese whey, as waste stream, was procured from dairy processing factories in New Borg El-Arab city and utilized as main cultivation media; replacing entirely, by such way, the microbiological media for fungal propagation and lipid accumulation. By using 1.0 M (HCl or NaOH), its pH was adjusted to 6 followed by sterilization. In 250 ml flasks contained about 70 ml of whey, the fungal lawns were inoculated and incubated at 28oC in an orbital shaking 150 rpm for 4 days, in triplicate. Thereafter, dry biomass, lipid yield and content were determined as described in details by El-shall et al. [9]. Extraction and transesterification of lipids were performed as briefly described in [22]. The transesterified lipids from both fungal strains were characterized by gas chromatography–mass spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FTIR). Initially, the samples were analyzed by Agilent 6890 Gas Chromatograph equipped with a straight deactivated 2 mm direct injector liner and a 15 m Alltech EC-5 column (250 µ I.D., 0.25 µ film thickness), to detect and quantify saturated and unsaturated contents of fatty acid. The operating conditions were held at 250 °C for inlet temperature, 280 °C for detector temperature and 35 °C initial oven temperature, which was held for 2 min then elevated to 300 °C for 23 min. The injection volume was 2 mL, with a split ratio of 10:1. Helium served as a carrier gas at a constant flow rate of 1 ml/min. The FAs profile was identified via comparison of its chromatographic peaks and retention times with those of WILEY 09 and NIST 11 mass spectral database. Notably, each individual peak was quantified by means of standards and their corresponding calibration curves. Regarding FTIR, KBr was used as a matrix in a disc method through a scanning spectrum ranged from 3500 to 500 cm−1 by Shimadzu FTIR-8400 S, Japan spectrophotometer at a resolution of 4 cm−1 [23].
Antibiofilm activity of lipid samples
The microtiter plate assay was applied to determine the antibiofilm activity of the SCOs qualitatively. Herein, Candida albicans (ATCC 10231), Staphylococcus aureus (ATCC 25923) and Pseudomonas aeruginosa (ATCC 27853) were used as representative strains for yeast forming biofilm, Gram-positive and Gram-negative bacteria, respectively. In brief, about 10 µL (1 × 105 CFU/mL) of overnight microbial culture was dispensed in U-bottom microtiter plate contained sterile Trypticase Soy Broth (TSB) supplemented with 1% w/v glucose (TSBG). Meanwhile, different concentrations of both SCOs (1–100 µg/mL) were pipetted in each inoculated well. Remarkably, wells contained TSBG without microbial inoculum containing 0.1% DMSO and inoculated wells lacking SCOs -treatments were run simultaneously as negative controls and positive controls, respectively. The plates were statically incubated for 24 h at 37 °C to empower the microbial propagation and biofilm maturation. Following incubation, the spent medium containing free-floating cells were decanted gently from each well. Then, 200 µl of sterile saline (0.9% NaCl) was used thrice to wash each well. Subsequently, the adherent cells were stained using Hucker’s crystal violet (0.1%, w/v) for 20 min at 37 °C. The excess dye was removed after incubation and the stained biofilms were washed off gently with deionized distilled water. To elute the attached cells, 200 µL of 95% ethanol was pipetted in each well and the absorbance was measured at 595 nm spectrophotometrically by microtiter ELISA reader (Tecan Infinite M200, Switzerland) to quantify biofilms [24]. The percent of biofilm suppression was calculated as described in the following formula:1\documentclass[12pt]{minimal}
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\begin{document}$$\text{Biofilm inhibition}\,\%=\left(\text{A}-\text{A}_{\mathrm o}\right)/\text{A}\ast 100$$\end{document}Biofilm inhibition%=A-Ao/A*100
Where A and A0 pointed out to the absorbance of the positive control and the treated wells, respectively.
The effect of SCOs samples on biofilm metabolic activity
The viability of biofilm cells and their respiratory activity was quantified using (MTT) assay. The assay based on the capability of surviving and metabolically active cells to reduce the yellow tetrazolium salt a (3-[4,5-dimethyl-2-thiazolyl]-2, 5-diphenyl-2 H-tetrazolium-bromide) to a purple formazan. The setting up of the experiment was previously described. Once the biofilm formed after 24 h incubation and washed, about 200 µL of 0.25 mg/mL MTT solution was added and gently pipetted with the content of each well. The 96-well tissue culture plate was incubated under dark and static condition for 2–3 h at 37 °C. The solution was withdrawn and replaced with 2% of dimethyl sulphoxide (DMSO) for solubilizing the insoluble formazan crystals. The absorbance was recorded at 570 nm using microtiter ELISA reader [23]. The higher absorbance reading reveals higher number of surviving cells in the biofilm. The effect of both oils on biofilm cells was calculated as the previous equation (Eq. 1).
The effect of SCOs samples on carbohydrate and protein contents of the biofilms
The biochemical components of the isolated biofilm in terms of total carbohydrate/ePS and total protein were detected before and after SCOs treatment. The phenol–sulfuric acid protocol was employed to quantify the carbohydrate content using glucose as a standard; while Bradford assay was utilized for determination protein concentration and BSA used as a standard [25].
The effect of SCOs samples on biofilm hydrophobicity
The hydrophobicity of untreated or control and SCOs-treated biofilm cells were measured through microbial adhesion to hydrocarbon (MATH) assay as described by [26]. Via such biphasic hydrocarbon/aqueous assay, the cell surface hydrophobicity (CSH) was expressed based on the change in optical density of the aqueous phase relative to the control. Initially, the biofilms were allowed to grow followed by decanting the planktonic cells, washing and suspension in phosphate buffer saline (PBS) (200 µL). Through scraping by a pipette tip, the biofilms became detached and resuspended in 3 mL of PBS buffer, then homogenously disruption by vortexing for 3 min; 0.4 mL of xylene (hydrocarbon) was added to 2 mL of biofilm suspensions and vortexed vigorously for 3 min. The overall suspensions were allowed to stand at room temperature for 15 min till the separation of hydrocarbon/ aqueous phase. Ultimately, the absorbance of biofilm cells that remained in aqueous phase (i.e., hydrophobicity %) was determined spectrophotometrically at 600 nm by the following equation (Eq. 2).2\documentclass[12pt]{minimal}
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\begin{document}$$\begin{aligned} &\text{Hydrophobicity inhibition}\,\%\\ & =1-\left(\text{OD600 nm positive control}\right)\\ &\quad -\left(\text{OD600 nm treated}\right)]/\\ &\qquad (\text{OD600 nm positive control})\times 100 \end{aligned}$$\end{document}Hydrophobicity inhibition%=1-OD600 nm positive control-OD600 nm treated]/(OD600 nm positive control)×100
The cells described as strongly hydrophobic, moderately hydrophobic and hydrophilic when the obtained values recorded > 50%, 20–50% and < 20%, respectively [23, 27].
The effect of SCOs samples on biofilm metabolic activity
The viability of biofilm cells and their respiratory activity was quantified using (MTT) assay. The assay based on the capability of surviving and metabolically active cells to reduce the yellow tetrazolium salt a (3-[4,5-dimethyl-2-thiazolyl]-2, 5-diphenyl-2 H-tetrazolium-bromide) to a purple formazan. The setting up of the experiment was previously described. Once the biofilm formed after 24 h incubation and washed, about 200 µL of 0.25 mg/mL MTT solution was added and gently pipetted with the content of each well. The 96-well tissue culture plate was incubated under dark and static condition for 2–3 h at 37 °C. The solution was withdrawn and replaced with 2% of dimethyl sulphoxide (DMSO) for solubilizing the insoluble formazan crystals. The absorbance was recorded at 570 nm using microtiter ELISA reader [23]. The higher absorbance reading reveals higher number of surviving cells in the biofilm. The effect of both oils on biofilm cells was calculated as the previous equation (Eq. 1).
The effect of SCOs samples on carbohydrate and protein contents of the biofilms
The biochemical components of the isolated biofilm in terms of total carbohydrate/ePS and total protein were detected before and after SCOs treatment. The phenol–sulfuric acid protocol was employed to quantify the carbohydrate content using glucose as a standard; while Bradford assay was utilized for determination protein concentration and BSA used as a standard [25].
The effect of SCOs samples on biofilm hydrophobicity
The hydrophobicity of untreated or control and SCOs-treated biofilm cells were measured through microbial adhesion to hydrocarbon (MATH) assay as described by [26]. Via such biphasic hydrocarbon/aqueous assay, the cell surface hydrophobicity (CSH) was expressed based on the change in optical density of the aqueous phase relative to the control. Initially, the biofilms were allowed to grow followed by decanting the planktonic cells, washing and suspension in phosphate buffer saline (PBS) (200 µL). Through scraping by a pipette tip, the biofilms became detached and resuspended in 3 mL of PBS buffer, then homogenously disruption by vortexing for 3 min; 0.4 mL of xylene (hydrocarbon) was added to 2 mL of biofilm suspensions and vortexed vigorously for 3 min. The overall suspensions were allowed to stand at room temperature for 15 min till the separation of hydrocarbon/ aqueous phase. Ultimately, the absorbance of biofilm cells that remained in aqueous phase (i.e., hydrophobicity %) was determined spectrophotometrically at 600 nm by the following equation (Eq. 2).2\documentclass[12pt]{minimal}
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\begin{document}$$\begin{aligned} &\text{Hydrophobicity inhibition}\,\%\\ & =1-\left(\text{OD600 nm positive control}\right)\\ &\quad -\left(\text{OD600 nm treated}\right)]/\\ &\qquad (\text{OD600 nm positive control})\times 100 \end{aligned}$$\end{document}Hydrophobicity inhibition%=1-OD600 nm positive control-OD600 nm treated]/(OD600 nm positive control)×100
The cells described as strongly hydrophobic, moderately hydrophobic and hydrophilic when the obtained values recorded > 50%, 20–50% and < 20%, respectively [23, 27].
Anticancer activity of lipid samples
Cell lines
Wi-38 cell line (Human normal fibroblast lung cells), CaCo-2 cell line (Human Colorectal adenocarcinoma, epithelial cells) and A549 cell line (Human Lung adenocarcinoma, epithelial cells) cell lines were purchased from ATCC.
Safety assay and anticancer activity
An In-vitro viability test was used to determine the safety patterns and the anticancer effects of the tested SCOs samples on non-cancerous (Wi-38) and cancerous cell lines (A549 and Caco-2) using 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2 H-tetrazolium (MTS) (Promega) assay according to the manual instructions. Briefly, 100 µL of (6 × 104 cells/mL) of the each overnight cell line culture were inoculated into 96 well plates. The inoculated plates were incubated overnight till semiconfluency. After incubation, 100 µL of oil samples at different concentrations (prepared in DMEM media) were added to the plates and the plates were incubated for further 2 days. The cellular viability was determined by quantifying the solubilized formazan in DMSO at 570 nm. The inhibition concentration at 50% (IC50) was quantified from the cytotoxicity % curve using GraphPad prism 9.
Selectivity index of oil samples
Cancer cell selectivity index of the examined SCOs-treated samples was calculated according to the method explained by [28] with a minor modification; SI = IC50nc/IC50cc, where IC50nc refers to the value of IC50 of the oil samples effects on normal cells, while IC50cc refers to the IC50 of the oil samples effects on cancer cell line.
Determination of caspase 3 activation
In brief, the untreated and treated cell lines were lysed in the supplemented lysis buffer and centrifuged. Supernatants were incubated with the kit reaction buffer and substrate at 37 °C, as illustrated in caspase-3 assay colorimetric kit (Abcam, US). After 2 h, the absorbance of all samples were assessed at 405 nm using microplate reader.
Determination of MMP2 and MMP9 inhibition
Following kit instructions (Abcam, US), serial concentrations of both SCOs were incubated with assay buffer and enzyme (MMP2 or MMP9) for 45 min at 37 °C. After adding substrate, absorbances were measured at 412 nm.
Cell lines
Wi-38 cell line (Human normal fibroblast lung cells), CaCo-2 cell line (Human Colorectal adenocarcinoma, epithelial cells) and A549 cell line (Human Lung adenocarcinoma, epithelial cells) cell lines were purchased from ATCC.
Safety assay and anticancer activity
An In-vitro viability test was used to determine the safety patterns and the anticancer effects of the tested SCOs samples on non-cancerous (Wi-38) and cancerous cell lines (A549 and Caco-2) using 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2 H-tetrazolium (MTS) (Promega) assay according to the manual instructions. Briefly, 100 µL of (6 × 104 cells/mL) of the each overnight cell line culture were inoculated into 96 well plates. The inoculated plates were incubated overnight till semiconfluency. After incubation, 100 µL of oil samples at different concentrations (prepared in DMEM media) were added to the plates and the plates were incubated for further 2 days. The cellular viability was determined by quantifying the solubilized formazan in DMSO at 570 nm. The inhibition concentration at 50% (IC50) was quantified from the cytotoxicity % curve using GraphPad prism 9.
Selectivity index of oil samples
Cancer cell selectivity index of the examined SCOs-treated samples was calculated according to the method explained by [28] with a minor modification; SI = IC50nc/IC50cc, where IC50nc refers to the value of IC50 of the oil samples effects on normal cells, while IC50cc refers to the IC50 of the oil samples effects on cancer cell line.
Determination of caspase 3 activation
In brief, the untreated and treated cell lines were lysed in the supplemented lysis buffer and centrifuged. Supernatants were incubated with the kit reaction buffer and substrate at 37 °C, as illustrated in caspase-3 assay colorimetric kit (Abcam, US). After 2 h, the absorbance of all samples were assessed at 405 nm using microplate reader.
Determination of MMP2 and MMP9 inhibition
Following kit instructions (Abcam, US), serial concentrations of both SCOs were incubated with assay buffer and enzyme (MMP2 or MMP9) for 45 min at 37 °C. After adding substrate, absorbances were measured at 412 nm.
Statistical analysis
In the present study, all experiments were performed trice and the results were averaged and represented as means ± SEM (Standard Error of Mean). All results were analyzed by one way ANOVA followed by Tukeys test. The statistical significance of collected data was accounted when P- value was ≤ 0.05, as per Graphpad Prism 5.03 software (Graph Pad Software Inc., La Jolla, CA, USA) [23].
Results and discussion
Economic production and characterization of fungal SCOs
The microbial lipids or SCOs, are categorized among the most promising natural feedstock for biofuel and nutraceuticals production. Remarkably, the proximity of their structure with fish oil or even vegetable oils had gained a momentum, especially those derived from fungi. That could be attributed to their large quantity of biomass with high lipids yield in a short growth cycle and facile biomass collection. Remarkably, the versatility in fungal growth conditions facilitated their cultivation on low-cost culture medium based on different wastes, which were continuously accumulated in the environment causing severe pollution. Such dual tasks, of investing the environmental contaminants in biotechnological products, consider being the fundamental pillar of sustainable green techno-economic productivity [9]. Several species of filamentous fungi were recorded in bioconversion of food processing wastes into value-added by-products such as Aspergillus, Rhizopus and Trichosporon [17]. Interestingly, Drechslera sp. and Alternaria sp. demonstrated significant potential for lipid production, especially under using of agro-industrial wastes as substrates [20, 21]. Hereupon, the current study focused on mycological production of SCOs from both fungal strains in an economic process via utilizing by-product of the cheese manufacturing industries (i.e., whey), as a sole nutrition source. Generally, it was recorded as highly nutritive waste stream full of proteins, vitamins, sugars, minerals, and other growth factors [29] and also reported previously as supporting material in fungal lipid production [30, 31]. Herein, the whey was utilized as a cultivation media without any addition of other nutrient sources. It facilitated the lipid accumulation yield in both filamentous fungi by 3.22 and 4.33 g/L, which representing 45.3 and 48.2% lipid content in Drechslera sp. and Alternaria sp., respectively. In comparison, the lipid content of Drechslera sp. and Alternaria sp. reached to 33.18 and 29%, with lipid yield evaluated by 3.65 and 5.6 g/L, respectively, upon replacing carbon source in their optimized media with agricultural wastes (i.e., orange peel and molasses). However, on optimized microbiological media (i.e., Czapek-Dox’s medium), their lipid contents were assessed by 40.75 and 50.3% for Drechslera sp. and Alternaria sp., respectively, reflecting the higher potentiality of whey stream as the main cultivation and production media in the lieu of carbon or nitrogen source [20, 21].
The characterstic features of SCOs from both oleaginous fungi were determined initially by identifying their components through investigating the fatty acid methyl esters profiles after acidic transesterification as shown in (Fig. 1) and (Table 1), which manifested the qualitative and quantitative differences in both examined profiles. Notably, the results of gas chromatography–mass spectrometry (GC-MS) showed common features among both profiles. Wherein, unsaturated fatty acids (USFAs) represented the dominate constituent in the profiles of both fungi by 62.18 and 53.15% for Alternaria sp. and Drechslera sp. respectively. Meanwhile, Palmitic acid-C16 (SFAs) was the most prevalent FA by 29.0 and 28.9% for Alternaria sp. and Drechslera sp. respectively, followed by Oleic acid -C18 (MUSFAs), which was evaluated by 24.7 and 28.8%, % for Alternaria sp. and Drechslera sp., correspondingly. On the other hand, the omega-6 (ɷ-6) poly unsaturated fatty acid (PUFAs) (i.e., Linolenic acid and γ-linolenic (C18) was the third major constituent in both profiles with the values of 26.89% and 15.72% for Alternaria sp. and Drechslera sp. respectively. Interestingly, an obvious percent of PUFAs (30.02%) was detected for Alternaria sp. compared to 17.93% for Drechslera sp. In accordance with our result, Lauryn et al. [32] demonstrated that cheese whey was ideal choice for Mucor circinelloides in SCOs production with GC-MS-profile contained predominantly oleic acid (41%), palmitic acid (23%), linoleic acid (11%), and γ-linolenic acid (9%).
Fig. 1GC-Ms analysis of fatty acid produced by (A) Alternaria sp. B Drechslera sp. cultivated on wheyTable 1Showing fatty acids (FAs) patterns of both SCOs extracted from Alternaria sp.and Drechslera sp.after their cultivation on whey mediaFatty acidsRetention timeSCOs of Alternaria sp.A-OSSCOs of Drechslera sp.D-OSCaprylic acid (C8)7.0070.880.23Myristoleic acid (C14)16.8380.520.43Myristic acid (C14)17.4530.380.55Palmitoleic acid (C16)21.2334.494.36Palmitic (C16)22.00929.0128.89Heptadecenoic24.2990.340.19Gama-linolenic (C18)26.1444.220.68Linolenic (C18)26.30922.6715.04Oleic (C18)26.14424.7828.86Elaidic (C18)26.3501.710.21Stearic (C18)26.5553.5113.31Cis-5,8,11,14,17-Eicosapentaenoic (C20)31.3150.630.40Cis-8,11,14-Eicosatrienoic (C20)31.7980.650.15Cis-11,14-Eicosadienoic (C20)32.1660.610.18Cis-11-Eicosenoic (C20)32.30400.17Cis-11,14,17-Eicosatrienoic (C20)32.4070.60.14Arachidic (C20)33.1270.480.27Heneicosanoic (C21)36.5130.430.11Cis-4,7,10,13,16,19-Docosahexaenoic (C22)37.7780.641.21Cis-13,16-Docosadienoic (C22)38.74500.14Erucic (C22)38.75700.79Behenoic (C22)39.5210.940.99Tricosanoic (C23)42.0820.620.54Nervonic (C24)43.9050.670.40Lignoceric (C24)44.4291.231.77Fatty acid typeTotal % of FAsSFAs37.8246.85USFAs62.1853.15MUFAs32.1735.22PUFAs30.0217.93ɷ-31.871.75ɷ-628.1516.18
The acquisition of infrared spectra of lipids has been attained senior concern thank to providing opulent information on their chemical components, besides it is a fast and economical technique [33]. Fig. 2 demonstrates the FTIR spectrum of both examined SCOs. Generally, the peaks at wavenumbers of 3742 and 3359 cm−1 of A-OS and 3200 cm−1 of D-OS indicated the existence of hydrate hydroxyl group (-OH). Similarly, as signposted by Nandiyanto et al. [34], a broad absorption band in the range of 3650 and 3250 cm−1 represented the sign of hydrogen bond. Besides, the band at 3010 cm−1 in both SCOs samples pointed out to the C = CH- vibration originated from unsaturated fatty acids; interestingly, such band could be used for examining the degree of unsaturation in oils as referred by Shapaval et al. [35]. In the same sense, Nandiyanto et al. [34] stated that the bands above 3000 cm−1 are representative of unsaturated compounds. Accordingly, this could reflect the superior unsaturation of fatty acids in Alternaria sp. than that observed in Drechslera sp., as more peaks were detected in the area above 3000 cm−1, which harmonized with results of GC-MS that confirmed the higher unsaturation degree in A-OS comparing to D-OS.
Fig. 2FTIR spectrum of oil samples from (A) Alternaria sp. (B) Drechslera sp
Notably, the spectral bands at 2889 cm−1 detected in D-OS characterizes the C-H stretching vibrations of lipids [36]. Meanwhile, Nandiyanto et al. [34] mentioned that the double bond groups such as carbonyl (C = C) were present in the region of 1500–2000 cm−1. So, remarkably, both fungal patterns confirmed the presence of carbonyl (C = C) peaks as detected at 1994 and 1648 cm−1 of A-OS and 1662 cm−1 of D-OS. While the presence of spectral band at that regions reflected the presence of crystallizable FAs [33]. However, the signature of CH2 asymmetric bending and CH2 vibration could be detected by the presence of peaks at wave numbers of 1403 and 1435 cm−1 [33, 37]. Whereas, typical bands at 1252, 1114, 1050, 986 and 948 cm−1 of A-OS-FTIR pattern and also 1273, 1096 and 993 cm−1 of D-OS FTIR pattern referred to C-O stretching of phospholipids [38–40]. Regarding to the spectral bands at 718 and 611 cm−1 of D-OS and A-OS profiles, respectively, could be attribute to alkyne C-H bend and CH2 rocking vibration, respectively [34, 41]. Likewise, Forfang et al. [42] elucidated that biological material (e.g., carbohydrates, proteins and lipids) exhibits CH stretching vibrations due to the presence of -CH3 and -CH2. Arguably, based on the above mentioned characterization techniques, the results of FTIR were deemed as strong evidence for the presence of functional groups that were related to intracellular lipids, which were detected and quantified by GC-MS analysis; emphasizing hereby the obvious discrepancies between SCOs of Alternaria sp. and Drechslera sp. in the content and structures.
Antibiofilm activity of SCOs
The presence of biofilm represents a serious threat to human health and surrounding ecosystem in various sectors. The biofilms injure medical equipment such as catheters, contact lenses, prosthetic devices, heart pacemakers, endoscopes, colonoscopes, dental plaques and dental irrigation units. Let alone their capability to invade human tissues causing severe infections [43]. However, the biofilms also adhere to food manufacturing equipment, air-conditioning units, petroleum pipelines and cooling towers, which symbolizes as evidences on industrial risk of biofilm. In the same sense, biofilms fixed themselves on external surfaces of marine vessels, water pipes, stones in a stream and sewage treatment plants, facilitating the accumulation of organic and inorganic materials with other organism such as algae, plants and protozoa in a phenomenon called biofouling. The problem of biofouling lies behind deteriorating the aqueous flow system with its fauna; causing the prevalence of microbial contamination and accelerated corrosion.
Against this backdrop, various mechanical removal approaches and chemical biocides were utilized to eliminate biofilms and prevent their hazard [44]. Nonetheless, the extensive use of antimicrobial agents generates multidrug resistance (MDR) phenomenon that led to ecological balance disruption and epidemic diseases. Therefore, modern insights are directed toward employing natural bioproducts as ecofriendly, biocompatible, safe and economic agents in defeating water/foodborne pathogens, which harmonized with aims of recent international events like COP28. Hence, the current investigation is undertaken to determine the in vitro antibiofilm activity of SCOs extracted from Alternaria sp. and Drechslera sp. against some MDR microbes-forming biofilm. This target was implemented through detecting the effect of SCOs on biofilm formation, viability, biochemical composition and hydrophobicity.
In fact, S. aureus, P. aeruginosa and C. albicans were opted due to their ubiquitous occurrence and concomitance with nosocomial/community-acquired infections. Besides, they exhibit the capability to colonize vast array of surfaces either abiotic or cellular interfaces, which lead to significant environmental and health threats. Therefore, crystal violet assay (CV) was employed to detect the antibiofilm influence of different doses of SCOs (1–100 µg/mL), which deemed as reliable and facile assay in staining the biofilm biomass entirely [45, 46]. As observed in Figs. (3 and 4), the inhibitory patterns of both SCOs samples displayed significant differences (P ≤ 0.05) in the biofilm development after treatment, as unveiled by ANOVA. Besides, the inhibitory power of both samples showed notoriously variation against examined pathogens. Namely, SCOs of Alternaria sp. (A-OS) suppressed the growth of P. aeruginosa biofilm at concentrations ranged from 1 µg /mL to 10 µg/mL by 7.01 ± 0.3% to 50.85 ± 2.68%, respectively; while SCOs of Drechslera sp. (D-OS) enhanced the growth of P. aeruginosa biofilm by 33.41 ± 6.24% and 5.83 ± 2.83% at exact concentrations, respectively. Remarkably, the antibiofilm potency increased with elevation of applied doses of both examined oil samples. Wherein, the biofilm of P. aeruginosa was inhibited significantly (P ≤ 0.05) at 100 µg/mL of A-OS and D-OS by 84.10 ± 0.445 and 47.41 ± 2.83%, correspondingly. On the other hand, about 90.37 ± 0.065% and 62.63 ± 5.82% inhibition was observed for S. aureus biofilm at 100 µg/mL of both SCOs in the same order. Furthermore, a pronounced and significant (P ≤ 0.05) fungicidal potency was noticed in blocking the biofilm formation of C. albicans that reached to 94.96 ± 0.21% and 78.67 ± 0.23% upon treating with A-OS and D-OS (100 µg/mL), respectively. Generally, as inferred from these results, there is an inter-species variation phenomenon in a dose dependent performance exerted by the examined SCOs samples. In agreement with our results, Murugan et al. [47], found a variation in the biofilm growth of Proteus sp., E. coli, Bacillus sp. and S. aureus; assigning that to the differences in the physiological behavior of different microbial species. Seemingly, the cell wall architecture, microbial physiology with varied metabolic performance and uptake/regulation systems are considered being the fundamental parameters in managing the tolerance and susceptibility profiles among inter and intra-species of the microbes in their response to any antagonistic agent [48].Fig. 3The Impact of A-OS on biofilm development by P. aeruginosa, S. aureus and C. albicans. A-Biofilm biomass suppression, B-Metabolic performance, C- EPS inhibition, D-Protein inhibition and E-Hydrophobicity inhibition. All values were expressed as mean ± SEM. Treatments at various doses were comparing to untreated control with significance at *P ≤ 0.05Fig. 4The Impact of D-OS on biofilm development by P. aeruginosa, S. aureus and C. albicans.
A-Biofilm biomass suppression, B-Metabolic performance, C- EPS inhibition, D-Protein inhibition and E-Hydrophobicity inhibition. All values were expressed as mean ± SEM. Treatments at various doses were comparing to untreated control with significance at *P ≤ 0.05
Effect of SCOs samples on biofilm metabolic activity
Actually, CV firmly stains the entire biofilm biomass, which includes polysaccharides in the mucilaginous mat conjugated with other biomolecules that are disseminated in an even manner on the live as well as dead cells surface. Subsequently, the overall metabolic performance of adhered microbial cells, which were treated with different concentrations of oil samples relative to untreated, was assessed calorimetrically using MTT assay. It is worth noting that tetrazolium salts (such as MTT (3-[4, 5- dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide), XTT (2,3-bis(2-methoxy-4-nitro-5-sulfo-phenyl)- 2 H-tetrazolium-5-carboxanilide) and TCC (2,3,5- triphenyl tetrazolium chloride) are frequently utilized in biological assays to investigate the viability of living cells. That occurs through enzymatic reduction of tetrazolium salt by the cellular NADH of metabolically active cells, which leads to the formation of colored formazan. Hence, different tetrazolium-based dyes were employed in various studies to determine biofilm viability in accompanying with other means like CV approach and microscale analysis [45, 46, 49].
Interestingly, both types of oil samples impacted on the viability of cells in the biofilm structure adversely, progressively and significantly (P ≤ 0.05) with increasing the doses of applied SCOs. Figs. (3 and 4) indicate that A-OS frustrated the propagation of active cell in P. aeruginosa biofilm matrix in all applied concentrations in the range of 2.35 ± 0.885% − 83.08 ± 0.235%. Conversely, D-OS flourished the growth of P. aeruginosa biofilm at low concentrations (1 µg/mL − 5 µg/mL) by the range of 16.72 ± 1.36% − 4.43 ± 1.36%; whereas, the viability of live cells curtailed to 48.12 ± 3.0% upon increasing the concentration to 100 µg/mL. Regarding to the biofilm of S. aureus, both oil samples inhibited the growth of active cells in linear concentration-dependent behavior; reaching to the maximum suppression by 85.9 ± 0.375 and 62.48 ± 1.52% for A-OS and D-OS, respectively at 100 µg/mL. In the same sense, the survival of C. albicans cells in biofilm network arrested by 86.54 ± 0.86% and 64.94 ± 1.48% under the treatment of A-OS and D-OS (100 µg/mL), respectively. As noticed, the results of biofilm inhibition were harmonized with that of metabolic activity. It is plausible to mention that viability and metabolic activity of all tested biofilm-forming pathogens correlated significantly with biofilm biomass (r ≥ 0.9, P = 0.00); reflecting hindrance impact of oil samples on active cells that are distributed within multilayer architecture of biofilm.
The effect of oil samples on biofilm’s carbohydrate and protein content
Carbohydrates or exopolysaccharides (ePs) and proteins represent the intrinsic constituents of EPS scaffold of the biofilm from both structure and function. As denoted by Gunn et al. [50] and Mosharaf et al. [51], the secreted proteins, adhesion proteins (e.g., lectins, Baplike proteins) and motility organelles configure the biofilm matrix proteins. However, galactose, mannose, glucose, arabinose, xylose, rhamnose, fucose, cellulose nanofibers, galacturonic acid and N-acetyl-glucosamine are the most abundant carbohydrates detected in slimy matrix of S. aureus, Enterococcus faecalis, Klebsiella pneumoniae, and P. aeruginosa [52]. As declared by Gunn et al. [50] the chemical constituents of the biofilm differ rely on the organism and was influenced by environmental parameters. Intriguingly, such specific components control biofilm integrity, maintain biofilm stability, configure its morphology, mediate cell-cell signaling, contribute in cell colonization/adherence and preserve its cells from adverse external stressors [23, 51].
Figure (3) depicts the effect of A-OS on biofilm content of carbohydrates or ePS, which diminished from 7.515 ± 0.13, 5.541 ± 0.147 and 6.59 ± 0.33 mg/mL in control untreated samples of S. aureus, P. aeruginosa, and C. albicans biofilms to 3.61 ± 0.34, 3.14 ± 0.335 and 2.99 ± 0.19 mg/mL at 10 ug/mL of A-OS, respectively; representing by such way inhibition percentages of 51.87 ± 4.55, 43.26 ± 5.9, and 54.5 ± 2.84%, respectively. However, the same concentration of D-OS reduced carbohydrate content of S. aureus, P. aeruginosa, and C. albicans biofilms to 5.100 ± 0.47, 4.98 ± 0.22, and 4.20 ± 0.335 mg/mL by 32.09 ± 0.536, 9.94 ± 4.04, and 36.23 ± 5.04% inhibition percentages, respectively (Fig. 4). In the same context, 10 ug/mL of A-OS reduced protein content of S. aureus, P. aeruginosa and C. albicans biofilms from 9.64 ± 0.095, 9.75 ± 0.065 and 9.34 ± 0.1 mg/mL in the control untreated samples to 5.92 ± 0.11, 6.94 ± 0.17 and 4.72 ± 0.16 mg/mL, which symbolize 38.54 ± 0.94, 33.47 ± 1.74 and 49.43 ± 1.69% inhibition percentages, respectively. Whereas, D-OS (10 ug/mL) diminished protein content to 7.44 ± 0.255, 8.40 ± 0.065 and 5.61 ± 0.37 mg/mL causing 22.81 ± 2.63, 13.84 ± 0.59 and 39.92 ± 3.89% inhibition.
Notably, significant and dramatic changes were observed in both contents upon elevating the concentrations till reached to the highest values at 100 ug/mL. Wherein, such concentration of A-OS caused in lowering the carbohydrate/ePS content of S. aureus, P. aeruginosa and C. albicans biofilms by 76.54 ± 7.04, 66.81 ± 2.51 and 80.54 ± 1.77%, respectively. While the carbohydrate/ePS reduction percentage reached 60.22 ± 2.02, 50.03 ± 1.95 and 64.26 ± 4.7% by D-OS for S. aureus, P. aeruginosa and C. albicans biofilms, respectively. Regarding the protein content, it lessened significantly to 63.94 ± 3.89, 57.27 ± 5.43 and 74.87 ± 2.49% upon applying 100 ug/mL of A-OS; however, D-OS (100 ug/mL) reduced it to 49.39 ± 1.96, 47.61 ± 2.03 and 59.07 ± 1.78% for S. aureus, P. aeruginosa and C. albicans biofilms, respectively.
Effect of oil samples on biofilm hydrophobicity
The adhesion capability of microbial cell deemed as intrinsic property to colonize any substrate and boost biofilm lifestyle easily. Interestingly, cell surface traits in the formed of ePS and hydrophobicity are decisive parameters that manage the entire adhesion process. In addition, surface characteristics and ambient environmental conditions rule the adhesion of the cells with the surface through number of interactions like hydrophobic, van der Waals and electrostatic. Remarkably, the cells with higher biofilm forming capacity possess higher hydrophobic nature that leads to potent adhesion and vice versa [53]. Given that the biofilm-forming cells with hydrophobic characteristics exhibit affinity to hydrocarbons (e.g., hexadecane, octene, xylene, etc.), the cells retained in the organic phase; generating low turbidity of aqueous phase and by such method (i.e., MATH), the hydrophobicity nature of the biofilm is detected [54].
In the current study, the hydrophobicity index (HI) recorded 65.72 ± 2.3, 51.61 ± 1.12 and 59.12 ± 5.43% for P. aeruginosa, S. aureus and C. albicans respectively; denoting a higher hydrophobicity property of P. aeruginosa than that exhibited by S. aureus and C. albicans. Upon applying different concentrations (10–100 µg/mL) of both oil, a noticeable reduction in HI was shown (Figs. 3 and 4); implying progressively transition to hydrophilicity state, which reached to the maximum at the highest applied doses. Wherein, A-OS (10 µg/mL) lessened hydrophobicity to 64.18 ± 4.23, 49.18 ± 1.73 and 58.62 ± 6.94% for P. aeruginosa, S. aureus and C. albicans, by 1.54 ± 4.23, 2.43 ± 1.73 and 0.498 ± 6.91% inhibition percentage in the respective order. However, A-OS (100 µg/mL) switched S. aureus and C. albicans biofilms to weak hydrophobicity state (i.e., became hydrophilic) by recording 18.5 ± 1.22 and 22.18 ± 0.94% HI, respectively; implementing 33.1 ± 1.22 and 36.94 ± 0.94% inhibition. While, the HI of P. aeruginosa biofilm altered to be moderate by recording 41.32 ± 1.65% and inhibition percentage recorded 24.04 ± 1.65%. On the other hand, D-OS (10 and 20 µg/mL) insignificantly enhanced the hydrophobicity of P. aeruginosa biofilm by 0.046 ± 2.55 and 0.032 ± 2.364%, respectively. Whereas, at 100 µg/mL inhibited its hydrophobicity by 15.96 ± 0.74%; maintaining HI in the potent region by recording 49.78 ± 0.74%. Regarding S. aureus and C. albicans biofilms, D-OS promoted their hydrophilic affinity in a dose-dependent behavior, reaching to the maximum at 100 µg/mL by recording 25.34 ± 0.422 and 37.89 ± 0.826%, correspondingly, which all remained in the moderate phase of hydrophobicity. Generally, albeit distinct structural variation in cells surface among the examined strains in our study, both oil samples substantiated their efficacy in influencing on hydrophobicity adversely. It is important to highlight that hydrophobicity reflects the microbial attachment or adhesiveness ability, which varies even from strain to strain and influenced by microbial age, microbial surface charge and growth medium [55]. In study performed by Kim et al. [56], 10 µg/ml of antibiofilm FAs (e.g., tricosanoic acids, palmitoleic, myristoleic acid, lauric acid, stearic, heptadecanoic and α-linolenic) reversed the biofilm of Cutibacterium acnes from hydrophobic region to hydrophilic region (hydrophobic index < 20%) simultaneously with biofilm inhibition, which agreed with our results.
Intriguingly, the results of the current investigation declared the existence of significant positive correlation between all examined variables (i.e., inhibition of biofilm, protein, ePS, viability and hydrophobicity) with SCOs concentrations, as signified by Pearson’s correlation coefficients (Table 2) (Figs. 5 and 6). Wherein, oil samples influenced negatively on the biofilm development through modulating microbial-surface interactions, in particular hydrophobic interactions through impacting on surface-associated exopolysaccharides and proteins. In consistent with our results, Pompilio et al. [57] attributed the higher hydrophobicity of Stenotrophomonas maltophilia biofilm to its higher exopolysaccharides content, which was positively correlated with biofilm development. Also, Mu et al. [58] manifested and explained the same finding in S. epidermidis biofilm. Otherwise, several reports documented the independence of biofilm formation on hydrophobicity [53, 59]. Nonetheless, there is a consensus among all studies regarding that the cell surface properties and overall physiological properties of microbes govern the process of biofilm development and maturation. Noteworthy mention that the hydrophobicity is fostered by the action of microbial appendages (e.g., pilli, fimbriae, fibrils, etc.) that scattered on the cell surface. Such organelles contain hydrophobic amino acid residues that expedite noncovalent attachment of the cells on any substratum [54, 55]. However, exopolysaccharides facilitate irreversible adhesion and sheltering the developed cells within the backbone of biofilm [23, 52].
Table 2Representing the correlation between biofilm inhibition with other studied factors (i.e., viability, ePS, protein and hydrophobicity) by the action of SCOsBiofilm typeExamined parameterPearson’s correlation coefficients (R2)(P-value)SCOs-1SCOs-2BiofilmViabilityePSProteinBiofilmViabilityePSProteinP. aeruginosaViability0.983 (0.00)0.961(0.00)ePS0.954 (0.00)0.985(0.00)0.941(0.00)0.971(0.00)Protein0.983 (0.00)0.968(0.00)0.943(0.00)0.934 (0.001)0.95(0.00)0.987(0.00)hydrophobicity0.926 (0.001)0.942(0.00)0.928 (0.001)0.911 (0.002)0.928 (0.001)0.942(0.00)0.984(0.00)0.986(0.00)S. aureusViability0.981 (0.00)0.976(0.00)ePS0.97 (0.00)0.984(0.00)0.986(0.00)0.982(0.00)Protein0.979 (0.00)0.975(0.00)0.941(0.00)0.984(0.00)0.982(0.00)0.992(0.00)hydrophobicity0.932 (0.001)0.968(0.00)0.931 (0.001)0.951(0.00)0.947(0.00)0.977(0.00)0.97(0.00)0.973(0.00)C. albicansViability0.975 (0.00)0.958(0.00)ePS0.984 (0.00)0.984(0.00)0.969(0.00)0.973(0.00)Protein0.937 (0.001)0.965(0.00)0.965(0.00)0.964(0.00)0.933 (0.001)0.986(0.00)hydrophobicity0.879 (0.004)0.932 (0.001)0.886(0.03)0.88(0.004)0.891 (0.003)0.971(0.00)0.95(0.00)0.9(0.002)Fig. 5Contour plot showing the correlation of biofilm suppression by A-OS versus EPS inhibition (left panel) and protein inhibition (right panel) with cell surface hydrophobicity. The diagram was plotted by Minitab 14 software. Different colors elucidate different levels of biofilm suppression. A & B - P. aeruginosa, C & D- S. aureus and E & F- C. albicansFig. 6Contour plot showing the correlation of biofilm suppression by D-OS versus EPS inhibition (left panel) and protein inhibition (right panel) with cell surface hydrophobicity. The diagram was plotted by Minitab 14 software. Different colors elucidate different levels of biofilm suppression. A & D - P. aeruginosa, B & E- S. aureus and C & F- C. albicans
Based on the previous results, it is conspicuous that the antibiofilm potency of SCOs samples appeared more evident against S. aureus. That could be attributed to its physiological and metabolic sensitivity, besides the nature and architecture of its cell wall, which seemed to contribute intrinsically in its susceptibility. Namely, the hydrophilic nature of gram-positive bacteria’s cell wall with their low content of lipids (1–4%) trigger the adsorption and penetration processes of exogenous materials interiorly easier. In contrast, the more complex structure of gram-negative bacterial cell wall with its abundant hydrophobic moieties (11–22% lipid content) serves as potent entry barrier toward detergents and hydrophobic molecules, hindering by such way the internal transportation of SCOs [60]. Strikingly, the superior biocidal potency of fatty acids, especially those of longer chain FAs (i.e., ≥ C12) against gram-positive bacteria such as B. subtilis, Micrococcus luteus, Propionibacterium acnes, Listeria monocytogenes and Clostridium difficile was reported tremendously by several research groups [56, 61–65], which coincident with our results. Additionally, Shukla et al. [62] reported that gram-negative bacteria displayed more resistance to medium- and long-chain FAs than gram-positive bacteria, which also agreed with our results; however, gram-negative bacteria were more susceptible to FAs with C6 or less in their chain. In the same context, [60] opined that yeast cells are more susceptible to medium chain-FA (i.e., C8-C12). Otherwise, [63, 66–68] stated the mycocidal potency of long chain-FA. Herein, both SCOs samples of the present study exhibited promising fungicidal performance versus C. albicans, which agreed that found by previously mentioned studies. Hereby, both oil samples could be harnessed in thwarting candidiasis in immunocompromised patients following secondary pneumonia infections conjugated with COVID-19.
Arguably, the biofilm development by P. aeruginosa, S. aureus and C. albicans was dose-dependently impeded by both oil samples, which could be ascribed to the intensification of detergent traits of FAs on account of their amphipathic nature, which could be bacteriostatic at lower concentrations and bactericidal at higher concentrations [56, 69, 70]. Subsequently, it is imperative to shed the light about the antagonistic strategy followed by oil against examined pathogens. However, as referred by Cepas et al. [66] and Kumar et al. [71] the precise biocidal strategy followed by FAs is obscure and undefined. Wherein, FAs frustrate microbial growth and biofilm development through violating multiple cellular targets nonspecifically, which seems being characteristic and should be invested in enfeebling drug resistance phenomena (MDR) [72].
The detrimental effects of FAs against treated biofilm commences with the cell membranes, which considered being the prime target in the multi-step process of biofilm formation. Wherein, FAs incorporated on the hydrophobic moieties of cell phospholipid layer causing membrane solubilization, integrity destabilization and apertures creation, either transient or permanent. That in turn elevated cell permeability level and cellular leakage. Besides, FAs subvert the nutrient uptake system, electron transport chain and oxidative phosphorylation which are the chief membrane-located processes. Additional presumptive scenario could be exerted by oil in the context of membrane damage is suppressing the functionality of membrane-associated proteins, which mediate fatty acid biosynthesis in the plasma membrane. Remarkably, Firoozabad et al. [73] demonstrated that the destructive effect of FAs on S. aureus biofilm was triggered through the inactivation of enzyme that is responsible for the fatty acid elongation step, which is enoyl-acyl carrier protein reductase (Fabl). On the other hand, Cepas and coauthors [66] assigned the inhibition of P. aeruginosa and C. albicans biofilms to the inhibition of Fabl by the action of FAs. Similarly, Lee et al. [67] attributed the anticandida potency of FAs to the hindrance of ergosterol production, which regulates fungal membrane synthesis and reinforces its structural. On the other hand, the treatment with FAs could enhance autolysis process by inducing autolytic enzyme in lieu of massive solubilization of the cell membrane by the surfactant-like action of FAs as suggested by Tsuchido et al. [74].
Interestingly, Bintari and Risandiansyah [75] scrutinized in silico the effect of FAs extracted from Cladophora sp. in dampening the activity of peptide deformylase in P. aeruginosa, E. faecium, E. coli, and S. aureus, which catalyzes protein maturation process. Meanwhile, FAs impair ePS production, fimbriae/pili formation and hydrophobicity, which thereafter frustrate motility and adhesion to substrates; ultimately block the microbial colonization and ruin irreversible aggregation and attachment. The finding by research groups of Kim et al. [56], Nicol et al. [76] and Kim et al. [77] supported these deleterious impacts. On genetic level, FAs drastically repressed DNA supercoiling/replication and down-regulated different genes such as hla, HWP1, CHT4, csgAB, fimH, flhD, motB, luxRS and NorA, which encode proteins responsible for alpha-hemolysin production, hyphal development, chitinase production, fimbriae synthesis, motility, quorum-sensing and efflux pump [63, 71, 77]. Additionally, anti-quorum sensing activity was detected for FAs in quenching the communication signaling and quorum sensing system among biofilm cells and with external environment [66, 76]. As referred by Nicol et al. [76], FAs inhibited the biofilm of Acinetobacter baumannii up to 38% through reducing quorum sensing regulator AbaR, which influenced adversely on communication signals among biofilm cells.
Taken together, the overall data revealed the superior antibiofilm potentiality of A-OS in defeating all examined types of biofilm-forming pathogens. That could be attributed to the profusion of USFAs (62.18%) comparing to D-OS, which contained 53.15% (Table 1-Fig. 1). Our finding is in line with the majority of the previously published investigations who declared the lower activity or bacteriostatic performance of SFAs [69, 78]. While USFAs with the same length carbon backbone exhibit influential potency in deteriorating biofilm development and obstructing the viability of its cells. Such potency of USFAs could be ascribed to their instability, propensity to oxidation and binding non-specifically to target sites like proteins [69, 78]. Otherwise, Khalilova et al. [79] demonstrated the remarkable antibacterial and antifungal properties of SFAs relative to USFAs, especially capric C10:0, lauric C12:0 and palmitic C16:0 acids.
Comparisons among studies are entangled and controversial due to the differences in the applied FAs, the treated microbe or even the altogether treatment process [69]. Notably, the nature of FAs (i.e., acid or its derivatives such as methyl, ester form, etc.), the applied dose and their solitary or combined case were deemed as the substantial features concerning FAs entity. However, the microbial factor includes microbial species types, isolation source, microbial cell load, microbial phase (i.e., planktonic or aggregated biomass), microbial metabolic activity and microbial physiology (i.e., sensitive or persister), which are undoubtedly differ among microbial genera till strain level. While the contact time, pH value and ionic strength of treatment milieu represent other influential treatment circumstances. All these reasons collectively could definitely influence on biocidal activity of applied FAs to different extends [60, 80]. Nevertheless, the molecular structure and shape of FAs remain the driving force that control the magnitude toxicity of FAs. Precisely, the carbon chain length, number of unsaturation and the geometric configuration around the double bond are the conclusive parameters that correlated proportionally to FAs antagonistic potentiality. As stated by McGaw et al. [60], Desbois and Smith [69] and Kim et al. [56], FAs that possess more carbon atoms (i.e. C16, C18, etc.) in their carbon backbone are more potent antimicrobial agents than those contained 10 or 12 carbons in their chain. Moreover, USFAs, properly PUFAs followed by MUFAs, manifested characteristic biocidal functionalities than SFAs [69]. In this regard, Feldlaufer et al. [78] found that palmitic acid (C16) didn’t show any inhibitory action against pathogens comparing to its unsaturated counterpart (i.e., palmitoleic acid (C16:1); confirming that FAs with 3, 4 or 6 double bonds display more antimicrobial activity. Strikingly, FAs stereochemistry also deemed as limiting factor, namely, the cis-isomer is highly active relative to trans configuration [60, 78]. That could be ascribed to the resemblance in the structure between SFAs and trans orientation USFAs [69]. In this sense, Feldlaufer et al. [78] documented that the introduction of double/multiple bonds, especially with FAs more than 14 carbon atoms in cis- orientation, plays imperative role in elevating antibiotic-like behavior. On the other hand, McGaw et al. [60] reported that SFAs with lower-chain length, MUFA and PUFAs with longer-chain structure exhibit considerable antimicrobial activity.
Remarkably, as indicated by the chromatographic profile (Fig. 1; Table 1), A-OS contained ample amount of omega (ω) fatty acids such as ω-5 (myristoleic acid, C14:1, 0.52%), ω-7 (palmitoleic acid, C16:1, 4.49%) and ω-9 (oleic acid C18:1, 24.78%). However, the predominance of ω-3 (cis-5,8,11,14,17-eicosapentaenoic, C20:5, 0.65%; Cis-4,7,10,13,16,19-docosahexaenoic, C22:6, 0.64% and Cis-11,14,17-Eicosatrienoic C20:3 0.60% ) and ω-6 (linolenic acid, C18:3, 22.67% and γ-linolenic acid, C18:3, 4.22%) contributed intensively in its robust effectiveness. It is worthy mention that ω-3 and ω-6 are frequently utilized as alternative food additives and therapeutic agents to maintain body homeostasis and enhancing the immune system, by the virtue of their functional values in lipid metabolism, antioxidant signaling pathway regulation and inflammatory processes modulation. Besides, their role in diabetes type two, ulcerative colitis, cardiovascular disease, Crohn’s disease, hypertension, alzheimer’s disease and cancer therapy couldn’t be neglected [62, 68, 73]. Meanwhile, their potency in defeating various microbial genera as antibacterial, antifungal or antibiofilm was also determined in various investigations [68, 79].
In the study conducted by Cepas et al. [66], the authors detected the inhibitory limit of linolenic acid by 1, 32 and 64 mg/mL against C. albicans, S. aureus and P. aeruginosa, respectively. Also, he and coauthors found that more than 250 mg/mL of stearic acid, γ-linolenic acid, arachidonic acid and palmitoleic acids were required to utterly block the growth of E. coli. While, 0.02 mg/mL of palmitoleic acids inhibited the biofilm of A. baumannii by 38% [76]. Comparing our results with previous studies and others, reflects the advantageous and promising properties of our SCOs that dampened biofilm activity, their cell viability, biochemical constituent and their hydrophobicity in sensible doses. That could be assigned to the synergistic effect mediated by the combination of SFAs, MUFAs and PUFAs collection. Strikingly, the naturally derived lipids from herbal and microbial sources share this common phenomenon. Namely, the presence of mixture of FAs that compete mutually on more than target site could implement their hostile functionality in prominent way. The results of the present research concurred with that reported by Dia and Jacoeb [81], Shukla et al. [62], Balkrishna et al. [72] and Panjaitan et al. [80]. In the context of synergistic activity, several reports accentuated the uplifting of antibiotic-like performance of FAs upon conjugating several FA together or supplementing with antibiotics, bacteriophages, organic compounds, enzymes and edible plant essential oils [63, 82–86].
Safety and anticancer activities of oil samples
The cytotoxicity results of oil samples on the non-cancerous cell line (Wi-38) indicated that the D-OS was safe treatment on the viability of these non-cancerous cells with IC50 25.8% followed by A-OS with IC50 value of 23.86% (Figs. 7A and 8). As a second-leading reason of death, the continuous amending in cancer treatment modalities is intensively implementing. The most widely options of cancer treatment for many decades are surgery, chemotherapy and radiation therapy, in a single treatments or in combination (Debela et al., 2021). Actually, pain and fatigue are the mainly harmful side effects of conventional therapies modality, besides, dosage selection difficulty, rapid drug metabolism, cytotoxicity and lack of tumor-specific treatments [87, 88]. Henceforth, utilizing fungal bioactive compounds such as SCOs considers being promising, complementary and alternative therapy to reduce cancer complications and improve the therapeutic efficacy [89].Fig. 7Cytotoxicity results of the oil samples on noncancerous (A) and cancerous cell lines (B and C)Fig. 8The IC50 values of the oil samples on the non-cancerous and cancerous cell lines (A), selectivity index (SI) against cancer cells (A549 and CaCo-2) (B), and morphological variations in the treated cancer cell lines relative to the untreated cells (C)
Concerning with the anticancer effects of the A-OS and D-OS, the MTS assay protocol showed that the oil sample D-OS is the most potent cytotoxic agent against both A549 and CaCo-2 cell lines with IC50 values of 2.55 and 3.425%, respectively (Figs. 7B and 8) and SI values of 10.1 and 7.5, respectively (Fig. 8). Furthermore, A-OS showed a potent effect against CaCo-2 cell line with IC50 8.275% (Fig. 8A) and SI value of 2.88 (Fig. 8B). D-OS was safer than A-OS on the viability of normal cells (Wi-38) with IC50 value that was 10-fold higher than its corresponding concentrations for inhibiting 50% growth of CaCo-2 and A549. Meanwhile, another oil sample (i.e., A-OS) had a higher IC50 for A549 growth inhibition than its cytotoxic dose on Wi-38. This declares that D-OS is more selective cytotoxic oil than A-OS against both studied cancer cell lines. Importantly, A-OS activated caspase 3 by 64.23 ± 1.18% and 53.77 ± 0.995% more effectively than D-OS (52.09 ± 0.222% and 49.72 ± 0.952%) in A549 and Caco-2 cells, respectively (Fig. 9). In a dose-dependent manner, A-OS exhibited stronger inhibitory effect on MMP2 and MMP9 with lower IC50 values (18.58% and 8.295%, respectively) than D-OS (23.61% and 13.16%, respectively) as shown in Fig. (10). Attractively, the polyunsaturated fatty acids (PUFAs) especially, ω-3 and ω-6 were recorded as selectively induced tumor cells apoptosis. The sensitivity of various cancer cells to different fatty acids were found to be variable depending on the type of cancer cell being tested and also the types and concentrations of the tested fatty acid [90]. Different research articles explained the positive effects of ω-6 fatty acids in controlling the human lung tumor cell growth in a concentration-dependent manner [91]. Furthermore, ω-3 PUFA triggers cancer cell apoptosis and synergizes to increase the sensitivity of tumor cells to conventional therapies, with interesting applications in cancers resistant to treatment [92]. According to Madhavi and Das [93], AA, GLA, DHA and EPA were found to be the greatest effective in inhibiting tumor cells growth comparing to ALA and LA that recorded lower effects even at higher concentrations. Kumar and Das [94] proved that linoleic acid at concentration of 40 µg/mL had the ability to inhibit cancer cells, whereas, the lower concentrations (5–10 µg/mL) enhanced the growth of nearly all types of cancer cells that were being tested in such study.Fig. 9Caspase 3 activation percentages in D-OS and A-OS treated A549 and Caco-2 cellsFig. 10Dose response curves of A-OS-and D-OS for (A) matrix metalloproteinase MMP2 and (B) MMP9 inhibition as well as (C) their IC50 values
This tumoricidal action of fatty acids could be attributed to the increase in the generation of free radicals in the tumor cells [90]. Moreover, there is an evidence indicating that ω-6 PUFA and LA can be involved in both pro- and anti-cancer processes. For example increased the proliferation of the breast carcinoma cell line (BT-474 cell line) and the human lung cancer cell line (A549) in vitro, as well as promoting colon and prostate tumorigenesis and tumor growth in animal models [95, 96]. On the other hand, a high dose of LA could inhibits the proliferation of the colon cancer cell line (Caco-2) [97], while a high intake of LA can also show a protective effect against cancer development [98]. Based on this theory, different literatures proved the anticancer leverages of fatty acids, at least partly owing to their proapoptotic effects, especially against colorectal cancer (e.g., Caco-2, HT-29, HCT116, LoVo, SW480 and SW620 cell lines) [99], MHCC97L hepatocellular carcinoma cell line [100] and LNCaP, DU145 and PC-3 prostate cancer cell lines [101]. Furthermore, dihomo-γ-linolenic acid proved selective cytotoxic effects against A549 lung cancer cell line without affecting the normal cells [102]. Remarkably, the activation of caspases is the primary indicator of apoptosis-mediated cancer cell death. Caspase 3 is one particular caspase whose activation causes irreversible cell death. Previous studies illustrated that hen egg yolk PUFAs and Fish oil-enriched PUFAs activated caspase 3 in the treated melanoma cells and breast cancer, respectively [10, 103]. Moreover, both MMP2 and MMP9 are considered the main contributors in mediating invasion and metastasis of lung and colon cancers [104, 105]. PUFA (ω-3 and ω-6 fatty acids) revealed high inhibition potency in suppressing both MMP2 and MMP9 activities [11, 13, 29].
Finally, the former results of anticancer and antibiofilm proficiency of SCOs could be symbolized as prospective contenders for prophylactic programs by the dint of their environmentally-sound and biocompatibility. Besides, their unspecific mode of action triggers the appearance of mutant phenotypes with FAs-resistance is less problematic than commercial drugs. In this avenue, SCOs of the present study deemed as promising alternative anti-infective agents in various biotechnological applications like agriculture, medicine, nutraceuticals, feed additives and the cosmetic formulations. Presently, studies are profoundly under way to examine the efficacy of our SCOs in combinatorial treatment or drug amalgamation strategy with metal/ polymer nanomaterials, in lieu of using single agent. Such nanoformulations composites would be employed as an alternative wound dressing material in vitro study. Also, their exploitation as an anti-acne agent in skin ointments or gels is also run in parallel. However, in the avenue of food technology synchronizing with exploiting higher nutritional value of SCOs, which are rich in ω-3 and ω-6, oral tablets of SCOs and probiotics would be designed for promoting indigenous microbiota to facilitate the digestion process in human and livestock.
Economic production and characterization of fungal SCOs
The microbial lipids or SCOs, are categorized among the most promising natural feedstock for biofuel and nutraceuticals production. Remarkably, the proximity of their structure with fish oil or even vegetable oils had gained a momentum, especially those derived from fungi. That could be attributed to their large quantity of biomass with high lipids yield in a short growth cycle and facile biomass collection. Remarkably, the versatility in fungal growth conditions facilitated their cultivation on low-cost culture medium based on different wastes, which were continuously accumulated in the environment causing severe pollution. Such dual tasks, of investing the environmental contaminants in biotechnological products, consider being the fundamental pillar of sustainable green techno-economic productivity [9]. Several species of filamentous fungi were recorded in bioconversion of food processing wastes into value-added by-products such as Aspergillus, Rhizopus and Trichosporon [17]. Interestingly, Drechslera sp. and Alternaria sp. demonstrated significant potential for lipid production, especially under using of agro-industrial wastes as substrates [20, 21]. Hereupon, the current study focused on mycological production of SCOs from both fungal strains in an economic process via utilizing by-product of the cheese manufacturing industries (i.e., whey), as a sole nutrition source. Generally, it was recorded as highly nutritive waste stream full of proteins, vitamins, sugars, minerals, and other growth factors [29] and also reported previously as supporting material in fungal lipid production [30, 31]. Herein, the whey was utilized as a cultivation media without any addition of other nutrient sources. It facilitated the lipid accumulation yield in both filamentous fungi by 3.22 and 4.33 g/L, which representing 45.3 and 48.2% lipid content in Drechslera sp. and Alternaria sp., respectively. In comparison, the lipid content of Drechslera sp. and Alternaria sp. reached to 33.18 and 29%, with lipid yield evaluated by 3.65 and 5.6 g/L, respectively, upon replacing carbon source in their optimized media with agricultural wastes (i.e., orange peel and molasses). However, on optimized microbiological media (i.e., Czapek-Dox’s medium), their lipid contents were assessed by 40.75 and 50.3% for Drechslera sp. and Alternaria sp., respectively, reflecting the higher potentiality of whey stream as the main cultivation and production media in the lieu of carbon or nitrogen source [20, 21].
The characterstic features of SCOs from both oleaginous fungi were determined initially by identifying their components through investigating the fatty acid methyl esters profiles after acidic transesterification as shown in (Fig. 1) and (Table 1), which manifested the qualitative and quantitative differences in both examined profiles. Notably, the results of gas chromatography–mass spectrometry (GC-MS) showed common features among both profiles. Wherein, unsaturated fatty acids (USFAs) represented the dominate constituent in the profiles of both fungi by 62.18 and 53.15% for Alternaria sp. and Drechslera sp. respectively. Meanwhile, Palmitic acid-C16 (SFAs) was the most prevalent FA by 29.0 and 28.9% for Alternaria sp. and Drechslera sp. respectively, followed by Oleic acid -C18 (MUSFAs), which was evaluated by 24.7 and 28.8%, % for Alternaria sp. and Drechslera sp., correspondingly. On the other hand, the omega-6 (ɷ-6) poly unsaturated fatty acid (PUFAs) (i.e., Linolenic acid and γ-linolenic (C18) was the third major constituent in both profiles with the values of 26.89% and 15.72% for Alternaria sp. and Drechslera sp. respectively. Interestingly, an obvious percent of PUFAs (30.02%) was detected for Alternaria sp. compared to 17.93% for Drechslera sp. In accordance with our result, Lauryn et al. [32] demonstrated that cheese whey was ideal choice for Mucor circinelloides in SCOs production with GC-MS-profile contained predominantly oleic acid (41%), palmitic acid (23%), linoleic acid (11%), and γ-linolenic acid (9%).
Fig. 1GC-Ms analysis of fatty acid produced by (A) Alternaria sp. B Drechslera sp. cultivated on wheyTable 1Showing fatty acids (FAs) patterns of both SCOs extracted from Alternaria sp.and Drechslera sp.after their cultivation on whey mediaFatty acidsRetention timeSCOs of Alternaria sp.A-OSSCOs of Drechslera sp.D-OSCaprylic acid (C8)7.0070.880.23Myristoleic acid (C14)16.8380.520.43Myristic acid (C14)17.4530.380.55Palmitoleic acid (C16)21.2334.494.36Palmitic (C16)22.00929.0128.89Heptadecenoic24.2990.340.19Gama-linolenic (C18)26.1444.220.68Linolenic (C18)26.30922.6715.04Oleic (C18)26.14424.7828.86Elaidic (C18)26.3501.710.21Stearic (C18)26.5553.5113.31Cis-5,8,11,14,17-Eicosapentaenoic (C20)31.3150.630.40Cis-8,11,14-Eicosatrienoic (C20)31.7980.650.15Cis-11,14-Eicosadienoic (C20)32.1660.610.18Cis-11-Eicosenoic (C20)32.30400.17Cis-11,14,17-Eicosatrienoic (C20)32.4070.60.14Arachidic (C20)33.1270.480.27Heneicosanoic (C21)36.5130.430.11Cis-4,7,10,13,16,19-Docosahexaenoic (C22)37.7780.641.21Cis-13,16-Docosadienoic (C22)38.74500.14Erucic (C22)38.75700.79Behenoic (C22)39.5210.940.99Tricosanoic (C23)42.0820.620.54Nervonic (C24)43.9050.670.40Lignoceric (C24)44.4291.231.77Fatty acid typeTotal % of FAsSFAs37.8246.85USFAs62.1853.15MUFAs32.1735.22PUFAs30.0217.93ɷ-31.871.75ɷ-628.1516.18
The acquisition of infrared spectra of lipids has been attained senior concern thank to providing opulent information on their chemical components, besides it is a fast and economical technique [33]. Fig. 2 demonstrates the FTIR spectrum of both examined SCOs. Generally, the peaks at wavenumbers of 3742 and 3359 cm−1 of A-OS and 3200 cm−1 of D-OS indicated the existence of hydrate hydroxyl group (-OH). Similarly, as signposted by Nandiyanto et al. [34], a broad absorption band in the range of 3650 and 3250 cm−1 represented the sign of hydrogen bond. Besides, the band at 3010 cm−1 in both SCOs samples pointed out to the C = CH- vibration originated from unsaturated fatty acids; interestingly, such band could be used for examining the degree of unsaturation in oils as referred by Shapaval et al. [35]. In the same sense, Nandiyanto et al. [34] stated that the bands above 3000 cm−1 are representative of unsaturated compounds. Accordingly, this could reflect the superior unsaturation of fatty acids in Alternaria sp. than that observed in Drechslera sp., as more peaks were detected in the area above 3000 cm−1, which harmonized with results of GC-MS that confirmed the higher unsaturation degree in A-OS comparing to D-OS.
Fig. 2FTIR spectrum of oil samples from (A) Alternaria sp. (B) Drechslera sp
Notably, the spectral bands at 2889 cm−1 detected in D-OS characterizes the C-H stretching vibrations of lipids [36]. Meanwhile, Nandiyanto et al. [34] mentioned that the double bond groups such as carbonyl (C = C) were present in the region of 1500–2000 cm−1. So, remarkably, both fungal patterns confirmed the presence of carbonyl (C = C) peaks as detected at 1994 and 1648 cm−1 of A-OS and 1662 cm−1 of D-OS. While the presence of spectral band at that regions reflected the presence of crystallizable FAs [33]. However, the signature of CH2 asymmetric bending and CH2 vibration could be detected by the presence of peaks at wave numbers of 1403 and 1435 cm−1 [33, 37]. Whereas, typical bands at 1252, 1114, 1050, 986 and 948 cm−1 of A-OS-FTIR pattern and also 1273, 1096 and 993 cm−1 of D-OS FTIR pattern referred to C-O stretching of phospholipids [38–40]. Regarding to the spectral bands at 718 and 611 cm−1 of D-OS and A-OS profiles, respectively, could be attribute to alkyne C-H bend and CH2 rocking vibration, respectively [34, 41]. Likewise, Forfang et al. [42] elucidated that biological material (e.g., carbohydrates, proteins and lipids) exhibits CH stretching vibrations due to the presence of -CH3 and -CH2. Arguably, based on the above mentioned characterization techniques, the results of FTIR were deemed as strong evidence for the presence of functional groups that were related to intracellular lipids, which were detected and quantified by GC-MS analysis; emphasizing hereby the obvious discrepancies between SCOs of Alternaria sp. and Drechslera sp. in the content and structures.
Antibiofilm activity of SCOs
The presence of biofilm represents a serious threat to human health and surrounding ecosystem in various sectors. The biofilms injure medical equipment such as catheters, contact lenses, prosthetic devices, heart pacemakers, endoscopes, colonoscopes, dental plaques and dental irrigation units. Let alone their capability to invade human tissues causing severe infections [43]. However, the biofilms also adhere to food manufacturing equipment, air-conditioning units, petroleum pipelines and cooling towers, which symbolizes as evidences on industrial risk of biofilm. In the same sense, biofilms fixed themselves on external surfaces of marine vessels, water pipes, stones in a stream and sewage treatment plants, facilitating the accumulation of organic and inorganic materials with other organism such as algae, plants and protozoa in a phenomenon called biofouling. The problem of biofouling lies behind deteriorating the aqueous flow system with its fauna; causing the prevalence of microbial contamination and accelerated corrosion.
Against this backdrop, various mechanical removal approaches and chemical biocides were utilized to eliminate biofilms and prevent their hazard [44]. Nonetheless, the extensive use of antimicrobial agents generates multidrug resistance (MDR) phenomenon that led to ecological balance disruption and epidemic diseases. Therefore, modern insights are directed toward employing natural bioproducts as ecofriendly, biocompatible, safe and economic agents in defeating water/foodborne pathogens, which harmonized with aims of recent international events like COP28. Hence, the current investigation is undertaken to determine the in vitro antibiofilm activity of SCOs extracted from Alternaria sp. and Drechslera sp. against some MDR microbes-forming biofilm. This target was implemented through detecting the effect of SCOs on biofilm formation, viability, biochemical composition and hydrophobicity.
In fact, S. aureus, P. aeruginosa and C. albicans were opted due to their ubiquitous occurrence and concomitance with nosocomial/community-acquired infections. Besides, they exhibit the capability to colonize vast array of surfaces either abiotic or cellular interfaces, which lead to significant environmental and health threats. Therefore, crystal violet assay (CV) was employed to detect the antibiofilm influence of different doses of SCOs (1–100 µg/mL), which deemed as reliable and facile assay in staining the biofilm biomass entirely [45, 46]. As observed in Figs. (3 and 4), the inhibitory patterns of both SCOs samples displayed significant differences (P ≤ 0.05) in the biofilm development after treatment, as unveiled by ANOVA. Besides, the inhibitory power of both samples showed notoriously variation against examined pathogens. Namely, SCOs of Alternaria sp. (A-OS) suppressed the growth of P. aeruginosa biofilm at concentrations ranged from 1 µg /mL to 10 µg/mL by 7.01 ± 0.3% to 50.85 ± 2.68%, respectively; while SCOs of Drechslera sp. (D-OS) enhanced the growth of P. aeruginosa biofilm by 33.41 ± 6.24% and 5.83 ± 2.83% at exact concentrations, respectively. Remarkably, the antibiofilm potency increased with elevation of applied doses of both examined oil samples. Wherein, the biofilm of P. aeruginosa was inhibited significantly (P ≤ 0.05) at 100 µg/mL of A-OS and D-OS by 84.10 ± 0.445 and 47.41 ± 2.83%, correspondingly. On the other hand, about 90.37 ± 0.065% and 62.63 ± 5.82% inhibition was observed for S. aureus biofilm at 100 µg/mL of both SCOs in the same order. Furthermore, a pronounced and significant (P ≤ 0.05) fungicidal potency was noticed in blocking the biofilm formation of C. albicans that reached to 94.96 ± 0.21% and 78.67 ± 0.23% upon treating with A-OS and D-OS (100 µg/mL), respectively. Generally, as inferred from these results, there is an inter-species variation phenomenon in a dose dependent performance exerted by the examined SCOs samples. In agreement with our results, Murugan et al. [47], found a variation in the biofilm growth of Proteus sp., E. coli, Bacillus sp. and S. aureus; assigning that to the differences in the physiological behavior of different microbial species. Seemingly, the cell wall architecture, microbial physiology with varied metabolic performance and uptake/regulation systems are considered being the fundamental parameters in managing the tolerance and susceptibility profiles among inter and intra-species of the microbes in their response to any antagonistic agent [48].Fig. 3The Impact of A-OS on biofilm development by P. aeruginosa, S. aureus and C. albicans. A-Biofilm biomass suppression, B-Metabolic performance, C- EPS inhibition, D-Protein inhibition and E-Hydrophobicity inhibition. All values were expressed as mean ± SEM. Treatments at various doses were comparing to untreated control with significance at *P ≤ 0.05Fig. 4The Impact of D-OS on biofilm development by P. aeruginosa, S. aureus and C. albicans.
A-Biofilm biomass suppression, B-Metabolic performance, C- EPS inhibition, D-Protein inhibition and E-Hydrophobicity inhibition. All values were expressed as mean ± SEM. Treatments at various doses were comparing to untreated control with significance at *P ≤ 0.05
Effect of SCOs samples on biofilm metabolic activity
Actually, CV firmly stains the entire biofilm biomass, which includes polysaccharides in the mucilaginous mat conjugated with other biomolecules that are disseminated in an even manner on the live as well as dead cells surface. Subsequently, the overall metabolic performance of adhered microbial cells, which were treated with different concentrations of oil samples relative to untreated, was assessed calorimetrically using MTT assay. It is worth noting that tetrazolium salts (such as MTT (3-[4, 5- dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide), XTT (2,3-bis(2-methoxy-4-nitro-5-sulfo-phenyl)- 2 H-tetrazolium-5-carboxanilide) and TCC (2,3,5- triphenyl tetrazolium chloride) are frequently utilized in biological assays to investigate the viability of living cells. That occurs through enzymatic reduction of tetrazolium salt by the cellular NADH of metabolically active cells, which leads to the formation of colored formazan. Hence, different tetrazolium-based dyes were employed in various studies to determine biofilm viability in accompanying with other means like CV approach and microscale analysis [45, 46, 49].
Interestingly, both types of oil samples impacted on the viability of cells in the biofilm structure adversely, progressively and significantly (P ≤ 0.05) with increasing the doses of applied SCOs. Figs. (3 and 4) indicate that A-OS frustrated the propagation of active cell in P. aeruginosa biofilm matrix in all applied concentrations in the range of 2.35 ± 0.885% − 83.08 ± 0.235%. Conversely, D-OS flourished the growth of P. aeruginosa biofilm at low concentrations (1 µg/mL − 5 µg/mL) by the range of 16.72 ± 1.36% − 4.43 ± 1.36%; whereas, the viability of live cells curtailed to 48.12 ± 3.0% upon increasing the concentration to 100 µg/mL. Regarding to the biofilm of S. aureus, both oil samples inhibited the growth of active cells in linear concentration-dependent behavior; reaching to the maximum suppression by 85.9 ± 0.375 and 62.48 ± 1.52% for A-OS and D-OS, respectively at 100 µg/mL. In the same sense, the survival of C. albicans cells in biofilm network arrested by 86.54 ± 0.86% and 64.94 ± 1.48% under the treatment of A-OS and D-OS (100 µg/mL), respectively. As noticed, the results of biofilm inhibition were harmonized with that of metabolic activity. It is plausible to mention that viability and metabolic activity of all tested biofilm-forming pathogens correlated significantly with biofilm biomass (r ≥ 0.9, P = 0.00); reflecting hindrance impact of oil samples on active cells that are distributed within multilayer architecture of biofilm.
The effect of oil samples on biofilm’s carbohydrate and protein content
Carbohydrates or exopolysaccharides (ePs) and proteins represent the intrinsic constituents of EPS scaffold of the biofilm from both structure and function. As denoted by Gunn et al. [50] and Mosharaf et al. [51], the secreted proteins, adhesion proteins (e.g., lectins, Baplike proteins) and motility organelles configure the biofilm matrix proteins. However, galactose, mannose, glucose, arabinose, xylose, rhamnose, fucose, cellulose nanofibers, galacturonic acid and N-acetyl-glucosamine are the most abundant carbohydrates detected in slimy matrix of S. aureus, Enterococcus faecalis, Klebsiella pneumoniae, and P. aeruginosa [52]. As declared by Gunn et al. [50] the chemical constituents of the biofilm differ rely on the organism and was influenced by environmental parameters. Intriguingly, such specific components control biofilm integrity, maintain biofilm stability, configure its morphology, mediate cell-cell signaling, contribute in cell colonization/adherence and preserve its cells from adverse external stressors [23, 51].
Figure (3) depicts the effect of A-OS on biofilm content of carbohydrates or ePS, which diminished from 7.515 ± 0.13, 5.541 ± 0.147 and 6.59 ± 0.33 mg/mL in control untreated samples of S. aureus, P. aeruginosa, and C. albicans biofilms to 3.61 ± 0.34, 3.14 ± 0.335 and 2.99 ± 0.19 mg/mL at 10 ug/mL of A-OS, respectively; representing by such way inhibition percentages of 51.87 ± 4.55, 43.26 ± 5.9, and 54.5 ± 2.84%, respectively. However, the same concentration of D-OS reduced carbohydrate content of S. aureus, P. aeruginosa, and C. albicans biofilms to 5.100 ± 0.47, 4.98 ± 0.22, and 4.20 ± 0.335 mg/mL by 32.09 ± 0.536, 9.94 ± 4.04, and 36.23 ± 5.04% inhibition percentages, respectively (Fig. 4). In the same context, 10 ug/mL of A-OS reduced protein content of S. aureus, P. aeruginosa and C. albicans biofilms from 9.64 ± 0.095, 9.75 ± 0.065 and 9.34 ± 0.1 mg/mL in the control untreated samples to 5.92 ± 0.11, 6.94 ± 0.17 and 4.72 ± 0.16 mg/mL, which symbolize 38.54 ± 0.94, 33.47 ± 1.74 and 49.43 ± 1.69% inhibition percentages, respectively. Whereas, D-OS (10 ug/mL) diminished protein content to 7.44 ± 0.255, 8.40 ± 0.065 and 5.61 ± 0.37 mg/mL causing 22.81 ± 2.63, 13.84 ± 0.59 and 39.92 ± 3.89% inhibition.
Notably, significant and dramatic changes were observed in both contents upon elevating the concentrations till reached to the highest values at 100 ug/mL. Wherein, such concentration of A-OS caused in lowering the carbohydrate/ePS content of S. aureus, P. aeruginosa and C. albicans biofilms by 76.54 ± 7.04, 66.81 ± 2.51 and 80.54 ± 1.77%, respectively. While the carbohydrate/ePS reduction percentage reached 60.22 ± 2.02, 50.03 ± 1.95 and 64.26 ± 4.7% by D-OS for S. aureus, P. aeruginosa and C. albicans biofilms, respectively. Regarding the protein content, it lessened significantly to 63.94 ± 3.89, 57.27 ± 5.43 and 74.87 ± 2.49% upon applying 100 ug/mL of A-OS; however, D-OS (100 ug/mL) reduced it to 49.39 ± 1.96, 47.61 ± 2.03 and 59.07 ± 1.78% for S. aureus, P. aeruginosa and C. albicans biofilms, respectively.
Effect of oil samples on biofilm hydrophobicity
The adhesion capability of microbial cell deemed as intrinsic property to colonize any substrate and boost biofilm lifestyle easily. Interestingly, cell surface traits in the formed of ePS and hydrophobicity are decisive parameters that manage the entire adhesion process. In addition, surface characteristics and ambient environmental conditions rule the adhesion of the cells with the surface through number of interactions like hydrophobic, van der Waals and electrostatic. Remarkably, the cells with higher biofilm forming capacity possess higher hydrophobic nature that leads to potent adhesion and vice versa [53]. Given that the biofilm-forming cells with hydrophobic characteristics exhibit affinity to hydrocarbons (e.g., hexadecane, octene, xylene, etc.), the cells retained in the organic phase; generating low turbidity of aqueous phase and by such method (i.e., MATH), the hydrophobicity nature of the biofilm is detected [54].
In the current study, the hydrophobicity index (HI) recorded 65.72 ± 2.3, 51.61 ± 1.12 and 59.12 ± 5.43% for P. aeruginosa, S. aureus and C. albicans respectively; denoting a higher hydrophobicity property of P. aeruginosa than that exhibited by S. aureus and C. albicans. Upon applying different concentrations (10–100 µg/mL) of both oil, a noticeable reduction in HI was shown (Figs. 3 and 4); implying progressively transition to hydrophilicity state, which reached to the maximum at the highest applied doses. Wherein, A-OS (10 µg/mL) lessened hydrophobicity to 64.18 ± 4.23, 49.18 ± 1.73 and 58.62 ± 6.94% for P. aeruginosa, S. aureus and C. albicans, by 1.54 ± 4.23, 2.43 ± 1.73 and 0.498 ± 6.91% inhibition percentage in the respective order. However, A-OS (100 µg/mL) switched S. aureus and C. albicans biofilms to weak hydrophobicity state (i.e., became hydrophilic) by recording 18.5 ± 1.22 and 22.18 ± 0.94% HI, respectively; implementing 33.1 ± 1.22 and 36.94 ± 0.94% inhibition. While, the HI of P. aeruginosa biofilm altered to be moderate by recording 41.32 ± 1.65% and inhibition percentage recorded 24.04 ± 1.65%. On the other hand, D-OS (10 and 20 µg/mL) insignificantly enhanced the hydrophobicity of P. aeruginosa biofilm by 0.046 ± 2.55 and 0.032 ± 2.364%, respectively. Whereas, at 100 µg/mL inhibited its hydrophobicity by 15.96 ± 0.74%; maintaining HI in the potent region by recording 49.78 ± 0.74%. Regarding S. aureus and C. albicans biofilms, D-OS promoted their hydrophilic affinity in a dose-dependent behavior, reaching to the maximum at 100 µg/mL by recording 25.34 ± 0.422 and 37.89 ± 0.826%, correspondingly, which all remained in the moderate phase of hydrophobicity. Generally, albeit distinct structural variation in cells surface among the examined strains in our study, both oil samples substantiated their efficacy in influencing on hydrophobicity adversely. It is important to highlight that hydrophobicity reflects the microbial attachment or adhesiveness ability, which varies even from strain to strain and influenced by microbial age, microbial surface charge and growth medium [55]. In study performed by Kim et al. [56], 10 µg/ml of antibiofilm FAs (e.g., tricosanoic acids, palmitoleic, myristoleic acid, lauric acid, stearic, heptadecanoic and α-linolenic) reversed the biofilm of Cutibacterium acnes from hydrophobic region to hydrophilic region (hydrophobic index < 20%) simultaneously with biofilm inhibition, which agreed with our results.
Intriguingly, the results of the current investigation declared the existence of significant positive correlation between all examined variables (i.e., inhibition of biofilm, protein, ePS, viability and hydrophobicity) with SCOs concentrations, as signified by Pearson’s correlation coefficients (Table 2) (Figs. 5 and 6). Wherein, oil samples influenced negatively on the biofilm development through modulating microbial-surface interactions, in particular hydrophobic interactions through impacting on surface-associated exopolysaccharides and proteins. In consistent with our results, Pompilio et al. [57] attributed the higher hydrophobicity of Stenotrophomonas maltophilia biofilm to its higher exopolysaccharides content, which was positively correlated with biofilm development. Also, Mu et al. [58] manifested and explained the same finding in S. epidermidis biofilm. Otherwise, several reports documented the independence of biofilm formation on hydrophobicity [53, 59]. Nonetheless, there is a consensus among all studies regarding that the cell surface properties and overall physiological properties of microbes govern the process of biofilm development and maturation. Noteworthy mention that the hydrophobicity is fostered by the action of microbial appendages (e.g., pilli, fimbriae, fibrils, etc.) that scattered on the cell surface. Such organelles contain hydrophobic amino acid residues that expedite noncovalent attachment of the cells on any substratum [54, 55]. However, exopolysaccharides facilitate irreversible adhesion and sheltering the developed cells within the backbone of biofilm [23, 52].
Table 2Representing the correlation between biofilm inhibition with other studied factors (i.e., viability, ePS, protein and hydrophobicity) by the action of SCOsBiofilm typeExamined parameterPearson’s correlation coefficients (R2)(P-value)SCOs-1SCOs-2BiofilmViabilityePSProteinBiofilmViabilityePSProteinP. aeruginosaViability0.983 (0.00)0.961(0.00)ePS0.954 (0.00)0.985(0.00)0.941(0.00)0.971(0.00)Protein0.983 (0.00)0.968(0.00)0.943(0.00)0.934 (0.001)0.95(0.00)0.987(0.00)hydrophobicity0.926 (0.001)0.942(0.00)0.928 (0.001)0.911 (0.002)0.928 (0.001)0.942(0.00)0.984(0.00)0.986(0.00)S. aureusViability0.981 (0.00)0.976(0.00)ePS0.97 (0.00)0.984(0.00)0.986(0.00)0.982(0.00)Protein0.979 (0.00)0.975(0.00)0.941(0.00)0.984(0.00)0.982(0.00)0.992(0.00)hydrophobicity0.932 (0.001)0.968(0.00)0.931 (0.001)0.951(0.00)0.947(0.00)0.977(0.00)0.97(0.00)0.973(0.00)C. albicansViability0.975 (0.00)0.958(0.00)ePS0.984 (0.00)0.984(0.00)0.969(0.00)0.973(0.00)Protein0.937 (0.001)0.965(0.00)0.965(0.00)0.964(0.00)0.933 (0.001)0.986(0.00)hydrophobicity0.879 (0.004)0.932 (0.001)0.886(0.03)0.88(0.004)0.891 (0.003)0.971(0.00)0.95(0.00)0.9(0.002)Fig. 5Contour plot showing the correlation of biofilm suppression by A-OS versus EPS inhibition (left panel) and protein inhibition (right panel) with cell surface hydrophobicity. The diagram was plotted by Minitab 14 software. Different colors elucidate different levels of biofilm suppression. A & B - P. aeruginosa, C & D- S. aureus and E & F- C. albicansFig. 6Contour plot showing the correlation of biofilm suppression by D-OS versus EPS inhibition (left panel) and protein inhibition (right panel) with cell surface hydrophobicity. The diagram was plotted by Minitab 14 software. Different colors elucidate different levels of biofilm suppression. A & D - P. aeruginosa, B & E- S. aureus and C & F- C. albicans
Based on the previous results, it is conspicuous that the antibiofilm potency of SCOs samples appeared more evident against S. aureus. That could be attributed to its physiological and metabolic sensitivity, besides the nature and architecture of its cell wall, which seemed to contribute intrinsically in its susceptibility. Namely, the hydrophilic nature of gram-positive bacteria’s cell wall with their low content of lipids (1–4%) trigger the adsorption and penetration processes of exogenous materials interiorly easier. In contrast, the more complex structure of gram-negative bacterial cell wall with its abundant hydrophobic moieties (11–22% lipid content) serves as potent entry barrier toward detergents and hydrophobic molecules, hindering by such way the internal transportation of SCOs [60]. Strikingly, the superior biocidal potency of fatty acids, especially those of longer chain FAs (i.e., ≥ C12) against gram-positive bacteria such as B. subtilis, Micrococcus luteus, Propionibacterium acnes, Listeria monocytogenes and Clostridium difficile was reported tremendously by several research groups [56, 61–65], which coincident with our results. Additionally, Shukla et al. [62] reported that gram-negative bacteria displayed more resistance to medium- and long-chain FAs than gram-positive bacteria, which also agreed with our results; however, gram-negative bacteria were more susceptible to FAs with C6 or less in their chain. In the same context, [60] opined that yeast cells are more susceptible to medium chain-FA (i.e., C8-C12). Otherwise, [63, 66–68] stated the mycocidal potency of long chain-FA. Herein, both SCOs samples of the present study exhibited promising fungicidal performance versus C. albicans, which agreed that found by previously mentioned studies. Hereby, both oil samples could be harnessed in thwarting candidiasis in immunocompromised patients following secondary pneumonia infections conjugated with COVID-19.
Arguably, the biofilm development by P. aeruginosa, S. aureus and C. albicans was dose-dependently impeded by both oil samples, which could be ascribed to the intensification of detergent traits of FAs on account of their amphipathic nature, which could be bacteriostatic at lower concentrations and bactericidal at higher concentrations [56, 69, 70]. Subsequently, it is imperative to shed the light about the antagonistic strategy followed by oil against examined pathogens. However, as referred by Cepas et al. [66] and Kumar et al. [71] the precise biocidal strategy followed by FAs is obscure and undefined. Wherein, FAs frustrate microbial growth and biofilm development through violating multiple cellular targets nonspecifically, which seems being characteristic and should be invested in enfeebling drug resistance phenomena (MDR) [72].
The detrimental effects of FAs against treated biofilm commences with the cell membranes, which considered being the prime target in the multi-step process of biofilm formation. Wherein, FAs incorporated on the hydrophobic moieties of cell phospholipid layer causing membrane solubilization, integrity destabilization and apertures creation, either transient or permanent. That in turn elevated cell permeability level and cellular leakage. Besides, FAs subvert the nutrient uptake system, electron transport chain and oxidative phosphorylation which are the chief membrane-located processes. Additional presumptive scenario could be exerted by oil in the context of membrane damage is suppressing the functionality of membrane-associated proteins, which mediate fatty acid biosynthesis in the plasma membrane. Remarkably, Firoozabad et al. [73] demonstrated that the destructive effect of FAs on S. aureus biofilm was triggered through the inactivation of enzyme that is responsible for the fatty acid elongation step, which is enoyl-acyl carrier protein reductase (Fabl). On the other hand, Cepas and coauthors [66] assigned the inhibition of P. aeruginosa and C. albicans biofilms to the inhibition of Fabl by the action of FAs. Similarly, Lee et al. [67] attributed the anticandida potency of FAs to the hindrance of ergosterol production, which regulates fungal membrane synthesis and reinforces its structural. On the other hand, the treatment with FAs could enhance autolysis process by inducing autolytic enzyme in lieu of massive solubilization of the cell membrane by the surfactant-like action of FAs as suggested by Tsuchido et al. [74].
Interestingly, Bintari and Risandiansyah [75] scrutinized in silico the effect of FAs extracted from Cladophora sp. in dampening the activity of peptide deformylase in P. aeruginosa, E. faecium, E. coli, and S. aureus, which catalyzes protein maturation process. Meanwhile, FAs impair ePS production, fimbriae/pili formation and hydrophobicity, which thereafter frustrate motility and adhesion to substrates; ultimately block the microbial colonization and ruin irreversible aggregation and attachment. The finding by research groups of Kim et al. [56], Nicol et al. [76] and Kim et al. [77] supported these deleterious impacts. On genetic level, FAs drastically repressed DNA supercoiling/replication and down-regulated different genes such as hla, HWP1, CHT4, csgAB, fimH, flhD, motB, luxRS and NorA, which encode proteins responsible for alpha-hemolysin production, hyphal development, chitinase production, fimbriae synthesis, motility, quorum-sensing and efflux pump [63, 71, 77]. Additionally, anti-quorum sensing activity was detected for FAs in quenching the communication signaling and quorum sensing system among biofilm cells and with external environment [66, 76]. As referred by Nicol et al. [76], FAs inhibited the biofilm of Acinetobacter baumannii up to 38% through reducing quorum sensing regulator AbaR, which influenced adversely on communication signals among biofilm cells.
Taken together, the overall data revealed the superior antibiofilm potentiality of A-OS in defeating all examined types of biofilm-forming pathogens. That could be attributed to the profusion of USFAs (62.18%) comparing to D-OS, which contained 53.15% (Table 1-Fig. 1). Our finding is in line with the majority of the previously published investigations who declared the lower activity or bacteriostatic performance of SFAs [69, 78]. While USFAs with the same length carbon backbone exhibit influential potency in deteriorating biofilm development and obstructing the viability of its cells. Such potency of USFAs could be ascribed to their instability, propensity to oxidation and binding non-specifically to target sites like proteins [69, 78]. Otherwise, Khalilova et al. [79] demonstrated the remarkable antibacterial and antifungal properties of SFAs relative to USFAs, especially capric C10:0, lauric C12:0 and palmitic C16:0 acids.
Comparisons among studies are entangled and controversial due to the differences in the applied FAs, the treated microbe or even the altogether treatment process [69]. Notably, the nature of FAs (i.e., acid or its derivatives such as methyl, ester form, etc.), the applied dose and their solitary or combined case were deemed as the substantial features concerning FAs entity. However, the microbial factor includes microbial species types, isolation source, microbial cell load, microbial phase (i.e., planktonic or aggregated biomass), microbial metabolic activity and microbial physiology (i.e., sensitive or persister), which are undoubtedly differ among microbial genera till strain level. While the contact time, pH value and ionic strength of treatment milieu represent other influential treatment circumstances. All these reasons collectively could definitely influence on biocidal activity of applied FAs to different extends [60, 80]. Nevertheless, the molecular structure and shape of FAs remain the driving force that control the magnitude toxicity of FAs. Precisely, the carbon chain length, number of unsaturation and the geometric configuration around the double bond are the conclusive parameters that correlated proportionally to FAs antagonistic potentiality. As stated by McGaw et al. [60], Desbois and Smith [69] and Kim et al. [56], FAs that possess more carbon atoms (i.e. C16, C18, etc.) in their carbon backbone are more potent antimicrobial agents than those contained 10 or 12 carbons in their chain. Moreover, USFAs, properly PUFAs followed by MUFAs, manifested characteristic biocidal functionalities than SFAs [69]. In this regard, Feldlaufer et al. [78] found that palmitic acid (C16) didn’t show any inhibitory action against pathogens comparing to its unsaturated counterpart (i.e., palmitoleic acid (C16:1); confirming that FAs with 3, 4 or 6 double bonds display more antimicrobial activity. Strikingly, FAs stereochemistry also deemed as limiting factor, namely, the cis-isomer is highly active relative to trans configuration [60, 78]. That could be ascribed to the resemblance in the structure between SFAs and trans orientation USFAs [69]. In this sense, Feldlaufer et al. [78] documented that the introduction of double/multiple bonds, especially with FAs more than 14 carbon atoms in cis- orientation, plays imperative role in elevating antibiotic-like behavior. On the other hand, McGaw et al. [60] reported that SFAs with lower-chain length, MUFA and PUFAs with longer-chain structure exhibit considerable antimicrobial activity.
Remarkably, as indicated by the chromatographic profile (Fig. 1; Table 1), A-OS contained ample amount of omega (ω) fatty acids such as ω-5 (myristoleic acid, C14:1, 0.52%), ω-7 (palmitoleic acid, C16:1, 4.49%) and ω-9 (oleic acid C18:1, 24.78%). However, the predominance of ω-3 (cis-5,8,11,14,17-eicosapentaenoic, C20:5, 0.65%; Cis-4,7,10,13,16,19-docosahexaenoic, C22:6, 0.64% and Cis-11,14,17-Eicosatrienoic C20:3 0.60% ) and ω-6 (linolenic acid, C18:3, 22.67% and γ-linolenic acid, C18:3, 4.22%) contributed intensively in its robust effectiveness. It is worthy mention that ω-3 and ω-6 are frequently utilized as alternative food additives and therapeutic agents to maintain body homeostasis and enhancing the immune system, by the virtue of their functional values in lipid metabolism, antioxidant signaling pathway regulation and inflammatory processes modulation. Besides, their role in diabetes type two, ulcerative colitis, cardiovascular disease, Crohn’s disease, hypertension, alzheimer’s disease and cancer therapy couldn’t be neglected [62, 68, 73]. Meanwhile, their potency in defeating various microbial genera as antibacterial, antifungal or antibiofilm was also determined in various investigations [68, 79].
In the study conducted by Cepas et al. [66], the authors detected the inhibitory limit of linolenic acid by 1, 32 and 64 mg/mL against C. albicans, S. aureus and P. aeruginosa, respectively. Also, he and coauthors found that more than 250 mg/mL of stearic acid, γ-linolenic acid, arachidonic acid and palmitoleic acids were required to utterly block the growth of E. coli. While, 0.02 mg/mL of palmitoleic acids inhibited the biofilm of A. baumannii by 38% [76]. Comparing our results with previous studies and others, reflects the advantageous and promising properties of our SCOs that dampened biofilm activity, their cell viability, biochemical constituent and their hydrophobicity in sensible doses. That could be assigned to the synergistic effect mediated by the combination of SFAs, MUFAs and PUFAs collection. Strikingly, the naturally derived lipids from herbal and microbial sources share this common phenomenon. Namely, the presence of mixture of FAs that compete mutually on more than target site could implement their hostile functionality in prominent way. The results of the present research concurred with that reported by Dia and Jacoeb [81], Shukla et al. [62], Balkrishna et al. [72] and Panjaitan et al. [80]. In the context of synergistic activity, several reports accentuated the uplifting of antibiotic-like performance of FAs upon conjugating several FA together or supplementing with antibiotics, bacteriophages, organic compounds, enzymes and edible plant essential oils [63, 82–86].
Effect of SCOs samples on biofilm metabolic activity
Actually, CV firmly stains the entire biofilm biomass, which includes polysaccharides in the mucilaginous mat conjugated with other biomolecules that are disseminated in an even manner on the live as well as dead cells surface. Subsequently, the overall metabolic performance of adhered microbial cells, which were treated with different concentrations of oil samples relative to untreated, was assessed calorimetrically using MTT assay. It is worth noting that tetrazolium salts (such as MTT (3-[4, 5- dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide), XTT (2,3-bis(2-methoxy-4-nitro-5-sulfo-phenyl)- 2 H-tetrazolium-5-carboxanilide) and TCC (2,3,5- triphenyl tetrazolium chloride) are frequently utilized in biological assays to investigate the viability of living cells. That occurs through enzymatic reduction of tetrazolium salt by the cellular NADH of metabolically active cells, which leads to the formation of colored formazan. Hence, different tetrazolium-based dyes were employed in various studies to determine biofilm viability in accompanying with other means like CV approach and microscale analysis [45, 46, 49].
Interestingly, both types of oil samples impacted on the viability of cells in the biofilm structure adversely, progressively and significantly (P ≤ 0.05) with increasing the doses of applied SCOs. Figs. (3 and 4) indicate that A-OS frustrated the propagation of active cell in P. aeruginosa biofilm matrix in all applied concentrations in the range of 2.35 ± 0.885% − 83.08 ± 0.235%. Conversely, D-OS flourished the growth of P. aeruginosa biofilm at low concentrations (1 µg/mL − 5 µg/mL) by the range of 16.72 ± 1.36% − 4.43 ± 1.36%; whereas, the viability of live cells curtailed to 48.12 ± 3.0% upon increasing the concentration to 100 µg/mL. Regarding to the biofilm of S. aureus, both oil samples inhibited the growth of active cells in linear concentration-dependent behavior; reaching to the maximum suppression by 85.9 ± 0.375 and 62.48 ± 1.52% for A-OS and D-OS, respectively at 100 µg/mL. In the same sense, the survival of C. albicans cells in biofilm network arrested by 86.54 ± 0.86% and 64.94 ± 1.48% under the treatment of A-OS and D-OS (100 µg/mL), respectively. As noticed, the results of biofilm inhibition were harmonized with that of metabolic activity. It is plausible to mention that viability and metabolic activity of all tested biofilm-forming pathogens correlated significantly with biofilm biomass (r ≥ 0.9, P = 0.00); reflecting hindrance impact of oil samples on active cells that are distributed within multilayer architecture of biofilm.
The effect of oil samples on biofilm’s carbohydrate and protein content
Carbohydrates or exopolysaccharides (ePs) and proteins represent the intrinsic constituents of EPS scaffold of the biofilm from both structure and function. As denoted by Gunn et al. [50] and Mosharaf et al. [51], the secreted proteins, adhesion proteins (e.g., lectins, Baplike proteins) and motility organelles configure the biofilm matrix proteins. However, galactose, mannose, glucose, arabinose, xylose, rhamnose, fucose, cellulose nanofibers, galacturonic acid and N-acetyl-glucosamine are the most abundant carbohydrates detected in slimy matrix of S. aureus, Enterococcus faecalis, Klebsiella pneumoniae, and P. aeruginosa [52]. As declared by Gunn et al. [50] the chemical constituents of the biofilm differ rely on the organism and was influenced by environmental parameters. Intriguingly, such specific components control biofilm integrity, maintain biofilm stability, configure its morphology, mediate cell-cell signaling, contribute in cell colonization/adherence and preserve its cells from adverse external stressors [23, 51].
Figure (3) depicts the effect of A-OS on biofilm content of carbohydrates or ePS, which diminished from 7.515 ± 0.13, 5.541 ± 0.147 and 6.59 ± 0.33 mg/mL in control untreated samples of S. aureus, P. aeruginosa, and C. albicans biofilms to 3.61 ± 0.34, 3.14 ± 0.335 and 2.99 ± 0.19 mg/mL at 10 ug/mL of A-OS, respectively; representing by such way inhibition percentages of 51.87 ± 4.55, 43.26 ± 5.9, and 54.5 ± 2.84%, respectively. However, the same concentration of D-OS reduced carbohydrate content of S. aureus, P. aeruginosa, and C. albicans biofilms to 5.100 ± 0.47, 4.98 ± 0.22, and 4.20 ± 0.335 mg/mL by 32.09 ± 0.536, 9.94 ± 4.04, and 36.23 ± 5.04% inhibition percentages, respectively (Fig. 4). In the same context, 10 ug/mL of A-OS reduced protein content of S. aureus, P. aeruginosa and C. albicans biofilms from 9.64 ± 0.095, 9.75 ± 0.065 and 9.34 ± 0.1 mg/mL in the control untreated samples to 5.92 ± 0.11, 6.94 ± 0.17 and 4.72 ± 0.16 mg/mL, which symbolize 38.54 ± 0.94, 33.47 ± 1.74 and 49.43 ± 1.69% inhibition percentages, respectively. Whereas, D-OS (10 ug/mL) diminished protein content to 7.44 ± 0.255, 8.40 ± 0.065 and 5.61 ± 0.37 mg/mL causing 22.81 ± 2.63, 13.84 ± 0.59 and 39.92 ± 3.89% inhibition.
Notably, significant and dramatic changes were observed in both contents upon elevating the concentrations till reached to the highest values at 100 ug/mL. Wherein, such concentration of A-OS caused in lowering the carbohydrate/ePS content of S. aureus, P. aeruginosa and C. albicans biofilms by 76.54 ± 7.04, 66.81 ± 2.51 and 80.54 ± 1.77%, respectively. While the carbohydrate/ePS reduction percentage reached 60.22 ± 2.02, 50.03 ± 1.95 and 64.26 ± 4.7% by D-OS for S. aureus, P. aeruginosa and C. albicans biofilms, respectively. Regarding the protein content, it lessened significantly to 63.94 ± 3.89, 57.27 ± 5.43 and 74.87 ± 2.49% upon applying 100 ug/mL of A-OS; however, D-OS (100 ug/mL) reduced it to 49.39 ± 1.96, 47.61 ± 2.03 and 59.07 ± 1.78% for S. aureus, P. aeruginosa and C. albicans biofilms, respectively.
Effect of oil samples on biofilm hydrophobicity
The adhesion capability of microbial cell deemed as intrinsic property to colonize any substrate and boost biofilm lifestyle easily. Interestingly, cell surface traits in the formed of ePS and hydrophobicity are decisive parameters that manage the entire adhesion process. In addition, surface characteristics and ambient environmental conditions rule the adhesion of the cells with the surface through number of interactions like hydrophobic, van der Waals and electrostatic. Remarkably, the cells with higher biofilm forming capacity possess higher hydrophobic nature that leads to potent adhesion and vice versa [53]. Given that the biofilm-forming cells with hydrophobic characteristics exhibit affinity to hydrocarbons (e.g., hexadecane, octene, xylene, etc.), the cells retained in the organic phase; generating low turbidity of aqueous phase and by such method (i.e., MATH), the hydrophobicity nature of the biofilm is detected [54].
In the current study, the hydrophobicity index (HI) recorded 65.72 ± 2.3, 51.61 ± 1.12 and 59.12 ± 5.43% for P. aeruginosa, S. aureus and C. albicans respectively; denoting a higher hydrophobicity property of P. aeruginosa than that exhibited by S. aureus and C. albicans. Upon applying different concentrations (10–100 µg/mL) of both oil, a noticeable reduction in HI was shown (Figs. 3 and 4); implying progressively transition to hydrophilicity state, which reached to the maximum at the highest applied doses. Wherein, A-OS (10 µg/mL) lessened hydrophobicity to 64.18 ± 4.23, 49.18 ± 1.73 and 58.62 ± 6.94% for P. aeruginosa, S. aureus and C. albicans, by 1.54 ± 4.23, 2.43 ± 1.73 and 0.498 ± 6.91% inhibition percentage in the respective order. However, A-OS (100 µg/mL) switched S. aureus and C. albicans biofilms to weak hydrophobicity state (i.e., became hydrophilic) by recording 18.5 ± 1.22 and 22.18 ± 0.94% HI, respectively; implementing 33.1 ± 1.22 and 36.94 ± 0.94% inhibition. While, the HI of P. aeruginosa biofilm altered to be moderate by recording 41.32 ± 1.65% and inhibition percentage recorded 24.04 ± 1.65%. On the other hand, D-OS (10 and 20 µg/mL) insignificantly enhanced the hydrophobicity of P. aeruginosa biofilm by 0.046 ± 2.55 and 0.032 ± 2.364%, respectively. Whereas, at 100 µg/mL inhibited its hydrophobicity by 15.96 ± 0.74%; maintaining HI in the potent region by recording 49.78 ± 0.74%. Regarding S. aureus and C. albicans biofilms, D-OS promoted their hydrophilic affinity in a dose-dependent behavior, reaching to the maximum at 100 µg/mL by recording 25.34 ± 0.422 and 37.89 ± 0.826%, correspondingly, which all remained in the moderate phase of hydrophobicity. Generally, albeit distinct structural variation in cells surface among the examined strains in our study, both oil samples substantiated their efficacy in influencing on hydrophobicity adversely. It is important to highlight that hydrophobicity reflects the microbial attachment or adhesiveness ability, which varies even from strain to strain and influenced by microbial age, microbial surface charge and growth medium [55]. In study performed by Kim et al. [56], 10 µg/ml of antibiofilm FAs (e.g., tricosanoic acids, palmitoleic, myristoleic acid, lauric acid, stearic, heptadecanoic and α-linolenic) reversed the biofilm of Cutibacterium acnes from hydrophobic region to hydrophilic region (hydrophobic index < 20%) simultaneously with biofilm inhibition, which agreed with our results.
Intriguingly, the results of the current investigation declared the existence of significant positive correlation between all examined variables (i.e., inhibition of biofilm, protein, ePS, viability and hydrophobicity) with SCOs concentrations, as signified by Pearson’s correlation coefficients (Table 2) (Figs. 5 and 6). Wherein, oil samples influenced negatively on the biofilm development through modulating microbial-surface interactions, in particular hydrophobic interactions through impacting on surface-associated exopolysaccharides and proteins. In consistent with our results, Pompilio et al. [57] attributed the higher hydrophobicity of Stenotrophomonas maltophilia biofilm to its higher exopolysaccharides content, which was positively correlated with biofilm development. Also, Mu et al. [58] manifested and explained the same finding in S. epidermidis biofilm. Otherwise, several reports documented the independence of biofilm formation on hydrophobicity [53, 59]. Nonetheless, there is a consensus among all studies regarding that the cell surface properties and overall physiological properties of microbes govern the process of biofilm development and maturation. Noteworthy mention that the hydrophobicity is fostered by the action of microbial appendages (e.g., pilli, fimbriae, fibrils, etc.) that scattered on the cell surface. Such organelles contain hydrophobic amino acid residues that expedite noncovalent attachment of the cells on any substratum [54, 55]. However, exopolysaccharides facilitate irreversible adhesion and sheltering the developed cells within the backbone of biofilm [23, 52].
Table 2Representing the correlation between biofilm inhibition with other studied factors (i.e., viability, ePS, protein and hydrophobicity) by the action of SCOsBiofilm typeExamined parameterPearson’s correlation coefficients (R2)(P-value)SCOs-1SCOs-2BiofilmViabilityePSProteinBiofilmViabilityePSProteinP. aeruginosaViability0.983 (0.00)0.961(0.00)ePS0.954 (0.00)0.985(0.00)0.941(0.00)0.971(0.00)Protein0.983 (0.00)0.968(0.00)0.943(0.00)0.934 (0.001)0.95(0.00)0.987(0.00)hydrophobicity0.926 (0.001)0.942(0.00)0.928 (0.001)0.911 (0.002)0.928 (0.001)0.942(0.00)0.984(0.00)0.986(0.00)S. aureusViability0.981 (0.00)0.976(0.00)ePS0.97 (0.00)0.984(0.00)0.986(0.00)0.982(0.00)Protein0.979 (0.00)0.975(0.00)0.941(0.00)0.984(0.00)0.982(0.00)0.992(0.00)hydrophobicity0.932 (0.001)0.968(0.00)0.931 (0.001)0.951(0.00)0.947(0.00)0.977(0.00)0.97(0.00)0.973(0.00)C. albicansViability0.975 (0.00)0.958(0.00)ePS0.984 (0.00)0.984(0.00)0.969(0.00)0.973(0.00)Protein0.937 (0.001)0.965(0.00)0.965(0.00)0.964(0.00)0.933 (0.001)0.986(0.00)hydrophobicity0.879 (0.004)0.932 (0.001)0.886(0.03)0.88(0.004)0.891 (0.003)0.971(0.00)0.95(0.00)0.9(0.002)Fig. 5Contour plot showing the correlation of biofilm suppression by A-OS versus EPS inhibition (left panel) and protein inhibition (right panel) with cell surface hydrophobicity. The diagram was plotted by Minitab 14 software. Different colors elucidate different levels of biofilm suppression. A & B - P. aeruginosa, C & D- S. aureus and E & F- C. albicansFig. 6Contour plot showing the correlation of biofilm suppression by D-OS versus EPS inhibition (left panel) and protein inhibition (right panel) with cell surface hydrophobicity. The diagram was plotted by Minitab 14 software. Different colors elucidate different levels of biofilm suppression. A & D - P. aeruginosa, B & E- S. aureus and C & F- C. albicans
Based on the previous results, it is conspicuous that the antibiofilm potency of SCOs samples appeared more evident against S. aureus. That could be attributed to its physiological and metabolic sensitivity, besides the nature and architecture of its cell wall, which seemed to contribute intrinsically in its susceptibility. Namely, the hydrophilic nature of gram-positive bacteria’s cell wall with their low content of lipids (1–4%) trigger the adsorption and penetration processes of exogenous materials interiorly easier. In contrast, the more complex structure of gram-negative bacterial cell wall with its abundant hydrophobic moieties (11–22% lipid content) serves as potent entry barrier toward detergents and hydrophobic molecules, hindering by such way the internal transportation of SCOs [60]. Strikingly, the superior biocidal potency of fatty acids, especially those of longer chain FAs (i.e., ≥ C12) against gram-positive bacteria such as B. subtilis, Micrococcus luteus, Propionibacterium acnes, Listeria monocytogenes and Clostridium difficile was reported tremendously by several research groups [56, 61–65], which coincident with our results. Additionally, Shukla et al. [62] reported that gram-negative bacteria displayed more resistance to medium- and long-chain FAs than gram-positive bacteria, which also agreed with our results; however, gram-negative bacteria were more susceptible to FAs with C6 or less in their chain. In the same context, [60] opined that yeast cells are more susceptible to medium chain-FA (i.e., C8-C12). Otherwise, [63, 66–68] stated the mycocidal potency of long chain-FA. Herein, both SCOs samples of the present study exhibited promising fungicidal performance versus C. albicans, which agreed that found by previously mentioned studies. Hereby, both oil samples could be harnessed in thwarting candidiasis in immunocompromised patients following secondary pneumonia infections conjugated with COVID-19.
Arguably, the biofilm development by P. aeruginosa, S. aureus and C. albicans was dose-dependently impeded by both oil samples, which could be ascribed to the intensification of detergent traits of FAs on account of their amphipathic nature, which could be bacteriostatic at lower concentrations and bactericidal at higher concentrations [56, 69, 70]. Subsequently, it is imperative to shed the light about the antagonistic strategy followed by oil against examined pathogens. However, as referred by Cepas et al. [66] and Kumar et al. [71] the precise biocidal strategy followed by FAs is obscure and undefined. Wherein, FAs frustrate microbial growth and biofilm development through violating multiple cellular targets nonspecifically, which seems being characteristic and should be invested in enfeebling drug resistance phenomena (MDR) [72].
The detrimental effects of FAs against treated biofilm commences with the cell membranes, which considered being the prime target in the multi-step process of biofilm formation. Wherein, FAs incorporated on the hydrophobic moieties of cell phospholipid layer causing membrane solubilization, integrity destabilization and apertures creation, either transient or permanent. That in turn elevated cell permeability level and cellular leakage. Besides, FAs subvert the nutrient uptake system, electron transport chain and oxidative phosphorylation which are the chief membrane-located processes. Additional presumptive scenario could be exerted by oil in the context of membrane damage is suppressing the functionality of membrane-associated proteins, which mediate fatty acid biosynthesis in the plasma membrane. Remarkably, Firoozabad et al. [73] demonstrated that the destructive effect of FAs on S. aureus biofilm was triggered through the inactivation of enzyme that is responsible for the fatty acid elongation step, which is enoyl-acyl carrier protein reductase (Fabl). On the other hand, Cepas and coauthors [66] assigned the inhibition of P. aeruginosa and C. albicans biofilms to the inhibition of Fabl by the action of FAs. Similarly, Lee et al. [67] attributed the anticandida potency of FAs to the hindrance of ergosterol production, which regulates fungal membrane synthesis and reinforces its structural. On the other hand, the treatment with FAs could enhance autolysis process by inducing autolytic enzyme in lieu of massive solubilization of the cell membrane by the surfactant-like action of FAs as suggested by Tsuchido et al. [74].
Interestingly, Bintari and Risandiansyah [75] scrutinized in silico the effect of FAs extracted from Cladophora sp. in dampening the activity of peptide deformylase in P. aeruginosa, E. faecium, E. coli, and S. aureus, which catalyzes protein maturation process. Meanwhile, FAs impair ePS production, fimbriae/pili formation and hydrophobicity, which thereafter frustrate motility and adhesion to substrates; ultimately block the microbial colonization and ruin irreversible aggregation and attachment. The finding by research groups of Kim et al. [56], Nicol et al. [76] and Kim et al. [77] supported these deleterious impacts. On genetic level, FAs drastically repressed DNA supercoiling/replication and down-regulated different genes such as hla, HWP1, CHT4, csgAB, fimH, flhD, motB, luxRS and NorA, which encode proteins responsible for alpha-hemolysin production, hyphal development, chitinase production, fimbriae synthesis, motility, quorum-sensing and efflux pump [63, 71, 77]. Additionally, anti-quorum sensing activity was detected for FAs in quenching the communication signaling and quorum sensing system among biofilm cells and with external environment [66, 76]. As referred by Nicol et al. [76], FAs inhibited the biofilm of Acinetobacter baumannii up to 38% through reducing quorum sensing regulator AbaR, which influenced adversely on communication signals among biofilm cells.
Taken together, the overall data revealed the superior antibiofilm potentiality of A-OS in defeating all examined types of biofilm-forming pathogens. That could be attributed to the profusion of USFAs (62.18%) comparing to D-OS, which contained 53.15% (Table 1-Fig. 1). Our finding is in line with the majority of the previously published investigations who declared the lower activity or bacteriostatic performance of SFAs [69, 78]. While USFAs with the same length carbon backbone exhibit influential potency in deteriorating biofilm development and obstructing the viability of its cells. Such potency of USFAs could be ascribed to their instability, propensity to oxidation and binding non-specifically to target sites like proteins [69, 78]. Otherwise, Khalilova et al. [79] demonstrated the remarkable antibacterial and antifungal properties of SFAs relative to USFAs, especially capric C10:0, lauric C12:0 and palmitic C16:0 acids.
Comparisons among studies are entangled and controversial due to the differences in the applied FAs, the treated microbe or even the altogether treatment process [69]. Notably, the nature of FAs (i.e., acid or its derivatives such as methyl, ester form, etc.), the applied dose and their solitary or combined case were deemed as the substantial features concerning FAs entity. However, the microbial factor includes microbial species types, isolation source, microbial cell load, microbial phase (i.e., planktonic or aggregated biomass), microbial metabolic activity and microbial physiology (i.e., sensitive or persister), which are undoubtedly differ among microbial genera till strain level. While the contact time, pH value and ionic strength of treatment milieu represent other influential treatment circumstances. All these reasons collectively could definitely influence on biocidal activity of applied FAs to different extends [60, 80]. Nevertheless, the molecular structure and shape of FAs remain the driving force that control the magnitude toxicity of FAs. Precisely, the carbon chain length, number of unsaturation and the geometric configuration around the double bond are the conclusive parameters that correlated proportionally to FAs antagonistic potentiality. As stated by McGaw et al. [60], Desbois and Smith [69] and Kim et al. [56], FAs that possess more carbon atoms (i.e. C16, C18, etc.) in their carbon backbone are more potent antimicrobial agents than those contained 10 or 12 carbons in their chain. Moreover, USFAs, properly PUFAs followed by MUFAs, manifested characteristic biocidal functionalities than SFAs [69]. In this regard, Feldlaufer et al. [78] found that palmitic acid (C16) didn’t show any inhibitory action against pathogens comparing to its unsaturated counterpart (i.e., palmitoleic acid (C16:1); confirming that FAs with 3, 4 or 6 double bonds display more antimicrobial activity. Strikingly, FAs stereochemistry also deemed as limiting factor, namely, the cis-isomer is highly active relative to trans configuration [60, 78]. That could be ascribed to the resemblance in the structure between SFAs and trans orientation USFAs [69]. In this sense, Feldlaufer et al. [78] documented that the introduction of double/multiple bonds, especially with FAs more than 14 carbon atoms in cis- orientation, plays imperative role in elevating antibiotic-like behavior. On the other hand, McGaw et al. [60] reported that SFAs with lower-chain length, MUFA and PUFAs with longer-chain structure exhibit considerable antimicrobial activity.
Remarkably, as indicated by the chromatographic profile (Fig. 1; Table 1), A-OS contained ample amount of omega (ω) fatty acids such as ω-5 (myristoleic acid, C14:1, 0.52%), ω-7 (palmitoleic acid, C16:1, 4.49%) and ω-9 (oleic acid C18:1, 24.78%). However, the predominance of ω-3 (cis-5,8,11,14,17-eicosapentaenoic, C20:5, 0.65%; Cis-4,7,10,13,16,19-docosahexaenoic, C22:6, 0.64% and Cis-11,14,17-Eicosatrienoic C20:3 0.60% ) and ω-6 (linolenic acid, C18:3, 22.67% and γ-linolenic acid, C18:3, 4.22%) contributed intensively in its robust effectiveness. It is worthy mention that ω-3 and ω-6 are frequently utilized as alternative food additives and therapeutic agents to maintain body homeostasis and enhancing the immune system, by the virtue of their functional values in lipid metabolism, antioxidant signaling pathway regulation and inflammatory processes modulation. Besides, their role in diabetes type two, ulcerative colitis, cardiovascular disease, Crohn’s disease, hypertension, alzheimer’s disease and cancer therapy couldn’t be neglected [62, 68, 73]. Meanwhile, their potency in defeating various microbial genera as antibacterial, antifungal or antibiofilm was also determined in various investigations [68, 79].
In the study conducted by Cepas et al. [66], the authors detected the inhibitory limit of linolenic acid by 1, 32 and 64 mg/mL against C. albicans, S. aureus and P. aeruginosa, respectively. Also, he and coauthors found that more than 250 mg/mL of stearic acid, γ-linolenic acid, arachidonic acid and palmitoleic acids were required to utterly block the growth of E. coli. While, 0.02 mg/mL of palmitoleic acids inhibited the biofilm of A. baumannii by 38% [76]. Comparing our results with previous studies and others, reflects the advantageous and promising properties of our SCOs that dampened biofilm activity, their cell viability, biochemical constituent and their hydrophobicity in sensible doses. That could be assigned to the synergistic effect mediated by the combination of SFAs, MUFAs and PUFAs collection. Strikingly, the naturally derived lipids from herbal and microbial sources share this common phenomenon. Namely, the presence of mixture of FAs that compete mutually on more than target site could implement their hostile functionality in prominent way. The results of the present research concurred with that reported by Dia and Jacoeb [81], Shukla et al. [62], Balkrishna et al. [72] and Panjaitan et al. [80]. In the context of synergistic activity, several reports accentuated the uplifting of antibiotic-like performance of FAs upon conjugating several FA together or supplementing with antibiotics, bacteriophages, organic compounds, enzymes and edible plant essential oils [63, 82–86].
Safety and anticancer activities of oil samples
The cytotoxicity results of oil samples on the non-cancerous cell line (Wi-38) indicated that the D-OS was safe treatment on the viability of these non-cancerous cells with IC50 25.8% followed by A-OS with IC50 value of 23.86% (Figs. 7A and 8). As a second-leading reason of death, the continuous amending in cancer treatment modalities is intensively implementing. The most widely options of cancer treatment for many decades are surgery, chemotherapy and radiation therapy, in a single treatments or in combination (Debela et al., 2021). Actually, pain and fatigue are the mainly harmful side effects of conventional therapies modality, besides, dosage selection difficulty, rapid drug metabolism, cytotoxicity and lack of tumor-specific treatments [87, 88]. Henceforth, utilizing fungal bioactive compounds such as SCOs considers being promising, complementary and alternative therapy to reduce cancer complications and improve the therapeutic efficacy [89].Fig. 7Cytotoxicity results of the oil samples on noncancerous (A) and cancerous cell lines (B and C)Fig. 8The IC50 values of the oil samples on the non-cancerous and cancerous cell lines (A), selectivity index (SI) against cancer cells (A549 and CaCo-2) (B), and morphological variations in the treated cancer cell lines relative to the untreated cells (C)
Concerning with the anticancer effects of the A-OS and D-OS, the MTS assay protocol showed that the oil sample D-OS is the most potent cytotoxic agent against both A549 and CaCo-2 cell lines with IC50 values of 2.55 and 3.425%, respectively (Figs. 7B and 8) and SI values of 10.1 and 7.5, respectively (Fig. 8). Furthermore, A-OS showed a potent effect against CaCo-2 cell line with IC50 8.275% (Fig. 8A) and SI value of 2.88 (Fig. 8B). D-OS was safer than A-OS on the viability of normal cells (Wi-38) with IC50 value that was 10-fold higher than its corresponding concentrations for inhibiting 50% growth of CaCo-2 and A549. Meanwhile, another oil sample (i.e., A-OS) had a higher IC50 for A549 growth inhibition than its cytotoxic dose on Wi-38. This declares that D-OS is more selective cytotoxic oil than A-OS against both studied cancer cell lines. Importantly, A-OS activated caspase 3 by 64.23 ± 1.18% and 53.77 ± 0.995% more effectively than D-OS (52.09 ± 0.222% and 49.72 ± 0.952%) in A549 and Caco-2 cells, respectively (Fig. 9). In a dose-dependent manner, A-OS exhibited stronger inhibitory effect on MMP2 and MMP9 with lower IC50 values (18.58% and 8.295%, respectively) than D-OS (23.61% and 13.16%, respectively) as shown in Fig. (10). Attractively, the polyunsaturated fatty acids (PUFAs) especially, ω-3 and ω-6 were recorded as selectively induced tumor cells apoptosis. The sensitivity of various cancer cells to different fatty acids were found to be variable depending on the type of cancer cell being tested and also the types and concentrations of the tested fatty acid [90]. Different research articles explained the positive effects of ω-6 fatty acids in controlling the human lung tumor cell growth in a concentration-dependent manner [91]. Furthermore, ω-3 PUFA triggers cancer cell apoptosis and synergizes to increase the sensitivity of tumor cells to conventional therapies, with interesting applications in cancers resistant to treatment [92]. According to Madhavi and Das [93], AA, GLA, DHA and EPA were found to be the greatest effective in inhibiting tumor cells growth comparing to ALA and LA that recorded lower effects even at higher concentrations. Kumar and Das [94] proved that linoleic acid at concentration of 40 µg/mL had the ability to inhibit cancer cells, whereas, the lower concentrations (5–10 µg/mL) enhanced the growth of nearly all types of cancer cells that were being tested in such study.Fig. 9Caspase 3 activation percentages in D-OS and A-OS treated A549 and Caco-2 cellsFig. 10Dose response curves of A-OS-and D-OS for (A) matrix metalloproteinase MMP2 and (B) MMP9 inhibition as well as (C) their IC50 values
This tumoricidal action of fatty acids could be attributed to the increase in the generation of free radicals in the tumor cells [90]. Moreover, there is an evidence indicating that ω-6 PUFA and LA can be involved in both pro- and anti-cancer processes. For example increased the proliferation of the breast carcinoma cell line (BT-474 cell line) and the human lung cancer cell line (A549) in vitro, as well as promoting colon and prostate tumorigenesis and tumor growth in animal models [95, 96]. On the other hand, a high dose of LA could inhibits the proliferation of the colon cancer cell line (Caco-2) [97], while a high intake of LA can also show a protective effect against cancer development [98]. Based on this theory, different literatures proved the anticancer leverages of fatty acids, at least partly owing to their proapoptotic effects, especially against colorectal cancer (e.g., Caco-2, HT-29, HCT116, LoVo, SW480 and SW620 cell lines) [99], MHCC97L hepatocellular carcinoma cell line [100] and LNCaP, DU145 and PC-3 prostate cancer cell lines [101]. Furthermore, dihomo-γ-linolenic acid proved selective cytotoxic effects against A549 lung cancer cell line without affecting the normal cells [102]. Remarkably, the activation of caspases is the primary indicator of apoptosis-mediated cancer cell death. Caspase 3 is one particular caspase whose activation causes irreversible cell death. Previous studies illustrated that hen egg yolk PUFAs and Fish oil-enriched PUFAs activated caspase 3 in the treated melanoma cells and breast cancer, respectively [10, 103]. Moreover, both MMP2 and MMP9 are considered the main contributors in mediating invasion and metastasis of lung and colon cancers [104, 105]. PUFA (ω-3 and ω-6 fatty acids) revealed high inhibition potency in suppressing both MMP2 and MMP9 activities [11, 13, 29].
Finally, the former results of anticancer and antibiofilm proficiency of SCOs could be symbolized as prospective contenders for prophylactic programs by the dint of their environmentally-sound and biocompatibility. Besides, their unspecific mode of action triggers the appearance of mutant phenotypes with FAs-resistance is less problematic than commercial drugs. In this avenue, SCOs of the present study deemed as promising alternative anti-infective agents in various biotechnological applications like agriculture, medicine, nutraceuticals, feed additives and the cosmetic formulations. Presently, studies are profoundly under way to examine the efficacy of our SCOs in combinatorial treatment or drug amalgamation strategy with metal/ polymer nanomaterials, in lieu of using single agent. Such nanoformulations composites would be employed as an alternative wound dressing material in vitro study. Also, their exploitation as an anti-acne agent in skin ointments or gels is also run in parallel. However, in the avenue of food technology synchronizing with exploiting higher nutritional value of SCOs, which are rich in ω-3 and ω-6, oral tablets of SCOs and probiotics would be designed for promoting indigenous microbiota to facilitate the digestion process in human and livestock.
Conclusion
This study highlighted the potency of the two oleaginous fungi (Alternaria sp. and Drechslera sp.), as promising factories, for their remarkable capability in economic production of ecologically friendly SCOs based on whey, as the main nutritive substrate. Fatty acid profile of both fungi revealed the presence of appreciated unsaturated fatty acids, which displayed superior anti-biofilm and anticancer activity. The overall data demonstrated antibiofilm potentiality of Alternaria oil sample in defeating all examined types of biofilm-forming pathogens comparing to Drechslera oil sample. On the other hand, data revealed that Drechslera oil sample is the most potent anticancer treatment against both A549 and CaCo-2 cell lines. Thus, the data gained from this study opens new perspectives in the field of biotechnology and considers superlative solution for the coming years to support cancer therapy and biofilm causing infections. |
Title: Menopausal characteristics and hormone replacement therapy in relation to long-term risk of cholecystectomy in women | Body:
Introduction
Gallbladder disease occurs largely as a result of formation of cholesterol gallstones, with the prevalence reaching 10%−30% in European countries (1). While most gallstones are clinically silent, up to 20% individuals with symptomatic gallstones have a 1%-2% likelihood annually of developing severe complications and requiring surgical removal of the gallbladder (2, 3). Cholecystectomy has greatly increased the number of hospitalizations and costs of healthcare resources, resulting in a substantial financial burden (4).
Women have a markedly higher risk of gallstones or cholecystectomy than men (5), indicating a role of sex steroid hormones in the pathogenesis of gallbladder disease. Estrogen has been suggested to have priming effects on the inflammatory and nociceptive pathways (6, 7), leading to symptoms that mimic cholecystitis. Elevated estrogen levels have been proposed to lead to supersaturation of biliary cholesterol, promoting the precipitation of solid cholesterol monohydrate crystals, which contributes to gallstone formation (8). However, evidence from epidemiological studies have been inconsistent regarding the relationship between length of women's reproductive years and gallbladder disease (9–11). On the other hand, several prospective observational studies have shown an increase in the risk of gallbladder disease or cholecystectomy associated with postmenopausal hormone replacement therapy (HRT) in women (12–15), supporting the lithogenicity of exogenous estrogen. As HRT use is commonly accompanied by early menopause (especially early surgical menopause), their independent and joint influences on the long-term risk of cholecystectomy remain to be assessed.
In the present study, using data from a large population-based prospective study (UK Biobank), we investigated the relationships of menopausal characteristics and HRT use as well as their joint influence on the risk of cholecystectomy in women.
Methods
Study population
The UK Biobank is a large prospective cohort study established to provide a resource for investigation of the genetic, environmental, and lifestyle factors associated with a wide range of diseases (16). From 2006 to 2010, the UK Biobank recruited ~500,000 participants who were aged 37–73 years, from 22 centers across England, Wales, and Scotland. At recruitment, participants provided a wide arrange of health-related information, underwent various physical measurements, and were collected for biological samples.
Of the 273,324 female participants, we excluded those with a history of cholecystectomy (n = 6,813), with disorders of gallbladder, biliary tract, or pancreas (n = 7,484), or with cancer at baseline (n = 29,496). We further excluded 834 participants who developed incident cancer in the duodenum, liver, gallbladder, biliary tract, or pancreas, and 18 participants who received liver transplantation during follow-up. Of the remaining 228,679 participants, we additionally excluded those without information on menopausal status or age at menopause (n = 43,460) or HRT use (n = 542). Finally, 184,677 women were included in the current analysis, among whom 126,717 (68.6%) had been menopausal at recruitment and 59,155 (32.0%) had ever used HRT (Supplementary Figure 1).
Assessments of menopausal characteristics and HRT use
Participants self-reported information on various menopausal characteristics through touchscreen-based questionnaires. These included menopause status (yes or no), age at menopause, histories of hysterectomy and oophorectomy and the corresponding age when received the surgery. Information on history of HRT use and the age at first/last HRT use was also collected via self-report, using the following questions: “Having you ever used hormone replacement therapy (HRT)?”; “How old were you when you first used HRT?”; and “How old were you when you last used HRT?”. According to the self-reported information, duration of using HRT was estimated by subtracting age at starting HRT from age at stopping the use. For this variable, women who reported “Still taking HRT,” “Do not know,” or “Prefer not to answer” were considered as lacking information on age at last use of HRT. For menopausal women, type of menopause (surgical or natural) was determined according to the reported information on age at menopause and age at hysterectomy or oophorectomy. A woman was deemed to be naturally-menopausal if the reported menopausal age was younger than the reported age at hysterectomy or oophorectomy, and otherwise surgical menopause.
Outcome ascertainment
The outcome of interest in this study was the occurrence of cholecystectomy during the follow-up period. We chose cholecystectomy because it is more clinically relevant than asymptomatic gallstones (17). The procedures of cholecystectomy in the UK Biobank were identified using hospital inpatient records obtained from the Hospital Episode Statistics for England, Scottish Morbidity Record data for Scotland, and the Patient Episode Database for Wales. Specifically, the incident cases of cholecystectomy were identified using the UK Biobank field IDs 41200 (operative procedures-main) and 41260 (date of first operative procedure-main), with the following codes: J18.1, J18.2, J18.3, J18.4, J18.5, J18.8, and J18.9.
Assessment of covariates
Information on sociodemographic factors, medical history, lifestyle behaviors, and medication use was collected at baseline by touchscreen questionnaires and nurse-led interviews. The Townsend deprivation index was calculated by combining four census variables (unemployment, non-car ownership, non-home ownership, and household overcrowding). BMI was calculated based on measured weight and height (kg/m2). Physical activity was assessed at baseline using the self-reported short-form International Physical Activity Questionnaire, and the data were summarized and reported in MET-h per week. To account for variations in alcohol content and drink volumes across beverages, amounts of daily alcohol consumption in grams were estimated from consumption of red wine, white wine, beer, spirit, and fortified wine using previously reported methods (18). Healthy dietary score was calculated as the sum of all the followings (assigning 1 point to each item): (1) Red meat <2 servings/week; (2) Processed meat <1 serving/week; (3) Fresh fruit ≥3 servings/day OR fresh vegetables ≥3 servings/day OR a combination ≥4.5 servings/day; (4) Whole grain ≥3 servings/day; (5) Refined grain <1.5 servings/day; (6) Fish ≥2 servings/week. Diabetes was defined as a self-reported physician's diagnosis or antidiabetic medication use, or an HbA1c level of ≥6.5%. More detailed information on the covariates is presented in Supplementary Table 1.
Statistical analysis
Baseline participant characteristics were reported for the whole study population and by incident cholecystectomy status. Continuous variables were presented as mean ± standard deviation (SD) and categorical variables as percentage. Missing data for continuous covariates were addressed using sex-specific median values, and categorical covariates were addressed with a missing indicator category.
We used multivariable Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of the aforementioned menopausal characteristics and HRT use with risk of cholecystectomy. Person-time of follow-up was calculated as the duration between the date of baseline evaluation and the date of the diagnosis of cholecystectomy, death, loss to follow-up, or the respective censoring dates for England (September 30, 2021), Scotland (July 31, 2021), and Wales (February 28, 2018), whichever occurred first. Three models were constructed to incrementally adjust for potential confounders. Model 1 was adjusted for age (years), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index. Model 2 was adjusted for the covariates in model 1 and was further adjusted for smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (NSAIDs; yes, no). Model 3 (full model) was further adjusted for HRT use (ever, never; for menopausal characteristics) or the status and type of menopause (pre-menopause, natural menopause, surgical menopause; for HRT use). A sensitivity analysis was performed by further adjusting for the healthy dietary score. Further sensitivity analyses were performed among women who had been menopausal at baseline, to reexamine the association of menopausal characteristics or HRT use with risk of cholecystectomy.
In addition, we performed a further analysis jointly considering HRT use and the status and type of menopause, with naturally-menopausal women who never used HRT as the reference. Statistical analyses were performed using Stata 15.0 software (StataCorp) and a two-sided P-value of <0.05 was considered statistically significant.
Study population
The UK Biobank is a large prospective cohort study established to provide a resource for investigation of the genetic, environmental, and lifestyle factors associated with a wide range of diseases (16). From 2006 to 2010, the UK Biobank recruited ~500,000 participants who were aged 37–73 years, from 22 centers across England, Wales, and Scotland. At recruitment, participants provided a wide arrange of health-related information, underwent various physical measurements, and were collected for biological samples.
Of the 273,324 female participants, we excluded those with a history of cholecystectomy (n = 6,813), with disorders of gallbladder, biliary tract, or pancreas (n = 7,484), or with cancer at baseline (n = 29,496). We further excluded 834 participants who developed incident cancer in the duodenum, liver, gallbladder, biliary tract, or pancreas, and 18 participants who received liver transplantation during follow-up. Of the remaining 228,679 participants, we additionally excluded those without information on menopausal status or age at menopause (n = 43,460) or HRT use (n = 542). Finally, 184,677 women were included in the current analysis, among whom 126,717 (68.6%) had been menopausal at recruitment and 59,155 (32.0%) had ever used HRT (Supplementary Figure 1).
Assessments of menopausal characteristics and HRT use
Participants self-reported information on various menopausal characteristics through touchscreen-based questionnaires. These included menopause status (yes or no), age at menopause, histories of hysterectomy and oophorectomy and the corresponding age when received the surgery. Information on history of HRT use and the age at first/last HRT use was also collected via self-report, using the following questions: “Having you ever used hormone replacement therapy (HRT)?”; “How old were you when you first used HRT?”; and “How old were you when you last used HRT?”. According to the self-reported information, duration of using HRT was estimated by subtracting age at starting HRT from age at stopping the use. For this variable, women who reported “Still taking HRT,” “Do not know,” or “Prefer not to answer” were considered as lacking information on age at last use of HRT. For menopausal women, type of menopause (surgical or natural) was determined according to the reported information on age at menopause and age at hysterectomy or oophorectomy. A woman was deemed to be naturally-menopausal if the reported menopausal age was younger than the reported age at hysterectomy or oophorectomy, and otherwise surgical menopause.
Outcome ascertainment
The outcome of interest in this study was the occurrence of cholecystectomy during the follow-up period. We chose cholecystectomy because it is more clinically relevant than asymptomatic gallstones (17). The procedures of cholecystectomy in the UK Biobank were identified using hospital inpatient records obtained from the Hospital Episode Statistics for England, Scottish Morbidity Record data for Scotland, and the Patient Episode Database for Wales. Specifically, the incident cases of cholecystectomy were identified using the UK Biobank field IDs 41200 (operative procedures-main) and 41260 (date of first operative procedure-main), with the following codes: J18.1, J18.2, J18.3, J18.4, J18.5, J18.8, and J18.9.
Assessment of covariates
Information on sociodemographic factors, medical history, lifestyle behaviors, and medication use was collected at baseline by touchscreen questionnaires and nurse-led interviews. The Townsend deprivation index was calculated by combining four census variables (unemployment, non-car ownership, non-home ownership, and household overcrowding). BMI was calculated based on measured weight and height (kg/m2). Physical activity was assessed at baseline using the self-reported short-form International Physical Activity Questionnaire, and the data were summarized and reported in MET-h per week. To account for variations in alcohol content and drink volumes across beverages, amounts of daily alcohol consumption in grams were estimated from consumption of red wine, white wine, beer, spirit, and fortified wine using previously reported methods (18). Healthy dietary score was calculated as the sum of all the followings (assigning 1 point to each item): (1) Red meat <2 servings/week; (2) Processed meat <1 serving/week; (3) Fresh fruit ≥3 servings/day OR fresh vegetables ≥3 servings/day OR a combination ≥4.5 servings/day; (4) Whole grain ≥3 servings/day; (5) Refined grain <1.5 servings/day; (6) Fish ≥2 servings/week. Diabetes was defined as a self-reported physician's diagnosis or antidiabetic medication use, or an HbA1c level of ≥6.5%. More detailed information on the covariates is presented in Supplementary Table 1.
Statistical analysis
Baseline participant characteristics were reported for the whole study population and by incident cholecystectomy status. Continuous variables were presented as mean ± standard deviation (SD) and categorical variables as percentage. Missing data for continuous covariates were addressed using sex-specific median values, and categorical covariates were addressed with a missing indicator category.
We used multivariable Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of the aforementioned menopausal characteristics and HRT use with risk of cholecystectomy. Person-time of follow-up was calculated as the duration between the date of baseline evaluation and the date of the diagnosis of cholecystectomy, death, loss to follow-up, or the respective censoring dates for England (September 30, 2021), Scotland (July 31, 2021), and Wales (February 28, 2018), whichever occurred first. Three models were constructed to incrementally adjust for potential confounders. Model 1 was adjusted for age (years), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index. Model 2 was adjusted for the covariates in model 1 and was further adjusted for smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (NSAIDs; yes, no). Model 3 (full model) was further adjusted for HRT use (ever, never; for menopausal characteristics) or the status and type of menopause (pre-menopause, natural menopause, surgical menopause; for HRT use). A sensitivity analysis was performed by further adjusting for the healthy dietary score. Further sensitivity analyses were performed among women who had been menopausal at baseline, to reexamine the association of menopausal characteristics or HRT use with risk of cholecystectomy.
In addition, we performed a further analysis jointly considering HRT use and the status and type of menopause, with naturally-menopausal women who never used HRT as the reference. Statistical analyses were performed using Stata 15.0 software (StataCorp) and a two-sided P-value of <0.05 was considered statistically significant.
Results
Participant characteristics
Mean age was 55.6 (± 8.2) years for the included women and the majority (95.5%) of them were ethnically white Europeans. Among the 184,677 women, there were 4,991 incident cases of cholecystectomy over a median of 12.7 years of follow-up. As compared with women who did not have cholecystectomy, those who had cholecystectomy had a higher Townsend deprivation index (lower socioeconomic status), were more likely to be smokers, were less likely to be alcohol drinkers, had lower levels of physical activity and higher levels of BMI, and they were more likely to have diabetes and use aspirin or NSAIDs (Table 1).
Table 1
Baseline participant characteristics according to incident cholecystectomy status.
Overall (n = 184,677)
Cholecystectomy
Yes (n = 4,991)
No (n = 179,686)
Age, year
55.6 ± 8.2
55.8 ± 8.1
55.6 ± 8.2
Ethnicity, %
White
95.5
96.7
95.5
Asian or Asian British
1.9
1.8
1.9
Black or Black British
1.8
0.9
1.8
Mixed
0.7
0.6
0.7
Townsend deprivation index
−1.4 ± 3.0
−1.1 ± 3.1
−1.4 ± 3.0
Smoking status, %
Never
60.9
57.7
61.0
Former
30.6
32.8
30.5
Current
8.5
9.5
8.5
Pack-years of smoking (current smokers)
24.0 ± 14.0
24.9 ± 13.8
24.0 ± 14.0
Drinking status, %
Never
5.7
6.7
5.7
Former
3.3
4.0
3.3
Current
91.0
89.3
91.0
Drinking amounts, g/day
9.6 ± 12.0
7.4 ± 10.8
9.6 ± 12.0
Physical activity, MET-h/week
42.2 ± 41.0
38.5 ± 40.0
42.3 ± 41.0
BMI, kg/m2
26.8 ± 5.0
29.7 ± 5.5
26.7 ± 5.0
Diabetes, %
3.8
4.6
3.8
Statin use, %
9.9
9.8
9.9
Aspirin use, %
8.9
9.8
8.9
Use of NSAIDs, %
17.0
20.1
16.9
BMI, body mass index; MET, metabolic equivalents; NSAIDs, non-steroidal anti-inflammatory drugs.
Menopausal characteristics and risk of cholecystectomy
After the full adjustment (model 3), menopausal women showed a similar risk of cholecystectomy as compared with non-menopausal women (Table 2). When considering type of menopause, however, women with surgical menopause (HR = 1.48; 95% CI: 1.29–1.71), but not natural menopause (HR = 1.04; 95% CI: 0.94–1.14) had a higher risk of cholecystectomy than non-menopausal women.
Table 2
Menopausal characteristics and risk of cholecystectomy in women.
Cases/total
Model 1, HR (95% CI)
Model 2, HR (95% CI)
Model 3, HR (95% CI)
Menopausal status
Non-menopausal (Ref.)
1,477/57,960
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Menopausal
3,514/126,717
1.15 (1.05–1.27)
1.13 (1.02–1.24)
1.05 (0.96–1.16)
Type of menopause
Non-menopausal (Ref.)
1,477/57,960
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Natural menopause
3,121/117,791
1.12 (1.02–1.23)
1.09 (0.99–1.20)
1.04 (0.94–1.14)
Surgical menopause
393/8,926
1.90 (1.66–2.18)
1.71 (1.49–1.95)
1.48 (1.29–1.71)
Hysterectomy
No (Ref.)
4,451/171,881
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Yes
535/12,641
1.67 (1.53–1.84)
1.56 (1.42–1.71)
1.45 (1.32–1.59)
Age at hysterectomy, year
No hysterectomy (Ref.)
4,451/171,881
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
<45
246/5,367
1.80 (1.58–2.05)
1.62 (1.42–1.84)
1.48 (1.29–1.69)
45–48
99/2,208
1.77 (1.45–2.16)
1.67 (1.37–2.05)
1.54 (1.26–1.88)
49–51
57/1,432
1.58 (1.21–2.06)
1.51 (1.16–1.96)
1.39 (1.07–1.81)
≥52
121/3,445
1.39 (1.16–1.67)
1.35 (1.12–1.62)
1.30 (1.08–1.56)
Oophorectomy
No (Ref.)
4,714/177,987
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Yes
227/5,469
1.58 (1.38–1.81)
1.45 (1.27–1.66)
1.31 (1.14–1.50)
Age at oophorectomy, year
No oophorectomy (Ref.)
4,714/177,987
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
<45
62/1,308
1.80 (1.40–2.32)
1.54 (1.20–1.98)
1.33 (1.03–1.71)
45–48
47/1,015
1.75 (1.31–2.33)
1.60 (1.20–2.14)
1.40 (1.05–1.88)
49–51
39/973
1.53 (1.12–2.10)
1.43 (1.05–1.97)
1.28 (0.93–1.76)
≥52
70/2,035
1.31 (1.03–1.66)
1.26 (0.99–1.60)
1.19 (0.94–1.51)
Model 1: age (year), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index.
Model 2: model 1 + smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Model 3: model 2 + hormone replacement therapy (ever, never).
Both hysterectomy (HR = 1.45; 95% CI: 1.32–1.59) and oophorectomy (HR = 1.31; 95% CI: 1.14–1.50) were associated with a higher risk of cholecystectomy. The associations tended be attenuated with increasing age at hysterectomy or oophorectomy, respectively. In particular, oophorectomy at ≥52 years old was not associated with risk of cholecystectomy (HR = 1.19; 95% CI: 0.94–1.51; Table 2). These associations for hysterectomy or oophorectomy and risk of cholecystectomy were similar when excluding non-menopausal women from the analyses (Supplementary Table 2).
We performed an additional analysis considering type of and age at menopause. Regardless of age at menopause, natural menopause was not associated with risk of cholecystectomy (Table 3). As compared with non-menopausal women, women with surgical menopause at <45 (HR = 1.57; 95% CI: 1.34–1.85) or 45 to 51 years old (HR = 1.42; 95% CI: 1.17–1.74), but not those with the surgery at ≥52 years old (HR = 1.20; 95% CI: 0.85–1.70) had a higher risk of cholecystectomy.
Table 3
Status and type of menopause and risk of cholecystectomy in women, according to age at menopause.
Type and age of menopause
Cases/total
Model 1, HR (95% CI)
Model 2, HR (95% CI)
Model 3, HR (95% CI)
Not menopausal (Ref.)
1,477/57,960
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Natural menopause at age <45 years
333/11,122
1.24 (1.08–1.42)
1.17 (1.02–1.34)
1.07 (0.93–1.23)
Natural menopause at age 45–51 years
1,498/56,697
1.10 (1.00–1.22)
1.09 (0.98–1.20)
1.04 (0.94–1.15)
Natural menopause at age ≥52 years
1,290/49,972
1.09 (0.98–1.22)
1.05 (0.95–1.18)
1.01 (0.90–1.13)
Surgical menopause at age <45 years
226/4,653
2.06 (1.75–2.41)
1.80 (1.53–2.11)
1.57 (1.34–1.85)
Surgical menopause at age 45–51 years
132/3,242
1.74 (1.43–2.12)
1.62 (1.34–1.98)
1.42 (1.17–1.74)
Surgical menopause at age ≥52 years
35/1,031
1.47 (1.04–2.08)
1.34 (0.95–1.90)
1.20 (0.85–1.70)
Model 1: age (year), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index.
Model 2: model 1 + smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Model 3: model 2 + hormone replacement therapy (ever, never).
HRT use and risk of cholecystectomy
After multivariable adjustment, HRT use (ever vs. never) was associated with a higher risk of cholecystectomy (HR = 1.29, 95% CI: 1.21–1.38). This association appeared to be independent of age when initiating HRT, duration of HRT use, and status and type of menopause (Table 4). For example, when compared with women who never used HRT, the highest risk was observed among women who ever used HRT and had surgical menopause (HR = 1.79; 95% CI: 1.58–2.02). These results remained significant after further adjustment of healthy dietary score. These associations for HRT use and risk of cholecystectomy were similar after excluding non-menopausal women from the analyses (Supplementary Table 3).
Table 4
Hormone replacement therapy (HRT) and risk of cholecystectomy in women.
Cases/total
Model 1, HR (95% CI)
Model 2, HR (95% CI)
Model 3, HR (95% CI)
HRT use
Never (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Ever
1,886/59,155
1.34 (1.25–1.43)
1.35 (1.27–1.44)
1.29 (1.21–1.38)
Age when initiated HRT, years
Never use (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
<45
384/10,550
1.49 (1.33–1.66)
1.40 (1.25–1.55)
1.28 (1.15–1.44)
45–48
519/15,161
1.41 (1.28–1.55)
1.42 (1.28–1.56)
1.36 (1.13–1.50)
49–51
464/15,356
1.26 (1.13–1.39)
1.33 (1.20–1.47)
1.29 (1.16–1.43)
≥52
349/13,430
1.09 (0.97–1.22)
1.17 (1.04–1.32)
1.16 (1.03–1.30)
Duration of HRT use, years
Never use (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
≤ 4
454/14,680
1.27 (1.15–1.41)
1.25 (1.13–1.39)
1.23 (1.10–1.36)
5–9
401/13,910
1.19 (1.07–1.33)
1.23 (1.10–1.37)
1.19 (1.07–1.33)
10–14
240/8,286
1.20 (1.04–1.37)
1.29 (1.12–1.47)
1.21 (1.06–1.40)
≥15
91/2,552
1.47 (1.19–1.82)
1.48 (1.20–1.84)
1.33 (1.07–1.66)
HRT use
Never (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
NA
Ever and pre-menopause
91/2,095
1.73 (1.41–2.14)
1.68 (1.36–2.07)
NA
Ever and natural menopause
1,489/50,290
1.24 (1.15–1.32)
1.26 (1.18–1.36)
NA
Ever & surgical menopause
306/6,770
1.92 (1.70–2.16)
1.79 (1.58–2.02)
NA
Model 1: age (years), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index.
Model 2: model 1 + smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Model 3: model 2 + status and type of menopause (pre-menopause, natural menopause, surgical menopause).
Joint analysis of menopausal characteristics and HRT use with risk of cholecystectomy
We then performed a further analysis jointly considering menopausal status, type of menopause, history of HRT use, and age at first HRT use. When compared with women who were naturally-menopausal and had never used HRT, non-menopausal women who ever used HRT had a 65% (HR = 1.65; 95% CI: 1.33–2.04) higher risk of cholecystectomy, while naturally-menopausal women who ever used HRT only had a modestly higher risk (regardless of time when starting HRT; Figure 1). The highest risk was observed for women who had surgical menopause and started HRT use before menopause (HR = 2.28, 95% CI: 1.70–3.04), followed by surgically-menopausal women who started HRT during or after the menopausal process (HR = 1.73; 95% CI: 1.51–1.98). These results were largely similar after further adjusting for the healthy dietary score (Supplementary Figure 2).
Figure 1
Joint association of menopause status, menopause type, and time at initiating HRT with risk of cholecystectomy in women. Results were adjusted for age (year), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), Townsend deprivation index, smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Participant characteristics
Mean age was 55.6 (± 8.2) years for the included women and the majority (95.5%) of them were ethnically white Europeans. Among the 184,677 women, there were 4,991 incident cases of cholecystectomy over a median of 12.7 years of follow-up. As compared with women who did not have cholecystectomy, those who had cholecystectomy had a higher Townsend deprivation index (lower socioeconomic status), were more likely to be smokers, were less likely to be alcohol drinkers, had lower levels of physical activity and higher levels of BMI, and they were more likely to have diabetes and use aspirin or NSAIDs (Table 1).
Table 1
Baseline participant characteristics according to incident cholecystectomy status.
Overall (n = 184,677)
Cholecystectomy
Yes (n = 4,991)
No (n = 179,686)
Age, year
55.6 ± 8.2
55.8 ± 8.1
55.6 ± 8.2
Ethnicity, %
White
95.5
96.7
95.5
Asian or Asian British
1.9
1.8
1.9
Black or Black British
1.8
0.9
1.8
Mixed
0.7
0.6
0.7
Townsend deprivation index
−1.4 ± 3.0
−1.1 ± 3.1
−1.4 ± 3.0
Smoking status, %
Never
60.9
57.7
61.0
Former
30.6
32.8
30.5
Current
8.5
9.5
8.5
Pack-years of smoking (current smokers)
24.0 ± 14.0
24.9 ± 13.8
24.0 ± 14.0
Drinking status, %
Never
5.7
6.7
5.7
Former
3.3
4.0
3.3
Current
91.0
89.3
91.0
Drinking amounts, g/day
9.6 ± 12.0
7.4 ± 10.8
9.6 ± 12.0
Physical activity, MET-h/week
42.2 ± 41.0
38.5 ± 40.0
42.3 ± 41.0
BMI, kg/m2
26.8 ± 5.0
29.7 ± 5.5
26.7 ± 5.0
Diabetes, %
3.8
4.6
3.8
Statin use, %
9.9
9.8
9.9
Aspirin use, %
8.9
9.8
8.9
Use of NSAIDs, %
17.0
20.1
16.9
BMI, body mass index; MET, metabolic equivalents; NSAIDs, non-steroidal anti-inflammatory drugs.
Menopausal characteristics and risk of cholecystectomy
After the full adjustment (model 3), menopausal women showed a similar risk of cholecystectomy as compared with non-menopausal women (Table 2). When considering type of menopause, however, women with surgical menopause (HR = 1.48; 95% CI: 1.29–1.71), but not natural menopause (HR = 1.04; 95% CI: 0.94–1.14) had a higher risk of cholecystectomy than non-menopausal women.
Table 2
Menopausal characteristics and risk of cholecystectomy in women.
Cases/total
Model 1, HR (95% CI)
Model 2, HR (95% CI)
Model 3, HR (95% CI)
Menopausal status
Non-menopausal (Ref.)
1,477/57,960
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Menopausal
3,514/126,717
1.15 (1.05–1.27)
1.13 (1.02–1.24)
1.05 (0.96–1.16)
Type of menopause
Non-menopausal (Ref.)
1,477/57,960
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Natural menopause
3,121/117,791
1.12 (1.02–1.23)
1.09 (0.99–1.20)
1.04 (0.94–1.14)
Surgical menopause
393/8,926
1.90 (1.66–2.18)
1.71 (1.49–1.95)
1.48 (1.29–1.71)
Hysterectomy
No (Ref.)
4,451/171,881
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Yes
535/12,641
1.67 (1.53–1.84)
1.56 (1.42–1.71)
1.45 (1.32–1.59)
Age at hysterectomy, year
No hysterectomy (Ref.)
4,451/171,881
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
<45
246/5,367
1.80 (1.58–2.05)
1.62 (1.42–1.84)
1.48 (1.29–1.69)
45–48
99/2,208
1.77 (1.45–2.16)
1.67 (1.37–2.05)
1.54 (1.26–1.88)
49–51
57/1,432
1.58 (1.21–2.06)
1.51 (1.16–1.96)
1.39 (1.07–1.81)
≥52
121/3,445
1.39 (1.16–1.67)
1.35 (1.12–1.62)
1.30 (1.08–1.56)
Oophorectomy
No (Ref.)
4,714/177,987
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Yes
227/5,469
1.58 (1.38–1.81)
1.45 (1.27–1.66)
1.31 (1.14–1.50)
Age at oophorectomy, year
No oophorectomy (Ref.)
4,714/177,987
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
<45
62/1,308
1.80 (1.40–2.32)
1.54 (1.20–1.98)
1.33 (1.03–1.71)
45–48
47/1,015
1.75 (1.31–2.33)
1.60 (1.20–2.14)
1.40 (1.05–1.88)
49–51
39/973
1.53 (1.12–2.10)
1.43 (1.05–1.97)
1.28 (0.93–1.76)
≥52
70/2,035
1.31 (1.03–1.66)
1.26 (0.99–1.60)
1.19 (0.94–1.51)
Model 1: age (year), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index.
Model 2: model 1 + smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Model 3: model 2 + hormone replacement therapy (ever, never).
Both hysterectomy (HR = 1.45; 95% CI: 1.32–1.59) and oophorectomy (HR = 1.31; 95% CI: 1.14–1.50) were associated with a higher risk of cholecystectomy. The associations tended be attenuated with increasing age at hysterectomy or oophorectomy, respectively. In particular, oophorectomy at ≥52 years old was not associated with risk of cholecystectomy (HR = 1.19; 95% CI: 0.94–1.51; Table 2). These associations for hysterectomy or oophorectomy and risk of cholecystectomy were similar when excluding non-menopausal women from the analyses (Supplementary Table 2).
We performed an additional analysis considering type of and age at menopause. Regardless of age at menopause, natural menopause was not associated with risk of cholecystectomy (Table 3). As compared with non-menopausal women, women with surgical menopause at <45 (HR = 1.57; 95% CI: 1.34–1.85) or 45 to 51 years old (HR = 1.42; 95% CI: 1.17–1.74), but not those with the surgery at ≥52 years old (HR = 1.20; 95% CI: 0.85–1.70) had a higher risk of cholecystectomy.
Table 3
Status and type of menopause and risk of cholecystectomy in women, according to age at menopause.
Type and age of menopause
Cases/total
Model 1, HR (95% CI)
Model 2, HR (95% CI)
Model 3, HR (95% CI)
Not menopausal (Ref.)
1,477/57,960
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Natural menopause at age <45 years
333/11,122
1.24 (1.08–1.42)
1.17 (1.02–1.34)
1.07 (0.93–1.23)
Natural menopause at age 45–51 years
1,498/56,697
1.10 (1.00–1.22)
1.09 (0.98–1.20)
1.04 (0.94–1.15)
Natural menopause at age ≥52 years
1,290/49,972
1.09 (0.98–1.22)
1.05 (0.95–1.18)
1.01 (0.90–1.13)
Surgical menopause at age <45 years
226/4,653
2.06 (1.75–2.41)
1.80 (1.53–2.11)
1.57 (1.34–1.85)
Surgical menopause at age 45–51 years
132/3,242
1.74 (1.43–2.12)
1.62 (1.34–1.98)
1.42 (1.17–1.74)
Surgical menopause at age ≥52 years
35/1,031
1.47 (1.04–2.08)
1.34 (0.95–1.90)
1.20 (0.85–1.70)
Model 1: age (year), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index.
Model 2: model 1 + smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Model 3: model 2 + hormone replacement therapy (ever, never).
HRT use and risk of cholecystectomy
After multivariable adjustment, HRT use (ever vs. never) was associated with a higher risk of cholecystectomy (HR = 1.29, 95% CI: 1.21–1.38). This association appeared to be independent of age when initiating HRT, duration of HRT use, and status and type of menopause (Table 4). For example, when compared with women who never used HRT, the highest risk was observed among women who ever used HRT and had surgical menopause (HR = 1.79; 95% CI: 1.58–2.02). These results remained significant after further adjustment of healthy dietary score. These associations for HRT use and risk of cholecystectomy were similar after excluding non-menopausal women from the analyses (Supplementary Table 3).
Table 4
Hormone replacement therapy (HRT) and risk of cholecystectomy in women.
Cases/total
Model 1, HR (95% CI)
Model 2, HR (95% CI)
Model 3, HR (95% CI)
HRT use
Never (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
Ever
1,886/59,155
1.34 (1.25–1.43)
1.35 (1.27–1.44)
1.29 (1.21–1.38)
Age when initiated HRT, years
Never use (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
<45
384/10,550
1.49 (1.33–1.66)
1.40 (1.25–1.55)
1.28 (1.15–1.44)
45–48
519/15,161
1.41 (1.28–1.55)
1.42 (1.28–1.56)
1.36 (1.13–1.50)
49–51
464/15,356
1.26 (1.13–1.39)
1.33 (1.20–1.47)
1.29 (1.16–1.43)
≥52
349/13,430
1.09 (0.97–1.22)
1.17 (1.04–1.32)
1.16 (1.03–1.30)
Duration of HRT use, years
Never use (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
1.00 (Ref.)
≤ 4
454/14,680
1.27 (1.15–1.41)
1.25 (1.13–1.39)
1.23 (1.10–1.36)
5–9
401/13,910
1.19 (1.07–1.33)
1.23 (1.10–1.37)
1.19 (1.07–1.33)
10–14
240/8,286
1.20 (1.04–1.37)
1.29 (1.12–1.47)
1.21 (1.06–1.40)
≥15
91/2,552
1.47 (1.19–1.82)
1.48 (1.20–1.84)
1.33 (1.07–1.66)
HRT use
Never (Ref.)
3,105/125,522
1.00 (Ref.)
1.00 (Ref.)
NA
Ever and pre-menopause
91/2,095
1.73 (1.41–2.14)
1.68 (1.36–2.07)
NA
Ever and natural menopause
1,489/50,290
1.24 (1.15–1.32)
1.26 (1.18–1.36)
NA
Ever & surgical menopause
306/6,770
1.92 (1.70–2.16)
1.79 (1.58–2.02)
NA
Model 1: age (years), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), and Townsend deprivation index.
Model 2: model 1 + smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Model 3: model 2 + status and type of menopause (pre-menopause, natural menopause, surgical menopause).
Joint analysis of menopausal characteristics and HRT use with risk of cholecystectomy
We then performed a further analysis jointly considering menopausal status, type of menopause, history of HRT use, and age at first HRT use. When compared with women who were naturally-menopausal and had never used HRT, non-menopausal women who ever used HRT had a 65% (HR = 1.65; 95% CI: 1.33–2.04) higher risk of cholecystectomy, while naturally-menopausal women who ever used HRT only had a modestly higher risk (regardless of time when starting HRT; Figure 1). The highest risk was observed for women who had surgical menopause and started HRT use before menopause (HR = 2.28, 95% CI: 1.70–3.04), followed by surgically-menopausal women who started HRT during or after the menopausal process (HR = 1.73; 95% CI: 1.51–1.98). These results were largely similar after further adjusting for the healthy dietary score (Supplementary Figure 2).
Figure 1
Joint association of menopause status, menopause type, and time at initiating HRT with risk of cholecystectomy in women. Results were adjusted for age (year), ethnic group (White, Asian or Asian British, Black or Black British, mixed ethnicities), Townsend deprivation index, smoking status (never, former, current), pack-years of smoking (for current smokers), drinking status (never, former, current), drinking amounts (g/day), total physical activity (MET-h/week), BMI (kg/m2), diabetes (yes, no), and use of statins (yes, no), aspirin (yes, no), and non-steroidal anti-inflammatory drugs (yes, no).
Discussion
In this analysis of large, population-based prospective study of middle-aged and older women, we assessed several menopausal characteristics and HRT use in relation to risk of cholecystectomy. Our findings showed that natural menopause, regardless of menopausal age, was not associated with risk of cholecystectomy, while surgical menopause, especially that occurred at a relatively younger age, was associated with a higher risk of cholecystectomy. Ever use of HRT was associated with a higher risk of cholecystectomy. In particular, women who were surgically-menopausal and started HRT before menopause had 2.28-fold risk of cholecystectomy when compared with women who were naturally-menopausal and never used HRT.
There are only a few studies regarding the effect of specific menopausal characteristics on gallbladder disease, and the findings have been conflicting. In the Biliary Tract Cancers pooling Project, increasing reproductive years were associated with an elevated risk of gallbladder cancer in non-Asian women (9), whereas in a case-control study, earlier age of menopause reflecting shorter reproductive lifespan was associated with a higher risk of gallbladder cancer (10). Moreover, in a case-control study in China, menopausal status was not associated with gallstones (19). In the present study, natural menopause, regardless of menopausal age, was not associated with risk of cholecystectomy, while surgical menopause was associated with a higher risk of cholecystectomy. Further stratification by age suggested that only surgical menopause occurred at a relatively younger age was associated with the risk of cholecystectomy. Such an association is not explained by HRT use following surgical menopause at a younger age, as there was a joint influence of HRT use and surgical menopause on risk of cholecystectomy. Furthermore, sensitivity analyses restricted to those who were menopausal at baseline showed that the associations of hysterectomy and bilateral oophorectomy with risk of cholecystectomy remained significant. Several explanations for the association between surgical menopause and risk of cholecystectomy have been proposed. Firstly, surgical menopause could result in a rapid loss of endogenous estrogen compared with natural menopause, which can alter cholesterol metabolism and promote gallstone formation. Secondly, experimental studies have shown that in the rodent ovariectomy model, fat mass, hepatic triglyceride content, and lipidosis were increased compared to controls (20), which may promote the precipitation of cholesterol in bile. Finally, changes in lifestyle factors such as diet and physical activity after undergoing uterine and ovarian removal surgeries may contribute to the development of gallbladder diseases. Future research is needed to explore the underlying mechanisms.
Several prospective observational studies have shown a relatively consistent relationship between HRT use and an elevated risk of gallbladder disease or cholecystectomy (12–15, 21). In the Heart and Estrogen/Progestin Replacement Study, a placebo-controlled trial of postmenopausal hormone therapy for coronary heart disease, treatment with estrogen plus progestin increased the risk for biliary tract surgery by 38% (22). A nested case-control study conducted using the UK Clinical Practice Research Datalink has suggested that combination HRT formulations and oral administrations were associated with higher risk of gallbladder cancer (23). The effect of HRT on the development of gallbladder disease was also evaluated in the Women's Health Initiative (WHI) hormone trials (24). In the WHI trials, 22,579 menopausal women without prior cholecystectomy were randomized to receive conjugated equine estrogen or placebo (for those with hysterectomy), or estrogen plus progestin or placebo (for those without hysterectomy). The results showed that HRT increased risk of any gallbladder disease or surgery after more than 5 years of follow-up (24). In addition, different formulations did not appear to influence the effect of hormone therapy, as the increases in the risk were similar for estrogen alone (HR = 1.67; 95% CI: 1.35–2.06) and estrogen with progestin (HR = 1.59; 95% CI: 1.28–1.97) (24). This is in line with results from the Million Women Study (12) but contradicted those from the French E3N study (13) in which oral estrogen alone, but not oral estrogen with progestin, was associated with a higher risk of cholecystectomy. With respect to hormone administration, the Million Women Study (12) found that the elevation in the risk of cholecystectomy with transdermal was substantially weaker than that with oral therapy. Likewise, in the French E3N study (13), transdermal estrogen, regardless of the formulations, was not associated with risk of cholecystectomy. Information on the formulations or administration (oral or transdermal) of hormones was not available in the UK Biobank, precluding further specific analyses. Moreover, women with hormone therapy in the WHI trials were more likely to receive cholecystectomy rather than other biliary tract surgeries (24), indicating the specificities of the effects of hormone therapy on gallbladder disease. In the current study, we found that ever use of HRT was associated with a higher risk of cholecystectomy, independent of menopausal characteristics, which is in agreement with the WHI findings (24).
Several biologically plausible pathways have been proposed to interpret the associations between HRT use and cholecystectomy. First, exogenous estrogen therapy could modify lipid metabolism, cause biliary cholesterol saturation, and promote precipitation of cholesterol in the bile (25). Second, HRT use inhibits chenodeoxycholic acid secretion and accelerates nucleation of cholesterol monohydrate crystals (8). Third, estrogen therapy could reduce gallbladder motility, which increases bile crystallization and contributes to gallstone formation (26). Finally, estrogen therapy has been suggested to have promoting effects on the inflammation (6), which is a pathological mechanism in the development of gallbladder disease (27).
Strengths of our study lie in its large sample size, the prospective design, and the long-term follow up. Our analysis simultaneously evaluated various menopausal characteristics (including menopausal status and age at and type of menopause) and HRT use as well as their independent and joint influence on the risk of cholecystectomy in women, after taking into account a wide arrange of known risk factors. Moreover, our study is further strengthened by the sensitivity analyses restricted to those who had been menopausal at baseline, which further validate the observed relationships between menopausal characteristics and risk of cholecystectomy.
Several potential limitations should be acknowledged when interpreting our findings. First, given the observational design of the current study, the potential influence residual confounding on our results cannot be entirely excluded. Second, information on menopausal characteristics and HRT use was collected through participants' self-report, which is prone to recall bias leading to misclassification of the exposures. Given the prospective design of the study, such misclassification is likely to be non-differential and could attenuate the examined relationships. Third, as mentioned above, we could not evaluate whether the association for HRT differed by formulations or administration of hormones because of the lack of data in the UK Biobank. Fourth, our analysis considered the risk for cholecystectomy which is more clinically relevant endpoint, and did not assess the risk for global gallbladder disease, although hormone therapy has been similarly associated with both endpoints in clinical trials (24). Finally, our analysis included middle-aged and older women in the UK who are predominantly of white ethnic background, and the findings need to be confirmed for other populations.
Conclusions
In summary, surgical menopause at a young age, rather than natural menopause, was associated with higher risk of cholecystectomy. Ever use of HRT was associated with a higher risk of cholecystectomy independently of other menopausal characteristics, with the highest risk for surgically-menopausal women who initiated HRT before menopause. |
Title: Pediatric Ovarian Yolk Sac Tumor Mimicking Appendicitis: A Case Report From a Low‐Resource Setting | Body:
1
Introduction
Ovarian yolk sac tumors (OYSTs), also known as endodermal sinus tumors, are rare and highly malignant germ cell tumors [1, 2]. They account for approximately 1% of all ovarian cancers and are most commonly diagnosed in children and young adults [3, 4]. The incidence of OYSTs is estimated to be about 0.1–0.3 per 100,000 females per year [5]. These tumors are characterized by rapid growth and early metastasis, making timely diagnosis and treatment critical for improving patient outcomes. Alpha‐fetoprotein (AFP) is often elevated in patients with OYSTs, serving as a crucial tumor marker for diagnosis and monitoring response to therapy [6].
In pediatric patients, ovarian yolk sac tumors are a rare but critical differential diagnosis when presenting with acute abdominal pain, which can often mimic more common conditions such as appendicitis. In Africa and other low‐resource settings, the burden of OYSTs, like other pediatric cancers, is not well‐documented because of limited cancer registries and diagnostic facilities [7]. Despite these limitations, germ cell tumors remain a significant portion of pediatric malignancies on the continent [5, 8]. Timely diagnosis and management are further hindered by the lack of specialized diagnostic tools and oncology services [9, 10]. This case highlights the diagnostic difficulty and need for a high index of suspicion when managing pediatric patients in these settings.
2
Case Presentation
2.1
Case History
A 6‐year‐old female presented to our center with a one‐month history of abdominal pain, initially localized to the right inguinal region and later generalized, accompanied by slight abdominal fullness and intermittent low‐grade fevers. She also experienced postprandial non‐projectile vomiting over the past 2 weeks but denied any weight loss, night sweats, cough, chest pain, abnormal vaginal discharge, abnormal bowel habits, or changes in urinary patterns.
On admission, she was fully conscious and alert, mildly pale, with a respiratory rate of 23 breaths/min, axillary temperature of 39.4°C, blood pressure of 98/67 mmHg, pulse rate of 149 beats per minute, and oxygen saturation of 97% on room air. Her abdomen was slightly distended with symmetrical contours, generalized tenderness, more pronounced in the right iliac region, and reduced bowel sounds. Her cardiovascular and respiratory examinations were normal. Based on the age and clinical presentation, the initial clinical diagnosis was acute appendicitis.
2.2
Methods
Laboratory investigations revealed a white blood cell count of 12.07 × 109/L, hemoglobin of 12.6 g/dL, and a platelet count of 440 × 109/L. Serum creatinine was 30 μmol/L, BUN was 2.22 mmol/L, serum potassium was 3.73 mmol/L, and sodium was 134.30 mmol/L. An abdominal ultrasound showed an intra‐abdominal mass of unclear origin. A CT scan of the abdomen and pelvis revealed an appendicular abscess and functional small bowel obstruction. The Alvarado's score was calculated to be 7/10, warranting immediate surgery.
In preparation for laparotomy, the patient was managed with intravenous ceftriaxone, metronidazole, and fluids, kept nil orally, and an NGT was inserted. After counseling the parents and obtaining consent, the child was taken for laparotomy. A lower midline incision was used because of the abscess, allowing better access for complete drainage and exploring the peritoneal cavity. Intraoperative findings however revealed a large (12 by 10 cm) right‐sided ovarian mass with features suggestive of a hemorrhagic ovarian cyst or ovarian tumor (Figure 1). Intraoperative consultation with the gynecology team led to the decision to perform a cystectomy, excising the mass while preserving the ovary and fallopian tube. Histological analysis later confirmed a right ovarian yolk sac tumor (Figure 2).
FIGURE 1
(A) Surgeon holding the ovarian mass attached to the right fallopian tube (yellow arrow showing fimbriae), (B) Post excision of the mass reserving the fallopian tube and ovary.
FIGURE 2
(A) Histopathology of ovarian yolk sac tumor demonstrating microscystic pattern characterized by loose meshwork of anastomosing channels and variably sized cysts; individual tumor cells have a signet ring‐like morphology and the cysts containing eosinophilic hyaline globules and amorphous, eosinophilic acellular basement membrane‐like material; H&E 200 × original magnification. (B) Photomicroscopy of the tumor displaying immunopositivity of the tumor cells with alpha‐fetoprotein (AFP) antibody; IHC 100 × original magnification.
2.3
Conclusion and Results
Postoperatively, the patient's recovery was uneventful and was discharged on Day 5. At her 4‐week follow‐up, her abdominal wound had healed well, and she was referred to the oncology department for further evaluation and management. Her follow‐up CBC was normal with a hemoglobin of 13 g/dL, β‐hCG was 0.43 mIU/mL, α‐fetoprotein was 0.500 IU/mL, and lactate dehydrogenase was 407 U/L. She remains asymptomatic after 6 months and continues 6‐monthly follow‐up to monitor tumor markers.
2.1
Case History
A 6‐year‐old female presented to our center with a one‐month history of abdominal pain, initially localized to the right inguinal region and later generalized, accompanied by slight abdominal fullness and intermittent low‐grade fevers. She also experienced postprandial non‐projectile vomiting over the past 2 weeks but denied any weight loss, night sweats, cough, chest pain, abnormal vaginal discharge, abnormal bowel habits, or changes in urinary patterns.
On admission, she was fully conscious and alert, mildly pale, with a respiratory rate of 23 breaths/min, axillary temperature of 39.4°C, blood pressure of 98/67 mmHg, pulse rate of 149 beats per minute, and oxygen saturation of 97% on room air. Her abdomen was slightly distended with symmetrical contours, generalized tenderness, more pronounced in the right iliac region, and reduced bowel sounds. Her cardiovascular and respiratory examinations were normal. Based on the age and clinical presentation, the initial clinical diagnosis was acute appendicitis.
2.2
Methods
Laboratory investigations revealed a white blood cell count of 12.07 × 109/L, hemoglobin of 12.6 g/dL, and a platelet count of 440 × 109/L. Serum creatinine was 30 μmol/L, BUN was 2.22 mmol/L, serum potassium was 3.73 mmol/L, and sodium was 134.30 mmol/L. An abdominal ultrasound showed an intra‐abdominal mass of unclear origin. A CT scan of the abdomen and pelvis revealed an appendicular abscess and functional small bowel obstruction. The Alvarado's score was calculated to be 7/10, warranting immediate surgery.
In preparation for laparotomy, the patient was managed with intravenous ceftriaxone, metronidazole, and fluids, kept nil orally, and an NGT was inserted. After counseling the parents and obtaining consent, the child was taken for laparotomy. A lower midline incision was used because of the abscess, allowing better access for complete drainage and exploring the peritoneal cavity. Intraoperative findings however revealed a large (12 by 10 cm) right‐sided ovarian mass with features suggestive of a hemorrhagic ovarian cyst or ovarian tumor (Figure 1). Intraoperative consultation with the gynecology team led to the decision to perform a cystectomy, excising the mass while preserving the ovary and fallopian tube. Histological analysis later confirmed a right ovarian yolk sac tumor (Figure 2).
FIGURE 1
(A) Surgeon holding the ovarian mass attached to the right fallopian tube (yellow arrow showing fimbriae), (B) Post excision of the mass reserving the fallopian tube and ovary.
FIGURE 2
(A) Histopathology of ovarian yolk sac tumor demonstrating microscystic pattern characterized by loose meshwork of anastomosing channels and variably sized cysts; individual tumor cells have a signet ring‐like morphology and the cysts containing eosinophilic hyaline globules and amorphous, eosinophilic acellular basement membrane‐like material; H&E 200 × original magnification. (B) Photomicroscopy of the tumor displaying immunopositivity of the tumor cells with alpha‐fetoprotein (AFP) antibody; IHC 100 × original magnification.
2.3
Conclusion and Results
Postoperatively, the patient's recovery was uneventful and was discharged on Day 5. At her 4‐week follow‐up, her abdominal wound had healed well, and she was referred to the oncology department for further evaluation and management. Her follow‐up CBC was normal with a hemoglobin of 13 g/dL, β‐hCG was 0.43 mIU/mL, α‐fetoprotein was 0.500 IU/mL, and lactate dehydrogenase was 407 U/L. She remains asymptomatic after 6 months and continues 6‐monthly follow‐up to monitor tumor markers.
3
Discussion
Ovarian neoplasms are classified by cell origin into three main groups: germ cell, sex cord‐stromal, and epithelial tumors. Prompt differentiation between benign and malignant lesions is essential to reduce morbidity and mortality and to prioritize fertility preservation, especially in pediatric patients. Although most ovarian tumors in pediatric populations are benign, approximately 10%–30% are malignant, with malignancies more common in adolescent girls. Pediatric ovarian tumors represent 1%–3% of all childhood cancers. Diagnosing these tumors is often challenging because of their nonspecific symptoms and varied, subtle imaging findings. Early and accurate diagnosis is key to managing these cases effectively and improving outcomes for pediatric patients [11].
In cases of ovarian tumors, surgeons often face challenging intraoperative decisions, particularly when encountering an unknown pathology. They must determine whether to perform a simple resection, attempt organ‐salvaging techniques, or proceed with staging based on real‐time findings. For many patients, including emergency cases, it is often impractical to conduct a biopsy with frozen sections or obtain a definitive pathological diagnosis during surgery [12]. Among serum tumor markers, AFP is particularly specific for malignant tumors, with others including beta‐human chorionic gonadotropin (β‐hCG), carcinoembryonic antigen (CEA), human placental alkaline phosphatase (hPLAP), and cancer antigen 125 (CA125) providing additional information. However, limitations arise, especially in resource‐limited settings, where these tumor markers may not be available during off‐hours or may take days to return results, hindering timely surgical decision‐making. Moreover, it is important to note that these markers are positive in only about 54% of malignant cases, meaning that a negative result does not conclusively rule out malignancy [13]. These constraints highlight the complexities of intraoperative management and the need for clinical judgment when comprehensive resources are not immediately accessible.
Ovarian yolk sac tumors, though rare, require a high index of suspicion, especially in pediatric patients presenting with abdominal masses and elevated AFP levels [2, 14]. In this case, the initial presentation mimicked appendicitis, which is a common diagnostic pitfall. The systemic inflammatory response caused by the tumor likely explains the fever and tachycardia. Advanced imaging and intraoperative findings were crucial in identifying the true nature of the mass.
Ovarian masses in children and adolescents often present with nonspecific abdominal symptoms, which can easily mimic other conditions, particularly acute appendicitis. Pomeranz et al. highlight that in a series, 59% of cases with ovarian cysts presented with acute abdominal pain, initially suspected to be appendicitis. Differential diagnoses for acute abdominal pain with fever are broad and should include conditions such as diverticulitis (including Meckel's diverticulitis), urolithiasis, enterocolitis, mesenteric adenitis, ovarian torsion, and pelvic inflammatory disease [15, 16] (PID). To accurately diagnose these conditions, various laboratory and imaging studies are valuable. However, clinicians must also consider the limitations of resource availability and the financial implications of extensive testing, especially in resource‐limited settings. A balanced approach to investigation can help in avoiding misdiagnosis while also ensuring efficient and accessible patient care.
This case occurred in a low‐resource setting where advanced diagnostic tools like MRI and preoperative tumor markers are limited. Laparoscopy was not considered due to the lack of specialized equipment and trained surgeon out of hours. Instead, clinical evaluation and basic imaging, such as ultrasound and CT scans, remain vital tools for diagnosis. Given the large size of the mass and its intra‐abdominal location, a lower midline incision was chosen to provide optimal access.
Although a cystectomy was performed to preserve ovarian function, this approach diverges from strict oncology guidelines, which recommend removal of the affected ovary and tube for better staging and prognosis. In resource‐rich settings, comprehensive fertility‐preserving surgical staging is the standard of care. However, in low‐resource settings, such decisions often prioritize immediate clinical outcomes and patient fertility, with follow‐up care coordinated postoperatively.
The patient was referred to the oncology department promptly after surgery for further management, including chemotherapy. Studies have shown that the BEP (bleomycin, etoposide, and cisplatin) regimen is effective in treating OYSTs, significantly improving survival rates [17, 18]. However, late presentation and limited access to comprehensive cancer care remain challenges in low‐resource settings.
To avoid similar misdiagnoses, clinicians should maintain a broad differential diagnosis for pediatric abdominal masses and use available diagnostic tools efficiently [19]. Multidisciplinary collaboration between surgeons, gynecologists, and oncologists is critical for ensuring optimal outcomes.
This case demonstrates the importance of considering ovarian yolk sac tumors in the differential diagnosis of pediatric patients with abdominal masses. Early recognition, appropriate surgical intervention, and histological confirmation are crucial for effective management. The patient's positive outcome underscores the efficacy of a multidisciplinary approach and adherence to standard treatment protocols. Continued follow‐up and chemotherapy are essential to ensure long‐term remission and monitor for recurrence. This case contributes to the body of knowledge on OYSTs and underscores the need for vigilance in diagnosing and managing rare pediatric tumors, particularly in low‐resource settings.
Author Contributions
William Nkenguye: writing – original draft. Alex Mremi: formal analysis, resources. Peter Minja: data curation, methodology. Jay Lodhia: conceptualization, writing – review and editing.
Ethics Statement
Approval was obtained from the departments of General surgery and the appropriate hospital institutional review board has approved the publication of this case report.
Consent
Written informed consent was obtained from the child's parents for publication for this case report; additionally, accompanying images have been censored to ensure that the patient cannot be identified. A copy of the consent is available on record. |
Title: Adapting and Validating a Patient Prompt List to Assist Localized Prostate Cancer Patients with Treatment Decision Making | Body:
1. Introduction
Prostate cancer is the most common cancer among American men. In 2024, 299,010 men are expected to be diagnosed, and 32,250 will die from the disease [1]. Despite the high prevalence of the disease, decision making in treatment is fraught with challenges. Some of the well-documented clinical and patient-level factors that complicate treatment care planning for patients with localized disease (stage T1 or T2) include the availability of different therapeutic options (e.g., surgery, radiation, and active surveillance), the existence of treatment side effects [2], uncertainty in predicting who will experience known side effects [3], and differences in patients’ tolerance of outcomes that compromise quality of life [4].
The duration of a cancer office visit can vary, but studies have shown that the average time for an ambulatory prostate cancer consultation is 18.5 min (SE ± 0.7 min) [5]. During a visit involving treatment decision making, physicians need to communicate a vast amount of information, including diagnosis and treatment options [6]. In addition to answering patient questions, physicians must also use this time to provide emotional support and perform knowledge checks to assess patients’ comprehension of the information that is presented to them [7].
Even though treatment consultations last slightly longer than general appointments, many newly diagnosed patients leave cancer treatment decision making visits feeling that their concerns have not been adequately addressed [8]. Men who are unable to express themselves effectively during the time constraints of an emotionally charged treatment consultation can become demoralized, anxious, and confused and make decisions that are misaligned with their preferences or lifestyles [9]. Cancer prevention and control experts can improve the patient treatment decision making experience by helping men to optimize the time they spend with physicians.
Question prompt lists are low-tech, easy-to-use aids that can help patients develop communication plans for a clinical interaction [10]. These lists can be generic or disease-specific and include specific questions or focus on general content areas. In the field of oncology, prompt lists have been used with multiple disease foci (e.g., brain [11], breast [12,13], head and neck [14], and melanoma [15]) in several countries (e.g., Australia [10,14,15,16], Canada [17], France [18], India [19], Japan [20], and the US [21]) at different points of a patient’s illness (e.g., genetic testing [22], treatment [19], and palliative care [14,18]). Prompt list studies have shown that this communication intervention can enhance patient engagement and improve the exchange of information, which in turn helps patients to find better solutions to their health problems. Despite the documented benefits of this form of communication assistance, there are a lack of validated prompt lists that have been designed for use with prostate cancer patients in the U.S.
The treatment decision making experience in the U.S. differs from other countries in many ways. For example, in nations with universal public health insurance (e.g., Canada, UK), treatment decisions are less influenced by out-of-pocket costs than in the U.S. [23]. The treatment culture for low-risk cancers tends to be more aggressive in the U.S. compared to other countries in which there is greater trust in long-term monitoring without immediate intervention [24]. The U.S. also has a higher number of specialized treatment centers that can give patients easier access to advanced therapies or different treatment options [25]. U.S. cancer patients also have greater access to information through online resources and advocacy groups than in countries where access to information can be more controlled or limited. Given these and other reasons not mentioned, a prompt list developed in other countries may fail to resonate with U.S. patients and providers without sociocultural tailoring.
This study sought to improve the quality of communication between prostate cancer patients and healthcare providers by adapting and validating a patient prompt list for use with localized prostate cancer patients who are facing treatment decisions. The validation process included (1) conducting a literature review of existing questions to generate a draft prompt list, (2) customizing the prompt list based on expert feedback [content validity indices], and (3) assessing the prompt list’s usefulness based on patient evaluations.
2. Materials and Methods
2.1. Participants
The data presented here are part of a larger study on quality of life among prostate cancer patients. The larger study collected participant observations, ethnographic interviews, Delphi panels, and survey research data from over 100 prostate cancer patients, their significant others, and treating healthcare providers. The current research analyzed a subset of unpublished Delphi panel data with healthcare providers and survey data from patients.
A total of 6 healthcare providers specializing in prostate cancer care and 30 patients were purposively recruited to participate at a teaching hospital and through community outlets. The provider group included 2 attending urologists, 2 urology fellows, an oncology nurse, and an oncology social worker. Providers were eligible to participate if they were employed at the recruiting hospital; licensed in their field; and had a minimum of two years experience of providing care to prostate cancer patients. Patients were eligible to participate if they self-identified as white or African American; could read and write English; and were treated for or monitoring localized prostate cancers (stage T1 or T2) within 5 years of recruitment. All healthcare providers were recruited through targeted outreach to department and tumor board meetings by the study PI. Patients were recruited via electronic medical records, word-of-mouth, posted flyers, and advertisements in local newspapers.
Ethical Approval and Informed Consent
All healthcare providers and patients provided written informed consent to participate prior to enrollment. All study-related information that existed in paper form was kept in a locked filed cabinet in the PI’s office. All study data that existed electronically were kept in a password-protected database on the PI’s work computer. The study was approved by the Institutional Review Boards at Roswell Park Cancer Institute (I-198711) and Georgia Southern University (H-14043).
2.2. Procedures
2.2.1. Literature Review and Selection of Patient Prompt List Questions
A comprehensive literature review was conducted to identify candidate questions that could be included on the treatment decision making prompt list for men with early stage disease. Members of the research team searched the PubMed database using the search terms prostate cancer, question prompt list, patient prompt list, and treatment information needs. This search returned no adaptable published prompt lists [26]. However, several articles included questions that were deemed potentially useful for creating a new prompt list [27,28,29,30,31,32]. After hard or electronic copies of the selected articles were obtained, the candidate questions were abstracted and catalogued in an Excel spreadsheet for healthcare providers to review.
2.2.2. Validation of the Patient Prompt List
Each healthcare provider was asked to independently review the list of 27 candidate questions and provide his/her opinion on each question’s relevance to a patient’s encounter during a treatment decision making consultation. The goal was to create a streamlined list that could be easily incorporated into a prostate cancer treatment consultation without overwhelming patients [33]. With this goal in mind, the experts were asked to prioritize questions that were essential and eliminate any that were redundant or less relevant. Each item was rated on a 4-point scale, with response options being 1 = not at all relevant, 2 = somewhat relevant, 3 = quite relevant, and 4 = highly relevant. The scores produced from experts’ ratings allowed us to produce two content validity indices.
The item-level content validity index (I-CVI) is an item-level validity measure. This index represents the proportion of individual experts who rate an item as quite or highly relevant. With this index, expert ratings of 1 (not at all relevant) or 2 (somewhat relevant) are considered not relevant, and ratings of 3 (quite relevant) or 4 (very relevant) are relevant. The I-CVI ranges from 0–1, with values ≥ 0.80 considered acceptable [34]. The CVI was calculated using the formula below.
I-CVI = Number of experts rating the item as 3 or 4/(Total number of experts)
The scale-level content validity index (S-CVI) is a scale-level index that measures the collective relevance of all items included in the prompt list. The S-CVI is computed by averaging the I-CVIs for all individual prompt list questions. The S-CVI ranges from 0–1, and values ≥ 0.90 are considered acceptable [35]. The S-CVI is calculated using the following formula:S-CVI = (∑I-CVI)/(Number of items)
Questions were retained on the prompt list if they met the I-CVI and S-CVI thresholds of acceptability mentioned above. Questions that did not meet the thresholds of acceptability for the I-CVI were revised and re-evaluated if a revision recommendation was made by a healthcare expert. The revised questions were then subjected to a second round of evaluation to determine if the changes improved their I-CVI scores. This iterative revision process helped to ensure that potentially valuable questions were not prematurely excluded. Questions that did not meet the retention cut-offs and did not receive any suggestions for revisions were eliminated from the list. Experts were also instructed to suggest items for inclusion on the prompt list that were not included from the literature review. This final step was included to ensure the prompt list was comprehensive and reflective of current clinical practice. All suggested questions were subject to the same evaluation process as the original questions, and their relevance was assessed via the I-CVI and S-CVI.
2.2.3. Patient Prompt List Usefulness Evaluation
After the prompt list was finalized, 30 patients were given a paper version of the tool to assess its applicability from a user’s perspective. Each patient was asked to provide feedback on three areas: (a) the importance of each question on the list, (b) the perceived usefulness of the prompt list to them personally, and (c) the perceived usefulness of the prompt list for other men diagnosed with prostate cancer.
The importance of the list was assessed with the question, “Think back to when you were diagnosed with prostate cancer. How important was it for you learn about ______ to make the best treatment decision?” Response options included 1 = not at all important, 2 = a little important, 3 = somewhat important, and 4 = very important. This item was designed to determine if the questions included on the prompt list addressed the core concerns that patients typically have when weighing their treatment options.
Perceived helpfulness to oneself was evaluated with the question, “Now that you have reviewed the prompt list in its entirety, how helpful would it had been for you to receive this list when you were first diagnosed?” Response options included 1 = not at all helpful, 2 = a little helpful, 3 = helpful, and 4 = very helpful. This feedback helped the research team understand how well the prompt list addressed common uncertainties or gaps in knowledge that men might face when deciding between treatment options.
Perceived helpfulness to others was evaluated with the question, “How helpful do you think this prompt list would be to other men who are newly diagnosed with prostate cancer?” Response options included: 1 = Not at all helpful, 2 = A little helpful, 3 = Helpful, and 4 = Very helpful. This question sought to capture participants’ perceptions of how the prompt list could benefit men facing prostate cancer for the first time.
2.2.4. Questions Grouping into Thematic Domains
Following the content validation and patient evaluation process, a subset of participants (3 patients, 2 health experts) were asked to participate in an individually moderated card sorting activity to organize the final prompt list questions into meaningful categories. Each participant met independently with a research staff member and was provided a packet containing each final prompt list question listed separately on an index card. Participants were instructed to sort all of the cards into at least two groups based on their perceptions of similarity. Participants could have provided labels for their card groupings, but labeling was not a requirement. No predefined categories were given or suggested to participants to allow for natural clustering.
The research team uploaded each participants’ grouped questions into NVivo 12™, which is a piece of qualitative data management and analysis software. The cluster analysis function was used to determine similarities between all participant produced groupings (nodes). Vertical dendrograms were produced to allow the research team to visually inspect common themes (nodes) across participants’ groupings using the ‘questions coding similarities’ clustering option. This qualitative analysis focused on the frequency with which prompt list questions were grouped together. The research team provided labels for each domain after agreement on the questions’ clustering was obtained.
2.3. Data Analysis
Both content validity indices were calculated in Microsoft Excel® (Excel 2019). Univariate statistics were produced in the Statistical Package for the Social Sciences (SPSS 25, IBM Corp. Armonk, NY, USA) to describe participants’ evaluations of the prompt list importance and its perceived helpfulness. NVivo 12™ was used to group prompt list questions into thematic domains.
2.1. Participants
The data presented here are part of a larger study on quality of life among prostate cancer patients. The larger study collected participant observations, ethnographic interviews, Delphi panels, and survey research data from over 100 prostate cancer patients, their significant others, and treating healthcare providers. The current research analyzed a subset of unpublished Delphi panel data with healthcare providers and survey data from patients.
A total of 6 healthcare providers specializing in prostate cancer care and 30 patients were purposively recruited to participate at a teaching hospital and through community outlets. The provider group included 2 attending urologists, 2 urology fellows, an oncology nurse, and an oncology social worker. Providers were eligible to participate if they were employed at the recruiting hospital; licensed in their field; and had a minimum of two years experience of providing care to prostate cancer patients. Patients were eligible to participate if they self-identified as white or African American; could read and write English; and were treated for or monitoring localized prostate cancers (stage T1 or T2) within 5 years of recruitment. All healthcare providers were recruited through targeted outreach to department and tumor board meetings by the study PI. Patients were recruited via electronic medical records, word-of-mouth, posted flyers, and advertisements in local newspapers.
Ethical Approval and Informed Consent
All healthcare providers and patients provided written informed consent to participate prior to enrollment. All study-related information that existed in paper form was kept in a locked filed cabinet in the PI’s office. All study data that existed electronically were kept in a password-protected database on the PI’s work computer. The study was approved by the Institutional Review Boards at Roswell Park Cancer Institute (I-198711) and Georgia Southern University (H-14043).
Ethical Approval and Informed Consent
All healthcare providers and patients provided written informed consent to participate prior to enrollment. All study-related information that existed in paper form was kept in a locked filed cabinet in the PI’s office. All study data that existed electronically were kept in a password-protected database on the PI’s work computer. The study was approved by the Institutional Review Boards at Roswell Park Cancer Institute (I-198711) and Georgia Southern University (H-14043).
2.2. Procedures
2.2.1. Literature Review and Selection of Patient Prompt List Questions
A comprehensive literature review was conducted to identify candidate questions that could be included on the treatment decision making prompt list for men with early stage disease. Members of the research team searched the PubMed database using the search terms prostate cancer, question prompt list, patient prompt list, and treatment information needs. This search returned no adaptable published prompt lists [26]. However, several articles included questions that were deemed potentially useful for creating a new prompt list [27,28,29,30,31,32]. After hard or electronic copies of the selected articles were obtained, the candidate questions were abstracted and catalogued in an Excel spreadsheet for healthcare providers to review.
2.2.2. Validation of the Patient Prompt List
Each healthcare provider was asked to independently review the list of 27 candidate questions and provide his/her opinion on each question’s relevance to a patient’s encounter during a treatment decision making consultation. The goal was to create a streamlined list that could be easily incorporated into a prostate cancer treatment consultation without overwhelming patients [33]. With this goal in mind, the experts were asked to prioritize questions that were essential and eliminate any that were redundant or less relevant. Each item was rated on a 4-point scale, with response options being 1 = not at all relevant, 2 = somewhat relevant, 3 = quite relevant, and 4 = highly relevant. The scores produced from experts’ ratings allowed us to produce two content validity indices.
The item-level content validity index (I-CVI) is an item-level validity measure. This index represents the proportion of individual experts who rate an item as quite or highly relevant. With this index, expert ratings of 1 (not at all relevant) or 2 (somewhat relevant) are considered not relevant, and ratings of 3 (quite relevant) or 4 (very relevant) are relevant. The I-CVI ranges from 0–1, with values ≥ 0.80 considered acceptable [34]. The CVI was calculated using the formula below.
I-CVI = Number of experts rating the item as 3 or 4/(Total number of experts)
The scale-level content validity index (S-CVI) is a scale-level index that measures the collective relevance of all items included in the prompt list. The S-CVI is computed by averaging the I-CVIs for all individual prompt list questions. The S-CVI ranges from 0–1, and values ≥ 0.90 are considered acceptable [35]. The S-CVI is calculated using the following formula:S-CVI = (∑I-CVI)/(Number of items)
Questions were retained on the prompt list if they met the I-CVI and S-CVI thresholds of acceptability mentioned above. Questions that did not meet the thresholds of acceptability for the I-CVI were revised and re-evaluated if a revision recommendation was made by a healthcare expert. The revised questions were then subjected to a second round of evaluation to determine if the changes improved their I-CVI scores. This iterative revision process helped to ensure that potentially valuable questions were not prematurely excluded. Questions that did not meet the retention cut-offs and did not receive any suggestions for revisions were eliminated from the list. Experts were also instructed to suggest items for inclusion on the prompt list that were not included from the literature review. This final step was included to ensure the prompt list was comprehensive and reflective of current clinical practice. All suggested questions were subject to the same evaluation process as the original questions, and their relevance was assessed via the I-CVI and S-CVI.
2.2.3. Patient Prompt List Usefulness Evaluation
After the prompt list was finalized, 30 patients were given a paper version of the tool to assess its applicability from a user’s perspective. Each patient was asked to provide feedback on three areas: (a) the importance of each question on the list, (b) the perceived usefulness of the prompt list to them personally, and (c) the perceived usefulness of the prompt list for other men diagnosed with prostate cancer.
The importance of the list was assessed with the question, “Think back to when you were diagnosed with prostate cancer. How important was it for you learn about ______ to make the best treatment decision?” Response options included 1 = not at all important, 2 = a little important, 3 = somewhat important, and 4 = very important. This item was designed to determine if the questions included on the prompt list addressed the core concerns that patients typically have when weighing their treatment options.
Perceived helpfulness to oneself was evaluated with the question, “Now that you have reviewed the prompt list in its entirety, how helpful would it had been for you to receive this list when you were first diagnosed?” Response options included 1 = not at all helpful, 2 = a little helpful, 3 = helpful, and 4 = very helpful. This feedback helped the research team understand how well the prompt list addressed common uncertainties or gaps in knowledge that men might face when deciding between treatment options.
Perceived helpfulness to others was evaluated with the question, “How helpful do you think this prompt list would be to other men who are newly diagnosed with prostate cancer?” Response options included: 1 = Not at all helpful, 2 = A little helpful, 3 = Helpful, and 4 = Very helpful. This question sought to capture participants’ perceptions of how the prompt list could benefit men facing prostate cancer for the first time.
2.2.4. Questions Grouping into Thematic Domains
Following the content validation and patient evaluation process, a subset of participants (3 patients, 2 health experts) were asked to participate in an individually moderated card sorting activity to organize the final prompt list questions into meaningful categories. Each participant met independently with a research staff member and was provided a packet containing each final prompt list question listed separately on an index card. Participants were instructed to sort all of the cards into at least two groups based on their perceptions of similarity. Participants could have provided labels for their card groupings, but labeling was not a requirement. No predefined categories were given or suggested to participants to allow for natural clustering.
The research team uploaded each participants’ grouped questions into NVivo 12™, which is a piece of qualitative data management and analysis software. The cluster analysis function was used to determine similarities between all participant produced groupings (nodes). Vertical dendrograms were produced to allow the research team to visually inspect common themes (nodes) across participants’ groupings using the ‘questions coding similarities’ clustering option. This qualitative analysis focused on the frequency with which prompt list questions were grouped together. The research team provided labels for each domain after agreement on the questions’ clustering was obtained.
2.2.1. Literature Review and Selection of Patient Prompt List Questions
A comprehensive literature review was conducted to identify candidate questions that could be included on the treatment decision making prompt list for men with early stage disease. Members of the research team searched the PubMed database using the search terms prostate cancer, question prompt list, patient prompt list, and treatment information needs. This search returned no adaptable published prompt lists [26]. However, several articles included questions that were deemed potentially useful for creating a new prompt list [27,28,29,30,31,32]. After hard or electronic copies of the selected articles were obtained, the candidate questions were abstracted and catalogued in an Excel spreadsheet for healthcare providers to review.
2.2.2. Validation of the Patient Prompt List
Each healthcare provider was asked to independently review the list of 27 candidate questions and provide his/her opinion on each question’s relevance to a patient’s encounter during a treatment decision making consultation. The goal was to create a streamlined list that could be easily incorporated into a prostate cancer treatment consultation without overwhelming patients [33]. With this goal in mind, the experts were asked to prioritize questions that were essential and eliminate any that were redundant or less relevant. Each item was rated on a 4-point scale, with response options being 1 = not at all relevant, 2 = somewhat relevant, 3 = quite relevant, and 4 = highly relevant. The scores produced from experts’ ratings allowed us to produce two content validity indices.
The item-level content validity index (I-CVI) is an item-level validity measure. This index represents the proportion of individual experts who rate an item as quite or highly relevant. With this index, expert ratings of 1 (not at all relevant) or 2 (somewhat relevant) are considered not relevant, and ratings of 3 (quite relevant) or 4 (very relevant) are relevant. The I-CVI ranges from 0–1, with values ≥ 0.80 considered acceptable [34]. The CVI was calculated using the formula below.
I-CVI = Number of experts rating the item as 3 or 4/(Total number of experts)
The scale-level content validity index (S-CVI) is a scale-level index that measures the collective relevance of all items included in the prompt list. The S-CVI is computed by averaging the I-CVIs for all individual prompt list questions. The S-CVI ranges from 0–1, and values ≥ 0.90 are considered acceptable [35]. The S-CVI is calculated using the following formula:S-CVI = (∑I-CVI)/(Number of items)
Questions were retained on the prompt list if they met the I-CVI and S-CVI thresholds of acceptability mentioned above. Questions that did not meet the thresholds of acceptability for the I-CVI were revised and re-evaluated if a revision recommendation was made by a healthcare expert. The revised questions were then subjected to a second round of evaluation to determine if the changes improved their I-CVI scores. This iterative revision process helped to ensure that potentially valuable questions were not prematurely excluded. Questions that did not meet the retention cut-offs and did not receive any suggestions for revisions were eliminated from the list. Experts were also instructed to suggest items for inclusion on the prompt list that were not included from the literature review. This final step was included to ensure the prompt list was comprehensive and reflective of current clinical practice. All suggested questions were subject to the same evaluation process as the original questions, and their relevance was assessed via the I-CVI and S-CVI.
2.2.3. Patient Prompt List Usefulness Evaluation
After the prompt list was finalized, 30 patients were given a paper version of the tool to assess its applicability from a user’s perspective. Each patient was asked to provide feedback on three areas: (a) the importance of each question on the list, (b) the perceived usefulness of the prompt list to them personally, and (c) the perceived usefulness of the prompt list for other men diagnosed with prostate cancer.
The importance of the list was assessed with the question, “Think back to when you were diagnosed with prostate cancer. How important was it for you learn about ______ to make the best treatment decision?” Response options included 1 = not at all important, 2 = a little important, 3 = somewhat important, and 4 = very important. This item was designed to determine if the questions included on the prompt list addressed the core concerns that patients typically have when weighing their treatment options.
Perceived helpfulness to oneself was evaluated with the question, “Now that you have reviewed the prompt list in its entirety, how helpful would it had been for you to receive this list when you were first diagnosed?” Response options included 1 = not at all helpful, 2 = a little helpful, 3 = helpful, and 4 = very helpful. This feedback helped the research team understand how well the prompt list addressed common uncertainties or gaps in knowledge that men might face when deciding between treatment options.
Perceived helpfulness to others was evaluated with the question, “How helpful do you think this prompt list would be to other men who are newly diagnosed with prostate cancer?” Response options included: 1 = Not at all helpful, 2 = A little helpful, 3 = Helpful, and 4 = Very helpful. This question sought to capture participants’ perceptions of how the prompt list could benefit men facing prostate cancer for the first time.
2.2.4. Questions Grouping into Thematic Domains
Following the content validation and patient evaluation process, a subset of participants (3 patients, 2 health experts) were asked to participate in an individually moderated card sorting activity to organize the final prompt list questions into meaningful categories. Each participant met independently with a research staff member and was provided a packet containing each final prompt list question listed separately on an index card. Participants were instructed to sort all of the cards into at least two groups based on their perceptions of similarity. Participants could have provided labels for their card groupings, but labeling was not a requirement. No predefined categories were given or suggested to participants to allow for natural clustering.
The research team uploaded each participants’ grouped questions into NVivo 12™, which is a piece of qualitative data management and analysis software. The cluster analysis function was used to determine similarities between all participant produced groupings (nodes). Vertical dendrograms were produced to allow the research team to visually inspect common themes (nodes) across participants’ groupings using the ‘questions coding similarities’ clustering option. This qualitative analysis focused on the frequency with which prompt list questions were grouped together. The research team provided labels for each domain after agreement on the questions’ clustering was obtained.
2.3. Data Analysis
Both content validity indices were calculated in Microsoft Excel® (Excel 2019). Univariate statistics were produced in the Statistical Package for the Social Sciences (SPSS 25, IBM Corp. Armonk, NY, USA) to describe participants’ evaluations of the prompt list importance and its perceived helpfulness. NVivo 12™ was used to group prompt list questions into thematic domains.
3. Results
3.1. Content Validity
The healthcare experts’ initial judgment of the prompt list questions is included in Table 1. After the first round of reviews, 14 items met the threshold for retention (Q1, Q3, Q5, Q7, Q8, Q10, Q11, Q12, Q13, Q16, Q22), and 13 items were recommended for deletion (Q4, Q9, Q14, Q15, Q18, Q19, Q20, Q21, Q24, Q25, Q26, Q27) because of redundancy or inappropriateness. The panel of experts thought that six questions were missing from the initial list that would be relevant to localized patients’ treatment decision making experiences (Q28, Q29, Q30, Q31, Q32, Q33) in the US. The S-CVI for the first round of prompt list questions fell below the 0.90 threshold of acceptability (S-CVI = 0.74).
The healthcare experts’ final version of the prompt list is shown in Table 2. The questions listed on the revision included the 14 items that were retained from the original review and the 6 items that were added based on expert suggestions. The I-CVIs for the questions on the revised prompt list ranged from 0.83 to 1.00. The S-CVI for the revised list of questions increased to 0.96.
3.2. Patients’ Ratings of the Importance of Prompt List Questions
Patients’ evaluations of the usefulness of the finalized prompt list are included in Figure 1. The proportions of men who thought the prompt list items were “important or strongly important” ranged from 63.4% for “Would I need a caregiver at any time?” to 100% for “What are the chances the cancer could come back after treatment?”
3.3. Usefulness of the Patient Prompt List to Oneself and Others
Patients’ judgements of the helpfulness of the 20-item prompt list, both to themselves and others are included in Figure 2. Most men in the sample viewed the prompt list as being “helpful or very helpful” to themselves (80%, N = 24) and “helpful or very helpful” to other men presenting with prostate cancer (83%, N = 25).
Similar positive evaluations were made regarding the prompt list being “helpful or very helpful” to oneself and others across demographic characteristics. Equal proportions of white people (80%, N = 12/15) and African Americans (80%, N = 12/15) rated the prompt list as “helpful or very helpful” to themselves. This positive evaluation was consistent across different education levels (100% < high school vs. 82.0% ≥ 12th grade) and age groups (75.0% < 65 years vs. 80.0% ≥ 65 years).
The majority of men in our racial (86.0% whites vs. 80.0% African American), age (75.0% < 65 years vs. 90.0% ≥ 65 years) and education groups (100% < high school vs. 78.0% ≥ 12th grade) also rated the prompt list as “helpful or very helpful” to other men.
3.4. Prompt List Questions Groupings by Thematic Domains
The card sorting activity resulted in the final 20 prompt list questions being organized into 5 interrelated domains. A brief description of each domain is provided below. A visual representation of questions grouped by domains is included in Figure 3.
Domain 1: Treatment Options and Information—This domain includes questions (Q1, Q3 Q10, Q11, Q12) that help patients gain a thorough understanding of the various treatment options available to them. Questions in this category focus on the mechanics and outcomes of each treatment and ways to gather more information. Examples include “What treatment options are available to me?” and “How does each treatment option work?” These questions are critical to treatment decision making because they help patients become well informed about their choices.
Domain 2: Side Effects, Risks, and Recovery—This domain (Q5, Q6, Q8, Q13, Q17, Q30, Q33) addresses the potential side effects and risks associated with each treatment option and the treatment recovery process. Questions such as “What are the risks (side effects) of each treatment?” and “How long will it take to recover from treatment?” help patients anticipate and prepare for the physical and emotional challenges they may face during and after treatment. These types of questions help men set realistic expectations and prepare for the practical aspects of their treatment journey.
Domain 3: Emotional and Social Support—This domain (Q20, Q22, Q31) focuses on the emotional and social aspects of coping with prostate cancer. Questions like “How should I talk to family/friends about my illness?” and “How do I find other men to talk to who had the same treatment?” are included in this domain. These questions underscore the importance of emotional well-being and social support as essential components of a holistic cancer care treatment plan.
Domain 4: Logistical and Practical Concerns—This domain covers practical issues that patients need to consider, such as insurance coverage, the need for a caregiver, and the logistics of receiving treatment. “What options will my insurance cover?” and “Will I need a caregiver at any time?” are key questions in this category. Addressing these concerns will help patients plan effectively for the financial and practical aspects of their treatment.
Domain 5: Future Health Considerations—This domain includes a single question that encourages patients to consider the long-term implications of their illness and treatment. The key question in this domain is “What are the chances the cancer could come back after treatment?”
3.1. Content Validity
The healthcare experts’ initial judgment of the prompt list questions is included in Table 1. After the first round of reviews, 14 items met the threshold for retention (Q1, Q3, Q5, Q7, Q8, Q10, Q11, Q12, Q13, Q16, Q22), and 13 items were recommended for deletion (Q4, Q9, Q14, Q15, Q18, Q19, Q20, Q21, Q24, Q25, Q26, Q27) because of redundancy or inappropriateness. The panel of experts thought that six questions were missing from the initial list that would be relevant to localized patients’ treatment decision making experiences (Q28, Q29, Q30, Q31, Q32, Q33) in the US. The S-CVI for the first round of prompt list questions fell below the 0.90 threshold of acceptability (S-CVI = 0.74).
The healthcare experts’ final version of the prompt list is shown in Table 2. The questions listed on the revision included the 14 items that were retained from the original review and the 6 items that were added based on expert suggestions. The I-CVIs for the questions on the revised prompt list ranged from 0.83 to 1.00. The S-CVI for the revised list of questions increased to 0.96.
3.2. Patients’ Ratings of the Importance of Prompt List Questions
Patients’ evaluations of the usefulness of the finalized prompt list are included in Figure 1. The proportions of men who thought the prompt list items were “important or strongly important” ranged from 63.4% for “Would I need a caregiver at any time?” to 100% for “What are the chances the cancer could come back after treatment?”
3.3. Usefulness of the Patient Prompt List to Oneself and Others
Patients’ judgements of the helpfulness of the 20-item prompt list, both to themselves and others are included in Figure 2. Most men in the sample viewed the prompt list as being “helpful or very helpful” to themselves (80%, N = 24) and “helpful or very helpful” to other men presenting with prostate cancer (83%, N = 25).
Similar positive evaluations were made regarding the prompt list being “helpful or very helpful” to oneself and others across demographic characteristics. Equal proportions of white people (80%, N = 12/15) and African Americans (80%, N = 12/15) rated the prompt list as “helpful or very helpful” to themselves. This positive evaluation was consistent across different education levels (100% < high school vs. 82.0% ≥ 12th grade) and age groups (75.0% < 65 years vs. 80.0% ≥ 65 years).
The majority of men in our racial (86.0% whites vs. 80.0% African American), age (75.0% < 65 years vs. 90.0% ≥ 65 years) and education groups (100% < high school vs. 78.0% ≥ 12th grade) also rated the prompt list as “helpful or very helpful” to other men.
3.4. Prompt List Questions Groupings by Thematic Domains
The card sorting activity resulted in the final 20 prompt list questions being organized into 5 interrelated domains. A brief description of each domain is provided below. A visual representation of questions grouped by domains is included in Figure 3.
Domain 1: Treatment Options and Information—This domain includes questions (Q1, Q3 Q10, Q11, Q12) that help patients gain a thorough understanding of the various treatment options available to them. Questions in this category focus on the mechanics and outcomes of each treatment and ways to gather more information. Examples include “What treatment options are available to me?” and “How does each treatment option work?” These questions are critical to treatment decision making because they help patients become well informed about their choices.
Domain 2: Side Effects, Risks, and Recovery—This domain (Q5, Q6, Q8, Q13, Q17, Q30, Q33) addresses the potential side effects and risks associated with each treatment option and the treatment recovery process. Questions such as “What are the risks (side effects) of each treatment?” and “How long will it take to recover from treatment?” help patients anticipate and prepare for the physical and emotional challenges they may face during and after treatment. These types of questions help men set realistic expectations and prepare for the practical aspects of their treatment journey.
Domain 3: Emotional and Social Support—This domain (Q20, Q22, Q31) focuses on the emotional and social aspects of coping with prostate cancer. Questions like “How should I talk to family/friends about my illness?” and “How do I find other men to talk to who had the same treatment?” are included in this domain. These questions underscore the importance of emotional well-being and social support as essential components of a holistic cancer care treatment plan.
Domain 4: Logistical and Practical Concerns—This domain covers practical issues that patients need to consider, such as insurance coverage, the need for a caregiver, and the logistics of receiving treatment. “What options will my insurance cover?” and “Will I need a caregiver at any time?” are key questions in this category. Addressing these concerns will help patients plan effectively for the financial and practical aspects of their treatment.
Domain 5: Future Health Considerations—This domain includes a single question that encourages patients to consider the long-term implications of their illness and treatment. The key question in this domain is “What are the chances the cancer could come back after treatment?”
4. Discussion
This study adapted and validated questions from the literature into a patient prompt list for men with localized prostate cancer to assist them with treatment decision making. The final 20-item prompt list achieved high content validity after a rigorous validation process with survivors and providers. Incorporating prostate cancer survivors into a retrospective analysis of the prompt list added value to the study because their opinions were based on hindsight. Having already been through the treatment decision making process, these men know what information they needed and how well this tool would have helped them fulfill their information deficiencies.
One of the strengths of the prompt list is its organization into five thematic areas. These patient- and physician-derived categories allows patients to engage with the prompt list in an individualized, yet structured manner. For instance, a patient who is primarily concerned about side effects can focus on that section first before moving to other areas. Conversely, a patient who is more concerned with long-term outcomes can navigate to those questions first before moving on the remaining areas. This personalization feature can help patients feel a sense of control, as it allows them to prioritize the issues that matter most to them, without feeling overwhelmed by information that may not be immediately relevant.
When placed in the context of ongoing efforts to enhance cancer care, our findings underscore the importance of rigorous content validation processes when developing patient-centered tools for new populations. The significant improvement in the scale-level content validity index (S-CVI) from 0.74 to 0.96 after expert feedback highlights the value of using a participatory approach. By leveraging the diverse insights of professionals across different specialties, the prompt list was also refined to better align with the complexities of the holistic patient experience.
One unexpected result from the study was the recommendation to delete 13 of the original 27 questions due to perceived redundancy. This was surprising because those questions were selected from a comprehensive literature review [27,28,29,30,31,32]. After placing the prompt list questions into domains, we saw there was overlapping content with some of the items that were being considered for inclusion. By removing the overlapping questions, the prompt list was streamlined for clarity and usability. We achieved our goal of developing a comprehensive yet brief tool to avoid overwhelming patients during a challenging time in their cancer journey. The final 20 items were designed to serve as gateway conversation starters to empower patients and open up dialogue. None of the five prompt list domains were lost as a result of the streamlining process.
4.1. Limitations
Despite its positive results, this study has some limitations that must be acknowledged. The sample size was relatively small and geographically limited. These two issues may limit the generalizability of our findings to broader populations (e.g., other racial, socioeconomic, or geographic groups). Future studies should include a larger and more diverse cohort to validate the tool more comprehensively. This can be achieved by partnering with multiple research sites across regions or using online platforms to enhance the recruitment pool. Since this study is an initial exploratory validation rather than a conclusive validation applicable to all settings, these issues were not considered in this research.
This study also relied on retrospective patient evaluations, which could introduce recall bias. With the passage of time, patients may not have accurately recalled their decision making experience, which could have led them to underestimate or overestimate the tool’s usefulness. Future research would benefit from real-time assessments with patients as they use the prompt list during actual treatment consultations. These additional studies could be longitudinal and track patient outcomes over several months to assess the long-term impact of the tool.
We did not measure the actual impact of prompt list use on measurable clinical outcomes, such as questions asked, decision making quality, or satisfaction with the clinical encounter. Future studies should incorporate standardized health and quality-of-life measures, such as patient-reported outcome measures (PROMs) or the SF-36, to enhance the prompt list’s credibility and demonstrate additional practical benefits. Measuring and demonstrating improvement in clinical outcomes can enhance the prompt list’s reputation as a useful resource for promoting patient-centered care.
4.2. Future Directions
Broadly disseminating the prompt list is critical to maximizing its use. One of the strengths of the tool is its ability to be adapted into other accessible formats. In telehealth settings, the prompt list can be included as a pre-appointment form to help patients select questions they want to discuss during their upcoming appointment. The list of questions can also be displayed on the computer screen while patients are in the virtual waiting room waiting to be connected to their providers. The prompt list could be included as an intake form in electronic health records to trigger physicians to address specific concerns that are identified by patients. In mobile applications and electronic devices, providers can send push notifications to remind patients to review the prompt list before their office visits. These future research projects should take place in the U.S. and non-U.S. settings.
4.1. Limitations
Despite its positive results, this study has some limitations that must be acknowledged. The sample size was relatively small and geographically limited. These two issues may limit the generalizability of our findings to broader populations (e.g., other racial, socioeconomic, or geographic groups). Future studies should include a larger and more diverse cohort to validate the tool more comprehensively. This can be achieved by partnering with multiple research sites across regions or using online platforms to enhance the recruitment pool. Since this study is an initial exploratory validation rather than a conclusive validation applicable to all settings, these issues were not considered in this research.
This study also relied on retrospective patient evaluations, which could introduce recall bias. With the passage of time, patients may not have accurately recalled their decision making experience, which could have led them to underestimate or overestimate the tool’s usefulness. Future research would benefit from real-time assessments with patients as they use the prompt list during actual treatment consultations. These additional studies could be longitudinal and track patient outcomes over several months to assess the long-term impact of the tool.
We did not measure the actual impact of prompt list use on measurable clinical outcomes, such as questions asked, decision making quality, or satisfaction with the clinical encounter. Future studies should incorporate standardized health and quality-of-life measures, such as patient-reported outcome measures (PROMs) or the SF-36, to enhance the prompt list’s credibility and demonstrate additional practical benefits. Measuring and demonstrating improvement in clinical outcomes can enhance the prompt list’s reputation as a useful resource for promoting patient-centered care.
4.2. Future Directions
Broadly disseminating the prompt list is critical to maximizing its use. One of the strengths of the tool is its ability to be adapted into other accessible formats. In telehealth settings, the prompt list can be included as a pre-appointment form to help patients select questions they want to discuss during their upcoming appointment. The list of questions can also be displayed on the computer screen while patients are in the virtual waiting room waiting to be connected to their providers. The prompt list could be included as an intake form in electronic health records to trigger physicians to address specific concerns that are identified by patients. In mobile applications and electronic devices, providers can send push notifications to remind patients to review the prompt list before their office visits. These future research projects should take place in the U.S. and non-U.S. settings.
5. Conclusions
This research lays the groundwork for future studies validating prompt lists across diverse populations and healthcare settings. It will also create opportunities for integrating this form of communication assistance into digital health platforms. |
Title: Efficacy and safety of direct-acting antiviral regimen for patients with hepatitis C virus genotype 2: a systematic review and meta-analysis | Body:
Background
Hepatitis C virus (HCV) is a blood-borne pathogen that could cause both chronic and acute hepatitis infection; around 50–80% of patients would develop a chronic inflammatory condition, which may lead to liver cirrhosis and hepatocellular carcinoma [1, 2]. Despite the implementation of universal precautions and blood safety measures, HCV infection continues to be a severe global public health burden [3]. The global estimated viraemic prevalence for hepatitis C virus infection was 0.7% (95% UI, 0.7–0.9), corresponding to 56.8 million (95% UI, 55.2–67.8) people infected with HCV in 2020 [4]. Besides, no prophylactic vaccine is available to prevent HCV infection, so the strategy to control HCV has to follow the treatment-as-prevention principle [1].
Since the emergence of well-tolerated oral direct-acting antivirals (DAAs) in early 2014, several major guidelines have recommended DAAs as the first-line treatment for patients infected with HCV instead of pegylated interferon (PEG-IFN) treatment, but therapy options vary according to factors such as HCV genotypes and patients’ status [5–7]. As a member of the Flaviviridae family, HCV is heterogeneous and can be classified into 7 genotypes and 67 subtypes, with genotype 1 being the most prevalent in the Americas, Europe, Australia, New Zealand, Central Asia and East Asia [8, 9]. Hepatitis C virus genotype 2 (HCV GT2) is the third predominant genotype in Asia, Africa and America, whereas the prevalence rate varies by geographical distribution, ranging from 62.9% in West Sub-Saharan Africa to 0.8% in North Africa and Middle East [10].
Nevertheless, compared with genotype 1 and 3, the evidence base for DAA therapies in GT2 patients is less extensive. Although there has been clinical evidence to support the novel DAAs entering the market, the majority of trials are open-label, single-arm studies that lack placebo comparators and primarily focus on individual DAA regimens. The comparative efficacy of individual combination therapies remains largely undetermined, primarily due to the scarcity of head-to-head trials Thus far, no studies have compared the efficacy and safety of all DAAs regimens for treating patients infected with HCV GT2.
To fill these gaps, this systematic review and meta-analysis focused on the studies about DAAs treatment for HCV GT2, to assess the comparative efficacy of DAAs regimens, and to identify the benefits and AEs associated with each DAAs intervention.
Methods
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines (PRISMA-P) were adhered to this meta-analysis and systematic review [11]. This systematic review was registered on the PROSPERO database for systematic reviews (CRD: 42022344032).
Eligibility criteria
We predefined criteria for inclusion in accordance with PICOS principles [12]. Clinical studies which investigated DAAs regimens for treating patients infected with HCV GT2 were eligible for inclusion. The involved population must be adults with diagnosed HCV GT2 infection, who never had HIV/HCV co-infected or decompensated cirrhosis. The detailed eligibility criteria are listed in Table 1.
Table 1Eligibility criteria based on PICOCharacteristicsEligibility criteriaPopulationAged over 18 years old with HCV GT2 infection.Limitation on children/adult patients with HIV/HCV co-infected or patients with decompensated cirrhosisInterventionsThe intervention group included at least one DAA, either as monotherapy or in combination with other treatments.ComparatorsNot applicableOutcome measuresPrimary outcome measures:Sustained virological response 12 weeks after completion of treatment, SVR12Secondary outcome measures:All adverse events associated with DAAs treatment(Including non-severe adverse events and serious adverse events)Study typeClinical trialRestrictionsLanguage of studies in Chinese or English
Eligibility criteria
We predefined criteria for inclusion in accordance with PICOS principles [12]. Clinical studies which investigated DAAs regimens for treating patients infected with HCV GT2 were eligible for inclusion. The involved population must be adults with diagnosed HCV GT2 infection, who never had HIV/HCV co-infected or decompensated cirrhosis. The detailed eligibility criteria are listed in Table 1.
Table 1Eligibility criteria based on PICOCharacteristicsEligibility criteriaPopulationAged over 18 years old with HCV GT2 infection.Limitation on children/adult patients with HIV/HCV co-infected or patients with decompensated cirrhosisInterventionsThe intervention group included at least one DAA, either as monotherapy or in combination with other treatments.ComparatorsNot applicableOutcome measuresPrimary outcome measures:Sustained virological response 12 weeks after completion of treatment, SVR12Secondary outcome measures:All adverse events associated with DAAs treatment(Including non-severe adverse events and serious adverse events)Study typeClinical trialRestrictionsLanguage of studies in Chinese or English
Search strategy
Six databases, including PubMed, Embase, Cochrane Library, Web of Science, CNKI, and Clinicaltrial.gov were searched for the relevant clinical trials. The last systematic search was performed on April 20, 2022. Search strategies were constructed on: (i) population: Chronic, Hepatitis C virus; (ii) Interventions: direct-acting antivirals, DAAs; (iii) study design: clinical trial, including single-arm studies (Supplementary Table S1).
Study selection
The process of selecting studies included two parts, through the literature software EndNote X9. Two reviewers independently screened articles by title and abstract based on eligibility criteria after removing duplicate records. Then, two researchers kept reviewing the remaining literature by full text. Any disputes between the two independent reviewers were resolved through discussion with the senior author.
Data extraction
Two researchers extracted information from the included studies independently. A third researcher resolved any further disagreements. The data extraction form comprises:
i.study design: author, NCT number, published year and country.ii.study design: population and design.iii.characteristics of patients: intervention, duration, with/without cirrhosis, treatment-experienced, treatment-naïve.iv.outcome measures: SVR12, the number of AEs and SAEs.v.any information for the assessment of the risk of bias.
Quality assessment
The RoB 2 tool was used for the methodological quality evaluation of the randomized controlled trials (RCTs), which evaluated the following five domains: the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome and selection of the reported result [13]. An outcome of low risk, high risk, or some concerns was reported for each domain. An overall assessment of the risk of bias was then determined for each study.
As for non-randomized studies, we used the risk of bias tool for nonrandomized studies (ROBINS-I) proposed by Cochrane Collaboration to assess the following five domains: (i) misclassification of interventions; (ii) deviations from intended intervention; (iii) missing data; (iv) measurement of outcomes; and (v) selection of the reported result [14]. An outcome of low, moderate, serious, critical, or no information for bias risk was reported for each domain. The combination of the five domains was then used to determine the overall risk of bias, whereas judgement for each domain was based on information extracted from each article.
Two researchers assessed the quality of all included studies for the quality assessment process independently. Any disagreements between the two independent reviewers were resolved by discussion and/or consultation with another researcher.
Statistical analyses
The SVR12 was regarded as the primary efficacy outcome, while the AEs was regarded as the secondary safety outcome. We extracted study-arm data from each study and summarised the intervention effect by calculating the proportion of patients reaching the SVR12 or with any AEs over the number of involved patients. Firstly, the overall pooled arm-specific proportion was conducted regardless of the cirrhosis status of patients, previous history of treatment, or treatment type. Then, subgroup analyses by all the above variables were conducted for specific efficacy outcomes. As for safety outcomes, any AEs reported were recorded and considered. Meta-analyses for SVR12 were conducted by using Freeman-Tukey double arcsine transformation, and a random effect model was performed for all meta-analyses because SVR12 rates were expected to follow the binomial distribution and approach the extreme boundaries [15]. The Bayesian Markov Chain Carlo (MCMC) was used to conduct the network meta-analysis. The MCMC approach was utilized three chains and was refined through 200,000 simulations, with every tenth simulation retained and the first 10,000 discarded as burn-in. We used software R v.4.1.2 and package ‘Meta’ v.5.2-0 & ‘gemtc’ to perform the meta-analysis, estimating and pooling the rate of SVR12 and AEs [16]. Unless otherwise stated, we set two-tailed statistical significance as P values < 0.05 for all analyses.
Assessment of heterogeneity
Heterogeneity of treatment effects was quantified by I2 statistics, with thresholds of 25%, 50% and 75%, where heterogeneity suggests low if (25%≤ I2 < 50%), moderate (50%≤ I2 < 75%), or high (I2 ≥ 75%), respectively [17].
Study selection
The process of selecting studies included two parts, through the literature software EndNote X9. Two reviewers independently screened articles by title and abstract based on eligibility criteria after removing duplicate records. Then, two researchers kept reviewing the remaining literature by full text. Any disputes between the two independent reviewers were resolved through discussion with the senior author.
Data extraction
Two researchers extracted information from the included studies independently. A third researcher resolved any further disagreements. The data extraction form comprises:
i.study design: author, NCT number, published year and country.ii.study design: population and design.iii.characteristics of patients: intervention, duration, with/without cirrhosis, treatment-experienced, treatment-naïve.iv.outcome measures: SVR12, the number of AEs and SAEs.v.any information for the assessment of the risk of bias.
Quality assessment
The RoB 2 tool was used for the methodological quality evaluation of the randomized controlled trials (RCTs), which evaluated the following five domains: the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome and selection of the reported result [13]. An outcome of low risk, high risk, or some concerns was reported for each domain. An overall assessment of the risk of bias was then determined for each study.
As for non-randomized studies, we used the risk of bias tool for nonrandomized studies (ROBINS-I) proposed by Cochrane Collaboration to assess the following five domains: (i) misclassification of interventions; (ii) deviations from intended intervention; (iii) missing data; (iv) measurement of outcomes; and (v) selection of the reported result [14]. An outcome of low, moderate, serious, critical, or no information for bias risk was reported for each domain. The combination of the five domains was then used to determine the overall risk of bias, whereas judgement for each domain was based on information extracted from each article.
Two researchers assessed the quality of all included studies for the quality assessment process independently. Any disagreements between the two independent reviewers were resolved by discussion and/or consultation with another researcher.
Statistical analyses
The SVR12 was regarded as the primary efficacy outcome, while the AEs was regarded as the secondary safety outcome. We extracted study-arm data from each study and summarised the intervention effect by calculating the proportion of patients reaching the SVR12 or with any AEs over the number of involved patients. Firstly, the overall pooled arm-specific proportion was conducted regardless of the cirrhosis status of patients, previous history of treatment, or treatment type. Then, subgroup analyses by all the above variables were conducted for specific efficacy outcomes. As for safety outcomes, any AEs reported were recorded and considered. Meta-analyses for SVR12 were conducted by using Freeman-Tukey double arcsine transformation, and a random effect model was performed for all meta-analyses because SVR12 rates were expected to follow the binomial distribution and approach the extreme boundaries [15]. The Bayesian Markov Chain Carlo (MCMC) was used to conduct the network meta-analysis. The MCMC approach was utilized three chains and was refined through 200,000 simulations, with every tenth simulation retained and the first 10,000 discarded as burn-in. We used software R v.4.1.2 and package ‘Meta’ v.5.2-0 & ‘gemtc’ to perform the meta-analysis, estimating and pooling the rate of SVR12 and AEs [16]. Unless otherwise stated, we set two-tailed statistical significance as P values < 0.05 for all analyses.
Assessment of heterogeneity
Heterogeneity of treatment effects was quantified by I2 statistics, with thresholds of 25%, 50% and 75%, where heterogeneity suggests low if (25%≤ I2 < 50%), moderate (50%≤ I2 < 75%), or high (I2 ≥ 75%), respectively [17].
Results
Characteristics of included studies
A total of 4,486 studies were yielded from database search, of which 703 duplicates were removed. After applying our inclusion criteria, we identified 31 articles for our meta-analysis, which included 35 clinical studies. All included articles were published within the past 10 years, with the majority being recent studies published within the past 5 years.
In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our search strategy and selection process are depicted in Fig. 1. Our search yielded a total of 31 articles, consisting of 9 randomized controlled trials (RCTs) and 26 non-randomized studies. Of the 35 clinical trials, 13 were conducted in phase 2, while 21 were conducted in phase 3; 1 clinical trial was conducted in phase 4. All 35 included studies were multicenter clinical trials.
Fig. 1Flowchart summarizing the selection and identification of studies based on PRISMA
2,968 participants with HCV GT2 from these 35 studies were eligible for the data synthesis, as presented in Table 2, consisting of 1,389 treatment-naive patients and 354 patients with cirrhosis. The sample size of the included clinical trials ranged from 18 to 458. All participants were diagnosed with HCV GT2 infection and received DAA therapy as their primary treatment.
Table 2Characteristics of included studies for systematic review and meta-analysisAuthorNCT NumberYearCountryPopulation and designInterventionParticipants (n)CirrhosisDurationSVR12Sponsors and Collaboratorsn (%)(week)n (%)Lawtiz et al., 2015 [48]NCT014585352015NANon-Randomized, open-label, sequential arm, phase 2, multicenter study, TN G1-3 PatientsOBV+PTV+RTN±RBV2001214(70.0%)AbbVieJacobson et al., 2013 [24]NCT01542788 (POSITRON)2013NA, ANZRandomized, blinded, placebo-controlled, phase 3, multicenter study, TN G2-3 patientsSOF+RBV14317(11.9%)12101(92.7%)Gilead SciencesJacobson et al., 2013 [24]NCT01604850 (FUSION)2013NA, ANZRandomized, open-label, active control, phase 3, multicenter study, TE G2-3 patientsSOF+RBV6819(27.9%)12 or 1661(89.7%)Gilead SciencesLawitz et al., 2013 [25]NCT01497366(FISSION)2013NA, EU, ANZRandomized, Open-label, multicenter, Phase 3 RCT, TN G1-3 PatientsSOF+RBV/PR137N/A12120(87.6%)Gilead SciencesZeuzem et al., 2014 [27]NCT01682720 (VALENCE)2014EURandomized, blinded, placebo-controlled, phase 3, multicenter, TN/TE G2-3 patientsSOF+RBV9111(12.1%)1268(93.2%)Gilead SciencesFoster et al., 2016 [49]NCT01616524 (PRINCIPAL)2016NA, LM, ASIA, EURandomized, blinded, controlled, phase 3, multinational, TN G2-3 patientsLambda+RBV+DCV45829(6.3%)12 or 24363(79.3%)Bristol-Myers SquibbOmata et al., 2014 [26]NCT019106362014JapanOpen-label, phase 3, multicenter, TN/TE G2 patientsSOF+RBV15317(11.1%)12148(96.7%)Gilead SciencesBrown et al., 2017 [50]NCT01932762(C-SCAPE)2017N/ARandomized, open-label, phase 2, multi-site, TN G2,4,5,6 patientsEBR±GZR±RBV5601243(76.8%)Merck Sharp & Dohme LLCDore et al., 2015 [51]NCT01257204(AI444-031)2015NA, EU, ANZRandomized, blinded, placebo-controlled, phase 2, multicenter, TN G2-3 patientsDCV+PEG+RBV7168(95.8)12 or 16 or 2457(80.3%)Bristol-Myers SquibbEverson et al., 2015 [52]NCT018587662015NARandomized, open-label, phase 2, multicenter, TN G1-6 patientsSOF+VEL1030888(65.2%)Gilead SciencesFeld et al., 2015 [19]NCT02201940(ASTRAL-1)2015NA, EU, ASIARandomized, blinded, placebo-controlled, phase 3, multicenter, TN/TE G1-2, 4-5 patientsSOF+VEL125N/A12104(100%)Gilead SciencesFoster et al., 2015 [20]NCT02220998(ASTRAL-2)2015NARandomized, open-label, phase 3, multicenter, TN/TE G2 patientsSOF+VEL/SOF+RBV26638(14.3%)12257(96.6%)Gilead SciencesFoster et al., 2015 [53]NCT019624412015NA, EU, ANZRandomized, open-label, phase 3, multicenter, TN/TE G2-3 patientsSOF+RBV+PEG48N/A12 or 16 or 2445(93.8%)Gilead SciencesAsselah et al., 2018 [32]NCT02243293(SURVEYOR-II)2018N/ARandomized, open-label, phase 3, multicenter, TN/TE G2 patientsGLE+PIB14508142(97.9%)AbbVieAsselah et al., 2018 [32]NCT02640482(ENDURANCE-2)2018N/ARandomized, blinded, placebo-controlled, phase 2, multicenter, TN/TE G2 patientsGLE+PIB302012201(99.5)AbbVieGane et al., 2016 [54]NCT023789612016NA, ANZNon-Randomized, open-label, phase 2, multicenter, TN/TE G2-3, 6 patientsSOF+VEL+VOX3314(42.4%)6 or 8 or 1231(93.9%)Gilead SciencesLawitz et al., 2017 [55]NCT023327072017N/ARandomized, open-label, phase 2, multicenter, TN G1-3 patientsGZR+RZR+UPR±RBV15157(37.7%)8 or 12141(93.4%)Merck Sharp & Dohme CorpToyoda et al., 2018 [30]NCT02707952(CERTAIN-1)2018ASIARandomized, open-label, phase 3, multicenter, TN/TE G2 patientsGLE+PIB1818(100%)1218(100%)AbbVieToyoda et al., 2018 [30]NCT02723084(CERTAIN-2)2018ASIARandomized, open-Label, active comparator, phase 3 multicenter, TN/TE G2 patientsGLE+PIB/SOF+RBV1360888(97.8%)AbbVieGane et al., 2017 [31]NCT02202980(LEPTON)2017ANZNon-randomized, open-label, phase 2, multicenter, TN/TE G2 patientsLDV+SOF532(3.8%)8 or 1245(84.9%)Gilead SciencesHo et al., 2017 [28]NCT02128542(VALOR-HCV)2017NAOpen-label, phase 4, multicenter, TN/TE G2 patientsSOF+RBV6666(100%)1252(78.9%)Gilead SciencesJacobson et al., 2017 [21]NCT02607800(POLARIS-2)2017NA, ANZ, EURandomized, open-label, phase 3, multicenter, TN G1-6 patientsSOF+VEL+VOX/SOF+VEL116N/A8 or 12114(98.3%)Gilead SciencesLawitz et al., 2019 [56]NCT02956629(MK-3682-041)2019N/ANon-randomized, open-label, phase 2, multi-site, TN/TE G1-6 patientsRZR+UPR4710(21.3%)1243(91.5%)Merck Sharp & Dohme CorpLawitz et al., 2019 [57]NCT02759315(MK-3682-035)2019NANon-randomized, open-label, phase 2, multi-center TN/TE G1-6 patientsRZR+UPR297(24.1%)1228(96.6%)Merck Sharp & Dohme LLCWei et al., 2018 [58]NCT026715002018ASIAOpen-label, Phase 3, multicenter, TN & TE G1-6 PatientsSOF+VEL64N/A1264(100%)Gilead SciencesWei et al.,2020 [34]NCT03222583(VOYAGE-1)2020ASIARandomized, blind, placebo-controlled, phase 3, multicenter, TN/TE G1-6 patientsGLE+PIB21008136(97.8%)AbbVieWei et al.,2020 [34]NCT03235349(VOYAGE-2)2020ASIAOpen-label, multicenter, phase 3, TN/TE G1-6 patientsGLE+PIB53531253(100%)AbbVieAsahina et al., 2018 [29]N/A2018ASIARandomized, open-label, phase 3, multicenter, TN & TE G2 patientsLDV+SOF/SOF+RBV13118(13.7%)12126(96.2%)Gilead SciencesIzumi et al., 2018 [59]N/A2018ASIAOpen-label study, phase 3, multicenter, TE G1-2 patientsSOF+VEL+RBV225(22.7%)12 or 2418(81.8%)Gilead SciencesRao et al., 2019 [60]NCT03416491(KW-136_II)2019ASIARandomized, open-label, phase 2, multicenter, TN G1-3,6 patientsCLV+SOF274(14.8%)1226(96.3%)Beijing Kawin TechnologyShafran et al., 2017 [61]NCT02292719(QUARTZ II-III)2017N/ARandomized, open-label, phase 2, multicenter, TN/TE G2-3 patientsOBV+PTV+RTN+SOF±RBV1906 or 813(68.4)AbbVieBrown et al., 2020 [33]NCT03089944(EXPEDITION-8)2020NA, EU, ASIAOpen-label, phase 3b, multicenter, TN G1-6 PatientsGLE+PIB26N/A826(100%)AbbVieAsselah et al.,2019 [22]NCT023467212019NA, EU, ASIAOpen-label, phase 3, multicenter, TN & TE G1,2,4,6 PatientsSOF+VEL20N/A1220 (100%)Gilead SciencesGao et al.,2020 [62]NCT03995485(KW-136_III)2020ASIAOpen-label, phase 3, multicenter, TN & TE G1-3, 6 PatientsCLV+SOF95N/A1291(95.8%)Beijing Kawin TechnologyHua et al., 2022 [63]NCT040702352022ASIARandomized, open-label, phase 2, multicenter, TN & TE G1-3, 6 PatientsAFV+DCV31N/A1231(100%)Nanjing Sanhome PharmaceuticalAFV: Alfosbuvir; SOF: Sofosbuvir; VEL: Velpatasvir; LDV: Ledipasvir; CLV: Coblopasvir; DCV: Daclatasvir; GLE: Glecaprevir; PIB: Pibrentasvir; EBR: Elbasvir; GRZ: Grazoprevir; RUZ: Ruzasvir; RTN: Ritonavir; UPR: Uprifosbuvir; VOX: Voxilaprevir; PR: Peginterferon and Ribavirin; RBV: Ribavirin; NA: North America; EU: Europe; ASIA: Asia; ANZ: Australia and New- Zealand; AFR: Africa, LM: Latin Ameica; TN: Treatment-naïve; TE: Treatment-experienced; NIH: National Institutes of Health; G: Genotype; N/A: Not applicable; RCT: Randomized controlled trials; NCT: ClinicalTrials.gov Identifier;
A total of 23 combinations of DAAs were investigated, duration of therapy ranged from 6 to 16 weeks with or without the addition of ribavirin. We excluded the 6-week regimens from our meta-analysis due to a limitation on sample size (n = 6). Among the 35 clinical trials included in this study, DAAs were regarded as the first-line treatment. According to the mechanism of action, DAAs were divided into four types [18]:
i.NS5A Inhibitor (Daclatasvir, Elbasvir, Ledipasvir, Ombitasvir, Ledipasvir, Pribrentasvir, Velpatasvir).ii.Protease NS3/4A Inhibitor (Glecaprevir, Paritaprevir, Simeprevir, Grazoprevir, Voxilaprevir).iii.Non-Nucleoside NS5B Polymerase Inhibitor (Dasabuvir, Deleobuvir).iv.NS5B Polymerase Inhibitor (Sofosbuvir).
Quality assessment
There are two domains were considered at low risk of bias in all studies, including classification of intervention and measurement of outcome (Supplementary Table S2 & Table S3). Of the 26 non-randomized trials included in our analysis, the quality was evaluated using the ROBINS-I tool, which assesses bias from five perspectives. Among these 26 studies, 12 were judged to be at moderate risk of bias due to deviations from the intended interventions. Bias due to missing data was considered moderate in 10 studies. Of the 9 randomized controlled trials assessed using the ROB 2 tool, 2 were judged to be at high risk of bias due to selection and performance. Overall, only 1 out of the 9 RCTs was considered to be at high risk of bias (Supplementary Figure S1).
Overall pooled SVR12 for DAA therapies
Since 2014, multiple regimens have been utilized for the treatment of hepatitis C virus genotype 2, with overall response rates showing considerable success. The characteristics of the 35 included studies (58 study-arms, n = 2,968) are reported (Supplementary Table S4). A total of 2,968 GT2 patients who received DAA therapy were included. As seen in Supplementary Figure S2, the pooled SVR12 was 94.62% (95% CI: 92.43-96.51%), but with substantial heterogeneity (I2 = 72.0%, P < 0.0001) caused by subgroups. Our network connected 10 mainstream treatment regimens for further indirectly comparison. Figure 2 illustrates the network of studies included in our network meta-analysis, comparing five different regimens. Notably, all regimens were compared directly to at least one other regimen, forming a well-connected network. The connection between SOF + RBV 12 and GLE + PIB 12 is depicted with a moderately thick line, indicating several studies have directly compared these two regimens. In contrast, the thinner line between SOF + VEL + VOX 8 and GLE + PIB 12 suggests fewer direct comparisons, relying more on indirect evidence. The assessment of inconsistency showed no significant differences between direct and indirect comparisons within this network. By comparing the main treatment regimens, we have confirmed that the 12-week SOF + VEL therapy and the 12-week GLE + PIB therapy are essentially equivalent in efficacy and are the treatment regimens with the highest response rates (Supplementary Figure S3).
We also did further analysis to indirectly compare the results between every two regimens (Table 3). Each cell in Table 3 shows the estimated difference in response rates between regimens with the corresponding 95% confidence intervals. For instance, the comparison between SOF + VEL-12 and GLE + PIB-12 shows a value of -1.05 (95% CI: -7.80, 5.58), suggesting that there is no significant difference in response rates between these regimens. We also highlighted interesting findings such as the comparison between SOF + VEL-12 and SOF + VEL + VOX-12, noting that adding voxilaprevir does not significantly change the response rate but may affect other clinical considerations like the duration of treatment or side-effect profile.
Fig. 2Forest plot of proportions of HCV GT2 patients reaching SVR12 weeks after the end of treatment with DAAs over patients receiving all doses of treatment, according to type of treatment. DAA regimen of SOF + VEL-12(a), SOF + RBV-12(b), SOF + RBV-16(c) LDV + SOF-12(d), GLE + PIB-8(e) and GLE + PIB-12(f)
Table 3Results of network meta-analysis between regimens in HCV genotype 2 patients (95% CI)LDV+SOF-12LDV+SOF-8GLE+PIB-12GLE+PIB-8SOF+RBV-12SOF+RBV-16SOF+RBV-24SOF+VEL-12SOF+VEL+VOX-12SOF+VEL+VOX-8LDV+SOF-12
LDV+SOF-12
-2.54 (-6.95, 1.12)3.67 (-1.44, 10.44)1.09 (-3.75, 6.23)-0.23 (-3.65, 3.19)-0.06 (-4.31, 4.20)27.73 (1.04, 94.57)2.60 (-2.20, 8.12)-35.13 (-123.10, 29.89)-18.58 (-65.60, 2.45)LDV+SOF-82.54 (-1.12, 6.95)
LDV+SOF-8
6.33 (0.02, 14.32)3.72 (-2.39, 10.30)2.35 (-2.68, 7.83)2.54 (-3.15, 8.50)30.34 (3.32, 97.32)5.27 (-0.87, 12.10)-32.36 (-120.76, 32.76)-16.02 (-63.14, 5.70)GLE+PIB-12-3.67 (-10.44, 1.44)-6.33 (-14.32, -0.02)
GLE+PIB-12
-2.53 (-6.95, 0.17)-3.88 (-9.65, 0.10)-3.72 (-10.08, 0.90)23.82 (-3.30, 90.57)-1.05 (-7.80, 4.78)-39.01 (-127.03, 25.80)-22.63 (-69.96, -0.89)GLE+PIB-8-1.09 (-6.23, 3.75)-3.72 (-10.30, 2.39)2.53 (-0.17, 6.95)
GLE+PIB-8
-1.31 (-5.08, 2.13)-1.16 (-5.69, 3.07)26.48 (-0.14, 93.36)1.54 (-3.57, 7.01)-36.19 (-124.13, 28.74)-19.78 (-66.87, 1.46)SOF+RBV-120.23 (-3.19, 3.65)-2.35 (-7.83, 2.68)3.88 (-0.10, 9.65)1.31 (-2.13, 5.08)
SOF+RBV-12
0.15 (-2.37, 2.72)27.90 (1.59, 94.76)2.80 (-0.64, 7.17)-34.88 (-122.79, 29.93)-18.36 (-65.25, 2.35)SOF+RBV-160.06 (-4.20, 4.31)-2.54 (-8.50, 3.15)3.72 (-0.90, 10.08)1.16 (-3.07, 5.69)-0.15 (-2.72, 2.37)
SOF+RBV-16
27.74 (1.36, 94.69)2.66 (-0.99, 7.14)-34.92 (-123.25, 29.90)-18.49 (-65.50, 2.24)SOF+RBV-24-27.73 (-94.57, -1.04)-30.34 (-97.32, -3.32)-23.82 (-90.57, 3.30)-26.48 (-93.36, 0.14)-27.90 (-94.76, -1.59)-27.74 (-94.69, -1.36)
SOF+RBV-24
-24.93 (-92.00, 1.61)-68.75 (-167.88, 9.81)-52.21 (-125.13, -7.81)SOF+VEL-12-2.60 (-8.12, 2.20)-5.27 (-12.10, 0.87)1.05 (-4.78, 7.80)-1.54 (-7.01, 3.57)-2.80 (-7.17, 0.64)-2.66 (-7.14, 0.99)24.93 (-1.61, 92.00)
SOF+VEL-12
-37.77 (-125.59, 26.95)-21.28 (-68.14, -1.15)SOF+VEL+VOX-1235.13 (-29.89, 123.10)32.36 (-32.76, 120.76)39.01 (-25.80, 127.03)36.19 (-28.74, 124.13)34.88 (-29.93, 122.79)34.92 (-29.90, 123.25)68.75 (-9.81, 167.88)37.77 (-26.95, 125.59)
SOF+VEL+VOX-12
12.95 (-44.16, 89.91)SOF+VEL+VOX-818.58 (-2.45, 65.60)16.02 (-5.70, 63.14)22.63 (0.89, 69.96)19.78 (-1.46, 66.87)18.36 (-2.35, 65.25)18.49 (-2.24, 65.50)52.21 (7.81, 125.13)21.28 (1.15, 68.14)-12.95 (-89.91, 44.16)
SOF+VEL+VOX-8
SVR12 analysis by participants’ treatment history
Totally, 48 study-arms provided data on whether patients had been treated or not, including 1,387 treatment-naïve patients and 414 treatment-experienced patients. As shown in Fig. 3, the pooled SVR12 estimated were 93.18% (95% CI 89.88–95.95%) for the treatment-naïve group and 95.08% (95% CI 89.54–98.94%) for the treatment-experienced group. Both groups show substantial and moderate heterogeneity in I2 = 70% and I2 = 62%, respectively. Compared with treatment-naive patients, treatment-experienced patients had a narrowly 1.9% higher chance of achieving SVR12.
Fig. 3Networks of studies. Evidence network of DAA-based regimens studied in chronic hepatitis C genotype 2 patients (The nodes represent each regimen, with the thickness of the connecting lines represents the number of studies. The edges between nodes indicate direct comparisons, with thicker edges denoting multiple study comparisons)
SVR12 analysis by participants’ cirrhosis status
Due to the indistinctive difference in treatment history, we moved to focus on the subgroup analysis by cirrhosis status for 381 patients with cirrhosis and 1,709 patients without cirrhosis. In total, 55 study-arms provided data for 20 different regimens in the cirrhotic status subgroup analysis (Fig. 4). Out of expectations, we found that cirrhotic patients had a comparatively high SVR12 of 97.17% (95% CI 93.01%, 99.73%), while non-cirrhotic patients only had 92.77% (95% CI 89.48%, 95.57%).
Fig. 4Forest plot of proportions of HCV GT2 patients reaching SVR12 with DAAs, according to pervious history of treatment
Analysis of adverse events for DAA therapies
Meta-analysis of 1198 DAA-treated patients’ AEs showed that fatigue was the most common adverse event (14.0%, 95% CI: 6.4-21.6%) followed by headache (13.1%, 95% CI: 9.2-17.1%), while death and serious AEs were uncommon (Table 4). Most AEs related to DAA regimens were transient, and specific medical intervention was unnecessary.
Table 4Meta-analysis of proportions of all adverse events for HCV genotype 2 patients treated with DAAs regimensCommon AEsDAA regimen and No. of event/No. of participantsProportion(95% CI)I2 statistic (P value)OBV+PTV+RTN+SOF+RBV-8 (n=1)OBV+PTV+RTN+SOF+RBV-6 (n=1)EBR/GZR+RBV-12(n=1)SOF+VEL-12(n=1)LDV+SOF-8(n=1)LDV+SOF-12(n=3)SOF+RBV-12(n=5)GLE+PIB-8 (n=2)GLE+PIB-12 (n=2)Fatigue3/106/912/3020/1345/276/15769/42517/23523/22014.0% [6.4; 21.6]92.8% (P<0.0001)Headache6/103/96/3024/1348/2718/15765/42530/23524/22013.1% [9.2; 17.1]76.7% (P<0.0001)Nausea3/105/3014/1345/277/15731/42521/23515/2205.7% [2.2; 10.8]89.6% (P<0.0001)Insomnia2/106/13422/4250.2% [0.0; 0.6]52.0% (P<0.001)Irritability4/1349/4250.1% [0.0; 0.5]0% (P =0.64)Pruritus2/96/13418/4253/2354/2201.3% [0.2; 2.4]53.3% (P<0.01)Nasopharyngitis8/13423/15776/4259/2352/2207.4% [2.5; 12.4]89.3% (P<0.001)Anaemia5/3074/4251/2205.7% [1.2; 10.3](P<0.0001)Blood bilirubin increased7/4251/2352/2202% [0; 0.8]58.3% (P<0.01)SAE1/1001/302/1342/274/15713/4253/2353/2201.5% [0.8; 2.1]0% (P =0.64)Death0002010000% [0; 0.2]0 (P =0.87)Any AE10/109/926/3092/13422/2795/157271/425133/235143/22073.1% [66.6; 79.1]77.4% (P<0.0001)
Sofosbuvir plus Velpatasvir with a duration of 12 weeks
The highest SVR12 rate was estimated for SOF + VEL for 12 weeks, with SVR12 100% (95% CI 99–100%; Fig. 5a). Among the 311 HCV GT2 patients who treated with SOF + VEL included from 4 arms, any virologic failure was not observed, even in the population with cirrhosis and previous treatment failure [19–22]. Only one study-arm reported the safety profile of SOF + VEL with 9 common AEs being headache (17.9%), fatigue (14.9%), nasopharyngitis (6.0%), nausea (10.4%), pruritus (4.5%), insomnia (4.5%), irritability (3.0%), cough (3.0%), and dyspepsia (0.7%) [23]. The rate of AEs is 68.7%, which was slightly lower among patients treated with SOF + VEL than patients’ pooled level (Supplementary Table S5). However, it should be noted that although 2 out of 134 participants were reported to have died during the post-treatment follow-up, further investigation revealed that the cause of death was attributed to metastatic cancer and cardiac arrest rather than the SOF + VEL treatment itself [20].
Fig. 5Forest plot of proportions of HCV GT2 patients reaching SVR12 with DAAs, according to cirrhotic status
Sofosbuvir plus Ribavirin with a duration of 12 and 16 weeks
As for the SOF + RBV regimen, the treatment of SOF + RBV for 12 weeks was used in 793 patients with HCV GT2 from 9 study-arms, which resulted in a pooled SVR12 of 94.6% (95% CI 92.8–96.4% I2 = 47%; Fig. 5b and c) [20, 24–30]. And the SVR12 of SOF + RBV for 16 weeks is 92.0% (95% CI 81.72–98.74%), which shows lower efficacy with longer treatment duration. Among the 425 patients who received SOF + RBV, 74 (17.4%) had anaemia, 69 (16.2%) had fatigue, 65 (15.3%) had headaches, and 26 types of AEs were reported in 5 studies totally. AEs were experienced by 63.8% of patients, and 13 SAEs occurred among 425 patients.
Ledipasvir plus Sofosbuvir with a duration of 8 and 12 weeks
The difference was observed when considering patients who received all doses of LDV + SOF for 8 or 12 weeks included from 4 study-arms [29, 31]. Treatment with LDV + SOF for 12 weeks resulted in an SVR12 (96.6%, 95% CI: 92.8-99.2%; Fig. 5d), compared with only 74.1% (95% CI: 54-89%) for a shorter duration of 8 weeks therapy. The most common AEs related to LDV + SOF are nasopharyngitis (14.6%) and headache (11.5%), while pruritus is not common. Specifically, some AEs like upper respiratory tract infection (URTI) (2.5%), gastroenteritis (2.1%), rash (1.9%), diarrhea (1.3%), hyperhidrosis (1.3%), pyrexia (1.3%) and back pain (0.6%) were only recorded in patients who received LDV + SOF.
Glecaprevir plus Pibrentasvir with a duration of 8 and 12 weeks
For the GLE + PIB regimen, the SVR12 rates were pooled from 4 studies with 693 patients infected with HCV GT2 [30, 32–34]. Comparing the pooled SVR12 with a random effect model between the two duration treatments shows no significant difference in SVR12 rates among the patients treated with 8 weeks (98.0%, 95% CI: 96.6-99.4%; Fig. 5e) and 12 weeks (100%, 95% CI: 99.0-100.0%; Fig. 5f). For safety, 10 kinds of AEs were observed in 455 patients treated with GLE + PIB for 8 or 12 weeks in total. Headache (11.9%), fatigue (8.8%) and nausea (7.9%) are the top three common AEs of patients after receiving all durations of GLE and PIB, while death cases were never observed. The rate of AEs in patients who received treatment with GLE + PIB (60.7%) is lower than the pooled rate (73.1%, 95% CI: 66.6-79.1%).
Characteristics of included studies
A total of 4,486 studies were yielded from database search, of which 703 duplicates were removed. After applying our inclusion criteria, we identified 31 articles for our meta-analysis, which included 35 clinical studies. All included articles were published within the past 10 years, with the majority being recent studies published within the past 5 years.
In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our search strategy and selection process are depicted in Fig. 1. Our search yielded a total of 31 articles, consisting of 9 randomized controlled trials (RCTs) and 26 non-randomized studies. Of the 35 clinical trials, 13 were conducted in phase 2, while 21 were conducted in phase 3; 1 clinical trial was conducted in phase 4. All 35 included studies were multicenter clinical trials.
Fig. 1Flowchart summarizing the selection and identification of studies based on PRISMA
2,968 participants with HCV GT2 from these 35 studies were eligible for the data synthesis, as presented in Table 2, consisting of 1,389 treatment-naive patients and 354 patients with cirrhosis. The sample size of the included clinical trials ranged from 18 to 458. All participants were diagnosed with HCV GT2 infection and received DAA therapy as their primary treatment.
Table 2Characteristics of included studies for systematic review and meta-analysisAuthorNCT NumberYearCountryPopulation and designInterventionParticipants (n)CirrhosisDurationSVR12Sponsors and Collaboratorsn (%)(week)n (%)Lawtiz et al., 2015 [48]NCT014585352015NANon-Randomized, open-label, sequential arm, phase 2, multicenter study, TN G1-3 PatientsOBV+PTV+RTN±RBV2001214(70.0%)AbbVieJacobson et al., 2013 [24]NCT01542788 (POSITRON)2013NA, ANZRandomized, blinded, placebo-controlled, phase 3, multicenter study, TN G2-3 patientsSOF+RBV14317(11.9%)12101(92.7%)Gilead SciencesJacobson et al., 2013 [24]NCT01604850 (FUSION)2013NA, ANZRandomized, open-label, active control, phase 3, multicenter study, TE G2-3 patientsSOF+RBV6819(27.9%)12 or 1661(89.7%)Gilead SciencesLawitz et al., 2013 [25]NCT01497366(FISSION)2013NA, EU, ANZRandomized, Open-label, multicenter, Phase 3 RCT, TN G1-3 PatientsSOF+RBV/PR137N/A12120(87.6%)Gilead SciencesZeuzem et al., 2014 [27]NCT01682720 (VALENCE)2014EURandomized, blinded, placebo-controlled, phase 3, multicenter, TN/TE G2-3 patientsSOF+RBV9111(12.1%)1268(93.2%)Gilead SciencesFoster et al., 2016 [49]NCT01616524 (PRINCIPAL)2016NA, LM, ASIA, EURandomized, blinded, controlled, phase 3, multinational, TN G2-3 patientsLambda+RBV+DCV45829(6.3%)12 or 24363(79.3%)Bristol-Myers SquibbOmata et al., 2014 [26]NCT019106362014JapanOpen-label, phase 3, multicenter, TN/TE G2 patientsSOF+RBV15317(11.1%)12148(96.7%)Gilead SciencesBrown et al., 2017 [50]NCT01932762(C-SCAPE)2017N/ARandomized, open-label, phase 2, multi-site, TN G2,4,5,6 patientsEBR±GZR±RBV5601243(76.8%)Merck Sharp & Dohme LLCDore et al., 2015 [51]NCT01257204(AI444-031)2015NA, EU, ANZRandomized, blinded, placebo-controlled, phase 2, multicenter, TN G2-3 patientsDCV+PEG+RBV7168(95.8)12 or 16 or 2457(80.3%)Bristol-Myers SquibbEverson et al., 2015 [52]NCT018587662015NARandomized, open-label, phase 2, multicenter, TN G1-6 patientsSOF+VEL1030888(65.2%)Gilead SciencesFeld et al., 2015 [19]NCT02201940(ASTRAL-1)2015NA, EU, ASIARandomized, blinded, placebo-controlled, phase 3, multicenter, TN/TE G1-2, 4-5 patientsSOF+VEL125N/A12104(100%)Gilead SciencesFoster et al., 2015 [20]NCT02220998(ASTRAL-2)2015NARandomized, open-label, phase 3, multicenter, TN/TE G2 patientsSOF+VEL/SOF+RBV26638(14.3%)12257(96.6%)Gilead SciencesFoster et al., 2015 [53]NCT019624412015NA, EU, ANZRandomized, open-label, phase 3, multicenter, TN/TE G2-3 patientsSOF+RBV+PEG48N/A12 or 16 or 2445(93.8%)Gilead SciencesAsselah et al., 2018 [32]NCT02243293(SURVEYOR-II)2018N/ARandomized, open-label, phase 3, multicenter, TN/TE G2 patientsGLE+PIB14508142(97.9%)AbbVieAsselah et al., 2018 [32]NCT02640482(ENDURANCE-2)2018N/ARandomized, blinded, placebo-controlled, phase 2, multicenter, TN/TE G2 patientsGLE+PIB302012201(99.5)AbbVieGane et al., 2016 [54]NCT023789612016NA, ANZNon-Randomized, open-label, phase 2, multicenter, TN/TE G2-3, 6 patientsSOF+VEL+VOX3314(42.4%)6 or 8 or 1231(93.9%)Gilead SciencesLawitz et al., 2017 [55]NCT023327072017N/ARandomized, open-label, phase 2, multicenter, TN G1-3 patientsGZR+RZR+UPR±RBV15157(37.7%)8 or 12141(93.4%)Merck Sharp & Dohme CorpToyoda et al., 2018 [30]NCT02707952(CERTAIN-1)2018ASIARandomized, open-label, phase 3, multicenter, TN/TE G2 patientsGLE+PIB1818(100%)1218(100%)AbbVieToyoda et al., 2018 [30]NCT02723084(CERTAIN-2)2018ASIARandomized, open-Label, active comparator, phase 3 multicenter, TN/TE G2 patientsGLE+PIB/SOF+RBV1360888(97.8%)AbbVieGane et al., 2017 [31]NCT02202980(LEPTON)2017ANZNon-randomized, open-label, phase 2, multicenter, TN/TE G2 patientsLDV+SOF532(3.8%)8 or 1245(84.9%)Gilead SciencesHo et al., 2017 [28]NCT02128542(VALOR-HCV)2017NAOpen-label, phase 4, multicenter, TN/TE G2 patientsSOF+RBV6666(100%)1252(78.9%)Gilead SciencesJacobson et al., 2017 [21]NCT02607800(POLARIS-2)2017NA, ANZ, EURandomized, open-label, phase 3, multicenter, TN G1-6 patientsSOF+VEL+VOX/SOF+VEL116N/A8 or 12114(98.3%)Gilead SciencesLawitz et al., 2019 [56]NCT02956629(MK-3682-041)2019N/ANon-randomized, open-label, phase 2, multi-site, TN/TE G1-6 patientsRZR+UPR4710(21.3%)1243(91.5%)Merck Sharp & Dohme CorpLawitz et al., 2019 [57]NCT02759315(MK-3682-035)2019NANon-randomized, open-label, phase 2, multi-center TN/TE G1-6 patientsRZR+UPR297(24.1%)1228(96.6%)Merck Sharp & Dohme LLCWei et al., 2018 [58]NCT026715002018ASIAOpen-label, Phase 3, multicenter, TN & TE G1-6 PatientsSOF+VEL64N/A1264(100%)Gilead SciencesWei et al.,2020 [34]NCT03222583(VOYAGE-1)2020ASIARandomized, blind, placebo-controlled, phase 3, multicenter, TN/TE G1-6 patientsGLE+PIB21008136(97.8%)AbbVieWei et al.,2020 [34]NCT03235349(VOYAGE-2)2020ASIAOpen-label, multicenter, phase 3, TN/TE G1-6 patientsGLE+PIB53531253(100%)AbbVieAsahina et al., 2018 [29]N/A2018ASIARandomized, open-label, phase 3, multicenter, TN & TE G2 patientsLDV+SOF/SOF+RBV13118(13.7%)12126(96.2%)Gilead SciencesIzumi et al., 2018 [59]N/A2018ASIAOpen-label study, phase 3, multicenter, TE G1-2 patientsSOF+VEL+RBV225(22.7%)12 or 2418(81.8%)Gilead SciencesRao et al., 2019 [60]NCT03416491(KW-136_II)2019ASIARandomized, open-label, phase 2, multicenter, TN G1-3,6 patientsCLV+SOF274(14.8%)1226(96.3%)Beijing Kawin TechnologyShafran et al., 2017 [61]NCT02292719(QUARTZ II-III)2017N/ARandomized, open-label, phase 2, multicenter, TN/TE G2-3 patientsOBV+PTV+RTN+SOF±RBV1906 or 813(68.4)AbbVieBrown et al., 2020 [33]NCT03089944(EXPEDITION-8)2020NA, EU, ASIAOpen-label, phase 3b, multicenter, TN G1-6 PatientsGLE+PIB26N/A826(100%)AbbVieAsselah et al.,2019 [22]NCT023467212019NA, EU, ASIAOpen-label, phase 3, multicenter, TN & TE G1,2,4,6 PatientsSOF+VEL20N/A1220 (100%)Gilead SciencesGao et al.,2020 [62]NCT03995485(KW-136_III)2020ASIAOpen-label, phase 3, multicenter, TN & TE G1-3, 6 PatientsCLV+SOF95N/A1291(95.8%)Beijing Kawin TechnologyHua et al., 2022 [63]NCT040702352022ASIARandomized, open-label, phase 2, multicenter, TN & TE G1-3, 6 PatientsAFV+DCV31N/A1231(100%)Nanjing Sanhome PharmaceuticalAFV: Alfosbuvir; SOF: Sofosbuvir; VEL: Velpatasvir; LDV: Ledipasvir; CLV: Coblopasvir; DCV: Daclatasvir; GLE: Glecaprevir; PIB: Pibrentasvir; EBR: Elbasvir; GRZ: Grazoprevir; RUZ: Ruzasvir; RTN: Ritonavir; UPR: Uprifosbuvir; VOX: Voxilaprevir; PR: Peginterferon and Ribavirin; RBV: Ribavirin; NA: North America; EU: Europe; ASIA: Asia; ANZ: Australia and New- Zealand; AFR: Africa, LM: Latin Ameica; TN: Treatment-naïve; TE: Treatment-experienced; NIH: National Institutes of Health; G: Genotype; N/A: Not applicable; RCT: Randomized controlled trials; NCT: ClinicalTrials.gov Identifier;
A total of 23 combinations of DAAs were investigated, duration of therapy ranged from 6 to 16 weeks with or without the addition of ribavirin. We excluded the 6-week regimens from our meta-analysis due to a limitation on sample size (n = 6). Among the 35 clinical trials included in this study, DAAs were regarded as the first-line treatment. According to the mechanism of action, DAAs were divided into four types [18]:
i.NS5A Inhibitor (Daclatasvir, Elbasvir, Ledipasvir, Ombitasvir, Ledipasvir, Pribrentasvir, Velpatasvir).ii.Protease NS3/4A Inhibitor (Glecaprevir, Paritaprevir, Simeprevir, Grazoprevir, Voxilaprevir).iii.Non-Nucleoside NS5B Polymerase Inhibitor (Dasabuvir, Deleobuvir).iv.NS5B Polymerase Inhibitor (Sofosbuvir).
Quality assessment
There are two domains were considered at low risk of bias in all studies, including classification of intervention and measurement of outcome (Supplementary Table S2 & Table S3). Of the 26 non-randomized trials included in our analysis, the quality was evaluated using the ROBINS-I tool, which assesses bias from five perspectives. Among these 26 studies, 12 were judged to be at moderate risk of bias due to deviations from the intended interventions. Bias due to missing data was considered moderate in 10 studies. Of the 9 randomized controlled trials assessed using the ROB 2 tool, 2 were judged to be at high risk of bias due to selection and performance. Overall, only 1 out of the 9 RCTs was considered to be at high risk of bias (Supplementary Figure S1).
Overall pooled SVR12 for DAA therapies
Since 2014, multiple regimens have been utilized for the treatment of hepatitis C virus genotype 2, with overall response rates showing considerable success. The characteristics of the 35 included studies (58 study-arms, n = 2,968) are reported (Supplementary Table S4). A total of 2,968 GT2 patients who received DAA therapy were included. As seen in Supplementary Figure S2, the pooled SVR12 was 94.62% (95% CI: 92.43-96.51%), but with substantial heterogeneity (I2 = 72.0%, P < 0.0001) caused by subgroups. Our network connected 10 mainstream treatment regimens for further indirectly comparison. Figure 2 illustrates the network of studies included in our network meta-analysis, comparing five different regimens. Notably, all regimens were compared directly to at least one other regimen, forming a well-connected network. The connection between SOF + RBV 12 and GLE + PIB 12 is depicted with a moderately thick line, indicating several studies have directly compared these two regimens. In contrast, the thinner line between SOF + VEL + VOX 8 and GLE + PIB 12 suggests fewer direct comparisons, relying more on indirect evidence. The assessment of inconsistency showed no significant differences between direct and indirect comparisons within this network. By comparing the main treatment regimens, we have confirmed that the 12-week SOF + VEL therapy and the 12-week GLE + PIB therapy are essentially equivalent in efficacy and are the treatment regimens with the highest response rates (Supplementary Figure S3).
We also did further analysis to indirectly compare the results between every two regimens (Table 3). Each cell in Table 3 shows the estimated difference in response rates between regimens with the corresponding 95% confidence intervals. For instance, the comparison between SOF + VEL-12 and GLE + PIB-12 shows a value of -1.05 (95% CI: -7.80, 5.58), suggesting that there is no significant difference in response rates between these regimens. We also highlighted interesting findings such as the comparison between SOF + VEL-12 and SOF + VEL + VOX-12, noting that adding voxilaprevir does not significantly change the response rate but may affect other clinical considerations like the duration of treatment or side-effect profile.
Fig. 2Forest plot of proportions of HCV GT2 patients reaching SVR12 weeks after the end of treatment with DAAs over patients receiving all doses of treatment, according to type of treatment. DAA regimen of SOF + VEL-12(a), SOF + RBV-12(b), SOF + RBV-16(c) LDV + SOF-12(d), GLE + PIB-8(e) and GLE + PIB-12(f)
Table 3Results of network meta-analysis between regimens in HCV genotype 2 patients (95% CI)LDV+SOF-12LDV+SOF-8GLE+PIB-12GLE+PIB-8SOF+RBV-12SOF+RBV-16SOF+RBV-24SOF+VEL-12SOF+VEL+VOX-12SOF+VEL+VOX-8LDV+SOF-12
LDV+SOF-12
-2.54 (-6.95, 1.12)3.67 (-1.44, 10.44)1.09 (-3.75, 6.23)-0.23 (-3.65, 3.19)-0.06 (-4.31, 4.20)27.73 (1.04, 94.57)2.60 (-2.20, 8.12)-35.13 (-123.10, 29.89)-18.58 (-65.60, 2.45)LDV+SOF-82.54 (-1.12, 6.95)
LDV+SOF-8
6.33 (0.02, 14.32)3.72 (-2.39, 10.30)2.35 (-2.68, 7.83)2.54 (-3.15, 8.50)30.34 (3.32, 97.32)5.27 (-0.87, 12.10)-32.36 (-120.76, 32.76)-16.02 (-63.14, 5.70)GLE+PIB-12-3.67 (-10.44, 1.44)-6.33 (-14.32, -0.02)
GLE+PIB-12
-2.53 (-6.95, 0.17)-3.88 (-9.65, 0.10)-3.72 (-10.08, 0.90)23.82 (-3.30, 90.57)-1.05 (-7.80, 4.78)-39.01 (-127.03, 25.80)-22.63 (-69.96, -0.89)GLE+PIB-8-1.09 (-6.23, 3.75)-3.72 (-10.30, 2.39)2.53 (-0.17, 6.95)
GLE+PIB-8
-1.31 (-5.08, 2.13)-1.16 (-5.69, 3.07)26.48 (-0.14, 93.36)1.54 (-3.57, 7.01)-36.19 (-124.13, 28.74)-19.78 (-66.87, 1.46)SOF+RBV-120.23 (-3.19, 3.65)-2.35 (-7.83, 2.68)3.88 (-0.10, 9.65)1.31 (-2.13, 5.08)
SOF+RBV-12
0.15 (-2.37, 2.72)27.90 (1.59, 94.76)2.80 (-0.64, 7.17)-34.88 (-122.79, 29.93)-18.36 (-65.25, 2.35)SOF+RBV-160.06 (-4.20, 4.31)-2.54 (-8.50, 3.15)3.72 (-0.90, 10.08)1.16 (-3.07, 5.69)-0.15 (-2.72, 2.37)
SOF+RBV-16
27.74 (1.36, 94.69)2.66 (-0.99, 7.14)-34.92 (-123.25, 29.90)-18.49 (-65.50, 2.24)SOF+RBV-24-27.73 (-94.57, -1.04)-30.34 (-97.32, -3.32)-23.82 (-90.57, 3.30)-26.48 (-93.36, 0.14)-27.90 (-94.76, -1.59)-27.74 (-94.69, -1.36)
SOF+RBV-24
-24.93 (-92.00, 1.61)-68.75 (-167.88, 9.81)-52.21 (-125.13, -7.81)SOF+VEL-12-2.60 (-8.12, 2.20)-5.27 (-12.10, 0.87)1.05 (-4.78, 7.80)-1.54 (-7.01, 3.57)-2.80 (-7.17, 0.64)-2.66 (-7.14, 0.99)24.93 (-1.61, 92.00)
SOF+VEL-12
-37.77 (-125.59, 26.95)-21.28 (-68.14, -1.15)SOF+VEL+VOX-1235.13 (-29.89, 123.10)32.36 (-32.76, 120.76)39.01 (-25.80, 127.03)36.19 (-28.74, 124.13)34.88 (-29.93, 122.79)34.92 (-29.90, 123.25)68.75 (-9.81, 167.88)37.77 (-26.95, 125.59)
SOF+VEL+VOX-12
12.95 (-44.16, 89.91)SOF+VEL+VOX-818.58 (-2.45, 65.60)16.02 (-5.70, 63.14)22.63 (0.89, 69.96)19.78 (-1.46, 66.87)18.36 (-2.35, 65.25)18.49 (-2.24, 65.50)52.21 (7.81, 125.13)21.28 (1.15, 68.14)-12.95 (-89.91, 44.16)
SOF+VEL+VOX-8
SVR12 analysis by participants’ treatment history
Totally, 48 study-arms provided data on whether patients had been treated or not, including 1,387 treatment-naïve patients and 414 treatment-experienced patients. As shown in Fig. 3, the pooled SVR12 estimated were 93.18% (95% CI 89.88–95.95%) for the treatment-naïve group and 95.08% (95% CI 89.54–98.94%) for the treatment-experienced group. Both groups show substantial and moderate heterogeneity in I2 = 70% and I2 = 62%, respectively. Compared with treatment-naive patients, treatment-experienced patients had a narrowly 1.9% higher chance of achieving SVR12.
Fig. 3Networks of studies. Evidence network of DAA-based regimens studied in chronic hepatitis C genotype 2 patients (The nodes represent each regimen, with the thickness of the connecting lines represents the number of studies. The edges between nodes indicate direct comparisons, with thicker edges denoting multiple study comparisons)
SVR12 analysis by participants’ cirrhosis status
Due to the indistinctive difference in treatment history, we moved to focus on the subgroup analysis by cirrhosis status for 381 patients with cirrhosis and 1,709 patients without cirrhosis. In total, 55 study-arms provided data for 20 different regimens in the cirrhotic status subgroup analysis (Fig. 4). Out of expectations, we found that cirrhotic patients had a comparatively high SVR12 of 97.17% (95% CI 93.01%, 99.73%), while non-cirrhotic patients only had 92.77% (95% CI 89.48%, 95.57%).
Fig. 4Forest plot of proportions of HCV GT2 patients reaching SVR12 with DAAs, according to pervious history of treatment
Analysis of adverse events for DAA therapies
Meta-analysis of 1198 DAA-treated patients’ AEs showed that fatigue was the most common adverse event (14.0%, 95% CI: 6.4-21.6%) followed by headache (13.1%, 95% CI: 9.2-17.1%), while death and serious AEs were uncommon (Table 4). Most AEs related to DAA regimens were transient, and specific medical intervention was unnecessary.
Table 4Meta-analysis of proportions of all adverse events for HCV genotype 2 patients treated with DAAs regimensCommon AEsDAA regimen and No. of event/No. of participantsProportion(95% CI)I2 statistic (P value)OBV+PTV+RTN+SOF+RBV-8 (n=1)OBV+PTV+RTN+SOF+RBV-6 (n=1)EBR/GZR+RBV-12(n=1)SOF+VEL-12(n=1)LDV+SOF-8(n=1)LDV+SOF-12(n=3)SOF+RBV-12(n=5)GLE+PIB-8 (n=2)GLE+PIB-12 (n=2)Fatigue3/106/912/3020/1345/276/15769/42517/23523/22014.0% [6.4; 21.6]92.8% (P<0.0001)Headache6/103/96/3024/1348/2718/15765/42530/23524/22013.1% [9.2; 17.1]76.7% (P<0.0001)Nausea3/105/3014/1345/277/15731/42521/23515/2205.7% [2.2; 10.8]89.6% (P<0.0001)Insomnia2/106/13422/4250.2% [0.0; 0.6]52.0% (P<0.001)Irritability4/1349/4250.1% [0.0; 0.5]0% (P =0.64)Pruritus2/96/13418/4253/2354/2201.3% [0.2; 2.4]53.3% (P<0.01)Nasopharyngitis8/13423/15776/4259/2352/2207.4% [2.5; 12.4]89.3% (P<0.001)Anaemia5/3074/4251/2205.7% [1.2; 10.3](P<0.0001)Blood bilirubin increased7/4251/2352/2202% [0; 0.8]58.3% (P<0.01)SAE1/1001/302/1342/274/15713/4253/2353/2201.5% [0.8; 2.1]0% (P =0.64)Death0002010000% [0; 0.2]0 (P =0.87)Any AE10/109/926/3092/13422/2795/157271/425133/235143/22073.1% [66.6; 79.1]77.4% (P<0.0001)
Sofosbuvir plus Velpatasvir with a duration of 12 weeks
The highest SVR12 rate was estimated for SOF + VEL for 12 weeks, with SVR12 100% (95% CI 99–100%; Fig. 5a). Among the 311 HCV GT2 patients who treated with SOF + VEL included from 4 arms, any virologic failure was not observed, even in the population with cirrhosis and previous treatment failure [19–22]. Only one study-arm reported the safety profile of SOF + VEL with 9 common AEs being headache (17.9%), fatigue (14.9%), nasopharyngitis (6.0%), nausea (10.4%), pruritus (4.5%), insomnia (4.5%), irritability (3.0%), cough (3.0%), and dyspepsia (0.7%) [23]. The rate of AEs is 68.7%, which was slightly lower among patients treated with SOF + VEL than patients’ pooled level (Supplementary Table S5). However, it should be noted that although 2 out of 134 participants were reported to have died during the post-treatment follow-up, further investigation revealed that the cause of death was attributed to metastatic cancer and cardiac arrest rather than the SOF + VEL treatment itself [20].
Fig. 5Forest plot of proportions of HCV GT2 patients reaching SVR12 with DAAs, according to cirrhotic status
Sofosbuvir plus Ribavirin with a duration of 12 and 16 weeks
As for the SOF + RBV regimen, the treatment of SOF + RBV for 12 weeks was used in 793 patients with HCV GT2 from 9 study-arms, which resulted in a pooled SVR12 of 94.6% (95% CI 92.8–96.4% I2 = 47%; Fig. 5b and c) [20, 24–30]. And the SVR12 of SOF + RBV for 16 weeks is 92.0% (95% CI 81.72–98.74%), which shows lower efficacy with longer treatment duration. Among the 425 patients who received SOF + RBV, 74 (17.4%) had anaemia, 69 (16.2%) had fatigue, 65 (15.3%) had headaches, and 26 types of AEs were reported in 5 studies totally. AEs were experienced by 63.8% of patients, and 13 SAEs occurred among 425 patients.
Ledipasvir plus Sofosbuvir with a duration of 8 and 12 weeks
The difference was observed when considering patients who received all doses of LDV + SOF for 8 or 12 weeks included from 4 study-arms [29, 31]. Treatment with LDV + SOF for 12 weeks resulted in an SVR12 (96.6%, 95% CI: 92.8-99.2%; Fig. 5d), compared with only 74.1% (95% CI: 54-89%) for a shorter duration of 8 weeks therapy. The most common AEs related to LDV + SOF are nasopharyngitis (14.6%) and headache (11.5%), while pruritus is not common. Specifically, some AEs like upper respiratory tract infection (URTI) (2.5%), gastroenteritis (2.1%), rash (1.9%), diarrhea (1.3%), hyperhidrosis (1.3%), pyrexia (1.3%) and back pain (0.6%) were only recorded in patients who received LDV + SOF.
Glecaprevir plus Pibrentasvir with a duration of 8 and 12 weeks
For the GLE + PIB regimen, the SVR12 rates were pooled from 4 studies with 693 patients infected with HCV GT2 [30, 32–34]. Comparing the pooled SVR12 with a random effect model between the two duration treatments shows no significant difference in SVR12 rates among the patients treated with 8 weeks (98.0%, 95% CI: 96.6-99.4%; Fig. 5e) and 12 weeks (100%, 95% CI: 99.0-100.0%; Fig. 5f). For safety, 10 kinds of AEs were observed in 455 patients treated with GLE + PIB for 8 or 12 weeks in total. Headache (11.9%), fatigue (8.8%) and nausea (7.9%) are the top three common AEs of patients after receiving all durations of GLE and PIB, while death cases were never observed. The rate of AEs in patients who received treatment with GLE + PIB (60.7%) is lower than the pooled rate (73.1%, 95% CI: 66.6-79.1%).
Discussion
In this up-to-date, evidence-based systematic review and meta-analysis, we combined SVR12 and AE data from 31 included studies wherever feasible through April 2022. Out of total 3,783 studies identified, we included 13 phase 2 studies and 22 phase 3 or 4 studies. Overall, our meta-analysis confirmed that DAAs therapy is highly effective and safe in the treatment of adult patients with HCV GT2.
The key finding of this study was that both SOF + VEL and GLE + PIB regimens, administered over 12 weeks, achieved the highest efficacy in treating HCV GT2. Additionally, the SOF + VEL regimen demonstrated a high safety profile for patients with GT2, evidenced by the lowest incidence of serious adverse events (1.5%) and comparatively milder side effects. Although both treatments exhibited a relatively high prevalence of adverse events (AEs), the GLE + PIB regimen reported a slightly lower overall AE rate (60.7%) compared to SOF + VEL (68.7%). Among the 58 study-arms, DAAs therapy allows around 80% of GT2 patients reach cure rates ≥ 90%, including those with cirrhosis and who had been treated before. Further analysis revealed an overall pooled SVR12 rate of 94.62% among 2,968 GT2 patients, which aligns with similar results reported for other genotypes [35, 36]. Besides, the therapy against HCV continues evolve. The DAAs regimen like GLE + PIB could shorten the duration into 8 weeks, while reaching 98% SVR12 rate among the GT2 patients. Compared to the 12-week duration of GLE + PIB, the rate of any adverse event in the 8-week group decreased from 65.0 to 56.6%. Shortening the DAA treatment duration for HCV patients can reduce the rate of side effects, there is also research demonstrating that shortened DAA treatment strategies are cost-effective for GT1 patients [37]. There was no observed difference in efficacy among subgroups based on previous treatment history or disease status. These findings align with results from a previous study on other genotypes, in which all patients with GT2 or GT3 were treated with sofosbuvir 400 mg and Velpatasvir 100 mg for 12 weeks and achieved SVR12 [38]. In clinical settings, the selection of a treatment regimen is influenced by various factors, including the accessibility and expense of DAAs, the patient’s tolerance, the potential for adverse reactions or drug-drug interactions, and the occurrence of resistance-associated substitutions (RAS).
However, achieving SVR12 is not the only goal of HCV treatment. While it is an important immediate goal, the ultimate goal is to reduce liver-related mortality and the incidence of hepatocellular carcinoma (HCC) resulting from chronic HCV infection [39]. Among the leading factors of HCC, chronic HCV infection is the primary cause of HCC in Australia [40]. More clinical benefits have been proven with the advent of DAA therapy, which could decrease the recurrence rate of HCC and improve survival [41]. HCV infection is not limited to liver-related symptoms and can also lead to extrahepatic manifestations (EHMs). These manifestations may include mixed cryoglobulinemia, non-Hodgkin lymphomas (NHL), cardiovascular disease, insulin resistance, type 2 diabetes, neurological and psychiatric diseases, and other rheumatic diseases [42]. Studies have shown that DAA treatment can improve or even resolve some EHMs associated with HCV infection [43–46].
In terms of safety, our analysis showed that fatigue (14.0%) was the most common side effect in GT2 patients, followed by headache (13.1%), and the pooled SAE rate was 1.5% (95% CI, 0.8-2.1%). These results confirm that DAAs are safe and well-tolerated. However, some SAEs reported in studies were considered irrelevant to the treatment itself, and therefore, they will not affect the safety evaluation of the DAAs regimen. The most frequent AEs, which included fatigue, headache, pruritus, and nasopharyngitis, were similar across regimens and occurred more frequently in regimens containing RBV. Only the sofosbuvir plus ledipasvir regimen had data for GT2 specifically, with nasopharyngitis (14.6%) as the most common adverse event. Even though the overall AE rates were higher than 50% and may reach 73.1%, the severity was considered mild to moderate, suggesting that treatment interruption or dose adjustment is not required. In assessing the safety profiles of SOF + VEL and GLE + PIB for treating Hepatitis C Virus genotype 2, SOF + VEL showed a 68.7% AE rate over 12 weeks with no treatment-related deaths, while GLE + PIB reported a slightly lower AE rate of 60.7% with no deaths across both 8 and 12-week durations. Despite similar efficacy, GLE + PIB’s marginally better safety profile, characterized by fewer AEs and no severe events, suggests it might be the preferable option, contingent on patient-specific considerations.
Our research has several strengths. It is the first systematic review and meta-analysis involving HCV GT2 patients treated with DAA, which included 35 studies. Moreover, it has a comprehensive subgroup analysis, including different regimens, treatment duration, the presence of cirrhosis, and treatment history. These data also seem to substantiate the suggestion that INF-free DAAs treatment is preferable for patients with any HCV genotypes [47].
There are also limitations to this study. Firstly, two-thirds of the included studies were not randomized controlled trials and were considered to have a low-moderate risk of bias. The single-arm study design has gradually replaced RCTs and becomes the mainstay of DAA therapy clinical trials, resulting in the infeasibility of directly comparing the major agents in DAA combinations. Secondly, the population of GT2 is relatively small, especially when compared to genotypes 1 and 3. Importantly, few studies have categorized patients based on specific genotypes or reported data according to patients’ characteristics, such as genotypes, treatment history, and cirrhosis status. Due to the absence of detailed data, we were unable to conduct further subgroup analyses to assess the efficacy and safety of DAA-based therapies across varying conditions (i.e., treatment history and cirrhosis status). Consequently, it was not feasible to aggregate the sustained virologic response at 12 weeks (SVR12) and the adverse events/serious adverse events (AE/SAE) data for all DAA regimen types. Moreover, only eight of the thirty-five eligible studies provided specific safety data for genotype 2, leading to potential incompleteness and bias in our findings.
Conclusions
Our systematic review and meta-analysis indicate that DAAs are effective in adults infected with chronic HCV GT2. The efficacy of the DAAs regimen in HCV GT2 patients is independent of patients’ previous treatment history and disease status. Among the various regimens evaluated, the combination of Sofosbuvir plus Velpatasvir for 12 weeks, as well as Glecaprevir plus Pibrentasvir for the same duration, were identified as the most effective and comparatively safe in managing chronic HCV GT2 infection. Both regimens achieved a sustained virological response 12 weeks post-treatment (SVR12) of 100% (95% CI 99–100%).
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1 |
Title: The Importance of Including Maternal Immune Activation in Animal Models of Hypoxic–Ischemic Encephalopathy | Body:
1. Introduction
Hypoxic–ischemic encephalopathy (HIE) is a perinatal brain injury characterized by a lack of oxygen and blood flow to the fetal brain. Its causes can be attributed to a wide array of intrauterine and peripartum events including chorioamnionitis, maternal hypoxia, maternal hypotension, placental abruption, cord prolapse and uterine rupture, among many others [1]. The outcomes of this early-life injury vary widely, ranging from normal development and cognition to severe disability and static encephalopathy [2,3,4]. Children with severe HIE have a mortality rate of 25–50%, and those who survive can have significant long-term deficits including cerebral palsy, epilepsy, learning deficits, and vision/hearing impairments [5,6]. It was originally thought that children who did not experience physical disabilities such as cerebral palsy were not at risk for other significant neurodevelopmental disabilities. However, more recent research suggests that in children with no motor deficits following HIE, there can be brain injury that is detectable through abnormal MRI and cognitive impairments detectable only in later childhood [7,8,9].
There are mixed data regarding sex differences in incidence of HIE and severity of outcomes after HIE, with some studies reporting greater incidence and severity for boys [10,11,12,13,14] and others reporting no difference [15]. For preterm infants, increased negative outcomes for males is well documented [10,11,16], with males also being overrepresented in spontaneous preterm births and complications as a whole [16,17,18]. In a study following 308 infants born at less than 25 weeks gestation, males were found to be more likely to develop cerebral palsy and impaired cognitive function than their female counterparts [11]. The mechanisms behind this sex difference are unknown but are currently hypothesized to be due to differences in microglia activation, reactions to oxidative stress, and cell death between males and females [19]. Sex differences in prevalence and outcomes for term infants are less clear, though some studies reported being male was a risk factor for HIE [12]. Much of the research conducted on sex differences in term infants has been conducted using animal models of HIE [20], and there is a need for more studies to fill in this gap in the literature [14].
The neural injury following a hypoxic–ischemic event occurs in two waves, referred to as primary and secondary energy failure. Primary energy failure occurs immediately following the decrease in blood flow and oxygen to the brain. The lack of oxygen and glucose decreases available energy and forces cells to switch to anaerobic metabolism, resulting in a decrease in ATP production, lactate buildup, and sodium/potassium pump failure [21]. The buildup of intracellular sodium leads to chronic depolarization and the buildup of intracellular calcium [22]. The buildup of free radicals such as oxidative stress also contributes to injury [23]. The compounded cellular stress during primary energy failure can lead to cell death through necrosis [23]. Following reperfusion of blood to the brain, there is a brief period of recovery referred to as the latency period, the length of which depends on the severity of injury [24]. Secondary energy failure occurs following the latency period, approximately 6–48 h following the hypoxic–ischemic insult [24]. Excitotoxicity due to the influx of sodium in primary energy failure as well as oxidative stress continue into secondary energy failure [25]. Additionally, mitochondrial dysfunction, cytotoxic edema, and inflammatory activation such as cytokine storms begin to contribute to injury during this time [26,27,28]. Apoptosis (programmed cell death) is heightened during secondary energy failure [29].
Currently, the only effective treatment for HIE is therapeutic hypothermia (TH) [8,9]. This therapy involves cooling the head and body of the infant to 33.5 °C for 72 h. This decrease in temperature slows cerebral metabolism, preserves glucose reserves in the brain, and inhibits the buildup of molecules such as lactate [30]. TH also decreases free radicals and dampens the neuroimmune response [30]. TH significantly improves neurological outcomes in HIE, leading to decreased rates of mortality, severe developmental delay, cerebral palsy, cognitive delay, and severe motor impairment [24]. However, there are some limitations to TH as a treatment. TH must be instituted within the first 6 h after birth to be effective [31]. Intrapartum hypoxia can be difficult to diagnose as there is no definitive diagnostic test for this condition [32]. Neonatal encephalopathy is typically diagnosed through clinical presentations of symptoms such as the Apgar score, abnormal fetal heart rate, or need for respiratory support [24]. The severity of encephalopathy is then characterized by the Sarnat score and can be further categorized by neuroimaging [33]. The possible overlooking of clinical symptoms, along with other barriers to care, can make starting treatment within those first 6 h difficult. Even with successful implementation of therapeutic hypothermia, up to 40% of children suffer from long-term neurodevelopmental disability [6,34]. Additionally, in low-income countries, TH has been shown to be ineffective or harmful due to increased bleeding and thrombocytopenia [35]. This results in there being no effective treatments for infants with HIE in these countries, which comprise the greatest worldwide burden of disease. The need for more therapies for HIE is critical, and to develop them, a robust and comprehensive understanding of the pathophysiology of HIE is needed.
Animal models have greatly contributed to our understanding of the pathophysiology underlying HIE injury and later outcomes. However, following TH, no treatments developed in animal models have been shown to be effective in humans. This points to the need to re-evaluate the translational relevancy of the animal models used to develop these treatments. HIE poses a unique challenge due to the high variability in risk factors, causes, location and severity of injury, and functional outcomes that are present in humans [36]. Many animal models of HIE utilize postnatal animals and lack the maternal–placental–fetal interface that largely characterizes HIE [37]. Some additionally include procedures such as carotid artery ligation that produce unilateral lesions not characteristic of HIE injury in humans [37]. Prenatal risk factors and causes should be considered for inclusion within animal models. Maternal immune activation (MIA) is a significant risk factor for HIE and can in fact directly cause hypoxic injury [38,39]. MIA is easy to include in animal models through the peripheral injection of bacterial or viral mimetics into pregnant animals, thereby incorporating maternal and fetal interaction [40]. An additional benefit of MIA is the expansive literature characterizing the molecular, immune, and behavioral outcomes, largely in rodent models [40,41,42]. The purpose of this review is to discuss the importance and relevancy of MIA, summarize the current animal models of HIE, and discuss how animal models of HIE can incorporate MIA to facilitate more translationally relevant models of HIE.
2. Maternal Immune Activation Is an Epidemiological Risk Factor for Hypoxic–Ischemic Encephalopathy
Maternal immune activation (MIA) is an environmental factor that is a significant risk factor for the development of HIE and cerebral palsy [43,44,45,46]. Chorioamnionitis diagnosis via clinical symptoms or histological analysis of the placenta was found to lead to a 4-fold increased risk of cerebral palsy in term infants [47]. In one study of 587 infants who were diagnosed with cerebral palsy, maternal infection preceding delivery was present as a risk factor in 41.4% of children compared to controls [48]. A systematic review of risk factors for cerebral palsy in developed countries identified chorioamnionitis as a significant risk factor for the development of this disability [44]. Furthermore, forty percent of children diagnosed with HIE without a sentinel birth event had been exposed to infection when examined [49,50]. Of 137 infants diagnosed with HIE who did not experience a sentinel event, histological evidence of chorioamnionitis was more likely to be seen in placental pathology that demonstrated increased polymorphonuclear leukocytes [49]. Another study found that 40% of infants presenting with HIE had chorioamnionitis alone, and 11% had both chorioamnionitis and another sentinel birth event [50]. Intrapartum fever was associated with a 3.1-fold increased risk of encephalopathy, and chorioamnionitis was associated with a 5.4-fold increased risk of encephalopathy [38]. In another study, for both preterm and term deliveries, infection at the time of delivery was associated with a twofold increased risk of developing cerebral palsy [51]. This epidemiological evidence points to the need for the inclusion of factors such as MIA into our understanding of HIE pathology.
3. Maternal Immune Activation Has Been Well Characterized in Neurodevelopmental Disorders
MIA is a well-studied risk factor in neurodevelopment, as maternal infections have been clinically associated with the development of neuropsychiatric disorders such as autism spectrum disorder (ASD) and schizophrenia, as well as the development of epilepsy [52]. There appears to be a male bias in these disorders, as ASD has a higher occurrence in boys than girls at a rate of 3:1 [53], and schizophrenia is slightly more common in males [54]. However, incidence rates of epilepsy are similar between males and females [55]. Large cohort studies investigating risk factors for childhood seizures and epilepsy have found increased risk due to overall maternal infection, and more specifically to influenza, maternal cystitis, pyelonephritis, persistent diarrhea, cough, and vaginal yeast infections [56,57]. Multiple types of maternal infections have been epidemiologically associated with increased risk of ASD and schizophrenia. The link between influenza and schizophrenia was initially observed following the 1918–1919 Spanish influenza epidemic [58]; however, additional cohort studies have found mixed results on the strength of this association [59,60]. Large cohort studies for other infections have found that maternal infection with toxoplasma gondii or bacterial genitourinary infections are risk factors for schizophrenia [61], with mixed evidence for herpes simplex 2 (HSV-2) and Cytomegalovirus (CMV) [62,63]. Meta-analyses of clinical studies have found associations between ASD and any maternal infection, with increased risk in patients who required hospitalization for these infections [64]. Outside of discrete infections, increased expression of inflammatory markers such as cytokines or C-reactive protein have also demonstrated increased risk for both ASD and schizophrenia diagnosis [65,66,67,68]. Given that children affected by HIE may have a wide array of neurodevelopmental and psychiatric impairments following injury, this evidence suggests that the significant disruptions in neurodevelopment that accompany MIA and HIE may not be specific to these disorders [69,70].
In rodent models, MIA leads to a milieu of long-lasting behavioral changes representative of the symptomology of neurodevelopmental disorders. Social behavior deficits have been characterized in MIA models, including a decrease in play behavior and ultrasonic vocalizations and an increase in self-grooming behavior [71,72]. Repetitive behaviors such as those seen in ASD are characterized in rodent models through the marble burying task, which is increased in MIA animals [73,74]. MIA disrupts working memory and latent inhibition post-puberty, replicating well-characterized cognitive deficits in humans with schizophrenia [75,76,77,78]. Prepulse inhibition (PPI) is a behavioral task used to assess sensorimotor gating, a core phenotype of schizophrenia, and in which MIA animals exhibit deficits in adulthood [79,80,81]. There are critical windows of neurodevelopment in which MIA is correlated with specific phenotypes [82]. In rodents, earlier gestational exposure (E9 to E12) to MIA has been shown to induce sensorimotor gating deficits and differences in reward-seeking behavior, whereas later gestational exposure (E16 to E18) impaired learning and memory as well as caused motor deficits [83,84]. Rodents exposed to MIA have also exhibited sleep disturbances and increased susceptibility to seizures [85,86,87,88]. Many of the negative outcomes following MIA are worse in males, which is consistent with humans, as neurodevelopmental disorders occur more often in boys [89]. This suggests that there may be a differential function of the immune system within development for males and females [90].
4. Maternal Immune Activation Disrupts Neurodevelopment Through Maternal and Fetal Inflammation
Rodent models are the most commonly used method to study the mechanisms of MIA. These models typically utilize peripheral injections of proinflammatory molecules such as lipopolysaccharide (LPS) or polyriboinosinic–polyribocytidylic acid (Poly I:C) at critical points during gestation. LPS is a bacterial cell wall component derived from Gram-negative bacteria that functions as an endotoxin and binds to toll-like receptor 4 (TLR4) to stimulate the release of cytokines and other immune molecules (Figure 1). There are two major downstream pathways following LPS binding: the MyD88-dependent pathway and the MyD88-independent pathway [91,92,93]. In the MyD88-dependent pathway, LPS binding triggers signaling via MYD88, TRIF, and IRAK4, IRAK2, and IRAK1 to ultimately initiate activation of transcription factors like NF-κB to trigger an inflammatory cytokine response [91]. The MyD88-independent pathway is also known as the TRIF-dependent pathway [92]. In this signaling pathway, LPS binding to TLR4 triggers TIR-domain-containing adaptor protein inducing IFN-β (TRIF) and TRIF-related adaptor molecule (TRAM) [94]. This triggers IRAF4, TRAF6, then c-Rel to stimulate IL-12 expression [93]. Poly(I:C) is a synthetic analogue of a double-stranded viral RNA that binds to toll-like receptor 3 (TLR3), triggering an immune reaction similar to a viral infection [94] (Figure 1). TLR3 is an intracellular receptor located on the endoplasmic reticulum, endosomes, and lysosomes that double-stranded RNA bind to and activate [95]. It is the only member of the receptor family solely dependent on TRIF signaling [96]. Upon binding, TRIF begins signaling cascades that lead to IRF3 and NF-κB activation [97]. The IRF3 pathway begins when TRIF recruits TNF receptor associated factor 3 (TRAF3), which then associates with TBK1 and IKKi. The TBK1 and IKKε complex then phosphorylates IRF3, which then translocates to the nucleus to trigger an inflammatory response, particularly those mediated via type 1 interferons [98]. TRIF can also activate NF-κB via activating the IKK complex, which will lead to increased cytokine production [97].
The immune response of the pregnant dam has traditionally been thought to be the main cause of neural and developmental changes within the fetus. Following the binding to toll-like receptors by pathogens or mimetics and the activation of proinflammatory genes (Figure 1), macrophages in the periphery and microglia in the CNS release cytokines and chemokines as a function of the innate immune system [99]. Cytokines are small signaling molecules that control the activation of other immune cells, and can be proinflammatory (IL-1, IL-6, TNFα, IL17, IFN, etc.) or anti-inflammatory (IL12 and IL6) [100]. Interferons (IFNs) are antiviral cytokines that can be activated by any pathogen. IFNs bind to immune cells and activate transcription of antiviral genes [101]. These include antiviral effectors as well as positive and negative regulators of the innate immune response [102]. Chemokines are small signaling molecules that bind to G-protein-coupled receptors on immune cells to mobilize them to areas of injury. Cytokines produced by the dam can cross through the placenta and blood–brain barrier to the fetal brain [103,104], or can cause injury to the placenta, which can alter blood flow to the fetus, causing tissue hypoxia and exacerbating injury (Figure 2) [39]. In particular, the proinflammatory cytokine IL-6 has been implicated in the development of abnormal behavioral changes, as a study in rats demonstrated that a single injection of IL-6 at gestational day 12.5 led to sensorimotor processing deficits in offspring that is often associated with a schizophrenic phenotype [103]. IL-6 may preferentially transfer from the mother to the fetus at specific periods of gestation. Radiolabeled IL-6 was able to cross into rat pups at mid gestation; however, it was not able to cross the placenta when injected in late gestation [105]. Furthermore, mouse pups exposed to Poly(I:C) at E12.5 had increased cytokine mRNA in the placenta, as well as increased cytokine expression in the brain [106]. Maternal cytokines can trigger maternal Th17 cells which release IL-17a, which has been shown to result in a cortical disorganization when injected at gestational day 18.5 in mice [107]. In the same study, MIA and IL-17a were associated with abnormal levels of proteins associated with cortical organization such as SATB2, TBR1, and CTIP2.
Newer research has found a more direct effect of MIA on the developing fetus. It was recently found that LPS can cross the placental barrier to enter the fetal compartment [108]. In CD-1 mice, FITC-labeled LPS was found in the maternal uterus, placenta, and fetal tissue after intravenous administration. This result was also replicated using 125I-labeled LPS: when injected into pregnant mice, 125I-LPS was found within the placental and the fetal tissues [109]. This indicates that direct LPS, or perhaps other pathogens within the fetus, may also contribute to the neurodevelopmental effects of early neuroinflammation. Following either transmission of cytokines to the fetal brain or direct LPS transmission, fetal microglia enter a proinflammatory state, releasing additional cytokines and chemokines [110]. The exact mechanisms by which maternal immune activation impacts the pathology of HIE and other long-term health consequences require further study [40,111,112].
Gestational age impacts the response to MIA [52,113]. Maternal infection has also been shown to increase the risk of premature birth, which has large cumulative impacts on neonatal health [113]. Infants exposed to MIA at a younger gestational age have poorer neurological outcomes when compared to infants exposed closer to term, though all infants exposed to MIA had worse neurological outcomes compared to infants who were not exposed [52]. There are also differences in the immune response in the preterm infant and the term infant. The fetal immune system is immature, as the adaptive immune system (responsible for specific pathogen responses and memory) does not develop until after birth [114]. As such, infants earlier in gestation are more susceptible to infection and its negative outcomes, as demonstrated by the increased risk of sepsis, with 60% of infants born at less than 25 weeks EGA (estimated gestational age) developing sepsis compared to 33% of infants born at 28 weeks EGA [115]. Preterm infants also have less complement activity in the serum compared to term infants [116,117]. Cumulatively, this results in significant impairment in the preterm infants’ ability to phagocytose pathogens and generate an inflammatory response, as well as a reduced ability to recruit other immune cells such as leukocytes and lymphocytes to the site of infection. Additionally, neutrophils in the preterm infant migrate more slowly and in decreased numbers compared to term infants, whose neutrophil migration does not differ from adults [118]. However, in a study of preterm infants exposed to infection, IL-6 was significantly elevated, suggesting that preterm infants are able to respond robustly to inflammatory conditions [119]. Overall, as gestational age decreases, the ability to mount a successful immune defense against pathogens decreases due to the immaturity of the immune system response, including the complement system, neutrophil migration, and cytokine response [117].
Activation of fetal microglia, the resident macrophages of the brain, by either cytokine transmission or direct pathogen binding are an important factor in neurodevelopmental disruption following MIA. In the embryonic and postnatal period, microglia exhibit a more ameboid phenotype, characterized by a larger soma and smaller projections, compared to the ramified phenotype in mature microglia, characterized by smaller soma size and longer, more complex projections [120]. Microglia play a critical role in neurodevelopmental processes such as synaptic pruning and neural precursor cell regulation (Figure 3) [121,122]. Studies have demonstrated that there are several mechanisms through which microglia regulate synapse formation [123,124]. One such mechanism is phagocytosis mediated by the fractalkine receptor CX3CR1 [125]. More recently, microglia triggering receptor expressed on myeloid cells 2 (TREM2) has been implicated in functional brain connectivity, with mice lacking this receptor experiencing pathogenic excitatory transmission and deficits in social behavior [123].
Following MIA, fetal microglia exhibit an increase in the ameboid phenotype typical of either activated or immature microglia (Figure 3) [110]. A single-cell RNAseq study of fetal brains following MIA found that microglia exhibit transcriptional changes that are associated with microglial activation, including an upregulation of Ccl12, Jund, Isg15, and Clec12a [126]. These microglial changes are associated with the release of additional cytokines and immune factors, thereby exacerbating the inflammatory response and its deleterious effects [110,127]. The activation of microglia during important periods of neurodevelopment also has direct impacts within the brain. There are many neurodevelopmental disruptions primarily within the cortex following MIA. These include over-proliferation of excitatory cortical neurons, decreased neurogenesis of inhibitory neurons, hypomyelination, and disrupted synaptic development and function [110,128,129,130]. These neurodevelopmental changes may be a consequence of microglia activation as they are highly involved in these processes [110,130]. Studies investigating the underlying causes of increased susceptibility to seizures following MIA have found many functional changes, including reactive astrogliosis, increases in hippocampal baseline excitability, shifts in EEG spectral power, imbalanced expression of ion transporters, and persistent depolarizing action of GABA [84,86,87,88,131]. These functional differences are likely due to these microglia-mediated neurodevelopmental alterations. The direct role of microglia is supported by the rescue of both behavioral and synaptic alterations following MIA by the depletion and repopulation of microglia [132]. Microglia also regulate the response of other cortical cells to MIA [126].
Figure 3
Microglia exhibit different phenotypes, transcriptional markers, and functions depending on developmental timing and activation state. There are some caveats to the transcriptional markers presented in Figure 3. Most developmental microglia have transcriptomes similar to homeostatic microglia. However, a subset referred to as proliferative-region-associated microglia (PAMs) have distinct transcriptional markers referenced here [133]. * Tmem119 and Hexb are often referred to as homeostatic markers. However, the expression of these genes does not change in proinflammatory microglia [134]. Therefore, it is more accurate to refer to these as general microglia markers. ** Many commonly used microglia identifiers are upregulated in proinflammatory microglia [135].
Microglia alterations are not the only possible mechanism of neurodevelopmental disruption following MIA (Figure 2) [136]. Inflammation has been shown to cause oxidative stress via reactive oxygen species (ROS) imbalances in the brain [137] and has been implicated with the development of ASD and schizophrenia [138,139]. This oxidative stress, in combination with other factors, can affect neurodevelopment by epigenetic changes, excitotoxicity, apoptosis, and mitochondrial dysfunction [136,139,140]. After an injection of LPS to a pregnant rat, pups were shown to have increased markers of oxidative stress in both the brain and the placenta, based on elevated malondialdehyde and carbonyl protein formation [104]. This demonstrates that maternal immune activation alone is enough to trigger an oxidative stress response [136]. Mitochondrial dysfunction in particular has been linked with ASD, with studies showing that people with ASD may already be more susceptible to oxidative stress as compared to healthy controls [141]. Mitochondrial dysfunction that can arise from oxidative stress can be due to DNA damage and altered proteins [142]. Further, dysfunctional mitochondria can further exacerbate already-existing oxidative stress, as mitochondria are a major source of ROS via the electron transport chain [143], creating a maladaptive feedback loop. Microglia have been implicated in this process, as activated microglia have been shown to increase ROS in the brain [144] and to play a role in NOX signaling, which is critical for regulating the oxidative stress response [145].
Although maternal infection is a known risk factor for developmental delays and disorders in humans, most cases of maternal infection or inflammation do not lead to these outcomes. Therefore, it is hypothesized that maternal immune activation may have a priming effect which leads to increased susceptibility to genetic or environmental “second hits”, further increasing the risk of developing various CNS disorders [79,146,147,148,149] depending on the timing, severity, and type of cumulative stressors [150]. In a study using a dual-hit model in mice, pregnant dams were given Poly(I:C) or saline. On postnatal day thirty or forty, offspring were exposed to unpredictable stress [151]. In adulthood, neither MIA nor unpredictable stress alone were enough to disrupt sensorimotor gating or increase their sensitivity to psychomimetic drugs; however, animals exposed to the dual hit demonstrated disruptions in both. In a two-hit model of prenatal and postnatal inflammation, only the presence of both hits was sufficient to induce spontaneous bursts of epileptiform activity [85]. The hypothesis that maternal immune activation can interact with other insults and immune challenges to increase deleterious outcomes helps to explain the significance of maternal inflammation in the rates of HIE.
The compounding effects of perinatal inflammation and later insults may be largely due to disruptions in microglial function. Microglia exhibit a blunted response to later immune challenges following MIA [110,152]. When microglia do not properly mount their proinflammatory response, pathogens and cellular debris are not cleared properly [153]. Prior inflammation can also be a driver in neurodevelopmental pathology through the infiltration of peripheral macrophages and other immune cells following the initial inflammatory hit. Studies have shown that the activation of TLR3 and TLR4 can have a cascade of consequences on the brain prior to injury, one of them being the weakening of the blood–brain barrier that allows peripheral immune cells to infiltrate the brain parenchyma [154]. These infiltrating peripheral macrophages (CCR2+) preferentially adopt a proinflammatory (CD68+, TNFα+) phenotype [155]. Over the course of several months, these cells can adopt a microglia-like phenotype (Tmem119+, Sall1+, or P2RY12+) or maintain their TNFα+MHCII+ phenotype.
Sex differences within MIA outcomes have also been noted, particularly in rodent models, with males often experiencing worse outcomes [156,157]. Males have been shown to exhibit more severe long-term behavioral changes than females, including increases in anxiety-like and depressive-like behaviors [158], as well as ASD-related behaviors such as social deficits [159,160]. Males exhibit larger deleterious effects in the placenta such as increased hemorrhage and growth restriction [161]. The hypoxic effects of MIA within the fetal cortex have also been shown to be worse in males [161]. There have been several proposed mechanisms for male susceptibility or female protection following MIA. Differing immune responses to MIA within male and female animals are likely involved. Males exhibit larger short-term cytokine responses to maternal immune activation in the placenta and the fetal brain [106,161]. This increased immune response may be due to the increased activation of microglia observed in males [162]. When cytokines were analyzed at a later timepoint, males expressed lower cytokine levels compared to females, suggesting that their baseline immunity may be decreased [158]. This is supported by a finding in adult mice in which males had decreased expression of CD68, a marker of phagocytic microglia [163]. Males also exhibited more significant changes in genes related to synaptic plasticity in the cortex after MIA [158]. The estrogen receptor ERβ has been proposed as a potential mediator of the sex differences in the fetal inflammatory response [164]. ERβ regulates the inflammatory response of microglia, as demonstrated through exaggerated inflammatory responses following reduced ERβ expression [165]. There may be sex-dependent patterns of ERβ expression in fetal brains, with decreased ERβ expression in female amygdala embryonic neurons [164]. This may also not be due to ERβ expression itself, but the sexually dependent expression of, and response to, steroid ligands. Additionally, there may be sex differences in the response to invading peripheral immune mediators such as regulatory T-cells following immune activation or injury. In females, invading regulatory T-cells have been shown to be neuroprotective, while in males, they promote neurodegeneration [166]. However, it should be noted that not all studies have found sex differences in behavioral outcomes or immune response within MIA models [167].
5. Commonly Used Animal Models of HIE and the Incorporation of MIA and Inflammation
Large animal models have proven critical to the field of neonatal brain research and have their own benefits that deepen clinical understanding of HIE. Sheep models of HIE are achieved via umbilical cord occlusion [168,169] or maternal hypoxia [155,170]. Umbilical cord occlusion typically occurs for at least 10 min, though there is variability in protocols between research groups [168,169,171]. Fetal oxygen levels can be titrated via modulating maternal oxygen levels and then measuring fetal blood flow and oxygen levels. These experiments are typically conducted on preterm sheep, at around 0.8–0.9 of gestation, due to having a brain growth trajectory that is more advanced than human infants [172,173]. To model HIE for preterm infants, 0.65-term sheep can be used [172,173]. As fetal sheep remain in the uterine environment during injury, this model optimally recapitulates the maternal–placental–fetal interface. Several studies within large animal models have utilized LPS to recapitulate the inflammatory environment of HIE [174,175]. LPS given to pregnant ewes via bolus injection was shown to decrease fetal O2, increase fetal PaCO2, and decrease fetal pH, reflecting a hypoxemic state with metabolic acidosis [176]. Chronic exposure to an inflammatory environment has been shown to alter white matter development within fetal sheep [175]. Studies such as this could give insight as to the mechanisms through which maternal infection could lead to a hypoxic–ischemic event through close monitoring of the fetus without losing the maternal interface. However, there is the drawback that these animal models focus solely on that of maternal immune activation without the presence of an acute hypoxic event.
Pigs have also been used to model HIE and are of particular interest due to both their white matter development and brain growth development, which closely mimic that of human infants [172]. One pig HIE model is achieved using anesthetized newborn piglets mechanically ventilated with 6% oxygen for 17–55 min, until they reach an EEG amplitude below 7 microvolts (a measure of encephalopathy) [177]. Another model used in pigs is achieved via occlusion of bilateral carotid arteries and subsequent hypoxia. This procedure utilizes phosphorus magnetic resonance spectroscopy in order to achieve low cerebral ATP levels [112]. Another piglet model uses hypoxia followed by complete asphyxia, following the protocol of 10% O2 for 30–45 min followed by 7–8 min of airway occlusion [172]. The intense monitoring of the piglets required by these models is an advantage as the hypoxic event can be highly titrated and consistent; however, the piglet models are all achieved extrauterine, and therefore, the maternal and placental environment cannot be replicated or investigated fully (Figure 4). The incorporation of prenatal inflammation and infection into pig models of HIE is a newer development, with a two-hit model being developed in 2019. In this model, LPS is infused into newborn pigs starting 4 h prior to hypoxia, with continued infusions for 48 h after the hypoxic event [178]. Newborn pigs that were primed with LPS showed greater microglial proliferation and astrogliosis compared to pigs who underwent hypoxia alone. This finding is particularly salient in regard to treatment of HIE, as the same model demonstrated that within LPS sensitized newborn piglets, therapeutic hypothermia was less effective as a treatment compared to those who were not sensitized [179]. Since therapeutic hypothermia is the only effective treatment for HIE, environmental and clinical factors that affect its efficacy are critical to examine further. A limitation of this study and model is that while these newborn pigs were exposed to infection, the maternal–fetal interface was lost as the transfusions occurred in the absence of the maternal compartment.
Primates have also been used to model HIE and have an advantage of most closely approximating human brain development (Figure 4). The primary model used today is the umbilical cord occlusion model, in which fetuses are surgically born and then subjected to umbilical cord occlusion lasting between 15 and 18 min [180]. Other primate models employ the use of saline-filled rubber sacs placed over the head of the fetus to induce total asphyxia in addition to umbilical cord occlusion or high-dose-oxytocin-induced prolonged partial asphyxia [181]. The major benefit to nonhuman primate models is their similar brain development and complexity compared to other large animals or rodents, with injured nonhuman primates demonstrating similar white matter injuries to that of human infants based on both MRI and histopathological results [182]. Nonhuman primates also show an increase in activated microglia and gliosis after brain injury [183]. These similarities between human infants and nonhuman primates could lead to more promising treatments, such as determining potential biomarkers of injury severity [184]. The effect of MIA on neurodevelopment has been explored in primate models [185,186,187,188], with the primary model being an intravenous Poly:IC injection into pregnant rhesus macaques [188]. Primates exposed to MIA demonstrated repetitive social behaviors similar to those seen in rodents [188], as well as elevated cytokines that were associated with these behaviors and persist past the neonatal period [185]. However, there is a lack of studies and primate models that incorporate MIA in conjunction with an acute hypoxic–ischemic injury, and this is a limitation of current primate models of HIE.
There are drawbacks to using larger animals and non-human primates as a model of HIE, particularly due to the difficulty of maintenance of the animals, which are costly to house (Figure 4). The techniques involved in these models are also technically challenging, sometimes requiring around-the-clock ICU care, and many institutions do not have the resources required to perform them [189]. There are also ethical considerations for large animal models, particularly for the nonhuman primate models. Furthermore, molecular and cellular tools have not been developed for larger animals as they have been for rodent models. This limits the ability to investigate the downstream molecular and cellular mechanisms of HIE within the larger animal model systems.
Rodents and small animals also provide valuable insight into the mechanisms of HIE. The current standard rodent model of HIE is the Rice–Vannucci model, in which a postnatal mouse or rat is subjected to a unilateral carotid artery ligation followed by global hypoxia (typically 8% oxygen) for 90 min [190]. The timing of this injury varies; however, it is often performed between postnatal day 7 and 10, which is considered equivalent to full-term human gestation brain development [191]. The result of this insult is a large unilateral hemispheric injury to the ipsilateral hemisphere with a significant amount of cell death. Mice subjected to this model have an injury that mimics a large unilateral cerebral infarction, with the expected hemiplegic motor deficits [192,193]. While Rice–Vannucci is the standard model, its clinical relevance has been questioned due to the nature of the induced injury. MRI scans of human neonates with HIE often show more global white matter and deep gray matter (basal ganglia, thalamus, and pons) injury unlike that of a hemispheric cerebral infarction seen in rodent models [194,195]. Mild HIE has also been associated with injury to watershed regions, not produced within the Rice–Vannucci model [196]. It is also possible for human neonates to have several injury patterns or diffuse cortical hypoxia. Due to these significant differences in injury patterns, the need to create rodent models beyond the Rice–Vannucci model is significant. It is also important to note that this model is achieved entirely using postnatal animals; therefore, it cannot replicate any maternal or placental factors that impact HIE and its outcome, and thus it could be missing key aspects of injury pathophysiology. The Rice–Vannucci model is not the only used model of HIE in rodents. Some models have found motor outcomes similar to cerebral palsy utilizing acute hypoxia [197,198]. Occlusion of the middle cerebral artery in neonatal mice and rats has also been used as an alternative to carotid artery ligation [192,199]. Some rodent models have used intrauterine hypoxia–ischemia, although not always in the context of HIE [200,201]. These limited animal models often produce more mild-to-moderate results in comparison to the more severe results of the Rice–Vannucci model.
Rodents are not the only small animal model that has been used to investigate HIE. In recent years, the use of ferrets as a model of HIE and infection has been utilized, as ferrets have the advantage of a similar white-to-gray-matter ratio and gyrencephalic cerebral cortex to infants that is not seen in rodent models [202,203]. Ferrets also pose less of a burden to maintain than larger animals such as pigs and sheep. The current model is achieved by injecting P10 ferret pups with LPS, followed by hypoxia and then hyperoxia. Ferret pups who underwent this procedure demonstrate developmental delays, abnormal gait, and white matter injury [204]. It is important to note that P10 ferret pups are considered equivalent to 24 weeks gestation in humans [202]. Brain injury outcomes vary based on gestational age, and preterm HIE is associated with cognitive and sensory deficits and white matter injury rather than the motor deficits and watershed injury commonly seen in term gestation HIE [205,206]. This model is also at a disadvantage in that it fails to replicate the maternal–fetal–placental interface, as both the injury and the immune activation are performed outside of the maternal environment.
Several new models of HIE within rodents incorporate the intrauterine inflammatory environment using MIA. Most of these models utilized systemic immune activation via an injection of LPS or Poly(I:C) into the dam, followed by a hypoxic–ischemic event, such as the Rice–Vannucci procedure. In rats, pups sensitized with a maternal LPS injection had a more robust microglia-mediated inflammatory response compared to pups that were not MIA-sensitized [207]. This indicates that MIA may sensitize microglia and lead to poorer neurological outcomes [207]. Other studies found that therapeutic hypothermia proved ineffective in pups immune-sensitized by direct LPS administration hours prior to Rice–Vannucci administration [208,209]. Environmental factors that impact the benefits of therapeutic hypothermia, such as intrauterine infection, should be focused areas of intensive study. Our laboratory recently developed a novel mouse model of neonatal HIE that both captures the inflammatory intrauterine environment commonly seen in human neonates as well as more accurately replicates the injury itself [2,210]. This is achieved via a two-hit model of maternal immune activation at late gestation followed by postnatal global hypoxia at term equivalence. This model allows investigation of both the maternal–placental–fetal system of inflammation, followed by a more clinically relevant global hypoxia that is akin to the pathophysiology in human neonates with HIE. These two hits result in developmental delays, motor impairments in adulthood, neuroinflammation, and transcriptional changes in microglia.
6. Considerations for the Incorporation of MIA into Animal Models of HIE
Investigators have begun to incorporate MIA or a direct immune priming of neonates into animal models of HIE. There are many variables that should be considered when creating these animal models. LPS has largely been used in HIE animal models due to the importance of chorioamnionitis as a risk factor, whereas the viral mimetic poly(I:C) may be less relevant for HIE [1]. Direct immune activation of pups has been used in animals as an immune priming prior to HIE insult. However, this loses maternal and placental interaction, and the use of MIA more translationally represents risk factors of HIE.
The dosage and timing of the injection of immune modulators is an important consideration, as both variables range significantly in the literature. In rodents LPS doses range from 25 μg/kg to over 1 mg/kg, although 100 μg/kg is the most frequently used dose [211,212]. Poly(I:C) doses in rodents typically range from 4 mg/kg to 20 mg/kg [211,212]. There are significantly fewer studies on the use of maternal immune activation in other animal models. In MIA models within pigs, poly(I:C) has been used at a dose of 1.0 mg/kg or 0.5 mg/kg [213]. PRRSV, a swine-specific virus, has also been used at 5 mL 1 × 105 50% tissue culture infectious dose (TCID50) [214,215]. LPS has been used at a dose of 2 μg/kg in pregnant gilts, although not within the context of MIA [216,217]. Further research would need to be conducted to determine if this dosage would be sufficient to elicit changes in offspring. One experiment in sheep utilized systemic LPS via the maternal JV catheter at 2 μg/kg of maternal body weight [176].
The injection of LPS into pregnant animals can cause complications that should be weighed. LPS is abortifacient in rodents [218,219]. In rodents, this may result in fetuses being reabsorbed prior to birth, particularly if the MIA is earlier in gestation; or it may result in non-viable pups at birth, particularly in the case of late gestation MIA [218]. Our experience with LPS at late gestation in mice revealed higher maternal hemorrhage and greater fetal loss at higher LPS dosages (>100 µg/kg), ultimately leading us to choose very lose dose LPS (50 µg/kg) for E18 MIA exposure [210]. The abortifacient impact of LPS has also been recorded in pigs, and so should be taken into consideration in large animal models [220]. Higher doses of LPS will cause higher abortifacient outcomes. Therefore, the dosage of LPS should be trialed to minimize fetal reabsorption and death, while maintaining cellular and molecular changes and behavioral phenotype.
The timing of both poly(I:C) and LPS can range all through gestation in rodents. However, poly(I:C) is most used in mid-gestation (E12.5 and E15), while LPS is most used either in mid (E15) or late (E18) gestation [218,219]. This reflects the differential susceptibility of the fetus to viral vs. bacterial infection at different gestational stages. Some viral infections such as zika virus and cytomegalovirus may be more deleterious in earlier gestation due to the effects on neural progenitor cells and cortical migration [221,222,223]. However, bacterial infections in the second and third trimester are particularly linked to ASD diagnosis [64]. Late gestational infection may be relevant to the timing of infection as a risk factor for HIE. Animals all have different rates of nervous system development relative to humans [224]. The specific timing of neural development such as gliogenesis, neurogenesis, myelination, synaptogenesis, synaptic pruning, and apoptosis should all be taken into consideration in the development of these models.
The methodology of hypoxic-ischemic insult should also be considered. Larger animal models more accurately replicate the human injury through umbilical cord occlusion, maternal hypoxia, or asphyxiation [172]. However, size or physiology can make these methods unusable or less effective in rodents. The Rice-Vannucci rodent model causes significant motor and cognitive impairments, but the hemispheric injury produced by the unilateral carotid artery ligation is not the best reflection of injury pattern in humans [193,194]. Some more recent rodent models have stepped away from this unilateral ligation through various techniques including maternal hypoxia, pup hypoxia in isolation, and cardiac arrest [192,197,225]. However, the phenotypes produced by these models are often less severe than the Rice-Vannucci model. While they might not be the best reflection of symptoms such as severe cerebral palsy, these models may more accurately represent mild to moderate HIE. The inclusion of risk factors such as MIA may serve to increase the severity of these models. HIE is an incredibly heterogenous injury, and investigators should weigh the type and severity of injury that will allow for them to answer the specific questions they seek to investigate.
7. Conclusions
The outcomes of HIE vary significantly depending on the risk factors present within the maternal–fetal interface, the type and extent of the hypoxic–ischemic event, and the resulting pattern of injury. Therapeutic hypothermia is not always effective in counteracting the devastating effects of HIE, either due to the limited timing of therapeutic efficacy or to individual variability. While this treatment was discovered in large animal models, the animal size and limited cellular and molecular tools pose many limitations and make these models inaccessible to many labs. Rodent models have greatly contributed to understanding the pathophysiology of HIE. However, none of the treatments that were promising in rodent models have proved effective in humans. This points to a significant need to develop animal models that recapitulate the many nuanced factors that contribute to the pathophysiology of disease and efficacy of treatment, while being accessible. MIA is an environmental factor that significantly contributes to the development and outcomes of HIE. MIA is easy to incorporate into HIE models, as it is already a commonly used rodent model of neurodevelopmental disorders such as schizophrenia and ASD, as well as a contributing factor to epilepsy susceptibility. MIA is non-invasive and involves a peripheral injection of non-infectious proinflammatory molecules, namely LPS or Poly(I:C). The mechanisms and outcomes of MIA have also been well characterized. The cognitive and behavioral deficits observed in models of HIE are primarily due to the activation of microglia and a disruption of their role within neurogenesis and synaptic development. MIA has also been shown to compound with second hits to create worse outcomes than either insult in isolation. The incorporation of MIA or inflammation in animal models of HIE has shown reduced efficacy of therapeutic hypothermia, possibly explaining some of the variability in humans. The cellular and molecular mechanisms involved in the interaction of MIA and HIE should be further investigated, and these models can be used as a more accurate way to investigate new therapies. The preclinical data obtained from the development of new animal models will hopefully lead to more personalized treatments and better outcomes for the children affected by this common and devastating disease. |
Title: A case report of successful treatment of pulmonary mucormycosis caused by Cunninghamella bertholletiae infection in a patient with T-lymphoblastic lymphoma | Body:
1
Introduction
The most common pathogens associated with mucormycosis are Rhizopus, Mucor, Lichtheimia, Apophysomyces, Cunninghamella, Rhizomucor, and Saksenaea. This infection primarily affects individuals with weakened immune systems, including those with diabetes, cancer, organ transplants, and those on long-term corticosteroid therapy. PM presents in the lungs, which are the third main location for the infection after the rhino-orbito-cerebral areas and the skin. C. bertholletiae is particularly linked to PM and has a higher mortality rate compared to other Mucorales (71 % vs 44 %) [1]. Patients with T-LBL often require intensive chemotherapy, leading to severe immunosuppression and increased vulnerability to opportunistic infections. PM involvement in T-LBL is rare and presents significant diagnostic and therapeutic challenges. In this case report, we describe the first documented survival of a T-LBL patient with PM caused by C. bertholletiae.
2
Case presentation
The patient, a 25-year-old male, was diagnosed with T-LBL without bone marrow involvement. He achieved complete remission after induction chemotherapy with the VDLP regimen (vincristine, daunorubicin, L-asparaginase, prednisone). He then received consolidation chemotherapy with the CAT regimen (cyclophosphamide, cytarabine, 6-mercaptopurine (6-MP). He developed high-grade fever (≥39.5 °C) and severe neutropenia during consolidation chemotherapy. Broad-spectrum antimicrobial therapy was initiated, including imipenem/cilastatin (500 mg intravenously every 8 hours), vancomycin (1000 mg intravenously every 12 hours) and voriconazole (loading dose of 6 mg/kg intravenously every 12 hours, followed by 4 mg/kg intravenously every 12 hours), but the specific pathogen remained unidentified with persistent fever. CT scan revealed multiple patchy and ground-glass opacities with indistinct margins (Fig. 1A). Due to the high risk of bleeding and anesthesia-related complications associated with fiberoptic bronchoscopy, we performed mNGS (Hangzhou Matridx Biotechnology Co.,Ltd.) of blood (Fig. 1H) to identify the causative agent, and the results revealed C. bertholletiae (Supplementary materials and methods, Sequencing data in NCBI: PRJNA1113529, 58 reads). And then, L-AmB therapy, initiated through a collaborative, multidisciplinary approach at a dose of 3 mg/kg for PM, effectively reduced the fever (below 38 °C) and improved hemoptysis. However, follow-up CT showed progression of pulmonary lesions and increased pleural effusion (Fig. 1B). After the recovery of thrombocytopenia, we performed mNGS (Hangzhou Matridx Biotechnology Co.,Ltd.) of BALF (Fig. 1I), confirming C. bertholletiae as the predominant pathogen (Supplementary materials and methods, Sequencing data in NCBI: PRJNA1113529, 628 reads)). We continued the antifungal treatment, resulting in a normalized temperature and the absence of hemoptysis. However, the third CT scan showed further enlargement of the infected area with consolidation and cavity formation (Fig. 1C). We attempted to increase the dose of L-AmB to 5 mg/kg and added posaconazole enteric-coated tablets at 300 mg/day. However, the patient experienced severe chills and shivering during the higher-dose L-AmB infusion. We then reduced the dose of L-AmB back to 3 mg/kg, and no further infusion reactions occurred. On day 72 of fever, the fourth follow-up CT scan revealed an obstructed bronchus (Fig. 1D).Fig. 1Chest CT scans, surgical specimens and mNGS of blood and BALF of the patient before and after treatment. Chest CT scans at 13 (A), 21 (B), 33(C), and 72 (D) days post-treatment demonstrated persistent fever and an obstructive mass in the basal segmental bronchus of the left lower lobe. The patient underwent video-assisted thoracoscopic surgery for lung resection (G), and no evidence of PM recurrence was observed on chest CT scan at one month (E) and seven months (F) after lobectomy. (H) The depth coverage distribution plot of the C. bertholletiae genome from blood. The X-axis represents the position on the reference genome (in base pairs, bp), while the Y-axis indicates the sequencing depth at each position. The reference genome length is 31,145,096 base pairs. The average depth is 0.01 %, meaning that the total number of bases in the sequencing reads is 0.0001 times the length of the reference genome. The coverage is 0.01 %, indicating that, on average, the sequencing reads cover 0.01 % of the total bases in the genome. (I) The depth coverage distribution plot of the C. bertholletiae genome from bronchoalveolar lavage fluid. The reference genome length is 31,145,096 base pairs. The average depth is 0.10 %, meaning that the total number of bases in the sequencing reads is 0.001 times the length of the reference genome. The coverage is 0.10 %, indicating that, on average, the sequencing reads cover 0.10 % of the total bases in the genome.Fig. 1
At this point, we were faced with a treatment dilemma. T-LBL, being an aggressive tumor, required immediate consolidation chemotherapy. However, active pulmonary mucormycosis posed a risk for systemic dissemination during chemotherapy, which could be fatal in severe cases. Following consultations with respiratory medicine, gastroenterology, thoracic surgery, anesthesiology, and hematology specialists, we decided to perform video-assisted thoracoscopic surgery (VATS) to resect the pulmonary lesion. The lesion showed consolidation (Fig. 1G). HE staining (Fig. 2A and B) and PAS staining (Fig. 2C) further confirmed the presence of fungi. During the perioperative period, we continued the antifungal therapy with L-AmB and posaconazole enteric-coated tablets.Fig. 2Lung tissue HE and PAS staining. (A) HE staining at 40 × magnification demonstrates the lumen uniformly filled with evenly distributed fungal hyphae. (B) Higher magnification (400 × ) of the HE-stained section reveals hyphae of consistent thickness, displaying septa and acute-angled branching. Spores are often located at one end of the hyphae, forming a racquet-like structure. (C) PAS staining at 400 × magnification highlights the hyphae, which are stained rose-red. Black arrows indicate the presence of fungi. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)Fig. 2
One month after the surgery, a follow-up CT scan showed no evidence of PM recurrence (Fig. 1E). Subsequently, he received one cycle of high-dose methotrexate (MTX), two cycles of autologous stem cell transplantation and low-dose MTX and 6-MP orally for maintenance therapy. He continued oral posaconazole enteric-coated tablets for secondary prophylaxis. Currently, seven months after the lobectomy, the CT scan shows no signs of lymphoma or PM relapse (Fig. 1F). The patient has resumed his college studies.
3
Discussion
Recent studies have identified HM as the main cause of PM, including acute leukemia (24 %), hematopoietic stem cell transplant (HSCT) (21 %), lymphoma (13 %), and others (4 %) [2,3]. The occurrence of PM in T-LBL patients infected with C. bertholletiae is actually rare. Early diagnosis of PM poses huge challenges, which was predominantly confirmed through postmortem examinations in the past [4]. Isolation of Mucorales from clinical specimens is difficult, with positive cultures observed in only 15 %–25 % of cases [5]. Efforts should also be directed towards obtaining specimens for non-culture methods, such as tissue puncture or excisional biopsy and mNGS of blood or BALF. However, invasive examinations or surgical procedures are contraindicated in patients with low platelet counts (<50,000/mm3), which is very common in hematological diseases, thus increasing the difficulty of diagnosis of PM [6]. mNGS provides a comprehensive approach to identify causative agents, with a significantly higher pathogen detection rate compared to traditional methods (80.21 % vs 25.00 %) [7]. Additionally, in patients with febrile neutropenia, it also shows a significantly higher fungal detection rate compared to the non-febrile neutropenia group (32.35 % vs 12.22 %) [7,8]. In this case, both blood and BALF mNGS identified C. bertholletiae (Fig. 1H and I), which played a crucial role in determining the cause of PM and initiating timely antifungal therapy. Fungal hyphae were also detected in the histopathological examination of the surgically resected lung tissue specimen.
There is a lack of prospective studies to determine the optimal treatment strategy for this rare infection. Current management primarily follows the guidelines for diagnosing and treating mucormycosis. Successful treatment of mucormycosis relies on three essential factors: surgery, antifungal therapy, and the management of underlying risk factors. The guideline group strongly recommends early and complete surgical resection of mucormycosis whenever feasible, along with systemic antifungal therapy [9]. Timely initiation of antifungal therapy is crucial, as a delay of 3 days in starting amphotericin B can increase the mortality rate (72 vs 33 %) [10]. In this case, the primary predisposing factor was neutropenia, which required active efforts to promote granulocyte recovery through appropriate supportive care measures. L-AmB is the recommended first-line treatment for mucormycosis at a dose of 5∼10 mg/kg/day. A high dose of L-AmB (10 mg/kg) is suggested for central nervous system infection, while 5 mg/kg/day is advised for PM. In real-world scenarios, patient tolerance to L-AmB dosages varies significantly. In our case, the patient was unable to tolerate a moderate dose of 5 mg/kg due to recurrent chills and high fever. According to the guideline recommendation, if a patient exhibits intolerance to L-AmB, reducing the dosage and considering the addition of isavuconazole or posaconazole may be appropriate. However, further research is needed to establish the optimal combination therapy regimen. The duration of therapy required to treat mucormycosis is uncertain. In our case, after two months of combined antifungal treatment, the dissemination of mucormycosis was effectively controlled. However, complete eradication was not achieved, resulting in residual consolidative lesions. The most common histopathologic findings in patients with pulmonary mucormycosis who underwent autopsy or lung biopsy include angioinvasion (100 %), hemorrhagic infarction (90 %), coagulative necrosis (85 %), and intra-alveolar hemorrhage (85 %) [11]. These changes impair the continuous fungicidal action of medications, leading to irreversible necrosis of lung tissue. Therefore, timely surgical resection of the lesions becomes crucial. Surgical resection of localized pulmonary lesions is an important therapeutic modality in the management of PM. In this case, we performed VATS, which resulted in the successful resection of the affected lung lobe. Surgical intervention allows for the elimination of the infectious focus, reducing the risk of dissemination and improving overall treatment outcomes.
4
Conclusions
In sum, the successful management of PM in a T-LBL patient highlights the crucial importance of early pathogen detection through mNGS of blood and BALF, prompt correction of underlying conditions, surgical intervention, and secondary prophylactic antifungal therapy. These findings emphasize the need for a multidisciplinary approach and personalized management strategies to optimize outcomes in complex cases involving hematological malignancies and invasive fungal infections.
5
Supplementary statement
In regard to the section on relevant past interventions with outcomes (5d), it is important to note that the patient in this case had no prior history of pulmonary mucormycosis infection. As a result, there were no previous medical records or interventions related to this specific condition. This case represents the patient's initial occurrence of pulmonary mucormycosis, and therefore, no relevant past interventions or outcomes are available for discussion or comparison.
6
Patient perspective
I feel extremely fortunate throughout the treatment of pulmonary mucormycosis. With accurate diagnosis and comprehensive treatment from my doctors, my condition has been effectively controlled. The utilization of mNGS technology swiftly identified the C. bertholletiae infection, providing a basis for prompt intervention. Additionally, surgical debridement played a significant role in my recovery. This experience has deeply emphasized the importance of multidisciplinary collaboration, and I am grateful to the medical team for their expertise and care, which helped me overcome the illness.
Prior to the publication of this case report, informed consent was obtained from the patient for the publication of all clinical information, images, and other data included in this manuscript. The patient was fully informed about the nature of the publication and its potential implications. Confidentiality was ensured, and all identifying information has been appropriately anonymized to protect the patient's privacy.
Funding
This work was supported by the Chongqing Science and Health Joint Medical Research Project (grant no. 2024QNXM053), Chongqing Natural Science Foundation (grant no. CSTB2024NSCQ-MSX0403), 10.13039/501100002858China Postdoctoral Science Foundation (grant no. 2022M720606), and Special support for postdoctoral of Chongqing (grant no. 2022CQBSHTB2064).
Ethics approval and consent to participate
The study was approved by the Second Affiliated Hospital of Chongqing Medical University.
Data availability statement
The data that support the findings of this study are available from the corresponding author, [Shengtao, Liao], upon reasonable request. Additionally, the mNGS data has been uploaded to the NCBI database (NCBI: PRJNA1113529).
Ethics declarations
Ethical approval was not required for the publication of this manuscript. The authors obtained informed consent from the patient.
CRediT authorship contribution statement
Qing Yang: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Kang Zhou: Writing – original draft, Visualization, Validation, Supervision, Investigation. Yan Shen: Writing – original draft, Investigation, Formal analysis, Data curation. Rong Huang: Writing – original draft, Formal analysis, Data curation. Li Liu: Writing – original draft, Formal analysis. Shengtao Liao: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: Engineering a dysbiotic biofilm model for testing root caries interventions through microbial modulation | Body:
Background
Despite the high prevalence of untreated dental caries [1], people are retaining more of their natural teeth as they age. This fact is followed by a rising incidence of root caries [2], which is a significant oral health concern and challenge posed by the progressive global aging of society. New approaches to the prevention and treatment of root caries are required, especially for aged and systemically compromised patients. A better understanding of root caries etiological mechanism, especially the interactions between dysbiotic microbial biofilms and host tissues, is essential for the development of new therapeutics. For instance, pro- and prebiotics have the potential to influence oral microbiota for promoting oral health and could be evaluated for their impact on microbial modulation on root caries dysbiotic biofilms. They could serve in the future as complementary components to root caries treatment and prevention strategies in combination with fluoride.
Exposure of dental roots to the oral environment significantly increases their risk of developing caries, which differ from enamel surfaces in several ways [3]. Root surfaces have a different organic and inorganic composition and morphology which make them more susceptible to biofilm accumulation and caries [3]. The acidic environment resulting from dietary sugar fermentation favours the growth and proliferation of aciduric and acidogenic bacteria [4]. As the dentine is exposed and collagen matrix become accessible for catabolism through the saliva or gingival fluid, proteolytic Gram-negative species can be selected [5], especially when root caries lesions extend beneath the gingival margin [6]. These species cohabit and act collaboratively to degrade the inorganic and organic components of the dental tissues, avoiding direct competition for specific nutrients (available in dietary sugars, saliva and gingival fluid). This fosters the establishment of compositionally and functionally diverse microbial communities on root surfaces [5]. The fact that root surfaces contain a considerable amount of organic material, as well as the presence of periodontal pathobionts with strong proteolytic activity in root surfaces biofilms [6], supports the theory that the microbiota might also play a role in the proteolytic stage of lesions formation [4, 7].
Designing a simple and reproducible laboratory-based method that accurately resemble a dysbiotic root caries biofilm is a crucial step in advancing novel prophylactic, diagnostic and therapeutic strategies targeting microbial functions. Various experimental models, including in vitro, ex vivo and human in situ models, have been employed to investigate the role of the microbiome in dental caries [8]. The ability to reproduce the biofilm community in the laboratory may increase the validity of the system to model microbiological events during caries lesion development [8, 9]. Despite achieving extensive demineralization with these methods mimicking cariogenic conditions, it is essential to recognize that natural biofilms are intricate ecosystems that cannot be fully studied when disassembled into their individual components. While simple methods using one of few microorganisms have significantly contributed to our understanding of certain factors contributing to cariogenicity, we must approach the extrapolation of these findings to real-life in vivo situations with caution and consider appropriate limitations.
This study aimed to engineer and optimize a dysbiotic root caries biofilm model for investigating microbial modulation. This model should be appropriate to demonstrate the potential of compounds in modulating root surface biofilm, and impacting root caries progression. Here, we investigated the effect of natural substances when used during biofilm development, or on a mature biofilm. In this context, we tested a phenolic lipid extracted from cashew nutshell liquid, which offers a combination of sustainability and bioactivity. This compound was previously shown to significantly reduce bacterial growth and collagenase activity [10]. Additionally, a cranberry extract was tested due to its anti-adhesion properties [11], antimicrobial capacity against oral biofilms [12] and potential as collagen cross-linker [13].
Materials and methods
This study was reported according to the CRIS guidelines (Checklist for Reporting in-vitro Studies) [14].
Study design
The experimental setup is illustrated in Fig. 1, showcasing the two different strategies employed. In the first strategy (“pre-treatment”), bovine root slabs were utilized, and test compounds were applied to the dentine slabs followed by cultivation for 10 days, with sucrose cycles simulating a cariogenic environment. This approach aimed to assess the compounds’ ability to modulate biofilms as pre-treatments to dentine and evaluate their potential for reducing lesion size compared to untreated controls. This pre-treatment strategy mimics the application of varnish products during dental treatments.Fig. 1Experimental design. Two strategies were used: Pre-treatment (using bovine slabs and root caries formation) and Post-treatment (using a Calgary Biofilm Device to treat mature biofilms)
The second strategy employed “post-treatment” exposure. Mature biofilms were first cultivated in a Calgary Biofilm Device (CBD; MBECTM Assay System, MBEC Biofilms Technology Ltd., Calgary, Alberta, Canada) for 7 days. Subsequently, they were exposed to the test compounds. This approach aimed to assess the anti-collagenase and antimicrobial activity of the test substances against established mature biofilms, mimicking the use of mouthwashes.
Samples
Initial inoculum and selection criteria for saliva donors
Four saliva donors were instructed to refrain from drinking and eating for 2 h. Non-stimulated saliva samples were collected through passive drooling and kept cool on ice to be delivered to the laboratory within an hour of collection. The following inclusion criteria were used to recruit the saliva donors: adults who self-reported either a caries experience (at least one tooth being restored or extracted due to caries) or a history of periodontal diseases (prior occurrence of gingivitis or periodontitis). This selection criterion was employed due to the similarity in microbial composition between individuals with inactive disease and those with active disease, as opposed to individuals without any history of caries [15, 16] or periodontal diseases [17]. Individuals with any history of diabetes, hypertension, arthritis, cancer, pregnant/lactating woman; who are using any kind of mouthwash, or used antibiotics in the last 3 months were excluded.
Description of devices preparation
Pre-treatment
Freshly extracted bovine incisors (N = 9) were cleaned of soft tissues and stored in fresh 0.5% chloramine for 48 h, at 4 °C. Then, stored in a humid environment (phosphate buffered saline- PBS) at 4 °C for no more than a month [18]. Following this disinfection protocol, roots were separated from the crowns under running water using a low-speed diamond saw (3000 rpm; Accutam, Struers). Then, four ~ 0.5 cm slices (transverse sections at axial plane) were sectioned from roots from cervical to apical direction, the first cut was positioned approximately 2 mm below the cement-enamel junction. The apical area was excluded. The entire buccolingual thickness of the root slices were maintained. The lingual surface of each slab was ground mechanically to obtain a flat surface. After preparation, specimens were affixed on the lid of a 24-well plate, leaving the buccal surface free. A post-preparation disinfection protocol with fresh 0.5% chloramine was performed for 48 h. In order to remove any chloramine residue from the specimens, a washing step was performed with sterile dH2O at 4 °C for 2 h [18], and storage at 4 °C in humidity (sterile PBS) for 1 week.
Randomization and allocation concealment of the slabs in each group were performed as follows: sequence generation was performed using random numbers tables generated for each tooth. Each tooth had its four slabs randomly assigned in four groups (1 slab per tooth in each group). The same investigator (ND-T) generated the randomisation sequence, sample collection and allocation to groups. Blinding was not feasible.
Post-treatment
To better mimic the root dentine biofilm and promote bacterial adhesion through collagen-binding proteins, we adapted the CBD by coating the hydroxyapatite pegs with a collagen coating solution (Sigma-Aldrich) to form a layer of collagen binding proteins. This modification allowed us to investigate not only adhesins but also other proteins involved in bacterial adhesion. CBD pegs were coated overnight with 200 μL of collagen coating solution (Sigma, concentration of 100 μL/0.32 cm2), at 25 °C, 65 rpm for 2 h. The pegs were then washed with sterile PBS.
For both strategies, another step with saliva coating was performed. The saliva was previously prepared by adding 2.5 mM dl-dithiothreitol, 50% of PBS and then filter-sterilised (filter membrane 0.22 µm pore size) to eliminate all contaminants and cells, leaving only the salivary proteins to form the pellicle at this stage. CBD and dentine specimens were coated with saliva for 1 h [19] at 37 °C; 65 rpm. Then, specimens were immersed in a proportion of 10% of initial inoculum added to SHI medium (proteose peptone, trypticase peptone, yeast extract, potassium chloride, sucrose, haemin, vitamin K, urea, arginine, mucin, blood, N-acetylmuramic acid-NAM) [20], which sustains the initial inoculum microbiota [21], and incubated in anaerobiosis and at 37 °C.
In vitro microbial modulation strategies
Pre-treatment of the dentine slabs
Dentine slabs from one group received pre-treatment with the anacardic acid-derivative LDT11 at 100 μg/mL. For details of the compound synthesis and chemical structure, see [22]; for details regarding its potential usefulness in Dentistry, see [10]. Another group of dentine slabs was pre-treated with 100 μg/mL of cranberry extract (commercially available Vaccinium macrocarpon extract 36:1, Health4All purest, Blackpool, UK, batch 307). Before the experiment, specimens were completely immersed in the compounds for 72 h to assure the substances penetration into the dentine. Control groups were kept immersed in PBS [23].
After the incubation of the dentine slabs with saliva inoculum for 24 h, a simulation of the demineralization-remineralization process was conducted through 24-h cycles: 6 h with SHI media supplemented with 1% sucrose followed by 18 h with only SHI media (protocol modified from [24]). This cycle was repeated 5 times (every 24 h during the initial 7 days and every 48 h during the subsequent 7 days).
For this experimental strategy, the groups were described as: negative control with high pH (G1; no inoculum, no pre-treatment), positive control with low pH (G2 + ; with inoculum, no pre-treatment, with sucrose cycles); positive control with high pH (G2 − ; with inoculum, no pre-treatment, no sucrose cycles), pre-treatment with LDT11 (G3; with inoculum, with LDT11 pre-treatment, with sucrose cycles); pre-treatment with cranberry (G4; with inoculum, with cranberry pre-treatment, with sucrose cycles). Pre-treatment strategy groups are summarised in the Supplementary Table 1.
Post-treatment strategy
The CBD device was employed to cultivate mature biofilms for 7 days. Subsequently, the pegs with the biofilms were subjected to treatment with the compounds. Chlorhexidine (CHX) was used as the positive control and PBS as the negative control. The groups were categorized as follows: CBD (Negative control treated with PBS), CHX (Positive control with CHX at 100 μg/mL), CBD_LDT11 (Anacardic acid-derivative LDT11 at 50 or 100 μg/mL) and CBD_Cran (Cranberry at 50 or 100 μg/mL). To determine these concentrations, we first conducted a pilot study to observe the dose–response curve for planktonic microbes. This was followed by a series of biocompatibility experiments on monolayer dental pulp cells and unispecies biofilm models. We then selected the concentrations that demonstrated the highest impact on collagenase inhibition, antimicrobial activity and biocompatibility [10].
Outcomes
The biofilms’ metatranscriptome was the main outcome of the study. Their antimicrobial activity and collagenase/gelatinase activity were also measured, as described below. In the pre-treatment group, mineral loss from dentine slabs was obtained via micro computed tomography (μ-CT).
pH monitoring
The pH of the media was monitored using a calibrated pH meter electrode: within the pre-treatment strategy, daily pH measurements of the media were taken, immediately before introducing sucrose and after the 6-h mark. In the post-treatment strategy, throughout the process of biofilm formation, pH measurements were taken in triplicate each day.
Metatranscriptome of biofilms
An aliquot of the harvested biofilms at the end of both strategies was mixed separately with RNAprotect, centrifuged and pellets stored at – 80 °C for further RNA extraction and sequencing. Total RNA yields ranged from 334.9 to 8526.3 ng (502.0 ± 118.8 ng). From the post-treatment analysis, only the groups exposed to one concentration (100 μg/mL) of the treatments was chosen. Total RNA samples were extracted from biofilms using the PowerFecal kit (QIAGEN), and used for library preparation. Briefly, all samples were checked for quantity and quality by agarose gel electrophoresis (RNA degradation and potential contamination), Nanodrop (tests RNA purity), Qubit (RNA concentration) and Agilent 2100 (RNA integrity). Then, the mRNA was purified including rRNA depletion and mRNA fragmentation, cDNA was synthetized, end repair and adaptor addition were performed and the fragments were enriched by PCR. The libraries were sequenced using Illumina (Novogene Co., Cambridge, UK).
Bioinformatic analysis was performed after removing low quality reads from raw data for analysis. First, the reads were assembled to conduct species classification analysis, and gene expression abundance analysis. Alignment of Unigenes with Bacteria, Fungi, Archaea and Viruses sequences extracted from NCBI’s NR database using DIAMOND software. Then homologous gene cluster analysis (eggNOG), carbohydrate enzyme analysis (CAZy) and other functional annotations were performed. Comparative analysis between groups was made, such as cluster analysis, PCA analysis and functional difference between samples (Novogene Co., Cambridge, UK).
Antimicrobial characterization and biofilm thickness
Biofilms grown on dentine slabs (pre-treatment) or pegs (post-treatment) were incubated with Filmtracer Live/Dead Biofilm Viability Kit (Molecular Probes, Inc.) by incubation in a 1:1 ratio of SYTO9 and propidium iodide, as previously described [10], and then imaged within 24 h using a confocal laser scanning microscope (Leica Microsystems). 3D images were generated using Leica Application Suit X software, v. 3.5.7.23225 (LAS X, https://www.leica-microsystems.com/products/microscope- software/p/leica-las-x-ls/) and the Biofilm Viability Checker was used to calculate the area of green and red fluorescence [25]. This analysis was performed on biological duplicates for each group, with imaging conducted in two different fields for each group, resulting in a total of 180–253 fields analysed per group (pre-treatment) and 881–1002 (post-treatment). Rates of live to dead cells were calculated and compared between groups. In the same images and their fluorescent signal intensities, it was possible to calculate the biofilm thickness and the coverage area through examination of biofilm structure in relation to the spatial localization by choosing this option in the 3D viewer of the software.
Collagenase/gelatinase activity of biofilms
To determine collagenase/gelatinase activity, the dysbiotic biofilms from both strategies were assayed in the EnzChek® Gelatinase/Collagenase Assay Kit using the DQ collagen type I (from bovine skin, fluorescein conjugate) and DQ gelatine as substrates (Molecular Probes, Inc.), following the supplier protocol.
In the pre-treatment strategy, biofilms were harvested with curettes after the end of the sucrose cycling period, washed twice with PBS and resuspended into 1 mL of PBS, from which 10 μL were added to the assay. The final collagen or gelatine concentration was 100 μg/mL. The assays were incubated in the experiment for 0, 2 and 24 h, in the shaker at room temperature. Fluorescence emission from the released fluorescent peptides at the collagen cleaving was monitored at 490–520 nm spectrum, at the Varioskan LUX Multimode Microplate Reader (ThermoScientific).
The gelatine hydrolysis of the biofilms from each the pre-treatment strategy was confirmed. Six replicates containing half of the biofilm formed in the dentine slabs were incubated anaerobically in nutrient agar (8 g/L) supplemented with gelatine (120 g/L) for 48 h, 72 h and 7 days. At these time points, the gelatine was assessed to ascertain either: partial hydrolysis (not all the gelatin used or a dense medium), total hydrolysis (completely liquid medium) or no gelatine hydrolysis.
For the post-treatment strategy, only the inhibition of gelatinase activity was performed as it was the single significant result in the pre-treatment strategy. Following 7-day biofilm formation and 30-min treatment with the natural compounds, CBD pegs containing the biofilms were immersed in DQ gelatine/buffer solution for enzymatic activity quantification. The fluorescence was measured at different time points (2 h, 7 h and 24 h) with incubation at room temperature (N = 7 pegs per group, experiment was carried out in duplicates).
Biofilms morphology
Scanning electron microscopy (SEM) was employed to confirm morphologic modifications of the biofilm in different conditions. Samples were fixed with 2.5% glutaraldehyde for 12 h, followed by ethanol series dehydration. After gold sputter coating, samples were visualized with a scanning electron microscope. Biofilms were washed by dipping them in sterile PBS to remove loosely adherent cells.
Confirmation of the root caries lesion formation
The outcome for the work on dentine slabs (pre-treatment strategy) was the presence or absence of the carious lesions confirmed by the mineral loss of the slabs through μ-CT scanning before and after the experimental model. The scans were performed using medium camera pixels with a special resolution of 10 μm and with Al + Cu filters (Skyscann, Bruker). Reconstruction was performed with the software interface (Nrecon). Differences between the reconstitute images before and after the experimental model and cariogenic challenges were created into the software and analyzed for the presence or absence of structure loss.
Sample size
Imaging (CSLM and μ-CT) and metatranscriptome analyses were performed in triplicates. SEM images were performed in one representative sample from each group in a descriptive analysis using different magnitudes. A sample size calculation was performed using the outcome “collagenase activity” (fluorescence values) of the enzyme control (Clostridium histolyticum collagenase) = 0.55, the average of oral bacteria (Streptococcus mutans, Veillonella parvula, Veillonella dispar, Escherichia coli, Porphyromonas gingivalis) = 0.43, and a standard deviation of 0.08, for a power of 80% and alpha of 5%. A sample size of n = 7 biofilms was considered enough for this outcome (http://powerandsamplesize.com/Calculators/Compare-k-Means/1-Way-ANOVA-Pairwise). A meaningful difference between groups was expected to be 10% in the microbial collagenase activity.
Statistical methods
Statistical analyses were performed using the GraphPad Prism 9 for Macbook. Statistical methods used to compare groups for pH: Mixed-models + Tukey, ANOVA + Dunnett’s (post-treatment main effect time). The antimicrobial and biofilm coverage were compared between groups using Kruskal–Wallis followed by the Dunn’s post hoc test.
Study design
The experimental setup is illustrated in Fig. 1, showcasing the two different strategies employed. In the first strategy (“pre-treatment”), bovine root slabs were utilized, and test compounds were applied to the dentine slabs followed by cultivation for 10 days, with sucrose cycles simulating a cariogenic environment. This approach aimed to assess the compounds’ ability to modulate biofilms as pre-treatments to dentine and evaluate their potential for reducing lesion size compared to untreated controls. This pre-treatment strategy mimics the application of varnish products during dental treatments.Fig. 1Experimental design. Two strategies were used: Pre-treatment (using bovine slabs and root caries formation) and Post-treatment (using a Calgary Biofilm Device to treat mature biofilms)
The second strategy employed “post-treatment” exposure. Mature biofilms were first cultivated in a Calgary Biofilm Device (CBD; MBECTM Assay System, MBEC Biofilms Technology Ltd., Calgary, Alberta, Canada) for 7 days. Subsequently, they were exposed to the test compounds. This approach aimed to assess the anti-collagenase and antimicrobial activity of the test substances against established mature biofilms, mimicking the use of mouthwashes.
Samples
Initial inoculum and selection criteria for saliva donors
Four saliva donors were instructed to refrain from drinking and eating for 2 h. Non-stimulated saliva samples were collected through passive drooling and kept cool on ice to be delivered to the laboratory within an hour of collection. The following inclusion criteria were used to recruit the saliva donors: adults who self-reported either a caries experience (at least one tooth being restored or extracted due to caries) or a history of periodontal diseases (prior occurrence of gingivitis or periodontitis). This selection criterion was employed due to the similarity in microbial composition between individuals with inactive disease and those with active disease, as opposed to individuals without any history of caries [15, 16] or periodontal diseases [17]. Individuals with any history of diabetes, hypertension, arthritis, cancer, pregnant/lactating woman; who are using any kind of mouthwash, or used antibiotics in the last 3 months were excluded.
Description of devices preparation
Pre-treatment
Freshly extracted bovine incisors (N = 9) were cleaned of soft tissues and stored in fresh 0.5% chloramine for 48 h, at 4 °C. Then, stored in a humid environment (phosphate buffered saline- PBS) at 4 °C for no more than a month [18]. Following this disinfection protocol, roots were separated from the crowns under running water using a low-speed diamond saw (3000 rpm; Accutam, Struers). Then, four ~ 0.5 cm slices (transverse sections at axial plane) were sectioned from roots from cervical to apical direction, the first cut was positioned approximately 2 mm below the cement-enamel junction. The apical area was excluded. The entire buccolingual thickness of the root slices were maintained. The lingual surface of each slab was ground mechanically to obtain a flat surface. After preparation, specimens were affixed on the lid of a 24-well plate, leaving the buccal surface free. A post-preparation disinfection protocol with fresh 0.5% chloramine was performed for 48 h. In order to remove any chloramine residue from the specimens, a washing step was performed with sterile dH2O at 4 °C for 2 h [18], and storage at 4 °C in humidity (sterile PBS) for 1 week.
Randomization and allocation concealment of the slabs in each group were performed as follows: sequence generation was performed using random numbers tables generated for each tooth. Each tooth had its four slabs randomly assigned in four groups (1 slab per tooth in each group). The same investigator (ND-T) generated the randomisation sequence, sample collection and allocation to groups. Blinding was not feasible.
Post-treatment
To better mimic the root dentine biofilm and promote bacterial adhesion through collagen-binding proteins, we adapted the CBD by coating the hydroxyapatite pegs with a collagen coating solution (Sigma-Aldrich) to form a layer of collagen binding proteins. This modification allowed us to investigate not only adhesins but also other proteins involved in bacterial adhesion. CBD pegs were coated overnight with 200 μL of collagen coating solution (Sigma, concentration of 100 μL/0.32 cm2), at 25 °C, 65 rpm for 2 h. The pegs were then washed with sterile PBS.
For both strategies, another step with saliva coating was performed. The saliva was previously prepared by adding 2.5 mM dl-dithiothreitol, 50% of PBS and then filter-sterilised (filter membrane 0.22 µm pore size) to eliminate all contaminants and cells, leaving only the salivary proteins to form the pellicle at this stage. CBD and dentine specimens were coated with saliva for 1 h [19] at 37 °C; 65 rpm. Then, specimens were immersed in a proportion of 10% of initial inoculum added to SHI medium (proteose peptone, trypticase peptone, yeast extract, potassium chloride, sucrose, haemin, vitamin K, urea, arginine, mucin, blood, N-acetylmuramic acid-NAM) [20], which sustains the initial inoculum microbiota [21], and incubated in anaerobiosis and at 37 °C.
Initial inoculum and selection criteria for saliva donors
Four saliva donors were instructed to refrain from drinking and eating for 2 h. Non-stimulated saliva samples were collected through passive drooling and kept cool on ice to be delivered to the laboratory within an hour of collection. The following inclusion criteria were used to recruit the saliva donors: adults who self-reported either a caries experience (at least one tooth being restored or extracted due to caries) or a history of periodontal diseases (prior occurrence of gingivitis or periodontitis). This selection criterion was employed due to the similarity in microbial composition between individuals with inactive disease and those with active disease, as opposed to individuals without any history of caries [15, 16] or periodontal diseases [17]. Individuals with any history of diabetes, hypertension, arthritis, cancer, pregnant/lactating woman; who are using any kind of mouthwash, or used antibiotics in the last 3 months were excluded.
Description of devices preparation
Pre-treatment
Freshly extracted bovine incisors (N = 9) were cleaned of soft tissues and stored in fresh 0.5% chloramine for 48 h, at 4 °C. Then, stored in a humid environment (phosphate buffered saline- PBS) at 4 °C for no more than a month [18]. Following this disinfection protocol, roots were separated from the crowns under running water using a low-speed diamond saw (3000 rpm; Accutam, Struers). Then, four ~ 0.5 cm slices (transverse sections at axial plane) were sectioned from roots from cervical to apical direction, the first cut was positioned approximately 2 mm below the cement-enamel junction. The apical area was excluded. The entire buccolingual thickness of the root slices were maintained. The lingual surface of each slab was ground mechanically to obtain a flat surface. After preparation, specimens were affixed on the lid of a 24-well plate, leaving the buccal surface free. A post-preparation disinfection protocol with fresh 0.5% chloramine was performed for 48 h. In order to remove any chloramine residue from the specimens, a washing step was performed with sterile dH2O at 4 °C for 2 h [18], and storage at 4 °C in humidity (sterile PBS) for 1 week.
Randomization and allocation concealment of the slabs in each group were performed as follows: sequence generation was performed using random numbers tables generated for each tooth. Each tooth had its four slabs randomly assigned in four groups (1 slab per tooth in each group). The same investigator (ND-T) generated the randomisation sequence, sample collection and allocation to groups. Blinding was not feasible.
Post-treatment
To better mimic the root dentine biofilm and promote bacterial adhesion through collagen-binding proteins, we adapted the CBD by coating the hydroxyapatite pegs with a collagen coating solution (Sigma-Aldrich) to form a layer of collagen binding proteins. This modification allowed us to investigate not only adhesins but also other proteins involved in bacterial adhesion. CBD pegs were coated overnight with 200 μL of collagen coating solution (Sigma, concentration of 100 μL/0.32 cm2), at 25 °C, 65 rpm for 2 h. The pegs were then washed with sterile PBS.
For both strategies, another step with saliva coating was performed. The saliva was previously prepared by adding 2.5 mM dl-dithiothreitol, 50% of PBS and then filter-sterilised (filter membrane 0.22 µm pore size) to eliminate all contaminants and cells, leaving only the salivary proteins to form the pellicle at this stage. CBD and dentine specimens were coated with saliva for 1 h [19] at 37 °C; 65 rpm. Then, specimens were immersed in a proportion of 10% of initial inoculum added to SHI medium (proteose peptone, trypticase peptone, yeast extract, potassium chloride, sucrose, haemin, vitamin K, urea, arginine, mucin, blood, N-acetylmuramic acid-NAM) [20], which sustains the initial inoculum microbiota [21], and incubated in anaerobiosis and at 37 °C.
Pre-treatment
Freshly extracted bovine incisors (N = 9) were cleaned of soft tissues and stored in fresh 0.5% chloramine for 48 h, at 4 °C. Then, stored in a humid environment (phosphate buffered saline- PBS) at 4 °C for no more than a month [18]. Following this disinfection protocol, roots were separated from the crowns under running water using a low-speed diamond saw (3000 rpm; Accutam, Struers). Then, four ~ 0.5 cm slices (transverse sections at axial plane) were sectioned from roots from cervical to apical direction, the first cut was positioned approximately 2 mm below the cement-enamel junction. The apical area was excluded. The entire buccolingual thickness of the root slices were maintained. The lingual surface of each slab was ground mechanically to obtain a flat surface. After preparation, specimens were affixed on the lid of a 24-well plate, leaving the buccal surface free. A post-preparation disinfection protocol with fresh 0.5% chloramine was performed for 48 h. In order to remove any chloramine residue from the specimens, a washing step was performed with sterile dH2O at 4 °C for 2 h [18], and storage at 4 °C in humidity (sterile PBS) for 1 week.
Randomization and allocation concealment of the slabs in each group were performed as follows: sequence generation was performed using random numbers tables generated for each tooth. Each tooth had its four slabs randomly assigned in four groups (1 slab per tooth in each group). The same investigator (ND-T) generated the randomisation sequence, sample collection and allocation to groups. Blinding was not feasible.
Post-treatment
To better mimic the root dentine biofilm and promote bacterial adhesion through collagen-binding proteins, we adapted the CBD by coating the hydroxyapatite pegs with a collagen coating solution (Sigma-Aldrich) to form a layer of collagen binding proteins. This modification allowed us to investigate not only adhesins but also other proteins involved in bacterial adhesion. CBD pegs were coated overnight with 200 μL of collagen coating solution (Sigma, concentration of 100 μL/0.32 cm2), at 25 °C, 65 rpm for 2 h. The pegs were then washed with sterile PBS.
For both strategies, another step with saliva coating was performed. The saliva was previously prepared by adding 2.5 mM dl-dithiothreitol, 50% of PBS and then filter-sterilised (filter membrane 0.22 µm pore size) to eliminate all contaminants and cells, leaving only the salivary proteins to form the pellicle at this stage. CBD and dentine specimens were coated with saliva for 1 h [19] at 37 °C; 65 rpm. Then, specimens were immersed in a proportion of 10% of initial inoculum added to SHI medium (proteose peptone, trypticase peptone, yeast extract, potassium chloride, sucrose, haemin, vitamin K, urea, arginine, mucin, blood, N-acetylmuramic acid-NAM) [20], which sustains the initial inoculum microbiota [21], and incubated in anaerobiosis and at 37 °C.
In vitro microbial modulation strategies
Pre-treatment of the dentine slabs
Dentine slabs from one group received pre-treatment with the anacardic acid-derivative LDT11 at 100 μg/mL. For details of the compound synthesis and chemical structure, see [22]; for details regarding its potential usefulness in Dentistry, see [10]. Another group of dentine slabs was pre-treated with 100 μg/mL of cranberry extract (commercially available Vaccinium macrocarpon extract 36:1, Health4All purest, Blackpool, UK, batch 307). Before the experiment, specimens were completely immersed in the compounds for 72 h to assure the substances penetration into the dentine. Control groups were kept immersed in PBS [23].
After the incubation of the dentine slabs with saliva inoculum for 24 h, a simulation of the demineralization-remineralization process was conducted through 24-h cycles: 6 h with SHI media supplemented with 1% sucrose followed by 18 h with only SHI media (protocol modified from [24]). This cycle was repeated 5 times (every 24 h during the initial 7 days and every 48 h during the subsequent 7 days).
For this experimental strategy, the groups were described as: negative control with high pH (G1; no inoculum, no pre-treatment), positive control with low pH (G2 + ; with inoculum, no pre-treatment, with sucrose cycles); positive control with high pH (G2 − ; with inoculum, no pre-treatment, no sucrose cycles), pre-treatment with LDT11 (G3; with inoculum, with LDT11 pre-treatment, with sucrose cycles); pre-treatment with cranberry (G4; with inoculum, with cranberry pre-treatment, with sucrose cycles). Pre-treatment strategy groups are summarised in the Supplementary Table 1.
Post-treatment strategy
The CBD device was employed to cultivate mature biofilms for 7 days. Subsequently, the pegs with the biofilms were subjected to treatment with the compounds. Chlorhexidine (CHX) was used as the positive control and PBS as the negative control. The groups were categorized as follows: CBD (Negative control treated with PBS), CHX (Positive control with CHX at 100 μg/mL), CBD_LDT11 (Anacardic acid-derivative LDT11 at 50 or 100 μg/mL) and CBD_Cran (Cranberry at 50 or 100 μg/mL). To determine these concentrations, we first conducted a pilot study to observe the dose–response curve for planktonic microbes. This was followed by a series of biocompatibility experiments on monolayer dental pulp cells and unispecies biofilm models. We then selected the concentrations that demonstrated the highest impact on collagenase inhibition, antimicrobial activity and biocompatibility [10].
Pre-treatment of the dentine slabs
Dentine slabs from one group received pre-treatment with the anacardic acid-derivative LDT11 at 100 μg/mL. For details of the compound synthesis and chemical structure, see [22]; for details regarding its potential usefulness in Dentistry, see [10]. Another group of dentine slabs was pre-treated with 100 μg/mL of cranberry extract (commercially available Vaccinium macrocarpon extract 36:1, Health4All purest, Blackpool, UK, batch 307). Before the experiment, specimens were completely immersed in the compounds for 72 h to assure the substances penetration into the dentine. Control groups were kept immersed in PBS [23].
After the incubation of the dentine slabs with saliva inoculum for 24 h, a simulation of the demineralization-remineralization process was conducted through 24-h cycles: 6 h with SHI media supplemented with 1% sucrose followed by 18 h with only SHI media (protocol modified from [24]). This cycle was repeated 5 times (every 24 h during the initial 7 days and every 48 h during the subsequent 7 days).
For this experimental strategy, the groups were described as: negative control with high pH (G1; no inoculum, no pre-treatment), positive control with low pH (G2 + ; with inoculum, no pre-treatment, with sucrose cycles); positive control with high pH (G2 − ; with inoculum, no pre-treatment, no sucrose cycles), pre-treatment with LDT11 (G3; with inoculum, with LDT11 pre-treatment, with sucrose cycles); pre-treatment with cranberry (G4; with inoculum, with cranberry pre-treatment, with sucrose cycles). Pre-treatment strategy groups are summarised in the Supplementary Table 1.
Post-treatment strategy
The CBD device was employed to cultivate mature biofilms for 7 days. Subsequently, the pegs with the biofilms were subjected to treatment with the compounds. Chlorhexidine (CHX) was used as the positive control and PBS as the negative control. The groups were categorized as follows: CBD (Negative control treated with PBS), CHX (Positive control with CHX at 100 μg/mL), CBD_LDT11 (Anacardic acid-derivative LDT11 at 50 or 100 μg/mL) and CBD_Cran (Cranberry at 50 or 100 μg/mL). To determine these concentrations, we first conducted a pilot study to observe the dose–response curve for planktonic microbes. This was followed by a series of biocompatibility experiments on monolayer dental pulp cells and unispecies biofilm models. We then selected the concentrations that demonstrated the highest impact on collagenase inhibition, antimicrobial activity and biocompatibility [10].
Outcomes
The biofilms’ metatranscriptome was the main outcome of the study. Their antimicrobial activity and collagenase/gelatinase activity were also measured, as described below. In the pre-treatment group, mineral loss from dentine slabs was obtained via micro computed tomography (μ-CT).
pH monitoring
The pH of the media was monitored using a calibrated pH meter electrode: within the pre-treatment strategy, daily pH measurements of the media were taken, immediately before introducing sucrose and after the 6-h mark. In the post-treatment strategy, throughout the process of biofilm formation, pH measurements were taken in triplicate each day.
Metatranscriptome of biofilms
An aliquot of the harvested biofilms at the end of both strategies was mixed separately with RNAprotect, centrifuged and pellets stored at – 80 °C for further RNA extraction and sequencing. Total RNA yields ranged from 334.9 to 8526.3 ng (502.0 ± 118.8 ng). From the post-treatment analysis, only the groups exposed to one concentration (100 μg/mL) of the treatments was chosen. Total RNA samples were extracted from biofilms using the PowerFecal kit (QIAGEN), and used for library preparation. Briefly, all samples were checked for quantity and quality by agarose gel electrophoresis (RNA degradation and potential contamination), Nanodrop (tests RNA purity), Qubit (RNA concentration) and Agilent 2100 (RNA integrity). Then, the mRNA was purified including rRNA depletion and mRNA fragmentation, cDNA was synthetized, end repair and adaptor addition were performed and the fragments were enriched by PCR. The libraries were sequenced using Illumina (Novogene Co., Cambridge, UK).
Bioinformatic analysis was performed after removing low quality reads from raw data for analysis. First, the reads were assembled to conduct species classification analysis, and gene expression abundance analysis. Alignment of Unigenes with Bacteria, Fungi, Archaea and Viruses sequences extracted from NCBI’s NR database using DIAMOND software. Then homologous gene cluster analysis (eggNOG), carbohydrate enzyme analysis (CAZy) and other functional annotations were performed. Comparative analysis between groups was made, such as cluster analysis, PCA analysis and functional difference between samples (Novogene Co., Cambridge, UK).
Antimicrobial characterization and biofilm thickness
Biofilms grown on dentine slabs (pre-treatment) or pegs (post-treatment) were incubated with Filmtracer Live/Dead Biofilm Viability Kit (Molecular Probes, Inc.) by incubation in a 1:1 ratio of SYTO9 and propidium iodide, as previously described [10], and then imaged within 24 h using a confocal laser scanning microscope (Leica Microsystems). 3D images were generated using Leica Application Suit X software, v. 3.5.7.23225 (LAS X, https://www.leica-microsystems.com/products/microscope- software/p/leica-las-x-ls/) and the Biofilm Viability Checker was used to calculate the area of green and red fluorescence [25]. This analysis was performed on biological duplicates for each group, with imaging conducted in two different fields for each group, resulting in a total of 180–253 fields analysed per group (pre-treatment) and 881–1002 (post-treatment). Rates of live to dead cells were calculated and compared between groups. In the same images and their fluorescent signal intensities, it was possible to calculate the biofilm thickness and the coverage area through examination of biofilm structure in relation to the spatial localization by choosing this option in the 3D viewer of the software.
Collagenase/gelatinase activity of biofilms
To determine collagenase/gelatinase activity, the dysbiotic biofilms from both strategies were assayed in the EnzChek® Gelatinase/Collagenase Assay Kit using the DQ collagen type I (from bovine skin, fluorescein conjugate) and DQ gelatine as substrates (Molecular Probes, Inc.), following the supplier protocol.
In the pre-treatment strategy, biofilms were harvested with curettes after the end of the sucrose cycling period, washed twice with PBS and resuspended into 1 mL of PBS, from which 10 μL were added to the assay. The final collagen or gelatine concentration was 100 μg/mL. The assays were incubated in the experiment for 0, 2 and 24 h, in the shaker at room temperature. Fluorescence emission from the released fluorescent peptides at the collagen cleaving was monitored at 490–520 nm spectrum, at the Varioskan LUX Multimode Microplate Reader (ThermoScientific).
The gelatine hydrolysis of the biofilms from each the pre-treatment strategy was confirmed. Six replicates containing half of the biofilm formed in the dentine slabs were incubated anaerobically in nutrient agar (8 g/L) supplemented with gelatine (120 g/L) for 48 h, 72 h and 7 days. At these time points, the gelatine was assessed to ascertain either: partial hydrolysis (not all the gelatin used or a dense medium), total hydrolysis (completely liquid medium) or no gelatine hydrolysis.
For the post-treatment strategy, only the inhibition of gelatinase activity was performed as it was the single significant result in the pre-treatment strategy. Following 7-day biofilm formation and 30-min treatment with the natural compounds, CBD pegs containing the biofilms were immersed in DQ gelatine/buffer solution for enzymatic activity quantification. The fluorescence was measured at different time points (2 h, 7 h and 24 h) with incubation at room temperature (N = 7 pegs per group, experiment was carried out in duplicates).
Biofilms morphology
Scanning electron microscopy (SEM) was employed to confirm morphologic modifications of the biofilm in different conditions. Samples were fixed with 2.5% glutaraldehyde for 12 h, followed by ethanol series dehydration. After gold sputter coating, samples were visualized with a scanning electron microscope. Biofilms were washed by dipping them in sterile PBS to remove loosely adherent cells.
Confirmation of the root caries lesion formation
The outcome for the work on dentine slabs (pre-treatment strategy) was the presence or absence of the carious lesions confirmed by the mineral loss of the slabs through μ-CT scanning before and after the experimental model. The scans were performed using medium camera pixels with a special resolution of 10 μm and with Al + Cu filters (Skyscann, Bruker). Reconstruction was performed with the software interface (Nrecon). Differences between the reconstitute images before and after the experimental model and cariogenic challenges were created into the software and analyzed for the presence or absence of structure loss.
Sample size
Imaging (CSLM and μ-CT) and metatranscriptome analyses were performed in triplicates. SEM images were performed in one representative sample from each group in a descriptive analysis using different magnitudes. A sample size calculation was performed using the outcome “collagenase activity” (fluorescence values) of the enzyme control (Clostridium histolyticum collagenase) = 0.55, the average of oral bacteria (Streptococcus mutans, Veillonella parvula, Veillonella dispar, Escherichia coli, Porphyromonas gingivalis) = 0.43, and a standard deviation of 0.08, for a power of 80% and alpha of 5%. A sample size of n = 7 biofilms was considered enough for this outcome (http://powerandsamplesize.com/Calculators/Compare-k-Means/1-Way-ANOVA-Pairwise). A meaningful difference between groups was expected to be 10% in the microbial collagenase activity.
Statistical methods
Statistical analyses were performed using the GraphPad Prism 9 for Macbook. Statistical methods used to compare groups for pH: Mixed-models + Tukey, ANOVA + Dunnett’s (post-treatment main effect time). The antimicrobial and biofilm coverage were compared between groups using Kruskal–Wallis followed by the Dunn’s post hoc test.
Sample size
Imaging (CSLM and μ-CT) and metatranscriptome analyses were performed in triplicates. SEM images were performed in one representative sample from each group in a descriptive analysis using different magnitudes. A sample size calculation was performed using the outcome “collagenase activity” (fluorescence values) of the enzyme control (Clostridium histolyticum collagenase) = 0.55, the average of oral bacteria (Streptococcus mutans, Veillonella parvula, Veillonella dispar, Escherichia coli, Porphyromonas gingivalis) = 0.43, and a standard deviation of 0.08, for a power of 80% and alpha of 5%. A sample size of n = 7 biofilms was considered enough for this outcome (http://powerandsamplesize.com/Calculators/Compare-k-Means/1-Way-ANOVA-Pairwise). A meaningful difference between groups was expected to be 10% in the microbial collagenase activity.
Statistical methods
Statistical analyses were performed using the GraphPad Prism 9 for Macbook. Statistical methods used to compare groups for pH: Mixed-models + Tukey, ANOVA + Dunnett’s (post-treatment main effect time). The antimicrobial and biofilm coverage were compared between groups using Kruskal–Wallis followed by the Dunn’s post hoc test.
Results
pH modulation within each experimental strategy
Figure 2 illustrates the pH change during the biofilm formation. For the pre-treatment strategy, the pH was measured before adding sucrose and 6 h after sucrose addition, confirming pH fluctuations in the cycles. Prior to exposure to sucrose cycling, the pH of the media in the cranberry-pre-treated dentine slab group was lower on day 2 when compared to day 4 (p = 0.0479), day 6 (p = 0.0211), day 8 (p < 0.0001) and day 10 (p = 0.0211), but recovered by day 4. In contrast, the untreated group (“No treatment, with sucrose cycle”) exhibited a higher pH by day 10 (p < 0.0001). The groups with cycles displayed lower pH levels compared to the control group without sucrose cycles, reaching the critical pH for root dentine demineralization. The group without treatment and not exposed to sucrose cycles increased the pH over time, remaining above the critical pH for root dentine demineralization at the end of the experiment. On day 1, no significant differences were observed between the groups, confirming the baseline pH similarity. However, the “no treatment (with sucrose cycles)” group had a significantly lower pH compared to the negative control (no biofilm) on day 2 (p = 0.0037), day 4 (p = 0.0046), day 6 (p = 0.0108), day 8 (p = 0.0027) and day 10 (p = 0.0209). This group also had a significantly lower pH than the “no treatment (no sucrose cycles)” group on day 2 (p = 0.0329), day 4 (p = 0.0176), day 8 (p = 0.0011) and day 10 (p = 0.0062) (Supplementary Table 2).Fig. 2Measurement of the pH of the media. Pre-treatment strategy: pH was measured immediately before and 6 h after adding sucrose to the media in the cycling. Post-treatment strategy: pH was measured daily, before the media change. Experiment performed using a pH meter electrode. Statistical analysis: Pre-treatment, before sucrose cycles = multiple comparisons regarding time points. Pre-treatment, after sucrose cycles = ANOVA, Dunnett’s multiple comparisons against no treatment (with sucrose cycles). Post-treatment = Statistical model for the main effect time. Mixed-effect model; Tukey's multiple comparisons test
Measurements of the media pH were conducted daily for the post-treatment strategy. A clear and statistically significant pH elevation was observed following the third day of biofilm formation: day 1 vs. day 5 (p = 0.0008), day 1 vs. day 6 (p < 0.0001), day 2 vs. day 3 (p = 0.0017), day 2 vs. day 4 (p = 0.0004), day 2 vs. day 5 (p < 0.0001), day 2 vs. day 6 (p < 0.0001), day 3 vs. day 6 (p = 0.0016), day 4 vs. day 6 (p = 0.0079) (Supplementary Table 2). This phenomenon could potentially be attributed to the increased complexity of the biofilms and the attachment of late colonizers. This trend aligns with the observed pH increase over successive days in the untreated group of the pre-treatment strategy.
Biofilms metatranscriptome–RNAseq
Sequencing output
The number of clean reads (filtered sequencing data) ranged from 3.78 × 107 to 4.74 × 107, and subsequent bioinformatic analysis is based on these reads. Clean bases ranged from 5.7G to 7G. The GC% content was lower in the saliva inoculum (43.36%) and higher (51.05%) in G2_3 (replicate of the pre-treatment strategy G2 + group–without pre-treatment, with sucrose cycles).
Microbial composition
As expected, there was a slight reduction in the number of active taxa within the laboratory culture when comparing the biofilms to the initial inoculum. The phyla Chlamydiota, Spirochaetota and Proteobacteria, as well as the Saccharibacteria oral taxon TM7x, were affected by the laboratory conditions and were found reduced in numbers following culturing. However, this diversity reduction was less noticeable in the biofilms grown on dentine slabs without treatment and without sucrose cycles (pre-treatment strategy, G2 − group). Nevertheless, the biofilm compositional complexity was confirmed, showing a great diversity of the active microbiome in both pre-treatment and post-treatment strategies, including reads mapped to microorganisms other than bacteria, such as archaea and viruses (Fig. 3).Fig. 3Taxonomic abundance clustering of the 35 most abundant taxa (Kingdom; phylum, and genus-level) within each sample. Samples were first selected based on species identification and abundance information. Taxa were then clustered based on both their taxonomic relationships and differences in abundance between samples. The clustering tree was constructed using the Bray–Curtis distance metric. On the right, the relative abundance distribution of each sample is shown, highlighting the top 10 taxa with the highest relative abundance at each taxonomic level. Remaining species are grouped as “Others”
The clustering tree based on Bray–Curtis distances struggled to differentiate between the biofilms from both strategies, particularly at higher taxonomic levels. At the genus level, the majority of samples subjected to sucrose cycles, regardless of pre-treatments, formed a clustered group. An exception was observed with two samples pre-treated with the anacardic acid LDT11. Conversely, all samples from the post-treatment strategy exhibited close clustering, implying that sucrose cycling significantly influenced sample composition. Taxa previously found in natural root caries lesions were the most active genus: Lactobacillus, Streptococcus, Bifidobacterium, Prevotella, Selenomonas, Veillonella, Oribacterium, Pseudomonas, Fusobacterium, Campylobacter, Actinomyces, Bacteroides, Neisseria, Staphylococcus, Haemophilus, Desulfitobacterium and Treponema. All samples that were exposed to sucrose cycles—pre-treated or not—massively increased the proportion of lactobacilli.
Concerning the non-bacterial content, the Thaumarchaeota and Euryarchaeota archaeal phyla were detected in the initial inoculum, with Thaumarchaeota exhibiting reads only in the groups subjected to sucrose cycles. On the other hand, reads affiliated with the Crenoarchaeota phylum were absent in the initial inoculum but were found in the groups treated with anacardic acid and cranberry, whether applied as pre or post-treatments, according to the relative abundance of unigenes. Virus reads were identified across all samples, and an unclassified virus phylum stood as the seventh most prevalent within the metatranscriptome across all conditions (Supplementary file 2, taxonomic analysis, unigene relative abundance table).
Differential gene expression and gene ontology term enrichment
There was lower co-expression (number of uniquely expressed genes shared between 2 groups) in the pre-treatment group (Fig. 4; Venn diagram): 921 genes sharing the same expression within all samples of the pre-treatment strategy, while there were 34,360 genes in the post-treatment groups. Analysis of differentially expressed genes revealed distinct expression patterns under different experimental conditions. Notably, the three groups exposed to sucrose clustered separately from those without sucrose addition, indicating a clear response to sucrose (Supplementary Fig. 1).Fig. 4RNA-seq coexpression (Venn diagram) and differential expression enrichment (GO terms—Gene Ontology). Much less co-expression in the pre-treatment is observed (Control 1 = G2 − ; Control 2 = G2 +). In the post-treatment, all groups shared similar expression in 34,360 genes. Cellular components (CC); biological processes (BP); metabolic functions (MF)
In the Gene Ontology (GO) terms enrichment analysis, both anacardic acid LDT11 and cranberry pre-treatments induced a higher level of gene expression alterations in biofilms: around 10 times more genes displayed significant differential expression in comparison to the post-treatment involving the same substances. In the post-treatment strategy, the anacardic acid derivative LDT11 provoked minimal changes in the cellular components of the mature biofilm but exerted substantial influence on biological processes and metabolic functions. Pre-treatment with anacardic acid LDT11 promoted changes in the metal binding in the biofilm growth in pre-treated with anacardic acid LDT11 when compared to controls without pre-treatment (> 500 genes with differential expression in each function). The same substance used as post-treatment did not change much the cellular components (CC) but did in the biological processes (BP) and metabolic functions (MF), although less than 60 genes had differential expression with the respective control without treatment. Of particular interest, the initial inoculum and the G2 − group (pre-treatment strategy without sucrose cycles) displayed no differentially expressed enriched GO terms. Similarly, the absence of enriched GO terms was observed when the biofilms harvested following the pre-treatment with anacardic acid LDT11 and cranberry were compared.
GO terms enrichment analysis of differentially expressed genes revealed that the control group (G2 −) exhibited enrichment for functions related to nitrogen compound metabolism and organic cyclic component metabolism compared to the sucrose-treated group (G2 +) (Supplementary Fig. 2). The branches represent hierarchical relationship (darker colour means higher enrichment degree of the biological process, cellular component or metabolic function).
Function annotation with EggNOG and carbohydrate metabolism
The analysis of functional abundance distances indicated that, in terms of metabolism, the pre-treatment variants exhibited greater dissimilarity compared to the post-treatment ones (Fig. 5A). The heatmap (EggNOG database) on Fig. 5B shows that biofilm growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters (G2 + group). In samples from the post-treatment strategy, no important dissimilarities were observed in the gene expression. The group not exposed to sucrose (G2 − group) had higher expression in amino acid, lipid, nucleotide and coenzyme transport and metabolism. Saliva inoculum presented higher expression defence mechanisms, RNA processing, translation, transcription, replication.Fig. 5Function annotation with Evolutionary Genealogy of Genes Non-supervised Orthologous Groups (EggNOG) and Carbohydrate metabolism (CAZy). A Functional annotation with eggNOG – clustering PCA: pre-treatment groups clustered farther apart in the functional abundance distance matrix, indicating that pre-treatments had a more profound impact on biofilm metabolic profiles. B Heatmap (EggNOG): Biofilms growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters. Post-treatment: do not change much the gene expression. C Static abundance chart on the first level of CAZy
Enriched GO terms can be observed in Fig. 5C, showing that biofilm growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters. Carbohydrate esterases (in green) had higher level of expression in the post-treated biofilms and the ones without sugar cycles, while glucosyltransferases (purple bars) were highly expressed in the pre-treated root slabs in the presence of sucrose, showing once more the cariogenicity of biofilms in our model.
We specifically observed the bacterial collagenases gene expression due to their importance to root caries biofilms. Out of the 32 annotated genes for collagenases U32, only four did not belong to Prevotella species. Interestingly, no reads were obtained for these genes in the anacardic acid LDT11 group, and just few reads in the cranberry group (Supplementary Fig. 3).
Antimicrobial effectiveness and biofilm thickness
We assessed the antimicrobial effectiveness of the compounds in both pre- and post-treatment applications (Fig. 6). Figure 6A illustrates examples of confocal 3D images depicting significant areas of dead cells in both the cranberry pre-treatment and sucrose control groups. This was confirmed by the Kruskal–Wallis test, followed by Dunn’s multiple comparisons against the control “No treatment (no sucrose cycles)”, that shows that the pre-treatment with cranberry extract significantly reduced biofilm viability (p < 0.0001). LDT11 exhibited comparable cell viability to the control group with sucrose cycles (p = 0.1209), suggesting a potential mitigation of the pH effects within the biofilm. Similarly, the group with no pre-treatment and no sucrose cycles also exhibited diminished viability relative to the other groups, except for the cranberry-treated group (p < 0.0001) (Fig. 6B).Fig. 6Antimicrobial activity of the compounds against complex biofilms when used as pre-treatment or post-treatment. A Confocal Light Scanning Microscopy (CLSM) 3D images for the pre-treatment strategy. B Proportion of viable cells (viability) measured with the biofilm viability checker using the CLSM images from a biological duplicate, with imaging conducted in two different fields for each group, resulting in a total of 180–253 fields analysed per group. Statistical analysis: Biofilm viability Checker; Kruskal–Wallis, Dunn’s–control “No treatment (sucrose cycles)”. C Confocal light scanning microscopy 3D images for the post-treatment strategy from a biological duplicate, with imaging conducted in two different fields for each group, resulting in a total of 881–1002 fields analysed per group. D Proportion of life and dead (L/D) cells in post-treated biofilms measured with the biofilm viability checker using the CLSM images. Statistical analysis: 2-way ANOVA, Tukey’s multiple comparison test. E Culture of the total aerobes and total anaerobes after 30 min of post-treatment. Statistical analysis: 2-way ANOVA, Tukey’s multiple comparison test
In the post-treatment strategy, following a 10-min incubation with the compounds, no noticeable changes were observed on the biofilm surfaces, with the exception of the positive control, chlorhexidine (CHX). However, after a 30-min treatment period, impacts were evident on the outer layers of the biofilms when exposed to higher concentrations of both anacardic acid and cranberry compounds (Fig. 6C). An examination of the internal layers of the biofilm revealed that, after a 10-min treatment, cranberry exhibited significantly stronger antimicrobial properties compared to anacardic acid LDT11, and their dosage effects appear to have opposite results: the proportion of live and dead cells was higher in LDT11 at 50 μg/mL compared to cranberry at 50 μg/mL (p < 0.0001), LDT11 at 50 μg/mL compared to cranberry at 100 μg/mL (p = 0.0013), LDT11 at 100 μg/mL compared to cranberry at 50 μg/mL (p = 0.0029) and LDT11 at 100 μg/mL compared to cranberry at 100 μg/mL (p = 0.0454). The no treatment group exhibited higher effects than cranberry at 50 μg/mL (p = 0.0479). LDT11 also showed less effect than chlorhexidine (CHX) (LDT11 at 50 μg/mL vs. CHX at 100 μg/mL—p < 0.0001, LDT11 at 100 μg/mL vs. CHX at 100 μg/mL—p = 0.0003), while cranberry did not differ from the positive control CHX, nor between its concentrations of 50 μg/mL and 100 μg/mL. Yet, this difference became less pronounced after a 30-min treatment, where no statistical differences between groups were observed (Fig. 6D, supplementary Table 3). However, the culture of total anaerobes and aerobes from the biofilms treated for 30 min demonstrated increased sensitivity, allowing for differences in the antimicrobial efficacy of CHX in comparison to the other groups (p < 0.0001) (Fig. 6E).
In the pre-treatment strategy, the sucrose cycles contributed to the development of a less dense, more porous biofilm structure. This observation aligns with the expected presence of extracellular polysaccharides that provide structural integrity to this biofilm matrix (Fig. 7A and B). In the presence of sucrose cycles, the biofilms exhibited a brownish coloration (Fig. 7A, purple arrow), whereas in the absence of sucrose cycles, they appeared more yellowish (Fig. 7A, blue arrow).Fig. 7Analysis of the biofilm characteristics. Pre-treatment strategy: A Biofilms depth: the confocal microscopy used the same images from Fig. 6A and C, and their fluorescent signal intensities, to calculate the biofilm thickness and the coverage area through examination of biofilm structure in relation to the spatial localization by choosing this option in the 3D viewer of the Leica Application Suit X software, v. 3.5.7.23225 (LAS X, https://www.leica-microsystems.com/products/microscope- software/p/leica-las-x-ls/), and representative biofilm photographs (blue and purple arrows represents no pre-treatment groups, without and with sucrose cycles, respectively). B Scanning electronic microscopy (SEM) at two magnifications, including the control group without pre-treatment and with sucrose cycles, and the pre-treated groups with LDT11 and cranberry. SEM images magnification: top row = 5.00 kV, 4–5 mm × 100 SE, 500 µm for all images; bottom row = 5.00 kV, 4.5 mm × 1 50 k SE, 30.0 µm; 5.00 kV, 4.5 mm × 1 3.00 k SE, 10.0 µm, and 5.00 kV, 5 mm × 1 3.00 k SE, 10.0 µm. Post-treatment strategy: C Biofilm depth; D Biofilm Coverage area after post-treatment for 10 and 30 min. The experiment was conducted using 4 distinct pegs per group, with each peg subjected to imaging in two separate regions
Both the cranberry and anacardic acid LDT11 groups exhibited numerous voids within the biofilm structure within the 3D images of the post-treatment strategy (Fig. 7C). By quantifying the area covered by the biofilms (Fig. 7D), we showed that cranberry facilitated the detachment of cells (anti-biofilm effect) in the post-treatment strategy (cranberry 50 μg/mL vs. CHX control at 10 and 30 min; p = 0.0130 and p = 0.0078, respectively; mixed-effect analysis), but not in the pre-treatment strategy.
Collagenase and gelatinase activity of biofilms
Investigating the inhibition of collagenases as a targeted approach for the treatment and prevention of root caries is promising, especially considering the likely role of microbial collagenases in the second stage of lesion formation. In this study, we examined whether the LDT11 and cranberry could demonstrate the modulation of these biofilms by inhibiting collagenases. Figure 8 presents the outcomes of the collagenase and gelatinase inhibition assessments (Fig. 8A and C), along with the gelatine hydrolysis experiments (Fig. 8B). In the pre-treatment strategy, collagenase activity of biofilms exhibited lower levels when collagen type 1 was the substrate than gelatine. The C. histolyticum positive control exhibited higher collagenase activity compared to biofilms from the control group (no pre-treatment, with sugar cycle) (p = 0.0389), LDT11 at 100 μg/mL (p = 0.0499) and cranberry at 100 μg/mL (p = 0.0153). No significant differences were found between the control (no pre-treatment, with sugar cycle) and the compounds. Also, pre-treatment with cranberry yielded biofilms with reduced gelatinase activity (p = 0.0114) (Fig. 8A). This observation was subsequently verified through the gelatine hydrolysis experiment, where biofilms harvested from the dentine after 10 days of biofilm and lesion development were placed in gelatine-containing media for 7 days, with subsequent observation of liquefaction occurrence. The proteases found in biofilms taken from dentine slabs were able to completely hydrolyse gelatine (tubes show positive hydrolysis when the gelatine was found in a liquid state at each time point), except for a few replicates pre-treated with cranberry or anacardic acid LDT11, or in controls without sugar cycles, that partially hydrolysed gelatine. Among these, the cranberry-treated group displayed a larger number of replicates with partial hydrolysis, indicating that the gelatin was not completely liquefied (Fig. 8B). For investigating collagenolytic activity within the post-treatment strategy, we exclusively employed fluorescent gelatine as the substrate. In Fig. 8C, fluorescence was measured at three time points 2 h, 7 h and 24 h at the post-treatment strategy: the higher the fluorescence the greater the gelatinase activity. Only CHX (positive control for collagenase inhibition) exhibited a significant reduction in gelatinase activity within the biofilms. At 2 h, no significant differences were observed between the no treatment group and the other groups. However, after 7 h and 24 h, a significant higher gelatinase inhibition was found at the CHX group when compared to no treatment group (p = 0.0293 and p = 0.0246, respectively).Fig. 8Collagenase and gelatinase activity of the biofilms with and without pre-treatments or post-treatments with the anacardic acid LDT11 and the cranberry extract. A Pre-treatment gelatinase and collagenase activity (EnzChek Gelatinase/Collagenase assay kit using the DQ collagen type I and DQ gelatine as substrates). Statistical analysis: 2-way ANOVA, Dunn’s multiple comparisons test. B Pre-treatment strategy: gelatine hydrolysis, assessed at two time points (48 h and 7 days). The bar graph indicates the number of replicates that were positive for hydrolysis. The image below the graph displays two tubes containing gelatine: the top tube shows positive hydrolysis with the gelatine in a liquid state, while the bottom tube shows negative hydrolysis with the gelatine remaining solid. C Post-treatment gelatinase activity (EnzChek Gelatinase/Collagenase assay kit using the DQ gelatine as substrate). Fluorescence was measured at three time points 2 h, 7 h and 24 h. Statistical analysis: 2-way ANOVA, Dunnett’s multiple comparisons test
Confirmation of root caries lesions development in the pre-treatment strategy
Visual inspection of the slabs revealed root caries-like lesions, evident through alterations in the colour of the dentine surface. Additionally, SEM images depicted deposits or sediments, some exhibiting filamentous characteristics, on the surfaces of groups pre-treated with anacardic acid LDT11 and cranberry (Supplementary Fig. 4). The µCT scanning of the dentine slabs, conducted before and after biofilm formation and cariogenic challenge, revealed visibly reduced mineral loss in all dentine slabs exposed to biofilms as a consequence from a successful dysbiosis-driven model. However, the group not exposed to sucrose cycles (G2 −) presented lower mineral loss than the group exposed to sucrose cycles without pre-treatments (G2 +). Slabs pre-treated with both anacardic acid LDT11 and cranberry seem to present lower mineral loss compared to the control G2 + . Dentine slabs maintained in the experimental condition without biofilm formation (no saliva inoculum) exhibited no mineral loss (G1) (Supplementary Fig. 5).
pH modulation within each experimental strategy
Figure 2 illustrates the pH change during the biofilm formation. For the pre-treatment strategy, the pH was measured before adding sucrose and 6 h after sucrose addition, confirming pH fluctuations in the cycles. Prior to exposure to sucrose cycling, the pH of the media in the cranberry-pre-treated dentine slab group was lower on day 2 when compared to day 4 (p = 0.0479), day 6 (p = 0.0211), day 8 (p < 0.0001) and day 10 (p = 0.0211), but recovered by day 4. In contrast, the untreated group (“No treatment, with sucrose cycle”) exhibited a higher pH by day 10 (p < 0.0001). The groups with cycles displayed lower pH levels compared to the control group without sucrose cycles, reaching the critical pH for root dentine demineralization. The group without treatment and not exposed to sucrose cycles increased the pH over time, remaining above the critical pH for root dentine demineralization at the end of the experiment. On day 1, no significant differences were observed between the groups, confirming the baseline pH similarity. However, the “no treatment (with sucrose cycles)” group had a significantly lower pH compared to the negative control (no biofilm) on day 2 (p = 0.0037), day 4 (p = 0.0046), day 6 (p = 0.0108), day 8 (p = 0.0027) and day 10 (p = 0.0209). This group also had a significantly lower pH than the “no treatment (no sucrose cycles)” group on day 2 (p = 0.0329), day 4 (p = 0.0176), day 8 (p = 0.0011) and day 10 (p = 0.0062) (Supplementary Table 2).Fig. 2Measurement of the pH of the media. Pre-treatment strategy: pH was measured immediately before and 6 h after adding sucrose to the media in the cycling. Post-treatment strategy: pH was measured daily, before the media change. Experiment performed using a pH meter electrode. Statistical analysis: Pre-treatment, before sucrose cycles = multiple comparisons regarding time points. Pre-treatment, after sucrose cycles = ANOVA, Dunnett’s multiple comparisons against no treatment (with sucrose cycles). Post-treatment = Statistical model for the main effect time. Mixed-effect model; Tukey's multiple comparisons test
Measurements of the media pH were conducted daily for the post-treatment strategy. A clear and statistically significant pH elevation was observed following the third day of biofilm formation: day 1 vs. day 5 (p = 0.0008), day 1 vs. day 6 (p < 0.0001), day 2 vs. day 3 (p = 0.0017), day 2 vs. day 4 (p = 0.0004), day 2 vs. day 5 (p < 0.0001), day 2 vs. day 6 (p < 0.0001), day 3 vs. day 6 (p = 0.0016), day 4 vs. day 6 (p = 0.0079) (Supplementary Table 2). This phenomenon could potentially be attributed to the increased complexity of the biofilms and the attachment of late colonizers. This trend aligns with the observed pH increase over successive days in the untreated group of the pre-treatment strategy.
Biofilms metatranscriptome–RNAseq
Sequencing output
The number of clean reads (filtered sequencing data) ranged from 3.78 × 107 to 4.74 × 107, and subsequent bioinformatic analysis is based on these reads. Clean bases ranged from 5.7G to 7G. The GC% content was lower in the saliva inoculum (43.36%) and higher (51.05%) in G2_3 (replicate of the pre-treatment strategy G2 + group–without pre-treatment, with sucrose cycles).
Microbial composition
As expected, there was a slight reduction in the number of active taxa within the laboratory culture when comparing the biofilms to the initial inoculum. The phyla Chlamydiota, Spirochaetota and Proteobacteria, as well as the Saccharibacteria oral taxon TM7x, were affected by the laboratory conditions and were found reduced in numbers following culturing. However, this diversity reduction was less noticeable in the biofilms grown on dentine slabs without treatment and without sucrose cycles (pre-treatment strategy, G2 − group). Nevertheless, the biofilm compositional complexity was confirmed, showing a great diversity of the active microbiome in both pre-treatment and post-treatment strategies, including reads mapped to microorganisms other than bacteria, such as archaea and viruses (Fig. 3).Fig. 3Taxonomic abundance clustering of the 35 most abundant taxa (Kingdom; phylum, and genus-level) within each sample. Samples were first selected based on species identification and abundance information. Taxa were then clustered based on both their taxonomic relationships and differences in abundance between samples. The clustering tree was constructed using the Bray–Curtis distance metric. On the right, the relative abundance distribution of each sample is shown, highlighting the top 10 taxa with the highest relative abundance at each taxonomic level. Remaining species are grouped as “Others”
The clustering tree based on Bray–Curtis distances struggled to differentiate between the biofilms from both strategies, particularly at higher taxonomic levels. At the genus level, the majority of samples subjected to sucrose cycles, regardless of pre-treatments, formed a clustered group. An exception was observed with two samples pre-treated with the anacardic acid LDT11. Conversely, all samples from the post-treatment strategy exhibited close clustering, implying that sucrose cycling significantly influenced sample composition. Taxa previously found in natural root caries lesions were the most active genus: Lactobacillus, Streptococcus, Bifidobacterium, Prevotella, Selenomonas, Veillonella, Oribacterium, Pseudomonas, Fusobacterium, Campylobacter, Actinomyces, Bacteroides, Neisseria, Staphylococcus, Haemophilus, Desulfitobacterium and Treponema. All samples that were exposed to sucrose cycles—pre-treated or not—massively increased the proportion of lactobacilli.
Concerning the non-bacterial content, the Thaumarchaeota and Euryarchaeota archaeal phyla were detected in the initial inoculum, with Thaumarchaeota exhibiting reads only in the groups subjected to sucrose cycles. On the other hand, reads affiliated with the Crenoarchaeota phylum were absent in the initial inoculum but were found in the groups treated with anacardic acid and cranberry, whether applied as pre or post-treatments, according to the relative abundance of unigenes. Virus reads were identified across all samples, and an unclassified virus phylum stood as the seventh most prevalent within the metatranscriptome across all conditions (Supplementary file 2, taxonomic analysis, unigene relative abundance table).
Differential gene expression and gene ontology term enrichment
There was lower co-expression (number of uniquely expressed genes shared between 2 groups) in the pre-treatment group (Fig. 4; Venn diagram): 921 genes sharing the same expression within all samples of the pre-treatment strategy, while there were 34,360 genes in the post-treatment groups. Analysis of differentially expressed genes revealed distinct expression patterns under different experimental conditions. Notably, the three groups exposed to sucrose clustered separately from those without sucrose addition, indicating a clear response to sucrose (Supplementary Fig. 1).Fig. 4RNA-seq coexpression (Venn diagram) and differential expression enrichment (GO terms—Gene Ontology). Much less co-expression in the pre-treatment is observed (Control 1 = G2 − ; Control 2 = G2 +). In the post-treatment, all groups shared similar expression in 34,360 genes. Cellular components (CC); biological processes (BP); metabolic functions (MF)
In the Gene Ontology (GO) terms enrichment analysis, both anacardic acid LDT11 and cranberry pre-treatments induced a higher level of gene expression alterations in biofilms: around 10 times more genes displayed significant differential expression in comparison to the post-treatment involving the same substances. In the post-treatment strategy, the anacardic acid derivative LDT11 provoked minimal changes in the cellular components of the mature biofilm but exerted substantial influence on biological processes and metabolic functions. Pre-treatment with anacardic acid LDT11 promoted changes in the metal binding in the biofilm growth in pre-treated with anacardic acid LDT11 when compared to controls without pre-treatment (> 500 genes with differential expression in each function). The same substance used as post-treatment did not change much the cellular components (CC) but did in the biological processes (BP) and metabolic functions (MF), although less than 60 genes had differential expression with the respective control without treatment. Of particular interest, the initial inoculum and the G2 − group (pre-treatment strategy without sucrose cycles) displayed no differentially expressed enriched GO terms. Similarly, the absence of enriched GO terms was observed when the biofilms harvested following the pre-treatment with anacardic acid LDT11 and cranberry were compared.
GO terms enrichment analysis of differentially expressed genes revealed that the control group (G2 −) exhibited enrichment for functions related to nitrogen compound metabolism and organic cyclic component metabolism compared to the sucrose-treated group (G2 +) (Supplementary Fig. 2). The branches represent hierarchical relationship (darker colour means higher enrichment degree of the biological process, cellular component or metabolic function).
Function annotation with EggNOG and carbohydrate metabolism
The analysis of functional abundance distances indicated that, in terms of metabolism, the pre-treatment variants exhibited greater dissimilarity compared to the post-treatment ones (Fig. 5A). The heatmap (EggNOG database) on Fig. 5B shows that biofilm growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters (G2 + group). In samples from the post-treatment strategy, no important dissimilarities were observed in the gene expression. The group not exposed to sucrose (G2 − group) had higher expression in amino acid, lipid, nucleotide and coenzyme transport and metabolism. Saliva inoculum presented higher expression defence mechanisms, RNA processing, translation, transcription, replication.Fig. 5Function annotation with Evolutionary Genealogy of Genes Non-supervised Orthologous Groups (EggNOG) and Carbohydrate metabolism (CAZy). A Functional annotation with eggNOG – clustering PCA: pre-treatment groups clustered farther apart in the functional abundance distance matrix, indicating that pre-treatments had a more profound impact on biofilm metabolic profiles. B Heatmap (EggNOG): Biofilms growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters. Post-treatment: do not change much the gene expression. C Static abundance chart on the first level of CAZy
Enriched GO terms can be observed in Fig. 5C, showing that biofilm growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters. Carbohydrate esterases (in green) had higher level of expression in the post-treated biofilms and the ones without sugar cycles, while glucosyltransferases (purple bars) were highly expressed in the pre-treated root slabs in the presence of sucrose, showing once more the cariogenicity of biofilms in our model.
We specifically observed the bacterial collagenases gene expression due to their importance to root caries biofilms. Out of the 32 annotated genes for collagenases U32, only four did not belong to Prevotella species. Interestingly, no reads were obtained for these genes in the anacardic acid LDT11 group, and just few reads in the cranberry group (Supplementary Fig. 3).
Sequencing output
The number of clean reads (filtered sequencing data) ranged from 3.78 × 107 to 4.74 × 107, and subsequent bioinformatic analysis is based on these reads. Clean bases ranged from 5.7G to 7G. The GC% content was lower in the saliva inoculum (43.36%) and higher (51.05%) in G2_3 (replicate of the pre-treatment strategy G2 + group–without pre-treatment, with sucrose cycles).
Microbial composition
As expected, there was a slight reduction in the number of active taxa within the laboratory culture when comparing the biofilms to the initial inoculum. The phyla Chlamydiota, Spirochaetota and Proteobacteria, as well as the Saccharibacteria oral taxon TM7x, were affected by the laboratory conditions and were found reduced in numbers following culturing. However, this diversity reduction was less noticeable in the biofilms grown on dentine slabs without treatment and without sucrose cycles (pre-treatment strategy, G2 − group). Nevertheless, the biofilm compositional complexity was confirmed, showing a great diversity of the active microbiome in both pre-treatment and post-treatment strategies, including reads mapped to microorganisms other than bacteria, such as archaea and viruses (Fig. 3).Fig. 3Taxonomic abundance clustering of the 35 most abundant taxa (Kingdom; phylum, and genus-level) within each sample. Samples were first selected based on species identification and abundance information. Taxa were then clustered based on both their taxonomic relationships and differences in abundance between samples. The clustering tree was constructed using the Bray–Curtis distance metric. On the right, the relative abundance distribution of each sample is shown, highlighting the top 10 taxa with the highest relative abundance at each taxonomic level. Remaining species are grouped as “Others”
The clustering tree based on Bray–Curtis distances struggled to differentiate between the biofilms from both strategies, particularly at higher taxonomic levels. At the genus level, the majority of samples subjected to sucrose cycles, regardless of pre-treatments, formed a clustered group. An exception was observed with two samples pre-treated with the anacardic acid LDT11. Conversely, all samples from the post-treatment strategy exhibited close clustering, implying that sucrose cycling significantly influenced sample composition. Taxa previously found in natural root caries lesions were the most active genus: Lactobacillus, Streptococcus, Bifidobacterium, Prevotella, Selenomonas, Veillonella, Oribacterium, Pseudomonas, Fusobacterium, Campylobacter, Actinomyces, Bacteroides, Neisseria, Staphylococcus, Haemophilus, Desulfitobacterium and Treponema. All samples that were exposed to sucrose cycles—pre-treated or not—massively increased the proportion of lactobacilli.
Concerning the non-bacterial content, the Thaumarchaeota and Euryarchaeota archaeal phyla were detected in the initial inoculum, with Thaumarchaeota exhibiting reads only in the groups subjected to sucrose cycles. On the other hand, reads affiliated with the Crenoarchaeota phylum were absent in the initial inoculum but were found in the groups treated with anacardic acid and cranberry, whether applied as pre or post-treatments, according to the relative abundance of unigenes. Virus reads were identified across all samples, and an unclassified virus phylum stood as the seventh most prevalent within the metatranscriptome across all conditions (Supplementary file 2, taxonomic analysis, unigene relative abundance table).
Differential gene expression and gene ontology term enrichment
There was lower co-expression (number of uniquely expressed genes shared between 2 groups) in the pre-treatment group (Fig. 4; Venn diagram): 921 genes sharing the same expression within all samples of the pre-treatment strategy, while there were 34,360 genes in the post-treatment groups. Analysis of differentially expressed genes revealed distinct expression patterns under different experimental conditions. Notably, the three groups exposed to sucrose clustered separately from those without sucrose addition, indicating a clear response to sucrose (Supplementary Fig. 1).Fig. 4RNA-seq coexpression (Venn diagram) and differential expression enrichment (GO terms—Gene Ontology). Much less co-expression in the pre-treatment is observed (Control 1 = G2 − ; Control 2 = G2 +). In the post-treatment, all groups shared similar expression in 34,360 genes. Cellular components (CC); biological processes (BP); metabolic functions (MF)
In the Gene Ontology (GO) terms enrichment analysis, both anacardic acid LDT11 and cranberry pre-treatments induced a higher level of gene expression alterations in biofilms: around 10 times more genes displayed significant differential expression in comparison to the post-treatment involving the same substances. In the post-treatment strategy, the anacardic acid derivative LDT11 provoked minimal changes in the cellular components of the mature biofilm but exerted substantial influence on biological processes and metabolic functions. Pre-treatment with anacardic acid LDT11 promoted changes in the metal binding in the biofilm growth in pre-treated with anacardic acid LDT11 when compared to controls without pre-treatment (> 500 genes with differential expression in each function). The same substance used as post-treatment did not change much the cellular components (CC) but did in the biological processes (BP) and metabolic functions (MF), although less than 60 genes had differential expression with the respective control without treatment. Of particular interest, the initial inoculum and the G2 − group (pre-treatment strategy without sucrose cycles) displayed no differentially expressed enriched GO terms. Similarly, the absence of enriched GO terms was observed when the biofilms harvested following the pre-treatment with anacardic acid LDT11 and cranberry were compared.
GO terms enrichment analysis of differentially expressed genes revealed that the control group (G2 −) exhibited enrichment for functions related to nitrogen compound metabolism and organic cyclic component metabolism compared to the sucrose-treated group (G2 +) (Supplementary Fig. 2). The branches represent hierarchical relationship (darker colour means higher enrichment degree of the biological process, cellular component or metabolic function).
Function annotation with EggNOG and carbohydrate metabolism
The analysis of functional abundance distances indicated that, in terms of metabolism, the pre-treatment variants exhibited greater dissimilarity compared to the post-treatment ones (Fig. 5A). The heatmap (EggNOG database) on Fig. 5B shows that biofilm growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters (G2 + group). In samples from the post-treatment strategy, no important dissimilarities were observed in the gene expression. The group not exposed to sucrose (G2 − group) had higher expression in amino acid, lipid, nucleotide and coenzyme transport and metabolism. Saliva inoculum presented higher expression defence mechanisms, RNA processing, translation, transcription, replication.Fig. 5Function annotation with Evolutionary Genealogy of Genes Non-supervised Orthologous Groups (EggNOG) and Carbohydrate metabolism (CAZy). A Functional annotation with eggNOG – clustering PCA: pre-treatment groups clustered farther apart in the functional abundance distance matrix, indicating that pre-treatments had a more profound impact on biofilm metabolic profiles. B Heatmap (EggNOG): Biofilms growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters. Post-treatment: do not change much the gene expression. C Static abundance chart on the first level of CAZy
Enriched GO terms can be observed in Fig. 5C, showing that biofilm growth over the dentine slabs without treatment, and exposed to sucrose cycles had more carbohydrate transporters. Carbohydrate esterases (in green) had higher level of expression in the post-treated biofilms and the ones without sugar cycles, while glucosyltransferases (purple bars) were highly expressed in the pre-treated root slabs in the presence of sucrose, showing once more the cariogenicity of biofilms in our model.
We specifically observed the bacterial collagenases gene expression due to their importance to root caries biofilms. Out of the 32 annotated genes for collagenases U32, only four did not belong to Prevotella species. Interestingly, no reads were obtained for these genes in the anacardic acid LDT11 group, and just few reads in the cranberry group (Supplementary Fig. 3).
Antimicrobial effectiveness and biofilm thickness
We assessed the antimicrobial effectiveness of the compounds in both pre- and post-treatment applications (Fig. 6). Figure 6A illustrates examples of confocal 3D images depicting significant areas of dead cells in both the cranberry pre-treatment and sucrose control groups. This was confirmed by the Kruskal–Wallis test, followed by Dunn’s multiple comparisons against the control “No treatment (no sucrose cycles)”, that shows that the pre-treatment with cranberry extract significantly reduced biofilm viability (p < 0.0001). LDT11 exhibited comparable cell viability to the control group with sucrose cycles (p = 0.1209), suggesting a potential mitigation of the pH effects within the biofilm. Similarly, the group with no pre-treatment and no sucrose cycles also exhibited diminished viability relative to the other groups, except for the cranberry-treated group (p < 0.0001) (Fig. 6B).Fig. 6Antimicrobial activity of the compounds against complex biofilms when used as pre-treatment or post-treatment. A Confocal Light Scanning Microscopy (CLSM) 3D images for the pre-treatment strategy. B Proportion of viable cells (viability) measured with the biofilm viability checker using the CLSM images from a biological duplicate, with imaging conducted in two different fields for each group, resulting in a total of 180–253 fields analysed per group. Statistical analysis: Biofilm viability Checker; Kruskal–Wallis, Dunn’s–control “No treatment (sucrose cycles)”. C Confocal light scanning microscopy 3D images for the post-treatment strategy from a biological duplicate, with imaging conducted in two different fields for each group, resulting in a total of 881–1002 fields analysed per group. D Proportion of life and dead (L/D) cells in post-treated biofilms measured with the biofilm viability checker using the CLSM images. Statistical analysis: 2-way ANOVA, Tukey’s multiple comparison test. E Culture of the total aerobes and total anaerobes after 30 min of post-treatment. Statistical analysis: 2-way ANOVA, Tukey’s multiple comparison test
In the post-treatment strategy, following a 10-min incubation with the compounds, no noticeable changes were observed on the biofilm surfaces, with the exception of the positive control, chlorhexidine (CHX). However, after a 30-min treatment period, impacts were evident on the outer layers of the biofilms when exposed to higher concentrations of both anacardic acid and cranberry compounds (Fig. 6C). An examination of the internal layers of the biofilm revealed that, after a 10-min treatment, cranberry exhibited significantly stronger antimicrobial properties compared to anacardic acid LDT11, and their dosage effects appear to have opposite results: the proportion of live and dead cells was higher in LDT11 at 50 μg/mL compared to cranberry at 50 μg/mL (p < 0.0001), LDT11 at 50 μg/mL compared to cranberry at 100 μg/mL (p = 0.0013), LDT11 at 100 μg/mL compared to cranberry at 50 μg/mL (p = 0.0029) and LDT11 at 100 μg/mL compared to cranberry at 100 μg/mL (p = 0.0454). The no treatment group exhibited higher effects than cranberry at 50 μg/mL (p = 0.0479). LDT11 also showed less effect than chlorhexidine (CHX) (LDT11 at 50 μg/mL vs. CHX at 100 μg/mL—p < 0.0001, LDT11 at 100 μg/mL vs. CHX at 100 μg/mL—p = 0.0003), while cranberry did not differ from the positive control CHX, nor between its concentrations of 50 μg/mL and 100 μg/mL. Yet, this difference became less pronounced after a 30-min treatment, where no statistical differences between groups were observed (Fig. 6D, supplementary Table 3). However, the culture of total anaerobes and aerobes from the biofilms treated for 30 min demonstrated increased sensitivity, allowing for differences in the antimicrobial efficacy of CHX in comparison to the other groups (p < 0.0001) (Fig. 6E).
In the pre-treatment strategy, the sucrose cycles contributed to the development of a less dense, more porous biofilm structure. This observation aligns with the expected presence of extracellular polysaccharides that provide structural integrity to this biofilm matrix (Fig. 7A and B). In the presence of sucrose cycles, the biofilms exhibited a brownish coloration (Fig. 7A, purple arrow), whereas in the absence of sucrose cycles, they appeared more yellowish (Fig. 7A, blue arrow).Fig. 7Analysis of the biofilm characteristics. Pre-treatment strategy: A Biofilms depth: the confocal microscopy used the same images from Fig. 6A and C, and their fluorescent signal intensities, to calculate the biofilm thickness and the coverage area through examination of biofilm structure in relation to the spatial localization by choosing this option in the 3D viewer of the Leica Application Suit X software, v. 3.5.7.23225 (LAS X, https://www.leica-microsystems.com/products/microscope- software/p/leica-las-x-ls/), and representative biofilm photographs (blue and purple arrows represents no pre-treatment groups, without and with sucrose cycles, respectively). B Scanning electronic microscopy (SEM) at two magnifications, including the control group without pre-treatment and with sucrose cycles, and the pre-treated groups with LDT11 and cranberry. SEM images magnification: top row = 5.00 kV, 4–5 mm × 100 SE, 500 µm for all images; bottom row = 5.00 kV, 4.5 mm × 1 50 k SE, 30.0 µm; 5.00 kV, 4.5 mm × 1 3.00 k SE, 10.0 µm, and 5.00 kV, 5 mm × 1 3.00 k SE, 10.0 µm. Post-treatment strategy: C Biofilm depth; D Biofilm Coverage area after post-treatment for 10 and 30 min. The experiment was conducted using 4 distinct pegs per group, with each peg subjected to imaging in two separate regions
Both the cranberry and anacardic acid LDT11 groups exhibited numerous voids within the biofilm structure within the 3D images of the post-treatment strategy (Fig. 7C). By quantifying the area covered by the biofilms (Fig. 7D), we showed that cranberry facilitated the detachment of cells (anti-biofilm effect) in the post-treatment strategy (cranberry 50 μg/mL vs. CHX control at 10 and 30 min; p = 0.0130 and p = 0.0078, respectively; mixed-effect analysis), but not in the pre-treatment strategy.
Collagenase and gelatinase activity of biofilms
Investigating the inhibition of collagenases as a targeted approach for the treatment and prevention of root caries is promising, especially considering the likely role of microbial collagenases in the second stage of lesion formation. In this study, we examined whether the LDT11 and cranberry could demonstrate the modulation of these biofilms by inhibiting collagenases. Figure 8 presents the outcomes of the collagenase and gelatinase inhibition assessments (Fig. 8A and C), along with the gelatine hydrolysis experiments (Fig. 8B). In the pre-treatment strategy, collagenase activity of biofilms exhibited lower levels when collagen type 1 was the substrate than gelatine. The C. histolyticum positive control exhibited higher collagenase activity compared to biofilms from the control group (no pre-treatment, with sugar cycle) (p = 0.0389), LDT11 at 100 μg/mL (p = 0.0499) and cranberry at 100 μg/mL (p = 0.0153). No significant differences were found between the control (no pre-treatment, with sugar cycle) and the compounds. Also, pre-treatment with cranberry yielded biofilms with reduced gelatinase activity (p = 0.0114) (Fig. 8A). This observation was subsequently verified through the gelatine hydrolysis experiment, where biofilms harvested from the dentine after 10 days of biofilm and lesion development were placed in gelatine-containing media for 7 days, with subsequent observation of liquefaction occurrence. The proteases found in biofilms taken from dentine slabs were able to completely hydrolyse gelatine (tubes show positive hydrolysis when the gelatine was found in a liquid state at each time point), except for a few replicates pre-treated with cranberry or anacardic acid LDT11, or in controls without sugar cycles, that partially hydrolysed gelatine. Among these, the cranberry-treated group displayed a larger number of replicates with partial hydrolysis, indicating that the gelatin was not completely liquefied (Fig. 8B). For investigating collagenolytic activity within the post-treatment strategy, we exclusively employed fluorescent gelatine as the substrate. In Fig. 8C, fluorescence was measured at three time points 2 h, 7 h and 24 h at the post-treatment strategy: the higher the fluorescence the greater the gelatinase activity. Only CHX (positive control for collagenase inhibition) exhibited a significant reduction in gelatinase activity within the biofilms. At 2 h, no significant differences were observed between the no treatment group and the other groups. However, after 7 h and 24 h, a significant higher gelatinase inhibition was found at the CHX group when compared to no treatment group (p = 0.0293 and p = 0.0246, respectively).Fig. 8Collagenase and gelatinase activity of the biofilms with and without pre-treatments or post-treatments with the anacardic acid LDT11 and the cranberry extract. A Pre-treatment gelatinase and collagenase activity (EnzChek Gelatinase/Collagenase assay kit using the DQ collagen type I and DQ gelatine as substrates). Statistical analysis: 2-way ANOVA, Dunn’s multiple comparisons test. B Pre-treatment strategy: gelatine hydrolysis, assessed at two time points (48 h and 7 days). The bar graph indicates the number of replicates that were positive for hydrolysis. The image below the graph displays two tubes containing gelatine: the top tube shows positive hydrolysis with the gelatine in a liquid state, while the bottom tube shows negative hydrolysis with the gelatine remaining solid. C Post-treatment gelatinase activity (EnzChek Gelatinase/Collagenase assay kit using the DQ gelatine as substrate). Fluorescence was measured at three time points 2 h, 7 h and 24 h. Statistical analysis: 2-way ANOVA, Dunnett’s multiple comparisons test
Confirmation of root caries lesions development in the pre-treatment strategy
Visual inspection of the slabs revealed root caries-like lesions, evident through alterations in the colour of the dentine surface. Additionally, SEM images depicted deposits or sediments, some exhibiting filamentous characteristics, on the surfaces of groups pre-treated with anacardic acid LDT11 and cranberry (Supplementary Fig. 4). The µCT scanning of the dentine slabs, conducted before and after biofilm formation and cariogenic challenge, revealed visibly reduced mineral loss in all dentine slabs exposed to biofilms as a consequence from a successful dysbiosis-driven model. However, the group not exposed to sucrose cycles (G2 −) presented lower mineral loss than the group exposed to sucrose cycles without pre-treatments (G2 +). Slabs pre-treated with both anacardic acid LDT11 and cranberry seem to present lower mineral loss compared to the control G2 + . Dentine slabs maintained in the experimental condition without biofilm formation (no saliva inoculum) exhibited no mineral loss (G1) (Supplementary Fig. 5).
Discussion
The objective of this research was to develop an in vitro cariogenic biofilm model and explore microbial modulation approaches for managing root caries. The presence of taxa previously found in natural root caries lesions (Fig. 3) such as Lactobacillus, Streptococcus, Bifidobacterium, Prevotella, Selenomonas, Veillonella, Actinomyces, Treponema and others confirms that we achieved the root caries biofilm engineering in vitro [5, 6, 26]. Two strategies were used to test substances that could modulate oral biofilms due to their bio-multifunctionality. In the first strategy, the tested compounds were incorporated before growing the biofilm (“pre-treatment strategy”). In the second strategy, mature biofilms were grown and then exposed to the compounds (“post-treatment strategy”). The advantages of the “post-treatment strategy” lie in its role as a viable substitute for artificial mouths, particularly in smaller oral microbiology laboratories, enabling rapid testing of various compounds, concentrations, and exposure times. However, a higher significance of the “pre-treatment strategy” to modulate the biofilms was observed, that can be attributed to its impact on biofilm formation, confirmed by the much less co-expression with the respective control in the metatranscriptome analysis than for the post-treated biofilms. Additionally, adding natural compounds to mature biofilms are less effective due to the biofilms’ resilient nature. While previous studies have explored microbiome modulation through surface pre-treatment or coating [27, 28], our focus is distinct, as we specifically target dentine collagen breakdown modulation to prevent/delay cavitation. Future clinical implications of these findings involve more feasibility to develop varnish products for application after professional tooth prophylaxis, as opposed to using mouthwashes, to implement substances for a dysbiosis reversal strategy in real-life scenarios.
Complex biofilms development and root caries experimental model
Saliva collection is a non-invasive and simple method, which should be regarded as an important advantage as ex vivo inoculum for in vitro biofilm engineering. The use of saliva as inoculum to grow microcosm biofilm in vitro has been reported as sufficient to mimic the complexity of the oral microbiome [29]. Although it is known that the microbial composition in dental biofilms is quite different from that of saliva, it is described in the literature that the initial inoculum is less important than the media chosen to form the microcosm biofilm [21, 29]. Microcosm biofilms are in vitro version of natural biofilms and have been explored as a microbiota model due to their ease of manipulation and control. This method allows us to create a rich and diverse microbiota needed to develop the microcosm biofilm from the initial saliva, which can be used to artificially develop caries lesions in the laboratory [24]. From a ‘defined inoculum’ enriched with a very nutritive media, a complex biofilm reflecting the complexity of the mouth was created. Usually, saliva donors are caries-free individuals presenting good oral health. Here, we decided to include those with either a history of caries, periodontitis or gingivitis, due to the fact that individuals with inactive oral diseases carry a more similar microbial composition to the ones with active diseases than the disease-free [15, 17]. However, it is interesting to note that the composition of the metabolically active microbiota formed on dentine slabs without pre-treatments was very similar in terms of diversity to the initial inoculum, except for a few species that were likely unculturable (Fig. 3). Also, we achieved the biofilm complexity with a relatively low number of saliva donors (N = 4).
To our knowledge, this is the first time that the presence of virus and archaeal phyla in microcosm oral biofilms was described, confirming the complexity attained and highlighting the model’s utility as a surrogate for clinical biofilms. Interestingly, Thaumarchaeota-associated reads were exclusively observed in the groups subjected to sucrose cycles. Their presence in natural dental caries biofilms has been demonstrated previously [30], possibly linked to their ability to produce ammonia (Supplementary file 2).
The microbial communities of the initial inoculum and the control group without sucrose or any treatments showed striking similarity. This indicates a consistent pattern of gene expression between the inoculum and the cultured biofilm without the addition of sucrose, affirming the suitability of the laboratory conditions for cultivating oral biofilms. We used the SHI media formulated by Tian et al. [20], that is better suited to sustain the initial inoculum composition compared to basal media or artificial saliva. SHI media can be described as a hybrid, encompassing elements of both, basal media and artificial saliva, while incorporating blood and acetylmuramic acid to enhance the growth of more demanding anaerobic microorganisms. This would be relevant in the context of a root caries model given the presence of numerous periodontal pathobionts within the microbiome, as we previously discussed. The ease of incorporating proteolytic bacteria in the CBD, particularly those with collagen-binding capabilities can be facilitated by the collagen coating applied before growing mature biofilm in the post-treatment strategy. Furthermore, SHI media has 0.5% of sucrose in its composition, meaning that all biofilms had a minimum amount of sucrose throughout the entire experiment, and the low pH and dysbiotic condition were achieved in the increment of this sucrose concentration.
In the pre-treatment analysis, the groups exposed to 1% sucrose cycles reached the critical pH for demineralisation of root dentine of approximately 5.8 to 6.0 (Fig. 2), resembling a dysbiotic favouring condition [31]. The massive increase of lactobacilli (Fig. 3), combined with the low pH and the overexpression of carbohydrate transporters and glucosyltransferases (Fig. 5), confirmed that we could drive the biofilms to dysbiosis in the laboratory. Furthermore, expression pattern of differential expression genes clustered according to the presence or absence of sucrose cycles, and the group not exposed to sucrose had higher expression in amino acid, lipid, nucleotide and coenzyme transport and metabolism (Figs. 4 and 5). These results confirming the importance of sucrose cycles to drive biofilms towards dysbiotic conditions can be interpreted according to Sheiham and James discussion about avoiding the term “multifactorial” to characterize dental caries as a disease [32]. While various clinical factors can impact mineral loss, our data supports the idea that caries dysbiotic biofilms can be achieved with a singular change in the fermentable sugar concentration and frequency. Though, this just emphasizes the pivotal role of fermentable sugars, even in root caries, in shifting the biofilm from a homeostatic to a dysbiotic state.
Our model was conducted at 37 °C under anaerobic conditions to mimic the cariogenic environment. Altering the environmental conditions to aerobic, for example, could simulate a less aggressive lesion. Biofilm fermentation, which is primarily responsible for caries lesion development, occurs under anaerobic conditions, breaking down sugars to produce acids like lactic acid. These acids are the strongest to demineralize the tooth surfaces, leading to caries lesions. In an aerobic environment, these bacteria shift their metabolism towards other pathways, as the presence of oxygen inhibits their ability to produce acids at the same rate.
A simpler model featuring only one or a few organisms would not accurately represent the complexity of the system. For example, recent findings from our team reveal that methanogen archaea are overexpressing genes associated with methanogenesis in caries-free biofilms compared to caries-active ones (unpublished data). This suggests their potential involvement in pH regulation within this environment. In our model, we observed the growth of archaea, viruses and other microorganisms. A previous study evaluated the impact of antiseptic treatment on in vitro oral biofilms using two models: a controllable 14-species community and a more representative biofilm using human tongue as inoculum. They found that both biofilms exhibited similar stress responses when exposed to CHX for 5 min every 24 h to simulate mouthwash use, characterized by rapid regrowth to initial bacterial concentrations. The researchers concluded that alternative treatments are needed to selectively target disease-associated bacteria in the biofilm without affecting commensal microorganisms. We believe our study presents such an alternative: our results demonstrate the potential of pre-treating teeth surfaces as an effective alternative to mouthwashes [33].
Collagenase activity in root caries biofilms
Matrix metalloproteinases (MMPs) are connected with the dentinal collagen degradation during the organic phase of root caries development, yet microbial collagenases could also participate in the process of collagen breakdown [4, 34, 35]. Microbial collagenase and MMPs exhibit distinct characteristics, potentially resulting in different outcomes in the dentinal collagen matrix. Unlike mammalian collagenases, which cleave collagen at a single site, bacterial collagenase from C. histolyticum performs multiple cleavages [36–38].
While such microbial collagenases activity has not been demonstrated experimentally [39, 40], we propose that bacteria are capable of breaking down the dentine collagen matrix due to their gene superexpression in root caries biofilms when compared to sound root surface biofilms [41]. However, in our dysbiotic biofilm model of the pre-treatment strategy, the collagenase activity was very low (Fig. 8). When compared to the isolated C. hystolyticum collagenase, only the control samples without pre-treatment and without sugar cycles showed any significant collagenase activity. Without pre-treatment or sucrose cycles, the control samples surprisingly exhibited significant collagenase activity (less sugar = more proteolytic microbes and then more collagenases). Despite the notably low collagenase activity during pre-treatment, arguably the most crucial outcome derived from this experiment is the affirmation that microbial collagenases within biofilms from carious lesions indeed are capable of breaking down gelatine (Fig. 8B).
Cranberry and anacardic acid are potential agents for modulating root caries biofilms
There is evidence on the multifunctionality of cranberry and its components, specifically proanthocyanidin. Cranberry’s antimicrobial effect against oral bacteria has been shown in planktonic cells [12], which we also showed here. The anti-proteolytic activity has been shown in proanthocyanidin-biomodified demineralized dentine matrix [13]. Cranberry proanthocyanidins anti-biofilm properties against Pseudomonas aeruginosa (disruption of preformed biofilms) [42] corroborates with our results on cells detaching in the mature biofilms. The pH of the media in the cranberry-pre-treated dentine slab group was lower on day 2 maybe it can explain the metatranscriptome dissimilarity. Also, it could explain the anti-biofilm characteristics observed in the post-treatment with cranberry. Next steps would involve the characterisation of the cranberry extracts to validate our data, using isolated components such as proanthoancin.
Furthermore, the presence of anacardic acid LDT11 resulted in no expression of the collagenase gene, and low expression in the presence of cranberry (Supplementary Fig. 3). Additionally, pre-treatment with anacardic acid LDT11 induced alterations in metal binding gene expression, potentially linked to its anti-enzymatic capacity (Fig. 4). Although we observed inhibition of collagenase gene expression, the enzyme activity was too low to detect inhibition by the compounds in our collagenase/gelatinase activity assay (Fig. 8). We believe that a longer incubation period might reveal more significant collagenase activity, suggesting that the model should be enhanced for this assay. Interestingly, cranberry showed potential for gelatinase inhibition, which warrants further exploration. Although CHX exhibited some gelatinase activity inhibition in the post-treatment strategy, due to its association with antimicrobial resistance, alternatives like cranberry may be more attractive clinically, although our results were only slightly significant.
Pre-treatment of root surfaces with LDT11 and cranberry has the potential to demonstrate a significant reduction in bacterial collagenase activity and enhanced modulation of dysbiotic biofilms compared to post-treatment application. Further investigation is needed regarding potential chelation with calcium in the experimental model in the presence of dentine specimens, as it may have constrained its effectiveness in this context. The deposits observed in the SEM can be suggestive of dentine biomodification by both anacardic acid and cranberry. This implies that the use of these compounds may also preserve the collagenolytic matrix in root tissues and in root caries management, which the trend was observed in the μ-CT analysis (Supplementary Fig. 5). Our data revealed a reduced area of mineral loss in the pre-treated samples, suggesting a potential decrease in lesion size. The next steps include further exploring this data on lesion size and testing the substances’ ability to promote cross-linking in dentine. Additionally, we have already synthesized a varnish containing the compounds for future testing.
Limitations
This model has some limitations. Although it is a complex biofilm model, it is still an in vitro study. Bovine dentine was used as a surrogate for human dentine due to its convenience and suitability for studies involving the activity of MMPs [36]. For instance, bovine teeth are suitable for caries development, as they are more uniform in terms of mineral composition and easier to manipulate in laboratory due to their size [43]. One could argue that CHX should also be used as a control in the pre-treatment strategy. However, we opted to use sucrose without pre-treatment as the control because it provides a relevant baseline for comparison, representing the condition where no compounds are introduced prior to the experimental treatments. This approach allows us to evaluate the natural progression and biofilm formation in the absence of any intervention, thereby serving as an effective negative control. Additionally, our focus was on the efficacy of post-treatment interventions in biofilm modulation by altering the surface. Using CHX, a potent antimicrobial agent, as a pre-treatment could significantly alter the initial conditions by influencing biofilm formation, introducing additional variables that might confound the results. Moreover, it is conceivable that the substantial alteration observed in the biofilm gene expression on the pre-treated surfaces may be attributed to the biomodification of the dentine surface and this could explain the microbiome modulation. However, further investigation is required to elucidate this aspect.
Complex biofilms development and root caries experimental model
Saliva collection is a non-invasive and simple method, which should be regarded as an important advantage as ex vivo inoculum for in vitro biofilm engineering. The use of saliva as inoculum to grow microcosm biofilm in vitro has been reported as sufficient to mimic the complexity of the oral microbiome [29]. Although it is known that the microbial composition in dental biofilms is quite different from that of saliva, it is described in the literature that the initial inoculum is less important than the media chosen to form the microcosm biofilm [21, 29]. Microcosm biofilms are in vitro version of natural biofilms and have been explored as a microbiota model due to their ease of manipulation and control. This method allows us to create a rich and diverse microbiota needed to develop the microcosm biofilm from the initial saliva, which can be used to artificially develop caries lesions in the laboratory [24]. From a ‘defined inoculum’ enriched with a very nutritive media, a complex biofilm reflecting the complexity of the mouth was created. Usually, saliva donors are caries-free individuals presenting good oral health. Here, we decided to include those with either a history of caries, periodontitis or gingivitis, due to the fact that individuals with inactive oral diseases carry a more similar microbial composition to the ones with active diseases than the disease-free [15, 17]. However, it is interesting to note that the composition of the metabolically active microbiota formed on dentine slabs without pre-treatments was very similar in terms of diversity to the initial inoculum, except for a few species that were likely unculturable (Fig. 3). Also, we achieved the biofilm complexity with a relatively low number of saliva donors (N = 4).
To our knowledge, this is the first time that the presence of virus and archaeal phyla in microcosm oral biofilms was described, confirming the complexity attained and highlighting the model’s utility as a surrogate for clinical biofilms. Interestingly, Thaumarchaeota-associated reads were exclusively observed in the groups subjected to sucrose cycles. Their presence in natural dental caries biofilms has been demonstrated previously [30], possibly linked to their ability to produce ammonia (Supplementary file 2).
The microbial communities of the initial inoculum and the control group without sucrose or any treatments showed striking similarity. This indicates a consistent pattern of gene expression between the inoculum and the cultured biofilm without the addition of sucrose, affirming the suitability of the laboratory conditions for cultivating oral biofilms. We used the SHI media formulated by Tian et al. [20], that is better suited to sustain the initial inoculum composition compared to basal media or artificial saliva. SHI media can be described as a hybrid, encompassing elements of both, basal media and artificial saliva, while incorporating blood and acetylmuramic acid to enhance the growth of more demanding anaerobic microorganisms. This would be relevant in the context of a root caries model given the presence of numerous periodontal pathobionts within the microbiome, as we previously discussed. The ease of incorporating proteolytic bacteria in the CBD, particularly those with collagen-binding capabilities can be facilitated by the collagen coating applied before growing mature biofilm in the post-treatment strategy. Furthermore, SHI media has 0.5% of sucrose in its composition, meaning that all biofilms had a minimum amount of sucrose throughout the entire experiment, and the low pH and dysbiotic condition were achieved in the increment of this sucrose concentration.
In the pre-treatment analysis, the groups exposed to 1% sucrose cycles reached the critical pH for demineralisation of root dentine of approximately 5.8 to 6.0 (Fig. 2), resembling a dysbiotic favouring condition [31]. The massive increase of lactobacilli (Fig. 3), combined with the low pH and the overexpression of carbohydrate transporters and glucosyltransferases (Fig. 5), confirmed that we could drive the biofilms to dysbiosis in the laboratory. Furthermore, expression pattern of differential expression genes clustered according to the presence or absence of sucrose cycles, and the group not exposed to sucrose had higher expression in amino acid, lipid, nucleotide and coenzyme transport and metabolism (Figs. 4 and 5). These results confirming the importance of sucrose cycles to drive biofilms towards dysbiotic conditions can be interpreted according to Sheiham and James discussion about avoiding the term “multifactorial” to characterize dental caries as a disease [32]. While various clinical factors can impact mineral loss, our data supports the idea that caries dysbiotic biofilms can be achieved with a singular change in the fermentable sugar concentration and frequency. Though, this just emphasizes the pivotal role of fermentable sugars, even in root caries, in shifting the biofilm from a homeostatic to a dysbiotic state.
Our model was conducted at 37 °C under anaerobic conditions to mimic the cariogenic environment. Altering the environmental conditions to aerobic, for example, could simulate a less aggressive lesion. Biofilm fermentation, which is primarily responsible for caries lesion development, occurs under anaerobic conditions, breaking down sugars to produce acids like lactic acid. These acids are the strongest to demineralize the tooth surfaces, leading to caries lesions. In an aerobic environment, these bacteria shift their metabolism towards other pathways, as the presence of oxygen inhibits their ability to produce acids at the same rate.
A simpler model featuring only one or a few organisms would not accurately represent the complexity of the system. For example, recent findings from our team reveal that methanogen archaea are overexpressing genes associated with methanogenesis in caries-free biofilms compared to caries-active ones (unpublished data). This suggests their potential involvement in pH regulation within this environment. In our model, we observed the growth of archaea, viruses and other microorganisms. A previous study evaluated the impact of antiseptic treatment on in vitro oral biofilms using two models: a controllable 14-species community and a more representative biofilm using human tongue as inoculum. They found that both biofilms exhibited similar stress responses when exposed to CHX for 5 min every 24 h to simulate mouthwash use, characterized by rapid regrowth to initial bacterial concentrations. The researchers concluded that alternative treatments are needed to selectively target disease-associated bacteria in the biofilm without affecting commensal microorganisms. We believe our study presents such an alternative: our results demonstrate the potential of pre-treating teeth surfaces as an effective alternative to mouthwashes [33].
Collagenase activity in root caries biofilms
Matrix metalloproteinases (MMPs) are connected with the dentinal collagen degradation during the organic phase of root caries development, yet microbial collagenases could also participate in the process of collagen breakdown [4, 34, 35]. Microbial collagenase and MMPs exhibit distinct characteristics, potentially resulting in different outcomes in the dentinal collagen matrix. Unlike mammalian collagenases, which cleave collagen at a single site, bacterial collagenase from C. histolyticum performs multiple cleavages [36–38].
While such microbial collagenases activity has not been demonstrated experimentally [39, 40], we propose that bacteria are capable of breaking down the dentine collagen matrix due to their gene superexpression in root caries biofilms when compared to sound root surface biofilms [41]. However, in our dysbiotic biofilm model of the pre-treatment strategy, the collagenase activity was very low (Fig. 8). When compared to the isolated C. hystolyticum collagenase, only the control samples without pre-treatment and without sugar cycles showed any significant collagenase activity. Without pre-treatment or sucrose cycles, the control samples surprisingly exhibited significant collagenase activity (less sugar = more proteolytic microbes and then more collagenases). Despite the notably low collagenase activity during pre-treatment, arguably the most crucial outcome derived from this experiment is the affirmation that microbial collagenases within biofilms from carious lesions indeed are capable of breaking down gelatine (Fig. 8B).
Cranberry and anacardic acid are potential agents for modulating root caries biofilms
There is evidence on the multifunctionality of cranberry and its components, specifically proanthocyanidin. Cranberry’s antimicrobial effect against oral bacteria has been shown in planktonic cells [12], which we also showed here. The anti-proteolytic activity has been shown in proanthocyanidin-biomodified demineralized dentine matrix [13]. Cranberry proanthocyanidins anti-biofilm properties against Pseudomonas aeruginosa (disruption of preformed biofilms) [42] corroborates with our results on cells detaching in the mature biofilms. The pH of the media in the cranberry-pre-treated dentine slab group was lower on day 2 maybe it can explain the metatranscriptome dissimilarity. Also, it could explain the anti-biofilm characteristics observed in the post-treatment with cranberry. Next steps would involve the characterisation of the cranberry extracts to validate our data, using isolated components such as proanthoancin.
Furthermore, the presence of anacardic acid LDT11 resulted in no expression of the collagenase gene, and low expression in the presence of cranberry (Supplementary Fig. 3). Additionally, pre-treatment with anacardic acid LDT11 induced alterations in metal binding gene expression, potentially linked to its anti-enzymatic capacity (Fig. 4). Although we observed inhibition of collagenase gene expression, the enzyme activity was too low to detect inhibition by the compounds in our collagenase/gelatinase activity assay (Fig. 8). We believe that a longer incubation period might reveal more significant collagenase activity, suggesting that the model should be enhanced for this assay. Interestingly, cranberry showed potential for gelatinase inhibition, which warrants further exploration. Although CHX exhibited some gelatinase activity inhibition in the post-treatment strategy, due to its association with antimicrobial resistance, alternatives like cranberry may be more attractive clinically, although our results were only slightly significant.
Pre-treatment of root surfaces with LDT11 and cranberry has the potential to demonstrate a significant reduction in bacterial collagenase activity and enhanced modulation of dysbiotic biofilms compared to post-treatment application. Further investigation is needed regarding potential chelation with calcium in the experimental model in the presence of dentine specimens, as it may have constrained its effectiveness in this context. The deposits observed in the SEM can be suggestive of dentine biomodification by both anacardic acid and cranberry. This implies that the use of these compounds may also preserve the collagenolytic matrix in root tissues and in root caries management, which the trend was observed in the μ-CT analysis (Supplementary Fig. 5). Our data revealed a reduced area of mineral loss in the pre-treated samples, suggesting a potential decrease in lesion size. The next steps include further exploring this data on lesion size and testing the substances’ ability to promote cross-linking in dentine. Additionally, we have already synthesized a varnish containing the compounds for future testing.
Limitations
This model has some limitations. Although it is a complex biofilm model, it is still an in vitro study. Bovine dentine was used as a surrogate for human dentine due to its convenience and suitability for studies involving the activity of MMPs [36]. For instance, bovine teeth are suitable for caries development, as they are more uniform in terms of mineral composition and easier to manipulate in laboratory due to their size [43]. One could argue that CHX should also be used as a control in the pre-treatment strategy. However, we opted to use sucrose without pre-treatment as the control because it provides a relevant baseline for comparison, representing the condition where no compounds are introduced prior to the experimental treatments. This approach allows us to evaluate the natural progression and biofilm formation in the absence of any intervention, thereby serving as an effective negative control. Additionally, our focus was on the efficacy of post-treatment interventions in biofilm modulation by altering the surface. Using CHX, a potent antimicrobial agent, as a pre-treatment could significantly alter the initial conditions by influencing biofilm formation, introducing additional variables that might confound the results. Moreover, it is conceivable that the substantial alteration observed in the biofilm gene expression on the pre-treated surfaces may be attributed to the biomodification of the dentine surface and this could explain the microbiome modulation. However, further investigation is required to elucidate this aspect.
Conclusion
In conclusion, a root caries-like biofilm and lesions were successfully created, demonstrating the successful development of caries-specific dysbiosis in vitro. The metatranscriptome analysis revealed a more pronounced effect when substances were used as a pre-treatment of dentine before the biofilm development and cariogenic challenges. These substances effectively reduced lesion sizes and delayed gelatine hydrolysis. Further research needs to be carried out to improve microbial control and modulation in order to maintain dental tooth health, and the current challenges are to reverse this dysbiosis in vitro and explore alternative methods for isolating microbial collagenolytic function in a laboratory setting. By doing so, we can test a broader range of substances and develop innovative treatments for root caries.
Supplementary Information
Additional file 1: Supplementary table 1. Summary of the pre-treatment strategy groups. Supplementary table 2. Summary of the statistical analysis for the pH monitoring in Figure 2. Supplementary table 3. Statistical analysis summary for the antimicrobial activity post-treatment strategy in Figure 6D. Supplementary table 4. Statistical analysis summary for the antimicrobial activity post-treatment strategy in Figure 6E. Supplementary Figure 1. Cluster analysis of differential expression genes. Hierarchical clustering analysis was carried out with the log10(Fragments Per Kilobase of transcript per Million mapped reads=FPKM+1) of union differential expression genes of all comparison groups under different experimental conditions. Genes in one cluster have similar expression levels. The x axis represents sample names, the y axis represents the corrected expression level value. Supplementary Figure 2 (A=Biological processes, B=Cellular components, C=Metabolic functions). TopGO DAG (Directed Acyclic Graph, DAG) of the enriched GO (Gene Ontology) term of differential expression genes and its hierarchical relation for the Control 2 (G2+) and Control 1 (G2-). Each node represents a GO term, and Top 10 GO terms are boxed. The darker the color is, the higher is the enrichment level of the term. The name and p-value of each term are present on the node. Supplementary Figure 3. Functional annotation with EggNOG (Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups) – unigene total absolute of the Collagenases U32 gene expression. Supplementary Figure 4. SEM images from dentine slabs after exposure to dysbiotic root caries-like biofilms and control (Control - = “G2-“, Control+sugar = “G2+”).Supplementary Figure 5. Examples of micro computed tomography (μ-CT) of dentine slabs confirming the presence of root caries-like lesions and mineral loss (blue areas represents the difference in the reference and target images, I.e., the demineralised area). A) 2D images for the groups exposed or not to sucrose cycles; B) 3D images for the following groups: G1; without inoculum; G2+; with inoculum, no pre-treatment, with sucrose cycles; G2- ; with inoculum, no pre-treatment, no sucrose cycles; G3; with inoculum, with LDT11 pre-treatment, with sucrose cycles; G4; with inoculum, with cranberry pre-treatment, with sucrose cycles.Additional file 2. Taxonomic analysis, unigene relative abundance table.
Supplementary Information
Additional file 1: Supplementary table 1. Summary of the pre-treatment strategy groups. Supplementary table 2. Summary of the statistical analysis for the pH monitoring in Figure 2. Supplementary table 3. Statistical analysis summary for the antimicrobial activity post-treatment strategy in Figure 6D. Supplementary table 4. Statistical analysis summary for the antimicrobial activity post-treatment strategy in Figure 6E. Supplementary Figure 1. Cluster analysis of differential expression genes. Hierarchical clustering analysis was carried out with the log10(Fragments Per Kilobase of transcript per Million mapped reads=FPKM+1) of union differential expression genes of all comparison groups under different experimental conditions. Genes in one cluster have similar expression levels. The x axis represents sample names, the y axis represents the corrected expression level value. Supplementary Figure 2 (A=Biological processes, B=Cellular components, C=Metabolic functions). TopGO DAG (Directed Acyclic Graph, DAG) of the enriched GO (Gene Ontology) term of differential expression genes and its hierarchical relation for the Control 2 (G2+) and Control 1 (G2-). Each node represents a GO term, and Top 10 GO terms are boxed. The darker the color is, the higher is the enrichment level of the term. The name and p-value of each term are present on the node. Supplementary Figure 3. Functional annotation with EggNOG (Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups) – unigene total absolute of the Collagenases U32 gene expression. Supplementary Figure 4. SEM images from dentine slabs after exposure to dysbiotic root caries-like biofilms and control (Control - = “G2-“, Control+sugar = “G2+”).Supplementary Figure 5. Examples of micro computed tomography (μ-CT) of dentine slabs confirming the presence of root caries-like lesions and mineral loss (blue areas represents the difference in the reference and target images, I.e., the demineralised area). A) 2D images for the groups exposed or not to sucrose cycles; B) 3D images for the following groups: G1; without inoculum; G2+; with inoculum, no pre-treatment, with sucrose cycles; G2- ; with inoculum, no pre-treatment, no sucrose cycles; G3; with inoculum, with LDT11 pre-treatment, with sucrose cycles; G4; with inoculum, with cranberry pre-treatment, with sucrose cycles.Additional file 2. Taxonomic analysis, unigene relative abundance table. |
Title: CPT1A‐IL‐10‐mediated macrophage metabolic and phenotypic alterations ameliorate acute lung injury | Body:
1
INTRODUCTION
Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a common acute respiratory failure due to diffuse pulmonary inflammation and oedema. ALI/ARDS is common in clinical syndrome, identified in 10.4% of intensive care unit admissions.
1
The pathophysiology of ALI/ARDS is intricate, involving the misregulation of numerous superposition and interacting pathways of inflammation, coagulation, as well as injury, resulting in limited pharmacological treatment options and an overall mortality rate of 43%.
2
One of the key immune cells in the lung, macrophages, plays a key regulatory role in the early inflammatory response and progression of ALI/ARDS.
3
Therefore, a deeper understanding of the altered functional phenotypes and cellular processes of macrophages in ALI/ARDS pathology may lead to novel therapeutic strategies.
During the past few years, a number of academic achievements have emerged, emphasising the crucial function of metabolic remodelling in the phenotypic adjustment and plasticity of macrophages.
4
Though the appearance of contradictory observations is emerging recently. In general, LPS or microbial stimuli‐activated proinflammatory macrophages (classically activated M1) typically exhibit increased glycolytic processes, impairment of mitochondrial oxidative phosphorylation (OXPHOS), disrupted tricarboxylic acid (TCA) cycle and overproduction of reactive oxygen species (ROS), along with elaboration of proinflammatory cytokines and increased microbial killing.
5
,
6
,
7
When comparing the metabolic differences between M1 macrophages and anti‐inflammatory macrophages (alternatively activated M2), we found that the latter exhibited a complete citric acid cycle (TCA cycle) and a more active mitochondrial OXPHOS process, which are key features of their anti‐inflammatory phenotype.
8
Another source of ATP supply in mitochondria is the oxidation of fatty acids, which is particularly important when glucose is underutilised. In addition to the field of bioenergetics,
9
fatty acid oxidation (FAO) has been reported to facilitate the transition of macrophages to the M2 phenotype since 2006.
10
It is now widely accepted that interleukin‐4‐rich macrophage differentiation (referred to as M2 polarisation in this study) is associated with elevated levels of FAO.
11
,
12
The evidence for confirmation comes primarily from the investigation of etomoxir, which has been shown to be effective as a carnitine acyltransferase 1 (CPT1) inhibitor.
13
,
14
CPT1, a rate‐limiting enzyme that plays an integral role in the FAO pathway, resides in the outer mitochondrial membrane. Its main function is to complex long‐chain fatty acid acyl coenzyme A (CoA) with carnitine, which in turn promotes the transport of this substance into mitochondria, where fatty acids undergo oxidation.
15
CPT1A, an active form of the CPT1 system, is deeply involved in the biology of many lung diseases, such as ALI, chronic obstructive pulmonary disease, and bronchial asthma.
16
However, the role of CPT1A and its‐mediated FAO in the regulation of macrophage phenotype during ALI/ARDS has not yet been thoroughly dissected.
Interleukin‐10 (IL‐10), a representative of regulatory cytokines, shows critical role in regulating innate as well as adaptive immune responses. It aims to reduce tissue damage caused by uncontrolled inflammation by suppressing the immune response.
17
The generation of IL‐10 by myeloid cells (e.g., macrophages and dendritic cells) is primarily driven by a series of pattern recognition receptor (PRRS) signals, which including ligands for Toll‐like receptors (TLRs) and inflammation promoting factors.
18
Recent findings have revealed that major metabolic regulatory turn points within macrophages can be activated in response to adjustments in nutritional and oxygen status, as well as in the signalling pathways of cytokine receptors such as IL‐10.
19
Ip et al.
20
revealed that lipopolysaccharide (LPS) induces the metabolic transition of macrophages to the glycolytic pathway, and at the same time, cytokine IL‐10 can neutralise this effect and catalyse the positive progress of OXPHOS. However, whether these metabolic switches, especially changes in FAO participant in the anti‐inflammatory capability of IL‐10 in the development of ALI, remained largely unknown.
In this study, we noted that macrophages in the ALI mouse model triggered by LPS had decreased levels of CPT1A level. By constructing mice harbouring a conditional depletion of macrophage CPT1A (Cre+
CPT1A
fl/fl), we identified a significant protective role of CPT1A in reducing lung inflammatory damage in ALI mice, as well as its anti‐inflammatory and mitochondria‐regulatory effects on bone marrow‐derived macrophages (BMDMs) challenged with LPS. Mechanistically, CPT1A could modulate metabolic alteration and polarisation of macrophage under LPS stimulation. Meanwhile, CPT1A can upregulate the expression of IL‐10/IL‐10 Receptor Alpha (IL‐10RA) by activating IL‐10 transcription factor. The introduction of exogenous IL‐10 into the Cre+
CPT1A
fl/fl macrophage successfully reversed the metabolic process related to the inflammatory response triggered by LPS, revealing the crucial role of the CPT1A‐IL‐10 axis in the regulation of macrophage polarisation in the progression of ALI/ARDS.
2
MATERIALS AND METHODS
2.1
Study approval
All the experimental mice were performed in line with the guidelines established by Shanghai Committee for Accreditation of Laboratory Animal. The protocol has been officially approved by the Laboratory Animal Research Center Review Board, Tongji University, Shanghai (approval number: TJBB03721106). Pentobarbital sodium was used as an anaesthetic during the operation, and related measures were taken to reduce the discomfort of experimental animals.
2.2
LPS‐induced ALI mouse model
The control (C57BL/6JNju), Wild type (WT) mice, B6.129P2‐Il10
tm1/Nju (IL‐10
−/−) mice, B6;129S‐Cpt1a
tm1(flox)Smoc mice (Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl) (6–8 weeks old, both male and female), and C57BL/6 mice (6–8 weeks old, male) were all acquired from Shanghai Model Organisms Center, Inc. (Shanghai, China). To create the conditional Cpt1a
tm1(flox)Smoc mice, a traditional embryonic stem (ES)‐based targeting strategy was employed. The vector, linearised and transfected into SCR012ES cells using an electroporation technique, contained a 2.7 kb 5′ homology arm, a .9 kb flox region, PGK‐Neo‐polyA, a 5.1 kb 3′ homology arm and an MC1‐TK‐polyA negative selection marker. Positive ES cells were expanded and injected into the blastocysts of C57BL/6J mice to produce the first generation of Cpt1aflox mice. The target mice were then generated by breeding Lyz2cre mice (obtained from Shanghai Model Organisms Center) with Cpt1aflox mice. The mice in the experiment were placed in a pathogen‐free environment, with a standard light/dark cycle and unrestricted intake of food and water. The animals were treated with LPS (10 mg/kg, Sigma‐Aldrich, L2630, USA) or its equivalent PBS by intranasal administration with or without simultaneous treatment with recombinant mouse IL‐10 (rIL‐10) (45 µg/kg, Biolegend, 575806, USA).
21
Mice were sacrificed 24 h time point after LPS stimulation for subsequent analysis of inflammation and damage.
2.3
Bronchoalveolar lavage fluid (Balf) collection for total and differential leukocytes counting
After anaesthetisation, Balf was collected through intratracheal injection of 800 µL ice‐cold sterile PBS, followed by carefully withdraw. Balf volume from each mouse was > 70% of the injected volume and basically consistent. The Balf samples were centrifuged for 10 min at 140 × g at 4°C. The Balf supernatant was stored at −80°C for subsequent dissection, while the deposited cells were suspended in PBS and the total number of cells was determined by means of a haemocytometer (treated with an equal volume of 3% glacial acetic acid to lyse red blood cells). The cells in the Balf were then transferred onto glass slides using a CytoSpin (StatSpin, USA) device and stained with Wright‐Giemsa (Baso Diagnostics, China) dye. Then, using a blinded method, differential leukocytes were classified and counted in a microscope on at least 200 cells per slide.
2.4
Local and systemic cytokines analysis
Blood samples were collected using a syringe, .5–.8 mL of fluid is extracted via cardiac puncture and transferred to an Eppendorf Micro Test tube. After the sample has been left at room temperature for at least 1 h, the serum of mice is separated by centrifugation at 2400 × g for 10 min. In line with the manufacturer's guidelines, the content of cytokines in Balf supernatant and serum of experimental mice was accurately evaluated by ELISA kit. Cytokine detection covers several products of Multisciences Biotech from China, included Interferon‐γ (IFN‐γ) (EK280/3‐96), interleukin‐6 (IL‐6) (EK206/3‐96), granulocyte colony‐stimulating factor (G‐CSF) (EK269/2‐96), CXC ligand 1/keratinocyte‐derived khemokine (CXCL1/KC) (EK296/2‐96) and IL‐10 (70‐EK210/4‐96).
2.5
Histopathological and immunologic analysis of mouse lung
In a separate experiment, mice's left lungs from different groups, without performing bronchoalveolar lavage, were dissected and fixed in 4% paraformaldehyde. The fixed lung tissue was then bedded in paraffin and made into 5 µm slices, which were then stained with a mixture of haematoxylin and eosin (H & E staining). An image capture process (involving six randomly generated sections) was performed, followed by an assessment of the blind identity using the ALI scoring mechanism
22
by two separate researchers already described. The degree of lung function impairment was assessed by taking into account five histological markers, including: (1) neutrophils in the alveolar space, (2) neutrophils in the interstitial space, (3) hyaline membranes, (4) proteinaceous debris filling the airspace, and (5) alveolar septal thickening. According to the conventional evaluation criteria, evaluation results of each element are zero, one or two points. These five factors are weighted according to the strength of association with ALI, while the normalisation process is performed according to the total number of fields. To assess the final damage, apply the following calculation (a continuous value number between 0 and 1): total injury score = [20 × (1) + 14 × (2) + 7 × (3) + 7 × (4) + 2 × (5)]/(number of fields × 100).
Additionally, the slides were immune‐stained with anti‐CPT1A (Abcam, ab234111, UK, 1:1000), anti‐IL‐10 (Servicebio, GB11108‐100, China, 1:500), and anti‐IL‐10RA (Abcam, ab225820, 1:100) antibodies to evaluate their distribution and expression. All images were captured using a Nikon Eclipse C1 microscope (Nikon, Japan), and then processed using ImageJ software (version 1.44p, National Institutes of Health, USA).
2.6
Extraction and culture of mouse BMDMs
BMDMs in the tibias and femurs of distinct mice (6–8 weeks old) were isolated following previously described methods.
23
The tibias and femurs were cut and flushed with a 1 mL syringe. Bone marrow cells were isolated by subjecting the suspension to a centrifugal force of 220 × g for 5 min under room temperature. Cells were then placed in Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) containing antibiotic cocktail 1% (penicillin‐streptomycin), Fetal Bovine Serum 10% (Qualified Heat Inactivated, Gibco, USA), and macrophage colony stimulating factor (M‐CSF, 20 ng/mL, PeproTech, 315‐02, USA). Such culture conditions were maintained for 7 days in order to stimulate the differentiation process of the cells. At the time of the fourth day, an equal amount of fresh complete medium (corresponding to half of the initial amount) was introduced into the cell culture dish. At the end of 7 days, the attached macrophages were harvested for further study.
2.7
Cell viability assay
Cell Counting Kit‐8 (Dojindo, Kumamoto, Japan) was used to evaluate the effect of different treatments on cell viability. In summary, BMDMs were seeded in 96‐well plates (the concentration is 5 × 104 cells every well) and allowed to incubate overnight at 37°C. After various treatments for 12/24 h, the cells were incubated at 37°C for approximately 1 to 2 h. During this time, 10 microliters of working reagent was added to each well, in line with the manufacturer's guidelines strictly. Subsequently, the absorbance at 450 nm wavelength was quantitatively analysed with the help of iMark microplate reader (Molecular Devices, Sunnyvale, USA).
2.8
Quantitative real‐time PCR (qRT‐PCR)
Total BMDMs RNA was extracted by Trizol reagent (Invitrogen, USA), followed by PrimeScript™ RT Master Mix (Takara, Japan, RR036A) converts RNA to cDNA. When using the QuantStudio™ 6 Flex Real‐Time System, the qRT‐PCR was executed by the TB Green Premix Ex Taq™ (Rox) (Takara, Japan, RR420A). 2−△△Ct method was used to quantitatively analyse the relative expression of mRNA. A series of specific primers produced in BioTNT (China) include lactate dehydrogenase A (LDHA), hypoxia‐inducible factor‐1 alpha (HIF‐1α), glucose transporter (GLUT), sterol regulatory element‐binding protein (SREBP), SREBP cleavage‐activating protein (SCAP), low‐density lipoprotein receptor (LDLr), IL‐1β, IL‐6, IL‐18, tumour necrosis factor (TNF)‐α, iNOS, IL‐12, IL‐10, IL‐10RA, PBX1, PREP1, MEIS1, ARG1, YM1 and β‐actin. Their sequences are detailed in Table S1.
2.9
Measurement of mitochondrial integrity and activity
BMDM cells were placed in a 4‐well imaging chambers containing cover‐glass bottom, and they were treated with LPS and/or rIL‐10 for 24 h. Thereafter, BMDMs were washed three times with PBS, a step intended to remove the growth medium. Subsequently, the cells were incubated with MitoTracker Red FM (Invitrogen, M22425, USA) for half an hour in order to stain and label the mitochondria. The cell nuclei were stained with Hoechst (Beyotime, C1017, China) prior to confocal imaging. Mitochondrial integrity was assessed using confocal microscopy (TCS SP8, Leica, Germany).
Mitochondrial ROS‐scavenging activity was evaluated using the MitoSOX Red (Invitrogen, M36008, USA) assay. Specifically, in this experiment, BMDM cells were seed into 96‐well black plates with 5 × 104 cells every well, and then expose to LPS and/or rIL‐10, respectively, for 24 h. BMDMs were then washed and incubated with 5 µM MitoSOX Red dye at 37°C in the dark for half an hour. Subsequently, a thorough washing operation was performed to wash the BMDM cells using an ice‐cold PBS solution. Subsequently, the average intensity of the fluorescence signal was measured and recorded with the aid of a SpectraMax M5/M5e fluorescent plate reader (Molecular Devices, USA) at wavelengths of 510/580 nm.
DCFH‐DA (Sigma‐Aldrich, D6883, USA) was used to quantitatively analyse the generation of ROS in cells in cooperation with the fluorescence detection device. In summary, this experiment used 96‐well black plates seeded with BMDMs at a cell density of 5×104 cells per well, followed by continuous 24 h treatment with LPS and/or rIL‐10. Cells were then washed, soaked in 10 µM DCFH‐DA, and stained at 37°C for half an hour in the absence of light. After three washes with cold PBS, the content of ROS was measured at 488/525 nm wavelength using SpectraMax M5/M5e fluorescent plate reader (Molecular Devices, USA).
2.10
Immunoblotting assay
By immunoblotting technique, for CPT1A, dynamin‐related protein 1 (DRP1), Fission1 (FIS1), optic atrophy 1 (OPA1), mitofusin 2 (MFN2), ARG1, iNOS, NLRP3, Caspase‐1 p10, and β‐actin were analysed. The total amount of protein in different types of murine BMDMs was extracted by RIPA lysate (Beyotime, China), then the concentration was measured using the BCA protein assay kit (Beyotime, China). Firstly, protein samples were analysed by 10% SDS‐Page gel electrophoresis, and protein bands were transferred to PVDF membrane. Next, the membrane was blocked with a mixture of Tris‐buffered saline with Tween 20(TBST, BioTNT, China) and 5% skim milk. The membrane was then incubated at 4°C and conjugated to a series of primary antibodies including anti‐CPT1A (Abcam, ab234111, UK), DRP1 (Cell Signaling Technology, CST, 8570, USA), FIS1 (CST, 32525), OPA1 (CST, 80471), MFN2 (CST, 9482), NLRP3 (CST, 15101), ARG1 (CST, 93668), iNOS (CST, 13120), Caspase‐1 p10 (Abcam, ab179515), and β‐actin (CST, 3700) at a dilution ratio of 1:1000. Next, the membrane was placed in a room temperature environment together with a secondary antibody, Anti‐rabbit IgG, HRP‐linked Antibody (CST, 7074), and Anti‐mouse IgG, HRP‐linked Antibody (CST, 7076), dilution ratio 1:2000. Two hours of culture bonding were performed. After three TBST washes, the bands were visualised using a Tanon 5200 Multifunction Automated Chemiluminescence Imaging System (Tanon Biotechnology, China), and the images were analysed using the ImageJ software.
2.11
Seahorse metabolic analysis
According to the manufacturer's operation manual, the XFe96 analyser of Agilent Company was used to carry out the experimental study of Seahorse. BMDMs were cultured in XFe96 plates and challenged with LPS for 12 h. Real‐time alterations in the oxygen consumption rate (OCR) of BMDMs were measured following the injection of 2 µM oligomycin, 1 µM FCCP, and a combination of .5 µM rotenone and antimycin A at specified time points. For extracellular acidification rate (ECAR) measurements, where indicated, cells were sequentially injected with 10 mM glucose, 1 µM oligomycin, and 100 mM 2‐DG. To evaluate FAO, BMDMs were pretreated with either bovine serum albumin alone (BSA) or palmitate‐conjugated BSA. The ratio of the oxygen consumption rate in cells with and without exogenous palmitic acid was used to assess the level of exogenous palmitate oxidation and thus detect FAO levels.
2.12
Determination of citrate, acetyl CoA and ATP
The production of citrate, acetyl CoA, and ATP in BMDMs from various groups was evaluated using the Citrate Assay Kit (Abcam, ab83396, UK), PicoProbe Acetyl CoA Assay Kit (Abcam, ab87546, UK), and Luminescent ATP Detection Assay Kit (Abcam, ab113849, UK), following the manufacturer's protocols. The formation of citrate, acetyl CoA and ATP in BMDMs from different cohorts was quantitatively evaluated. The concentrations of citric acid and acetyl CoA were accurately measured at 535/587 nm wavelength using the fluorescent plate reader. ATP levels were measured in cell lysates using a luminescence‐based assay.
2.13
Phagocytosis experiment
The phagocytic capacity of the BMDMs was detected using pHrodo Green E. coli BioParticles Conjugate (Invitrogen, P35366, USA), operated in line with the manufacturer's protocols. The phagocytic capacity was determined by both fluorescence plate reader and confocal microscopy at 509/533 nm.
2.14
The construction of single‐cell RNA sequencing (scRNA‐seq) library and data analysis
The construction of the scRNA‐seq library (Genome Sequence Archive with accession ID CRA008837) using the 10× Genomics platform was described in detail previously.
21
Briefly, the lung tissue is discreetly cut into small pieces and subjected to a decomposition process in order to obtain a suspension consisting of individual cells, which is then filtered by means of a 100‐µm cell strainer. Processing of the fresh cell suspension was performed in strict accordance with the immediate instructions of the manufacturer of the 10× Chromium 3′ v3 kit (10× Genomics, Pleasanton, CA). On the NovaSeq 6000 platform (Illumina, Inc., San Diego, CA), the library was construction, as well as the sequence analysis was performed. The initial sequence read data was converted to fastq files using Illumina bcl2fastq2 Conversion Software v2.20 (available at https://support.illumina.com/downloads/bcl2fastq‐conversion‐software‐v2‐20.html). Utilise FastQC software v0.11.9 (available at https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to rigorously evaluate the quality of sequence data. Standard cell ranger pipelines were used for sequence processing and alignment to the GRcm39 genome with default parameters (https://support.10xgenomics.com/single‐cell‐gene‐expression/software/pipelines/latest/). Cell samples from WT control mice and WT LPS‐treated mice were finally analysed, with 1129 cells in the control group and 4478 cells in the LPS‐treated group. By applying a non‐linear dimensionality reduction technique and a graphical model clustering method, a series of key cluster divisions are set according to the prototype marker, included are epithelial cells (identified by Epcam), endothelial cells (identified by Pecam1), fibroblasts (identified by Col1a2), neutrophils (identified by Ly6g), monocytes–macrophages (identified by Fcgr1 and Mrc1), T‐lymphocytes (characterised by Cd3e), B lymphocytes (marked by Cd19), dendritic cells (indicated by Cd83) and natural killer cells (marked by Klrb1c) (Figure S1a and b). By applying the likelihood‐ratio test of single‐cell gene expression, genes with significant differences in expression can be identified. This approach is similar to the one implemented by Seurat's v6 FindAllMarkers function.
2.15
RNA‐sequence library preparation and data processing
A total of 3 × 106 BMDM cells per well were seeded in the 6 cm dish, and then the cells were exposed to LPS for 12 h. Cell samples were then collected and Trizol reagent (Invitrogen, Carlsbad, CA, USA) was applied in line with the manufacturer's manual to obtain total RNA. RNA samples were identified by spectrophotometric analysis, followed by detailed quality assessment using the Agilent 2100 Bioanalyzer (Agilent Technologies). The RNA integrity of all test samples remained good, with scores generally exceeding 7.0. A sequencing library is construct by a kit named TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, CA, USA). The library was subsequently sequenced on the Illumina Novaseq 6000 platform (Macrogen, Seoul, South Korea). The platform uses a 2 × 150bp paired‐end read sequence.
Fastp software (https://github.com/OpenGene/fastp) tool removes reads that contain linker contamination, low‐quality bases and indeterminate bases. The quality of the sequence was checked using the fastp tool. HISAT2 (https://ccb.jhu.edu/software/hisat2) was aligned to map reads to the Mouse Genome Assembly GRCm39 reference genome. The mapped reads of each sample were assembled by StringTie (https://ccb.jhu.edu/software/stringtie). The official website of the StringTie should operate with its default configuration. Subsequently, all transcriptomes from all samples were merged to reconstruct a comprehensive transcriptome by gffcompare (https://github.com/gpertea/gffcompare). Table attainment was assessed for all transcripts in units of FPKM (i.e., the number of complete exon fragments/mapped reads in millions × exon length in kB). Significantly differentially expressed mRNAs were detected with a fold change of more than 2 or less than .5 and p‐value < .05 using edgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html). Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of differentially expressed mRNAs were interpreted.
2.16
Statistical analysis
In addition to scRNA‐seq data, data are shown as mean ± standard error (± SEM) of at least three independent experiments or biological samples. Additionally, by means of GraphPad Prism software (version 8.0.1, GraphPad Software, Inc., San Diego, CA), the data obtained from the experiment were explored by performing a two‐tailed unpaired Student's t‐test, one‐way ANOVA followed by Bonferroni's post hoc test. The Shapiro–Wilk test is applied to assess the normality of the samples. p Value less than .05 was considered statistically significant. Detailed statistical values are provided in the figure legends.
2.1
Study approval
All the experimental mice were performed in line with the guidelines established by Shanghai Committee for Accreditation of Laboratory Animal. The protocol has been officially approved by the Laboratory Animal Research Center Review Board, Tongji University, Shanghai (approval number: TJBB03721106). Pentobarbital sodium was used as an anaesthetic during the operation, and related measures were taken to reduce the discomfort of experimental animals.
2.2
LPS‐induced ALI mouse model
The control (C57BL/6JNju), Wild type (WT) mice, B6.129P2‐Il10
tm1/Nju (IL‐10
−/−) mice, B6;129S‐Cpt1a
tm1(flox)Smoc mice (Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl) (6–8 weeks old, both male and female), and C57BL/6 mice (6–8 weeks old, male) were all acquired from Shanghai Model Organisms Center, Inc. (Shanghai, China). To create the conditional Cpt1a
tm1(flox)Smoc mice, a traditional embryonic stem (ES)‐based targeting strategy was employed. The vector, linearised and transfected into SCR012ES cells using an electroporation technique, contained a 2.7 kb 5′ homology arm, a .9 kb flox region, PGK‐Neo‐polyA, a 5.1 kb 3′ homology arm and an MC1‐TK‐polyA negative selection marker. Positive ES cells were expanded and injected into the blastocysts of C57BL/6J mice to produce the first generation of Cpt1aflox mice. The target mice were then generated by breeding Lyz2cre mice (obtained from Shanghai Model Organisms Center) with Cpt1aflox mice. The mice in the experiment were placed in a pathogen‐free environment, with a standard light/dark cycle and unrestricted intake of food and water. The animals were treated with LPS (10 mg/kg, Sigma‐Aldrich, L2630, USA) or its equivalent PBS by intranasal administration with or without simultaneous treatment with recombinant mouse IL‐10 (rIL‐10) (45 µg/kg, Biolegend, 575806, USA).
21
Mice were sacrificed 24 h time point after LPS stimulation for subsequent analysis of inflammation and damage.
2.3
Bronchoalveolar lavage fluid (Balf) collection for total and differential leukocytes counting
After anaesthetisation, Balf was collected through intratracheal injection of 800 µL ice‐cold sterile PBS, followed by carefully withdraw. Balf volume from each mouse was > 70% of the injected volume and basically consistent. The Balf samples were centrifuged for 10 min at 140 × g at 4°C. The Balf supernatant was stored at −80°C for subsequent dissection, while the deposited cells were suspended in PBS and the total number of cells was determined by means of a haemocytometer (treated with an equal volume of 3% glacial acetic acid to lyse red blood cells). The cells in the Balf were then transferred onto glass slides using a CytoSpin (StatSpin, USA) device and stained with Wright‐Giemsa (Baso Diagnostics, China) dye. Then, using a blinded method, differential leukocytes were classified and counted in a microscope on at least 200 cells per slide.
2.4
Local and systemic cytokines analysis
Blood samples were collected using a syringe, .5–.8 mL of fluid is extracted via cardiac puncture and transferred to an Eppendorf Micro Test tube. After the sample has been left at room temperature for at least 1 h, the serum of mice is separated by centrifugation at 2400 × g for 10 min. In line with the manufacturer's guidelines, the content of cytokines in Balf supernatant and serum of experimental mice was accurately evaluated by ELISA kit. Cytokine detection covers several products of Multisciences Biotech from China, included Interferon‐γ (IFN‐γ) (EK280/3‐96), interleukin‐6 (IL‐6) (EK206/3‐96), granulocyte colony‐stimulating factor (G‐CSF) (EK269/2‐96), CXC ligand 1/keratinocyte‐derived khemokine (CXCL1/KC) (EK296/2‐96) and IL‐10 (70‐EK210/4‐96).
2.5
Histopathological and immunologic analysis of mouse lung
In a separate experiment, mice's left lungs from different groups, without performing bronchoalveolar lavage, were dissected and fixed in 4% paraformaldehyde. The fixed lung tissue was then bedded in paraffin and made into 5 µm slices, which were then stained with a mixture of haematoxylin and eosin (H & E staining). An image capture process (involving six randomly generated sections) was performed, followed by an assessment of the blind identity using the ALI scoring mechanism
22
by two separate researchers already described. The degree of lung function impairment was assessed by taking into account five histological markers, including: (1) neutrophils in the alveolar space, (2) neutrophils in the interstitial space, (3) hyaline membranes, (4) proteinaceous debris filling the airspace, and (5) alveolar septal thickening. According to the conventional evaluation criteria, evaluation results of each element are zero, one or two points. These five factors are weighted according to the strength of association with ALI, while the normalisation process is performed according to the total number of fields. To assess the final damage, apply the following calculation (a continuous value number between 0 and 1): total injury score = [20 × (1) + 14 × (2) + 7 × (3) + 7 × (4) + 2 × (5)]/(number of fields × 100).
Additionally, the slides were immune‐stained with anti‐CPT1A (Abcam, ab234111, UK, 1:1000), anti‐IL‐10 (Servicebio, GB11108‐100, China, 1:500), and anti‐IL‐10RA (Abcam, ab225820, 1:100) antibodies to evaluate their distribution and expression. All images were captured using a Nikon Eclipse C1 microscope (Nikon, Japan), and then processed using ImageJ software (version 1.44p, National Institutes of Health, USA).
2.6
Extraction and culture of mouse BMDMs
BMDMs in the tibias and femurs of distinct mice (6–8 weeks old) were isolated following previously described methods.
23
The tibias and femurs were cut and flushed with a 1 mL syringe. Bone marrow cells were isolated by subjecting the suspension to a centrifugal force of 220 × g for 5 min under room temperature. Cells were then placed in Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) containing antibiotic cocktail 1% (penicillin‐streptomycin), Fetal Bovine Serum 10% (Qualified Heat Inactivated, Gibco, USA), and macrophage colony stimulating factor (M‐CSF, 20 ng/mL, PeproTech, 315‐02, USA). Such culture conditions were maintained for 7 days in order to stimulate the differentiation process of the cells. At the time of the fourth day, an equal amount of fresh complete medium (corresponding to half of the initial amount) was introduced into the cell culture dish. At the end of 7 days, the attached macrophages were harvested for further study.
2.7
Cell viability assay
Cell Counting Kit‐8 (Dojindo, Kumamoto, Japan) was used to evaluate the effect of different treatments on cell viability. In summary, BMDMs were seeded in 96‐well plates (the concentration is 5 × 104 cells every well) and allowed to incubate overnight at 37°C. After various treatments for 12/24 h, the cells were incubated at 37°C for approximately 1 to 2 h. During this time, 10 microliters of working reagent was added to each well, in line with the manufacturer's guidelines strictly. Subsequently, the absorbance at 450 nm wavelength was quantitatively analysed with the help of iMark microplate reader (Molecular Devices, Sunnyvale, USA).
2.8
Quantitative real‐time PCR (qRT‐PCR)
Total BMDMs RNA was extracted by Trizol reagent (Invitrogen, USA), followed by PrimeScript™ RT Master Mix (Takara, Japan, RR036A) converts RNA to cDNA. When using the QuantStudio™ 6 Flex Real‐Time System, the qRT‐PCR was executed by the TB Green Premix Ex Taq™ (Rox) (Takara, Japan, RR420A). 2−△△Ct method was used to quantitatively analyse the relative expression of mRNA. A series of specific primers produced in BioTNT (China) include lactate dehydrogenase A (LDHA), hypoxia‐inducible factor‐1 alpha (HIF‐1α), glucose transporter (GLUT), sterol regulatory element‐binding protein (SREBP), SREBP cleavage‐activating protein (SCAP), low‐density lipoprotein receptor (LDLr), IL‐1β, IL‐6, IL‐18, tumour necrosis factor (TNF)‐α, iNOS, IL‐12, IL‐10, IL‐10RA, PBX1, PREP1, MEIS1, ARG1, YM1 and β‐actin. Their sequences are detailed in Table S1.
2.9
Measurement of mitochondrial integrity and activity
BMDM cells were placed in a 4‐well imaging chambers containing cover‐glass bottom, and they were treated with LPS and/or rIL‐10 for 24 h. Thereafter, BMDMs were washed three times with PBS, a step intended to remove the growth medium. Subsequently, the cells were incubated with MitoTracker Red FM (Invitrogen, M22425, USA) for half an hour in order to stain and label the mitochondria. The cell nuclei were stained with Hoechst (Beyotime, C1017, China) prior to confocal imaging. Mitochondrial integrity was assessed using confocal microscopy (TCS SP8, Leica, Germany).
Mitochondrial ROS‐scavenging activity was evaluated using the MitoSOX Red (Invitrogen, M36008, USA) assay. Specifically, in this experiment, BMDM cells were seed into 96‐well black plates with 5 × 104 cells every well, and then expose to LPS and/or rIL‐10, respectively, for 24 h. BMDMs were then washed and incubated with 5 µM MitoSOX Red dye at 37°C in the dark for half an hour. Subsequently, a thorough washing operation was performed to wash the BMDM cells using an ice‐cold PBS solution. Subsequently, the average intensity of the fluorescence signal was measured and recorded with the aid of a SpectraMax M5/M5e fluorescent plate reader (Molecular Devices, USA) at wavelengths of 510/580 nm.
DCFH‐DA (Sigma‐Aldrich, D6883, USA) was used to quantitatively analyse the generation of ROS in cells in cooperation with the fluorescence detection device. In summary, this experiment used 96‐well black plates seeded with BMDMs at a cell density of 5×104 cells per well, followed by continuous 24 h treatment with LPS and/or rIL‐10. Cells were then washed, soaked in 10 µM DCFH‐DA, and stained at 37°C for half an hour in the absence of light. After three washes with cold PBS, the content of ROS was measured at 488/525 nm wavelength using SpectraMax M5/M5e fluorescent plate reader (Molecular Devices, USA).
2.10
Immunoblotting assay
By immunoblotting technique, for CPT1A, dynamin‐related protein 1 (DRP1), Fission1 (FIS1), optic atrophy 1 (OPA1), mitofusin 2 (MFN2), ARG1, iNOS, NLRP3, Caspase‐1 p10, and β‐actin were analysed. The total amount of protein in different types of murine BMDMs was extracted by RIPA lysate (Beyotime, China), then the concentration was measured using the BCA protein assay kit (Beyotime, China). Firstly, protein samples were analysed by 10% SDS‐Page gel electrophoresis, and protein bands were transferred to PVDF membrane. Next, the membrane was blocked with a mixture of Tris‐buffered saline with Tween 20(TBST, BioTNT, China) and 5% skim milk. The membrane was then incubated at 4°C and conjugated to a series of primary antibodies including anti‐CPT1A (Abcam, ab234111, UK), DRP1 (Cell Signaling Technology, CST, 8570, USA), FIS1 (CST, 32525), OPA1 (CST, 80471), MFN2 (CST, 9482), NLRP3 (CST, 15101), ARG1 (CST, 93668), iNOS (CST, 13120), Caspase‐1 p10 (Abcam, ab179515), and β‐actin (CST, 3700) at a dilution ratio of 1:1000. Next, the membrane was placed in a room temperature environment together with a secondary antibody, Anti‐rabbit IgG, HRP‐linked Antibody (CST, 7074), and Anti‐mouse IgG, HRP‐linked Antibody (CST, 7076), dilution ratio 1:2000. Two hours of culture bonding were performed. After three TBST washes, the bands were visualised using a Tanon 5200 Multifunction Automated Chemiluminescence Imaging System (Tanon Biotechnology, China), and the images were analysed using the ImageJ software.
2.11
Seahorse metabolic analysis
According to the manufacturer's operation manual, the XFe96 analyser of Agilent Company was used to carry out the experimental study of Seahorse. BMDMs were cultured in XFe96 plates and challenged with LPS for 12 h. Real‐time alterations in the oxygen consumption rate (OCR) of BMDMs were measured following the injection of 2 µM oligomycin, 1 µM FCCP, and a combination of .5 µM rotenone and antimycin A at specified time points. For extracellular acidification rate (ECAR) measurements, where indicated, cells were sequentially injected with 10 mM glucose, 1 µM oligomycin, and 100 mM 2‐DG. To evaluate FAO, BMDMs were pretreated with either bovine serum albumin alone (BSA) or palmitate‐conjugated BSA. The ratio of the oxygen consumption rate in cells with and without exogenous palmitic acid was used to assess the level of exogenous palmitate oxidation and thus detect FAO levels.
2.12
Determination of citrate, acetyl CoA and ATP
The production of citrate, acetyl CoA, and ATP in BMDMs from various groups was evaluated using the Citrate Assay Kit (Abcam, ab83396, UK), PicoProbe Acetyl CoA Assay Kit (Abcam, ab87546, UK), and Luminescent ATP Detection Assay Kit (Abcam, ab113849, UK), following the manufacturer's protocols. The formation of citrate, acetyl CoA and ATP in BMDMs from different cohorts was quantitatively evaluated. The concentrations of citric acid and acetyl CoA were accurately measured at 535/587 nm wavelength using the fluorescent plate reader. ATP levels were measured in cell lysates using a luminescence‐based assay.
2.13
Phagocytosis experiment
The phagocytic capacity of the BMDMs was detected using pHrodo Green E. coli BioParticles Conjugate (Invitrogen, P35366, USA), operated in line with the manufacturer's protocols. The phagocytic capacity was determined by both fluorescence plate reader and confocal microscopy at 509/533 nm.
2.14
The construction of single‐cell RNA sequencing (scRNA‐seq) library and data analysis
The construction of the scRNA‐seq library (Genome Sequence Archive with accession ID CRA008837) using the 10× Genomics platform was described in detail previously.
21
Briefly, the lung tissue is discreetly cut into small pieces and subjected to a decomposition process in order to obtain a suspension consisting of individual cells, which is then filtered by means of a 100‐µm cell strainer. Processing of the fresh cell suspension was performed in strict accordance with the immediate instructions of the manufacturer of the 10× Chromium 3′ v3 kit (10× Genomics, Pleasanton, CA). On the NovaSeq 6000 platform (Illumina, Inc., San Diego, CA), the library was construction, as well as the sequence analysis was performed. The initial sequence read data was converted to fastq files using Illumina bcl2fastq2 Conversion Software v2.20 (available at https://support.illumina.com/downloads/bcl2fastq‐conversion‐software‐v2‐20.html). Utilise FastQC software v0.11.9 (available at https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to rigorously evaluate the quality of sequence data. Standard cell ranger pipelines were used for sequence processing and alignment to the GRcm39 genome with default parameters (https://support.10xgenomics.com/single‐cell‐gene‐expression/software/pipelines/latest/). Cell samples from WT control mice and WT LPS‐treated mice were finally analysed, with 1129 cells in the control group and 4478 cells in the LPS‐treated group. By applying a non‐linear dimensionality reduction technique and a graphical model clustering method, a series of key cluster divisions are set according to the prototype marker, included are epithelial cells (identified by Epcam), endothelial cells (identified by Pecam1), fibroblasts (identified by Col1a2), neutrophils (identified by Ly6g), monocytes–macrophages (identified by Fcgr1 and Mrc1), T‐lymphocytes (characterised by Cd3e), B lymphocytes (marked by Cd19), dendritic cells (indicated by Cd83) and natural killer cells (marked by Klrb1c) (Figure S1a and b). By applying the likelihood‐ratio test of single‐cell gene expression, genes with significant differences in expression can be identified. This approach is similar to the one implemented by Seurat's v6 FindAllMarkers function.
2.15
RNA‐sequence library preparation and data processing
A total of 3 × 106 BMDM cells per well were seeded in the 6 cm dish, and then the cells were exposed to LPS for 12 h. Cell samples were then collected and Trizol reagent (Invitrogen, Carlsbad, CA, USA) was applied in line with the manufacturer's manual to obtain total RNA. RNA samples were identified by spectrophotometric analysis, followed by detailed quality assessment using the Agilent 2100 Bioanalyzer (Agilent Technologies). The RNA integrity of all test samples remained good, with scores generally exceeding 7.0. A sequencing library is construct by a kit named TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, CA, USA). The library was subsequently sequenced on the Illumina Novaseq 6000 platform (Macrogen, Seoul, South Korea). The platform uses a 2 × 150bp paired‐end read sequence.
Fastp software (https://github.com/OpenGene/fastp) tool removes reads that contain linker contamination, low‐quality bases and indeterminate bases. The quality of the sequence was checked using the fastp tool. HISAT2 (https://ccb.jhu.edu/software/hisat2) was aligned to map reads to the Mouse Genome Assembly GRCm39 reference genome. The mapped reads of each sample were assembled by StringTie (https://ccb.jhu.edu/software/stringtie). The official website of the StringTie should operate with its default configuration. Subsequently, all transcriptomes from all samples were merged to reconstruct a comprehensive transcriptome by gffcompare (https://github.com/gpertea/gffcompare). Table attainment was assessed for all transcripts in units of FPKM (i.e., the number of complete exon fragments/mapped reads in millions × exon length in kB). Significantly differentially expressed mRNAs were detected with a fold change of more than 2 or less than .5 and p‐value < .05 using edgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html). Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of differentially expressed mRNAs were interpreted.
2.16
Statistical analysis
In addition to scRNA‐seq data, data are shown as mean ± standard error (± SEM) of at least three independent experiments or biological samples. Additionally, by means of GraphPad Prism software (version 8.0.1, GraphPad Software, Inc., San Diego, CA), the data obtained from the experiment were explored by performing a two‐tailed unpaired Student's t‐test, one‐way ANOVA followed by Bonferroni's post hoc test. The Shapiro–Wilk test is applied to assess the normality of the samples. p Value less than .05 was considered statistically significant. Detailed statistical values are provided in the figure legends.
3
RESULTS
3.1
Macrophage CPT1A deficiency contributes to more severe lung injury upon LPS challenge
Our previous scRNA‐seq data (Genome Sequence Archive with accession ID CRA008837) showed that CPT1A mainly expressed in macrophages and fibroblasts and was reduced by LPS challenge (Figure S1a–d). Meanwhile, we constructed a classical ALI mouse model via LPS inhalation (10 mg/kg) and observed reduced CPT1A expression in lung macrophages of ALI mice compared to the control (Figure S1e), which indicated a potential relationship between decreased CPT1A expression in macrophage upon LPS exposure and ALI pathogenesis.
To prove this, we generated mice harbouring a conditional depletion of CPT1A in macrophages (Cre+
CPT1A
fl/fl). By use of the ALI mouse model, control (Cre−
CPT1A
fl/fl) and Cre+
CPT1A
fl/fl mice were injected with LPS or PBS. Their local and systemic inflammatory responses were then measured and analysed 24 h after treatment. As shown in Figure 1a, Balf inflammatory cell infiltration was dramatically elevated by LPS challenge, especially neutrophils, and was further exacerbated by CPT1A deficiency in macrophages. IFN‐γ, IL‐6, G‐CSF and CXCL1/KC in Balf and serum were elevated rapidly after LPS stimulation, while CPT1A depletion in macrophages intensified the increase at 24 h except for IFN‐γ in Balf (Figure 1b and c). We also observed elevated IL‐10 level in both Balf and serum after LPS challenge. Interestingly, the level of IL‐10 was much lower in Cre+
CPT1A
fl/fl mice compared with the Cre−
CPT1A
fl/fl ones (Figure 1b and c).
FIGURE 1
CPT1A deficiency in macrophage contributes to more severe lung injury in the ALI mouse model. (a) Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice were administrated nasally with PBS or LPS (10 mg/kg). After 24 h, mice were euthanised and Balf was collected and estimated for total cell number, macrophage, neutrophil and lymphocyte numbers. n = 6 biologically independent samples. Balf total cell, **p = .0044, ****p < .0001. Macrophage, ****p < .0001. Neutrophil, ***p = .0002, ****p < .0001. Lymphocyte, **p = .002, ****p < .0001. (b, c) Secretion of cytokines IFN‐γ, IL‐6, G‐CSF, CXCL1/KC and IL‐10 in Balf and serum was assessed by ELISA kits. n = 6 biologically independent samples. (b) *p = .0212, **p = .0017, ****p < .0001. (c) *p = .0368 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), *p = .0116 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group), **p = .0046, *p = .0448, ****p < .0001. (d, e) Quantitative analysis of immunoblotting assay identifying the activation of NLRP3/Caspase‐1 pathway for lung inflammation evaluation. n = 6 biologically independent samples. (d) ***p = .0002 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0009 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group), ****p < .0001. (e) **p = .0086, ****p < .0001. (f) Representative pictures of H&E‐stained lung tissue sections from each group. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. Lung injury score was determined by 5 pathophysiological features. n = 6 biologically independent samples. ****p < .0001. (g) Immunohistochemical staining and quantitative analysis of IL‐10 in Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice. Red arrowheads indicate IL‐10‐expressed macrophage within lungs. n = 30 each group from 6 biologically independent samples. ****p < .0001. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
To further estimate the role of macrophage CPT1A expression in LPS‐triggered inflammatory lung injuries, we applied whole lung lysates for immunoblotting detection and lung sections for histological staining. In Figures S2a and 1d and e, we observed that the NLRP3/Caspase‐1 signalling pathway associated with inflammatory response in ALI mouse lung was promoted when CPT1A gene was absent in macrophages. Lung structure can be evaluated from five different perspectives: observing the alveolar neutrophils, assessing the interstitial neutrophils, detecting the hyaline membranes, analysing the proteinaceous debris and measuring the septal thickening. We observed no obvious pathological change when depleting macrophage CPT1A under physiological condition; however, exacerbated pulmonary damage in Cre+
CPT1A
fl/fl mice could be identified compared with the Cre−
CPT1A
fl/fl ones at 24 h after LPS stimulation (Figure 1f).
Next, we evaluated the effect of CPT1A on IL‐10 expression on tissue scales by staining mouse lung sections with IL‐10 and its receptor, IL‐10RA. After LPS inhalation, infiltrated macrophages lack of CPT1A expressed lower levels of IL‐10 (Figure 1g), as well as IL‐10RA (Figure S2b) compared with Cre−
CPT1A
fl/fl mice. In a nutshell, the expression of CPT1A gene in BMDMs shows a crucial role in the progression of ALI induced by LPS, which can affect the regulation of IL‐10 and its receptor.
3.2
CPT1A regulates aberrant inflammation and mitochondrial stability in macrophages upon LPS challenge
We then investigated the effects of CPT1A on inflammatory response and metabolic patterns of macrophage ex vivo by isolating and culturing mouse bone marrow derived macrophages (BMDMs) (Figure 2a). Macrophage CPT1A depletion was verified by immunoblotting that BMDMs isolated from Cre+
CPT1A
fl/fl mice showed a complete absence of CPT1A expression (Figure 2b). BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice were stimulated with LPS and subjected to RNA‐seq. Heatmaps of all differential gene expression results were presented in Figure S3a. A total of 2357 differentially expressed genes (DEGs) were identified between Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl macrophages in the control group, and in the experimental group, the number was 1069 (Figure S3b). The most highly DEGs in Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl BMDMs showed that CPT1A depletion had a significant impact on cellular metabolism (Fabp5, Ldhb, Ldha, Acsbg1 and Acadvl) and proinflammatory cytokine production (Nlrp3, Il1b, Il33), especially under LPS stimulation (Figure S3c). In terms of consistency, it was observed that the enrichment analysis of GO presented a similar pattern with the pathway analysis of KEGG (Figure S3d and e). By use of qRT‐PCR, we determined that in the absence of CPT1A, LPS administration induced more IL‐1β, IL‐18, IL‐6 and tumour necrosis factor (TNF)‐α, cytokines that promote inflammation production (Figure 2c). In addition, we also investigated the mechanism of NLRP3 inflammasome activation, which is responsible for the secretion of IL‐1β and IL‐18.
24
The results showed that loss of CPT1A enhanced LPS‐induced the NLRP3/Caspase‐1 signalling pathway activation in BMDMs (Figures S4a and 2d and e).
FIGURE 2
CPT1A regulates aberrant inflammation and mitochondrial stability in macrophages upon LPS challenge. (a) Schematic diagram of the extraction, separation and cultivation of BMDMs in vitro. Some figure elements were created with BioRender.com. (b) Macrophage CPT1A depletion was verified by immunoblotting in BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice. n = 4 biologically independent samples. (c) After LPS stimulation (100 ng/mL) for 6 h, the induction of cytokines IL‐1β, IL‐18, IL‐6 and TNF‐α mRNA expression in each group was analysed by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. ***p = .0007, ***p = .0004, ****p < .0001. (d, e) After LPS (100 ng/mL) treatment for 24 h, quantitative immunoblotting analysis indicating the effect of CPT1A expression on NLRP3/Caspase‐1 p10 level for inflammasome activation in BMDMs. n = 3 biologically independent samples. (d) ***p = .0002 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0001 (Cre−
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+Con group), *p = .0451 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), *p = .0243 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group). (e) **p = .0015 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), *p = .0411, ***p = .0002, **p = .0023 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group). (f, g) Microplate reader assay showing the effect of CPT1A on the production of mitochondrial (f) and intracellular ROS (g) induced by LPS (100 ng/mL) in BMDMs. n = 6 biologically independent samples. (f) *p = .0114 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0002, *p = .0243. (g) *p = .0268, ****p < .0001. (h) Confocal microscopy images showing the mitochondrial mass of BMDMs from each group using MitoTracker probe. Scale bars, 20 µm. (i–l) Quantitative immunoblotting analysis indicating the effect of CPT1A on the expression of OPA1, MFN2, DRP1 and FIS1 for mitochondrial dynamics in BMDMs. n = 6 biologically independent samples. (i) *p = .013 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0002 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), *p = .0397 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group). (j) **p = .0026 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), **p = .0035 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group). (k) **p = .0063 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), **p = .0028 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group), ***p = .0004. (l) *p = .027 (Cre−
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+Con group), *p = .0154 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), **p = .0049. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
CPT1A mainly localised in mitochondrial membrane and has been reported to maintain mitochondrial homeostasis and attenuate excessive inflammatory response in renal tubular epithelial cells under profibrotic stimuli.
25
To answer the question of whether macrophage CPT1A expression prevented mitochondrial dysfunction, we examined ROS generation after LPS stimulation and discovered that LPS‐induced mitochondrial and intracellular ROS level elevated in macrophages with or without CPT1A, and Cre+
CPT1A
fl/fl BMDMs exhibited even higher ROS level compared to Cre−
CPT1A
fl/fl cells using MitoSOX Red probe and DCFH‐DA fluorescent probe under stimulus, respectively (Figure 2f and g), demonstrating the potential mitochondrial ROS‐scavenging activity of CPT1A in macrophages. Thereafter, we stained and imaged the active mitochondria in the cell using MitoTracker fluorescent dye, and observed that a decrease in fluorescence brightness indicates signs of mitochondrial damage. As revealed in Figure 2h, the fluorescence intensity of MitoTracker was decreased upon LPS challenge, which could be further reduced by CPT1A depletion. As a dynamic organelle, the maintenance of the mitochondrial homeostasis highly dependents on its fission and fusion process.
26
It has been shown that once the balance between fission and fusion in mitochondria is disturbed, its oxidation is affected.
27
To explore the potential role of CPT1A on macrophage mitochondrial dynamics, we analysed the expression levels of OPA1 and MFN2, as well as the fission proteins DRP1 and FIS1. Results showed that LPS treatment dramatically reduced OPA1 and MFN2 expression, while increased DRP1 and FIS1 expression, which indicates abnormal mitochondrial dynamics (Figures S4b and 2i–l). Besides, CPT1A depletion intensified the decrease of OPA1 and elevation of DRP1 in BMDMs (Figures S4b and 2i and k). The findings suggest that CPT1A expression participated in maintaining mitochondrial homeostasis, thereby protecting macrophages against excess inflammation.
Since genetic inactivation of CPT1A can disrupt mitochondrial homeostasis and induce inflammation, we asked whether pharmacological CPT1A modulation could augment bioenergetic and inflammatory reaction under LPS stimulation. To test this, we applied the well‐established CPT1A activator L‐carnitine (Lca), as well as its inhibitor etomoxir (Eto),
15
and proved their independence of the cytotoxicity in BMDMs (Figure S5a and b). Compared with LPS group, L‐carnitine significantly increased the level of CPT1A expression, which could be restrained by etomoxir pretreatment (Figure S5c). Lca can significantly down‐regulate the levels of inflammatory mediators IL‐1β and TNF‐α, and effectively inhibit the activation of NLRP3/Caspase‐1 signalling pathway, which has been clearly verified in the data. At the same time, etomoxir showed a trend opposite to our observations (Figure S5d–f). Additionally, Lca prevented abnormal mitochondrial dynamics by elevating fusion proteins (OPA1, MFN2) and reducing the fission protein DRP1; conversely, CPT1A inhibition by etomoxir decreased fusion proteins MFN2 and increased mitochondrial fission protein FIS1 (Figure S5g), which further demonstrated the importance of CPT1A in regulating the abnormal inflammatory response and mitochondrial stability of macrophages stimulated by LPS.
3.3
CPT1A modulates metabolic reprogramming from glycolysis to FAO in LPS‐stimulated macrophages
The heatmap of DEGs showed that many FAO‐associated genes were down‐regulated by CPT1A depletion in LPS‐stimulated BMDMs compared with the Cre−
CPT1A
fl/fl group (Figure 3a). To further assess the effect of CPT1A expression on metabolic plasticity upon activation, we examined ECAR and OCR in BMDM isolated from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice. We determined glycolytic function by oligomycin‐induced OXPHOS blockage and discovered that absence of CPT1A promoted basal glycolytic function in the presence of LPS (Figure 3b and e). Simultaneously, we found that palmitate‐induced OCR was significantly lower in BMDMs isolated from Cre+
CPT1A
fl/fl compared to Cre−
CPT1A
fl/fl mice under baseline situation, suggesting a relatively low level of fatty acid metabolism. After LPS stimulation, FAO level dropped in both groups, and the decline was more significant in Cre+
CPT1A
fl/fl group (Figure 3d). Compared with Cre−
CPT1A
fl/fl, Cre+
CPT1A
fl/fl BMDMs showed a lower‐level of baseline oxygen consumption, and OCR, basal respiration, and respiration capacity elevation upon LPS challenge (Figure 3c and f). These metabolic alterations may be due to the glycolysis‐related oxygen consumption. Besides, levels of acetyl CoA, citrate and ATP in Cre+
CPT1A
fl/fl macrophages was decreased compared to Cre−
CPT1A
fl/fl ones both in the absence and presence of LPS, indicating a low activity of FAO‐OXPHOS (Figure 3g).
FIGURE 3
CPT1A modulates metabolic reprogramming from glycolysis to FAO, polarisation and IL‐10 production in LPS‐stimulated macrophages. (a) Heat map of DEGs in Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl BMDMs after 12 h stimulation with LPS (100 ng/mL). (b, c) ECAR and OCR of BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice were measured with a Seahorse XFe96 analyser. Where indicated, cells were injected with 10 mM glucose (GLU), 1 µM oligomycin (OLI) and 100 mM 2‐DG sequentially for ECAR (b). For OCR, BMDMs were pretreated with 170 µM palmitate‐BSA FAO substrate or 40 µM CPT1A inhibitor Etomoxir, followed by injection of 2 µM OLI, 1 µM FCCP, and a combination of .5 µM rotenone and antimycin A (AA) at the time points indicated (c). n = 6 biologically independent samples at each data point. (d–f) The FAO level (d), glycolysis level and glycolysis capacity (e), and basal respiration and respiration capacity (f) of BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice with or without LPS stimulation. n = 6 biologically independent samples. (d) *p = .0312, **p = .0019. (e) *p = .0329, **p = .0022. (f) ****p < .0001. (g) After 12 h treatment with LPS (100 ng/mL) in BMDMs, levels of intracellular FAO metabolites, acetyl CoA and citrate, as well as ATP were measured. n = 6 biologically independent samples. Acetyl CoA, ***p = .0001 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0006 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), ****p < .0001. Citrate, *p = .0241, ****p < .0001. ATP, *p = .0449, ***p = .0001, ****p < .0001. (h, i) Induction of LDHA, HIF‐1α, GLUT, SCAP, SREBP and LDLr mRNA expression in Cre−
CPT1A
fl/fl or Cre+
CPT1A
fl/fl BMDMs was estimated by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. (h) ****p < .0001. (i) *p = .0147, ***p = .0002, ****p < .0001. (j) Diagram of the modulatory effects of CPT1A on macrophage activation and polarisation upon LPS stimulation. Some figure elements were created with BioRender.com. (k) Induction of iNOS, IL‐12, YM1 and IL‐10 mRNA expression in Cre−
CPT1A
fl/fl or Cre+
CPT1A
fl/fl BMDMs was analysed via qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. iNOS, ***p = .0002, ****p < .0001. IL‐12, ****p < .0001. YM1, ***p = .0002, ****p < .0001. IL‐10, **p = .0012, ***p = .0005, *p = 0.0443. (l) The secretion level of IL‐10 by BMDMs was examined by ELISA assay 24 h after LPS stimulation (100 ng/mL). n = 6 biologically independent samples. ****p < .0001. (m) Quantitative immunoblotting analysis indicating the effect of CPT1A on ARG1 and iNOS expression for macrophage polarisation in BMDMs. n = 3 biologically independent samples. iNOS, **p = .007, *p = .021. ARG1, **p = .0024 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), **p = .0082 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl +LPS group), ****p < .0001. (n) The phagocytic capacity of BMDMs from Cre−
CPT1A
fl/fl or Cre+
CPT1A
fl/fl mice was evaluated by use of the pHrod Green E. coli BioParticles through both confocal microscopy (showed in the left panel; scale bars, 20 µm) and fluorescence plate reader (showed in the right panel). n = 6 biologically independent samples. *p = .045, **p = .0013, ****p < .0001. (o) The levels of IL‐10RA, PBX1, MEIS1 and PREP1 mRNA expression in BMDMs were analysed by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. IL‐10RA, ****p < .0001. PBX1, *p = .0196, ****p < .0001. MEIS1, ***p = .0004, ****p < .0001. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
In addition, we estimated metabolic alterations of macrophages with or without CPT1A expression upon LPS challenge via key regulators measurement. We found that under LPS challenge, there was an upregulation trend in mRNA expression of LDHA, GLUT and HIF‐1α
28
,
29
in macrophages, and the elevation is more obvious under CPT1A knockout condition (Figure 3h). In contrast, while LPS activation enhanced the expression levels of LDLr, SREBP, and SCAP
9
,
30
,
31
in Cre−
CPT1A
fl/fl ones, these gene expression were obvious repressed in Cre+
CPT1A
fl/fl BMDMs under the same stimulation (Figure 3i). Notably, these alterations could not be attributed to the cellular cytotoxicity induced by CPT1A defect or LPS administration (Figure S6). Together, the above results suggest that defect of FAO function in CPT1A‐absent macrophages may lead to the active mobilisation of glycolysis, and CPT1A plays a pivotal role in maintaining stable cellular energy metabolism in macrophages upon LPS challenge.
3.4
CPT1A shifts macrophages polarisation and modulates IL‐10 production in the ALI model
As mentioned above, FAO is linked to the M2 state of macrophage,
32
so we speculated that CPT1A, as the major rate‐limiting enzyme of FAO, may affect the polarisation of macrophage under LPS stimulation (Figure 3j). As shown in Figure 3k, stimulation with LPS significantly increased the M1 marker proteins iNOS and IL‐12 expression, which could be further elevated by CPT1A deficiency. Conversely, M2 markers YM1 and IL‐10 gene expression was decreased in Cre+
CPT1A
fl/fl BMDMs compared to the Cre−
CPT1A
fl/fl ones upon LPS challenge (Figure 3k). At protein expression level, macrophage CPT1A defect enhanced M1 marker iNOS while reduced M2 marker ARG1 regardless of whether they were activated by LPS (Figures 3m and S7a). M2 macrophages display significant anti‐inflammatory properties compared to proinflammatory M1 macrophages and play a significant part in the phagocytic function.
33
Herein, by use of the pHrod Green E. coli BioParticles, we observed lower phagocytosis activity of Cre+
CPT1A
fl/fl BMDMs than Cre−
CPT1A
fl/fl ones upon LPS administration (Figure 3n).
As we have observed a potential regulation effect of IL‐10/IL‐10RA expression by CPT1A on tissue scales, we then carried out in vivo studies which showed that both airway and peripheral IL‐10 secretion were suppressed by the depletion of macrophage CPT1A (Figure 1). Consistently, CPT1A knockout not only inhibited the mRNA level and secretion of IL‐10 (Figure 3k and l), but also reduced the expression of its receptor IL‐10RA in LPS‐stimulated BMDMs (Figure 3o), which indicates a potential autocrine effect of IL‐10 in macrophages. Additionally, CPT1A depletion also decreased the expression of PBX1, MEIS1, but not PREP1 upon LPS stimulation (Figure 3o), which could mediate transcriptional activation of IL‐10 during phagocytosis.
18
Overall, the data provide evidence that CPT1A was able to drive the macrophages polarisation between M1/M2 phenotype and promote IL‐10 production, which might modulate the excess inflammatory response during ALI.
3.5
IL‐10 generation is required for the anti‐inflammatory activity of macrophages in the ALI model
Given that IL‐10, as a cytokine with anti‐inflammatory capabilities, plays a crucial role in regulating the immune response and, as previously mentioned, its activity can be regulated by CPT1A, we explored the role of IL‐10 in the anti‐inflammatory effects of CPT1A in the ALI model. WT mice and IL‐10 null mice were treated with LPS or PBS in combination, with rIL‐10 or PBS, followed by detailed testing after an interval of 24 h. The study finally showed that when the level of IL‐10 was reduced, airway inflammation was aggravated, especially the accumulation of macrophages and neutrophils in the inflammatory response triggered by LPS was enhanced. Accordingly, rIL‐10 supplementation was effective in slowing the growth of such inflammatory cells (Figure 4a). Local and systematic inflammation, manifested by IFN‐γ, IL‐6, G‐CSF and CXCL1/KC secretion in Balf and serum, was also aggravated by IL‐10 knockout and alleviated upon rIL‐10 treatment (Figure 4b and c). We also collected lung tissues for immunoblotting assay and histological staining. As shown in Figure 4d and e, IL‐10 deficiency, which further exacerbates the extent of damage to the ALI lung tissue. However, supplementation with rIL‐10 was effective in reversing the damage. The study revealed that endogenous IL‐10 production plays a maintenance role in inflammatory injury of ALI.
FIGURE 4
IL‐10 is essential in the restoration of ALI models, which could protect the anti‐inflammatory activities of macrophages from the mitochondrial dysfunction under LPS challenge. (a) WT and IL‐10−/−
mice were administrated nasally with PBS or LPS (10 mg/kg) with or without rIL‐10 treatment (45 µg/kg). After 24 h, mice were euthanised and Balf was collected and estimated for total cell number, macrophage, neutrophil, and lymphocyte numbers. n = 6 biologically independent samples. Balf total cell, *p = .014 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .0114 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), **p = .0014 (WT+LPS group vs. WT+rIL‐10+LPS group), **p = .0018 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. Macrophage, *p = .0251, ***p = .0002, ****p < .0001. Neutrophil, **p = .0011 (WT+LPS group vs. WT+rIL‐10+LPS group), **p = .0079 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0001, ****p < .0001. Lymphocyte, ****p < .0001. (b, c) Production of cytokines IFN‐γ, IL‐6, G‐CSF and CXCL1/KC in Balf and serum was assessed via ELISA kits. n = 6 biologically independent samples. (b) IFN‐γ, *p = .0216 (WT+Con group vs. WT+LPS group), *p = .0333 (WT+LPS group vs. WT+rIL‐10+LPS group), ***p = .0004, ****p < .0001. IL‐6, **p = .0011 (WT+Con group vs. WT+LPS group), **p = .0064, ***p = .0002 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. G‐CSF, *p = .0445, **p = .0012, ***p = .0002, ****p < .0001. CXCL1/KC, *p = .0127 (WT+Con group vs. WT+LPS group), *p = .0447 (WT+LPS group vs. WT+rIL‐10+LPS group), ****p < .0001. (c) IFN‐γ, **p = .0039, ****p < .0001. IL‐6, **p = .0028, ***p = .0005 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0002 (WT+LPS group vs. IL‐10
−/−+LPS group), ****p < .0001. G‐CSF, **p = .0088, ****p < .0001. CXCL1/KC, **p = .0034 (WT+Con group vs. WT+LPS group), **p = .0013 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. (d) Immunoblotting and its quantitative analysis indicating the effect of IL‐10 on the NLRP3/Caspase‐1 activation for lung inflammation. n = 6 biologically independent samples. NLRP3, **p = .0099, ***p = .0009, ****p < .0001. Caspase‐1 p10, ***p = .0003, **p = .0042, ****p < .0001. (e) Representative images of H&E‐stained lung tissue sections of WT or IL‐10
−/− mice after LPS stimulation for 24 h with or without rIL‐10 treatment. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. Lung damage was determined by 5 pathophysiological features to obtain the total injury score. n = 6 biologically independent samples. ****p < .0001. (f) After LPS stimulation (100 ng/mL) for 6 h with or without rIL‐10 pretreatment, the mRNA expressions of cytokines IL‐1β, and IL‐6 were examined by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. ****p < .0001. (g) Immunoblotting and its quantitative analysis showing the effect of IL‐10 on the NLRP3/Caspase‐1 activation for macrophage inflammation after 24 h of LPS stimulation (100 ng/mL). n = 3 biologically independent samples. NLRP3, **p = .0077 (WT+Con group vs. WT+LPS group), **p = .0026 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group). Caspase‐1 p10, **p = .0046 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), **p = .0036 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .0344 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), *p = .0419 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group). (h, i) Microplate reader assay exhibiting the effect of IL‐10 expression on the production of mitochondrial (h) and intracellular ROS (i) induced by LPS (100 ng/mL) in BMDMs. n = 6 biologically independent samples. (h) *p = .0122, ***p = .0005, ****p < .0001. (i) **p = .0044, ****p < .0001. (j) Confocal microscopy images showing the mitochondrial mass of BMDMs from each group using MitoTracker probe. Scale bars, 20 µm. (k) Immunoblotting and its quantitative analysis indicating the effect of IL‐10 on the expression of OPA1, MFN2, DRP1 and FIS1 for mitochondrial dynamics in BMDMs. n = 3 biologically independent samples. OPA1, *p = .0145 (WT+Con group vs. WT+LPS group), *p = .015 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), *p = .012. MFN2, **p = .0098, ***p = .0003, *p = .0159 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), *p = .0238 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group). DRP1, **p = .0082, *p = .0358 (WT+LPS group vs. WT+rIL‐10+LPS group), *p = .0043 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), *p = .0157 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .0131 (WT +rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. FIS1, ****p < .0001, **p = .0024, *p = .0332. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
To explore the role of IL‐10 in the inflammatory response and mitochondrial stability of ALI macrophages, we isolated BMDMs from WT or IL‐10
−/− mice and observed them for 24 h under LPS stimulation with or without rIL‐10 treatment as a variable. In the case of insufficient endogenous production of IL‐10, LPS significantly promoted the production of IL‐1β and IL‐6, two inflammatory mediators, whereas the addition of exogenous rIL‐10 effectively reduced their synthesis (Figure 4f). In parallel, BMDM studies revealed that depletion of IL‐10 accelerated LPS‐induced activation of the NLRP3/Caspase‐1 signalling pathway, while exogenous IL‐10 suppressed this inflammation reaction (Figure 4g). As for mitochondrial integrity and function, we discovered that IL‐10 deficiency exacerbated mitochondrial damage using MitoTracker fluorescent probe upon LPS challenge, which was rescued by rIL‐10 in BMDMs (Figure 4j). In addition, LPS‐triggered mitochondrial and intracellular ROS could also be increased by IL‐10 depletion, otherwise restrained by rIL‐10 administration (Figure 4h and i). When it came to mitochondrial dynamics, we demonstrated that lack of IL‐10 further reduced OPA1 expression while elevated DRP1 and FIS1 expression under LPS challenge; moreover, rIL‐10 treatment could partially reverse the changes (Figure 4k). Collectively, we speculate that IL‐10‐regulated mitochondrial homeostasis augments macrophage inflammatory effect in mice ALI model.
3.6
IL‐10 alters metabolic profiles and polarisation of macrophages upon LPS challenge
The changes of OCR and ECAR, as a measure of FAO‐OXPHOS and glycolysis were measured in WT and IL‐10
−/− BMDMs stimulated by LPS. As shown in Figure 5a and c, IL‐10
−/− BMDMs had higher ECAR basal levels and glycolytic capacity, indicating increased dependence on glycolysis when compared to WT BMDMs. Additionally, reduced basal OCR, ATP‐linked mitochondrial respiration and FAO‐coupled OCR were observed in IL‐10
−/− BMDMs, suggesting the decreased predominance of FAO‐OXPHOS over glycolysis (Figure 5b, d and e). A decrease in acetyl CoA, citrate and ATP content was observed in IL‐10‐depleted BMDMs under LPS‐stimulated conditions. However, with the introduction of recombinant IL‐10, an increase in the level of acetyl CoA was observed, to levels similar to those of the WT phenotype (Figure 5f). By measuring the metabolism‐related gene signature of macrophages, we found that LPS stimulation dramatically increased the expression of glycolysis‐related genes LDHA, HIF‐1α, GLUT in WT and IL‐10−/‐
BMDMs, while the exaggerated glycolysis induced by IL‐10 depletion could be partly alleviated by the addition of exogenous IL‐10 addition (Figure 5g). Meanwhile, the FAO‐associated genes LDLr and SCAP were downregulated in IL‐10−/‐
BMDMs after LPS challenge, then was elevated by rIL‐10 treatment (Figure 5h). Overall, the results showed that IL‐10 inhibited LPS‐induced glycolysis and FAO‐OXPHOS in macrophages.
FIGURE 5
IL‐10 alters metabolic profiles and polarisation of macrophages upon LPS challenge. (a, b) ECAR (a) and OCR (b) of BMDMs from WT and IL‐10
−/− mice were measured with a Seahorse XFe96 analyser. n = 6 biologically independent samples at each data point. (c–e) The glycolysis level and glycolysis capacity (c), as well as basal respiration and Respiration capacity (d), and FAO level (e) of BMDMs from WT and IL‐10
−/− mice with or without LPS stimulation (100 ng/mL). n = 6 biologically independent samples. (c) ***p = .0004, **p = .0083, ****p < .0001. (d) ****p < .0001. (e) ***p = .0003, **p = .0029, *p = .0224. (f) After 12 h treatment with LPS (100 ng/mL) with or without rIL‐10 addition in BMDMs, generation of ATP, as well as intracellular FAO metabolites, citrate and acetyl CoA were measured. n = 6 biologically independent samples. *p = .0093, *p = .0145, ***p = .0006 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0001 (WT+Con group vs. WT+LPS group), *p = .017, ***p = .0004 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0001 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), ****p < .0001. (g, h) Induction of LDHA, HIF‐1α and GLUT (g), as well as SCAP, SREBP and LDLr (h) mRNA expression in WT and IL‐10
−/− BMDMs was estimated by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. (g) *p = .0407 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), *p = .0164 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .035, **p = .0038, ***p = .0008, ****p < .0001. (h) **p = .0034, **p = .0063, *p = .0202, **p = .0061,****p < .0001. (i) Induction of iNOS, IL‐12, ARG1 and YM1 mRNA expression in WT and IL‐10
−/− BMDMs was analysed via qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. **p = .0018, *p = .0245, *p = .0286, **p = .0025, ***p = .0004, ****p < .0001. (j, k) Immunoblotting (j) and its quantitative analysis (k) verifying the effect of IL‐10 on iNOS and ARG1 expression for macrophage polarisation in BMDMs with or without LPS (100 ng/mL) challenge. n = 3 biologically independent samples. iNOS, *p = .0287, **p = .0052. ARG1, **p = .0011, *p = .011 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), *p = .0112 (WT+LPS group vs. IL‐10
−/−+LPS group). (l) The phagocytic capacity of BMDMs from WT and IL‐10
−/− mice was evaluated using the pHrod Green E. coli BioParticles through both confocal microscopy (showed in the left panel; scale bars, 20 µm) and fluorescence plate reader (showed in the right panel). n = 6 biologically independent samples. **p = .002 (WT+LPS group vs. WT +rIL‐10+LPS group), **p = .0088 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
Subsequently, we assessed the expression of M1/M2 markers in WT and IL‐10‐deficient BMDMs after LPS stimulation to explore the role of IL‐10 in macrophage polarisation. As shown in Figure 5i, in comparison with the WT ones, BMDMs from IL‐10
−/− mice showed an apparent increase in the M1 markers iNOS and IL‐12 and a reduce in the M2 markers ARG1 and YM1 after LPS challenge. Coherently, IL‐10 depletion also upregulated iNOS while inhibited ARG1 at protein expression level in LPS‐stimulated macrophages (Figure 5j and k). LPS‐impaired phagocytic capacity of macrophages could be recovered by rIL‐10 addition, though endogenous IL‐10 had no significant impact on this process. Our quantitative analysis results also indicate that the fluorescence intensity of LPS stimulation groups was significantly reduced compared with that of WT groups (Figure 5l). Notably, the above reactions could not be explained by cellular cytotoxicity resulted from IL‐10 defect and/or LPS challenge (Figure S8a and b). These data suggest that IL‐10 may inhibit aberrant inflammation in ALI by modulating the balance of macrophage polarisation.
3.7
Exogenous IL‐10 supplement confers protection against the aggravated ALI induced by CPT1A deficiency
To further clarify the role of IL‐10 in the protective effects of macrophage CPT1A in ALI models, we firstly supplemented Cre+
CPT1A
fl/fl mice with exogenous IL‐10 together with LPS challenge. Results showed that Cre+
CPT1A
fl/fl mice receiving LPS + rIL‐10 administration had less airway inflammatory cells, especially macrophages and neutrophils aggregation, as well as decreased IFN‐γ, IL‐6, G‐CSF, CXCL1/KC levels in Balf and serum, compared with Cre+
CPT1A
fl/fl ALI mice without IL‐10 supplement (Figure 6a–c). Furthermore, rIL‐10 treatment inhibited inflammatory pathway activation (Figures S2a and 6d) and reduced diffuse damage in lungs of Cre+
CPT1A
fl/fl ALI mice (Figure 6e). These data verify that aggravation of inflammatory damage caused by macrophage CPT1A deficiency within ALI lungs could be rescued by exogenous IL‐10.
FIGURE 6
Exogenous IL‐10 supplement reverses the aggravated ALI, macrophages metabolic alterations and polarisation caused by CPT1A deficiency in LPS‐stimulated model. (a) Exogenous rIL‐10 (45 µg/kg) was administrated to Cre+
CPT1A
fl/fl mice simultaneously with LPS (10 mg/kg) challenge. After 24 h, mice were euthanised and Balf was collected and estimated for total cell number, macrophage, neutrophil, and lymphocyte numbers. n = 4 biologically independent samples. *p = .0133, ***p = .0006, **p = .0041. (b, c) Production of cytokines IFN‐γ, IL‐6, G‐CSF, and CXCL1/KC in Balf and serum was assessed via ELISA kits. n = 4 biologically independent samples. (b) ***p = .0002, ****p < .0001, **p = .0011, *p = .031. (c) **p = .0013, **p = .0017, ****p < .0001, *p = .0144. (d) Quantitative immunoblotting analysis indicating the protective effect of rIL‐10 on the NLRP3/Caspase‐1 activation for lung inflammation in Cre+
CPT1A
fl/fl mice. n = 4 biologically independent samples. *p = .0272, **p = .0072. (e) Representative images of H&E‐stained lung tissue sections of Cre+
CPT1A
fl/fl mice after LPS challenge with or without rIL‐10 treatment. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. n = 4 biologically independent samples. ****p < .0001. (f) After LPS stimulation for 6 h with or without rIL‐10 pretreatment, the mRNA expressions of cytokines IL‐1β, IL‐18, IL‐6 and TNF‐α in Cre+
CPT1A
fl/fl BMDMs were examined by qRT‐PCR. Data are expressed as fold change. n = 4 biologically independent samples. **p = .0021, *p = .0206, *p = .0157. (g) Quantitative immunoblotting analysis showing the effect of rIL‐10 on the NLRP3/Caspase‐1 activation for macrophage inflammation after 24 h of LPS stimulation. n = 4 biologically independent samples. NLRP3, *p = .0166, Caspase‐1 p10, *p = .0145. (h) Confocal microscopy images showing the mitochondrial mass of BMDMs from each group using MitoTracker probe. Scale bars, 20 µm. (i) Microplate reader assay exhibiting the effect of rIL‐10 on mitochondrial and intracellular ROS production induced by LPS in Cre+
CPT1A
fl/fl BMDMs. n = 4 biologically independent samples. **p = .004. (j) Quantitative immunoblotting analysis indicating the effect of rIL‐10 on the expression of OPA1, MFN2, DRP1 and FIS1 for mitochondrial dynamics in Cre+
CPT1A
fl/fl BMDMs. n = 4 biologically independent samples. OPA1, **p = .0082. MFN2, *p = .0124. DRP1, **p = .0032. FIS1, *p = .013. (k, l) Induction of LDHA, HIF‐1α and GLUT (k), as well as SCAP, SREBP and LDLr (l) mRNA expression in Cre+
CPT1A
fl/fl BMDMs with or without rIL‐10 addition was estimated by qRT‐PCR. Data are expressed as fold change. n = 4 biologically independent samples. (k) HIF‐1α, ***p = .0002, GLUT, ****p < .0001. (l) SCAP, **p = .0029, SREBP, **p = .0053, LDLr, **p = .0029. (m) After 12 h treatment with LPS with or without rIL‐10 supplement in BMDMs, production of ATP, as well as intracellular FAO metabolites, citrate and acetyl CoA were measured. n = 4 biologically independent samples. ATP, *p = .0173. citrate, **p = .0011. Acetyl CoA, **p = .003. (n) Induction of iNOS, IL‐12, ARG1 and YM1 mRNA expression in Cre+
CPT1A
fl/fl BMDMs was analysed via qRT‐PCR. Data are expressed as fold change. n = 4 biologically independent samples. iNOS, ***p = .0003. IL‐12, ****p < .0001. (o) Quantitative immunoblotting analysis verifying the effect of rIL‐10 on iNOS and ARG1 expression for macrophage polarisation in Cre+
CPT1A
fl/fl BMDMs upon LPS challenge. n = 4 biologically independent samples. iNOS, *p = .0142. ARG1, *p = .0179. (p) The phagocytic capacity of Cre+
CPT1A
fl/fl BMDMs with or without rIL‐10 treatment was evaluated by the pHrod Green E. coli BioParticles through both confocal microscopy (showed in the upper panel; scale bars, 20 µm) and fluorescence plate reader (showed in the lower panel). n = 4 biologically independent samples. ***p = .0002. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using unpaired Student's t‐test.
We next asked whether the exacerbated inflammation in macrophages by CPT1A depletion could be alleviated by IL‐10 addition. As shown in Figure 6f, in the experimental model of Cre+
CPT1A
fl/fl BMDMs, the overproduction of cytokines IL‐1β, IL‐18, IL‐6 and TNF‐α by LPS stimulation was effectively reduced by the introduction of exogenous IL‐10. In addition, with the presence of rIL‐10, the LPS‐induced inflammatory response was inhibited in Cre+
CPT1A
fl/fl BMDM cells, especially the activation of the NLRP3/Caspase‐1 inflammatory pathway was suppressed (Figures S4a and 6g). These findings suggest that exogenous IL‐10 could compensate for excessive inflammation of macrophages caused by CPT1A deficiency.
To investigate whether the abnormal mitochondrial function described above in Cre+
CPT1A
fl/fl BMDMs attributed to the lack of IL‐10, MitoTracker Red was used to stain the mitochondrial content of cells. The results showed that rIL‐10 treatment significantly restored the LPS‐induced decrease in fluorescence of Cre+
CPT1A
fl/fl macrophages (Figure 6h). In line with the result, we proved that both levels of ROS were decreased in LPS‐stimulated Cre+
CPT1A
fl/fl macrophages in the presence of IL‐10 by using mitochondria‐specific ROS indicator MitoSOX and intracellular ROS probe DCFH‐DA, demonstrating a potential positive effect of IL‐10 on mitochondrial ROS‐scavenging activity within Cre+
CPT1A
fl/fl macrophages (Figure 6i). As for the study of mitochondrial dynamics, we found that the addition of rIL‐10 significantly increased the levels of OPA1 and MFN2, two mitochondrial fusion proteins, and effectively reduced the expression of DRP1 and FIS1 mitochondrial fission proteins in LPS‐induced Cre+
CPT1A
fl/fl macrophages (Figures S4b and 6j). Collectively, IL‐10 addition in Cre+
CPT1A
fl/fl BMDMs was able to restore the mitochondrial damage induced by CPT1A deficiency under LPS stimulation. Notably, compared with WT BMDMs, the CPT1A activator Lca could neither dampen inflammatory response (Figure S9a–c) nor improve mitochondrial dynamics (Figure S9d) in IL‐10−/−
BMDMs, implying that IL‐10 acts as a crucial intermediate regulator for the protective effect of macrophage CPT1A on ALI/ARDS.
3.8
IL‐10 restores metabolic reprogramming and shifts polarisation caused by CPT1A deficiency in LPS‐stimulated macrophages
Because of the observed effects of IL‐10 on the aberrant macrophage functional status induced by CPT1A depletion, we investigated whether IL‐10 modulated metabolic changes during this process. As shown in Figure 6k and l, rIL‐10 addition significantly reduced the expression of glycolysis‐related genes HIF‐1α and GLUT, while upregulated FAO‐associated genes SCAP, SREBP and LDLr in Cre+
CPT1A
fl/fl BMDMs upon LPS challenge, suggesting an increased FAO/glycolytic flux induced by IL‐10. Besides, levels of FAO intermediate metabolites acetyl CoA, citrate and ATP were all elevated by rIL‐10 administration in LPS‐stimulated Cre+
CPT1A
fl/fl BMDMs (Figure 6m).
Regarding macrophage polarisation, we demonstrated that higher expression of M1 markers iNOS and IL‐12, and lower expression of M2 markers ARG1 and YM1 in Cre+
CPT1A
fl/fl BMDMs after LPS stimulation could be partly reversed by rIL‐10 addition (Figure 6n). Meanwhile, the IL‐10‐induced decrement in iNOS/ARG1 protein level was also confirmed in LPS‐challenged Cre+
CPT1A
fl/fl BMDMs (Figures S7a and 6o). Consistently, impaired phagocytic capacity in CPT1A‐deficient macrophages upon LPS treatment was improved by IL‐10 supplement (Figure 6p). Overall, these data suggest that the changes in metabolic profiles and macrophage polarisation caused by CPT1A depletion are at least partly attributed to CPT1A‐regulated IL‐10 expression in the ALI model.
3.1
Macrophage CPT1A deficiency contributes to more severe lung injury upon LPS challenge
Our previous scRNA‐seq data (Genome Sequence Archive with accession ID CRA008837) showed that CPT1A mainly expressed in macrophages and fibroblasts and was reduced by LPS challenge (Figure S1a–d). Meanwhile, we constructed a classical ALI mouse model via LPS inhalation (10 mg/kg) and observed reduced CPT1A expression in lung macrophages of ALI mice compared to the control (Figure S1e), which indicated a potential relationship between decreased CPT1A expression in macrophage upon LPS exposure and ALI pathogenesis.
To prove this, we generated mice harbouring a conditional depletion of CPT1A in macrophages (Cre+
CPT1A
fl/fl). By use of the ALI mouse model, control (Cre−
CPT1A
fl/fl) and Cre+
CPT1A
fl/fl mice were injected with LPS or PBS. Their local and systemic inflammatory responses were then measured and analysed 24 h after treatment. As shown in Figure 1a, Balf inflammatory cell infiltration was dramatically elevated by LPS challenge, especially neutrophils, and was further exacerbated by CPT1A deficiency in macrophages. IFN‐γ, IL‐6, G‐CSF and CXCL1/KC in Balf and serum were elevated rapidly after LPS stimulation, while CPT1A depletion in macrophages intensified the increase at 24 h except for IFN‐γ in Balf (Figure 1b and c). We also observed elevated IL‐10 level in both Balf and serum after LPS challenge. Interestingly, the level of IL‐10 was much lower in Cre+
CPT1A
fl/fl mice compared with the Cre−
CPT1A
fl/fl ones (Figure 1b and c).
FIGURE 1
CPT1A deficiency in macrophage contributes to more severe lung injury in the ALI mouse model. (a) Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice were administrated nasally with PBS or LPS (10 mg/kg). After 24 h, mice were euthanised and Balf was collected and estimated for total cell number, macrophage, neutrophil and lymphocyte numbers. n = 6 biologically independent samples. Balf total cell, **p = .0044, ****p < .0001. Macrophage, ****p < .0001. Neutrophil, ***p = .0002, ****p < .0001. Lymphocyte, **p = .002, ****p < .0001. (b, c) Secretion of cytokines IFN‐γ, IL‐6, G‐CSF, CXCL1/KC and IL‐10 in Balf and serum was assessed by ELISA kits. n = 6 biologically independent samples. (b) *p = .0212, **p = .0017, ****p < .0001. (c) *p = .0368 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), *p = .0116 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group), **p = .0046, *p = .0448, ****p < .0001. (d, e) Quantitative analysis of immunoblotting assay identifying the activation of NLRP3/Caspase‐1 pathway for lung inflammation evaluation. n = 6 biologically independent samples. (d) ***p = .0002 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0009 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group), ****p < .0001. (e) **p = .0086, ****p < .0001. (f) Representative pictures of H&E‐stained lung tissue sections from each group. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. Lung injury score was determined by 5 pathophysiological features. n = 6 biologically independent samples. ****p < .0001. (g) Immunohistochemical staining and quantitative analysis of IL‐10 in Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice. Red arrowheads indicate IL‐10‐expressed macrophage within lungs. n = 30 each group from 6 biologically independent samples. ****p < .0001. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
To further estimate the role of macrophage CPT1A expression in LPS‐triggered inflammatory lung injuries, we applied whole lung lysates for immunoblotting detection and lung sections for histological staining. In Figures S2a and 1d and e, we observed that the NLRP3/Caspase‐1 signalling pathway associated with inflammatory response in ALI mouse lung was promoted when CPT1A gene was absent in macrophages. Lung structure can be evaluated from five different perspectives: observing the alveolar neutrophils, assessing the interstitial neutrophils, detecting the hyaline membranes, analysing the proteinaceous debris and measuring the septal thickening. We observed no obvious pathological change when depleting macrophage CPT1A under physiological condition; however, exacerbated pulmonary damage in Cre+
CPT1A
fl/fl mice could be identified compared with the Cre−
CPT1A
fl/fl ones at 24 h after LPS stimulation (Figure 1f).
Next, we evaluated the effect of CPT1A on IL‐10 expression on tissue scales by staining mouse lung sections with IL‐10 and its receptor, IL‐10RA. After LPS inhalation, infiltrated macrophages lack of CPT1A expressed lower levels of IL‐10 (Figure 1g), as well as IL‐10RA (Figure S2b) compared with Cre−
CPT1A
fl/fl mice. In a nutshell, the expression of CPT1A gene in BMDMs shows a crucial role in the progression of ALI induced by LPS, which can affect the regulation of IL‐10 and its receptor.
3.2
CPT1A regulates aberrant inflammation and mitochondrial stability in macrophages upon LPS challenge
We then investigated the effects of CPT1A on inflammatory response and metabolic patterns of macrophage ex vivo by isolating and culturing mouse bone marrow derived macrophages (BMDMs) (Figure 2a). Macrophage CPT1A depletion was verified by immunoblotting that BMDMs isolated from Cre+
CPT1A
fl/fl mice showed a complete absence of CPT1A expression (Figure 2b). BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice were stimulated with LPS and subjected to RNA‐seq. Heatmaps of all differential gene expression results were presented in Figure S3a. A total of 2357 differentially expressed genes (DEGs) were identified between Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl macrophages in the control group, and in the experimental group, the number was 1069 (Figure S3b). The most highly DEGs in Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl BMDMs showed that CPT1A depletion had a significant impact on cellular metabolism (Fabp5, Ldhb, Ldha, Acsbg1 and Acadvl) and proinflammatory cytokine production (Nlrp3, Il1b, Il33), especially under LPS stimulation (Figure S3c). In terms of consistency, it was observed that the enrichment analysis of GO presented a similar pattern with the pathway analysis of KEGG (Figure S3d and e). By use of qRT‐PCR, we determined that in the absence of CPT1A, LPS administration induced more IL‐1β, IL‐18, IL‐6 and tumour necrosis factor (TNF)‐α, cytokines that promote inflammation production (Figure 2c). In addition, we also investigated the mechanism of NLRP3 inflammasome activation, which is responsible for the secretion of IL‐1β and IL‐18.
24
The results showed that loss of CPT1A enhanced LPS‐induced the NLRP3/Caspase‐1 signalling pathway activation in BMDMs (Figures S4a and 2d and e).
FIGURE 2
CPT1A regulates aberrant inflammation and mitochondrial stability in macrophages upon LPS challenge. (a) Schematic diagram of the extraction, separation and cultivation of BMDMs in vitro. Some figure elements were created with BioRender.com. (b) Macrophage CPT1A depletion was verified by immunoblotting in BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice. n = 4 biologically independent samples. (c) After LPS stimulation (100 ng/mL) for 6 h, the induction of cytokines IL‐1β, IL‐18, IL‐6 and TNF‐α mRNA expression in each group was analysed by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. ***p = .0007, ***p = .0004, ****p < .0001. (d, e) After LPS (100 ng/mL) treatment for 24 h, quantitative immunoblotting analysis indicating the effect of CPT1A expression on NLRP3/Caspase‐1 p10 level for inflammasome activation in BMDMs. n = 3 biologically independent samples. (d) ***p = .0002 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0001 (Cre−
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+Con group), *p = .0451 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), *p = .0243 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group). (e) **p = .0015 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), *p = .0411, ***p = .0002, **p = .0023 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group). (f, g) Microplate reader assay showing the effect of CPT1A on the production of mitochondrial (f) and intracellular ROS (g) induced by LPS (100 ng/mL) in BMDMs. n = 6 biologically independent samples. (f) *p = .0114 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0002, *p = .0243. (g) *p = .0268, ****p < .0001. (h) Confocal microscopy images showing the mitochondrial mass of BMDMs from each group using MitoTracker probe. Scale bars, 20 µm. (i–l) Quantitative immunoblotting analysis indicating the effect of CPT1A on the expression of OPA1, MFN2, DRP1 and FIS1 for mitochondrial dynamics in BMDMs. n = 6 biologically independent samples. (i) *p = .013 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0002 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), *p = .0397 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group). (j) **p = .0026 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), **p = .0035 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group). (k) **p = .0063 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), **p = .0028 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl+LPS group), ***p = .0004. (l) *p = .027 (Cre−
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+Con group), *p = .0154 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), **p = .0049. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
CPT1A mainly localised in mitochondrial membrane and has been reported to maintain mitochondrial homeostasis and attenuate excessive inflammatory response in renal tubular epithelial cells under profibrotic stimuli.
25
To answer the question of whether macrophage CPT1A expression prevented mitochondrial dysfunction, we examined ROS generation after LPS stimulation and discovered that LPS‐induced mitochondrial and intracellular ROS level elevated in macrophages with or without CPT1A, and Cre+
CPT1A
fl/fl BMDMs exhibited even higher ROS level compared to Cre−
CPT1A
fl/fl cells using MitoSOX Red probe and DCFH‐DA fluorescent probe under stimulus, respectively (Figure 2f and g), demonstrating the potential mitochondrial ROS‐scavenging activity of CPT1A in macrophages. Thereafter, we stained and imaged the active mitochondria in the cell using MitoTracker fluorescent dye, and observed that a decrease in fluorescence brightness indicates signs of mitochondrial damage. As revealed in Figure 2h, the fluorescence intensity of MitoTracker was decreased upon LPS challenge, which could be further reduced by CPT1A depletion. As a dynamic organelle, the maintenance of the mitochondrial homeostasis highly dependents on its fission and fusion process.
26
It has been shown that once the balance between fission and fusion in mitochondria is disturbed, its oxidation is affected.
27
To explore the potential role of CPT1A on macrophage mitochondrial dynamics, we analysed the expression levels of OPA1 and MFN2, as well as the fission proteins DRP1 and FIS1. Results showed that LPS treatment dramatically reduced OPA1 and MFN2 expression, while increased DRP1 and FIS1 expression, which indicates abnormal mitochondrial dynamics (Figures S4b and 2i–l). Besides, CPT1A depletion intensified the decrease of OPA1 and elevation of DRP1 in BMDMs (Figures S4b and 2i and k). The findings suggest that CPT1A expression participated in maintaining mitochondrial homeostasis, thereby protecting macrophages against excess inflammation.
Since genetic inactivation of CPT1A can disrupt mitochondrial homeostasis and induce inflammation, we asked whether pharmacological CPT1A modulation could augment bioenergetic and inflammatory reaction under LPS stimulation. To test this, we applied the well‐established CPT1A activator L‐carnitine (Lca), as well as its inhibitor etomoxir (Eto),
15
and proved their independence of the cytotoxicity in BMDMs (Figure S5a and b). Compared with LPS group, L‐carnitine significantly increased the level of CPT1A expression, which could be restrained by etomoxir pretreatment (Figure S5c). Lca can significantly down‐regulate the levels of inflammatory mediators IL‐1β and TNF‐α, and effectively inhibit the activation of NLRP3/Caspase‐1 signalling pathway, which has been clearly verified in the data. At the same time, etomoxir showed a trend opposite to our observations (Figure S5d–f). Additionally, Lca prevented abnormal mitochondrial dynamics by elevating fusion proteins (OPA1, MFN2) and reducing the fission protein DRP1; conversely, CPT1A inhibition by etomoxir decreased fusion proteins MFN2 and increased mitochondrial fission protein FIS1 (Figure S5g), which further demonstrated the importance of CPT1A in regulating the abnormal inflammatory response and mitochondrial stability of macrophages stimulated by LPS.
3.3
CPT1A modulates metabolic reprogramming from glycolysis to FAO in LPS‐stimulated macrophages
The heatmap of DEGs showed that many FAO‐associated genes were down‐regulated by CPT1A depletion in LPS‐stimulated BMDMs compared with the Cre−
CPT1A
fl/fl group (Figure 3a). To further assess the effect of CPT1A expression on metabolic plasticity upon activation, we examined ECAR and OCR in BMDM isolated from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice. We determined glycolytic function by oligomycin‐induced OXPHOS blockage and discovered that absence of CPT1A promoted basal glycolytic function in the presence of LPS (Figure 3b and e). Simultaneously, we found that palmitate‐induced OCR was significantly lower in BMDMs isolated from Cre+
CPT1A
fl/fl compared to Cre−
CPT1A
fl/fl mice under baseline situation, suggesting a relatively low level of fatty acid metabolism. After LPS stimulation, FAO level dropped in both groups, and the decline was more significant in Cre+
CPT1A
fl/fl group (Figure 3d). Compared with Cre−
CPT1A
fl/fl, Cre+
CPT1A
fl/fl BMDMs showed a lower‐level of baseline oxygen consumption, and OCR, basal respiration, and respiration capacity elevation upon LPS challenge (Figure 3c and f). These metabolic alterations may be due to the glycolysis‐related oxygen consumption. Besides, levels of acetyl CoA, citrate and ATP in Cre+
CPT1A
fl/fl macrophages was decreased compared to Cre−
CPT1A
fl/fl ones both in the absence and presence of LPS, indicating a low activity of FAO‐OXPHOS (Figure 3g).
FIGURE 3
CPT1A modulates metabolic reprogramming from glycolysis to FAO, polarisation and IL‐10 production in LPS‐stimulated macrophages. (a) Heat map of DEGs in Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl BMDMs after 12 h stimulation with LPS (100 ng/mL). (b, c) ECAR and OCR of BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice were measured with a Seahorse XFe96 analyser. Where indicated, cells were injected with 10 mM glucose (GLU), 1 µM oligomycin (OLI) and 100 mM 2‐DG sequentially for ECAR (b). For OCR, BMDMs were pretreated with 170 µM palmitate‐BSA FAO substrate or 40 µM CPT1A inhibitor Etomoxir, followed by injection of 2 µM OLI, 1 µM FCCP, and a combination of .5 µM rotenone and antimycin A (AA) at the time points indicated (c). n = 6 biologically independent samples at each data point. (d–f) The FAO level (d), glycolysis level and glycolysis capacity (e), and basal respiration and respiration capacity (f) of BMDMs from Cre−
CPT1A
fl/fl and Cre+
CPT1A
fl/fl mice with or without LPS stimulation. n = 6 biologically independent samples. (d) *p = .0312, **p = .0019. (e) *p = .0329, **p = .0022. (f) ****p < .0001. (g) After 12 h treatment with LPS (100 ng/mL) in BMDMs, levels of intracellular FAO metabolites, acetyl CoA and citrate, as well as ATP were measured. n = 6 biologically independent samples. Acetyl CoA, ***p = .0001 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), ***p = .0006 (Cre+
CPT1A
fl/fl+Con group vs. Cre+
CPT1A
fl/fl+LPS group), ****p < .0001. Citrate, *p = .0241, ****p < .0001. ATP, *p = .0449, ***p = .0001, ****p < .0001. (h, i) Induction of LDHA, HIF‐1α, GLUT, SCAP, SREBP and LDLr mRNA expression in Cre−
CPT1A
fl/fl or Cre+
CPT1A
fl/fl BMDMs was estimated by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. (h) ****p < .0001. (i) *p = .0147, ***p = .0002, ****p < .0001. (j) Diagram of the modulatory effects of CPT1A on macrophage activation and polarisation upon LPS stimulation. Some figure elements were created with BioRender.com. (k) Induction of iNOS, IL‐12, YM1 and IL‐10 mRNA expression in Cre−
CPT1A
fl/fl or Cre+
CPT1A
fl/fl BMDMs was analysed via qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. iNOS, ***p = .0002, ****p < .0001. IL‐12, ****p < .0001. YM1, ***p = .0002, ****p < .0001. IL‐10, **p = .0012, ***p = .0005, *p = 0.0443. (l) The secretion level of IL‐10 by BMDMs was examined by ELISA assay 24 h after LPS stimulation (100 ng/mL). n = 6 biologically independent samples. ****p < .0001. (m) Quantitative immunoblotting analysis indicating the effect of CPT1A on ARG1 and iNOS expression for macrophage polarisation in BMDMs. n = 3 biologically independent samples. iNOS, **p = .007, *p = .021. ARG1, **p = .0024 (Cre−
CPT1A
fl/fl+Con group vs. Cre−
CPT1A
fl/fl+LPS group), **p = .0082 (Cre−
CPT1A
fl/fl+LPS group vs. Cre+
CPT1A
fl/fl +LPS group), ****p < .0001. (n) The phagocytic capacity of BMDMs from Cre−
CPT1A
fl/fl or Cre+
CPT1A
fl/fl mice was evaluated by use of the pHrod Green E. coli BioParticles through both confocal microscopy (showed in the left panel; scale bars, 20 µm) and fluorescence plate reader (showed in the right panel). n = 6 biologically independent samples. *p = .045, **p = .0013, ****p < .0001. (o) The levels of IL‐10RA, PBX1, MEIS1 and PREP1 mRNA expression in BMDMs were analysed by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. IL‐10RA, ****p < .0001. PBX1, *p = .0196, ****p < .0001. MEIS1, ***p = .0004, ****p < .0001. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
In addition, we estimated metabolic alterations of macrophages with or without CPT1A expression upon LPS challenge via key regulators measurement. We found that under LPS challenge, there was an upregulation trend in mRNA expression of LDHA, GLUT and HIF‐1α
28
,
29
in macrophages, and the elevation is more obvious under CPT1A knockout condition (Figure 3h). In contrast, while LPS activation enhanced the expression levels of LDLr, SREBP, and SCAP
9
,
30
,
31
in Cre−
CPT1A
fl/fl ones, these gene expression were obvious repressed in Cre+
CPT1A
fl/fl BMDMs under the same stimulation (Figure 3i). Notably, these alterations could not be attributed to the cellular cytotoxicity induced by CPT1A defect or LPS administration (Figure S6). Together, the above results suggest that defect of FAO function in CPT1A‐absent macrophages may lead to the active mobilisation of glycolysis, and CPT1A plays a pivotal role in maintaining stable cellular energy metabolism in macrophages upon LPS challenge.
3.4
CPT1A shifts macrophages polarisation and modulates IL‐10 production in the ALI model
As mentioned above, FAO is linked to the M2 state of macrophage,
32
so we speculated that CPT1A, as the major rate‐limiting enzyme of FAO, may affect the polarisation of macrophage under LPS stimulation (Figure 3j). As shown in Figure 3k, stimulation with LPS significantly increased the M1 marker proteins iNOS and IL‐12 expression, which could be further elevated by CPT1A deficiency. Conversely, M2 markers YM1 and IL‐10 gene expression was decreased in Cre+
CPT1A
fl/fl BMDMs compared to the Cre−
CPT1A
fl/fl ones upon LPS challenge (Figure 3k). At protein expression level, macrophage CPT1A defect enhanced M1 marker iNOS while reduced M2 marker ARG1 regardless of whether they were activated by LPS (Figures 3m and S7a). M2 macrophages display significant anti‐inflammatory properties compared to proinflammatory M1 macrophages and play a significant part in the phagocytic function.
33
Herein, by use of the pHrod Green E. coli BioParticles, we observed lower phagocytosis activity of Cre+
CPT1A
fl/fl BMDMs than Cre−
CPT1A
fl/fl ones upon LPS administration (Figure 3n).
As we have observed a potential regulation effect of IL‐10/IL‐10RA expression by CPT1A on tissue scales, we then carried out in vivo studies which showed that both airway and peripheral IL‐10 secretion were suppressed by the depletion of macrophage CPT1A (Figure 1). Consistently, CPT1A knockout not only inhibited the mRNA level and secretion of IL‐10 (Figure 3k and l), but also reduced the expression of its receptor IL‐10RA in LPS‐stimulated BMDMs (Figure 3o), which indicates a potential autocrine effect of IL‐10 in macrophages. Additionally, CPT1A depletion also decreased the expression of PBX1, MEIS1, but not PREP1 upon LPS stimulation (Figure 3o), which could mediate transcriptional activation of IL‐10 during phagocytosis.
18
Overall, the data provide evidence that CPT1A was able to drive the macrophages polarisation between M1/M2 phenotype and promote IL‐10 production, which might modulate the excess inflammatory response during ALI.
3.5
IL‐10 generation is required for the anti‐inflammatory activity of macrophages in the ALI model
Given that IL‐10, as a cytokine with anti‐inflammatory capabilities, plays a crucial role in regulating the immune response and, as previously mentioned, its activity can be regulated by CPT1A, we explored the role of IL‐10 in the anti‐inflammatory effects of CPT1A in the ALI model. WT mice and IL‐10 null mice were treated with LPS or PBS in combination, with rIL‐10 or PBS, followed by detailed testing after an interval of 24 h. The study finally showed that when the level of IL‐10 was reduced, airway inflammation was aggravated, especially the accumulation of macrophages and neutrophils in the inflammatory response triggered by LPS was enhanced. Accordingly, rIL‐10 supplementation was effective in slowing the growth of such inflammatory cells (Figure 4a). Local and systematic inflammation, manifested by IFN‐γ, IL‐6, G‐CSF and CXCL1/KC secretion in Balf and serum, was also aggravated by IL‐10 knockout and alleviated upon rIL‐10 treatment (Figure 4b and c). We also collected lung tissues for immunoblotting assay and histological staining. As shown in Figure 4d and e, IL‐10 deficiency, which further exacerbates the extent of damage to the ALI lung tissue. However, supplementation with rIL‐10 was effective in reversing the damage. The study revealed that endogenous IL‐10 production plays a maintenance role in inflammatory injury of ALI.
FIGURE 4
IL‐10 is essential in the restoration of ALI models, which could protect the anti‐inflammatory activities of macrophages from the mitochondrial dysfunction under LPS challenge. (a) WT and IL‐10−/−
mice were administrated nasally with PBS or LPS (10 mg/kg) with or without rIL‐10 treatment (45 µg/kg). After 24 h, mice were euthanised and Balf was collected and estimated for total cell number, macrophage, neutrophil, and lymphocyte numbers. n = 6 biologically independent samples. Balf total cell, *p = .014 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .0114 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), **p = .0014 (WT+LPS group vs. WT+rIL‐10+LPS group), **p = .0018 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. Macrophage, *p = .0251, ***p = .0002, ****p < .0001. Neutrophil, **p = .0011 (WT+LPS group vs. WT+rIL‐10+LPS group), **p = .0079 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0001, ****p < .0001. Lymphocyte, ****p < .0001. (b, c) Production of cytokines IFN‐γ, IL‐6, G‐CSF and CXCL1/KC in Balf and serum was assessed via ELISA kits. n = 6 biologically independent samples. (b) IFN‐γ, *p = .0216 (WT+Con group vs. WT+LPS group), *p = .0333 (WT+LPS group vs. WT+rIL‐10+LPS group), ***p = .0004, ****p < .0001. IL‐6, **p = .0011 (WT+Con group vs. WT+LPS group), **p = .0064, ***p = .0002 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. G‐CSF, *p = .0445, **p = .0012, ***p = .0002, ****p < .0001. CXCL1/KC, *p = .0127 (WT+Con group vs. WT+LPS group), *p = .0447 (WT+LPS group vs. WT+rIL‐10+LPS group), ****p < .0001. (c) IFN‐γ, **p = .0039, ****p < .0001. IL‐6, **p = .0028, ***p = .0005 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0002 (WT+LPS group vs. IL‐10
−/−+LPS group), ****p < .0001. G‐CSF, **p = .0088, ****p < .0001. CXCL1/KC, **p = .0034 (WT+Con group vs. WT+LPS group), **p = .0013 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. (d) Immunoblotting and its quantitative analysis indicating the effect of IL‐10 on the NLRP3/Caspase‐1 activation for lung inflammation. n = 6 biologically independent samples. NLRP3, **p = .0099, ***p = .0009, ****p < .0001. Caspase‐1 p10, ***p = .0003, **p = .0042, ****p < .0001. (e) Representative images of H&E‐stained lung tissue sections of WT or IL‐10
−/− mice after LPS stimulation for 24 h with or without rIL‐10 treatment. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. Lung damage was determined by 5 pathophysiological features to obtain the total injury score. n = 6 biologically independent samples. ****p < .0001. (f) After LPS stimulation (100 ng/mL) for 6 h with or without rIL‐10 pretreatment, the mRNA expressions of cytokines IL‐1β, and IL‐6 were examined by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. ****p < .0001. (g) Immunoblotting and its quantitative analysis showing the effect of IL‐10 on the NLRP3/Caspase‐1 activation for macrophage inflammation after 24 h of LPS stimulation (100 ng/mL). n = 3 biologically independent samples. NLRP3, **p = .0077 (WT+Con group vs. WT+LPS group), **p = .0026 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group). Caspase‐1 p10, **p = .0046 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), **p = .0036 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .0344 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), *p = .0419 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group). (h, i) Microplate reader assay exhibiting the effect of IL‐10 expression on the production of mitochondrial (h) and intracellular ROS (i) induced by LPS (100 ng/mL) in BMDMs. n = 6 biologically independent samples. (h) *p = .0122, ***p = .0005, ****p < .0001. (i) **p = .0044, ****p < .0001. (j) Confocal microscopy images showing the mitochondrial mass of BMDMs from each group using MitoTracker probe. Scale bars, 20 µm. (k) Immunoblotting and its quantitative analysis indicating the effect of IL‐10 on the expression of OPA1, MFN2, DRP1 and FIS1 for mitochondrial dynamics in BMDMs. n = 3 biologically independent samples. OPA1, *p = .0145 (WT+Con group vs. WT+LPS group), *p = .015 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), *p = .012. MFN2, **p = .0098, ***p = .0003, *p = .0159 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), *p = .0238 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group). DRP1, **p = .0082, *p = .0358 (WT+LPS group vs. WT+rIL‐10+LPS group), *p = .0043 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), *p = .0157 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .0131 (WT +rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. FIS1, ****p < .0001, **p = .0024, *p = .0332. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
To explore the role of IL‐10 in the inflammatory response and mitochondrial stability of ALI macrophages, we isolated BMDMs from WT or IL‐10
−/− mice and observed them for 24 h under LPS stimulation with or without rIL‐10 treatment as a variable. In the case of insufficient endogenous production of IL‐10, LPS significantly promoted the production of IL‐1β and IL‐6, two inflammatory mediators, whereas the addition of exogenous rIL‐10 effectively reduced their synthesis (Figure 4f). In parallel, BMDM studies revealed that depletion of IL‐10 accelerated LPS‐induced activation of the NLRP3/Caspase‐1 signalling pathway, while exogenous IL‐10 suppressed this inflammation reaction (Figure 4g). As for mitochondrial integrity and function, we discovered that IL‐10 deficiency exacerbated mitochondrial damage using MitoTracker fluorescent probe upon LPS challenge, which was rescued by rIL‐10 in BMDMs (Figure 4j). In addition, LPS‐triggered mitochondrial and intracellular ROS could also be increased by IL‐10 depletion, otherwise restrained by rIL‐10 administration (Figure 4h and i). When it came to mitochondrial dynamics, we demonstrated that lack of IL‐10 further reduced OPA1 expression while elevated DRP1 and FIS1 expression under LPS challenge; moreover, rIL‐10 treatment could partially reverse the changes (Figure 4k). Collectively, we speculate that IL‐10‐regulated mitochondrial homeostasis augments macrophage inflammatory effect in mice ALI model.
3.6
IL‐10 alters metabolic profiles and polarisation of macrophages upon LPS challenge
The changes of OCR and ECAR, as a measure of FAO‐OXPHOS and glycolysis were measured in WT and IL‐10
−/− BMDMs stimulated by LPS. As shown in Figure 5a and c, IL‐10
−/− BMDMs had higher ECAR basal levels and glycolytic capacity, indicating increased dependence on glycolysis when compared to WT BMDMs. Additionally, reduced basal OCR, ATP‐linked mitochondrial respiration and FAO‐coupled OCR were observed in IL‐10
−/− BMDMs, suggesting the decreased predominance of FAO‐OXPHOS over glycolysis (Figure 5b, d and e). A decrease in acetyl CoA, citrate and ATP content was observed in IL‐10‐depleted BMDMs under LPS‐stimulated conditions. However, with the introduction of recombinant IL‐10, an increase in the level of acetyl CoA was observed, to levels similar to those of the WT phenotype (Figure 5f). By measuring the metabolism‐related gene signature of macrophages, we found that LPS stimulation dramatically increased the expression of glycolysis‐related genes LDHA, HIF‐1α, GLUT in WT and IL‐10−/‐
BMDMs, while the exaggerated glycolysis induced by IL‐10 depletion could be partly alleviated by the addition of exogenous IL‐10 addition (Figure 5g). Meanwhile, the FAO‐associated genes LDLr and SCAP were downregulated in IL‐10−/‐
BMDMs after LPS challenge, then was elevated by rIL‐10 treatment (Figure 5h). Overall, the results showed that IL‐10 inhibited LPS‐induced glycolysis and FAO‐OXPHOS in macrophages.
FIGURE 5
IL‐10 alters metabolic profiles and polarisation of macrophages upon LPS challenge. (a, b) ECAR (a) and OCR (b) of BMDMs from WT and IL‐10
−/− mice were measured with a Seahorse XFe96 analyser. n = 6 biologically independent samples at each data point. (c–e) The glycolysis level and glycolysis capacity (c), as well as basal respiration and Respiration capacity (d), and FAO level (e) of BMDMs from WT and IL‐10
−/− mice with or without LPS stimulation (100 ng/mL). n = 6 biologically independent samples. (c) ***p = .0004, **p = .0083, ****p < .0001. (d) ****p < .0001. (e) ***p = .0003, **p = .0029, *p = .0224. (f) After 12 h treatment with LPS (100 ng/mL) with or without rIL‐10 addition in BMDMs, generation of ATP, as well as intracellular FAO metabolites, citrate and acetyl CoA were measured. n = 6 biologically independent samples. *p = .0093, *p = .0145, ***p = .0006 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0001 (WT+Con group vs. WT+LPS group), *p = .017, ***p = .0004 (WT+rIL‐10+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ***p = .0001 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), ****p < .0001. (g, h) Induction of LDHA, HIF‐1α and GLUT (g), as well as SCAP, SREBP and LDLr (h) mRNA expression in WT and IL‐10
−/− BMDMs was estimated by qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. (g) *p = .0407 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), *p = .0164 (WT+LPS group vs. IL‐10
−/−+LPS group), *p = .035, **p = .0038, ***p = .0008, ****p < .0001. (h) **p = .0034, **p = .0063, *p = .0202, **p = .0061,****p < .0001. (i) Induction of iNOS, IL‐12, ARG1 and YM1 mRNA expression in WT and IL‐10
−/− BMDMs was analysed via qRT‐PCR. Data are expressed as fold change. n = 6 biologically independent samples. **p = .0018, *p = .0245, *p = .0286, **p = .0025, ***p = .0004, ****p < .0001. (j, k) Immunoblotting (j) and its quantitative analysis (k) verifying the effect of IL‐10 on iNOS and ARG1 expression for macrophage polarisation in BMDMs with or without LPS (100 ng/mL) challenge. n = 3 biologically independent samples. iNOS, *p = .0287, **p = .0052. ARG1, **p = .0011, *p = .011 (IL‐10
−/−+Con group vs. IL‐10
−/−+LPS group), *p = .0112 (WT+LPS group vs. IL‐10
−/−+LPS group). (l) The phagocytic capacity of BMDMs from WT and IL‐10
−/− mice was evaluated using the pHrod Green E. coli BioParticles through both confocal microscopy (showed in the left panel; scale bars, 20 µm) and fluorescence plate reader (showed in the right panel). n = 6 biologically independent samples. **p = .002 (WT+LPS group vs. WT +rIL‐10+LPS group), **p = .0088 (IL‐10
−/−+LPS group vs. IL‐10
−/−+rIL‐10+LPS group), ****p < .0001. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using one‐way ANOVA followed by Bonferroni's post hoc test.
Subsequently, we assessed the expression of M1/M2 markers in WT and IL‐10‐deficient BMDMs after LPS stimulation to explore the role of IL‐10 in macrophage polarisation. As shown in Figure 5i, in comparison with the WT ones, BMDMs from IL‐10
−/− mice showed an apparent increase in the M1 markers iNOS and IL‐12 and a reduce in the M2 markers ARG1 and YM1 after LPS challenge. Coherently, IL‐10 depletion also upregulated iNOS while inhibited ARG1 at protein expression level in LPS‐stimulated macrophages (Figure 5j and k). LPS‐impaired phagocytic capacity of macrophages could be recovered by rIL‐10 addition, though endogenous IL‐10 had no significant impact on this process. Our quantitative analysis results also indicate that the fluorescence intensity of LPS stimulation groups was significantly reduced compared with that of WT groups (Figure 5l). Notably, the above reactions could not be explained by cellular cytotoxicity resulted from IL‐10 defect and/or LPS challenge (Figure S8a and b). These data suggest that IL‐10 may inhibit aberrant inflammation in ALI by modulating the balance of macrophage polarisation.
3.7
Exogenous IL‐10 supplement confers protection against the aggravated ALI induced by CPT1A deficiency
To further clarify the role of IL‐10 in the protective effects of macrophage CPT1A in ALI models, we firstly supplemented Cre+
CPT1A
fl/fl mice with exogenous IL‐10 together with LPS challenge. Results showed that Cre+
CPT1A
fl/fl mice receiving LPS + rIL‐10 administration had less airway inflammatory cells, especially macrophages and neutrophils aggregation, as well as decreased IFN‐γ, IL‐6, G‐CSF, CXCL1/KC levels in Balf and serum, compared with Cre+
CPT1A
fl/fl ALI mice without IL‐10 supplement (Figure 6a–c). Furthermore, rIL‐10 treatment inhibited inflammatory pathway activation (Figures S2a and 6d) and reduced diffuse damage in lungs of Cre+
CPT1A
fl/fl ALI mice (Figure 6e). These data verify that aggravation of inflammatory damage caused by macrophage CPT1A deficiency within ALI lungs could be rescued by exogenous IL‐10.
FIGURE 6
Exogenous IL‐10 supplement reverses the aggravated ALI, macrophages metabolic alterations and polarisation caused by CPT1A deficiency in LPS‐stimulated model. (a) Exogenous rIL‐10 (45 µg/kg) was administrated to Cre+
CPT1A
fl/fl mice simultaneously with LPS (10 mg/kg) challenge. After 24 h, mice were euthanised and Balf was collected and estimated for total cell number, macrophage, neutrophil, and lymphocyte numbers. n = 4 biologically independent samples. *p = .0133, ***p = .0006, **p = .0041. (b, c) Production of cytokines IFN‐γ, IL‐6, G‐CSF, and CXCL1/KC in Balf and serum was assessed via ELISA kits. n = 4 biologically independent samples. (b) ***p = .0002, ****p < .0001, **p = .0011, *p = .031. (c) **p = .0013, **p = .0017, ****p < .0001, *p = .0144. (d) Quantitative immunoblotting analysis indicating the protective effect of rIL‐10 on the NLRP3/Caspase‐1 activation for lung inflammation in Cre+
CPT1A
fl/fl mice. n = 4 biologically independent samples. *p = .0272, **p = .0072. (e) Representative images of H&E‐stained lung tissue sections of Cre+
CPT1A
fl/fl mice after LPS challenge with or without rIL‐10 treatment. Scale bars in the upper panel, 200 µm; scale bars in the lower panel, 50 µm. n = 4 biologically independent samples. ****p < .0001. (f) After LPS stimulation for 6 h with or without rIL‐10 pretreatment, the mRNA expressions of cytokines IL‐1β, IL‐18, IL‐6 and TNF‐α in Cre+
CPT1A
fl/fl BMDMs were examined by qRT‐PCR. Data are expressed as fold change. n = 4 biologically independent samples. **p = .0021, *p = .0206, *p = .0157. (g) Quantitative immunoblotting analysis showing the effect of rIL‐10 on the NLRP3/Caspase‐1 activation for macrophage inflammation after 24 h of LPS stimulation. n = 4 biologically independent samples. NLRP3, *p = .0166, Caspase‐1 p10, *p = .0145. (h) Confocal microscopy images showing the mitochondrial mass of BMDMs from each group using MitoTracker probe. Scale bars, 20 µm. (i) Microplate reader assay exhibiting the effect of rIL‐10 on mitochondrial and intracellular ROS production induced by LPS in Cre+
CPT1A
fl/fl BMDMs. n = 4 biologically independent samples. **p = .004. (j) Quantitative immunoblotting analysis indicating the effect of rIL‐10 on the expression of OPA1, MFN2, DRP1 and FIS1 for mitochondrial dynamics in Cre+
CPT1A
fl/fl BMDMs. n = 4 biologically independent samples. OPA1, **p = .0082. MFN2, *p = .0124. DRP1, **p = .0032. FIS1, *p = .013. (k, l) Induction of LDHA, HIF‐1α and GLUT (k), as well as SCAP, SREBP and LDLr (l) mRNA expression in Cre+
CPT1A
fl/fl BMDMs with or without rIL‐10 addition was estimated by qRT‐PCR. Data are expressed as fold change. n = 4 biologically independent samples. (k) HIF‐1α, ***p = .0002, GLUT, ****p < .0001. (l) SCAP, **p = .0029, SREBP, **p = .0053, LDLr, **p = .0029. (m) After 12 h treatment with LPS with or without rIL‐10 supplement in BMDMs, production of ATP, as well as intracellular FAO metabolites, citrate and acetyl CoA were measured. n = 4 biologically independent samples. ATP, *p = .0173. citrate, **p = .0011. Acetyl CoA, **p = .003. (n) Induction of iNOS, IL‐12, ARG1 and YM1 mRNA expression in Cre+
CPT1A
fl/fl BMDMs was analysed via qRT‐PCR. Data are expressed as fold change. n = 4 biologically independent samples. iNOS, ***p = .0003. IL‐12, ****p < .0001. (o) Quantitative immunoblotting analysis verifying the effect of rIL‐10 on iNOS and ARG1 expression for macrophage polarisation in Cre+
CPT1A
fl/fl BMDMs upon LPS challenge. n = 4 biologically independent samples. iNOS, *p = .0142. ARG1, *p = .0179. (p) The phagocytic capacity of Cre+
CPT1A
fl/fl BMDMs with or without rIL‐10 treatment was evaluated by the pHrod Green E. coli BioParticles through both confocal microscopy (showed in the upper panel; scale bars, 20 µm) and fluorescence plate reader (showed in the lower panel). n = 4 biologically independent samples. ***p = .0002. Data are presented as mean ± SEM and analysed with a 95% confidence interval. p Values were calculated using unpaired Student's t‐test.
We next asked whether the exacerbated inflammation in macrophages by CPT1A depletion could be alleviated by IL‐10 addition. As shown in Figure 6f, in the experimental model of Cre+
CPT1A
fl/fl BMDMs, the overproduction of cytokines IL‐1β, IL‐18, IL‐6 and TNF‐α by LPS stimulation was effectively reduced by the introduction of exogenous IL‐10. In addition, with the presence of rIL‐10, the LPS‐induced inflammatory response was inhibited in Cre+
CPT1A
fl/fl BMDM cells, especially the activation of the NLRP3/Caspase‐1 inflammatory pathway was suppressed (Figures S4a and 6g). These findings suggest that exogenous IL‐10 could compensate for excessive inflammation of macrophages caused by CPT1A deficiency.
To investigate whether the abnormal mitochondrial function described above in Cre+
CPT1A
fl/fl BMDMs attributed to the lack of IL‐10, MitoTracker Red was used to stain the mitochondrial content of cells. The results showed that rIL‐10 treatment significantly restored the LPS‐induced decrease in fluorescence of Cre+
CPT1A
fl/fl macrophages (Figure 6h). In line with the result, we proved that both levels of ROS were decreased in LPS‐stimulated Cre+
CPT1A
fl/fl macrophages in the presence of IL‐10 by using mitochondria‐specific ROS indicator MitoSOX and intracellular ROS probe DCFH‐DA, demonstrating a potential positive effect of IL‐10 on mitochondrial ROS‐scavenging activity within Cre+
CPT1A
fl/fl macrophages (Figure 6i). As for the study of mitochondrial dynamics, we found that the addition of rIL‐10 significantly increased the levels of OPA1 and MFN2, two mitochondrial fusion proteins, and effectively reduced the expression of DRP1 and FIS1 mitochondrial fission proteins in LPS‐induced Cre+
CPT1A
fl/fl macrophages (Figures S4b and 6j). Collectively, IL‐10 addition in Cre+
CPT1A
fl/fl BMDMs was able to restore the mitochondrial damage induced by CPT1A deficiency under LPS stimulation. Notably, compared with WT BMDMs, the CPT1A activator Lca could neither dampen inflammatory response (Figure S9a–c) nor improve mitochondrial dynamics (Figure S9d) in IL‐10−/−
BMDMs, implying that IL‐10 acts as a crucial intermediate regulator for the protective effect of macrophage CPT1A on ALI/ARDS.
3.8
IL‐10 restores metabolic reprogramming and shifts polarisation caused by CPT1A deficiency in LPS‐stimulated macrophages
Because of the observed effects of IL‐10 on the aberrant macrophage functional status induced by CPT1A depletion, we investigated whether IL‐10 modulated metabolic changes during this process. As shown in Figure 6k and l, rIL‐10 addition significantly reduced the expression of glycolysis‐related genes HIF‐1α and GLUT, while upregulated FAO‐associated genes SCAP, SREBP and LDLr in Cre+
CPT1A
fl/fl BMDMs upon LPS challenge, suggesting an increased FAO/glycolytic flux induced by IL‐10. Besides, levels of FAO intermediate metabolites acetyl CoA, citrate and ATP were all elevated by rIL‐10 administration in LPS‐stimulated Cre+
CPT1A
fl/fl BMDMs (Figure 6m).
Regarding macrophage polarisation, we demonstrated that higher expression of M1 markers iNOS and IL‐12, and lower expression of M2 markers ARG1 and YM1 in Cre+
CPT1A
fl/fl BMDMs after LPS stimulation could be partly reversed by rIL‐10 addition (Figure 6n). Meanwhile, the IL‐10‐induced decrement in iNOS/ARG1 protein level was also confirmed in LPS‐challenged Cre+
CPT1A
fl/fl BMDMs (Figures S7a and 6o). Consistently, impaired phagocytic capacity in CPT1A‐deficient macrophages upon LPS treatment was improved by IL‐10 supplement (Figure 6p). Overall, these data suggest that the changes in metabolic profiles and macrophage polarisation caused by CPT1A depletion are at least partly attributed to CPT1A‐regulated IL‐10 expression in the ALI model.
4
DISCUSSION
In the late 19th century, Elias Metchnikoff first revealed the two main functions of macrophages: phagocytosis and elimination of microorganisms. Over the past few decades, macrophages have undergone evolution, and their functions have become increasingly sophisticated, widely involved in host defence, tissue stability and repair, pathology processes, and development. In order to manipulate these diverse functional pools, the innate immune system is managed by enabling a variety of activation states. Elaborate regulation of macrophage activation is essential for managing the inflammatory process and maintaining tissue homeostasis, especially in the therapy of diverse diseases associated with inflammation, such as ALI/ARDS. The process of lipid transformation plays a central role in macrophages’ physiological functions, which has a profound impact on the cell's biology, influencing cellular energy balance, the structure and composition of the cell membrane, and signalling processes.
9
Regulation of fatty acid metabolism, such as the production and breakdown of fatty acids, is essential for the proper functioning of macrophages and the effectiveness of the overall immune system.
34
Recently, the emerging field of ‘immunometabolism’ has been dedicated to unravelling the complex interactions between cellular metabolism and immune response, thereby contributing insights to the birth of innovative therapeutic regimens. The distinct metabolic profiles in activated macrophage are essential for appropriate immune cell function. Herein, we provided evidence that CPT1A, the key regulator of FAO, participated in metabolic alteration of activated macrophages and was able to modulate aberrant inflammation in the ALI model via upregulating IL‐10 production under physiological conditions, which could also maintain mitochondrial stability and drive the polarisation of macrophages into anti‐inflammatory direction. The defect of CPT1A‐IL‐10 axis interfered with macrophage metabolism, leading to the M1‐like macrophage polarisation upon LPS challenge, accompanied with aggravated inflammatory response and mitochondrial ROS (Figure 7). This study helps to reveal how macrophages regulate their immune and metabolic functions in disease States, thus pointing out a new possible direction for the development of therapies for ALI/ARDS.
FIGURE 7
Illustration of the proposed mechanisms of the potent protective activity of CPT1A‐IL‐10 axis in LPS‐induced ALI mice. Some figure elements were created with BioRender.com.
Over the past decades, numerous studies have shown that FAO not only controls the bioenergy balance in macrophages, but also by generate certain metabolites to regulate signal transduction pathways and gene expression.
35
The activity of fatty acid oxidation and fatty acid synthesis cycle has been monitored in M2‐differentiated macrophages, and these processes are closely related to cellular oxidative metabolism and ATP production.
36
Oxidative metabolic processes, facilitated by FAO, can activate M2 macrophages through multiple pathways, involving the modulation of bioenergetics and the generation of nuclear and cytoplasmic Acyl‐CoA stores that serve as carbon sources for histone acetylation and stimulate the expression of genes that enhance the regulation of M2 macrophage functions such as fibrosis and tissue repair.
37
However, some studies have cast doubt on the effectiveness of FAO in the anti‐inflammatory regulation of macrophage activation. Research has proven that the M2 polarisation process is not inhibited in macrophages deficient in the CPT2 gene.
14
Therefore, further studies are essential to thoroughly unravel the effects of FAO on macrophage adaptation and characteristic transitions. In the present study, LPS challenge induced a more significant conversion of M1‐polarised macrophage with genetically deficient of CPT1A (Figure 3). Meanwhile, CPT1A‐deficient macrophages exhibited increased glycolysis and decreased FAO‐OXPHOS compared to the control ones (Figure 3). External stimuli often induce macrophages to consume more energy in a short time, when glycolysis becomes more active. At the same time, elevated glycolysis level and activated pentose phosphate pathway facilitate rapid ATP production and the synthesis of biosynthetic intermediates essential for generating proinflammatory factors. Meanwhile, the inhibition of OXPHOS results in the generation of substantial amounts of bactericidal ROS.
19
Several literatures have focused on macrophage CPT1A expression and regarded it as a promising therapeutic strategy for inflammatory diseases.
16
Other researchers pointed out that CPT1A inhibition could attenuate free FA‐induced inflammation in macrophages.
38
These are consistent with our data showing that CPT1A protected against aberrant inflammation and maintained mitochondrial stability in macrophages when exposed to LPS (Figure 2), whereas CPT1A deficiency exacerbated pulmonary injury in an ALI mouse model (Figure 1). Therefore, CPT1A might be able to modify the metabolic process associated with inflammatory responses.
IL‐10 is a key immunomodulatory factor, which is produced by activated immune cells and plays a crucial part in controlling the process of excessive inflammatory response.
39
IL‐10, whether endogenous or exogenous, has been shown to significantly mediate the inflammatory response in ALI mice
21
and take part in the progression of immune cell recruitment, especially neutrophils,
40
,
41
,
42
in multiple mouse ALI models, which is also proved in our previous findings.
21
Ip et al.
20
have found that IL‐10 could be involved in the regulation of inflammation‐induced changes in macrophage metabolic program. In this study, we observed that IL‐10 effectively blocked the metabolic transition of OXPHOS to glycolysis and supported the stability of mitochondrial structure during the LPS activated macrophages (Figures 4 and 5). Besides, IL‐10 was also found to enhance mitochondrial FAO and suppress macrophage polarisation towards M1 phenotype challenged with LPS (Figure 5), underscoring the pivotal role of IL‐10 in regulating the crosstalk between cell metabolism and immune function. In addition, our data suggest that CPT1A‐mediated fatty acid oxidation plays a protective role in macrophages during the ALI progression, which is largely regulated by IL‐10‐related signalling pathways. Exogenous IL‐10 supplementation to CPT1A‐deficient macrophages could effectively improve FAO level and decrease excessive inflammation caused by LPS (Figure 6), while CPT1A agonist (L‐carnitine) addition did not correct the aberrant inflammation and impaired mitochondrial dynamics in IL‐10
−/− macrophages after LPS stimulation (Figure S9). Accordingly, we postulate that IL‐10 may be situated downstream of CPT1A, assuming the regulatory role in FAO and macrophage activation. The immune‐regulatory effects of IL‐10 hinges on the interactions with its receptor (IL‐10R), which is composed of a heterodimer of two subunits: high‐affinity IL‐10RA and low‐affinity IL‐10RB.
18
Our data showed that CPT1A deficiency could not only inhibit the mRNA level and secretion of IL‐10, but also reduce the expression of its receptor IL‐10RA in LPS‐stimulated BMDMs (Figure 3). To address mechanistic links to IL‐10 gene modulation, we investigated the transcription factor necessary for IL‐10 expression in BMDMs, and discovered the existence of CPT1A culminated in activation of PBX1 and MEIS1 (Figure 3).
43
,
44
Therefore, we hypothesised that CPT1A‐predominant FAO at least partly depends on the capacity of IL‐10 production in macrophages. Although IL‐10 has previously been illustrated to take part in regulating cellular glycolysis, our study complements the gap in the role of CPT1A‐IL‐10 axis in immunoinflammatory modulation by elevating macrophage FAO level.
There are some shortcomings in the current academic exploration. We showed that the FAO rate‐limiting enzyme CPT1A suppressed the inflammatory responses of macrophages in an IL‐10‐dependent manner, leading to the alleviation of acute lung injury. However, the potential role of CPT1A in regulating other cell types in the lungs, and their contributions to the inflammatory response during the progression of ALI/ARDS, remains to be explored. A second limitation is that the underlying mechanism by which CPT1A regulates the expression of IL‐10/IL‐10RA in macrophages upon LPS exposure was not fully illustrated in this study. Additionally, the clinical relevance of CPT1A in acute pulmonary inflammatory diseases in humans needs to be validated using clinical biopsy, serum and Balf samples. Furthermore, the Lyz2‐cre system used to generate the conditional Cre+
CPT1A
fl/fl mice is primarily expressed in mouse myeloid cells, such as neutrophils and monocytes in the bone marrow. This leads to the possibility that other myeloid cell lines are also affected in our mouse model. Last but not the least, IL‐10 has been illustrated could inhibit glucose uptake and glycolysis. However, according to our results that IL‐10 increased cellular ATP, citrate and acetyl‐CoA in macrophages lacking CPT1A, which were expected to already have limited mitochondrial FAO capacity. We deemed due to the reduced glycolysis mediated by IL‐10, which is expected to further constrain substrate (pyruvate) availability for mitochondrial metabolism; yet our data suggest that mitochondrial metabolism is increased – presumably due to enhanced amino acid catabolism, which is also crucial for maintaining proper immune cell function during the immune response. The alternatively activated progress of macrophage could lead to significant changes in amino acid catabolism, including arginine and proline metabolism.
45
By contrast, classically activated M1‑type macrophages mainly rely on glutamine metabolism.
7
,
46
Therefore, changes in amino acid metabolism can affect the disease process by regulating the activation state of macrophages, which is also worthy of further investigation in the future.
In conclusion, we identified the reduced expression of CPT1A in macrophages of LPS‐induced ALI mouse model. By constructing mice harbouring a conditional depletion of macrophage CPT1A (Cre+
CPT1A
fl/fl), we intuitively observed the protective role of CPT1A in inhibiting neutrophil accumulation, and reducing lung inflammatory damage. By means of mouse BMDMs, CPT1A exhibited preeminent anti‐inflammatory and mitochondria‐regulatory effects on the cells challenged by LPS. Mechanistically, CPT1A could not only modulate metabolic reprogramming between glycolysis and FAO‐OXPHOS but also suppress macrophage polarisation towards M1 phenotype induced by LPS. Besides, CPT1A has capable of increasing the expression of IL‐10/IL‐10RA by activating the transcription factors. When lack of endogenous IL‐10, similar reactions occurred both in ALI mice and macrophages upon LPS stimulation in comparison with the Cre+
CPT1A
fl/fl group. We further ascertained that the addition of exogenous IL‐10 to Cre+
CPT1A
fl/fl macrophages effectively reversed the metabolic program associated with the inflammatory response caused by LPS. However, CPT1A agonist supplementation could not correct the aberrant inflammation and impaired mitochondrial dynamics in IL‐10
−/− macrophages. Consequently, we anticipated that CPT1A‐IL‐10 axis, bridging cellular metabolism and immunological function in macrophages, may pave the way for new approaches to address inflammatory diseases, particularly ALI/ARDS, where effective treatments are currently lacking.
AUTHOR CONTRIBUTIONS
Wei Gao, Kun Wang and Qiang Li are responsible for the content of the manuscript. They conceived and designed the study. Muyun Wang, Ximing Liao, Haiyang Hu, Di Wu, Jing Gao and Linlin Meng carry out experiments with technical guidance from Wei Gao, Qiang Li and Kun Wang, Feilong Wang gives guidance on the Seahorse experiments. Muyun Wang, Wujian Xu, Shaoyong Gao, Jing Hua and Yuanyuan Wang coordinated to analyse the data and write the manuscript. Wei Gao, Kun Wang and Qiang Li carefully revised the entire article. All authors read and approved the final manuscript.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interests.
ETHICS STATEMENT
All animal experiments were implemented under the guidelines of Shanghai Committee for Accreditation of Laboratory Animal, and the research protocol was approved by the Laboratory Animal Research Center Review Board of Tongji University (Permit Number: TJBB03721106) (Shanghai, China). All surgery was performed under pentobarbital sodium anaesthesia with efforts to minimise animal suffering.
Supporting information
Supporting information |
Title: Analysis of the shorter drug survival times for Janus kinase inhibitors and interleukin-17 inhibitors compared with tumor necrosis factor inhibitors in a real-world cohort of axial spondyloarthritis patients - a retrospective analysis from the RHADAR network | Body:
Introduction
In addition to disease activity and treatment response measures, treatment persistence is another crucial factor when considering and comparing treatment efficacy in axial spondyloarthritis (axSpA), a rheumatic disease with the focus on the axial skeleton [1–3]. In this context, the term “drug survival” was introduced to depict the likelihood of patients staying on a specific medication [2, 3]. Drug survival is thus used as an approximation of the efficacy and safety of a drug [2, 3].
Studying drug survival in a real-world scenario in outpatient care distinct from specialized study centers is difficult due to the challenge of collecting suitable and adequate data [4]. New digital tools and smart databases can help with collecting and evaluating patients’ data for study purposes, especially when the disease is seldom like axSpA. The RHADAR network was founded to aggregate real-world data on patients with rheumatic diseases [4]. It currently consists of seven rheumatic practices and one outpatient department of a hospital in Germany, specialized in rheumatology, that supply pseudonymized data for aggregation into the RHADAR joint database [4].
In axSpA, treatment options with disease-modifying antirheumatic drugs (DMARD) have evolved over the last decade. Three drug classes with different modes of action (MoA) have been approved for moderate to severe axSpA in Europe and the USA as of February 2024, specifically tumor necrosis factor alpha inhibitors (TNFi), interleukin (IL)-17 inhibitors (IL-17i), and Janus kinase inhibitors (JAKi) [5, 6]. While drug survival of TNFi and IL-17i has been studied intensively over the last decade, sufficient data for JAKi, the drug class most recently approved, are still not widely available [3, 7–11]. Similarly, the impact of orally available agents such as JAKi on real-world persistence with bDMARDs delivered by more invasive methods still needs to be further studied in axSpA.
In this analysis, we aimed to assess the drug survival of JAKi compared with bDMARDs (TNFi and IL-17i) among German outpatients treated for axSpA under real-world conditions.
Methods
Study design
Using the RHADAR database, a retrospective analysis was performed for axSpA patients who initiated treatment with a TNFi, IL-17i, or JAKi between January 15th 2015 and October 17th 2023. All approved IL-17i (ixekizumab, secukinumab), TNFi (adalimumab, certolizumab, etanercept, golimumab, and infliximab), and JAKi (tofacitinib and upadacitinib) were clustered by MoA and included in the analysis. Bimekizumab was not included in the analysis due to the short period since its regulatory approval for axSpA (June 7th 2023) [12]. Similar analyses on drug survival of JAKi and different bDMARDS among German psoriatic arthritis outpatients from the RHADAR database using the same methodology have already been published elsewhere [13].
Patients
Patients included in the analyses had a diagnosis of axSpA as indicated by International Classification of Disease-10 (ICD-10) codes (German 2023 version) and had previously given written informed consent to be included in the RHADAR database and to allow the use of their pseudonymized data for analyses. All patients were treated in outpatient departments in Germany (i.e. seven rheumatologic practices and one outpatient department of a rheumatologic hospital). The treating rheumatologists assessed the relevant demographic factors and axSpA activity parameters, including Bath Ankylosing Spondylitis Disease Activity Index (BASDAI, ranging from 0 to 10), Bath Ankylosing Spondylitis Functional Index (BASFI, range 0 to 10), Ankylosing Spondylitis Disease Activity Score (ASDAS with a range from 0 to > 3.5), and laboratory parameters, during routine clinical care [14].
Study size
A sample size calculation was not performed due to the retrospective character of the study. Accordingly, the number of database entries determined the number of axSpA patients available for the analysis.
Ethical approval
The patients gave written informed consent for pseudonymized Inclusion in RHADAR database and for analysis of their data. The ethics committee of the Medical Faculty of the University of Wuerzburg (DE/EKBY13) stated that the study represents a retrospective data analysis of pseudonymzied data assessed in clinical routine care. Therefore, ethical approval by German law is not required when the publication of data is performed in anonymous form (Date February 19th, 2024, No 20240122 03).
Statistical analysis
We performed a Kaplan-Meier (KM) analysis for comparison of the drug persistence of each MoA over 24 months, similar to our previous work concerning PsA patients [13]. An event was defined as discontinuation of a bDMARD or JAKi therapy. Demographic parameters, axSpA specific disease parameters, combined medication, and comorbidities were used to characterize the overall and MoA-specific patient populations. For sample characterization, the following descriptive measures were used: absolute and relative frequencies, means with standard deviation (SD), 95% confidence intervals (CI) of the mean, and medians with interquartile range (25th and 75th percentiles). If not stated otherwise, mean (SD) values are reported.
To assess the influence of MoA on drug discontinuation risk over time, we conducted Cox regression analysis with the MoA exhibiting the longest drug persistence serving as reference. Drug survival rates at month x were calculated by the formula (n [at risk at month x) + n [cumulative censored at month x]) ÷ n [at risk at month 0]. The corresponding model was adjusted for age, sex, and disease duration as potential confounders. Hazard ratios (HRs) with corresponding 95% CIs were reported for each MoA as well as each covariate (age, gender, disease duration). Statistical significance was set at p ≤ 0.05. Statistical analyses were performed using R software (version 4.4.0) and RStudio (version 2023.12.1 + 402) [15, 16]. For dichotomous variable distribution analysis, 95% CIs, odds ratios (OR), and chi-square tests were utilized. Prism Version 5 was utilized for these analyses. Missing values were not imputed to preserve the original data integrity. To evaluate treatment response for discontinued therapies, BASDAI and ASDAS changes from start of the discontinued therapy to the start of the subsequent prescription were calculated.
Study design
Using the RHADAR database, a retrospective analysis was performed for axSpA patients who initiated treatment with a TNFi, IL-17i, or JAKi between January 15th 2015 and October 17th 2023. All approved IL-17i (ixekizumab, secukinumab), TNFi (adalimumab, certolizumab, etanercept, golimumab, and infliximab), and JAKi (tofacitinib and upadacitinib) were clustered by MoA and included in the analysis. Bimekizumab was not included in the analysis due to the short period since its regulatory approval for axSpA (June 7th 2023) [12]. Similar analyses on drug survival of JAKi and different bDMARDS among German psoriatic arthritis outpatients from the RHADAR database using the same methodology have already been published elsewhere [13].
Patients
Patients included in the analyses had a diagnosis of axSpA as indicated by International Classification of Disease-10 (ICD-10) codes (German 2023 version) and had previously given written informed consent to be included in the RHADAR database and to allow the use of their pseudonymized data for analyses. All patients were treated in outpatient departments in Germany (i.e. seven rheumatologic practices and one outpatient department of a rheumatologic hospital). The treating rheumatologists assessed the relevant demographic factors and axSpA activity parameters, including Bath Ankylosing Spondylitis Disease Activity Index (BASDAI, ranging from 0 to 10), Bath Ankylosing Spondylitis Functional Index (BASFI, range 0 to 10), Ankylosing Spondylitis Disease Activity Score (ASDAS with a range from 0 to > 3.5), and laboratory parameters, during routine clinical care [14].
Study size
A sample size calculation was not performed due to the retrospective character of the study. Accordingly, the number of database entries determined the number of axSpA patients available for the analysis.
Ethical approval
The patients gave written informed consent for pseudonymized Inclusion in RHADAR database and for analysis of their data. The ethics committee of the Medical Faculty of the University of Wuerzburg (DE/EKBY13) stated that the study represents a retrospective data analysis of pseudonymzied data assessed in clinical routine care. Therefore, ethical approval by German law is not required when the publication of data is performed in anonymous form (Date February 19th, 2024, No 20240122 03).
Statistical analysis
We performed a Kaplan-Meier (KM) analysis for comparison of the drug persistence of each MoA over 24 months, similar to our previous work concerning PsA patients [13]. An event was defined as discontinuation of a bDMARD or JAKi therapy. Demographic parameters, axSpA specific disease parameters, combined medication, and comorbidities were used to characterize the overall and MoA-specific patient populations. For sample characterization, the following descriptive measures were used: absolute and relative frequencies, means with standard deviation (SD), 95% confidence intervals (CI) of the mean, and medians with interquartile range (25th and 75th percentiles). If not stated otherwise, mean (SD) values are reported.
To assess the influence of MoA on drug discontinuation risk over time, we conducted Cox regression analysis with the MoA exhibiting the longest drug persistence serving as reference. Drug survival rates at month x were calculated by the formula (n [at risk at month x) + n [cumulative censored at month x]) ÷ n [at risk at month 0]. The corresponding model was adjusted for age, sex, and disease duration as potential confounders. Hazard ratios (HRs) with corresponding 95% CIs were reported for each MoA as well as each covariate (age, gender, disease duration). Statistical significance was set at p ≤ 0.05. Statistical analyses were performed using R software (version 4.4.0) and RStudio (version 2023.12.1 + 402) [15, 16]. For dichotomous variable distribution analysis, 95% CIs, odds ratios (OR), and chi-square tests were utilized. Prism Version 5 was utilized for these analyses. Missing values were not imputed to preserve the original data integrity. To evaluate treatment response for discontinued therapies, BASDAI and ASDAS changes from start of the discontinued therapy to the start of the subsequent prescription were calculated.
Results
Study population
A new prescription for a bDMARD or JAKi therapy was reported for 1222 axSpA cases with additional patient characteristics (668 males [54.7%] and 554 females [45.3%]) from the RHADAR database. Patient characteristics obtained at the start of the therapy are detailed in Table 1. A mean (SD) age of 47.0 years (13.4 years) was found. The average BASDAI was 4.3 (2.3) and the mean ASDAS was 2.5 (0.9).
Table 1Patient characteristics at treatment initiation (N = 1222)Valid (n)Valid (%)Mean95%CI (lower)95%CI (upper)SDSEMMedian25% Quantile75% QuantileGender (male)66854.7NANANANANANANANAGender (female)55445.3NANANANANANANANAAge1222100.047.046.247.713.40.447.037.057.0Disease duration (years)104785.712.611.913.311.30.39.04.019.0BASDAI80766.04.34.24.52.30.14.42.56.0BASFI59548.73.63.43.82.70.13.21.35.7ASDAS42134.52.52.42.60.90.02.51.83.1ESR (mm/h)63752.118.617.320.017.50.712.08.024.0CRP (mg/dl)77663.50.70.60.92.00.10.30.10.6Pain (0-100)36830.141.738.844.527.91.541.016.064.2Morning stiffness (min)34228.070.451.489.3178.69.730.016.260.0
Treatment persistence
During the analyzed period, 1222 new treatments with bDMARDs (TNFi [954 cases], IL-17i [190 cases]) or JAKi (78 cases) were reported. The median drug survival was 31 months for TNFi, 25 months for IL-17i, and 18 months for JAKi. Figure 1 shows the KM diagram for drug survival of the different MoA. Most discontinuations occurred within the first 12 months of treatment initiation. After this initial period, discontinuation rates decreased, leading to fairly constant persistence rates for a given drug class from 1 to 2 years (Table 2). The corresponding 1-year drug survival probability was 84.4% for TNFi, followed by 75.3% for IL-17i and 65.4% for JAKi. A 2-year drug survival rate of 79.6% was found for TNFi compared with 72.6% for IL-17i and 62.8% for JAKi (Table 2).
Fig. 1Drug persistence in axSpA patients
Table 2Risk tableMode of actionMonthAt risk (n)At risk (%)Cumulative events (n)Cumulative censored (n)Strata size (n)Drug survivalpropabilityIL-17i019010050190100.0%IL-17i1212264482119075.3%IL-17i249952533919072.6%JAKi0781008078100.0%JAKi1246592757865.4%JAKi24263329237862.8%TNFi0954100190954100.0%TNFi127027415510395484.4%TNFi245595919820095479.6%
Cox regression with TNFi serving as the reference drug class indicated JAKi (HR 1.91 [95% CI 1.22–2.99]) and IL-17i (HR 1.43 [95% CI 1.02–2.01]) to have a significantly higher probability of drug discontinuation compared to TNFi. No difference was observed between JAKi and IL-17i in a supplemental Cox regression with IL-17i serving as the reference drug class (HR 1.33 [95% CI 0.79–2.24]).
Female gender (with male gender as reference) (HR = 1.97 [95% CI 1.50–2.59 ]) and shorter disease duration (HR = 0.98 [95% CI 0.97–0.99]) were found as independent covariates affecting drug survival in Cox regression analyses.
Changes in disease activity during treatment
Due to the structure of the RHADAR database, a direct data query for reasons of drug discontinuation was not possible. To gain insights into whether adverse events or loss of effectiveness were the main cause for drug discontinuation, we analyzed the available changes regarding BASDAI and ASDAS during treatment for each MoA (Table 3). Corresponding change scores ranged from − 0.5 to − 0.7 for BASDAI and were equal to − 0.1 for ASDAS for all drug classes included. We only found mild negative delta values and no differences in the mean delta BASDAI or mean delta ASDAS between the three MoA, indicating that the main cause for treatment change was primary loss of efficacy and not adverse events. No difference was seen between the three MoA.
Table 3BASDAI and ASDAS changes from start of a new therapy until discontinuationAssessmentTNFi(n = 198)IL-17i(n = 53)JAKi(n = 29)n (%)Mean(95% CI)n (%)Mean(95% CI)n (%)Mean(95% CI)BASDAI106 (53.5)− 0.5 (0.2 − 0.8)30 (56.6)− 0.7 (0.2 − 1.1)18 (62.1)-0.6 (0.1–1.2)ASDAS48 (24.2)-0.1 (-0.1-0.3)13 (24.5)− 0.1 (-0.1 -0.3)10 (34.5)-0.1 (0.0–0.3)
Subgroup analysis stratified by drug classes
Potential effects of varying group characteristics were analyzed to reveal potential impacts on drug survival (see Table 4 for an overview of the characteristics by drug class and supplemental section S1 for additional details).
Table 4Patient characteristics stratified by treatment MoACharacteristicTNFi(n = 954 )IL-17i(n = 190)JAKi(n = 78)n (%)Mean(95% CI)n (%)Mean(95% CI)n (%)Mean(95% CI)Age954 (100)46.7 (45.8–47.6)190 (100)47.0 (45.3–48.8)78 (100)50.0 (47.0–53.0)Gender (male)528 (55.3)-105 (55.3)-35 (44.9)-Gender (female)426 (44.7)-85 (44.7)-43 (55.1)-Disease duration (years)823 (86.3)12.6 (11.8–13.4)160 (84.2)12.7 (10.9–14.4)64 (82.1)12.7 (10.1–15.2)BASDAI624(65.4)4.1 (3.9–4.2)127 (66.8)5.3 (4.9–5.6)56 (71.8)5.2 (4.6–5.7)BASFI461 (48.3)3.3 (3.1–3.6)91 (47.9)4.6 (4.1–5.2)43 (55.1)4.7 (3.9–5.6)ASDAS311 (32.6)2.4 (2.3–2.5)70 (36.8)2.8 (2.6–3.0)40 (51.3)3.0 (2.7–3.3)ESR (mm/h)487 (51.0)18.6 (17.0–20.1)102 (53.7)18.3 (15.1–21.6)48 (61.5)19.9 (14.9–24.9)CRP (mg/dl)589 (61.7)0.6 (0.5–0.8)130 (68.4)1.1 (0.5–1.8)57 (73.1)0.8 (0.4–1.2)Pain (0-100)273 (28.6)37.0 (33.8–40.2)64 (33.7)54.8 (48.7–61.0)31 (39.7)55.3 (45.4–65.3)
Patients in the TNFi subgroup had slightly lower disease activity parameter (BASDAI, ASDAS) compared with IL-17i- and JAKi-treated patients (BASDAI of 4.1 [TNFi] vs. 5.2 [JAKi] vs. 5.3 [IL-17i], ASDAS of 2.4 [TNFi] vs. 3.0 [JAKi] vs. 2.8 [IL-17i]) when considering the 95% confidence intervals. No difference was found between the JAKi and IL-17i subgroups. TNFi-treated patients had an average of 0.5 previous bDMARD/tsDMARD treatments, IL-17i-treated patients received an average of 1.1 prior treatments, and JAKi-treated patients had an average of 1.9 previous therapies.
Simultaneous conventional synthetic DMARD therapies and relevant comorbidities with influence on drug survival
Combination with a conventional synthetic DMARD (csDMARD) and comorbidities might affect drug survival [17–21]. Firstly, we therefore analyzed the RHADAR database to assess whether the bDMARD or JAKi therapies were administered as monotherapy or in combination with a csDMARD. There were no significant differences in the frequencies of concomitant treatment among the three drug classes (TNFi 11.0%, JAKi 16.7%, IL-17i 8.9%) (see also Supplemental section S2). Methotrexate was the most common csDMARD used in concomitant therapy (IL-17i 8.4%, JAKi 15.4%, and TNFi 10.3%). Sulfasalazine was seldom used in combined therapy (IL-17i 0.5%, JAKi 1.3%, TNFi 0.6%) and hydroxychloroquine only in one TNFi patient (0.1%).
Secondly, we analyzed the drug classes for their distribution of reported comorbidities based on the database entries (Table 5). While osteoarthritis rates were similar among the three subgroups, depression was less common in the TNFi (2.7%) compared with the IL-17i (6.3%) and JAKi (5.1%) groups. Coronary heart disease as well as overall cardiovascular risk factors (except for obesity) were similarly distributed. Smoking and diabetes mellitus were not reported in the JAKi group. Regarding axSpA-specific comorbidities, the highest rate of uveitis was found in the JAKi group (16.7% vs. 5.3% [IL-17i] vs. 4.4% [TNFi]) and Crohn’s disease/ inflammatory bowel diseases were only reported in the TNFi group (2.3%).
Table 5Comorbidities by drug classComorbidityaTNFi(n = 954)IL-17i(n = 190)JAKi(n = 78)Osteoarthritis169 (17.7%)45 (23.7%)18 (23.1%)Uveitis42 (4.4%)10 (5.3%)13 (16.7)Depression26 (2.7%)12 (6.3%)4 (5.1%)Chronic inflammatory bowel disease22 (2.3%)00Coronary heart disease39 (4.1%)11 (5.8%)4 (5.1%)Obesity35 (3.7%)11 (5.8%)5 (6.4%)Cardiovascular risk factors340 (35.6%)78 (41.1%)24 (30.8%)Missing110 (11.5%)17 (9.0%)1 (1.3%)No comorbidity21 (2.2%)2 (1.1%)1 (1.3%)Data are reported as n (%)aBased on ICD-10 codes: osteoarthritis, M15-M19; depression, F32; coronary heart disease, I25; obesity, E66; cardiovascular risk factors, I10, E78, E11-E14; F17; uveitis, H20; chronic inflammatory bowel disease/ Crohn´s disease K50
Study population
A new prescription for a bDMARD or JAKi therapy was reported for 1222 axSpA cases with additional patient characteristics (668 males [54.7%] and 554 females [45.3%]) from the RHADAR database. Patient characteristics obtained at the start of the therapy are detailed in Table 1. A mean (SD) age of 47.0 years (13.4 years) was found. The average BASDAI was 4.3 (2.3) and the mean ASDAS was 2.5 (0.9).
Table 1Patient characteristics at treatment initiation (N = 1222)Valid (n)Valid (%)Mean95%CI (lower)95%CI (upper)SDSEMMedian25% Quantile75% QuantileGender (male)66854.7NANANANANANANANAGender (female)55445.3NANANANANANANANAAge1222100.047.046.247.713.40.447.037.057.0Disease duration (years)104785.712.611.913.311.30.39.04.019.0BASDAI80766.04.34.24.52.30.14.42.56.0BASFI59548.73.63.43.82.70.13.21.35.7ASDAS42134.52.52.42.60.90.02.51.83.1ESR (mm/h)63752.118.617.320.017.50.712.08.024.0CRP (mg/dl)77663.50.70.60.92.00.10.30.10.6Pain (0-100)36830.141.738.844.527.91.541.016.064.2Morning stiffness (min)34228.070.451.489.3178.69.730.016.260.0
Treatment persistence
During the analyzed period, 1222 new treatments with bDMARDs (TNFi [954 cases], IL-17i [190 cases]) or JAKi (78 cases) were reported. The median drug survival was 31 months for TNFi, 25 months for IL-17i, and 18 months for JAKi. Figure 1 shows the KM diagram for drug survival of the different MoA. Most discontinuations occurred within the first 12 months of treatment initiation. After this initial period, discontinuation rates decreased, leading to fairly constant persistence rates for a given drug class from 1 to 2 years (Table 2). The corresponding 1-year drug survival probability was 84.4% for TNFi, followed by 75.3% for IL-17i and 65.4% for JAKi. A 2-year drug survival rate of 79.6% was found for TNFi compared with 72.6% for IL-17i and 62.8% for JAKi (Table 2).
Fig. 1Drug persistence in axSpA patients
Table 2Risk tableMode of actionMonthAt risk (n)At risk (%)Cumulative events (n)Cumulative censored (n)Strata size (n)Drug survivalpropabilityIL-17i019010050190100.0%IL-17i1212264482119075.3%IL-17i249952533919072.6%JAKi0781008078100.0%JAKi1246592757865.4%JAKi24263329237862.8%TNFi0954100190954100.0%TNFi127027415510395484.4%TNFi245595919820095479.6%
Cox regression with TNFi serving as the reference drug class indicated JAKi (HR 1.91 [95% CI 1.22–2.99]) and IL-17i (HR 1.43 [95% CI 1.02–2.01]) to have a significantly higher probability of drug discontinuation compared to TNFi. No difference was observed between JAKi and IL-17i in a supplemental Cox regression with IL-17i serving as the reference drug class (HR 1.33 [95% CI 0.79–2.24]).
Female gender (with male gender as reference) (HR = 1.97 [95% CI 1.50–2.59 ]) and shorter disease duration (HR = 0.98 [95% CI 0.97–0.99]) were found as independent covariates affecting drug survival in Cox regression analyses.
Changes in disease activity during treatment
Due to the structure of the RHADAR database, a direct data query for reasons of drug discontinuation was not possible. To gain insights into whether adverse events or loss of effectiveness were the main cause for drug discontinuation, we analyzed the available changes regarding BASDAI and ASDAS during treatment for each MoA (Table 3). Corresponding change scores ranged from − 0.5 to − 0.7 for BASDAI and were equal to − 0.1 for ASDAS for all drug classes included. We only found mild negative delta values and no differences in the mean delta BASDAI or mean delta ASDAS between the three MoA, indicating that the main cause for treatment change was primary loss of efficacy and not adverse events. No difference was seen between the three MoA.
Table 3BASDAI and ASDAS changes from start of a new therapy until discontinuationAssessmentTNFi(n = 198)IL-17i(n = 53)JAKi(n = 29)n (%)Mean(95% CI)n (%)Mean(95% CI)n (%)Mean(95% CI)BASDAI106 (53.5)− 0.5 (0.2 − 0.8)30 (56.6)− 0.7 (0.2 − 1.1)18 (62.1)-0.6 (0.1–1.2)ASDAS48 (24.2)-0.1 (-0.1-0.3)13 (24.5)− 0.1 (-0.1 -0.3)10 (34.5)-0.1 (0.0–0.3)
Subgroup analysis stratified by drug classes
Potential effects of varying group characteristics were analyzed to reveal potential impacts on drug survival (see Table 4 for an overview of the characteristics by drug class and supplemental section S1 for additional details).
Table 4Patient characteristics stratified by treatment MoACharacteristicTNFi(n = 954 )IL-17i(n = 190)JAKi(n = 78)n (%)Mean(95% CI)n (%)Mean(95% CI)n (%)Mean(95% CI)Age954 (100)46.7 (45.8–47.6)190 (100)47.0 (45.3–48.8)78 (100)50.0 (47.0–53.0)Gender (male)528 (55.3)-105 (55.3)-35 (44.9)-Gender (female)426 (44.7)-85 (44.7)-43 (55.1)-Disease duration (years)823 (86.3)12.6 (11.8–13.4)160 (84.2)12.7 (10.9–14.4)64 (82.1)12.7 (10.1–15.2)BASDAI624(65.4)4.1 (3.9–4.2)127 (66.8)5.3 (4.9–5.6)56 (71.8)5.2 (4.6–5.7)BASFI461 (48.3)3.3 (3.1–3.6)91 (47.9)4.6 (4.1–5.2)43 (55.1)4.7 (3.9–5.6)ASDAS311 (32.6)2.4 (2.3–2.5)70 (36.8)2.8 (2.6–3.0)40 (51.3)3.0 (2.7–3.3)ESR (mm/h)487 (51.0)18.6 (17.0–20.1)102 (53.7)18.3 (15.1–21.6)48 (61.5)19.9 (14.9–24.9)CRP (mg/dl)589 (61.7)0.6 (0.5–0.8)130 (68.4)1.1 (0.5–1.8)57 (73.1)0.8 (0.4–1.2)Pain (0-100)273 (28.6)37.0 (33.8–40.2)64 (33.7)54.8 (48.7–61.0)31 (39.7)55.3 (45.4–65.3)
Patients in the TNFi subgroup had slightly lower disease activity parameter (BASDAI, ASDAS) compared with IL-17i- and JAKi-treated patients (BASDAI of 4.1 [TNFi] vs. 5.2 [JAKi] vs. 5.3 [IL-17i], ASDAS of 2.4 [TNFi] vs. 3.0 [JAKi] vs. 2.8 [IL-17i]) when considering the 95% confidence intervals. No difference was found between the JAKi and IL-17i subgroups. TNFi-treated patients had an average of 0.5 previous bDMARD/tsDMARD treatments, IL-17i-treated patients received an average of 1.1 prior treatments, and JAKi-treated patients had an average of 1.9 previous therapies.
Simultaneous conventional synthetic DMARD therapies and relevant comorbidities with influence on drug survival
Combination with a conventional synthetic DMARD (csDMARD) and comorbidities might affect drug survival [17–21]. Firstly, we therefore analyzed the RHADAR database to assess whether the bDMARD or JAKi therapies were administered as monotherapy or in combination with a csDMARD. There were no significant differences in the frequencies of concomitant treatment among the three drug classes (TNFi 11.0%, JAKi 16.7%, IL-17i 8.9%) (see also Supplemental section S2). Methotrexate was the most common csDMARD used in concomitant therapy (IL-17i 8.4%, JAKi 15.4%, and TNFi 10.3%). Sulfasalazine was seldom used in combined therapy (IL-17i 0.5%, JAKi 1.3%, TNFi 0.6%) and hydroxychloroquine only in one TNFi patient (0.1%).
Secondly, we analyzed the drug classes for their distribution of reported comorbidities based on the database entries (Table 5). While osteoarthritis rates were similar among the three subgroups, depression was less common in the TNFi (2.7%) compared with the IL-17i (6.3%) and JAKi (5.1%) groups. Coronary heart disease as well as overall cardiovascular risk factors (except for obesity) were similarly distributed. Smoking and diabetes mellitus were not reported in the JAKi group. Regarding axSpA-specific comorbidities, the highest rate of uveitis was found in the JAKi group (16.7% vs. 5.3% [IL-17i] vs. 4.4% [TNFi]) and Crohn’s disease/ inflammatory bowel diseases were only reported in the TNFi group (2.3%).
Table 5Comorbidities by drug classComorbidityaTNFi(n = 954)IL-17i(n = 190)JAKi(n = 78)Osteoarthritis169 (17.7%)45 (23.7%)18 (23.1%)Uveitis42 (4.4%)10 (5.3%)13 (16.7)Depression26 (2.7%)12 (6.3%)4 (5.1%)Chronic inflammatory bowel disease22 (2.3%)00Coronary heart disease39 (4.1%)11 (5.8%)4 (5.1%)Obesity35 (3.7%)11 (5.8%)5 (6.4%)Cardiovascular risk factors340 (35.6%)78 (41.1%)24 (30.8%)Missing110 (11.5%)17 (9.0%)1 (1.3%)No comorbidity21 (2.2%)2 (1.1%)1 (1.3%)Data are reported as n (%)aBased on ICD-10 codes: osteoarthritis, M15-M19; depression, F32; coronary heart disease, I25; obesity, E66; cardiovascular risk factors, I10, E78, E11-E14; F17; uveitis, H20; chronic inflammatory bowel disease/ Crohn´s disease K50
Discussion
In this study, we provide new data on the drug survival of different drug classes (TNFi, IL-17i, and JAKi) among German axSpA outpatients treated during routine care since approval of JAKi. Most new treatments were initiated with a TNFi, followed by IL-17i and JAKi. The highest drug survival probability was found for TNFi, followed by IL-17i. The lowest rates of persistence were seen in JAKi-treated patients. The most common time period for treatment discontinuation was during the first 12 months after treatment initiation due to lack of effectiveness. Discontinuation subsequently declined, and drug persistence rates remained fairly constant between 12 and 24 months. Using Cox regression models, we found significantly higher adjusted HRs for JAKi and IL-17i drug discontinuation than for TNFi. The risk for discontinuation of IL-17i did not differ significantly from JAKi.
Comparing our study population with the reported German average axSpA population, our patients were slightly younger (mean of 47.0 years [13.4 years]) than those described in two claims database studies (61.2 and 56.5 years), but the male to female ratio was quite similar [22, 23]. Neither database analyses reported disease parameters such as BASDAI [22, 23]. Two other smaller studies of German axSpA cohorts reported generally similar characteristics to our cohort for age (55.9 years/56.1 years) and gender distribution (46.4% female proportion for both) as well as for BASDAI (4.5 for both) [24, 25]. Therefore, we conclude that our study population is representative of the German axSpA population and that our findings are likely to be transferable to other axSpA patients in Germany.
A 2020 meta-analysis of drug survival for all TNFi in ankylosing spondylitis (AS) revealed drug survival rates of 76.0% at one year and around 60–70% at year 2 which is slightly lower than our reported 1-year and 2-year drug survival rate [7]. However, in the study mentioned, the drug survival rates for TNFi showed high variability ranging from 30 to 100% (supplementary file to [7]). Accordingly, interpreting this discrepancy between our data and these reported metadata is challenging. One explanation might be the dualism in the German healthcare system: only treated outpatients were included in the RHADAR database. Disease severity in these patients might be lower than those with more refractory or difficult-to-manage axSpA, who are more likely to be treated in specialized clinical departments and not in rheumatologic practices. Additionally, the TNFi patients in our cohort had a mean of 0.5 previous treatments and, therefore, were often bDMARD naïve, with the administered TNFi being their first-line treatment. Importantly, first-line treatments were found to have higher retention rates than second- or third-line treatments [26]. In line with this observation, the aforementioned meta-analysis found drug retention rates of around 82% for etanercept and of around 80% for adalimumab in bDMARD-naïve patients at year 1, which is similar to our reported rate [7]. A further explanation could be that the broader availability of magnetic resonance imaging and improved diagnostic procedures in Germany over recent years may help to better distinguish flares and disease activity from back pain and degenerative disorders, leading to fewer treatment switches due to perceived treatment failure.
Our observed retention rates for IL-17i were in in line with other reported real-world studies among axSpA patients [7, 9, 10]. Additionally, we found a slightly higher drug discontinuation probability for IL-17i compared to TNFi. The explanation for that might be that the IL-17i treated patients in our cohort had seen more lines of treatment than the TNFi-treated patients. A study of secukinumab retention in axSpA patients revealed that drug survival was better when secukinumab was administered as first-line therapy rather than as a second- or third-line agent [8]. But when used as first-line bDMARD, drug survival of secukinumab did not differ from adalimumab or other TNFi [3, 9]. On the other hand, an observational cohort study of the 5 Nordish registries with almost 11 000 treatment courses indicated that drug retention of secukinumab might be inferior to adalimumab in the third line or higher [9].
Real-world data on the drug persistence of JAKi in axSpA are still not widely available. We only discovered one recent published study from the Spanish BIOBADASER registry with a similar sample size [11]. Their reported retention rate for all JAKi (1-year survival rate of 82.9%) was much higher than our reported rate, but at year 2, their reported survival rate of 64.0% was quite similar to the rates observed in our cohort [11]. When interpreting this discrepancy in the 1-year drug survival, it should be noted that the BIOBADASER study only registered 62 treatment courses with JAKi in axSpA, which corresponds to only 3/4 of the number of cases in our cohort [11]. Their Kaplan Meier curve was correspondingly angularly shaped [11]. Similar to our cohort, JAKi were mainly used in the third line or later, so that the more favorable 1-year survival cannot be easily explained by JAKi usage in an earlier line of therapy [11]. Our data might therefore be more accurate and depict the real-world drug survival more precisely due to the higher number of reported cases, but further studies will be needed to confirm our observations [11]. Interestingly, the probability for discontinuation of JAKi was significantly higher than for TNFi in adjusted Cox regression models in our cohort. For JAKi, this is surprising given studies revealing a strong patient preference for oral over subcutaneous therapies [27, 28]. The analysis from the Spanish BIOBADASER registry found no difference for drug discontinuation between TNFi and JAKi in their axSpA patients which might be interpreted with caution due to the smaller number of treatment courses as mentioned before [11]. On the other hand, the Danish DANBIO registry data and data published from the RHADAR database also showed significantly lower persistence rates for tofacitinib/JAKi compared with TNFi or IL-17i for PsA [3, 13]. When interpreting our drug survival rates of JAKi, it must be taken into account that JAKi-treated patients in our cohort had more previous treatments and higher disease activity parameters than TNFi-treated patients. A suitable interpretation of these findings might be that JAKi does not typically represent the first-line treatment of German axSpA outpatients and is a drug class reserved for more refractory forms of disease, which is in line with the findings reported for JAKi treated patients of the Spanish BIOBADASER register [11]. This would be consistent with observations on shorter drug survival times for patients having received a larger number of previous treatments [9, 11, 26]. Concomitant medication and the presence of osteoarthritis or population characteristics like age did not differ between the drug classes and, therefore, do not appear to explain the observed differences as in other registries [11]. To analyze whether the reason for drug discontinuation differed between the MoA we looked for the change in BASDAI and ASDAS values between treatment initiation and treatment switch as a surrogate for treatment response. The mean delta values exhibited only a mild negative delta and did not differ between the three MoA, indicating that the main reason for drug discontinuation for all MoA was non-response and not adverse events, which is in line with finding of other registries [9–11, 26].
It is also possible that the publication of the ORAL surveillance study data, which revealed an increased risk of cardiovascular events and cancer with tofacitinib, as well as the subsequent changes in the warnings and precautions section of the SmPCs of the JAKi may have prompted clinicians or patients to switch treatments [29, 30].
The findings of our study must be interpreted with caution due to several limiting factors. As a retrospective study based on data from a real-world clinical registry, drug survival was not evaluated prospectively, and groups were not matched or stratified. The RHADAR database is a registry with automatic data transfer from rheumatologic practices; these data are not monitored, which explains the considerable rate of missing values for some parameters. Not all available disease parameters were assessed in all patients, as it is common in routine clinical care, and reasons for discontinuation were not provided. For analysis purposes, specific agents within a drug class were aggregated, which may have affected the reported results. This effect might be more relevant for TNFi than other drug classes due to the greater number of agents in the TNFi drug class. Other studies have reported differences in drug persistence among TNFi inhibitors in patients with axSpA [3, 26]. Our analyses did not distinguish between AS (radiographic axSpA) and non-radiographic axSpA. Some studies, but not all, have found that patients with AS have a prolonged drug survival on bDMARDs compared with patients with non-radiographic axSpA [31–33].
Despite these limitations related to real-world studies, our findings provide unique and valuable insights into real-world drug retention rates of bDMARDs and JAKi in patients with axSpA during a time frame in which multiple agents with different MoA and modes of administration were available. High costs and the difficulty of performing study activities during routine outpatient care make it challenging to perform real-world drug survival studies. However, these studies are critical because they depict outcomes observed during routine care and may provide information to guide medical and economic decision-making in the healthcare sector.
Based on our data, we conclude that TNFi is still the preferred drug class for patients with axSpA who are initiating treatment with a new bDMARD or JAKi in the German outpatient sector. JAKi appear to be reserved for more severe or refractory forms of axSpA, and this preference could potentially be further intensified in the future by the findings of the ORAL surveillance study and the corresponding Pharmacolovigilance Risk Assessment Committee recommendations [29, 30]. Higher usage as first-line agents in patients with less severe or refractory disease might explain the longer drug survivals for TNFi compared with JAKi and IL-17i, but other factors, including effectiveness, may also play a role.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1 |
Title: Mortality Rate and Years of Life Lost Due to Breast and Gynecologic Cancers in Southern Iran 2004–2019: A Population-Based Study | Body:
Introduction
Breast cancer is the most common cancer in women worldwide (1, 2) with approximately 1.7 million cases diagnosed and more than 7 billion USD medical costs annually (3, 4). The annual rate of breast cancer had 3.1% increase from 1980 to 2010 (5). Based on reports from WHO in 2020, half a million women in low- and middle-income countries pass away because of breast cancer, accounting for almost 75% of worldwide deaths from the disease (6). In Iranian populations, breast cancer is one of the most common cancers, with the incidence of ranging 22–120 per 100,000 women (7). Age-standardized Years of Life Lost rate (ASYR) had an increase from 85.1 (65.6 to 110.4) per 100,000 people in 1990 to 196.1 (156.5 to 245.7) per 100,000 people in 2010, followed by an insignificant decline of 20%, which reached to 151.1 (119.7 to 190.6) per 100,000 people in 2015 (8).
The annual economic burden of gynecological cancers in Iran was estimated at 51 million USD, consisting the direct costs of 32 million USD and the indirect costs of 19 million USD (9). About 240,000 women diagnose with ovarian cancer every year in the world to make it the seventh most common women cancers (2). Almost 70% of ovarian cancers are detected at the advanced stage; that is, the 5-year survival of women with such cancers is estimated as only 30% (10). Its’ incidence and mortality have been increased in Iran in the last decade (11).
Cervical cancer is the second most common cancer amongst women (12). The estimated age-standardized incidence rate (ASR) of cervical neoplasm is about 13.1 per 100,000 women globally (13). The mean cervical cancer ASR is reported as 2.5 per 100,000 women in Iran, with a mortality-to-incidence ratio of 42% (9).
Previous studies have been only focused on mortality, incidence or prevalence of breast, ovary, or cervical cancers in Iran. Few researches have addressed the issue of Years of Life Lost (YLL) attributed to the breast, ovary and cervical cancers in Iran.
We aimed to evaluate and track the trends of the mortality rate and YLLs of breast, ovary and cervical cancer in Southern Iran women from 2004 to 2019.
Materials and Methods
Data
In this population-based study, mortality rate and YLL due to the breast and gynecologic (i.e., uterine, vaginal and vulvar) cancers were assessed in the Fars Province, southern Iran, during 2004–2019. This data was collected from “Electronic Death Registry System (EDRS)”, Fars Province. We included all deaths due to the just-mentioned cancers amongst females aged >20 yr, stratified by the age groups (i.e., 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, and >80 yr). The corresponding ICD-10 codes were C50 and C53–56. Duplicated events were excluded by any similarities in national identification ID as well as the date of death.
Statistical analysis
The population of the Fars Province was estimated using local health centers’ databases and national census reports from 1996 to 2016, which was adjusted with respect to the estimated annual population growth as well as the standard population for low-and middle-income countries in 2013 (14).
The crude mortality rate (CMR) and ASR per 100,000 people were estimated using the Statistical Package for Social Sciences (SPSS) (Ver. 22.0 for Windows, Released 2013. Armonk, NY: IBM Corp.). The trends of CMR and ASR for each studied cancer during 2004–2019 were assessed by χ2 test. The significance level of p value was set at .05.
To estimate YLL, the WHO’s 2015 “YLL template”, which ran in Microsoft Excel spreadsheet (2016), was used. YLLs were calculated by the method represented in the WHO’s second edition of “National burden of disease studies: a practical guide” booklet in 2001 (15).
YLL=N Ce(ra) / (β+r)2 [e−(β+r) (L+a) [−(β+r) (L+a)−1]−e−(β+r)a [−(β+r) a−1]]
N= the number of deaths is at a certain age and gender.L= the standard of living of the deceased is the same age and gender.r = Discounting Rate is equal to 0.03.β = the contract rate in calculating the age value is equal to 0.04.C is a modified constant value equal to 0.1658 and β is equal to 0.04 and these two numbers estimate the value of different ages (x).a = the age at which death occurrede = constant; equivalent to 2.71.
Moreover, to assess the trend of YLL rates per 1,000 people during the studied period, joinpoint regression based on the log-linear model was applied. Joinpoint regression analysis describes changing trends over successive segments of time as well as the levels of increase or decrease within each segment. The resulting line segment between join points is described by the annual percent change (APC) that is based on the slope of the line segment and the average annual percent change (AAPC). The analysis for the trend was carried out by Joinpoint Regression Program 4.9.0.0.
Ethics approval
This study was approved by the local Ethics Committee of Shiraz University of Medical Sciences (code: IR.SUMS.REC.1399.772).
Data
In this population-based study, mortality rate and YLL due to the breast and gynecologic (i.e., uterine, vaginal and vulvar) cancers were assessed in the Fars Province, southern Iran, during 2004–2019. This data was collected from “Electronic Death Registry System (EDRS)”, Fars Province. We included all deaths due to the just-mentioned cancers amongst females aged >20 yr, stratified by the age groups (i.e., 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, and >80 yr). The corresponding ICD-10 codes were C50 and C53–56. Duplicated events were excluded by any similarities in national identification ID as well as the date of death.
Statistical analysis
The population of the Fars Province was estimated using local health centers’ databases and national census reports from 1996 to 2016, which was adjusted with respect to the estimated annual population growth as well as the standard population for low-and middle-income countries in 2013 (14).
The crude mortality rate (CMR) and ASR per 100,000 people were estimated using the Statistical Package for Social Sciences (SPSS) (Ver. 22.0 for Windows, Released 2013. Armonk, NY: IBM Corp.). The trends of CMR and ASR for each studied cancer during 2004–2019 were assessed by χ2 test. The significance level of p value was set at .05.
To estimate YLL, the WHO’s 2015 “YLL template”, which ran in Microsoft Excel spreadsheet (2016), was used. YLLs were calculated by the method represented in the WHO’s second edition of “National burden of disease studies: a practical guide” booklet in 2001 (15).
YLL=N Ce(ra) / (β+r)2 [e−(β+r) (L+a) [−(β+r) (L+a)−1]−e−(β+r)a [−(β+r) a−1]]
N= the number of deaths is at a certain age and gender.L= the standard of living of the deceased is the same age and gender.r = Discounting Rate is equal to 0.03.β = the contract rate in calculating the age value is equal to 0.04.C is a modified constant value equal to 0.1658 and β is equal to 0.04 and these two numbers estimate the value of different ages (x).a = the age at which death occurrede = constant; equivalent to 2.71.
Moreover, to assess the trend of YLL rates per 1,000 people during the studied period, joinpoint regression based on the log-linear model was applied. Joinpoint regression analysis describes changing trends over successive segments of time as well as the levels of increase or decrease within each segment. The resulting line segment between join points is described by the annual percent change (APC) that is based on the slope of the line segment and the average annual percent change (AAPC). The analysis for the trend was carried out by Joinpoint Regression Program 4.9.0.0.
Ethics approval
This study was approved by the local Ethics Committee of Shiraz University of Medical Sciences (code: IR.SUMS.REC.1399.772).
Results
During 2004–2019 in the Fars Province, 1886 and 829 deaths occurred due to breast and gynecologic cancers, respectively. Generally, ASR due to breast and gynecologic cancers showed a significant increasing trend from 2004 to 2019 in the Fars Province, with 5.3 per 100,000 people (95% CI: 4.6, 6.0) in 2004 to 16.5 per 100,000 people (95% CI: 15.4, 17.7) in 2019 for breast cancer (P <0.001), 1.2 per 100,000 people (95% CI: 0.9, 1.5) in 2004 to 3.8 per 100,000 people (95% CI: 3.3, 4.3) in 2019 for cervical/uterine/vaginal/vulvar cancers (P <0.001), and 0.2 per 100,000 people (95% CI: 0.1, 0.3) in 2004 to 3.2 per 100,000 people (95% CI: 2.7, 3.7) in 2019 for ovary cancer (P <0.001) (Table 1).
Table 1:
Absolute mortality, CMR (per 100,000 people), ASR (per 100,000 people), and YLL due to breast and gynecologic cancers in southern Iran, 2004–2019 [“cervical cancer” is also included uterine/vaginal/vulvar cancers]
Year
Death, n
CMR
ASR (95% CI)
YLL, n
Breast
Cervical†
Ovary
Breast
Cervical†
Ovary
Breast
Cervical†
Ovary
Breast
Cervical†
Ovary
2004
41
9
2
3.8
0.8
0.2
5.3 (4.6, 6.0)
1.2 (0.9, 1.5)
0.2 (0.1, 0.3)
814
125
35
2005
49
18
3
4.4
1.6
0.3
6.0 (5.2, 6.7)
2.4 (2.0, 2.8)
0.4 (0.2, 0.6)
933
319
64
2006
62
30
8
5.4
2.6
0.7
7.4 (6.6, 8.2)
3.7 (3.2, 4.3)
1.1 (0.8, 1.4)
1,177
509
143
2007
82
32
11
6.9
2.7
0.9
9.4 (8.5, 10.3)
3.7 (3.2, 4.3)
1.4 (1.1, 1.7)
1,570
597
174
2008
98
23
24
8.0
1.9
2.0
10.4 (9.4, 11.3)
2.7 (2.3, 3.2)
2.6 (2.1, 3.1)
1,829
350
462
2009
74
15
20
5.9
1.2
1.6
7.2 (6.4, 8.0)
1.5 (1.1, 1.8)
2.1 (1.6, 2.6)
1,342
259
377
2010
85
19
19
6.5
1.5
1.5
8.2 (7.4, 9.1)
1.9 (1.5, 2.3)
1.8 (1.4, 2.2)
1,503
304
334
2011
106
26
29
7.9
1.9
2.2
9.9 (9.0, 10.8)
2.6 (2.1, 3.0)
3.0 (2.5, 3.5)
1,807
448
525
2012
111
24
23
8.1
1.8
1.7
9.6 (8.6, 10.5)
2.1 (1.6, 2.5)
1.9 (1.5, 2.3)
1,401
293
288
2013
115
24
29
8.3
1.7
2.1
10.3 (9.4, 11.2)
2.3 (1.8, 2.8)
2.5 (2.1, 2.9)
2,126
407
510
2014
122
42
25
8.7
3.0
1.8
10.4 (9.5, 11.3)
3.7 (3.1, 4.2)
2.3 (1.9, 2.7)
2,118
703
393
2015
166
22
40
11.7
1.5
2.8
13.8 (12.8, 14.8)
1.8 (1.4, 2.2)
3.4 (2.9, 3.9)
2,787
308
654
2016
154
33
41
10.7
2.3
2.8
12.2 (11.2, 13.2)
2.8 (2.4, 3.3)
3.5 (3.0, 4.0)
2,795
562
683
2017
195
31
42
13.5
2.1
2.9
15.5 (14.4, 16.6)
2.6 (2.2, 3.0)
3.5 (3.0, 4.0)
3,537
507
721
2018
213
45
33
14.7
3.1
2.3
16.7 (15.5, 17.8)
3.6 (3.1, 4.2)
2.7 (2.2, 3.2)
3,624
778
564
2019
213
46
41
14.6
3.1
2.8
16.5 (15.4, 17.7)
3.8 (3.3, 4.3)
3.2 (2.7, 3.7)
3,714
703
657
Total
1,886
439
390
9.0
2.1
1.9
11.1 (10.9, 11.3)
2.7 (2.6, 2.8)
2.4 (2.3, 2.5)
33,077
7,172
6,584
P
-
-
-
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
-
-
-
Abbreviations: CMR, crude mortality rate; ASR, age-standardized rate; YLL, years of life lost; CI, confidence interval.
†
“Cervical cancer” is also included uterine/vaginal/vulvar cancers.
The total YLL due to breast, cervical/uterine/vaginal/vulvar, and ovary cancers’ deaths in the 16-year period were 33,077, 7,172 and 6,584 yr, respectively. The highest YLLs were observed in 50–59-year-old females, followed by 40–49- and 60–69-year-old females for deaths due to breast and gynecologic cancers. In addition, extreme age groups (20–29- and >80-year-old females) comprised the lowest YLL (Fig. 1).
Fig. 1:
YLL due to breast and gynecologic cancers in different age groups, southern Iran, 2004–2019 [“cervical cancer” is also included uterine/vaginal/vulvar cancers]
According to the join point regression, the 16- year trend of YLL rate due to premature mortality in breast cancer was increasing: annual percent change (APC) was 7.6% (95% CI 5.5–9.7, P<0.001). The model did not show any joinpoint, and hence, the AAPC (average annual percent change) is the same as APC (Fig. 2).
Fig. 2:
16-year trend of YLL (per 1,000 people) due to breast (A), cervical (B) and ovary (C) cancers in southern Iran, 2004–2019 [“cervical cancer” is also included uterine/vaginal/vulvar cancers]
In addition, the trend of YLL rate due to premature mortality in ovary cancer was increasing: average annual percent change (AAPC) was 19.0% (95% CI 12.2–26.1, P<0.001). The model shows one join point in 2008 when the APC was 71.6% (95% CI 38.8– 112.0, P<0.001) (Fig. 2).
Moreover, the trend of YLL rate due to premature mortality in cervix cancer was increasing: Average annual percent change (AAPC) was 9.2% (95% CI −9.2 to 31.3, P>0.05). The model shows two join point in 2006 and 2009, when the APC was 105.5% (95% CI −15.1 to 397.5, P=0.097) and −25.4% (95% CI −69.2 to 80.7, P= 0.467) (Fig. 2).
Discussion
During 2004–2019 in the southern Iran, the highest YLLs were observed in 50–59-year-old females, followed by 40–49- and 60–69-year-old females due to breast and gynecologic cancers, respectively. Notably, the lowest YLLs were observed in extreme age groups (20–29- and >80-year-old females). The 16-year trend of YLLs due to breast cancer premature death was constantly increasing. This trend showed a significant increasing pattern from 2009 to 2019 for cervical/uterine/vaginal/vulvar cancers premature death. Moreover, the 16-year trend of YLLs due to ovary cancer premature death was significantly increasing with one join point in 2008.
In our study, mortality rate of breast cancer had an increase from 5.3 per 100,000 people in 2004 to 16.5 per 100,000 people in 2019 in south of Iran. Similarly, the mean breast cancer mortality rate in middle east and north Africa region (MENA) is shown an increase from 8.70 per 100,000 people in 2005 to 9.72 per 100,000 people in 2015 (7). In another study, Sharifian et al. (16), reported an increase in YLLs due to breast cancer in Iran, which was 152.8 yr per 100,000 people in 1990 to 175.64 yr per 100,000 people in 2010. Furthermore, during 2004–2019, we found that the APC of YLLs due to breast cancer premature death was 7.6%. The APC estimated by Ataeinia et al. (8), was 4.8% during 1990–2015. In contrast, it was decreased from 24.5 per 100,000 people in 2004 to 19.4 per 100.000 people in 2019 in United States (US) (17). This decrease postulated the role of generalized use of mammography and improvement of breast cancer treatment in US. (2). in this respect, in Iran, only 18% of breast cancer patients are estimated to detect in stage 1 (7). More than 80% of these patients initially diagnose with stage II in Iran (18). Moreover, in a multi-center study in Iran, a significant percentage of cases (96%) were diagnosed at stage II or III (19). Up-trend of YLLs due to breast cancer is observed in south of Iran, which this might be because of progress in diagnosis, as well as the Iranian population changes such as increase in life expectancy and urbanization (20,21). Therefore, screening programs should be prioritized to detect patients at early stages, coupled with early treatment’s allocation (8, 22).
Another finding of this study was 0.2 death per 100,000 women due to ovarian cancer in 2004 that was increased to 3.2 per 100,000 women in 2019. In-line with or finding, Sharifian et al. (11), showed an increase in ASR and crude rate of ovarian cancer mortality in Iran from 2004 to 2013. Life style changes (e.g., low fiber intake, overweight and obesity, etc.), which are the risk factors of ovarian cancer, might be led to such increase (11, 23, 24).
Eastern Mediterranean Region had an increase in disability adjusted life years (DALYs) due to cervical cancers from 2000 to 2017 (25). In our study, the aggregated mortality rate in cervical, uterine, vaginal, and vulvar cancers had an upsurge from 1.2 in 2004 to 3.8 in 2019 with a corresponding APC of 7.8%. Worth noting, a significant increasing trend was just observed after 2009. According to the 2019 Global Burdon of disease (GBD) report, the mortality and YLLs of cervical, uterine and ovarian cancers in south of Iran showed an increase (44.81 to 351.78% and 145.69 to 668.03%, respectively) from 1990 to 2019 (26). Another study from Iran represented a decreasing trend in mortality due to cervical cancer and corpus uterine cancer during 1990–2016 (27). This difference might be because of population diversities, as our population is accounted for as the geographical area with the highest incidence of cervical cancer in Iran as well as the second highest ASR of mortality due to cervical cancer (28).
Last by not least, the highest YLLs were observed in 50–59-year-old females, followed by 40–49- and 60–69-year-old females for deaths due to breast and gynecologic cancers. In Western world, breast cancer occurs at older ages, while the average age of 50 is the peak age of breast cancer in Asian counties (29, 30). In Iran, during 2005–2006, the age group of 50–54 had the highest mortality rate of breast cancer (31). While in another Iranian study, 49% of patients were diagnosed during 35–49 yr of age and 27% during 50–64 yr of age (32). By and large, our result in consistent with in-line studies; that is, highest incidence and mortality in the fifth and sixth decades in Iran, in addition to the diagnosis tendency toward stages II and III, might be led to a high YLLs.
Some limitations of this study include the possibility of undercounting the cases of death due to cancer, whereas some of the strengths of the study are the wide range of time and the appropriate sample size. This study is one of the few studies which analyses the trend of the years of life lost due to women’s cancer.
Conclusion
Mortality rate and YLLs due to breast and gynecologic cancers have been increased in southern Iran during the last twenty years. Early detection programs and allocating treatments at early stages should be prioritized.
Journalism Ethics considerations
Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors. |
Title: Impact of Attendance to a Pharmacist-Managed Medication Adherence Clinic on Glycemic Control and Risk Factors for Non-Completion Among Persons with Type 2 Diabetes Mellitus in Selangor, Malaysia | Body:
Introduction
The global prevalence of diabetes mellitus (DM) is escalating.1 By 2030, this prevalence is expected to increase by 42% in developed countries and a striking 170% in developing countries.2 This trend is particularly alarming in developing regions, attributed to rapid population growth, aging populations, unhealthy local diets, urbanization, increasing obesity rates, unhealthy lifestyles, and limited access to adequate healthcare.3 As these challenges grow, so does the cost of managing the disease, prompting the development of strategies for early diagnosis, effective care, and primary prevention.4 In response to this escalating crisis, various pharmacist-led intervention programs have been implemented globally. These include Medication Therapy Management (MTM),5 Medicine Use Reviews (MUR),6 Home Medication Reviews (HMR),7 and Medication Therapy Adherence Clinics (MTAC).8 Each of these programs shares a common goal: to improve patient adherence to treatment and enhance overall treatment outcomes.
Specifically in Malaysia, the Diabetes Medication Therapy Adherence Clinic (DMTAC) program was established in 2004, focusing on patients with diabetes. Pharmacists in this program are required to complete training as mandated by the Malaysian Ministry of Health, and the program is governed by protocols and service modules updated periodically by the Pharmaceutical Services Program.9 Eligible patients for DMTAC are those receiving treatment in hospitals and health clinics, identified by criteria such as uncontrolled diabetes, issues with medication adherence, and failure to meet individual HbA1c targets. The protocol requires each patient to attend a minimum of four appointments and demonstrate a medication-related knowledge score of 100%, as measured by the DFIT score (dosage, frequency, indication, time, and exhibit good medication adherence to be eligible for discharge from the service. They are also required to achieve targeted HbA1c results in at least two readings. Patients may be discontinued from the service if they fail to attend for six consecutive months or miss three consecutive sessions. These criteria are established to ensure the realistic achievement of set objectives.
Numerous studies have evaluated the effectiveness of DMTAC in achieving optimal diabetes control and medication adherence.10–16 These studies recruited diabetes patients and compared outcomes between those who completed their DMTAC appointments, those who did not, and patients who were not enrolled in the DMTAC service. However, their limitations included being conducted at single centers and involving small sample sizes. Additionally, a systematic review involving 18 studies highlighted a significant patient dropout rate from the DMTAC program.15 To date, no research has specifically focused on identifying the factors that influence patient attendance at DMTAC, revealing a critical gap in the information necessary to enhance the service. This gap underscores the need for an assessment of DMTAC’s effectiveness on a larger, more diverse sample and an investigation into the factors associated with patient attendance and appointment adherence. The aim of this study is to assess the outcomes of the current DMTAC service by comparing regular attendees with those who have missed appointments. The insights gained from this research will contribute to developing sustainable improvements for the DMTAC service.
Materials and Methods
The study was conducted through a retrospective analysis of patient records who attended the DMTAC program from January 2018 to December 2020 at three health clinics and two hospitals in the state of Selangor, Malaysia. Ethical approval for this study was obtained from the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia (NMRR-19-3816-5209). The requirement for obtaining informed consent from patients was exempted because all collected data were completely anonymized, adhering to the ethical standards set forth in the Declaration of Helsinki. The reporting of this study aligns with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines specific to cohort studies.17 A list of patients with type 2 diabetes, aged ≥ 18 years, who attended at least one DMTAC session during the period from January 2018 to December 2020, was screened for inclusion as study samples. Patients were excluded from the study if they had no HbA1c level measurements during the study period, HbA1c measurements taken less than three months before the first DMTAC session, HbA1c measurements taken more than one year after the last DMTAC session, or if they had incomplete records. The patients were divided into two groups: (1) those who completed and (2) those who did not complete. The complete attendance group consisted of patients who attended the DMTAC program for a minimum of four sessions (≥ 4 sessions), while the did not complete group comprised those who missed follow-up sessions before the fourth session (< 4 sessions), in accordance with the criteria outlined in the published DMTAC protocol.9 Patient data were retrospectively collected from medical records using a data collection form, employing convenience sampling method.
In a previous study conducted by Butt et al (2016) at a teaching hospital in Kuala Lumpur, it was found that the data in each group of patients tested had a normal distribution with a standard deviation (SD) of 1.8.18 Assuming a true minimum difference in HbA1c between the control group and the test group of 1, estimates calculated through the “PS” software indicated that a total of 69 samples were required in each group to reject the null hypothesis that the population means between the two groups were equal with a probability (power) of 0.9. With an estimated 35% of incomplete data, the minimum required sample size was 190 samples, or 95 samples for each group.19 In assessing the risk factors for non-completion at the clinic, this research follows the guidelines set by Green (1991) regarding the appropriate sample size for examining individual predictors in regression analysis.20 The author recommends the formula N > 104 + M to determine the sample size, where N represents the sample size and M is the number of independent variables involved. In this study, the logistic regression analysis incorporates eight independent variables. Consequently, based on Green’s formula, the minimum required sample size is calculated to be 112.
The primary outcome evaluated in this study was the change in clinical outcomes from the initial (baseline) to the final DMTAC session. Clinical outcomes included glycemic control parameters such as the most recent readings of HbA1c and fasting blood sugar (FBS). HbA1c is an accurate indicator of glycemic control over the three months preceding a measurement.21 A HbA1c level > 7% indicates poor glycemic control.22 All reported medication adherence levels were recorded irrespective of the assessment tool used by the pharmacists. Medication knowledge scores were assessed for each patient’s DMTAC visit.
Descriptive statistics were reported for the entire population and separately between two groups, namely the complete and incomplete attendance groups. Categorical variables were summarized using frequencies, percentages, and continuous variables as means [standard deviation (SD)] for age, duration of diabetes diagnosis, number of medications, comorbid diagnoses, HbA1c readings, and FBS readings. Medians [interquartile range (IQR)] were used for gender, ethnicity, educational level, employment status, and medication adherence levels. Normal distribution for continuous variables was tested using the Kolmogorov–Smirnov method. For group comparisons, the Student’s T-test was used for continuous variables, and the chi-square test was used for categorical variables.
A two-way repeated measures ANOVA was used to assess changes at two time points and differences between groups for HbA1c and FBS measurements. The Wilcoxon signed-rank test was used to compare medication adherence means between initial and final adherence for both groups. Binary logistic regression was employed to identify potential risk factors and associations for DMTAC non-completion. Odds ratios (OR) and corresponding 95% confidence intervals were calculated for all variables. Statistical analysis was conducted using IBM SPSS Statistics version 21.0.23 All statistical tests were two-tailed, and a value of p < 0.05 was considered statistically significant.
Results
An analysis of the patient database across all participating facilities revealed that 198 patient profiles met the specified criteria and were subsequently included in this study. Of these, 49% (n=98) of patients completed a minimum of four DMTAC sessions (complete) while 50.5% (n=100) attended fewer than four sessions (did not complete). The average age (SD) for the entire cohort was 56.52 (12.91) years, and 97 patients (49.0%) were male (Table 1). The majority identified as Malay (n=99, 50.0%), had an unspecified level of education (n=72, 36.4%), and were not employed (n=111, 56.1%). There were no significant differences between the complete and did not complete groups at baseline regarding sociodemographic, clinical characteristics, and demographic features, except for Malay ethnicity (n=99, 50.0%, P = 0.018).Table 1Distribution of Subjects According to Sociodemographic and Clinical Characteristics (N=198), MalaysiaCharacteristicsAll patients (N=198)Complete (n=98)Incomplete (n=100)P valuesNumber of visits (mode)Baseline characteristics41 Age, years, mean (SD)56.5 (12.9)58.0 (12.9)55.0 (12.8)0.103 Male97 (49.0)59 (54.6)38 (42.2)0.063 Race Malay99 (50.0)43 (43.8)56 (56.0)0.018 Chinese37 (18.7)26 (26.5)11 (11.0) Indian62 (31.3)29 (29.6)33 (33.0)Years diagnosed, mean (SD)10.43 (8.0)9.3 (7.4)11.6 (8.5)0.051Numbers of medications5.86 (2.1)5.8 (2.0)5.9 (2.3)0.701Concurrent diagnosis2.6 (0.1)2.7 (0.9)2.5 (1.0)0.200DFIT Score, mean (SD)93.3 (9.6)93.6 (9.3)93.0 (9.9)0.668HbA1c, %, mean (SD)10.6 (2.2)10.7 (2.3)10.5 (2.2)0.747FBS, mmol/l, mean (SD)20.9 (4.2)9.6 (3.3)11.6 (4.6)0.055Education Primary34 (17.2)19 (19.4)15 (15.0)0.417 Secondary57 (28.8)29 (29.6)28 (28.0) Tertiary35 (7.7)13 (13.3)22 (22.0) Unknown72 (36.4)37 (37.8)35 (35.0)Working status Working82 (41.4)38 (38.8)44 (44.0)0.066 Not working111 (56.1)55 (56.1)56 (56.0) Unknown5 (2.5)5 (5.1)0 (0.0)Adherence Poor70 (35.4)37 (39.4)33 (33.0)0.368 Moderate104 (52.5)50 (53.2)54 (54.0) Good20 (10.1)7 (7.4)13 (13.0) Unknown4 (2)4 (0.0)Notes: All values are reported as no.(%) unless otherwise noted.Abbreviations: DFIT, medication dosage, frequency, indication, and timing; FBS, fasting blood sugar level; HbA1c, target glycated haemoglobin level; SD, standard deviation.
For the complete group, HbA1c measurements were significantly different between the first measurement (10.83; 95% CI: 10.31-11.34) and the last measurement (9.61; 95% CI: 9.13-10.09) (P < 0.001) (Table 2). For the did not complete group, HbA1c measurements were also significantly different between the first measurement (10.40; 95% CI: 9.84-10.97) and the last measurement (9.94; 95% CI: 9.42-10.47) (P < 0.029). There was no statistically significant difference in HbA1c levels between the complete group and the did not complete group at the beginning of the test. Likewise, there was no statistically significant difference in HbA1C between the complete group and the did not complete group at the end of the test.Table 2Clinical Measures for Mean Scores and Adjusted Mean Scores for Clinical Outcome Measures Among Adult Diabetic PatientsNDescriptive StatisticsAdjusted MeanP valueMean (SD)Mean (95% CI)First MeasurementFinal MeasurementFirst MeasurementFinal MeasurementHbA1C (n=156)Complete group8510.83 (2.52)9.61 (2.24)10.83 (10.31,11.34)9.61 (9.13,10.09)<0.001Incomplete group7110.40 (2.28)9.94 (2.22)10.40 (9.84, 10.97)9.94 (9.42, 10.47)0.029Between group differences,P value––0.2730.360FBS (n=63)Complete group4210.14 (3.86)7.73 (3.06)10.14 (8.97,11.31)7.73 (6.67,8.79)<0.001Incomplete group219.85 (3.61)9.84 (4.08)9.85 (8.20, 11.50)9.84 (8.35, 11.34)0.996Between group differences,P value––0.7750.024Abbreviations: CI, confidence interval; FBS, fasting blood sugar level; HbA1c, target glycated haemoglobin level; SD, standard deviation.
For the complete group, FBS measurements were statistically different between the first measurement (10.14; 95% CI: 8.97-11.31) and the last measurement (7.73; 95% CI: 6.67-8.79) (P < 0.001) (Table 2). In contrast, for the did not complete group, FBS measurements showed no statistically significant difference between the first measurement and the last measurement. FBS values were not significantly different between the complete group and the did not complete group at the beginning of the test. However, FBS values were significantly different between the complete group and the did not complete group at the end of the test (P = 0.024).
The Friedman test was utilized alongside the repeated measures ANOVA to analyze the data, particularly because some datasets were non-normally distributed. This non-parametric test was specifically applied to examine the did not complete group. It supported the findings of the repeated measures ANOVA by demonstrating significant changes over time, as indicated by a p-value of <0.001. In addition, baseline FBS measurements were analyzed for between-group comparisons, yielding a p-value of 0.610. This result suggested no significant statistical differences between the groups at the study’s inception. However, a distinct shift was observed in the final FBS measurements. The analysis of these concluding measurements indicated a statistically significant difference between the groups, as evidenced by a p-value of 0.037. This implies that while the initial FBS levels were similar across the groups, by the end of the study period, significant disparities in FBS levels had developed.
For the complete group, the highest percentage of samples that had moderate medication adherence in the first measurement showed the greatest improvement to good adherence in the last measurement (38.3%). Meanwhile, for the did not complete group, the highest percentage of samples was those with the same moderate adherence level in both the first and last measurements (42.0%). There was a significant change in medication adherence between the first and last measurements (P < 0.001) for both the complete and did not complete groups (Table 3).Table 3Comparison of Level of Medication Adherence According to DMTAC SessionGroupsAdherence (Final)P valueaPoorModerateGoodComplete (n=94)Adherence (First)Poor6 (6.4%)21 (22.3%)10 (10.6%)<0.001Moderate2 (2.1%)12 (12.8%)36 (38.3%)Good0 (0.0%)1 (1.1%)6 (6.4%)Incomplete (n=100)Adherence (First)Poor20 (20.0%)11 (11.0%)2 (2.0%)<0.001Moderate0 (0.0%)42 (42.0%)12 (12.0%)Good0 (0.0%)2 (2.0%)11 (11.0%)Notes: aWilcoxon Signed Ranks test was applied to compare the first and final adherence.Abbreviation: DMTAC, diabetes medication therapy adherence clinic.
A logistic regression analysis was conducted to identify risk factors associated with DMTAC non-completion (Table 4). Among the sociodemographic and clinical characteristics included in this analysis, it was found that gender, education level, and duration since diabetes diagnosis were significantly related to DMTAC non-completion (P < 0.05). Male patients were found to be 65.20% less likely to miss DMTAC appointments compared to female patients after adjusting for all factors (OR = 0.35; 95% CI = 0.14, 0.85). Patients with lower education levels were also found to have a 72.30% lower risk of DMTAC non-completion compared to patients with higher education levels after adjusting for all factors (OR = 0.28; 95% CI = 0.08, 0.99). Additionally, for each year increase in the duration since diabetes diagnosis, patients had a 1.06% higher risk of DMTAC non-completion (OR = 1.06; 95% CI = 1.01, 1.11). No significant associations were found between age, employment status, and the number of comorbid diagnoses with DMTAC non-completion.Table 4Multivariate Analysis of Factors Associated with for Not Completing DMTAC SessionVariablesB(SE)Adjusted OR (95% CI)Wald (df)P valueFemaleRef.Male−1.06(0.46)0.35(0.14,0.85)5.31(1)0.021Race IndianRef. Malay0.55(0.42)1.73(0.76,3.91)1.73(1)0.189 Chinese−0.66(0.53)0.52(0.18,1.47)1.53(1)0.216Age0.00(0.02)1.00(0.96,1.03)0.04(1)0.850Education Level TertiaryRef. Unknown−0.80(0.57)0.45(0.15,1.35)2.03(1)0.154 Primary−1.28(0.65)0.28(0.08,0.99)3.89(1)0.049 Secondary−0.98(0.58)0.38(0.12,1.17)2.86(1)0.091Working status NoRef. Yes0.33(0.49)1.39(0.53,3.61)0.45(1)0.504 UnknownNENENENEYears diagnosed with diabetes0.06(0.03)1.06(1.01,1.11)5.18(1)0.023Concurrent diagnosis−0.01(0.19)0.99(0.68,1.45)0.00(1)0.973HbA1C level at DMTAC first session−0.05(0.08)0.95(0.80,1.12)0.41(1)0.523Abbreviations: B, logistic regression coefficient; CI, confidence interval; df, degree of freedom; DMTAC, diabetes medication therapy adherence clinic; HbA1c, target glycated haemoglobin level; NE, not estimable; OD, odds ratio; Ref., reference group; SD, standard deviation; SE, standard error.
Discussion
The study aimed to assess the impact of patients’ completion in the DMTAC program on blood glucose control (HbA1c and FBS levels) and medication adherence, while also identifying associated risks with DMTAC program non-completion. Five facilities were involved in this study, and a total of 198 patient records attending during the study period were analyzed. No differences were found in baseline patient characteristics between the two groups under study, except for ethnicity. Malay patients constituted the largest group in the study, which was expected since the study used a convenient sampling technique, and Malays make up the majority ethnic group in Malaysia (51%).24 In most studies involving patient record sampling, it’s common to observe a higher representation of Malay patients.25,26
The study revealed significant changes between the initial and final measurements of HbA1c levels in both the complete and did not complete groups, indicating improved glycemic control when patients attended DMTAC. A study conducted in Kedah, Malaysia, showed that the total number of DMTAC sessions had no impact on the final HbA1c outcomes of patients.10 This may suggest that some patients could benefit from fewer DMTAC sessions, particularly if the conducted sessions can focus on addressing the specific problems of individual patients. Additionally, the study found that patients who attended at least four DMTAC sessions exhibited a stronger association with better FBS levels when compared to the did not complete group. However, it is worth noting that no significant changes were observed in HbA1c levels. This lack of significant findings could potentially be attributed to the small sample size, which consisted of only 21 individuals in the complete group with final HbA1c readings. Additionally, it is important to consider that FBS levels were measured on the final visit itself, whereas HbA1c might have been measured weeks or even months after the final visit. This difference in timing may reflect a more accurate impact of short-term glucose control. Furthermore, it is essential to acknowledge that participants who completed the required visits may have been discharged from the DMTAC or may not have returned for follow-up visits after completing their initial four visits. As a result, they could potentially experience a reduction in HbA1c levels over time. However, this reduction may not have reached statistical significance when compared to the did not complete group, as the frequency of their visits would have decreased.
Furthermore, the study identified that among patients who fully attended DMTAC services, those with a moderate level of medication adherence were the most likely to improve to a higher adherence level in their last DMTAC session. The analysis also indicated differences in medication adherence rates among patients between the complete attendance and attrition groups. More patients achieved good and moderate medication adherence by the end of DMTAC sessions in the complete attendance group compared to those who dropped out. These findings illustrate the effectiveness of DMTAC in improving patients’ medication adherence rates. These results are consistent with other studies that have investigated the effectiveness of DMTAC in enhancing medication adherence among patients.10–13,15,16 However, a network meta-analysis suggested that the most effective intervention strategies for long-term medication adherence should encompass multiple components, including technical aspects of behavior change.27
Additionally, this study was able to assess several factors associated with DMTAC program non-completion. The analysis revealed that male patients were less likely to drop out from DMTAC services. This factor may be related to local sociocultural factors, and similar findings have been reported in several other studies examining patient attendance rates for healthcare appointments.28–31 Furthermore, the analysis indicated that patients with lower levels of education were at a reduced risk of attrition from the services. This is likely because individuals with lower education levels have limited access and capabilities to seek information about medications and diseases independently.32 Therefore, patients in this category have a greater incentive to attend appointments to obtain better information about their condition. However, even though patients with higher levels of education possess more knowledge and understanding of their condition, this does not necessarily translate into better treatment adherence.33
The findings of this study have demonstrated the positive impact of pharmacist-led services on diabetes management. However, these findings need to be interpreted with caution due to certain limitations. One significant limitation of this study is that retrospective data collections are susceptible to variations in data completeness and quality when compared to the rigorous data collection protocols typically found in prospective studies, such as clinical trials. Additionally, given that the study was conducted across multiple centers, there was no centralized protocol for measuring HbA1c (glycated hemoglobin) and FBS (fasting blood sugar). This lack of standardization resulted in variations in the strategies used to measure blood glucose control at each facility. Consequently, a considerable number of samples had to be excluded from the analysis due to the unavailability of data, particularly for HbA1c. Furthermore, despite the inclusion of facility-related factors in the analysis, there remains a possibility that the effectiveness of the intervention program is strongly influenced by the skills of the individuals overseeing the program. This introduces an additional layer of complexity when interpreting the results.
Conclusion
In conclusion, this study highlights the positive impact of pharmacist-led DMTAC services on diabetes management, demonstrating improvements in FBS and adherence among patients attending at least four sessions. Additionally, the study identified several factors associated with DMTAC non-completion, including female gender, higher educational level, and longer duration since diabetes diagnosis. These findings emphasize the importance of tailoring interventions to address specific patient needs and challenges, ultimately contributing to the enhancement of diabetes care and patient outcomes. Further research and the development of standardized measurement protocols are warranted to validate and build upon these results. |
Title: Iatrogenic ureteral injury diagnosed after colon cancer surgery: A case report of a rare and challenging complication | Body:
1
Introduction
Iatrogenic ureteral injury (IUI) is an unfortunate and rare complication during colorectal surgery. While IUI remains a rare event, short and long-term complications are life-threatening ranging from intraperitoneal urinoma to septic shock and a serious risk of permanent renal failure [1,2]. Although treating ureteric injuries has seen a great evolution in the last decades, it remains a challenging entity even in contemporary practice. This work has been reported in line with the SCARE criteria [3].
2
Case presentation
An 88-year-old patient with a history of type 2 diabetes, hypertension, and laparoscopic cholecystectomy 10 years ago, was admitted to our surgery ward for symptoms of large bowel obstruction evolving for 24 h. An abdominal computed tomography (CT) scan showed a large bowel obstruction secondary to a metastatic sigmoid tumor. The patient underwent an urgent laparotomy, and a discharge colostomy was performed. At the end of the first week of the postoperative course, an abdominal examination revealed a non-functional retracted stoma associated with abdominal distension.
The patient underwent a second laparotomy. Intraoperatively, a perforation in the sigmoid tumor without peritoneal effusion was identified. After a difficult dissection, the patient had an oncological sigmoidectomy with Hartman's colostomy. An active drainage was placed in the pouch of Douglas. Twenty-four hours later, urinary output in the urinary catheter was less than 100 cc, contrasting with intraperitoneal active drainage of 1600 cc of yellow-amber fluid. Further analysis of the drainage fluid was performed, showing creatinine and urea levels 10 times higher than normal values. Retrograde ureterography and CT urography showed an active extravasation of contrast on the left, associated with a urinoma extending from the left pararenal fossa to the left iliac fossa (Fig. 1). These findings suggested a left ureter injury. After a multidisciplinary meeting, we decided to perform a surgical nephrostomy. Percutaneous nephrostomy was not feasible as the urinary tract was not dilated.Fig. 1Imaging findings after the ureteral injury. (a) Retrograde ureterography showing a complete section of the left ureter (White arrow) with the presence of a urinoma (White star). (b) axial view, (c) coronal view and (d) sagittal view depicting the presence of an injury of the left ureter (White arrows) with extravasation of contrast agent (Yellow arrowheads) and a urinoma in the left iliac fossa (White stars). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)Fig. 1Fig. 2Intraoperative view. (a), (b), (c), and (d) sequentially illustrate the progressive stages of dissection, revealing the injured ureter (White arrows).Fig. 2
The patient was reoperated, three weeks later, after convenient preparation. Intraoperatively, a 300-cc urinoma located in the left iliac fossa was evacuated. We found a total section of the pelvic left ureter with necrosis of the distal segment (Fig. 2). After a meticulous and difficult dissection of the proximal segment of the left ureter, a ureterostomy was performed, along with a resection of the necrosed ureter's distal segment. Supplementary video provides more details of the intraoperative findings. One week later, the patient succumbed to the effects of heart failure.
3
Discussion
Low anterior and abdominoperineal resection of the rectum, along with sigmoid resection are the most frequent causes of ureteral injury in digestive surgery, with a documented overall incidence ranging between 0 and 1.5 % [1,4,5]. Identified risk factors of IUI encompass advanced cancer patients, malnourished patients, prior surgeries, obesity, history of radiation exposure, procedures in teaching centers, and operations related to endometriosis, diverticulitis, or bowel perforation [1,6,7]. In our case, the patient exhibited multiple risk factors, including advanced cancer, obesity, and a history of prior surgery. Ideally, a IUI is repaired directly if observed during surgery. Unfortunately, 50 to 70 % are still diagnosed in the postoperative phase [[8], [9], [10]]. When suspected, diagnosis is confirmed through retrograde ureterography and CT urography [11].
The primary objective of management is to establish a continuous flow of urine to avert potential complications. The majority of IUI cases manifest in the distal ureter. Given their proximity to the bladder, these injuries are typically addressed through ureteroneocystostomy. Dealing with complete sections of the abdominal ureter presents greater challenges, particularly if attempts at repair are made more than two weeks after the initial injury. In instances of delayed presentation (beyond two weeks) where ureteric stenting is not feasible, the conventional approach is to postpone surgery for a period of 3 to 6 months, allowing for the resolution of inflammation and tissue regeneration [12]. In our specific case, the injury was located in the pelvic ureter, and due to the associated uroperitoneum, the patient underwent a reoperation three weeks post-injury. We opted for a temporary proximal diversion, planning to reschedule the patient for auto-transplantation at a later date.
Preventing IUI in colorectal surgery is of paramount importance. This process initiates in the preoperative phase with a meticulous assessment of ureteral and colic anatomy through comprehensive review of preoperative imaging. Detecting any anomalies in urologic organs prior to surgery is crucial, as their discovery intraoperatively can pose a significant challenge for surgeons. Intraoperative precautions hinge on unequivocal identification of the ureter before proceeding with the ligation or section of the mesocolon. Moreover, in cases where anticipated ureter resection is warranted to adhere to carcinologic imperatives or whenever there is uncertainty regarding potential ureter injury, seeking assistance from a more seasoned and specialized surgeon is the right call [6]. Recently, the utilization of ureteral catheters prior to colorectal surgery has gained traction, although it remains a topic of ongoing discussion [9,13,14]. Studies have indicated that while catheter placement enhances the likelihood of intraoperative detection, it does not necessarily guarantee a reduced incidence of IUI. While further research is imperative, this tool may prove beneficial in intricate pelvic surgeries, cases involving advanced tumors, and in obese patients [6,7].
4
Conclusion
IUI remains a seldom-seen complication in colorectal surgery. It is imperative to prioritize both preoperative and intraoperative measures to prevent IUI, ensuring optimal outcomes. When the diagnosis of a IUI is established, a treatment strategy should be meticulously devised and executed by a skilled and experienced surgeon. Planning for treatment can be intricate, involving careful consideration of the opportune timing and the most suitable technique for ureteral reconstruction.
The following is the supplementary data related to this article.Supplementary videoUreterostomy for Iatrogenic ureteral injury. Commencing with the initial stages of left ureter dissection, culminating in the creation of the ureterostomy by the conclusion of the intervention.Supplementary video
Abbreviations
IUIIatrogenic ureteral injuryCTComputed tomography
Consent for publication
Written informed consent was obtained from the family for publication and any accompanying images after disidentification. A copy of the written consent is available for review by the Editor-in-Chief of this journal on request.
Ethical approval
Ethical approval was deemed unnecessary by the institutional ethics committee of Menzel Bourguiba Hospital, Bizerta, Tunisia, as the paper is reporting a single case that emerged during normal practice. Ethical approval is not required in our institution for case reports.
Funding
Nothing to declare.
Author contribution
Anis Hasnaoui: Conceptualization, Writing-Reviewing and Editing. Racem Trigui: writing-Original draft preparation. Ahmed Ghaieth Dhahak: Data curation. Mariem Nouira: Data curation. Mourad Gargouri: Data interpretation. Imen Ganzoui: Data curation. All authors read and approved the final manuscript.
Guarantor
Anis Hasnaoui
Conflict of interest statement
Nothing to declare. |
Title: The Support Needs of Patients Requesting Medical Aid in Dying and Their Relatives: A Qualitative Study Using Semi-Structured Interviews and Written Narratives | Body:
Introduction
Medical aid in dying (MAID), including euthanasia and physician-assisted suicide, has become an increasingly important legal and medical issue. Euthanasia involves the practice in which a health practitioner ends a person’s life at their voluntary request by administering lethal drugs. Physician-assisted suicide involves the practice in which a health practitioner provides or prescribes to a patient, at his or her voluntary request, a lethal medication which the patient subsequently self-administers to end their own life [1]. At present, jurisdictions with MAID legislation cover about 300 million people across the globe [1]. Furthermore, prevalence rates of MAID continue to rise in jurisdictions where the practice is lawful [2–4]. Several jurisdictions are currently debating a possible enactment of MAID legislation.
MAID entails a complex process as patients requesting it and their relatives may experience a range of challenges, difficulties and undesired outcomes throughout their MAID trajectories [5]. These trajectories include the moment when the patient expresses an explicit desire for MAID, the assessment of the request, the performance of MAID, and the period following the performance of MAID [5, 6]. Patients, for example, sometimes feel that their autonomy and values are not respected, and may feel that healthcare professionals and relatives minimize their unbearable suffering [5, 7, 8]. Relatives, for example, may feel unprepared for the performance of MAID, can experience post-traumatic stress after the performance, and may feel excluded by healthcare professionals during the assessment of the MAID request [9–14]. These challenges, difficulties, and undesired outcomes may indicate that the support needs of patients and relatives are not adequately fulfilled, for instance, because their healthcare professionals do not know their support needs or have not assessed them properly [6, 7, 9, 15].
The existing scientific literature provides little direction on the specific support needs of patients and relatives throughout their MAID trajectories. Studies have primarily focused on describing their general experiences with MAID practice, their attitudes towards MAID, problems and difficulties they encounter in MAID practice, and their interactions with healthcare professionals [8, 16]. A better understanding of the support needs of patients and relatives in MAID practice could guide healthcare professionals in anticipating undesired outcomes and improving the quality of life of patients and relatives [17]. We sought to answer the following research question: what support needs do patients requesting MAID and their relatives experience throughout their MAID trajectories?
Methods
Research Paradigm and Study Design
We took on a social constructionist lens, assuming that support needs are social and dynamic constructs [18]. Due to the explorative nature of our research aim, we employed a qualitative design capturing the lived experiences of MAID practice among patients and relatives, using 1) semi-structured interviews with patients and relatives and 2) personal written narratives from patients through qualitative questionnaires.
In this study, we defined the medical aid in dying trajectory as beginning when the patient expresses an explicit desire for medical aid in dying, followed by the assessment of the request, the performance of medical aid in dying, and the period following the performance of medical aid in dying [5, 6].
Study Context
We conducted this study from December 2021 to September 2022 in Flanders and Brussels (Belgium). For a patient to be eligible for MAID, specific criteria must be met which are stipulated in the Belgian Act on Euthanasia [19]. In Belgium, medical aid in dying is mainly performed in the home setting (54% of all registered cases in 2020–2021), in hospitals (30%), and in nursing homes (13%) [20].
Research Participants, Recruitment, and Data Collection
We included patients and relatives having lived experiences with MAID practice. Eligibility criteria for patients were: 1) having expressed a desire for MAID to relatives (friends or family members) or healthcare professionals, or having formally requested MAID to an attending physician, or having received the formal approval from an attending physician to receive MAID; 2) being comfortable with being interviewed in Dutch; 3) residence in Flanders or Brussels, Belgium; and 4) being 18 years or older. Eligibility criteria for relatives of patients were: 1) being a family member or friend of a person who has expressed a desire for MAID or has formally requested MAID, or who has received the formal approval to receive MAID, or who received MAID prior to the interview (between 3 months and 3 years), 2) fluency in Dutch; 3) residence in Flanders or Brussels, Belgium; and 4) being 18 years or older.
We recruited participants between December 2021 and June 2022 using purposive and snowball sampling. We aimed to recruit a heterogeneous study sample in terms of sociodemographic characteristics, health conditions and principal care settings. More specifically, purposive sampling occurred through healthcare and patient organizations and associations, stakeholders, and the professional network of the End-of-Life Care Research Group in Flanders (Belgium). Snowball sampling was applied by inviting potential participants who were identified by those already included for participation in the study. Interested participants could register on a website or contact the research team. We conducted an eligibility assessment before inviting them to participate in the study. Eligible patients were allowed to participate via a semi-structured interview or a personal written narrative using an online qualitative questionnaire (LimeSurvey). We ensured that the interviewer was unfamiliar to the participants. Prior to study participation, we disclosed the identity of the interviewer to participants. Moreover, we offered participants the opportunity to get in touch with the interviewer for any questions or concerns regarding the study.
For the semi-structured interviews, we used a topic guide consisting of the following topics: 1) general experiences with MAID practice; 2) experienced practices regarding MAID; 3) experienced good practices regarding MAID, and 4) experienced support needs regarding MAID. The specific questions in the topic guide were iteratively refined throughout the data collection process. Support needs were conceptualized as the support or guidance a participant experienced as essential to meet his or her basic needs or to achieve a desired outcome in MAID practice [21]. Participants could choose whether the interview would be performed online by Whereby (http://Whereby.com) or in person at a location of their choice. XX (second author, M.Sc. in Social work, Ph.D. in Health Sciences, senior researcher, female) conducted one interview, while XX (first author, M.Sc. in Sociology, Ph.D. candidate, male) conducted the other interviews. Both are researchers with previous experience in conducting qualitative health research. Interviews were recorded and transcribed verbatim. After every interview, field notes were made to document unique observations and contextual information. For the personal written narratives, we sent an online qualitative questionnaire (LimeSurvey) to eligible patients who preferred this method of study participation [22]. The questionnaire included open-ended questions reflecting the themes of the semi-structured topic guide. We requested patients to respond to these questions by writing down their narratives and experiences, which we converted into transcripts for data analysis [22]. Data collection was informed by inductive thematic saturation in which we defined the saturation point as the stage at which no new meaning related to support needs emerges across the narratives of patients and relatives [23].
Data Analysis
We used a qualitative content analytic approach [24, 25]. In the first phase, XX and XX (last author, M.Sc. in Sociology, Ph.D. in Health Sciences, senior researcher, female) applied open inductive coding and categorization of the raw data, analyzing patients’ and relatives’ experiences separately. We linked initial codes to text fragments, aiming at identifying initial codes describing participants’ lived experiences of support needs regarding medical aid in dying. Support needs were conceptualized following the definition used during data collection [21]. XX and XX independently created a coding framework based on seven transcripts. These frameworks were then compared; inconsistencies and differences were discussed until a preliminary coding framework was reached. Next, XX used the coding framework to analyze the remaining transcripts, modifying it when new codes emerged. We used Nvivo 12 for the coding process. In the second phase, XX merged codes similar in understanding into meaningful clusters to identify types of support needs. Moreover, we concluded that saturation was reached after the 11th interview for patients and the 18th for relatives, as no new meaning in relation to support needs had arisen [23]. In the third phase, the identified clusters were discussed in group meetings with all co-authors. In this third phase, we also concluded that most types of support needs of patients are identical to those of relatives, with only a few different types. For clarity in reporting, the latter are described separately in the results section of this article. Again, inconsistencies were addressed until an agreement on final meaningful clusters, i.e., types of support needs, was reached. The field notes were used to help us organize the codes and identify meaningful clusters.
In terms of reflexivity, the research team included four health sociologists (SV, SD, KC, JC, and LD), one social worker (JG), and one bioethicist (FM), all with prior experience in qualitative methods related to end-of-life practices. None of the researchers hold strong normative positions for or against medical aid in dying. Each team member adopted a critical perspective, advocating for rigorous evaluation of medical aid in dying legislation and practices. To uphold objectivity in data interpretation, the research team convened regular discussion sessions.
Ethical Considerations
The Ethics Committee of the XX and XX approved our study (XX; 15 September 2021). We utilized pseudonyms for all participants in the transcripts and removed any identifying information. All participants provided written or oral informed consent to participate in the study.
Research Paradigm and Study Design
We took on a social constructionist lens, assuming that support needs are social and dynamic constructs [18]. Due to the explorative nature of our research aim, we employed a qualitative design capturing the lived experiences of MAID practice among patients and relatives, using 1) semi-structured interviews with patients and relatives and 2) personal written narratives from patients through qualitative questionnaires.
In this study, we defined the medical aid in dying trajectory as beginning when the patient expresses an explicit desire for medical aid in dying, followed by the assessment of the request, the performance of medical aid in dying, and the period following the performance of medical aid in dying [5, 6].
Study Context
We conducted this study from December 2021 to September 2022 in Flanders and Brussels (Belgium). For a patient to be eligible for MAID, specific criteria must be met which are stipulated in the Belgian Act on Euthanasia [19]. In Belgium, medical aid in dying is mainly performed in the home setting (54% of all registered cases in 2020–2021), in hospitals (30%), and in nursing homes (13%) [20].
Research Participants, Recruitment, and Data Collection
We included patients and relatives having lived experiences with MAID practice. Eligibility criteria for patients were: 1) having expressed a desire for MAID to relatives (friends or family members) or healthcare professionals, or having formally requested MAID to an attending physician, or having received the formal approval from an attending physician to receive MAID; 2) being comfortable with being interviewed in Dutch; 3) residence in Flanders or Brussels, Belgium; and 4) being 18 years or older. Eligibility criteria for relatives of patients were: 1) being a family member or friend of a person who has expressed a desire for MAID or has formally requested MAID, or who has received the formal approval to receive MAID, or who received MAID prior to the interview (between 3 months and 3 years), 2) fluency in Dutch; 3) residence in Flanders or Brussels, Belgium; and 4) being 18 years or older.
We recruited participants between December 2021 and June 2022 using purposive and snowball sampling. We aimed to recruit a heterogeneous study sample in terms of sociodemographic characteristics, health conditions and principal care settings. More specifically, purposive sampling occurred through healthcare and patient organizations and associations, stakeholders, and the professional network of the End-of-Life Care Research Group in Flanders (Belgium). Snowball sampling was applied by inviting potential participants who were identified by those already included for participation in the study. Interested participants could register on a website or contact the research team. We conducted an eligibility assessment before inviting them to participate in the study. Eligible patients were allowed to participate via a semi-structured interview or a personal written narrative using an online qualitative questionnaire (LimeSurvey). We ensured that the interviewer was unfamiliar to the participants. Prior to study participation, we disclosed the identity of the interviewer to participants. Moreover, we offered participants the opportunity to get in touch with the interviewer for any questions or concerns regarding the study.
For the semi-structured interviews, we used a topic guide consisting of the following topics: 1) general experiences with MAID practice; 2) experienced practices regarding MAID; 3) experienced good practices regarding MAID, and 4) experienced support needs regarding MAID. The specific questions in the topic guide were iteratively refined throughout the data collection process. Support needs were conceptualized as the support or guidance a participant experienced as essential to meet his or her basic needs or to achieve a desired outcome in MAID practice [21]. Participants could choose whether the interview would be performed online by Whereby (http://Whereby.com) or in person at a location of their choice. XX (second author, M.Sc. in Social work, Ph.D. in Health Sciences, senior researcher, female) conducted one interview, while XX (first author, M.Sc. in Sociology, Ph.D. candidate, male) conducted the other interviews. Both are researchers with previous experience in conducting qualitative health research. Interviews were recorded and transcribed verbatim. After every interview, field notes were made to document unique observations and contextual information. For the personal written narratives, we sent an online qualitative questionnaire (LimeSurvey) to eligible patients who preferred this method of study participation [22]. The questionnaire included open-ended questions reflecting the themes of the semi-structured topic guide. We requested patients to respond to these questions by writing down their narratives and experiences, which we converted into transcripts for data analysis [22]. Data collection was informed by inductive thematic saturation in which we defined the saturation point as the stage at which no new meaning related to support needs emerges across the narratives of patients and relatives [23].
Data Analysis
We used a qualitative content analytic approach [24, 25]. In the first phase, XX and XX (last author, M.Sc. in Sociology, Ph.D. in Health Sciences, senior researcher, female) applied open inductive coding and categorization of the raw data, analyzing patients’ and relatives’ experiences separately. We linked initial codes to text fragments, aiming at identifying initial codes describing participants’ lived experiences of support needs regarding medical aid in dying. Support needs were conceptualized following the definition used during data collection [21]. XX and XX independently created a coding framework based on seven transcripts. These frameworks were then compared; inconsistencies and differences were discussed until a preliminary coding framework was reached. Next, XX used the coding framework to analyze the remaining transcripts, modifying it when new codes emerged. We used Nvivo 12 for the coding process. In the second phase, XX merged codes similar in understanding into meaningful clusters to identify types of support needs. Moreover, we concluded that saturation was reached after the 11th interview for patients and the 18th for relatives, as no new meaning in relation to support needs had arisen [23]. In the third phase, the identified clusters were discussed in group meetings with all co-authors. In this third phase, we also concluded that most types of support needs of patients are identical to those of relatives, with only a few different types. For clarity in reporting, the latter are described separately in the results section of this article. Again, inconsistencies were addressed until an agreement on final meaningful clusters, i.e., types of support needs, was reached. The field notes were used to help us organize the codes and identify meaningful clusters.
In terms of reflexivity, the research team included four health sociologists (SV, SD, KC, JC, and LD), one social worker (JG), and one bioethicist (FM), all with prior experience in qualitative methods related to end-of-life practices. None of the researchers hold strong normative positions for or against medical aid in dying. Each team member adopted a critical perspective, advocating for rigorous evaluation of medical aid in dying legislation and practices. To uphold objectivity in data interpretation, the research team convened regular discussion sessions.
Ethical Considerations
The Ethics Committee of the XX and XX approved our study (XX; 15 September 2021). We utilized pseudonyms for all participants in the transcripts and removed any identifying information. All participants provided written or oral informed consent to participate in the study.
Results
Study Sample
We identified 18 patients and 23 relatives as eligible for study participation, all of whom were invited to participate. 15 patients and 21 relatives (n = 36) eventually participated, whereas the other invited individuals chose not to participate or to drop out without providing their motives. We conducted nine semi-structured interviews with patients (five online interviews and four in person at the patient’s main residence), one dyadic semi-structured interview with a patient and a relative (in person at the main residence of the patient), and 20 semi-structured interviews with relatives (11 online interviews and 9 interviews in person at the main residence of the relative), and included five written narratives from patients. The mean length of the semi-structured interviews with patients was 78 min (range: 28–130 min), and with relatives 82 min (range: 54–127 min). The main characteristics of the participants are listed in Tables 1, 2.
TABLE 1
Participant characteristics of patients requesting medical aid in dying (N = 15) (Belgium, 2021–2022).
N
Biological sex
Female
12
Male
3
Age
<30 years
1
30–40 years
4
41–50 years
4
51–60 years
3
>60 years
3
Highest level of education
Secondary school
6
Higher education
9
Phase in the MAID trajectory
a
Holding an explicit desire for MAID that has been expressed to relatives or healthcare professionals
4
Exploration of the eligibility for MAID by the attending physician following a formal request
5
Preparing the performance of MAID as the attending physician has agreed to grant the request
6
Main medical diagnosis
Psychiatric disorder
7
Connective tissue disorder
2
Chronic pain syndrome
2
Neurodegenerative disorder
2
Cancer
2
Main residence
a
Home
13
Hospital
1
Nursing home
1
MAID, medical aid in dying.
a
Timing of the interview or written narrative.
TABLE 2
Participant characteristics of relatives (N = 21) (Belgium, 2021–2022).
N
Sex
Female
18
Male
3
Age
<30 years
1
30–40 years
6
41–50 ears
2
51–60 years
6
<61 years
6
Highest level of education
Secondary school
2
Higher education
19
Relationship with the patient
Partner
2
Parent
3
Sibling
1
Child
9
Grandchild
2
Close friend
1
Living together with the patient
a
No
18
Yes
3
Family carer
a
Yes
11
No
9
The phase of MAID practice
a
The patient holds an explicit desire for MAID, that has been expressed to relatives or healthcare professionals
0
Exploration of the eligibility for MAID by the attending physician following a formal request
2
Preparing the performance of MAID as the attending physician has agreed to grant the request
3
MAID was carried out
b
<12 months
7
12–24 months
5
25–36 months
3
>36 months
1
Patient’s main medical diagnosis
a
Cancer
12
Neurodegenerative disorder
3
Psychiatric disorder
2
Chronic pain syndrome
2
Polypathology
2
Patient’s sex
Female
13
Male
8
Patient’s age
a
<30 years
1
30–40 years
2
41–50 years
1
51–60 years
2
61–70 years
4
71–80 years
7
>80 years
4
Patient’s main residence
a
Home
14
Hospital
4
Nursing home
3
MAID, medical aid in dying.
a
Timing of the interview or when MAID was carried out.
b
In one case, the relative did not attend the performance of MAID.
Support Needs Experienced Throughout the Medical Aid in Dying Trajectory
We identified eight types of support needs experienced by patients and relatives throughout their MAID trajectories (Figure 1). Identified support needs appeared to differ in their intensity of experience and seemed to be partially linked to a specific phase of the MAID trajectory, e.g., assessment or day of performance. We discuss each theme or type of support need separately for analytic clarity. However, they are interconnected in practice. In the following paragraphs, references to “participants” include both patients and relatives. Furthermore, some exemplary quotes are provided in Table 3.
FIGURE 1
Eight types of support needs were identified that patients and their relatives might experience throughout their medical aid in dying trajectories. Support for maximizing daily functioning was only reported by patients, while support for making sense of the patient’s desire for medical aid in dying only in relatives. (Belgium, 2021–2022).
TABLE 3
Participant’s quotes regarding types of support needs (Belgium, 2021–2022).
Support for maximizing daily functioning
“Well, you won’t recover if you have a chronic disease. So it’s not about recovering. It’s a different way of looking at me. I also know that, in my case, being at home is not the ideal situation because I don’t have the same support as in the hospital. There, you got pain management, a nursing team helping you out of bed and giving you all the medication, and if they have to, even putting the food in your mouth. I don’t need that kind of support. That is way too much, of course. But just a bit more support, so I am more comfortable at home. This would be helpful, so I can stand on my own feet and stay here at home with my dog until it’s time for my MAID.”
- Patient preparing the performance of MAID-
Support for making sense of the patient’s desire for MAID
“I think it would have been better if we were a bit more informed about her suffering, and especially about the severity of her suffering. Of course, that’s very personal, but that would have helped us to evaluate her situation in a realistic way and perhaps to assist her better. A bit like raising awareness. Now, I can finally imagine what her suffering was all about, but before … Mum makes the decision for MAID and stops all treatments. Two weeks later, she was dead, and we were still unsure of why she had chosen to go through with MAID.”
-Relative of a patient who received MAID-
Support for managing meaningful activities
“I would prefer to call it the need for a master of ceremonies. Perhaps that’s a weird word, but someone who actually guides us in our farewell ritual. When I say farewell ritual, everyone thinks of the farewell ritual in the mortuary. So I don’t need him for that, but for a farewell ritual on the day of the performance, together with my family. Someone who helps you in looking for alternative ways to say goodbye. Someone who normalizes the whole situation on the day itself, and in which my brother and mother are given a special part.”
-Patient preparing the performance of MAID-
Support for navigating existential questions
“I also think the philosophy around it is important. What does this mean in a more existential context? I am not religious, but what does such a MAID request from your child mean? What is the impact of that? Have I been a good parent? Is it something genetic? What was my role in her decision to make the request? It’s not just the practical side that matters, but also, how do I deal with this? What does it mean? Should I ask myself questions about how I stand in life? These questions are ever-present. Fortunately, I don't get completely overwhelmed by these questions, otherwise, I would spiral into depression. However, I still contemplate them regularly. I have numerous questions, observations, and reflections that are difficult to be answered. It’s a state of mind that persists even when I am in bed at night. This can lead to mental turmoil, making it impossible to get any sleep. So yeah, then it would be nice to have someone to listen to them and acknowledge my concerns. That could provide some comfort, yes.”
-Relative of a patient whose attending physician is assessing the eligibility for MAID-
Support for psycho-emotional regulation
“That moment itself [after performance], you are in the flow of the funeral and family. But the night after, it was, it was really awful. Yes, you are in shock. When someone you love dies due to a sudden accident or illness, you are in shock because you have not been able to prepare for it. We had a year to prepare ourselves for her death as we had arranged her MAID well in advance. But I was in shock. […] A month later, I ended up with a psychologist through my general practitioner. I didn't know what to do with what I felt or didn't feel. Is it normal for me to think such things? And then someone who says to you ‘That’s completely normal’ is just enough.”
-Relative of a patient who received MAID-
Support for facilitating social interaction
“It would be useful to have someone as a backup, indeed. I have always had a good relationship with my brothers, you know. One of them is Catholic, and the others are atheistic. Anyway, when we are together, that can cause some problems, and even more now when I have asked for MAID. Sometimes, you can really feel the tension. But you can’t always be diplomatic if that is what I want. But it would help if there is a moderator. We often meet as brothers, but we don’t really talk to each other about our feelings. That’s difficult. So yes, a moderator to help us say what we still want to say to each other before I get MAID.”
–Patient whose attending physician is assessing the eligibility for MAID-
Support for understanding the process toward MAID
“Perhaps the oncologist could have framed it better: from the moment you decide that you want MAID, this and that still have to happen first. We didn't know that. Mom called the hospital to say she would like to receive MAID. All the necessary paperwork was in order. However, at that point, she was told that a second doctor was needed. We also didn't know that you couldn't come into the hospital and have it done right away.”
-Relative of a patient who received MAID-
Support for handling organizational and practical matters
“One thing that was lacking in terms of support during my hospital stay was the absence of social service to help me with the arrangements necessary for the performance of MAID. I had to carry out everything on my own, without any prior experience of making a written request, knowing whom to notify, or what to take into account. That was a time-consuming and energy-draining process, and it would have been helpful if there was a social service to rely on. Someone who could be present when arranging these things; and also help me locate a notary in the area. The practical details that needed to be taken care of were a burden, and while those around me have tried to help me, I don’t think it should have been their responsibility to shoulder this.”
-Patient preparing the performance of MAID-
MAID, medical aid in dying.
Support for Maximizing Daily Functioning
This support need was only found in patients. Patients repeatedly spoke of their unbearable suffering as a daily reality around which they had organized their lives. They described in many ways their ongoing struggle with accepting this suffering. Although patients recognized that no practical support could eradicate their suffering, some felt that support could ease their struggle by maximizing their daily functioning throughout their MAID trajectories. They favored practical support in carrying out daily activities and tasks, e.g., aid to travel to healthcare facilities. Some patients experienced such support as essential to maintaining a sense of self-worth. On the other hand, others experienced such support as pivotal to being able to stay at home independently and to receive MAID in their familiar environment.
Support for Making Sense of the Patient’s Desire for MAID
This support need was only identified in relatives. Relatives needed support for making sense of the patient’s desire for MAID and its broader context, such as the nature of the unbearable suffering. Most relatives needed this to 1) facilitate closure and accept the patient’s desire and 2) evaluate their potential role in alleviating the suffering. Relatives stated that this support need partially stemmed from their limited understanding of the desire for MAID as patients and healthcare professionals often reduced the desire to the patient’s medical condition. Moreover, relatives indicated that this support need was particularly strong in the early phases of the MAID trajectory, when they first learned of the patient’s desire or had just become involved in the process.
Support for Managing Meaningful Activities
Patients and relatives (hereafter referred to as participants) felt the need to experience moments and activities that gave purpose and meaning to their MAID trajectories as ways to make the most of the remaining time prior to the performance. In practice, these meaningful moments and activities reflected participants’ values, passions, lives, roles, and identities, particularly those of patients. These included, for example, preparing memorials, sharing (life) stories and memories, and participating in rituals and traditions. Participants particularly experienced a farewell ritual as one of the most meaningful activities. Moreover, relatives also identified being a family carer for the patient in his or her MAID trajectory as a meaningful activity. However, participants needed support to manage meaningful activities as they often faced difficulties and challenges. Additionally, patients with a non-terminal condition most often found their MAID trajectories to be long and purposeless and stipulated that they required support in finding meaningful activities prior to the performance.
Support for Navigating Existential Questions
Participants expressed having many existential questions and contemplating life and death throughout their MAID trajectories. This involved questioning and reflecting upon, for example, the value of life, when it is justified to request MAID, what will come after death, whether requesting MAID equals failing relatives (experienced by patients), or when one is ready to die. Consequently, participants emphasized their support need for navigating existential questions as like having a sounding board since these questions sometimes led to feelings of loneliness and distress. Furthermore, this support need appeared more acute in patients than in relatives.
Support for Psycho-Emotional Regulation
A rapid succession of negative and positive emotions characterized participants’ MAID trajectories. They particularly stressed the need for guidance in controlling and coping with negative emotions. Participants often felt overwhelmed by the intensity of their emotions and faced difficulties processing them individually. Patients’ negative emotions included anxiety due to the uncertain outcome of the MAID request, distress caused by the unpredictable progression of the unbearable suffering, and fear of death and the unknown afterlife. Negative emotions of relatives included feelings of loss and anticipatory grief throughout their MAID trajectories, stress when the request was being assessed, and tension upon receiving the news of the patient’s request.
The need for psycho-emotional regulation seemed to differ in intensity among participants. In their accounts, participants experiencing adverse events in their MAID trajectory emphasized their need for psycho-emotional regulation more strongly compared to those not experiencing adverse events. Examples of adverse events included witnessing the patient’s discomfort, complications or delays in accessing MAID, and communication issues with providers. Additionally, family carers or relatives very close to the patient also stressed their need for psycho-emotional regulation more firmly in their accounts compared with others. These relatives attributed this need in part to the fact that they neglected their emotions due to prioritizing the patient’s emotions as the focal point throughout the MAID trajectories.
Support for Facilitating Social Interaction
Participants felt a strong need for social connection and sharing their MAID trajectories with those close to them, who were often indicated as the most important individuals in their trajectories. However, many participants reported problems establishing and maintaining social interactions, and wished for support to facilitate this.
The support need for facilitating social interaction was materialized by participants in several ways. First, some participants required support to resolve social conflicts and to improve social bonds, e.g., conflict resolution through moderated family conversations. This support need was partly related to obtaining peace of mind before the performance. Moreover, some patients considered postponing the performance if social conflicts were not resolved. Second, a few patients needed social support in communicating their MAID desires or requests to relatives. Third, some relatives perceived this support as facilitating family engagement in the MAID trajectories. These relatives experienced family engagement as necessary to adequately support patients themselves in their MAID trajectories. Relatives who were at times not being engaged, experienced frustration or anger, sometimes resulting in psychological problems.
Support for Understanding the Process Toward MAID
In their MAID trajectories, participants required support for understanding the process toward MAID regarding 1) the MAID legislation and procedure, and 2) the attending physician’s decision-making regarding MAID. First, participants found it challenging to comprehend the legal modalities of MAID, especially the eligibility criteria. These difficulties led to confusion and ambiguity, such as whether patients with advanced dementia have access to MAID via an advance directive. In addition, participants wanted guidance in understanding the MAID procedure, including the formal steps one must complete before the performance. Second, participants required guidance in understanding the attending physician’s decision-making process for granting or rejecting the MAID request. Participants wanted to know the specific motives or reasoning behind the decision, partly due to their experiences of vagueness from attending physicians.
Support for Handling Organizational and Practical Matters
Participants required assistance with various organizational and practical matters throughout their MAID trajectories, such as deciding on the ideal location and timing of performance or arranging the patient’s funeral. They also needed help with administrative tasks, e.g., completing a written MAID request to meet the legal requirement.
Participants explained that this support need partly stemmed from their desire to focus on issues that mattered more to them than organization and practical matters, such as social activities. Organizational and practical support was perceived as a way to reduce the administrative burden often experienced in MAID practice. On the other hand, some patients wished for this support to be in order with all administrative requirements as a sense of control or relief. That was more acute in relatives than patients because handling organizational and practical matters was perceived as one of their important roles since patients often lacked strength due to their general deterioration.
Study Sample
We identified 18 patients and 23 relatives as eligible for study participation, all of whom were invited to participate. 15 patients and 21 relatives (n = 36) eventually participated, whereas the other invited individuals chose not to participate or to drop out without providing their motives. We conducted nine semi-structured interviews with patients (five online interviews and four in person at the patient’s main residence), one dyadic semi-structured interview with a patient and a relative (in person at the main residence of the patient), and 20 semi-structured interviews with relatives (11 online interviews and 9 interviews in person at the main residence of the relative), and included five written narratives from patients. The mean length of the semi-structured interviews with patients was 78 min (range: 28–130 min), and with relatives 82 min (range: 54–127 min). The main characteristics of the participants are listed in Tables 1, 2.
TABLE 1
Participant characteristics of patients requesting medical aid in dying (N = 15) (Belgium, 2021–2022).
N
Biological sex
Female
12
Male
3
Age
<30 years
1
30–40 years
4
41–50 years
4
51–60 years
3
>60 years
3
Highest level of education
Secondary school
6
Higher education
9
Phase in the MAID trajectory
a
Holding an explicit desire for MAID that has been expressed to relatives or healthcare professionals
4
Exploration of the eligibility for MAID by the attending physician following a formal request
5
Preparing the performance of MAID as the attending physician has agreed to grant the request
6
Main medical diagnosis
Psychiatric disorder
7
Connective tissue disorder
2
Chronic pain syndrome
2
Neurodegenerative disorder
2
Cancer
2
Main residence
a
Home
13
Hospital
1
Nursing home
1
MAID, medical aid in dying.
a
Timing of the interview or written narrative.
TABLE 2
Participant characteristics of relatives (N = 21) (Belgium, 2021–2022).
N
Sex
Female
18
Male
3
Age
<30 years
1
30–40 years
6
41–50 ears
2
51–60 years
6
<61 years
6
Highest level of education
Secondary school
2
Higher education
19
Relationship with the patient
Partner
2
Parent
3
Sibling
1
Child
9
Grandchild
2
Close friend
1
Living together with the patient
a
No
18
Yes
3
Family carer
a
Yes
11
No
9
The phase of MAID practice
a
The patient holds an explicit desire for MAID, that has been expressed to relatives or healthcare professionals
0
Exploration of the eligibility for MAID by the attending physician following a formal request
2
Preparing the performance of MAID as the attending physician has agreed to grant the request
3
MAID was carried out
b
<12 months
7
12–24 months
5
25–36 months
3
>36 months
1
Patient’s main medical diagnosis
a
Cancer
12
Neurodegenerative disorder
3
Psychiatric disorder
2
Chronic pain syndrome
2
Polypathology
2
Patient’s sex
Female
13
Male
8
Patient’s age
a
<30 years
1
30–40 years
2
41–50 years
1
51–60 years
2
61–70 years
4
71–80 years
7
>80 years
4
Patient’s main residence
a
Home
14
Hospital
4
Nursing home
3
MAID, medical aid in dying.
a
Timing of the interview or when MAID was carried out.
b
In one case, the relative did not attend the performance of MAID.
Support Needs Experienced Throughout the Medical Aid in Dying Trajectory
We identified eight types of support needs experienced by patients and relatives throughout their MAID trajectories (Figure 1). Identified support needs appeared to differ in their intensity of experience and seemed to be partially linked to a specific phase of the MAID trajectory, e.g., assessment or day of performance. We discuss each theme or type of support need separately for analytic clarity. However, they are interconnected in practice. In the following paragraphs, references to “participants” include both patients and relatives. Furthermore, some exemplary quotes are provided in Table 3.
FIGURE 1
Eight types of support needs were identified that patients and their relatives might experience throughout their medical aid in dying trajectories. Support for maximizing daily functioning was only reported by patients, while support for making sense of the patient’s desire for medical aid in dying only in relatives. (Belgium, 2021–2022).
TABLE 3
Participant’s quotes regarding types of support needs (Belgium, 2021–2022).
Support for maximizing daily functioning
“Well, you won’t recover if you have a chronic disease. So it’s not about recovering. It’s a different way of looking at me. I also know that, in my case, being at home is not the ideal situation because I don’t have the same support as in the hospital. There, you got pain management, a nursing team helping you out of bed and giving you all the medication, and if they have to, even putting the food in your mouth. I don’t need that kind of support. That is way too much, of course. But just a bit more support, so I am more comfortable at home. This would be helpful, so I can stand on my own feet and stay here at home with my dog until it’s time for my MAID.”
- Patient preparing the performance of MAID-
Support for making sense of the patient’s desire for MAID
“I think it would have been better if we were a bit more informed about her suffering, and especially about the severity of her suffering. Of course, that’s very personal, but that would have helped us to evaluate her situation in a realistic way and perhaps to assist her better. A bit like raising awareness. Now, I can finally imagine what her suffering was all about, but before … Mum makes the decision for MAID and stops all treatments. Two weeks later, she was dead, and we were still unsure of why she had chosen to go through with MAID.”
-Relative of a patient who received MAID-
Support for managing meaningful activities
“I would prefer to call it the need for a master of ceremonies. Perhaps that’s a weird word, but someone who actually guides us in our farewell ritual. When I say farewell ritual, everyone thinks of the farewell ritual in the mortuary. So I don’t need him for that, but for a farewell ritual on the day of the performance, together with my family. Someone who helps you in looking for alternative ways to say goodbye. Someone who normalizes the whole situation on the day itself, and in which my brother and mother are given a special part.”
-Patient preparing the performance of MAID-
Support for navigating existential questions
“I also think the philosophy around it is important. What does this mean in a more existential context? I am not religious, but what does such a MAID request from your child mean? What is the impact of that? Have I been a good parent? Is it something genetic? What was my role in her decision to make the request? It’s not just the practical side that matters, but also, how do I deal with this? What does it mean? Should I ask myself questions about how I stand in life? These questions are ever-present. Fortunately, I don't get completely overwhelmed by these questions, otherwise, I would spiral into depression. However, I still contemplate them regularly. I have numerous questions, observations, and reflections that are difficult to be answered. It’s a state of mind that persists even when I am in bed at night. This can lead to mental turmoil, making it impossible to get any sleep. So yeah, then it would be nice to have someone to listen to them and acknowledge my concerns. That could provide some comfort, yes.”
-Relative of a patient whose attending physician is assessing the eligibility for MAID-
Support for psycho-emotional regulation
“That moment itself [after performance], you are in the flow of the funeral and family. But the night after, it was, it was really awful. Yes, you are in shock. When someone you love dies due to a sudden accident or illness, you are in shock because you have not been able to prepare for it. We had a year to prepare ourselves for her death as we had arranged her MAID well in advance. But I was in shock. […] A month later, I ended up with a psychologist through my general practitioner. I didn't know what to do with what I felt or didn't feel. Is it normal for me to think such things? And then someone who says to you ‘That’s completely normal’ is just enough.”
-Relative of a patient who received MAID-
Support for facilitating social interaction
“It would be useful to have someone as a backup, indeed. I have always had a good relationship with my brothers, you know. One of them is Catholic, and the others are atheistic. Anyway, when we are together, that can cause some problems, and even more now when I have asked for MAID. Sometimes, you can really feel the tension. But you can’t always be diplomatic if that is what I want. But it would help if there is a moderator. We often meet as brothers, but we don’t really talk to each other about our feelings. That’s difficult. So yes, a moderator to help us say what we still want to say to each other before I get MAID.”
–Patient whose attending physician is assessing the eligibility for MAID-
Support for understanding the process toward MAID
“Perhaps the oncologist could have framed it better: from the moment you decide that you want MAID, this and that still have to happen first. We didn't know that. Mom called the hospital to say she would like to receive MAID. All the necessary paperwork was in order. However, at that point, she was told that a second doctor was needed. We also didn't know that you couldn't come into the hospital and have it done right away.”
-Relative of a patient who received MAID-
Support for handling organizational and practical matters
“One thing that was lacking in terms of support during my hospital stay was the absence of social service to help me with the arrangements necessary for the performance of MAID. I had to carry out everything on my own, without any prior experience of making a written request, knowing whom to notify, or what to take into account. That was a time-consuming and energy-draining process, and it would have been helpful if there was a social service to rely on. Someone who could be present when arranging these things; and also help me locate a notary in the area. The practical details that needed to be taken care of were a burden, and while those around me have tried to help me, I don’t think it should have been their responsibility to shoulder this.”
-Patient preparing the performance of MAID-
MAID, medical aid in dying.
Support for Maximizing Daily Functioning
This support need was only found in patients. Patients repeatedly spoke of their unbearable suffering as a daily reality around which they had organized their lives. They described in many ways their ongoing struggle with accepting this suffering. Although patients recognized that no practical support could eradicate their suffering, some felt that support could ease their struggle by maximizing their daily functioning throughout their MAID trajectories. They favored practical support in carrying out daily activities and tasks, e.g., aid to travel to healthcare facilities. Some patients experienced such support as essential to maintaining a sense of self-worth. On the other hand, others experienced such support as pivotal to being able to stay at home independently and to receive MAID in their familiar environment.
Support for Making Sense of the Patient’s Desire for MAID
This support need was only identified in relatives. Relatives needed support for making sense of the patient’s desire for MAID and its broader context, such as the nature of the unbearable suffering. Most relatives needed this to 1) facilitate closure and accept the patient’s desire and 2) evaluate their potential role in alleviating the suffering. Relatives stated that this support need partially stemmed from their limited understanding of the desire for MAID as patients and healthcare professionals often reduced the desire to the patient’s medical condition. Moreover, relatives indicated that this support need was particularly strong in the early phases of the MAID trajectory, when they first learned of the patient’s desire or had just become involved in the process.
Support for Managing Meaningful Activities
Patients and relatives (hereafter referred to as participants) felt the need to experience moments and activities that gave purpose and meaning to their MAID trajectories as ways to make the most of the remaining time prior to the performance. In practice, these meaningful moments and activities reflected participants’ values, passions, lives, roles, and identities, particularly those of patients. These included, for example, preparing memorials, sharing (life) stories and memories, and participating in rituals and traditions. Participants particularly experienced a farewell ritual as one of the most meaningful activities. Moreover, relatives also identified being a family carer for the patient in his or her MAID trajectory as a meaningful activity. However, participants needed support to manage meaningful activities as they often faced difficulties and challenges. Additionally, patients with a non-terminal condition most often found their MAID trajectories to be long and purposeless and stipulated that they required support in finding meaningful activities prior to the performance.
Support for Navigating Existential Questions
Participants expressed having many existential questions and contemplating life and death throughout their MAID trajectories. This involved questioning and reflecting upon, for example, the value of life, when it is justified to request MAID, what will come after death, whether requesting MAID equals failing relatives (experienced by patients), or when one is ready to die. Consequently, participants emphasized their support need for navigating existential questions as like having a sounding board since these questions sometimes led to feelings of loneliness and distress. Furthermore, this support need appeared more acute in patients than in relatives.
Support for Psycho-Emotional Regulation
A rapid succession of negative and positive emotions characterized participants’ MAID trajectories. They particularly stressed the need for guidance in controlling and coping with negative emotions. Participants often felt overwhelmed by the intensity of their emotions and faced difficulties processing them individually. Patients’ negative emotions included anxiety due to the uncertain outcome of the MAID request, distress caused by the unpredictable progression of the unbearable suffering, and fear of death and the unknown afterlife. Negative emotions of relatives included feelings of loss and anticipatory grief throughout their MAID trajectories, stress when the request was being assessed, and tension upon receiving the news of the patient’s request.
The need for psycho-emotional regulation seemed to differ in intensity among participants. In their accounts, participants experiencing adverse events in their MAID trajectory emphasized their need for psycho-emotional regulation more strongly compared to those not experiencing adverse events. Examples of adverse events included witnessing the patient’s discomfort, complications or delays in accessing MAID, and communication issues with providers. Additionally, family carers or relatives very close to the patient also stressed their need for psycho-emotional regulation more firmly in their accounts compared with others. These relatives attributed this need in part to the fact that they neglected their emotions due to prioritizing the patient’s emotions as the focal point throughout the MAID trajectories.
Support for Facilitating Social Interaction
Participants felt a strong need for social connection and sharing their MAID trajectories with those close to them, who were often indicated as the most important individuals in their trajectories. However, many participants reported problems establishing and maintaining social interactions, and wished for support to facilitate this.
The support need for facilitating social interaction was materialized by participants in several ways. First, some participants required support to resolve social conflicts and to improve social bonds, e.g., conflict resolution through moderated family conversations. This support need was partly related to obtaining peace of mind before the performance. Moreover, some patients considered postponing the performance if social conflicts were not resolved. Second, a few patients needed social support in communicating their MAID desires or requests to relatives. Third, some relatives perceived this support as facilitating family engagement in the MAID trajectories. These relatives experienced family engagement as necessary to adequately support patients themselves in their MAID trajectories. Relatives who were at times not being engaged, experienced frustration or anger, sometimes resulting in psychological problems.
Support for Understanding the Process Toward MAID
In their MAID trajectories, participants required support for understanding the process toward MAID regarding 1) the MAID legislation and procedure, and 2) the attending physician’s decision-making regarding MAID. First, participants found it challenging to comprehend the legal modalities of MAID, especially the eligibility criteria. These difficulties led to confusion and ambiguity, such as whether patients with advanced dementia have access to MAID via an advance directive. In addition, participants wanted guidance in understanding the MAID procedure, including the formal steps one must complete before the performance. Second, participants required guidance in understanding the attending physician’s decision-making process for granting or rejecting the MAID request. Participants wanted to know the specific motives or reasoning behind the decision, partly due to their experiences of vagueness from attending physicians.
Support for Handling Organizational and Practical Matters
Participants required assistance with various organizational and practical matters throughout their MAID trajectories, such as deciding on the ideal location and timing of performance or arranging the patient’s funeral. They also needed help with administrative tasks, e.g., completing a written MAID request to meet the legal requirement.
Participants explained that this support need partly stemmed from their desire to focus on issues that mattered more to them than organization and practical matters, such as social activities. Organizational and practical support was perceived as a way to reduce the administrative burden often experienced in MAID practice. On the other hand, some patients wished for this support to be in order with all administrative requirements as a sense of control or relief. That was more acute in relatives than patients because handling organizational and practical matters was perceived as one of their important roles since patients often lacked strength due to their general deterioration.
Support for Maximizing Daily Functioning
This support need was only found in patients. Patients repeatedly spoke of their unbearable suffering as a daily reality around which they had organized their lives. They described in many ways their ongoing struggle with accepting this suffering. Although patients recognized that no practical support could eradicate their suffering, some felt that support could ease their struggle by maximizing their daily functioning throughout their MAID trajectories. They favored practical support in carrying out daily activities and tasks, e.g., aid to travel to healthcare facilities. Some patients experienced such support as essential to maintaining a sense of self-worth. On the other hand, others experienced such support as pivotal to being able to stay at home independently and to receive MAID in their familiar environment.
Support for Making Sense of the Patient’s Desire for MAID
This support need was only identified in relatives. Relatives needed support for making sense of the patient’s desire for MAID and its broader context, such as the nature of the unbearable suffering. Most relatives needed this to 1) facilitate closure and accept the patient’s desire and 2) evaluate their potential role in alleviating the suffering. Relatives stated that this support need partially stemmed from their limited understanding of the desire for MAID as patients and healthcare professionals often reduced the desire to the patient’s medical condition. Moreover, relatives indicated that this support need was particularly strong in the early phases of the MAID trajectory, when they first learned of the patient’s desire or had just become involved in the process.
Support for Managing Meaningful Activities
Patients and relatives (hereafter referred to as participants) felt the need to experience moments and activities that gave purpose and meaning to their MAID trajectories as ways to make the most of the remaining time prior to the performance. In practice, these meaningful moments and activities reflected participants’ values, passions, lives, roles, and identities, particularly those of patients. These included, for example, preparing memorials, sharing (life) stories and memories, and participating in rituals and traditions. Participants particularly experienced a farewell ritual as one of the most meaningful activities. Moreover, relatives also identified being a family carer for the patient in his or her MAID trajectory as a meaningful activity. However, participants needed support to manage meaningful activities as they often faced difficulties and challenges. Additionally, patients with a non-terminal condition most often found their MAID trajectories to be long and purposeless and stipulated that they required support in finding meaningful activities prior to the performance.
Support for Navigating Existential Questions
Participants expressed having many existential questions and contemplating life and death throughout their MAID trajectories. This involved questioning and reflecting upon, for example, the value of life, when it is justified to request MAID, what will come after death, whether requesting MAID equals failing relatives (experienced by patients), or when one is ready to die. Consequently, participants emphasized their support need for navigating existential questions as like having a sounding board since these questions sometimes led to feelings of loneliness and distress. Furthermore, this support need appeared more acute in patients than in relatives.
Support for Psycho-Emotional Regulation
A rapid succession of negative and positive emotions characterized participants’ MAID trajectories. They particularly stressed the need for guidance in controlling and coping with negative emotions. Participants often felt overwhelmed by the intensity of their emotions and faced difficulties processing them individually. Patients’ negative emotions included anxiety due to the uncertain outcome of the MAID request, distress caused by the unpredictable progression of the unbearable suffering, and fear of death and the unknown afterlife. Negative emotions of relatives included feelings of loss and anticipatory grief throughout their MAID trajectories, stress when the request was being assessed, and tension upon receiving the news of the patient’s request.
The need for psycho-emotional regulation seemed to differ in intensity among participants. In their accounts, participants experiencing adverse events in their MAID trajectory emphasized their need for psycho-emotional regulation more strongly compared to those not experiencing adverse events. Examples of adverse events included witnessing the patient’s discomfort, complications or delays in accessing MAID, and communication issues with providers. Additionally, family carers or relatives very close to the patient also stressed their need for psycho-emotional regulation more firmly in their accounts compared with others. These relatives attributed this need in part to the fact that they neglected their emotions due to prioritizing the patient’s emotions as the focal point throughout the MAID trajectories.
Support for Facilitating Social Interaction
Participants felt a strong need for social connection and sharing their MAID trajectories with those close to them, who were often indicated as the most important individuals in their trajectories. However, many participants reported problems establishing and maintaining social interactions, and wished for support to facilitate this.
The support need for facilitating social interaction was materialized by participants in several ways. First, some participants required support to resolve social conflicts and to improve social bonds, e.g., conflict resolution through moderated family conversations. This support need was partly related to obtaining peace of mind before the performance. Moreover, some patients considered postponing the performance if social conflicts were not resolved. Second, a few patients needed social support in communicating their MAID desires or requests to relatives. Third, some relatives perceived this support as facilitating family engagement in the MAID trajectories. These relatives experienced family engagement as necessary to adequately support patients themselves in their MAID trajectories. Relatives who were at times not being engaged, experienced frustration or anger, sometimes resulting in psychological problems.
Support for Understanding the Process Toward MAID
In their MAID trajectories, participants required support for understanding the process toward MAID regarding 1) the MAID legislation and procedure, and 2) the attending physician’s decision-making regarding MAID. First, participants found it challenging to comprehend the legal modalities of MAID, especially the eligibility criteria. These difficulties led to confusion and ambiguity, such as whether patients with advanced dementia have access to MAID via an advance directive. In addition, participants wanted guidance in understanding the MAID procedure, including the formal steps one must complete before the performance. Second, participants required guidance in understanding the attending physician’s decision-making process for granting or rejecting the MAID request. Participants wanted to know the specific motives or reasoning behind the decision, partly due to their experiences of vagueness from attending physicians.
Support for Handling Organizational and Practical Matters
Participants required assistance with various organizational and practical matters throughout their MAID trajectories, such as deciding on the ideal location and timing of performance or arranging the patient’s funeral. They also needed help with administrative tasks, e.g., completing a written MAID request to meet the legal requirement.
Participants explained that this support need partly stemmed from their desire to focus on issues that mattered more to them than organization and practical matters, such as social activities. Organizational and practical support was perceived as a way to reduce the administrative burden often experienced in MAID practice. On the other hand, some patients wished for this support to be in order with all administrative requirements as a sense of control or relief. That was more acute in relatives than patients because handling organizational and practical matters was perceived as one of their important roles since patients often lacked strength due to their general deterioration.
Discussion
Using semi-structured interviews and written narratives, we identified various support needs among patients and relatives across their MAID trajectories i.e., from the moment when the patient conceives an explicit desire for MAID till the period following the performance of MAID. Participants needed support for maximizing daily functioning (only reported by patients), making sense of the patient’s desire for MAID (only reported by relatives), managing meaningful activities, navigating existential questions, psycho-emotional regulation, facilitating social interaction, understanding the process toward MAID, and handling organizational and practical matters.
Our study suggests that MAID constitutes a multidimensional practice for patients and relatives involving various support needs throughout their MAID trajectories. These support needs essentially reflect a much broader aspiration of patients and families for definition and experience of the conditions for a good death [26–29]. Although echoing some degree of specificity in terms of context and practice – e.g., making sense of the patient’s desire for MAID and understanding the process toward MAID-most identified support needs seem to correspond to the multidimensional support needs of patients and relatives in other end-of-life trajectories [30–32]. Our findings could be seen to align with Manfred Max-Neef’s theory which states that (support) needs across individuals and practices are highly similar, as opposed to traditional theories asserting considerable variation in (support) needs [33]. Hence, an inter-professional approach can be warranted to meet the multidimensional support needs throughout the MAID trajectory as it can be challenging for a single professional to address all of these needs alone.
Our study illustrates that, in line with other dying experiences, patients and relatives experience their MAID trajectory less as a medical practice and more as a social and personal process [34, 35]. This is substantiated by our finding that participants needed support for facilitating social interaction while emphasizing their intense need for social connection and sharing their final moments throughout their MAID trajectories. The social character of MAID practice is also evident in relatives’ need for support to be engaged in the MAID trajectories, making them active participants. These results build on previous studies indicating that MAID should be equally understood and approached as a social and relational phenomenon [8, 12, 36]. This implies that the MAID trajectories of patients and relatives are partly nurtured by the dynamics and quality of their relationships, which may influence their MAID experiences considerably [12, 36]. In that way, our findings provide empirical evidence for the suggestions of Canadian scholars that MAID support would benefit from both a patient- and family-centered approach [7, 13, 37]. Furthermore, “MAID as an existential practice” is substantiated by the fact that participants needed support for navigating existential questions, managing meaningful activities, and making sense of unbearable suffering in the case of relatives. This is in keeping with the study of Tuva et al., which found that patients near the end of life longed for care focusing on “living a meaningful life” [30]. Furthermore, these support needs may result from the typical existential experiences that are often reported among patients and relatives when death is imminent [38–40]. Moreover, our participants experienced farewell rituals in their medical aid in dying trajectories as particularly meaningful. That corroborates previous research, in which relatives perceived these farewell rituals as an advantage of medical aid in dying practice as they experienced that in other end-of-life practices, they would have less room to organize them due to the unpredictable nature of the moment of death [12, 41]. Successful farewell rituals may provide individuals with emotional energy and facilitate closure [41, 42]. Furthermore, our study suggests that patients and their relatives struggle to organize and manage meaningful and meaning-making activities. A plausible reason is that such tasks might come with an emotional burden and some unfamiliarity [5].
Our study further raises the interesting question about which support model would be best suited to meet the support needs of patients and relatives in medical aid in dying practice. In line with others [7, 43, 44], we argue that a palliative care approach seems to be highly suitable, based on our findings’ alignment with the principles and goals of palliative care: 1) patients requesting medical aid in dying and relatives require support for the multidimensional needs they experience with a strong emphasis on psychological, social and existential needs; 2) an inter-professional approach is advisable to fulfill the multitude of these support needs adequately; and 3) patient-and-family centeredness appears the most appropriate approach. These requirements mean that palliative care professionals and services are well suited to address the support needs of patients and relatives in medical aid in dying practice. Thus, palliative care and medical aid in dying practice should not necessarily be viewed as opposing options but might be integrated, as in Belgium [45]. The integration of palliative care and medical aid in dying practice continues to be the subject of intense international debate [46, 47]. Our study provides empirical support in favor of this integration. We suggest that jurisdictions with (proposed) assisted dying legislation should consider in earnest to what extent and how palliative care professionals can be engaged, and organizations can be strengthened, to support patients and relatives throughout their medical aid in dying trajectories.
Limitations and Strengths
With regard to study limitations, first, some psychological adjustments and recall bias regarding earlier experiences in the medical aid in dying trajectories may be possible (e.g., because of memory limitations and emotional influences), resulting in reporting predominantly positive or negative experiences. Recall bias, may have been possible, for example, for those relatives who were included in the study during their bereaved phase or after the administration of medical aid in dying. Second, we could have missed some specific support needs of patients with terminal conditions as we mainly recruited patients with non-terminal conditions. We mitigated this by including numerous medical aid in dying experiences from relatives of patients with terminal conditions. On the other hand, various study strengths can be acknowledged. We used several strategies to enhance the rigor and trustworthiness of our findings: method triangulation, investigator triangulation, and ongoing reflection [48]. We also used a combination of semi-structured interviews and written narratives. The latter method of data collection was presented at initial contact with patients to include those more difficult to engage with an interview format, aiding inclusivity and reducing potential recruitment bias. We obtained a saturation of types of support needs arising from the lived experiences of both patients and relatives.
Implications for Clinical Practice and Future Research
First, our findings offer valuable insights that can inform healthcare professionals, educational curricula, practice tools –e.g. guidelines for medical aid in dying- and training-programs about which support needs can improve the medical aid in dying experiences and outcomes of patients and relatives. Secondly, our study provides empirical evidence for key components that should be included in a needs-oriented approach to care for or support medical aid in dying practice, such as a person-centered approach. Thirdly, healthcare professionals should pay attention to the non-medical support needs of patients and relatives, with particular attention to their social and existential needs. In this regard, promising approaches to facilitating social interaction between patients and relatives throughout their medical aid in dying trajectories may include family therapeutic methods, family group discussions, and nurse-delivered dyadic interventions [49–53]. Following this, future research could investigate the most effective type of support to fulfill the social and existential support needs of patients and relatives in medical aid in dying practice, given the current lack of research on this topic.
Conclusion
Our study reveals that patients and relatives might experience multidimensional support needs throughout their medical aid in dying trajectories. Our findings suggest that they experience their medical aid in dying trajectories as explicitly social/existential in nature. As their support needs essentially correspond with those experienced in other trajectories at the end of life, support for patients and relatives in medical aid in dying practice might benefit from a palliative care approach.
Limitations and Strengths
With regard to study limitations, first, some psychological adjustments and recall bias regarding earlier experiences in the medical aid in dying trajectories may be possible (e.g., because of memory limitations and emotional influences), resulting in reporting predominantly positive or negative experiences. Recall bias, may have been possible, for example, for those relatives who were included in the study during their bereaved phase or after the administration of medical aid in dying. Second, we could have missed some specific support needs of patients with terminal conditions as we mainly recruited patients with non-terminal conditions. We mitigated this by including numerous medical aid in dying experiences from relatives of patients with terminal conditions. On the other hand, various study strengths can be acknowledged. We used several strategies to enhance the rigor and trustworthiness of our findings: method triangulation, investigator triangulation, and ongoing reflection [48]. We also used a combination of semi-structured interviews and written narratives. The latter method of data collection was presented at initial contact with patients to include those more difficult to engage with an interview format, aiding inclusivity and reducing potential recruitment bias. We obtained a saturation of types of support needs arising from the lived experiences of both patients and relatives.
Implications for Clinical Practice and Future Research
First, our findings offer valuable insights that can inform healthcare professionals, educational curricula, practice tools –e.g. guidelines for medical aid in dying- and training-programs about which support needs can improve the medical aid in dying experiences and outcomes of patients and relatives. Secondly, our study provides empirical evidence for key components that should be included in a needs-oriented approach to care for or support medical aid in dying practice, such as a person-centered approach. Thirdly, healthcare professionals should pay attention to the non-medical support needs of patients and relatives, with particular attention to their social and existential needs. In this regard, promising approaches to facilitating social interaction between patients and relatives throughout their medical aid in dying trajectories may include family therapeutic methods, family group discussions, and nurse-delivered dyadic interventions [49–53]. Following this, future research could investigate the most effective type of support to fulfill the social and existential support needs of patients and relatives in medical aid in dying practice, given the current lack of research on this topic.
Conclusion
Our study reveals that patients and relatives might experience multidimensional support needs throughout their medical aid in dying trajectories. Our findings suggest that they experience their medical aid in dying trajectories as explicitly social/existential in nature. As their support needs essentially correspond with those experienced in other trajectories at the end of life, support for patients and relatives in medical aid in dying practice might benefit from a palliative care approach. |
Title: Abdominal Pain in Inflammatory Bowel Disease-Epidemiology, Pathophysiology, and Management: A Narrative Review | Body:
Key Summary Points
Abdominal pain is a major symptom of inflammatory bowel disease (IBD), significantly impacting patients' quality of life.This study aimed to review recent research findings on the epidemiology, pathophysiology, and management of abdominal pain in IBD.The study highlighted the complex interplay between abdominal pain, disease progression, and therapeutic interventions for IBD.Emerging trends and challenges in managing IBD-related abdominal pain were identified, emphasizing the need for patient-centered approaches and integrative therapies to improve quality of life.
Introduction
Inflammatory bowel disease (IBD) comprises a group of chronic, non-specific inflammatory intestinal disorders with unclear etiology, which primarily include ulcerative colitis (UC) and Crohn’s disease (CD) [1]. Both UC and CD lead to intestinal inflammation and ulcers, which clinically manifest as presenting symptoms such as diarrhea, bloody stools, and abdominal pain. The distinction is mainly based on the location, extent, depth, pattern, and complications of the lesions [2]. Abdominal pain is common in IBD patients, with studies indicating a high incidence rate and a duration of up to 1–2 years, significantly affecting patients’ quality of life (QoL) [3].
Currently, there are no specific medications for IBD-related abdominal pain. Patients are usually administered acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), and opioids for pain relief [4]. The long-term use of these medications can lead to adverse effects. For example, opioids [5, 6], such as morphine, tramadol, hydromorphone, and fentanyl, can cause hepatotoxicity, renal dysfunction, slowed intestinal motility, and exacerbated symptoms of IBD. NSAIDs [7], such as ibuprofen, naproxen sodium, and diclofenac sodium can cause intestinal damage, ulcer formation, and bleeding, and increase the risk of IBD occurrence or recurrence. Acetaminophen can potentially cause damage to the cardiovascular system with long-term use [8, 9]. In addition, the side effects of these treatments exacerbate symptoms and reduce patient compliance [10].
In addition to the economic burden, abdominal pain and/or fear have a negative impact on the quality of life (QoL) of patients with IBD [11]. Recent studies have shown that an increasing number of patients on mesalazine, a commonly used IBD treatment, experience intolerable symptoms, including worsened diarrhea and abdominal pain [12]. As there are currently no effective and safe treatments for IBD-associated abdominal pain, thorough research into the pathogenesis and intervention mechanisms of IBD-associated abdominal pain, the development of practical, patient-centered intervention strategies, and the safe, rational, appropriate, and effective use of relevant medications for IBD are of great importance [13, 14].
In a longitudinal study on the association between abdominal pain and the increased risk of future medical resource utilization (HRU) in IBD, 162 consecutively enrolled IBD patients (mean age 44.0 years; 99 female, 63 male; 115 CD, 45 ulcerative colitis UC, two indeterminate colitis) were included. A total of 121 patients (74.7%) exhibited HRU (mean age 43.6 years; 73 female: 48 male; 84 CD, 36 UC, one IC) preceding the follow-up appointment. Abdominal pain (OR = 2.18, 95% CI 1.04–4.35, p = 0.04) at the index appointment was the only study variable significantly associated with HRU on bivariate analysis [15]. These findings reinforced the importance of regularly screening for and effectively treating abdominal pain in IBD. Therefore, it is important for both clinicians and researchers to gain a better understanding of this abdominal pain symptom to develop effective and targeted therapeutic interventions. This review summarizes the current knowledge on the pathophysiology of abdominal pain in IBD and also reviews the current evidence behind therapies and interventions for the management of abdominal pain.Fig. 1Pathophysiologic mechanisms of abdominal pain in inflammatory bowel disease (IBD). Transient Receptor Potential Vanilloid 1 (TRPV1); Acute inflammation and post-inflammation; Coexisting irritable bowel syndrome; Gut microbiota and metabolites
This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.
Definition and Epidemiology of Abdominal Pain in IBD
Abdominal pain is an important reason for patients with IBD to seek treatment. Abdominal pain in IBD manifests differently between patients with CD and UC. CD primarily presents as cramping pain in the right lower abdomen and around the navel, often occurring after meals and temporarily subsiding after bowel movements. In acute cases, there may be persistent abdominal pain with tenderness and rebound tenderness, which requires differentiation from acute appendicitis. Abdominal pain in UC is usually localized to the lower abdomen, although the pain locations vary slightly depending on the location of the lesions. For example, pain occurs in the cecum and ascending colon in the right lower abdomen, pain in the descending colon in the left lower abdomen, and pain in the sigmoid colon and rectum in the lower abdomen, pubic region, and perineal area. Lower abdominal pain in UC is often accompanied by a feeling of urgency, and pain subsides after defecation [16].
Abdominal pain in IBD can be classified as either chronic or acute. Chronic abdominal pain refers to persistent or recurrent visceral pain lasting longer than 3 months, with IBD-induced abdominal pain falling under the category of chronic secondary abdominal pain [17]. Acute abdominal pain is caused by non-traumatic reasons lasting no more than 5 days [18].
Symptoms of abdominal pain occur in 62% of patients in IBD [19]. Studies suggest that patients with CD are more likely to experience abdominal pain than patients with UC [20]. Conversely, other studies have indicated a comparable frequency of abdominal pain in these IBD subtypes [3]. Abdominal pain is more common in female patients with IBD than in male patients [21]. Abdominal pain is also common in pediatric patients with IBD, with over 50% of adolescent patients with IBD experiencing abdominal pain [22]. A survey conducted in the UK found that up to 50% of patients with CD and 37% of those with ulcerative colitis reported pain, irrespective of whether IBD was in relapse or in remission. Of those reporting pain, a high level of pain (pain ≥ 7/10) was scored by 54% of patients with CD and 42% with UC.4. In a survey of people with IBD from 21 European countries (n = 4990), 62% reported daily pain and 28.5% reported regular analgesic use between flares [9].
Impact of Abdominal Pain on Patients with IBD
Abdominal pain is a common symptom in patients with IBD. It occurs not only in the active phase but also during remission and has a lasting impact on patients’ daily lives. A study conducted in the United States found that 50–70% of IBD patients experience abdominal pain, making it the most common reason for emergency room visits [23].
In-depth interviews conducted by Newton et al. with 21 adults and 14 adolescents about their summary health-related quality of life (HRQoL) and their experience of symptoms in UC revealed that all patients experienced abdominal pain symptoms, which lead to emotional and psychological distress, such as anxiety, fear, and depression, thus impairing the patient’s QoL [24]. In the Swiss IBD Cohort Study (SIBDCS), Zeitz et al. surveyed 2152 IBD patients for pain intensity, location of pain, and the impact of pain on daily life and social activities. The study found that 71% of the patients experienced pain during the course of the disease, with abdominal pain (59.5%) and back pain (38.3%) being the main causes [3]. In addition, patients with pain had a significantly lower overall QoL than patients without pain. A retrospective cohort study of 303 IBD patients conducted by Venkata Subhash Gorrepati et al. found that IBD-related abdominal pain leads to weight loss and malnutrition among patients [25]. Barnes et al. conducted an online survey with 670 respondents from three tertiary IBD centers, social media, and the Crohn’s & Colitis Australia (CCA) association on factors associated with insomnia in patients with IBD. The results of this study suggest that abdominal pain is one of the most important contributing factors to insomnia [26].
Numerous studies have shown that abdominal pain impairs the QoL and health of patients with IBD. Therefore, there is an urgent need for treatment interventions that target abdominal pain in patients with IBD. A study of 400 patients with IBD by Louis et al. found that abdominal pain was the most important factor in assessing treatment preferences, with a relative attribute importance (RAI) of 33%. To reduce abdominal pain from severe to moderate or mild, patients were willing to accept an additional risk of mild-to-moderate side effects of 18.8% and 30.6%, respectively [27].
Diagnosis and Evaluation of Abdominal Pain in IBD
Pain is a subjective sensation experienced by humans. Therefore, the methods for recognizing and evaluating abdominal pain in IBD differ significantly from other signs and must be understood by asking patients about the degree, frequency, intensity, and characteristics of abdominal pain. Clinically, objective methods are also being sought to quantify the diagnosis of abdominal pain. Therefore, there are numerous scales or questionnaires that have been developed for the diagnosis of IBD and the severity of abdominal pain based on the degree of pain or IBD. In addition, imaging techniques such as magnetic resonance imaging have also been applied to diagnose and evaluate abdominal pain in IBD.Fig. 2Suggestions for pain management with pharmacological treatment, non-pharmacological treatment, and psychological intervention for abdominal pain in inflammatory bowel disease (IBD)
Visual Analog Scale (VAS)
The VAS returns to the visual analog scale proposed by Hayes et al. in 1971 for clinical pain assessment [28]. In recent years, Chinese pain experts have conducted research on the status and validity of international and national pain scales and published the “Chinese Expert Consensus on the Application of Pain Assessment Scales (2020 Edition)” [29]. Pain scales are divided into three categories: unidimensional, multidimensional, and neuropathic pain screening. The VAS is a unidimensional pain intensity assessment scale that is characterized by accuracy, simplicity, and high sensitivity. The VAS is a simple self-assessment method consisting of a 10-cm horizontal line with no pain at the left end and the most intense pain at the right end. Patients mark their pain intensity on the line, and the score is calculated in centimeters. VAS questionnaires can be customized as needed. For example, researchers seeking to assess gastrointestinal symptoms and well-being in patients with irritable bowel syndrome(IBS) have developed a series of seven 100-mm visual analog scales to assess abdominal pain, diarrhea, constipation, bloating, vomiting, nausea, psychological well-being, and the impact of intestinal symptoms on daily life [30]. More recently, some researchers have pointed out the limitations of the VAS, such as subjectivity, measurement error, limitation of a single item, and lack of description, and have proposed the VAS-RRR, which can simultaneously collect ratings, rankings, and data from pairwise comparisons [31].
Eleven-point Numeric Rating Scale (NRS-11)
The NRS-11 requires patients to describe their pain intensity using 11 numbers from 0 to 10, with 0 being no pain, 0–3 being mild pain, 3–7 being moderate pain, > 7 being severe pain, and 10 being the most intense pain [32]. The NRS is a numerical representation of the VAS method, with the advantage of being more intuitive. Patients are asked to express their perceived pain intensity using numbers (0–10), which is easy to understand and express, and convenient to record. Farrar et al. analyzed data from ten double-blind, placebo-controlled, multicenter clinical trials on chronic pain, involving 2724 patients. The study showed that a reduction of about 2 points or about 30% on the NRS-11 usually represents a clinically significant change, providing a standard definition for chronic pain treatment clinical trials, andenhancing the comparability, validity, and clinical applicability of research results [33]. Janssen et al. used the NRS-11 to assess abdominal pain symptoms in 429 out of 559 (76.7%) patients with IBD in remission. The study found that 198 out of 429 (42.6%) had abdominal pain symptoms (score ≥ 3), which were related to psychosocial factors [34]. More comprehensive treatment methods for IBD patients with abdominal pain can improve the quality of care and subjective well-being. The NRS-11 is also used in various clinical studies on abdominal pain [32, 35].
Short Inflammatory Bowel Disease Questionnaire (SIBDQ)
The SIBDQ is a simplified version of the Inflammatory Bowel Disease Questionnaire (IBDQ) by Irvine et al., which reduces the original 32 questions of the IBDQ to ten questions, covering the four dimensions of intestinal symptoms, emotional status, social function, and systemic symptoms [36]. The advantages of the SIBDQ are its brevity and ease of completion and scoring. Some researchers examined patients with UC simultaneously with the SIBDQ and the Simple Clinical Colitis Activity Index (SCCAI) and analyzed the internal consistency, test–retest reliability, sensitivity, and criterion validity of the SIBDQ. It was found that the SIBDQ can sensitively reflect changes in disease activity in patients with UC and is highly correlated with SCCAI. It is considered an effective, reliable, and responsive assessment method for measuring the HRQoL of patients [37]. In China, researchers also had 201 patients with IBD complete the Chinese version of the Short Inflammatory Bowel Disease Questionnaire (C-SIBDQ) [38] to assess its reliability, validity, and stability. They concluded that the C-SIBDQ has high reliability, validity, and stability and can be used as a rating scale to assess the QoL of patients with IBD.
Magnetic Resonance Imaging Technology
Bao et al. used functional magnetic resonance imaging (fMRI) and found that patients with CD with abdominal pain showed lower regional homogeneity (ReHo) values in the insula, middle cingulate cortex (MCC), and auxiliary motor area, while showing higher ReHo values in the temporal pole. Patients without abdominal pain have lower ReHo values in the hippocampus/parahippocampal cortex and higher ReHo values in the dorsomedial prefrontal cortex, which indicates a close correlation between abnormal activity in the insula and MCC and the severity of CD with and without abdominal pain [39]. Lv et al. determined the changes in metabolites in the bilateral anterior cingulate cortex (ACC) of CD patients with abdominal pain by using proton magnetic resonance spectroscopy (1H-MRS). The results showed that the levels of glutamic acid (Glu)/(creatine + phosphocreatine, total creatine, tCr) and glutamic acid + glutamine (Glx)/tCr in the bilateral ACC of CD with abdominal pain were higher than those without abdominal pain, and were closely related to the pain VAS score [40]. Some scholars have also applied blood-oxygen level-dependent (BOLD6) fMRI for abdominal pain perception to determine the immune response of anti-tumor necrosis factor (TNF) therapy in CD patients [41].
Visual Analog Scale (VAS)
The VAS returns to the visual analog scale proposed by Hayes et al. in 1971 for clinical pain assessment [28]. In recent years, Chinese pain experts have conducted research on the status and validity of international and national pain scales and published the “Chinese Expert Consensus on the Application of Pain Assessment Scales (2020 Edition)” [29]. Pain scales are divided into three categories: unidimensional, multidimensional, and neuropathic pain screening. The VAS is a unidimensional pain intensity assessment scale that is characterized by accuracy, simplicity, and high sensitivity. The VAS is a simple self-assessment method consisting of a 10-cm horizontal line with no pain at the left end and the most intense pain at the right end. Patients mark their pain intensity on the line, and the score is calculated in centimeters. VAS questionnaires can be customized as needed. For example, researchers seeking to assess gastrointestinal symptoms and well-being in patients with irritable bowel syndrome(IBS) have developed a series of seven 100-mm visual analog scales to assess abdominal pain, diarrhea, constipation, bloating, vomiting, nausea, psychological well-being, and the impact of intestinal symptoms on daily life [30]. More recently, some researchers have pointed out the limitations of the VAS, such as subjectivity, measurement error, limitation of a single item, and lack of description, and have proposed the VAS-RRR, which can simultaneously collect ratings, rankings, and data from pairwise comparisons [31].
Eleven-point Numeric Rating Scale (NRS-11)
The NRS-11 requires patients to describe their pain intensity using 11 numbers from 0 to 10, with 0 being no pain, 0–3 being mild pain, 3–7 being moderate pain, > 7 being severe pain, and 10 being the most intense pain [32]. The NRS is a numerical representation of the VAS method, with the advantage of being more intuitive. Patients are asked to express their perceived pain intensity using numbers (0–10), which is easy to understand and express, and convenient to record. Farrar et al. analyzed data from ten double-blind, placebo-controlled, multicenter clinical trials on chronic pain, involving 2724 patients. The study showed that a reduction of about 2 points or about 30% on the NRS-11 usually represents a clinically significant change, providing a standard definition for chronic pain treatment clinical trials, andenhancing the comparability, validity, and clinical applicability of research results [33]. Janssen et al. used the NRS-11 to assess abdominal pain symptoms in 429 out of 559 (76.7%) patients with IBD in remission. The study found that 198 out of 429 (42.6%) had abdominal pain symptoms (score ≥ 3), which were related to psychosocial factors [34]. More comprehensive treatment methods for IBD patients with abdominal pain can improve the quality of care and subjective well-being. The NRS-11 is also used in various clinical studies on abdominal pain [32, 35].
Short Inflammatory Bowel Disease Questionnaire (SIBDQ)
The SIBDQ is a simplified version of the Inflammatory Bowel Disease Questionnaire (IBDQ) by Irvine et al., which reduces the original 32 questions of the IBDQ to ten questions, covering the four dimensions of intestinal symptoms, emotional status, social function, and systemic symptoms [36]. The advantages of the SIBDQ are its brevity and ease of completion and scoring. Some researchers examined patients with UC simultaneously with the SIBDQ and the Simple Clinical Colitis Activity Index (SCCAI) and analyzed the internal consistency, test–retest reliability, sensitivity, and criterion validity of the SIBDQ. It was found that the SIBDQ can sensitively reflect changes in disease activity in patients with UC and is highly correlated with SCCAI. It is considered an effective, reliable, and responsive assessment method for measuring the HRQoL of patients [37]. In China, researchers also had 201 patients with IBD complete the Chinese version of the Short Inflammatory Bowel Disease Questionnaire (C-SIBDQ) [38] to assess its reliability, validity, and stability. They concluded that the C-SIBDQ has high reliability, validity, and stability and can be used as a rating scale to assess the QoL of patients with IBD.
Magnetic Resonance Imaging Technology
Bao et al. used functional magnetic resonance imaging (fMRI) and found that patients with CD with abdominal pain showed lower regional homogeneity (ReHo) values in the insula, middle cingulate cortex (MCC), and auxiliary motor area, while showing higher ReHo values in the temporal pole. Patients without abdominal pain have lower ReHo values in the hippocampus/parahippocampal cortex and higher ReHo values in the dorsomedial prefrontal cortex, which indicates a close correlation between abnormal activity in the insula and MCC and the severity of CD with and without abdominal pain [39]. Lv et al. determined the changes in metabolites in the bilateral anterior cingulate cortex (ACC) of CD patients with abdominal pain by using proton magnetic resonance spectroscopy (1H-MRS). The results showed that the levels of glutamic acid (Glu)/(creatine + phosphocreatine, total creatine, tCr) and glutamic acid + glutamine (Glx)/tCr in the bilateral ACC of CD with abdominal pain were higher than those without abdominal pain, and were closely related to the pain VAS score [40]. Some scholars have also applied blood-oxygen level-dependent (BOLD6) fMRI for abdominal pain perception to determine the immune response of anti-tumor necrosis factor (TNF) therapy in CD patients [41].
Pathophysiology Mechanisms of Abdominal Pain in IBD
Transient Receptor Potential Vanilloid 1 (TRPV1)
TRPV1 receptors are also involved in visceral hypersensitivity. Upon activation, TRPV1 elicits burning and pain sensations and releases neuropeptides that trigger neurogenic inflammation, including calcitonin gene-related peptide (CGRP) and substance P (SP), from the peripheral terminals [42–45]. Alterations in TRPV1 expression and functionality have been associated with various gastrointestinal disorders such as gastroesophageal reflux disease, IBS, and active IBD.
In their study, Ayesha Akbar et al. found that the number of TRPV1-positive nerve fibers in the rectosigmoid mucosa of patients with IBD showing symptoms of IBS was significantly higher than that of patients with IBD without IBS symptoms and the control group. This number was positively correlated with the scores for abdominal pain. However, there were no significant differences between the three groups in terms of other neuromarkers and inflammatory indicators. Multiple linear regression analysis showed that TRPV1 was the only significant predictor of abdominal pain [46]. Yiangou et al. investigated the density of TRPV1 in the colon tissue of patients with IBD and the control group using immunoblotting and immunostaining methods. They found a significant increase in the immunoreactivity of TRPV1 in the colonic nerve fibers of patients with IBD during active disease, suggesting that TRPV1 plays an important role in IBD-related pain and gastrointestinal motility disorders [47].
Studies have shown upregulation of TRPV1 expression in patients with acute exacerbations of IBD, acute colitis mouse models, and patients with IBD with chronic abdominal pain or endoscopic remission [48]. Exogenous TRPV1 neurons are involved in the response to abdominal pain [49]. In addition, TRPV1 sensitization mediates visceral hypersensitivity during UC remission, which contributes significantly to the occurrence of occasional or persistent refractory abdominal pain in some UC patients during endoscopic remission [50].
Acute Inflammatory and Post-inflammatory Abdominal Pain in IBD
Post-inflammatory abdominal pain refers to abdominal pain during the remission phase of IBD. Studies have found that in the same group of IBD patients, post-inflammatory abdominal pain is less severe and of shorter duration compared to abdominal pain during the active phase after relevant drug interventions [51]. During acute inflammation in IBD, an abundance of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), mediators, for example, SP and serotonin, and neuropeptides, including SP, nerve growth factor, and CGRP are released from damaged tissue or immune cells (mast cells) [52]. These molecules activate visceral afferent neurons, leading to the sensitization of nociceptors [53]. Abdominal pain is triggered by the activation of visceral nociceptors in response to potentially injurious stimuli in the gut, such as bloating, which depolarizes nerve endings and transmits pain signals to the CNS.
Intestinal inflammation often persists during remission in patients with IBD. It impairs intestinal motility and causes either accelerated or slowed bowel movements, leading to abdominal pain, diarrhea, or constipation [54]. Nociceptors become sensitized after tissue damage or inflammation, lowering the pain threshold and making previously ineffective stimuli effective [55]. Peripheral sensitization in IBD results from inflammatory stimuli that excite visceral sensory neurons, lowering the pain threshold and increasing pain perception. Inflammation also leads to central sensitization, which increases the input from visceral sensory neurons to the spinal cord and brain, facilitating the activation of higher-order sensory neurons and amplifying pain signals [56].
TRPV1 is also involved in the development of post-inflammatory pain. SP levels increase during both the acute and recovery phases of colitis. When sensory nerve endings are exposed to SP for prolonged periods, TRPV1 is sensitized, leading to post-inflammatory pain [57]. Ephrin-B2/EphB signaling contributes to post-inflammatory visceral hypersensitivity (VHS). Theofanous et al. [58] demonstrated in mouse models of post-inflammatory TNBS colitis and maternal separation (MS) that Ephrin-B2/EphB signaling contributes to VHS and may represent a novel therapeutic target. Enterochromaffin (EC) cells, which are excitatory enteroendocrine and neuroendocrine cells, recognize environmental and endogenous stimuli that trigger or exacerbate pain, including nutritional stimuli, microbial metabolites, inflammatory factors, mechanical stretch, stress-induced hormones, and neurotransmitters. EC can produce persistent intestinal hypersensitivity in a persistently activated state, even without inflammation [59].
Coexisting Irritable Bowel Syndrome
A common cause of pain in IBD is the simultaneous presence of irritable bowel syndrome, referred to as “inflammatory bowel syndrome (IBS)” or “IBD-IBS” [60, 61].
Chronic abdominal pain in IBD shares common pathophysiological features with IBS and likely represents an overlap between the two processes, including mild mucosal inflammation [62], neuroimmune interactions [63], and alterations in the gut microbiota [64]. Studies have found that patients with IBD and IBS have phenomena such as small intestinal bacterial overgrowth (SIBO), intestinal methanogen overgrowth (IMO), small intestinal fungal overgrowth (SIFO), and mast cell activation syndrome (MCAS) in their intestines, which contribute to the occurrence of abdominal pain [61]. Fecal calprotectin (FC) can be used to distinguish IBS from active IBD. However, the diagnostic threshold of FC for the differentiation between the two is not clear. In addition, chronic pain and discomfort in IBD-IBS abdominal pain are also attributed to dysregulation of the brain–gut axis and neuroendocrine dysfunction [65]. Dysregulation of the brain–gut axis can lead to unfavorable emotional states such as depression and anxiety in patients with IBD [66]. Patients with IBD accompanied by anxiety or depression are more prone to symptoms such as IBS-related abdominal pain [67]. The overlap between IBS and IBD may explain why certain medications that relieve IBS symptoms, such as pinaverium bromide and trimebutine, show some efficacy in IBD-related abdominal pain [68]. Despite further confirmation of the causal relationship between IBD and IBS by the randomized Mendel trial, the efficacy of the use of neuromodulators and gut–brain behavioral therapy in patients with IBD-related abdominal pain is not as satisfactory according to IBS guidelines [69].
Gut Microbiota and Metabolites
Signaling molecules from the gut microbiota of many patients with IBD, including microbial by-products, metabolites, neurotransmitters, and neuromodulators, may play a crucial role in mediating abdominal pain through peripheral and central sensitization [70]. Studies have shown that germ-free mice exhibit VHS at birth, which is associated with an increased expression of spinal Toll-like receptors and cytokines. This decreases after colonization with conventional gut microbiota, indicating a regulatory role of the symbiotic gut microbiota in maintaining the balance of excitability of colonic sensory [71]. Oral administration of specific Lactobacillus strains can induce the expression of μ-opioid and cannabinoid receptors in intestinal epithelial cells, mediating an analgesic function in the gut, similar to the effects of morphine [72]. Bacterial overgrowth in the small intestine of children is closely associated with chronic diarrhea and abdominal pain [73].
Several clinical studies have shown that the use of antibiotics alleviates abdominal pain in patients with IBD [74]. Clinical studies have also demonstrated the efficacy of probiotics such as Lactobacillus rhamnosus GG, mixtures of Bifidobacterium infantis M-63, M-16 V, and BB536, and Lactobacillus acidophilus NCFM in relieving abdominal pain [75]. In addition, metabolites produced by the gut microbiota, such as short-chain fatty acids, can sensitize peripheral pain-sensitive neurons via protein kinase C (PKC), promote histamine and 5-HT secretion, and trigger abdominal pain [76]. Conversely, the probiotic strain Escherichia coli Nissle 1917 (EcN) can produce an analgesic peptide, C12AsnGABAOH, which can penetrate the epithelial barrier and reduce calcium influx triggered by the activation of nociceptors in sensory neurons via GABAB receptors, thereby alleviating abdominal pain [77]. Overall, these previous studies suggest a link between the gut microbiota, its metabolites, and abdominal pain (Fig. 1).
Transient Receptor Potential Vanilloid 1 (TRPV1)
TRPV1 receptors are also involved in visceral hypersensitivity. Upon activation, TRPV1 elicits burning and pain sensations and releases neuropeptides that trigger neurogenic inflammation, including calcitonin gene-related peptide (CGRP) and substance P (SP), from the peripheral terminals [42–45]. Alterations in TRPV1 expression and functionality have been associated with various gastrointestinal disorders such as gastroesophageal reflux disease, IBS, and active IBD.
In their study, Ayesha Akbar et al. found that the number of TRPV1-positive nerve fibers in the rectosigmoid mucosa of patients with IBD showing symptoms of IBS was significantly higher than that of patients with IBD without IBS symptoms and the control group. This number was positively correlated with the scores for abdominal pain. However, there were no significant differences between the three groups in terms of other neuromarkers and inflammatory indicators. Multiple linear regression analysis showed that TRPV1 was the only significant predictor of abdominal pain [46]. Yiangou et al. investigated the density of TRPV1 in the colon tissue of patients with IBD and the control group using immunoblotting and immunostaining methods. They found a significant increase in the immunoreactivity of TRPV1 in the colonic nerve fibers of patients with IBD during active disease, suggesting that TRPV1 plays an important role in IBD-related pain and gastrointestinal motility disorders [47].
Studies have shown upregulation of TRPV1 expression in patients with acute exacerbations of IBD, acute colitis mouse models, and patients with IBD with chronic abdominal pain or endoscopic remission [48]. Exogenous TRPV1 neurons are involved in the response to abdominal pain [49]. In addition, TRPV1 sensitization mediates visceral hypersensitivity during UC remission, which contributes significantly to the occurrence of occasional or persistent refractory abdominal pain in some UC patients during endoscopic remission [50].
Acute Inflammatory and Post-inflammatory Abdominal Pain in IBD
Post-inflammatory abdominal pain refers to abdominal pain during the remission phase of IBD. Studies have found that in the same group of IBD patients, post-inflammatory abdominal pain is less severe and of shorter duration compared to abdominal pain during the active phase after relevant drug interventions [51]. During acute inflammation in IBD, an abundance of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), mediators, for example, SP and serotonin, and neuropeptides, including SP, nerve growth factor, and CGRP are released from damaged tissue or immune cells (mast cells) [52]. These molecules activate visceral afferent neurons, leading to the sensitization of nociceptors [53]. Abdominal pain is triggered by the activation of visceral nociceptors in response to potentially injurious stimuli in the gut, such as bloating, which depolarizes nerve endings and transmits pain signals to the CNS.
Intestinal inflammation often persists during remission in patients with IBD. It impairs intestinal motility and causes either accelerated or slowed bowel movements, leading to abdominal pain, diarrhea, or constipation [54]. Nociceptors become sensitized after tissue damage or inflammation, lowering the pain threshold and making previously ineffective stimuli effective [55]. Peripheral sensitization in IBD results from inflammatory stimuli that excite visceral sensory neurons, lowering the pain threshold and increasing pain perception. Inflammation also leads to central sensitization, which increases the input from visceral sensory neurons to the spinal cord and brain, facilitating the activation of higher-order sensory neurons and amplifying pain signals [56].
TRPV1 is also involved in the development of post-inflammatory pain. SP levels increase during both the acute and recovery phases of colitis. When sensory nerve endings are exposed to SP for prolonged periods, TRPV1 is sensitized, leading to post-inflammatory pain [57]. Ephrin-B2/EphB signaling contributes to post-inflammatory visceral hypersensitivity (VHS). Theofanous et al. [58] demonstrated in mouse models of post-inflammatory TNBS colitis and maternal separation (MS) that Ephrin-B2/EphB signaling contributes to VHS and may represent a novel therapeutic target. Enterochromaffin (EC) cells, which are excitatory enteroendocrine and neuroendocrine cells, recognize environmental and endogenous stimuli that trigger or exacerbate pain, including nutritional stimuli, microbial metabolites, inflammatory factors, mechanical stretch, stress-induced hormones, and neurotransmitters. EC can produce persistent intestinal hypersensitivity in a persistently activated state, even without inflammation [59].
Coexisting Irritable Bowel Syndrome
A common cause of pain in IBD is the simultaneous presence of irritable bowel syndrome, referred to as “inflammatory bowel syndrome (IBS)” or “IBD-IBS” [60, 61].
Chronic abdominal pain in IBD shares common pathophysiological features with IBS and likely represents an overlap between the two processes, including mild mucosal inflammation [62], neuroimmune interactions [63], and alterations in the gut microbiota [64]. Studies have found that patients with IBD and IBS have phenomena such as small intestinal bacterial overgrowth (SIBO), intestinal methanogen overgrowth (IMO), small intestinal fungal overgrowth (SIFO), and mast cell activation syndrome (MCAS) in their intestines, which contribute to the occurrence of abdominal pain [61]. Fecal calprotectin (FC) can be used to distinguish IBS from active IBD. However, the diagnostic threshold of FC for the differentiation between the two is not clear. In addition, chronic pain and discomfort in IBD-IBS abdominal pain are also attributed to dysregulation of the brain–gut axis and neuroendocrine dysfunction [65]. Dysregulation of the brain–gut axis can lead to unfavorable emotional states such as depression and anxiety in patients with IBD [66]. Patients with IBD accompanied by anxiety or depression are more prone to symptoms such as IBS-related abdominal pain [67]. The overlap between IBS and IBD may explain why certain medications that relieve IBS symptoms, such as pinaverium bromide and trimebutine, show some efficacy in IBD-related abdominal pain [68]. Despite further confirmation of the causal relationship between IBD and IBS by the randomized Mendel trial, the efficacy of the use of neuromodulators and gut–brain behavioral therapy in patients with IBD-related abdominal pain is not as satisfactory according to IBS guidelines [69].
Gut Microbiota and Metabolites
Signaling molecules from the gut microbiota of many patients with IBD, including microbial by-products, metabolites, neurotransmitters, and neuromodulators, may play a crucial role in mediating abdominal pain through peripheral and central sensitization [70]. Studies have shown that germ-free mice exhibit VHS at birth, which is associated with an increased expression of spinal Toll-like receptors and cytokines. This decreases after colonization with conventional gut microbiota, indicating a regulatory role of the symbiotic gut microbiota in maintaining the balance of excitability of colonic sensory [71]. Oral administration of specific Lactobacillus strains can induce the expression of μ-opioid and cannabinoid receptors in intestinal epithelial cells, mediating an analgesic function in the gut, similar to the effects of morphine [72]. Bacterial overgrowth in the small intestine of children is closely associated with chronic diarrhea and abdominal pain [73].
Several clinical studies have shown that the use of antibiotics alleviates abdominal pain in patients with IBD [74]. Clinical studies have also demonstrated the efficacy of probiotics such as Lactobacillus rhamnosus GG, mixtures of Bifidobacterium infantis M-63, M-16 V, and BB536, and Lactobacillus acidophilus NCFM in relieving abdominal pain [75]. In addition, metabolites produced by the gut microbiota, such as short-chain fatty acids, can sensitize peripheral pain-sensitive neurons via protein kinase C (PKC), promote histamine and 5-HT secretion, and trigger abdominal pain [76]. Conversely, the probiotic strain Escherichia coli Nissle 1917 (EcN) can produce an analgesic peptide, C12AsnGABAOH, which can penetrate the epithelial barrier and reduce calcium influx triggered by the activation of nociceptors in sensory neurons via GABAB receptors, thereby alleviating abdominal pain [77]. Overall, these previous studies suggest a link between the gut microbiota, its metabolites, and abdominal pain (Fig. 1).
Management of Abdominal Pain in IBD
Pharmacological Treatment
Modern Medicine Treatment
While there are currently no targeted medications used clinically specifically for abdominal pain in IBD, many researchers are exploring pharmacological treatments tailored to the pathogenesis of abdominal pain in IBD. In a study by Spagnuolo et al., 43 patients with IBD were randomly divided into two groups. The treatment group received mesalazine combined with a mixture of β-glucan, myoinositol, and digestive enzymes, whereas the control group received mesalazine alone. After 4 weeks of treatment, abdominal pain improved significantly in the treatment group, suggesting that a mixture of β-glucan, myoinositol, and digestive enzymes can improve intestinal inflammation and increase intestinal motility, thereby relieving abdominal pain [78].
Opioids are generally used clinically to treat abdominal pain. According to a cross-sectional analysis of data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2006 to 2017, 35.5% of patients with IBD experiencing abdominal pain received opioids in the emergency department [79]. In a survey conducted in the UK, 12% of IBD outpatients had taken opioids within 12 months, and 11% of patients with IBD continued to take opioids after discharge [80]. The long-term use of opioids can have adverse effects on the gastrointestinal tract and central nervous system, including opioid-induced constipation, drug-induced bowel syndrome, and dependence, and there is a lack of clinical evidence for the efficacy of opioids in the treatment of chronic non-cancer abdominal pain [7]. In a study that focused on abdominal pain symptoms in patients with CD, Coates et al. found that opioid medications did not improve abdominal pain symptoms or QoL [81].
Lindstrom et al. found that OLORINAB is a small molecule that selectively activates the cannabinoid receptor 2 (CB2 receptor) and can be administered orally for the treatment of visceral pain associated with IBD [82]. No signs of toxicity were observed in animal studies. Yacyshyn et al. randomly assigned 14 patients with CD to OLORINAB at doses of 25 mg or 100 mg three times daily for 8 weeks. The results showed that at week 8, the mean (SD) change from baseline in average abdominal pain score at peak OLORINAB plasma concentrations was − 4.61 (1.77) in the 25-mg group (P = 0.0043) and − 4.57 (2.17) in the 100-mg group (P = 0.0036). The change from baseline at week 8 in the mean (SD) number of pain-free days per week was + 1.60 (2.61) in the 25-mg group and + 2.33 (3.62) in the 100-mg group, and no analgesics were required during the treatment period [83].
Risankizumab, a monoclonal antibody that inhibits inflammatory cytokines, lowers the serum levels of IL-22 in patients with CD by blocking IL-23 signaling, thereby improving clinical symptoms and endoscopic scores. D’Haens et al. randomly assigned 1477 patients with moderate-to-severe UC to either risankizumab 600 mg, 1200 mg, or a placebo administered intravenously every 4 weeks for 12 weeks. The results showed that the clinical remission rates of abdominal pain scores in the risankizumab 600 mg and 1200 mg groups were 35% and 40%, respectively, compared to 19% in the placebo group, confirming the efficacy of risankizumab in improving abdominal pain [84].
Upadacitinib, an oral selective JAK-1 inhibitor, suppresses the JAK-STAT signaling pathway, thereby reducing the production and release of inflammatory cytokines and relieving intestinal inflammation. In a randomized, double-blind, placebo-controlled study by Ghosh et al. [85], 250 patients with moderate UC were divided into five groups that received either placebo or upadacitinib at doses of 7.5, 15, 30, or 45 mg once daily for 8 weeks. Of the patients in the 45 mg upadacitinib group, 37.5% reported no abdominal pain compared to 13.0% in the placebo group, suggesting that upadacitinib at a dose of 45 mg once daily improves the symptoms of abdominal pain in UC. Danese et al. conducted two identical, double-blind, multicenter, placebo-controlled phase III trials in which 660 UC patients were randomly assigned to the upadacitinib treatment group and 328 patients to the placebo (PBO) group and received 15 mg or 30 mg of upadacitinib or PBO once daily over a 52-week treatment period. In this study, at weeks 2, 4, 6, and 8, a larger percentage of UPA-treated patients reported no abdominal pain when compared with PBO-treated patients (30.5 vs. 15.5%, 39.1 vs. 22.3%, 47.5 vs. 23.5%, and 50.2 vs. 23.8%, respectively). The differences at each time point were statistically significant (P < 0.001) and maintained improvement in abdominal pain symptoms over the 52-week treatment period (55.3% and 45.9% vs. 20.8% for UPA 15 mg and UPA 30 mg vs. PBO; P < 0.001) [86].
Dysbiosis of gut microbiota can be a major contributor to IBD, leading to intestinal barrier damage and inflammation. Therefore, modulation of the gut microbiota is a crucial aspect of IBD treatment, which is usually achieved through the use of antibiotics, probiotics, prebiotics, and transplantation of fecal microbiota [87]. Numerous clinical trials have shown that antibiotic therapy is effective in relieving abdominal pain [74]. In a study by Castiglione et al., 29 patients with CD and small intestinal bacterial overgrowth received either metronidazole 250 mg bid (group A) or ciprofloxacin 500 mg bid (group B) for 10 days, resulting in the improvement of abdominal pain symptoms in 50% of group A patients and 43% of group B patients [88]. In a randomized controlled trial conducted by Palumbo et al. involving 60 patients with moderate-to-severe UC, the control group received mesalazine alone, whereas the observation group received mesalazine combined with probiotics for 2 years [89].
Certain tricyclic antidepressants (TCAs), such as amitriptyline and desipramine, are used to treat neuropathic pain. In a retrospective cohort study of patients with mild or inactive IBD taking TCAs, 85.2% of patients with IBD experienced symptoms of abdominal pain. After TCA treatment, patients with IBD experienced a moderate improvement in overall well-being, while patients with UC showed greater treatment efficacy than patients with CD [90]. In addition, the American Gastroenterological Association recommends the use of antispasmodic medications in patients with IBD experiencing IBS/functional symptoms, which are effective in alleviating abdominal pain symptoms [91].
NSAIDs usually show a favorable response to non-visceral inflammatory abdominal pain and can alleviate axial/peripheral arthritis associated with IBD [92]. Nevertheless, many clinicians approach NSAID therapy for IBD-related abdominal pain with caution [93]. Reports suggest that frequent (≥ 5 times/month) use of NSAIDs is associated with active CD [94].
Mirikizumab, an anti-IL-23p19 antibody, demonstrated efficacy and safety in patients with moderately to severely active UC in the LUCENT phase 3 trials. At week 4, there was a higher rate of abdominal pain improvement (mirikizumab 45.5% vs. placebo (PBO)27.9%). At week 12, mirikizumab patients versus PBO achieved abdominal pain improvement(66.4 vs. 49.2%). Mirikizumab-treated patients sustained symptom control versus placebo patients in maintenance until week 52 (76.2 vs. 47.2%) [95].
Tofacitinib is an oral, small molecule, JAK inhibitor for the treatment of moderate-to-severe UC that was approved in Europe and the USA in 2018. In a real-world survey in the United States and five European countries, 642 (642/2049) patients with UC received tofacitinib. Abdominal pain/cramps symptoms were reported in the first weeks of treatment, and decreased with time. At week [52 +], the mean reduction from treatment initiation to current in abdominal pain symptoms was 2.2 (to a current mean score of 0.9) [96].
Considering the symptom overlap between IBD and IBS, antispasmodic agents (pinaverium bromide, etc.) have also been used to treat abdominal pain. Pinaverium bromide (PB) [N-(bromo-2-dimethoxy-4,5-benzyl)-N([(dimethyl-6,6 norpinanyl-2)-2 ethoxy]-2 ethyl morpholinium bromide] is a quaternary ammonium derivative that acts as an antispasmodic agent by blocking both muscarinic receptors and calcium channels in the gut smooth muscle cells. In a randomized, placebo-controlled trial, 285 IBS-Rome III patients received at least one dose of PB 100 mg plus simethicone (S) 300 mg or placebo. The results showed that PB + S was superior in abdominal pain (effect size: 31%, P = 0.038) [97].
Pharmacological Treatment
Modern Medicine Treatment
While there are currently no targeted medications used clinically specifically for abdominal pain in IBD, many researchers are exploring pharmacological treatments tailored to the pathogenesis of abdominal pain in IBD. In a study by Spagnuolo et al., 43 patients with IBD were randomly divided into two groups. The treatment group received mesalazine combined with a mixture of β-glucan, myoinositol, and digestive enzymes, whereas the control group received mesalazine alone. After 4 weeks of treatment, abdominal pain improved significantly in the treatment group, suggesting that a mixture of β-glucan, myoinositol, and digestive enzymes can improve intestinal inflammation and increase intestinal motility, thereby relieving abdominal pain [78].
Opioids are generally used clinically to treat abdominal pain. According to a cross-sectional analysis of data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2006 to 2017, 35.5% of patients with IBD experiencing abdominal pain received opioids in the emergency department [79]. In a survey conducted in the UK, 12% of IBD outpatients had taken opioids within 12 months, and 11% of patients with IBD continued to take opioids after discharge [80]. The long-term use of opioids can have adverse effects on the gastrointestinal tract and central nervous system, including opioid-induced constipation, drug-induced bowel syndrome, and dependence, and there is a lack of clinical evidence for the efficacy of opioids in the treatment of chronic non-cancer abdominal pain [7]. In a study that focused on abdominal pain symptoms in patients with CD, Coates et al. found that opioid medications did not improve abdominal pain symptoms or QoL [81].
Lindstrom et al. found that OLORINAB is a small molecule that selectively activates the cannabinoid receptor 2 (CB2 receptor) and can be administered orally for the treatment of visceral pain associated with IBD [82]. No signs of toxicity were observed in animal studies. Yacyshyn et al. randomly assigned 14 patients with CD to OLORINAB at doses of 25 mg or 100 mg three times daily for 8 weeks. The results showed that at week 8, the mean (SD) change from baseline in average abdominal pain score at peak OLORINAB plasma concentrations was − 4.61 (1.77) in the 25-mg group (P = 0.0043) and − 4.57 (2.17) in the 100-mg group (P = 0.0036). The change from baseline at week 8 in the mean (SD) number of pain-free days per week was + 1.60 (2.61) in the 25-mg group and + 2.33 (3.62) in the 100-mg group, and no analgesics were required during the treatment period [83].
Risankizumab, a monoclonal antibody that inhibits inflammatory cytokines, lowers the serum levels of IL-22 in patients with CD by blocking IL-23 signaling, thereby improving clinical symptoms and endoscopic scores. D’Haens et al. randomly assigned 1477 patients with moderate-to-severe UC to either risankizumab 600 mg, 1200 mg, or a placebo administered intravenously every 4 weeks for 12 weeks. The results showed that the clinical remission rates of abdominal pain scores in the risankizumab 600 mg and 1200 mg groups were 35% and 40%, respectively, compared to 19% in the placebo group, confirming the efficacy of risankizumab in improving abdominal pain [84].
Upadacitinib, an oral selective JAK-1 inhibitor, suppresses the JAK-STAT signaling pathway, thereby reducing the production and release of inflammatory cytokines and relieving intestinal inflammation. In a randomized, double-blind, placebo-controlled study by Ghosh et al. [85], 250 patients with moderate UC were divided into five groups that received either placebo or upadacitinib at doses of 7.5, 15, 30, or 45 mg once daily for 8 weeks. Of the patients in the 45 mg upadacitinib group, 37.5% reported no abdominal pain compared to 13.0% in the placebo group, suggesting that upadacitinib at a dose of 45 mg once daily improves the symptoms of abdominal pain in UC. Danese et al. conducted two identical, double-blind, multicenter, placebo-controlled phase III trials in which 660 UC patients were randomly assigned to the upadacitinib treatment group and 328 patients to the placebo (PBO) group and received 15 mg or 30 mg of upadacitinib or PBO once daily over a 52-week treatment period. In this study, at weeks 2, 4, 6, and 8, a larger percentage of UPA-treated patients reported no abdominal pain when compared with PBO-treated patients (30.5 vs. 15.5%, 39.1 vs. 22.3%, 47.5 vs. 23.5%, and 50.2 vs. 23.8%, respectively). The differences at each time point were statistically significant (P < 0.001) and maintained improvement in abdominal pain symptoms over the 52-week treatment period (55.3% and 45.9% vs. 20.8% for UPA 15 mg and UPA 30 mg vs. PBO; P < 0.001) [86].
Dysbiosis of gut microbiota can be a major contributor to IBD, leading to intestinal barrier damage and inflammation. Therefore, modulation of the gut microbiota is a crucial aspect of IBD treatment, which is usually achieved through the use of antibiotics, probiotics, prebiotics, and transplantation of fecal microbiota [87]. Numerous clinical trials have shown that antibiotic therapy is effective in relieving abdominal pain [74]. In a study by Castiglione et al., 29 patients with CD and small intestinal bacterial overgrowth received either metronidazole 250 mg bid (group A) or ciprofloxacin 500 mg bid (group B) for 10 days, resulting in the improvement of abdominal pain symptoms in 50% of group A patients and 43% of group B patients [88]. In a randomized controlled trial conducted by Palumbo et al. involving 60 patients with moderate-to-severe UC, the control group received mesalazine alone, whereas the observation group received mesalazine combined with probiotics for 2 years [89].
Certain tricyclic antidepressants (TCAs), such as amitriptyline and desipramine, are used to treat neuropathic pain. In a retrospective cohort study of patients with mild or inactive IBD taking TCAs, 85.2% of patients with IBD experienced symptoms of abdominal pain. After TCA treatment, patients with IBD experienced a moderate improvement in overall well-being, while patients with UC showed greater treatment efficacy than patients with CD [90]. In addition, the American Gastroenterological Association recommends the use of antispasmodic medications in patients with IBD experiencing IBS/functional symptoms, which are effective in alleviating abdominal pain symptoms [91].
NSAIDs usually show a favorable response to non-visceral inflammatory abdominal pain and can alleviate axial/peripheral arthritis associated with IBD [92]. Nevertheless, many clinicians approach NSAID therapy for IBD-related abdominal pain with caution [93]. Reports suggest that frequent (≥ 5 times/month) use of NSAIDs is associated with active CD [94].
Mirikizumab, an anti-IL-23p19 antibody, demonstrated efficacy and safety in patients with moderately to severely active UC in the LUCENT phase 3 trials. At week 4, there was a higher rate of abdominal pain improvement (mirikizumab 45.5% vs. placebo (PBO)27.9%). At week 12, mirikizumab patients versus PBO achieved abdominal pain improvement(66.4 vs. 49.2%). Mirikizumab-treated patients sustained symptom control versus placebo patients in maintenance until week 52 (76.2 vs. 47.2%) [95].
Tofacitinib is an oral, small molecule, JAK inhibitor for the treatment of moderate-to-severe UC that was approved in Europe and the USA in 2018. In a real-world survey in the United States and five European countries, 642 (642/2049) patients with UC received tofacitinib. Abdominal pain/cramps symptoms were reported in the first weeks of treatment, and decreased with time. At week [52 +], the mean reduction from treatment initiation to current in abdominal pain symptoms was 2.2 (to a current mean score of 0.9) [96].
Considering the symptom overlap between IBD and IBS, antispasmodic agents (pinaverium bromide, etc.) have also been used to treat abdominal pain. Pinaverium bromide (PB) [N-(bromo-2-dimethoxy-4,5-benzyl)-N([(dimethyl-6,6 norpinanyl-2)-2 ethoxy]-2 ethyl morpholinium bromide] is a quaternary ammonium derivative that acts as an antispasmodic agent by blocking both muscarinic receptors and calcium channels in the gut smooth muscle cells. In a randomized, placebo-controlled trial, 285 IBS-Rome III patients received at least one dose of PB 100 mg plus simethicone (S) 300 mg or placebo. The results showed that PB + S was superior in abdominal pain (effect size: 31%, P = 0.038) [97].
Modern Medicine Treatment
While there are currently no targeted medications used clinically specifically for abdominal pain in IBD, many researchers are exploring pharmacological treatments tailored to the pathogenesis of abdominal pain in IBD. In a study by Spagnuolo et al., 43 patients with IBD were randomly divided into two groups. The treatment group received mesalazine combined with a mixture of β-glucan, myoinositol, and digestive enzymes, whereas the control group received mesalazine alone. After 4 weeks of treatment, abdominal pain improved significantly in the treatment group, suggesting that a mixture of β-glucan, myoinositol, and digestive enzymes can improve intestinal inflammation and increase intestinal motility, thereby relieving abdominal pain [78].
Opioids are generally used clinically to treat abdominal pain. According to a cross-sectional analysis of data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2006 to 2017, 35.5% of patients with IBD experiencing abdominal pain received opioids in the emergency department [79]. In a survey conducted in the UK, 12% of IBD outpatients had taken opioids within 12 months, and 11% of patients with IBD continued to take opioids after discharge [80]. The long-term use of opioids can have adverse effects on the gastrointestinal tract and central nervous system, including opioid-induced constipation, drug-induced bowel syndrome, and dependence, and there is a lack of clinical evidence for the efficacy of opioids in the treatment of chronic non-cancer abdominal pain [7]. In a study that focused on abdominal pain symptoms in patients with CD, Coates et al. found that opioid medications did not improve abdominal pain symptoms or QoL [81].
Lindstrom et al. found that OLORINAB is a small molecule that selectively activates the cannabinoid receptor 2 (CB2 receptor) and can be administered orally for the treatment of visceral pain associated with IBD [82]. No signs of toxicity were observed in animal studies. Yacyshyn et al. randomly assigned 14 patients with CD to OLORINAB at doses of 25 mg or 100 mg three times daily for 8 weeks. The results showed that at week 8, the mean (SD) change from baseline in average abdominal pain score at peak OLORINAB plasma concentrations was − 4.61 (1.77) in the 25-mg group (P = 0.0043) and − 4.57 (2.17) in the 100-mg group (P = 0.0036). The change from baseline at week 8 in the mean (SD) number of pain-free days per week was + 1.60 (2.61) in the 25-mg group and + 2.33 (3.62) in the 100-mg group, and no analgesics were required during the treatment period [83].
Risankizumab, a monoclonal antibody that inhibits inflammatory cytokines, lowers the serum levels of IL-22 in patients with CD by blocking IL-23 signaling, thereby improving clinical symptoms and endoscopic scores. D’Haens et al. randomly assigned 1477 patients with moderate-to-severe UC to either risankizumab 600 mg, 1200 mg, or a placebo administered intravenously every 4 weeks for 12 weeks. The results showed that the clinical remission rates of abdominal pain scores in the risankizumab 600 mg and 1200 mg groups were 35% and 40%, respectively, compared to 19% in the placebo group, confirming the efficacy of risankizumab in improving abdominal pain [84].
Upadacitinib, an oral selective JAK-1 inhibitor, suppresses the JAK-STAT signaling pathway, thereby reducing the production and release of inflammatory cytokines and relieving intestinal inflammation. In a randomized, double-blind, placebo-controlled study by Ghosh et al. [85], 250 patients with moderate UC were divided into five groups that received either placebo or upadacitinib at doses of 7.5, 15, 30, or 45 mg once daily for 8 weeks. Of the patients in the 45 mg upadacitinib group, 37.5% reported no abdominal pain compared to 13.0% in the placebo group, suggesting that upadacitinib at a dose of 45 mg once daily improves the symptoms of abdominal pain in UC. Danese et al. conducted two identical, double-blind, multicenter, placebo-controlled phase III trials in which 660 UC patients were randomly assigned to the upadacitinib treatment group and 328 patients to the placebo (PBO) group and received 15 mg or 30 mg of upadacitinib or PBO once daily over a 52-week treatment period. In this study, at weeks 2, 4, 6, and 8, a larger percentage of UPA-treated patients reported no abdominal pain when compared with PBO-treated patients (30.5 vs. 15.5%, 39.1 vs. 22.3%, 47.5 vs. 23.5%, and 50.2 vs. 23.8%, respectively). The differences at each time point were statistically significant (P < 0.001) and maintained improvement in abdominal pain symptoms over the 52-week treatment period (55.3% and 45.9% vs. 20.8% for UPA 15 mg and UPA 30 mg vs. PBO; P < 0.001) [86].
Dysbiosis of gut microbiota can be a major contributor to IBD, leading to intestinal barrier damage and inflammation. Therefore, modulation of the gut microbiota is a crucial aspect of IBD treatment, which is usually achieved through the use of antibiotics, probiotics, prebiotics, and transplantation of fecal microbiota [87]. Numerous clinical trials have shown that antibiotic therapy is effective in relieving abdominal pain [74]. In a study by Castiglione et al., 29 patients with CD and small intestinal bacterial overgrowth received either metronidazole 250 mg bid (group A) or ciprofloxacin 500 mg bid (group B) for 10 days, resulting in the improvement of abdominal pain symptoms in 50% of group A patients and 43% of group B patients [88]. In a randomized controlled trial conducted by Palumbo et al. involving 60 patients with moderate-to-severe UC, the control group received mesalazine alone, whereas the observation group received mesalazine combined with probiotics for 2 years [89].
Certain tricyclic antidepressants (TCAs), such as amitriptyline and desipramine, are used to treat neuropathic pain. In a retrospective cohort study of patients with mild or inactive IBD taking TCAs, 85.2% of patients with IBD experienced symptoms of abdominal pain. After TCA treatment, patients with IBD experienced a moderate improvement in overall well-being, while patients with UC showed greater treatment efficacy than patients with CD [90]. In addition, the American Gastroenterological Association recommends the use of antispasmodic medications in patients with IBD experiencing IBS/functional symptoms, which are effective in alleviating abdominal pain symptoms [91].
NSAIDs usually show a favorable response to non-visceral inflammatory abdominal pain and can alleviate axial/peripheral arthritis associated with IBD [92]. Nevertheless, many clinicians approach NSAID therapy for IBD-related abdominal pain with caution [93]. Reports suggest that frequent (≥ 5 times/month) use of NSAIDs is associated with active CD [94].
Mirikizumab, an anti-IL-23p19 antibody, demonstrated efficacy and safety in patients with moderately to severely active UC in the LUCENT phase 3 trials. At week 4, there was a higher rate of abdominal pain improvement (mirikizumab 45.5% vs. placebo (PBO)27.9%). At week 12, mirikizumab patients versus PBO achieved abdominal pain improvement(66.4 vs. 49.2%). Mirikizumab-treated patients sustained symptom control versus placebo patients in maintenance until week 52 (76.2 vs. 47.2%) [95].
Tofacitinib is an oral, small molecule, JAK inhibitor for the treatment of moderate-to-severe UC that was approved in Europe and the USA in 2018. In a real-world survey in the United States and five European countries, 642 (642/2049) patients with UC received tofacitinib. Abdominal pain/cramps symptoms were reported in the first weeks of treatment, and decreased with time. At week [52 +], the mean reduction from treatment initiation to current in abdominal pain symptoms was 2.2 (to a current mean score of 0.9) [96].
Considering the symptom overlap between IBD and IBS, antispasmodic agents (pinaverium bromide, etc.) have also been used to treat abdominal pain. Pinaverium bromide (PB) [N-(bromo-2-dimethoxy-4,5-benzyl)-N([(dimethyl-6,6 norpinanyl-2)-2 ethoxy]-2 ethyl morpholinium bromide] is a quaternary ammonium derivative that acts as an antispasmodic agent by blocking both muscarinic receptors and calcium channels in the gut smooth muscle cells. In a randomized, placebo-controlled trial, 285 IBS-Rome III patients received at least one dose of PB 100 mg plus simethicone (S) 300 mg or placebo. The results showed that PB + S was superior in abdominal pain (effect size: 31%, P = 0.038) [97].
Traditional Chinese Medicine (TCM) Treatment
IBD in TCM can be categorized as “Jiu Li”, “Li Ji”, and “Chang Pi”, and results from a weakened spleen due to external pathogens, irregular diet, and emotional imbalance. The main pathological factors include dampness, stagnant heat, and toxic heat [98]. In TCM, abdominal pain in IBD is associated with “inadequate nutrition leads to pain”, “stagnation leads to pain”, and “excessive heat leads to pain”, which is a complex pattern of deficiency and excess [99]. A notable aspect of TCM treatment is syndrome differentiation, in which an appropriate medication is administered based on the patient’s symptoms [100].
Jingwen et al. randomly divided 58 UC patients into a treatment group and a control group, with the control group receiving conventional treatment with modern drugs, while the treatment group received TCM syndrome differentiation treatment in addition to conventional treatment with modern drugs. In the treatment group, various Chinese herbal formulas were selected based on the patients’ TCM differentiation, such as Fuzi Lizhong Tang combined with Sishen Wan, Wumei Pills, Baitouweng Tang, and Tongxie Yaofang combined with Sini San. These formulas have effects such as dispelling cold, relieving pain, warming the middle and stopping diarrhea, and harmonizing Qi, which can alleviate the symptoms of patients’ abdominal pain. The results of the study showed that the scores for abdominal pain in both groups were lower after treatment than before (the study group: before treatment 3.24 ± 0.25, after treatment 1.05 ± 0.26 vs. the control group: before treatment 3.29 ± 0.22, after treatment 2.15 ± 0.32), with the scores in the treatment group being significantly lower than those in the control group (P < 0.05) [101].
Dai Yancheng et al. conducted a randomized controlled trial to investigate the effects of the spleen-strengthening and bowel-cleansing formula on the QoL of patients with splenic deficiency-moisture syndrome type UC. They divided 120 patients with mild to moderately active UC into an experimental group and a control group, each of which was treated with a spleen-strengthening and bowel-cleansing formula and 5-aminosalicylic acid for 8 weeks. The results showed a significant difference in the relief of abdominal pain between the two groups (total effective rate 94.5% vs. 81.0%), with the experimental group outperforming the control group [102].
In addition to oral Chinese medicine, Chinese medicine enema or retention enema also demonstrates its effectiveness and safety [103]. Retention enemas with Qingchang Decoction can result in clinical and mucosal remission of left-sided ulcerative colitis [104]. Yuping et al. conducted a randomized controlled trial comparing Qingchang suppositories (experimental group) and mesalazine suppositories (control group) for UC treatment (E1 or E2). The results showed that the comprehensive efficacy rate in the treatment group was 91.49% compared with 87.23% in the control group. Regarding the efficacy of TCM syndrome, the rates were 97.87% and 91.48%, respectively, with the treatment group outperforming the control group. In addition, the Qingchang suppository showed superior efficacy over the SASP suppository in improving symptoms, such as diarrhea, abdominal pain, bloating, tenesmus, and anal burning caused by damp heat in the intestine in mild to moderately active UC below the left half of the colon [105].
Non-pharmacological Treatment
Acupuncture
The potential beneficial mechanisms of acupuncture in the treatment of IBD include increasing vagal nerve activity to reduce disease activity and inflammation, improving malnutrition, restoring intestinal barrier function, reducing VHS, and relieving depression/anxiety and pain in patients [106]. Systematic reviews and meta-analyses have shown that acupuncture alone or combined with conventional medication may be more effective than conventional medication in the treatment of UC compared to conventional medication. In a Swedish study [107] of 147 patients with UC, 48.3% received conventional medical treatment, with 21 of them attempting acupuncture treatment. The feedback on the results showed that most of them (83.1%) found acupuncture helpful, including relieving pain, improving general well-being, and alleviating symptoms [108].
Acupuncture treatment of CD generally targets acupuncture points such as CV12 (Zhongwan), ST37 (Shangjuxu), SP6 (Sanyinjiao), LR3 (Taichong), and KI3 (Taixi) [99]. Chunhui et al. conducted two randomized controlled trials of acupuncture treatment for patients with CD. The observation group received acupuncture and moxibustion, while the control group received sham acupuncture and moxibustion. In both studies, the observation group showed better improvement in abdominal pain than the control group [109, 110]. In another study by Yang Ling et al. [111], it was found that the group receiving moxibustion with herbs (consisting of Aconiti radix, Cinnamomi ramulus, Moschus, Coptidis rhizoma, Salviae Miltiorrhizae Radix, and Carthami flos) was superior to the ginger-separated moxibustion group (consisting of ginger slices) in reducing the duration of abdominal pain (P = 0.032) and improving the scores.
Dietary Management
Diet is a decisive factor influencing the occurrence and course of IBD. Different dietary patterns such as the Mediterranean diet, vegetarian/vegan diet, and a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) can alter the function of the intestinal microbiota, immune system, and intestinal mucosal barrier, thereby influencing inflammatory responses. Certain dietary factors, such as high fat, high sugar, high animal protein, low fiber, and processed foods, can increase the risk of IBD onset and exacerbate the disease, leading to abdominal pain [112]. de Graaf et al. investigated the relationship between the dietary index and intestinal inflammation and gastrointestinal symptoms in patients with IBD [113]. By calculating the Adaptive Dietary Inflammatory Index (ADII) in 238 patients with IBD, the study found a close correlation between the ADII score and abdominal pain in IBD (b = 0.194, p = 0.003).
The FODMAP diet is poorly absorbed by the intestine, leading to osmotic and fermentative effects that cause functional symptoms. The low-FODMAP diet has been shown to be very effective in patients with IBS; therefore, many studies have been conducted in recent years on whether a low-FODMAP diet can improve clinical symptoms in patients with IBD [114]. Tapete et al. conducted a randomized controlled trial in which 30 patients with IBD in remission were divided into a low-FODMAP diet group and a standard diet group for 6–8 weeks [115]. The results showed a significant improvement in abdominal pain in the low-FODMAP diet group, demonstrating the effectiveness of the low-FODMAP diet in controlling abdominal pain in patients with IBD in remission. Tapete et al. also conducted a study in which 50 patients with IBD were randomly assigned to either a high or low-FODMAP diet for 4 weeks and then switched to the other group for another 4 weeks [116]. The results showed that a low-FODMAP diet significantly reduced abdominal symptoms in patients with IBD.
Transcranial Direct Current Stimulation (tDCS)
Transcranial direct current stimulation (tDCS) is a method of brain stimulation that involves passing a weak current (1–2 mA) across the cortex using at least two electrodes. tDCS has been explored for its potential to alleviate symptoms of psychiatric and neurological disorders, including chronic pain. It is considered a promising treatment due to its cost-effectiveness, portability, safety, and ease of use compared to other neuromodulation methods. Studies have shown that tDCS can influence brain regions associated with pain perception and modulation [117]. Twenty patients with either CD or UC with chronic abdominal pain (CAP) were included in the study aimed to investigate the effects of tDCS. Anodal or sham tDCS was applied over the primary motor cortex for five consecutive days (2 mA, 20 min). The results showed that there was a significant reduction of abdominal pain in the anodal tDCS group compared with sham tDCS (right side of the abdomen (mean of active group: 10.86 kg; pre: 1.53 6 0.81 kg; post: 2.39 6 1.53 kg; vs. mean of sham group: 20.175 kg; pre: 1.78 6 0.84 kg; post: 1.62 6 0.89 kg). This effect was evident in changes in VAS and pressure pain threshold on the left and right sides of the abdomen. In addition, 1 week after stimulation, pain reduction remained significantly decreased in the right side of the abdomen. TDCS proved to be an effective and clinically relevant therapeutic strategy for CAP in IBD [118]. Neeb et al. conducted a study that included 36 patients with IBD and chronic pain in a double-blind, randomized, placebo-controlled trial, assigning participants to either a placebo treatment or tDCS for 5 days. MRI scans were performed before and after the treatment. Compared to the placebo group, the active tDCS group showed significantly higher resting-state functional connectivity within the visual medial network (p = 0.027) and the right frontoparietal network (p = 0.048). Increased connectivity was observed in the visual cortex, amygdala, cingulum, and insula. No significant results were found in structural MRI (DTI, VBM). These findings suggest that tDCS can enhance functional connectivity in specific brain networks, potentially offering a novel therapeutic strategy for managing chronic abdominal pain in IBD patients [119].
Psychological Intervention
Hypnotherapy
Hypnotherapy is a form of psychological treatment in which patients are put into a sleep-like state to change their perceptions, emotions, and behaviors. Hypnotherapy can relieve pain in patients with IBD by reducing stress responses, improving emotional states, enhancing self-efficacy, and regulating gut motility [120].
Lee et al. conducted a randomized controlled trial with 40 patients with CD aged 12 to 18 years and divided them into a hypnotherapy intervention group and a waiting control group. The hypnotherapy intervention group received a personal hypnosis session, training in self-hypnosis, and recordings for 8 weeks. The results showed a significant improvement in the severity of abdominal pain in the hypnotherapy intervention group (P = 0.03), suggesting that hypnotherapy can relieve abdominal pain by modulating the nervous system, improving psychological factors, and controlling inflammatory responses [121].
Keefer et al. conducted a randomized controlled trial in which 54 patients in remission from UC were treated with gut-oriented hypnotherapy. The observation group received gut-oriented hypnotherapy, whereas the control group received attention-control therapy. Both therapies were conducted by professional psychologists in an outpatient setting once a week for 40 min each, for seven sessions. The results showed that the severity of abdominal pain was lower in the observation group than in the control group both after treatment and at follow-up. It is believed that gut-directed hypnotherapy regulates the muscle tone and peristalsis of the patient’s intestines, activates their self-healing power, promotes the repair of the intestinal mucosa, and inhibits the release of inflammatory factors, thereby reducing inflammation and irritation of the intestines by altering symptom perception, improving intestinal motility, and strengthening the immune system [122].
Cognitive behavioral therapy (CBT) is a psychosocial intervention method that aims to reduce pain and improve QoL by changing patients’ cognitive and behavioral responses to pain through techniques such as psychoeducation, understanding cognitive behavioral models, relaxation exercises, breathing techniques, and cognitive restructuring. It enables patients to gain better self-management skills, understand pain and themselves better, and accept pain [123]. A meta-analysis [124] showed that CBT helps reduce anxiety and depression and improves the QoL of patients with IBD. In clinical research, CBT is currently mainly used for sleep disorders in IBD, but there are also several studies in which CBT is used to improve abdominal pain symptoms caused by other diseases, such as functional abdominal pain disorder (FAPD) and IBS [125].
Levy et al. conducted a prospective, randomized longitudinal study over 12 months involving 200 children with FAPD and their parents [126]. The observation group received a social learning and cognitive behavioral therapy (SLCBT) intervention, whereas the control group received an educational and supportive intervention. The results showed that children in the SLCBT group had a more significant improvement in the severity of gastrointestinal symptoms and pain management than those in the educational and supportive groups. Parents in the SLCBT group also showed a significant reduction in attention to their children’s symptoms and maladaptive beliefs about their children’s pain. Several studies have shown that children and adolescents with IBD are more likely to experience anxiety, depression, social problems (interactions in the social environment), and family dysfunction. CBT, including teaching coping skills or supportive non-directive therapy (SNDT) (therapists listen empathetically and reflect on what the patient presents), provides brief cognitive behavioral interventions for children with IBD and their parents, improving their functioning and QoL. In some patients, it can reduce disease activity and relieve the symptoms of abdominal pain and bloating [127–129]. Therefore, CBT is also a promising direction for research on the treatment of abdominal pain symptoms in IBD (Fig. 2).
Non-pharmacological Treatment
Acupuncture
The potential beneficial mechanisms of acupuncture in the treatment of IBD include increasing vagal nerve activity to reduce disease activity and inflammation, improving malnutrition, restoring intestinal barrier function, reducing VHS, and relieving depression/anxiety and pain in patients [106]. Systematic reviews and meta-analyses have shown that acupuncture alone or combined with conventional medication may be more effective than conventional medication in the treatment of UC compared to conventional medication. In a Swedish study [107] of 147 patients with UC, 48.3% received conventional medical treatment, with 21 of them attempting acupuncture treatment. The feedback on the results showed that most of them (83.1%) found acupuncture helpful, including relieving pain, improving general well-being, and alleviating symptoms [108].
Acupuncture treatment of CD generally targets acupuncture points such as CV12 (Zhongwan), ST37 (Shangjuxu), SP6 (Sanyinjiao), LR3 (Taichong), and KI3 (Taixi) [99]. Chunhui et al. conducted two randomized controlled trials of acupuncture treatment for patients with CD. The observation group received acupuncture and moxibustion, while the control group received sham acupuncture and moxibustion. In both studies, the observation group showed better improvement in abdominal pain than the control group [109, 110]. In another study by Yang Ling et al. [111], it was found that the group receiving moxibustion with herbs (consisting of Aconiti radix, Cinnamomi ramulus, Moschus, Coptidis rhizoma, Salviae Miltiorrhizae Radix, and Carthami flos) was superior to the ginger-separated moxibustion group (consisting of ginger slices) in reducing the duration of abdominal pain (P = 0.032) and improving the scores.
Acupuncture
The potential beneficial mechanisms of acupuncture in the treatment of IBD include increasing vagal nerve activity to reduce disease activity and inflammation, improving malnutrition, restoring intestinal barrier function, reducing VHS, and relieving depression/anxiety and pain in patients [106]. Systematic reviews and meta-analyses have shown that acupuncture alone or combined with conventional medication may be more effective than conventional medication in the treatment of UC compared to conventional medication. In a Swedish study [107] of 147 patients with UC, 48.3% received conventional medical treatment, with 21 of them attempting acupuncture treatment. The feedback on the results showed that most of them (83.1%) found acupuncture helpful, including relieving pain, improving general well-being, and alleviating symptoms [108].
Acupuncture treatment of CD generally targets acupuncture points such as CV12 (Zhongwan), ST37 (Shangjuxu), SP6 (Sanyinjiao), LR3 (Taichong), and KI3 (Taixi) [99]. Chunhui et al. conducted two randomized controlled trials of acupuncture treatment for patients with CD. The observation group received acupuncture and moxibustion, while the control group received sham acupuncture and moxibustion. In both studies, the observation group showed better improvement in abdominal pain than the control group [109, 110]. In another study by Yang Ling et al. [111], it was found that the group receiving moxibustion with herbs (consisting of Aconiti radix, Cinnamomi ramulus, Moschus, Coptidis rhizoma, Salviae Miltiorrhizae Radix, and Carthami flos) was superior to the ginger-separated moxibustion group (consisting of ginger slices) in reducing the duration of abdominal pain (P = 0.032) and improving the scores.
Dietary Management
Diet is a decisive factor influencing the occurrence and course of IBD. Different dietary patterns such as the Mediterranean diet, vegetarian/vegan diet, and a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) can alter the function of the intestinal microbiota, immune system, and intestinal mucosal barrier, thereby influencing inflammatory responses. Certain dietary factors, such as high fat, high sugar, high animal protein, low fiber, and processed foods, can increase the risk of IBD onset and exacerbate the disease, leading to abdominal pain [112]. de Graaf et al. investigated the relationship between the dietary index and intestinal inflammation and gastrointestinal symptoms in patients with IBD [113]. By calculating the Adaptive Dietary Inflammatory Index (ADII) in 238 patients with IBD, the study found a close correlation between the ADII score and abdominal pain in IBD (b = 0.194, p = 0.003).
The FODMAP diet is poorly absorbed by the intestine, leading to osmotic and fermentative effects that cause functional symptoms. The low-FODMAP diet has been shown to be very effective in patients with IBS; therefore, many studies have been conducted in recent years on whether a low-FODMAP diet can improve clinical symptoms in patients with IBD [114]. Tapete et al. conducted a randomized controlled trial in which 30 patients with IBD in remission were divided into a low-FODMAP diet group and a standard diet group for 6–8 weeks [115]. The results showed a significant improvement in abdominal pain in the low-FODMAP diet group, demonstrating the effectiveness of the low-FODMAP diet in controlling abdominal pain in patients with IBD in remission. Tapete et al. also conducted a study in which 50 patients with IBD were randomly assigned to either a high or low-FODMAP diet for 4 weeks and then switched to the other group for another 4 weeks [116]. The results showed that a low-FODMAP diet significantly reduced abdominal symptoms in patients with IBD.
Transcranial Direct Current Stimulation (tDCS)
Transcranial direct current stimulation (tDCS) is a method of brain stimulation that involves passing a weak current (1–2 mA) across the cortex using at least two electrodes. tDCS has been explored for its potential to alleviate symptoms of psychiatric and neurological disorders, including chronic pain. It is considered a promising treatment due to its cost-effectiveness, portability, safety, and ease of use compared to other neuromodulation methods. Studies have shown that tDCS can influence brain regions associated with pain perception and modulation [117]. Twenty patients with either CD or UC with chronic abdominal pain (CAP) were included in the study aimed to investigate the effects of tDCS. Anodal or sham tDCS was applied over the primary motor cortex for five consecutive days (2 mA, 20 min). The results showed that there was a significant reduction of abdominal pain in the anodal tDCS group compared with sham tDCS (right side of the abdomen (mean of active group: 10.86 kg; pre: 1.53 6 0.81 kg; post: 2.39 6 1.53 kg; vs. mean of sham group: 20.175 kg; pre: 1.78 6 0.84 kg; post: 1.62 6 0.89 kg). This effect was evident in changes in VAS and pressure pain threshold on the left and right sides of the abdomen. In addition, 1 week after stimulation, pain reduction remained significantly decreased in the right side of the abdomen. TDCS proved to be an effective and clinically relevant therapeutic strategy for CAP in IBD [118]. Neeb et al. conducted a study that included 36 patients with IBD and chronic pain in a double-blind, randomized, placebo-controlled trial, assigning participants to either a placebo treatment or tDCS for 5 days. MRI scans were performed before and after the treatment. Compared to the placebo group, the active tDCS group showed significantly higher resting-state functional connectivity within the visual medial network (p = 0.027) and the right frontoparietal network (p = 0.048). Increased connectivity was observed in the visual cortex, amygdala, cingulum, and insula. No significant results were found in structural MRI (DTI, VBM). These findings suggest that tDCS can enhance functional connectivity in specific brain networks, potentially offering a novel therapeutic strategy for managing chronic abdominal pain in IBD patients [119].
Psychological Intervention
Hypnotherapy
Hypnotherapy is a form of psychological treatment in which patients are put into a sleep-like state to change their perceptions, emotions, and behaviors. Hypnotherapy can relieve pain in patients with IBD by reducing stress responses, improving emotional states, enhancing self-efficacy, and regulating gut motility [120].
Lee et al. conducted a randomized controlled trial with 40 patients with CD aged 12 to 18 years and divided them into a hypnotherapy intervention group and a waiting control group. The hypnotherapy intervention group received a personal hypnosis session, training in self-hypnosis, and recordings for 8 weeks. The results showed a significant improvement in the severity of abdominal pain in the hypnotherapy intervention group (P = 0.03), suggesting that hypnotherapy can relieve abdominal pain by modulating the nervous system, improving psychological factors, and controlling inflammatory responses [121].
Keefer et al. conducted a randomized controlled trial in which 54 patients in remission from UC were treated with gut-oriented hypnotherapy. The observation group received gut-oriented hypnotherapy, whereas the control group received attention-control therapy. Both therapies were conducted by professional psychologists in an outpatient setting once a week for 40 min each, for seven sessions. The results showed that the severity of abdominal pain was lower in the observation group than in the control group both after treatment and at follow-up. It is believed that gut-directed hypnotherapy regulates the muscle tone and peristalsis of the patient’s intestines, activates their self-healing power, promotes the repair of the intestinal mucosa, and inhibits the release of inflammatory factors, thereby reducing inflammation and irritation of the intestines by altering symptom perception, improving intestinal motility, and strengthening the immune system [122].
Cognitive behavioral therapy (CBT) is a psychosocial intervention method that aims to reduce pain and improve QoL by changing patients’ cognitive and behavioral responses to pain through techniques such as psychoeducation, understanding cognitive behavioral models, relaxation exercises, breathing techniques, and cognitive restructuring. It enables patients to gain better self-management skills, understand pain and themselves better, and accept pain [123]. A meta-analysis [124] showed that CBT helps reduce anxiety and depression and improves the QoL of patients with IBD. In clinical research, CBT is currently mainly used for sleep disorders in IBD, but there are also several studies in which CBT is used to improve abdominal pain symptoms caused by other diseases, such as functional abdominal pain disorder (FAPD) and IBS [125].
Levy et al. conducted a prospective, randomized longitudinal study over 12 months involving 200 children with FAPD and their parents [126]. The observation group received a social learning and cognitive behavioral therapy (SLCBT) intervention, whereas the control group received an educational and supportive intervention. The results showed that children in the SLCBT group had a more significant improvement in the severity of gastrointestinal symptoms and pain management than those in the educational and supportive groups. Parents in the SLCBT group also showed a significant reduction in attention to their children’s symptoms and maladaptive beliefs about their children’s pain. Several studies have shown that children and adolescents with IBD are more likely to experience anxiety, depression, social problems (interactions in the social environment), and family dysfunction. CBT, including teaching coping skills or supportive non-directive therapy (SNDT) (therapists listen empathetically and reflect on what the patient presents), provides brief cognitive behavioral interventions for children with IBD and their parents, improving their functioning and QoL. In some patients, it can reduce disease activity and relieve the symptoms of abdominal pain and bloating [127–129]. Therefore, CBT is also a promising direction for research on the treatment of abdominal pain symptoms in IBD (Fig. 2).
Hypnotherapy
Hypnotherapy is a form of psychological treatment in which patients are put into a sleep-like state to change their perceptions, emotions, and behaviors. Hypnotherapy can relieve pain in patients with IBD by reducing stress responses, improving emotional states, enhancing self-efficacy, and regulating gut motility [120].
Lee et al. conducted a randomized controlled trial with 40 patients with CD aged 12 to 18 years and divided them into a hypnotherapy intervention group and a waiting control group. The hypnotherapy intervention group received a personal hypnosis session, training in self-hypnosis, and recordings for 8 weeks. The results showed a significant improvement in the severity of abdominal pain in the hypnotherapy intervention group (P = 0.03), suggesting that hypnotherapy can relieve abdominal pain by modulating the nervous system, improving psychological factors, and controlling inflammatory responses [121].
Keefer et al. conducted a randomized controlled trial in which 54 patients in remission from UC were treated with gut-oriented hypnotherapy. The observation group received gut-oriented hypnotherapy, whereas the control group received attention-control therapy. Both therapies were conducted by professional psychologists in an outpatient setting once a week for 40 min each, for seven sessions. The results showed that the severity of abdominal pain was lower in the observation group than in the control group both after treatment and at follow-up. It is believed that gut-directed hypnotherapy regulates the muscle tone and peristalsis of the patient’s intestines, activates their self-healing power, promotes the repair of the intestinal mucosa, and inhibits the release of inflammatory factors, thereby reducing inflammation and irritation of the intestines by altering symptom perception, improving intestinal motility, and strengthening the immune system [122].
Cognitive behavioral therapy (CBT) is a psychosocial intervention method that aims to reduce pain and improve QoL by changing patients’ cognitive and behavioral responses to pain through techniques such as psychoeducation, understanding cognitive behavioral models, relaxation exercises, breathing techniques, and cognitive restructuring. It enables patients to gain better self-management skills, understand pain and themselves better, and accept pain [123]. A meta-analysis [124] showed that CBT helps reduce anxiety and depression and improves the QoL of patients with IBD. In clinical research, CBT is currently mainly used for sleep disorders in IBD, but there are also several studies in which CBT is used to improve abdominal pain symptoms caused by other diseases, such as functional abdominal pain disorder (FAPD) and IBS [125].
Levy et al. conducted a prospective, randomized longitudinal study over 12 months involving 200 children with FAPD and their parents [126]. The observation group received a social learning and cognitive behavioral therapy (SLCBT) intervention, whereas the control group received an educational and supportive intervention. The results showed that children in the SLCBT group had a more significant improvement in the severity of gastrointestinal symptoms and pain management than those in the educational and supportive groups. Parents in the SLCBT group also showed a significant reduction in attention to their children’s symptoms and maladaptive beliefs about their children’s pain. Several studies have shown that children and adolescents with IBD are more likely to experience anxiety, depression, social problems (interactions in the social environment), and family dysfunction. CBT, including teaching coping skills or supportive non-directive therapy (SNDT) (therapists listen empathetically and reflect on what the patient presents), provides brief cognitive behavioral interventions for children with IBD and their parents, improving their functioning and QoL. In some patients, it can reduce disease activity and relieve the symptoms of abdominal pain and bloating [127–129]. Therefore, CBT is also a promising direction for research on the treatment of abdominal pain symptoms in IBD (Fig. 2).
Conclusions
Abdominal pain, one of the most common and distressing symptoms of IBD, has a significant impact on patients’ QoL. However, the current understanding and awareness of abdominal pain in IBD remain insufficient in clinical practice [45]. Abdominal pain is mainly diagnosed based on the patient’s subjective perceptions, which are assessed using various severity rating scales. Although this method has a certain reference value, it lacks objectivity and accuracy. Therefore, developing more objective and accurate diagnostic tools is important for future clinical practice.
As far as treatment is concerned, the options for treating abdominal pain in IBD are currently limited. Although some medications, such as mesalamine, cannabinoid receptor agonists, and anti-inflammatory drugs, have shown some efficacy in relieving symptoms, these medications are not suitable for all patients and often have side effects. In addition, integrative therapies such as Chinese herbs, acupuncture, dietary management, and psychological interventions have also shown promise in relieving abdominal pain in IBD [130].
In general, the management of abdominal pain in IBD requires a multidisciplinary approach aimed at minimizing patient suffering and improving the QoL. Clinicians should consider individual patient differences during treatment and combine different therapeutic methods to create personalized treatment plans. In addition, patients’ psychological status and lifestyle should be included in the treatment to provide comprehensive support and intervention. |
Title: m | Body:
1
Introduction
Lung adenocarcinoma (LUAD) is one of the most lethal cancers and is characterized by difficulty in early diagnosis, high rates of recurrence and metastasis, and a dismal prognosis.[
1
,
2
] Depending on the stage, treatments for LUAD vary and include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy.[
3
] The majority of LUAD cases are diagnosed at an advanced stage and are inoperable, with limited therapeutic alternatives; thus, chemotherapy remains the front‐line strategy for treating LUAD.[
4
] Platinum compounds, including cisplatin [also known as cis‐diamminedichloroplatinum (II), CDDP], remain the cornerstone of LUAD chemotherapy.[
5
] Through DNA–platinum adduct formation and the stimulation of apoptotic signaling in cancer cells, cisplatin therapy provides patients a remarkable survival advantage; however, cisplatin resistance restricts its therapeutic effectiveness and contributes to cancer recurrence in patients with LUAD.[
6
,
7
] Therefore, the discovery of reliable prognostic indicators for the response or resistance to cisplatin and a deeper understanding of the molecular mechanisms underlying cisplatin chemoresistance are essential.
Apart from intrinsic causes, cisplatin resistance in LUAD can be acquired through various mechanisms, which can be categorized as follows: increased DNA repair or tolerance, amplified cisplatin detoxification, inhibition of apoptosis, and reduced intracellular accumulation of cisplatin.[
8
,
9
] Cisplatin primarily targets genomic DNA, resulting in a multitude of DNA lesions that impede transcription and replication.[
10
] To fix the DNA damage caused by cisplatin, cells activate the DNA damage response (DDR) and engage an intricate network of mechanisms, including DNA damage repair pathways.[
9
] In actuality, cisplatin‐induced DNA damage is repaired via nucleotide excision repair (NER), interstrand crosslink (ICL) repair, mismatch repair (MMR), homologous recombination (HR) and nonhomologous end joining (NHEJ) in a variety of cancers, including lung cancer.[
11
] Our previous research revealed that TRIM44 enhances BRCA1‐mediated homologous recombination, thereby facilitating cisplatin chemoresistance through the deubiquitylation of FLNA.[
12
]
The structure‐specific endonuclease ERCC1‐XPF, a multifunctional heterodimer, is essential for DNA damage repair, including NER, HR, and ICL repair.[
13
,
14
] The essential interaction between the ERCC1 and XPF proteins involves the dimerization of their hydrophobic C‐terminal regions through double helix–hairpin–helix motifs within their HhH2 domains, spanning a distance of 1534 Å, which results in stable heterodimer formation.[
15
] This dimerization serves to protect each protein from degradation due to aggregation.[
16
] Utilizing cross‐saturation techniques, ERCC1 residues from Arg234 to Leu294 have been shown to interact with XPF residues ranging from Gln849 to Ala906.[
16
] Elevated expression of ERCC1 is associated with poor therapeutic outcomes following cisplatin administration in multiple malignancies, including LUAD.[
17
,
18
] By inhibiting the protein–protein interaction between ERCC1 and XPF, this inhibitor enhances the sensitivity of LUAD cells to platinum‐based drugs.[
19
] However, the specific factors and molecular mechanisms that govern the interaction between ERCC1 and XPF remain to be fully elucidated.
Alternative splicing (AS) occurs in over 95% of human protein‐encoding genes and serves as a major evolutionary driver as well as the primary cause of proteome diversity.[
20
] Dysregulated RNA splicing is a prevalent factor contributing to genetic diseases, including cancer.[
21
] Cancer‐associated splicing dysregulation increases cell proliferation, inhibits apoptosis, promotes the metastatic potential, enables immunological evasion, and confers drug resistance.[
22
,
23
] Eukaryotic precursor mRNA (pre‐mRNA) splicing is orchestrated by a large and intricate ribonucleoprotein complex known as the spliceosome.[
24
] Its roles include defining splice locations and catalyzing RNA splicing reactions.[
25
] The spliceosome comprises five small nuclear ribonucleoproteins (snRNPs): U1, U2, U4, U5, and U6. Each snRNP consists of a uridine‐rich small nuclear RNA (U snRNA) that ranges from 100 to 300 nucleotides in length, Sm proteins (including SNRPB/B′, D1, D2, D3, E, F, and G), and numerous associated proteins.[
26
] Key spliceosome‐related genes have been identified and experimentally validated in the context of malignant tumors.[
27
,
28
,
29
] As an illustration, SNRPB is known to modulate cisplatin resistance in lung cancer via the ERK signaling pathway.[
30
] However, the specific spliceosome‐associated genes related to cisplatin resistance are still largely unidentified.
In this study, SNRPA was identified as a novel gene associated with platinum resistance using computational techniques. We established stable cell lines with SNRPA knockout, knockdown, and overexpression to confirm the role of SNRPA in cisplatin chemoresistance. The phenotypes of the cisplatin‐resistant LUAD cells were found to be modulated by SNRPA expression. We discovered that SNRPA increases the production of ERCC1 transcripts that include exon 8 [referred to as ERCC1‐E8(+) or simply E8 (+)] and that SNRPA‐induced cisplatin resistance, as well as DNA damage repair, were partially dependent on ERCC1‐E8(+). Furthermore, the stability of the SNRPA mRNA was shown to be epigenetically regulated by embryonic lethal abnormal vision‐like protein 1 (ELAVL1), which affected SNRPA expression. These findings increase our understanding of the functions, mechanisms, and epigenetic regulation of SNRPA, thus providing potential therapeutic targets for combating chemoresistance.
2
Results
2.1
Identification of Spliceosome‐Related Genes Governing Platinum‐Based Drug Resistance
To identify spliceosome‐related genes potentially involved in chemoresistance to platinum‐based drugs, we obtained the annotation information for 137 spliceosome‐related genes from the Spliceosome database (http://spliceosomedb.ucsc.edu/) (Table S1, Supporting Information). This study included 1205 patients whose gene expression information and clinicopathological features were available in The Cancer Genome Atlas (TCGA, n = 518) and the Gene Expression Omnibus (GEO, n = 687) databases. A flowchart of the bioinformatics analysis is detailed in Figure S1 (Supporting Information). Compared with those in normal lung tissues, the expression of spliceosome‐related genes that were differentially expressed in LUAD tissues was determined by analyzing transcriptome gene expression datasets from lung tissues in TCGA and GEO databases (Figure
1a,b). A Venn diagram revealed 18 dysregulated spliceosome‐related genes at the intersection of TCGA and GEO datasets (Figure 1c). Within TCGA‐LUAD cohort, for 143 patients who received platinum‐based chemotherapy (platinum treatment group) and 348 who received no chemotherapy (non‐chemo group), we conducted a Cox regression analysis. Univariate and multivariate analyses identified elevated SNRPA expression as an independent prognostic indicator for OS in the cohort treated with platinum‐based chemotherapy but not in the non‐chemo group (Figure 1d,e). These results prompted us to choose SNRPA as a candidate spliceosome‐related gene for further investigation into its role in cisplatin resistance.
Figure 1
Identification of spliceosome‐related genes that modulate platinum‐based drug resistance. a,b) Heatmaps showcasing the differential gene expression profiles, highlighting genes that are dysregulated in LUAD samples relative to healthy lung tissue counterparts based on TCGA (a) and GEO (b) databases. c) Venn diagram depicting the intersection of upregulated spliceosome‐related genes obtained from TCGA and GEO databases. d,e) Forest plots of overall survival (OS) revealed the results of univariate and multivariate regression analyses of the platinum‐based treatment group (d) and the non‐chemo group (e). f) Violin plots depicting the comparative analysis of SNRPA mRNA expression profiles, contrasting LUAD tissues with non‐neoplastic lung counterparts, based on the TCGA and GEO databases. g) Western blot revealed the protein expression of SNRPA using fresh frozen LUAD (n = 9) and paired normal adjacent tissue samples (n = 9), and the other results are shown in Figure S2a (Supporting Information). h,i) Kaplan–Meier survival plots delineating the OS stratification for patients with LUAD who were categorized on the basis of elevated versus diminished SNRPA expression levels in the platinum treatment group (h) and non‐chemo group (i) (Log rank test; TCGA: n = 592; GSE2514: n = 39). All data are presented as the mean ± SD (n = 3). The p values in panels (f) and (g) were calculated by Student's t‐test. ***p < 0.001.
Compared to normal lung tissues, hierarchical clustering analysis revealed that SNRPA expression was elevated in LUAD tissues, based on the public datasets (Figure 1f). Western blot further confirmed increased expression of the SNRPA protein in fresh frozen LUAD tissue samples (Figure 1g; Figure S2a, Supporting Information). The Kaplan–Meier analysis indicated that high SNRPA expression predicted a shorter overall survival time for LUAD patients who received platinum‐based chemotherapy; however, no significant difference was observed in the non‐chemo group (Figure 1h,i).
2.2
SNRPA Reinforces LUAD Cell Cisplatin Resistance both In Vitro and In Vivo
To examine whether SNRPA contributed to cisplatin‐induced acquired drug resistance, we assessed the expression levels of SNRPA in cisplatin‐treated LUAD cells. We observed a gradual increase in SNRPA expression after treatment with increasing cisplatin doses or a prolonged treatment duration in the cisplatin‐sensitive A549 and H1299 cells, indicating a dose‐ and time‐dependent relationship (Figure
2a; Figure S2b, Supporting Information). Furthermore, compared with their cisplatin‐sensitive parental counterparts, cisplatin‐resistant cells (A549/DDP and H1299/DDP) presented substantially increased SNRPA expression (Figure 2b; Figure S3a, Supporting Information).
Figure 2
The depletion of SNRPA reverses the cisplatin chemoresistance of cisplatin‐resistant cells. a) Western blot representing the levels of SNRPA expression after treatment with increasing cisplatin dosages or the time increasing durations of cisplatin treatment (left panel). The bar graphs showed SNRPA protein expression based on the average grayscale value of the target protein (right panel). b) Western blot analyses demonstrating the quantification of SNRPA protein expression in cell lines characterized by sensitivity and resistance to cisplatin treatment. c) SNRPA expression in A549/DDP cells transfected with CRISPR negative control clones (NC), the SNRPA CRISPR/Cas9 knockout plasmid‐1 (KO‐1) or the SNRPA CRISPR/Cas9 knockout plasmid‐1 (KO‐2). d) Comparative analysis of both the mRNA and protein expression levels of SNRPA in H1299/DDP cells transfected with shNC (NC), shSNRPA‐1 (sh‐1), or shSNRPA‐2 (sh‐2). e) CCK‐8 analysis, after 48 h of cisplatin treatment, illustrated cell viability across the designated cellular groups (n = 4). f) Colonies were visualized by staining with crystal violet (left panel), while the bar graphs provided a statistical evaluation of the colony counts (right panel). g) EdU assays of the proliferation of the indicated cells in a 10 µM cisplatin solution. h) Representative images illustrating the results of Annexin V‐Fluor 647 APC/7‐AAD dual staining to evaluate apoptosis in cells treated with 10 µM cisplatin for 24 h. i) The expression of apoptosis‐associated proteins in A549/DDP cells. j) The left panel displayed representative bioluminescence images of xenograft tumors generated from NC or KO cells. Bar graphs were generated to quantify the bioluminescence luciferase signal emitted from the xenograft tumors, providing a statistical comparison between groups. k,m) The left panel showed images of xenograft tumors in the designated groups (k). These panels depict the outcomes of t‐tests applied to compare the mean tumor volume (l) and weight (m) across different groups. n) IHC analyses were performed on xenograft tumor tissues to examine the expression patterns of SNRPA, Bcl‐2, BAX, and γ‐H2AX. All data are presented as the mean ± SD (n≥3). The p values in panels (a), (c), (d), (f) and (g) were calculated using one‐way ANOVA. The p values in panels (j), (l) and (m) were calculated by Student's t‐test. The p values in panels (e) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
Consequently, in functional studies, cisplatin‐resistant cells were used to establish a loss‐of‐function model, whereas parental LUAD cells served to create a gain‐of‐function model. Using CRISPR/Cas9 technology, we achieved successful knockout of the SNRPA mRNA and protein levels in SNRPA‐deficient CRISPR clones (KO1 and KO2) of A549/DDP via lentiviral constructs, as evidenced by comparisons with CRISPR negative control clones (NC) (Figure 2c). To enhance the validity of our findings, we designed specific shRNAs targeting SNRPA, as well as the negative control, and transfected them in H1299/DDP cells (Figure 2d). Notably, in the absence of cisplatin treatment, neither SNRPA knockdown nor knockout influenced the proliferation, apoptosis or DNA damage of chemotherapy‐resistant LUAD cells (Figure S2c‐e, Supporting Information). Similarly, in vivo experiments without cisplatin treatment revealed that SNRPA knockout did not affect tumor growth (Figure S2f‐i, Supporting Information).
Consistent with our expectations, SNRPA knockout cells demonstrated a lower 50% inhibitory concentration (IC50) when treated with progressively increasing concentrations of cisplatin (Figure 2e). Plate colony formation and EdU incorporation assays confirmed that the downregulation of SNRPA reduced cisplatin resistance (Figure 2f,g). Consistent with these findings, the resistance of H1299/DDP cells to cisplatin was reversed, as confirmed by CCK8, plate colony formation and EdU incorporation assays (Figure S3b‐d, Supporting Information). Flow cytometry analysis and Western blot were subsequently performed to evaluate the effect of SNRPA depletion on cisplatin‐induced apoptosis. SNRPA silencing noticeably escalated the proportion of apoptotic A549/DDP cells treated with cisplatin, which coincided with decreased expression of the anti‐apoptotic protein Bcl‐2 and an upsurge in the pro‐apoptotic protein BAX (Figure 2h,i; Figure S3e, Supporting Information). In the in vivo cisplatin resistance experiment, xenografts (NC and KO) derived from A549/DDP derivative cells (CRISPR negative control clones and SNRPA‐deficient CRISPR clones) were established in nude athymic mice. The experimental procedure for cisplatin injection was carried out as detailed in Figure S3g (Supporting Information). Compared with the NC group injected with NC cells, SNRPA knockout attenuated cisplatin resistance in LUAD cells located in the axillary region of nude mice, as evidenced by reduced luciferase activity (Figure 2j), decreased tumor volume (Figure 2k,l), and reduced tumor weight (Figure 2m). In consecutive tissue sections derived from xenografts, IHC staining demonstrated diminished BCL2 expression and augmented BAX expression in specimens from the KO group compared with those from the NC group (Figure 2n).
As indicated in Figures 2b and S3a (Supporting Information), cisplatin‐sensitive parental cells were employed to establish a gain‐of‐function model. We successfully established stable SNRPA‐overexpressing clones through lentiviral infection, which was validated by quantitative real‐time polymerase chain reaction (qRT‒PCR) and Western blot analyses (Figure S4a,b, Supporting Information). CCK‐8, colony formation, and EdU assays were employed to assess the cytotoxicity of cisplatin. The CCK‐8 results revealed that SNRPA overexpression induced the resistance of A549 and H1299 cells to cisplatin (Figure S5a, Supporting Information). Cell proliferation was evaluated through colony formation assays (Figure S5b, Supporting Information) and EdU experiments (Figure S5c, Supporting Information) under cisplatin treatment conditions. Flow cytometry and Western blot analysis elucidated that the overexpression of SNRPA inhibited cisplatin‐induced apoptosis (Figure S5d,e, Supporting Information). Consistent with the in vitro results, the spontaneous xenograft tumors formed by stable SNRPA‐overexpressing clones (SNRPA) displayed higher luciferase activity, as well as larger tumor volumes and weights, than those from the control (Ctrl) group (Figure S5f‐i, Supporting Information). However, without cisplatin treatment, overexpression of SNRPA did not affect in vitro LUAD cell proliferation, apoptosis or DNA damage and in vivo tumor growth (Figure S4c‐i, Supporting Information). Based on these findings, we concluded that SNRPA can induce cisplatin chemoresistance in LUAD.
2.3
SNRPA can Blunt Cisplatin‐Induced DNA Damage
To elucidate the mechanism behind SNRPA‐induced cisplatin resistance, we sequenced the mRNA transcriptomes of the NC and KO1 clones to perform a differential expression (DE) analysis. The DE analysis identified 324 upregulated genes and 368 downregulated genes after SNRPA knockout (P < 0.05, absolute log2 Fold change (FC) > 1) (Figure
3a). Enrichment analysis of the DE genes using GO revealed many significant signal transduction pathways after SNRPA knockout. Among them, four pathways are significantly and statistically involved in the response to drug and response to DNA damage stimulus (Figure 3b). Phosphorylated H2AX (γ‐H2AX) is a known indicator of DNA damage and repair.[
31
,
32
]
γ‐H2AX nuclear foci were observed to investigate the effects of SNRPA on DNA damage. The increased presence of γ‐H2AX foci following SNRPA knockout indicated a reduced DNA repair capability, as indicated by immunofluorescence staining and Western blot analyses (Figure 3c,d). In contrast, SNRPA overexpression enhanced DNA repair, contributing to a reduced presence of γ‐H2AX foci (Figure 3e,f). Animal experiments revealed an increase in γ‐H2AX expression in tumor tissues isolated from KO mice compared with those from the NC group (Figure 2n). These findings indicate that SNRPA enhances the repair of DNA damage induced by cisplatin in LUAD.
Figure 3
SNRPA promotes DNA damage repair. a) Volcano plot showing differentially expressed genes between SNRPA knockout cells (SNRPA‐KO1) and negative control cells (NC). b) Gene Ontology (GO) enrichment analysis was performed to delineate the biological processes involving SNRPA. The table illustrates that four pathways are significantly and statistically involved in the response to drug and response to DNA damage stimulus following SNRPA knockout. c) The expression levels of the γ‐H2AX protein in the specified cell lines following treatment with cisplatin and subsequent knockout or knockdown of SNRPA were assessed. ## represents exogenous Flag‐SNRPA, # represents endogenous SNRPA. d) Immunofluorescence co‐localization of γ‐H2AX and DAPI exhibited γ‐H2AX nuclear foci. The increased presence of γH2AX indicated reduced DNA repair after DNA damage. e) The expression of the γ‐H2AX protein was assessed in the specified cells treated with cisplatin after SNRPA overexpression. f) Localization of γ‐H2AX and DAPI revealed γ‐H2AX nuclear foci. The decreased presence of γH2AX indicated enhanced DNA repair after DNA damage.
2.4
SNRPA can Control exon 8 Skipping of ERCC1 Gene and the Formation of the ERCC1–XPF Complex
To explore the role of SNRPA in AS and the mechanisms of SNRPA‐induced DNA repair, we performed a genomic structure analysis based on transcriptome sequencing results. This analysis identified 1416 significant endogenous AS events regulated by SNRPA, with significant changes in percent spliced in (PSI) values (absolute ΔPSI ≥ 15%, P < 0.05) (Figure
4a; Table S2, Supporting Information). SNRPA regulates various AS types, including intron retention (IR), alternative 5′ splice sites (A5SS), alternative 3′ splice sites (A3SS), mutually exclusive exons (MXE), and exon skipping (SE). The predominant AS event was SE (N = 1185), with 681 SE events showing increased exon skipping (∆PSI ≥ +15%) and 504 SE events exhibiting decreased exon skipping (∆PSI ≤ −15%) (Figure 4a,b). To identify the AS events regulating DNA repair, we intersected genes that underwent AS with those related to DNA repair (GOBP_DNA_REPAIR)(Table S3, Supporting Information). We found 67 alternative splicing genes, which involve 98 alternative splicing events, related to DNA repair (Figure 4c). Among these AS events, ERCC1 isoforms with deletion of exon VIII have been reported to have increased cisplatin sensitivity compared to the wild type.[
33
] Encouragingly, SNRPA knockout led to an increased exclusion of exon 8, generating more ERCC1 transcripts with exon 8 exclusion [ERCC1‐E8(−)] and fewer ERCC1 transcripts with exon 8 inclusion [ERCC1‐E8(+)] compared with the NC group (Figure 4d). The proportion of ERCC1‐E8(−) was elevated after SNRPA knockout (Figure 4e). Specific primers targeting exon 7 and exon 9, which span exon 8, for RT‒PCR were designed to distinguish whether exon 8 was included or excluded (Figure 4f). The findings of the genomic structure analysis were validated via agarose gel electrophoresis. SNRPA depletion resulted in an increased ratio of ERCC1‐E8(−) expression and a decreased ratio of ERCC1‐E8(+) expression, which is referred to as the spliced‐out ratio [SOR, i.e., ERCC1‐E8(−)/ERCC1‐E8(+)] (Figure 4g,i), whereas the overexpression of SNRPA elicited the opposite effect, significantly reducing the SOR of ERCC1‐E8 (Figure 4h,j). In summary, SNRPA promotes alternative splicing of the ERCC1 precursor mRNA to form ERCC1‐E8(+).
Figure 4
SNRPA suppresses exon 8 skipping of ERCC1 gene and promotes the formation of the ERCC1–XPF complex. a) Heatmap of significantly different AS events after SNRPA knockout (absolute ΔPSI ≥ 15%, P < 0.05). The color gradient indicates the percent spliced in (PSI) value of each event in each duplicate sample. b) Bar plot representing significantly different AS events. PSI = splice in/(splice in + splice out). ΔPSI = PSI(KO)‐PSI(NC). n = the number of AS events. Exon skipping (SE), intron retention (RI), mutually exclusive exons (MXE), alternative 5′ splice sites (A5SS), and alternative 3′ splice sites (A3SS). c) Venn diagram showing the intersection of 67 genes between differential AS event‐related genes and DNA repair‐related genes. d) Sashimi plot visualizing RNA‐seq reads mapped to exon 7, 8 and 9 of the ERCC1 mRNA after SNRPA knockout in A549/DDP cells. e) Scatter plot of the PSI for the ERCC1 exon 8 skipping event in NC cells and KO cells. f) Schematic representation (constitutive exon 8 and variable exons 7 and 9) of the splice variants [ERCC1‐E8(+) and ERCC1‐E8(−)]. g,i) Expression of ERCC1 transcripts with [ERCC1‐E8(+)] and without [ERCC1‐E8(−)] exon 8 after SNRPB knockdown in A549/DDP and 1299/DDP cells and the spliced out ratio [SOR, i.e., ERCC1‐E8(−)/ERCC1‐E8(+)]. h,j) Expression of ERCC1 transcripts with and without exon 8 after SNRPB overexpression in A549 and H1299 cells. Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information (h‐j). k) Schematic diagram of the wild‐type long ERCC1 protein. The ERCC1 residues Arg234 to Leu294 constitute the XPF binding domain. Exon 8 of ERCC1 encodes Sec235 to Gly258. l,m) Immunoprecipitation assays were conducted to assess XPF, which was immunoprecipitated by ERCC1 after SNRPA dysregulation. All data are presented as the mean ± SD (n = 3). The p values in panels (e), (h) and (j) were calculated by Student's t‐test. The p values in panels (g) and (i) were calculated using one‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
ERCC1 forms a heterodimer with XPF, which is a structure‐specific endonuclease that is vital for DNA damage repair.[
34
] The wild‐type long ERCC1 gene structure consists of 10 exons and encodes 297 amino acids (Figure 4k). The wild‐type elongated ERCC1 protein is composed of five primary α‐helical structures, designated H1 to H5. The H1 and H2 helices collectively form the initial HhH motif, whereas the second HhH motif is composed of the H4 and H5 helices.[
15
] The two HhH motifs are XPF protein‐binding domains. ERCC1 residues Arg234 to Leu294 are considered indispensable binding sites for XPF.[
16
] Through the National Center for Biotechnology Information (NCBI), we found that exon 8 of the ERCC1 gene encodes residues Ser235 to Gly258, which are in the first HhH motif of the XPF protein‐binding domain (Figure 4k). A reasonable assumption is that the upregulated exon VIII‐deficient ERCC1 variant attenuates the excision repair capacity of ERCC1 and augments the susceptibility of cancer cells to cisplatin treatment.[
33
] As expected, SNRPA depletion markedly reduced the recruitment of XPF to ERCC1 (Figure 4l). Conversely, SNRPA overexpression promoted the interaction between XPF and ERCC1 (Figure 4m).
2.5
ERCC1‐E8(+) is Essential for SNRPA‐Modulated Cisplatin Resistance and DNA Repair
Cells employ the ERCC1–XPF complex to repair DNA damage.[
34
] Given that the amino acids encoded by exon 8 of ERCC1 are involved in the interaction between ERCC1 and XPF, we investigated whether SNRPA‐induced cisplatin resistance and DNA repair depended on exon 8. First, siRNAs targeting ERCC1‐E8(+) (siE8‐1 and siE8‐2) were transfected into cisplatin‐resistant cells. Through agarose gel electrophoresis assays, we confirmed the successful silencing of ERCC1‐E8(+) in A549/DDP and H1299/DDP cells (Figure S6a,b, Supporting Information). Next, we discovered that ERCC1‐E8(+) knockdown did not influence the proliferation and apoptosis of chemotherapy‐resistant LUAD cells without cisplatin treatment (Figure S6c,d, Supporting Information). The results of the CCK‐8 experiments assessing cisplatin cytotoxicity demonstrated a significant reversal of cisplatin resistance in A549/DDP and H1299/DDP cells upon the silencing of ERCC1‐E8(+) compared with that in the control group (Figure S7a, Supporting Information). The results of the colony formation (Figure S7b, Supporting Information) and EdU (Figure S7c,d, Supporting Information) assays indicated that the knockdown of ERCC1‐E8(+) significantly reduced the survival and proliferation of A549/DDP and H1299/DDP cells treated with cisplatin. The flow cytometry analysis of apoptosis and Western blot analysis of apoptotic markers determined that ERCC1‐E8(+) silencing promoted apoptosis (Figure S7e–g, Supporting Information). Consistently, Western blot analysis showed a significant increase in the expression levels of the DNA damage biomarker γ‐H2AX in cells after the knockdown of ERCC1‐E8(+) (Figure S7g, Supporting Information). These results underscore the crucial role of the ERCC1‐E8(+) isoform in promoting cisplatin resistance and DNA repair in LUAD cells.
The overexpression of SNRPA decreased the SOR of ERCC1‐E8 in A549 and H1299 cells (Figure
5a,b). Consistent with our expectations, the silencing of ERCC1‐E8(+) significantly reversed SNRPA‐enhanced cisplatin resistance, as evidenced by CCK‐8 (Figure 5c; Figure S8a, Supporting Information), colony formation (Figure 5d; Figure S8b, Supporting Information), EdU (Figure 5e; Figure S8c, Supporting Information) and apoptosis‐related (Figure 5f,g; Figure S8d, Supporting Information) assays. Additionally, SNRPA overexpression enhanced DNA repair, contributing to the reduced presence of γ‐H2AX foci, whereas the silencing of ERCC1‐E8(+) led to increased expression of γ‐H2AX (Figure 5h,i; Figure S8e, Supporting Information). These results underscore the crucial role of exon 8 of ERCC1 in SNRPA‐induced cisplatin resistance in LUAD cells.
Figure 5
ERCC1‐E8(+) is critical for SNRPA‐orchestrated cisplatin resistance and DNA damage repair. a,b) Agarose gel showing the SOR of ERCC1‐E8 in cisplatin‐resistant A549 and H1299 cells after ERCC1‐E8(+) knockdown (siExon 8 or siE8‐1, siE8‐2) and SNRPA overexpression. Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information. c) The viability of the designated cells was quantified utilizing a CCK‐8 assay following 48 h of exposure to cisplatin (n = 4). d) The specified cells were subjected to 14 days of treatment with 10 µM cisplatin, as assessed by a colony formation assay. Staining of the resultant colonies were stained with crystal violet (left panel), and a quantitative analysis of the colony frequency is presented in the bar graphs (right panel). e) An EdU incorporation assay was employed to measure the proliferation rates of the cells of interest under treatment with a 10 µM dose of cisplatin. f) The left panel displays representative images of Annexin V‐FITC/PI staining of the specified cells following 24 h of treatment with 10 µM cisplatin. g,h) Western blots showed the expression of apoptosis‐associated proteins and γ‐H2AX, a known indicator of DNA damage and repair; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPAs. i) Co‐localization of γ‐H2AX and DAPI immunofluorescence revealed γ‐H2AX nuclear foci. All data are presented as the mean ± SD (n ≥ 3). The p values in panels (a), (b), (d) and (e) were calculated using one‐way ANOVA. The p values in panels (c) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
2.6
The Stability of the SNRPA mRNA, which is Modified by m6A Methylation, is Regulated by the ELAVL1 Protein
m6A RNA methylation is considered to facilitate cisplatin resistance.[
35
] m6A dot plot assays demonstrated elevated levels of overall m6A methylation in the cisplatin‐resistant LUAD cell lines as opposed to their parental counterparts (Figure
6a). Furthermore, using the online bioinformatic tool MEME (https://meme‐suite.org/meme/tools/meme), the typical m6A motif DRACH (D = A, G or U; R = A or G; H = A, U or C) was identified in the SNRPA mRNA (Figure 6b). To test the prediction of the presence of the SNRPA mRNA m6A methylation, methylated RNA immunoprecipitation (MeRIP) for the enrichment of methylated SNRPA was performed (Figure S9a, Supporting Information). MeRIP‐qPCR experiments revealed that, compared with the IgG control, SNRPA exhibited significant m6A methylation in LUAD cells (Figure 6c). Using the RMVar (https://rmvar.renlab.org/), RM2target (http://rm2target.ca nceromics.org/), and RMbase (https://rna.sysu.edu.cn/rmbase/index.php) online databases, we predicted seven m6A regulators that might be involved in SNRPA mRNA m6A methylation (Figure 6d). Among them, ELAVL1 [also known as human antigen R (HuR)] was most significantly overexpressed in LUAD tissues and had the lowest p value compared with normal lung tissues (Figure 6e; Figure S9b,c, Supporting Information). Pearson's correlation analysis revealed that the most significant correlation was observed between the mRNA expression levels of SNRPA and ELAVL1 among the seven m6A regulators (R = 0.45; p < 0.001; Figure 6f; Figure S9d,e, Supporting Information).
Figure 6
SNRPA expression and mRNA stability are regulated by ELAVL1. a) Dot blot assays illustrated the global m6A level in the total RNA of cisplatin‐resistant LUAD cell lines and their corresponding parental counterparts. b) The typical m6A motif RRACH (D = A, G or U; R = A or G; H = A, U or C) is shown in the SNRPA mRNA using MEME. c) The bar graphs showed that the m6A modification occurred in SNRPA mRNA, as determined via MeRIP‐qPCR. Anti‐m6A: m6A antibody. d) Venn diagram illustrating the m6A regulators controlling SNRPA, which were predicted by RM2target, RMbase, and RMVar. e) The expression levels of the ELAVL1 gene in tumor and normal tissues obtained from TCGA‐LUAD database were depicted using a violin plot. f) Pearson's correlation analysis of ELAVL1 and SNRPA expression in TCGA‐LUAD dataset. g) The probability of ELAVL1 binding to SNRPA was predicted by RPISeq. RF: random forest algorithm. SVM: Support vector machine algorithm. h) RIP‐qPCR revealed that the SNRPA mRNA was enriched by the ELAVL1 antibody. i) RNA pulldown analysis revealed the immunoprecipitation of ELAVL1 and IGF2BP with biotin‐labeled SNRPA probes. Red arrows: ELAVL1; green arrows: IGF2BP1. j) Immunoprecipitation assays confirmed the molecular interaction between IGF2BP1 and ELAVL1. k,l) qRT‒PCR and Western blot analyses revealed that the depletion of ELAVL1 (shELAVL‐1 and shELAVL‐2) resulted in a subsequent decrease in SNRPA expression in A549/DDP and H1299/DDP cells. m) Using an actinomycin D (Act D) pulse‐chase experiment, the half‐lives of the SNRPA mRNA in the indicated cells treated with 5 µg mL−1 Act D were observed. n,o) Western blot was utilized to evaluate the protein expression levels of ELAVL1 and SNRPA in targeted cells treated with the lysosome inhibitor NH4Cl (left panel) or the proteasome inhibitor MG132 (right panel), each for a period of 8 h. p) The stability of the SNRPA protein displayed by Western blot in LUAD cells treated with CHX in different time gradients. All data are presented as the mean ± SD (n = 3). The p values in panels (c), (e) and (h) were calculated by Student's t‐test. The p values in panels (k) were calculated using one‐way ANOVA. The p values in panels (m) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
Recent studies have indicated that ELAVL1 can bind to mRNA, thereby increasing their stability.[
36
,
37
] The online tool RPISeq (http://pridb.gdcb.iastate.edu/RPISeq/) was used to predict potential interactions between the ELAVL1 protein and SNRPA mRNA (Figure 6g). RNA binding protein immunoprecipitation (RIP) and qRT‒PCR assays demonstrated that the SNRPA mRNA was greatly enriched by the ELAVL1 antibody (Figure 6h; Figure S10a, Supporting Information). IGF2BP proteins recognize m6A‐modified mRNAs, recruiting ELAVL1 as an RNA stabilizer to prevent mRNA degradation.[
38
] RNA‐binding protein pull‐down assay (RNA pull‐down) assays followed by Western blot analyses were conducted to further confirm the interaction between ELAVL1 and the SNRPA mRNA, as well as between IGF2BP and the SNRPA mRNA. The results revealed that the ELAVL1 and IGF2BP proteins were pulled down by the biotinylated SNRPA mRNA probes (Figure 6i). The association between ELAVL1 and IGF2BP was confirmed through IP assays. IGF2BP was identified in the complex that precipitated with ELAVL1, and reciprocally, ELAVL1 was subsequently detected in the IP of IGF2BP (Figure 6j).
qRT‒PCR and Western blot analyses revealed that ELAVL1 depletion (shELAVL1‐1 and shELAVL1‐2) resulted in a subsequent decrease in SNRPA expression (Figure 6k,l). Next, we wondered whether the ELAVL1‐mediated effect on SNRPA expression occurs at the mRNA or protein level. An actinomycin D (Act D) chase assay revealed that silencing ELAVL1 diminished the stability of the SNRPA mRNA in LUAD cells (Figure 6m). However, the proteasome inhibitor MG132 and the lysosome inhibitor NH4Cl did not reverse the reduction in the SNRPA protein level elicited by ELAVL1 silencing (Figure 6n,o). In addition, ELAVL1 knockdown did not affect the half‐life of SNRPA following treatment with actinomycin (CHX) (Figure 6p; Figure S11a, Supporting Information). Together, our data suggest that ELAVL1 binds to the SNRPA mRNA, promoting its stability through the m6A modification and increasing SNRPA expression.
2.7
SNRPA is Essential for the Increase in Cisplatin Resistance, DNA Repair, and ERCC1‐E8(+) Expression Mediated by ELAVL1
The roles and mechanisms of ELAVL1 in cisplatin resistance are unknown. Without cisplatin treatment, ELAVL1 knockdown inhibited cell viability but promoted apoptosis and DNA damage in chemotherapy‐resistant LUAD cells (Figure S10, Supporting Information). Cisplatin did not inhibit cell proliferation or induce apoptosis in chemotherapy‐resistant LUAD cells, however the combination of ELAVL1 silencing and DNA damage cisplatin showed a more significant inhibition of cell viability and a greater promotion of apoptosis and DNA damage than either cisplatin or shRNA‐ELAVL1 alone (Figure S10, Supporting Information).
As shown in Figures
7a and S12a (Supporting Information), upon ELAVL1 knockdown, SNRPA was overexpressed. Indeed, after ELAVL1 silencing, the resistance of A549/DDP and H1299/DDP cells was reversed (Figure S11b–e, Supporting Information), apoptotic cells were markedly increased with cisplatin treatment (Figure S11f,g, Supporting Information). Moreover, ELAVL1 depletion inhibited DNA repair (Figure 7g,h; Figure S12g, Supporting Information) and ERCC1‐E8(+) expression in the presence of cisplatin (Figure 7i; Figure S12i, Supporting Information). Consistent with our expectation, SNRPA overexpression effectively restored the ELAVL1 knockdown‐mediated reduction in cisplatin resistance and DNA repair and the downregulation of ERCC1‐E8(+) (Figure 7a–i; Figure S12, Supporting Information). To ascertain compelling in vivo evidence for the indispensability of SNRPA in ELAVL1‐mediated augmentation of cisplatin resistance, athymic mice were stratified into the following four cohorts: (I) NC, (II) shELAVL1, (III) shELAVL1+Vector, and (IV) shELAVL1+SNRPA. Relative to athymic mice inoculated with NC cells, those inoculated with shELAVL1 cells exhibited a reduced tumor burden (Figure 7j–n), intimating that ELAVL1 may potentiate cisplatin resistance. Compared with the third group (III), the fourth group (IV) exhibited an increased tumor size (Figure 7j–n). IHC assays revealed elevated levels of BAX and γ‐H2AX in group (II) than in group (I), whereas the expression of BCL2 was markedly diminished (Figure 7o). SNRPA overexpression counteracted the effects of ELAVL depletion on cisplatin sensitivity and DNA damage repair, as evidenced by decreased BAX and γ‐H2AX expression and increased BCL2 expression (Figure 7o). Our results support the conjecture that ELAVL1 may promote cisplatin resistance in an SNRPA‐dependent manner.
Figure 7
ELAVL1 promotes cisplatin resistance, DNA repair and ERCC1‐E8(+) expression in an SNRPA‐dependent manner. a) Western blot analysis was utilized to determine the levels of ELAVL1 and SNRPA expression in cisplatin‐resistant cell lines following the overexpression of SNRPA based on the knockdown of ELAVL1; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPA. b) Cell survival in the defined populations was quantified after 48 h of cisplatin exposure using CCK‐8 assays (n = 4). c) Colonies were visualized with crystal violet staining (left panel), and a quantitative assessment of colony numbers was presented in the accompanying bar graphs for statistical interpretation (right panel). d) EdU assays showing the proliferation of the indicated cells in a 10 µM cisplatin solution. e) The left panel presents micrographs illustrating the staining of the designated cells with Annexin V‐FITC/PI following exposure to 10 µM cisplatin for 24 h. f) The expression levels of proteins associated with apoptosis were analyzed using Western blot; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPA. g) Western blot was used to assess the expression of γ‐H2AX, a well‐known indicator of DNA damage and repair; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPA. h) Immunofluorescence co‐localization of γ‐H2AX and DAPI in A549/DDP cells. i) Agarose gel showing that SNRPA overexpression effectively restored the ELAVL1 knockdown‐mediated downregulation of ERCC1‐E8(+). Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information. j‐n) Athymic nude mice were allocated into four experimental cohorts: the NC, shELAVL1 (shEL), shELAVL1+Vector (shEL+Vector), and shELAVL1+SNRPA‐Flag (shEL+SNRPA) cohorts. j,k) Bioluminescence images of xenograft tumors were shown. The bioluminescent signals were quantified and are presented in bar graphs. Additionally, at the culmination of the experiment, photographic documentation of the xenograft tumors from the 4 groups was performed (l). Subsequent statistical assessments were conducted to compare the mean tumor weights (m) and volumes (n) across the groups. o) IHC analyses were carried out to determine the protein expression levels within the tumors. All data are presented as the mean ± SD (n ≥ 3). The p values in panels (d), (i), (k), (m) and (n) were calculated using one‐way ANOVA. The p values in panels (b) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
2.8
SNRPA Expression is Correlated with Therapeutic Resistance to Cisplatin in Clinical LUAD Specimens
To investigate the association between SNRPA expression and cisplatin resistance, LUAD specimens from patients who had received cisplatin treatment were selected. IHC results showed that cisplatin‐resistant patients (PFS < 6 months) exhibited higher SNRPA expression levels than cisplatin‐sensitive patients (PFS ≥ 6 months Figure
8a). A greater proportion of cisplatin‐resistant patients exhibited elevated levels of SNRPA expression in comparison to cisplatin‐sensitive patients (Figure 8b). Kaplan–Meier analysis based on 100 LUAD patients treated with cisplatin demonstrated that high SNRPA expression predicted poor overall survival (Figure 8c). The relationship of the intensity and distribution between SNRPA and ELAVL1 was positive, suggesting that ELAVL1 might upregulate SNRPA expression (Figure 8d). Using fresh frozen LUAD (T) and normal adjacent (N) tissue samples, we found that the expression of ELAVL1 was upregulated in LUAD tissue compared with the normal tissue (Figure 8e; Figure S13a, Supporting Information). Moreover, the spearman correlation suggests that there was a significant positive correlation between the protein expression of ELAVL1 and SNRPA (Figure 8f). Meanwhile, ERCC1‐E8 (+) expression were significantly higher in resistant patient tissues compared to sensitive tissues, as indicated by the lower spliced‐out ratio (SOR), i.e., ERCC1‐E8 (−)/ERCC1‐E8 (+), of ERCC Exon 8 (Figure 8g; Figure S13b, Supporting Information). These findings, combined with the experimental data derived from LUAD cell lines and xenograft models, reveal that m6A‐driven SNRPA can contribute to cisplatin resistance through SNRPA‐mediated apoptosis and DNA repair.
Figure 8
Correlations between the expression levels of SNRPA and ELAVL1 in tissues obtained from individuals diagnosed with LUAD. a,b) The left panel shows the results of IHC staining for SNRPA in LUAD tissues, which were categorized into cisplatin‐sensitive (PFS ≥ 6 months) and cisplatin‐resistant (PFS < 6 months) subgroups. The right panel quantifies the frequency of high (red bar) versus low (blue bar) SNRPA expression correlated with cisplatin responsiveness. b) This bar graph delineates the distribution of cisplatin‐resistant (red bar) versus cisplatin‐sensitive (blue bar) patients in the high‐ and low‐SNRPA expression cohorts. c) Kaplan–Meier survival plots delineating the OS stratification for 100 patients treated with cisplatin from Harbin Medical University Cancer Hospital. d) Consecutive LUAD tissue sections from two individuals display the IHC staining intensity for ELAVL1 and SNRPA. Case 1 correlates with high SNRPA expression, while Case 2 aligns with low SNRPA expression in LUAD tissues. e) Western blot revealed the protein expression of ELAVL1 using fresh frozen LUAD (n = 9) and normal adjacent tissues (n = 9), the others could be seen in Figure S13a (Supporting Information). f) Correlation between the expression of ELAVL1 and SNRPA based on Western blot results from nine pairs of fresh frozen tissues. g) Agarose gel electrophoresis showing the expression of ERCC1‐E8 (+) and ERCC1‐E8 (−) in fresh frozen cisplatin‐sensitive (S) (n = 9) and cisplatin‐resistant (R) (n = 9) tissue samples (left panel). Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information. The bar graph shows the spliced‐out ratio (SOR) of ERCC Exon 8 (right panel). SOR: ERCC1‐E8 (−)/ERCC1‐E8 (+). Additional data can be seen in Figure S13b (Supporting Information). h) A schematic representation illustrating the molecular mechanism by which m6A‐modified SNRPA, which regulates the AS of ERCC1, contributes to the development of cisplatin resistance. All data are presented as the mean ± SD (n ≥ 3). The p values in panels (e) were calculated by Student's t‐test. The p values in panels (a), (b) and (d) were calculated by Chi‐square test. **p < 0.01; ***p < 0.001.
2.1
Identification of Spliceosome‐Related Genes Governing Platinum‐Based Drug Resistance
To identify spliceosome‐related genes potentially involved in chemoresistance to platinum‐based drugs, we obtained the annotation information for 137 spliceosome‐related genes from the Spliceosome database (http://spliceosomedb.ucsc.edu/) (Table S1, Supporting Information). This study included 1205 patients whose gene expression information and clinicopathological features were available in The Cancer Genome Atlas (TCGA, n = 518) and the Gene Expression Omnibus (GEO, n = 687) databases. A flowchart of the bioinformatics analysis is detailed in Figure S1 (Supporting Information). Compared with those in normal lung tissues, the expression of spliceosome‐related genes that were differentially expressed in LUAD tissues was determined by analyzing transcriptome gene expression datasets from lung tissues in TCGA and GEO databases (Figure
1a,b). A Venn diagram revealed 18 dysregulated spliceosome‐related genes at the intersection of TCGA and GEO datasets (Figure 1c). Within TCGA‐LUAD cohort, for 143 patients who received platinum‐based chemotherapy (platinum treatment group) and 348 who received no chemotherapy (non‐chemo group), we conducted a Cox regression analysis. Univariate and multivariate analyses identified elevated SNRPA expression as an independent prognostic indicator for OS in the cohort treated with platinum‐based chemotherapy but not in the non‐chemo group (Figure 1d,e). These results prompted us to choose SNRPA as a candidate spliceosome‐related gene for further investigation into its role in cisplatin resistance.
Figure 1
Identification of spliceosome‐related genes that modulate platinum‐based drug resistance. a,b) Heatmaps showcasing the differential gene expression profiles, highlighting genes that are dysregulated in LUAD samples relative to healthy lung tissue counterparts based on TCGA (a) and GEO (b) databases. c) Venn diagram depicting the intersection of upregulated spliceosome‐related genes obtained from TCGA and GEO databases. d,e) Forest plots of overall survival (OS) revealed the results of univariate and multivariate regression analyses of the platinum‐based treatment group (d) and the non‐chemo group (e). f) Violin plots depicting the comparative analysis of SNRPA mRNA expression profiles, contrasting LUAD tissues with non‐neoplastic lung counterparts, based on the TCGA and GEO databases. g) Western blot revealed the protein expression of SNRPA using fresh frozen LUAD (n = 9) and paired normal adjacent tissue samples (n = 9), and the other results are shown in Figure S2a (Supporting Information). h,i) Kaplan–Meier survival plots delineating the OS stratification for patients with LUAD who were categorized on the basis of elevated versus diminished SNRPA expression levels in the platinum treatment group (h) and non‐chemo group (i) (Log rank test; TCGA: n = 592; GSE2514: n = 39). All data are presented as the mean ± SD (n = 3). The p values in panels (f) and (g) were calculated by Student's t‐test. ***p < 0.001.
Compared to normal lung tissues, hierarchical clustering analysis revealed that SNRPA expression was elevated in LUAD tissues, based on the public datasets (Figure 1f). Western blot further confirmed increased expression of the SNRPA protein in fresh frozen LUAD tissue samples (Figure 1g; Figure S2a, Supporting Information). The Kaplan–Meier analysis indicated that high SNRPA expression predicted a shorter overall survival time for LUAD patients who received platinum‐based chemotherapy; however, no significant difference was observed in the non‐chemo group (Figure 1h,i).
2.2
SNRPA Reinforces LUAD Cell Cisplatin Resistance both In Vitro and In Vivo
To examine whether SNRPA contributed to cisplatin‐induced acquired drug resistance, we assessed the expression levels of SNRPA in cisplatin‐treated LUAD cells. We observed a gradual increase in SNRPA expression after treatment with increasing cisplatin doses or a prolonged treatment duration in the cisplatin‐sensitive A549 and H1299 cells, indicating a dose‐ and time‐dependent relationship (Figure
2a; Figure S2b, Supporting Information). Furthermore, compared with their cisplatin‐sensitive parental counterparts, cisplatin‐resistant cells (A549/DDP and H1299/DDP) presented substantially increased SNRPA expression (Figure 2b; Figure S3a, Supporting Information).
Figure 2
The depletion of SNRPA reverses the cisplatin chemoresistance of cisplatin‐resistant cells. a) Western blot representing the levels of SNRPA expression after treatment with increasing cisplatin dosages or the time increasing durations of cisplatin treatment (left panel). The bar graphs showed SNRPA protein expression based on the average grayscale value of the target protein (right panel). b) Western blot analyses demonstrating the quantification of SNRPA protein expression in cell lines characterized by sensitivity and resistance to cisplatin treatment. c) SNRPA expression in A549/DDP cells transfected with CRISPR negative control clones (NC), the SNRPA CRISPR/Cas9 knockout plasmid‐1 (KO‐1) or the SNRPA CRISPR/Cas9 knockout plasmid‐1 (KO‐2). d) Comparative analysis of both the mRNA and protein expression levels of SNRPA in H1299/DDP cells transfected with shNC (NC), shSNRPA‐1 (sh‐1), or shSNRPA‐2 (sh‐2). e) CCK‐8 analysis, after 48 h of cisplatin treatment, illustrated cell viability across the designated cellular groups (n = 4). f) Colonies were visualized by staining with crystal violet (left panel), while the bar graphs provided a statistical evaluation of the colony counts (right panel). g) EdU assays of the proliferation of the indicated cells in a 10 µM cisplatin solution. h) Representative images illustrating the results of Annexin V‐Fluor 647 APC/7‐AAD dual staining to evaluate apoptosis in cells treated with 10 µM cisplatin for 24 h. i) The expression of apoptosis‐associated proteins in A549/DDP cells. j) The left panel displayed representative bioluminescence images of xenograft tumors generated from NC or KO cells. Bar graphs were generated to quantify the bioluminescence luciferase signal emitted from the xenograft tumors, providing a statistical comparison between groups. k,m) The left panel showed images of xenograft tumors in the designated groups (k). These panels depict the outcomes of t‐tests applied to compare the mean tumor volume (l) and weight (m) across different groups. n) IHC analyses were performed on xenograft tumor tissues to examine the expression patterns of SNRPA, Bcl‐2, BAX, and γ‐H2AX. All data are presented as the mean ± SD (n≥3). The p values in panels (a), (c), (d), (f) and (g) were calculated using one‐way ANOVA. The p values in panels (j), (l) and (m) were calculated by Student's t‐test. The p values in panels (e) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
Consequently, in functional studies, cisplatin‐resistant cells were used to establish a loss‐of‐function model, whereas parental LUAD cells served to create a gain‐of‐function model. Using CRISPR/Cas9 technology, we achieved successful knockout of the SNRPA mRNA and protein levels in SNRPA‐deficient CRISPR clones (KO1 and KO2) of A549/DDP via lentiviral constructs, as evidenced by comparisons with CRISPR negative control clones (NC) (Figure 2c). To enhance the validity of our findings, we designed specific shRNAs targeting SNRPA, as well as the negative control, and transfected them in H1299/DDP cells (Figure 2d). Notably, in the absence of cisplatin treatment, neither SNRPA knockdown nor knockout influenced the proliferation, apoptosis or DNA damage of chemotherapy‐resistant LUAD cells (Figure S2c‐e, Supporting Information). Similarly, in vivo experiments without cisplatin treatment revealed that SNRPA knockout did not affect tumor growth (Figure S2f‐i, Supporting Information).
Consistent with our expectations, SNRPA knockout cells demonstrated a lower 50% inhibitory concentration (IC50) when treated with progressively increasing concentrations of cisplatin (Figure 2e). Plate colony formation and EdU incorporation assays confirmed that the downregulation of SNRPA reduced cisplatin resistance (Figure 2f,g). Consistent with these findings, the resistance of H1299/DDP cells to cisplatin was reversed, as confirmed by CCK8, plate colony formation and EdU incorporation assays (Figure S3b‐d, Supporting Information). Flow cytometry analysis and Western blot were subsequently performed to evaluate the effect of SNRPA depletion on cisplatin‐induced apoptosis. SNRPA silencing noticeably escalated the proportion of apoptotic A549/DDP cells treated with cisplatin, which coincided with decreased expression of the anti‐apoptotic protein Bcl‐2 and an upsurge in the pro‐apoptotic protein BAX (Figure 2h,i; Figure S3e, Supporting Information). In the in vivo cisplatin resistance experiment, xenografts (NC and KO) derived from A549/DDP derivative cells (CRISPR negative control clones and SNRPA‐deficient CRISPR clones) were established in nude athymic mice. The experimental procedure for cisplatin injection was carried out as detailed in Figure S3g (Supporting Information). Compared with the NC group injected with NC cells, SNRPA knockout attenuated cisplatin resistance in LUAD cells located in the axillary region of nude mice, as evidenced by reduced luciferase activity (Figure 2j), decreased tumor volume (Figure 2k,l), and reduced tumor weight (Figure 2m). In consecutive tissue sections derived from xenografts, IHC staining demonstrated diminished BCL2 expression and augmented BAX expression in specimens from the KO group compared with those from the NC group (Figure 2n).
As indicated in Figures 2b and S3a (Supporting Information), cisplatin‐sensitive parental cells were employed to establish a gain‐of‐function model. We successfully established stable SNRPA‐overexpressing clones through lentiviral infection, which was validated by quantitative real‐time polymerase chain reaction (qRT‒PCR) and Western blot analyses (Figure S4a,b, Supporting Information). CCK‐8, colony formation, and EdU assays were employed to assess the cytotoxicity of cisplatin. The CCK‐8 results revealed that SNRPA overexpression induced the resistance of A549 and H1299 cells to cisplatin (Figure S5a, Supporting Information). Cell proliferation was evaluated through colony formation assays (Figure S5b, Supporting Information) and EdU experiments (Figure S5c, Supporting Information) under cisplatin treatment conditions. Flow cytometry and Western blot analysis elucidated that the overexpression of SNRPA inhibited cisplatin‐induced apoptosis (Figure S5d,e, Supporting Information). Consistent with the in vitro results, the spontaneous xenograft tumors formed by stable SNRPA‐overexpressing clones (SNRPA) displayed higher luciferase activity, as well as larger tumor volumes and weights, than those from the control (Ctrl) group (Figure S5f‐i, Supporting Information). However, without cisplatin treatment, overexpression of SNRPA did not affect in vitro LUAD cell proliferation, apoptosis or DNA damage and in vivo tumor growth (Figure S4c‐i, Supporting Information). Based on these findings, we concluded that SNRPA can induce cisplatin chemoresistance in LUAD.
2.3
SNRPA can Blunt Cisplatin‐Induced DNA Damage
To elucidate the mechanism behind SNRPA‐induced cisplatin resistance, we sequenced the mRNA transcriptomes of the NC and KO1 clones to perform a differential expression (DE) analysis. The DE analysis identified 324 upregulated genes and 368 downregulated genes after SNRPA knockout (P < 0.05, absolute log2 Fold change (FC) > 1) (Figure
3a). Enrichment analysis of the DE genes using GO revealed many significant signal transduction pathways after SNRPA knockout. Among them, four pathways are significantly and statistically involved in the response to drug and response to DNA damage stimulus (Figure 3b). Phosphorylated H2AX (γ‐H2AX) is a known indicator of DNA damage and repair.[
31
,
32
]
γ‐H2AX nuclear foci were observed to investigate the effects of SNRPA on DNA damage. The increased presence of γ‐H2AX foci following SNRPA knockout indicated a reduced DNA repair capability, as indicated by immunofluorescence staining and Western blot analyses (Figure 3c,d). In contrast, SNRPA overexpression enhanced DNA repair, contributing to a reduced presence of γ‐H2AX foci (Figure 3e,f). Animal experiments revealed an increase in γ‐H2AX expression in tumor tissues isolated from KO mice compared with those from the NC group (Figure 2n). These findings indicate that SNRPA enhances the repair of DNA damage induced by cisplatin in LUAD.
Figure 3
SNRPA promotes DNA damage repair. a) Volcano plot showing differentially expressed genes between SNRPA knockout cells (SNRPA‐KO1) and negative control cells (NC). b) Gene Ontology (GO) enrichment analysis was performed to delineate the biological processes involving SNRPA. The table illustrates that four pathways are significantly and statistically involved in the response to drug and response to DNA damage stimulus following SNRPA knockout. c) The expression levels of the γ‐H2AX protein in the specified cell lines following treatment with cisplatin and subsequent knockout or knockdown of SNRPA were assessed. ## represents exogenous Flag‐SNRPA, # represents endogenous SNRPA. d) Immunofluorescence co‐localization of γ‐H2AX and DAPI exhibited γ‐H2AX nuclear foci. The increased presence of γH2AX indicated reduced DNA repair after DNA damage. e) The expression of the γ‐H2AX protein was assessed in the specified cells treated with cisplatin after SNRPA overexpression. f) Localization of γ‐H2AX and DAPI revealed γ‐H2AX nuclear foci. The decreased presence of γH2AX indicated enhanced DNA repair after DNA damage.
2.4
SNRPA can Control exon 8 Skipping of ERCC1 Gene and the Formation of the ERCC1–XPF Complex
To explore the role of SNRPA in AS and the mechanisms of SNRPA‐induced DNA repair, we performed a genomic structure analysis based on transcriptome sequencing results. This analysis identified 1416 significant endogenous AS events regulated by SNRPA, with significant changes in percent spliced in (PSI) values (absolute ΔPSI ≥ 15%, P < 0.05) (Figure
4a; Table S2, Supporting Information). SNRPA regulates various AS types, including intron retention (IR), alternative 5′ splice sites (A5SS), alternative 3′ splice sites (A3SS), mutually exclusive exons (MXE), and exon skipping (SE). The predominant AS event was SE (N = 1185), with 681 SE events showing increased exon skipping (∆PSI ≥ +15%) and 504 SE events exhibiting decreased exon skipping (∆PSI ≤ −15%) (Figure 4a,b). To identify the AS events regulating DNA repair, we intersected genes that underwent AS with those related to DNA repair (GOBP_DNA_REPAIR)(Table S3, Supporting Information). We found 67 alternative splicing genes, which involve 98 alternative splicing events, related to DNA repair (Figure 4c). Among these AS events, ERCC1 isoforms with deletion of exon VIII have been reported to have increased cisplatin sensitivity compared to the wild type.[
33
] Encouragingly, SNRPA knockout led to an increased exclusion of exon 8, generating more ERCC1 transcripts with exon 8 exclusion [ERCC1‐E8(−)] and fewer ERCC1 transcripts with exon 8 inclusion [ERCC1‐E8(+)] compared with the NC group (Figure 4d). The proportion of ERCC1‐E8(−) was elevated after SNRPA knockout (Figure 4e). Specific primers targeting exon 7 and exon 9, which span exon 8, for RT‒PCR were designed to distinguish whether exon 8 was included or excluded (Figure 4f). The findings of the genomic structure analysis were validated via agarose gel electrophoresis. SNRPA depletion resulted in an increased ratio of ERCC1‐E8(−) expression and a decreased ratio of ERCC1‐E8(+) expression, which is referred to as the spliced‐out ratio [SOR, i.e., ERCC1‐E8(−)/ERCC1‐E8(+)] (Figure 4g,i), whereas the overexpression of SNRPA elicited the opposite effect, significantly reducing the SOR of ERCC1‐E8 (Figure 4h,j). In summary, SNRPA promotes alternative splicing of the ERCC1 precursor mRNA to form ERCC1‐E8(+).
Figure 4
SNRPA suppresses exon 8 skipping of ERCC1 gene and promotes the formation of the ERCC1–XPF complex. a) Heatmap of significantly different AS events after SNRPA knockout (absolute ΔPSI ≥ 15%, P < 0.05). The color gradient indicates the percent spliced in (PSI) value of each event in each duplicate sample. b) Bar plot representing significantly different AS events. PSI = splice in/(splice in + splice out). ΔPSI = PSI(KO)‐PSI(NC). n = the number of AS events. Exon skipping (SE), intron retention (RI), mutually exclusive exons (MXE), alternative 5′ splice sites (A5SS), and alternative 3′ splice sites (A3SS). c) Venn diagram showing the intersection of 67 genes between differential AS event‐related genes and DNA repair‐related genes. d) Sashimi plot visualizing RNA‐seq reads mapped to exon 7, 8 and 9 of the ERCC1 mRNA after SNRPA knockout in A549/DDP cells. e) Scatter plot of the PSI for the ERCC1 exon 8 skipping event in NC cells and KO cells. f) Schematic representation (constitutive exon 8 and variable exons 7 and 9) of the splice variants [ERCC1‐E8(+) and ERCC1‐E8(−)]. g,i) Expression of ERCC1 transcripts with [ERCC1‐E8(+)] and without [ERCC1‐E8(−)] exon 8 after SNRPB knockdown in A549/DDP and 1299/DDP cells and the spliced out ratio [SOR, i.e., ERCC1‐E8(−)/ERCC1‐E8(+)]. h,j) Expression of ERCC1 transcripts with and without exon 8 after SNRPB overexpression in A549 and H1299 cells. Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information (h‐j). k) Schematic diagram of the wild‐type long ERCC1 protein. The ERCC1 residues Arg234 to Leu294 constitute the XPF binding domain. Exon 8 of ERCC1 encodes Sec235 to Gly258. l,m) Immunoprecipitation assays were conducted to assess XPF, which was immunoprecipitated by ERCC1 after SNRPA dysregulation. All data are presented as the mean ± SD (n = 3). The p values in panels (e), (h) and (j) were calculated by Student's t‐test. The p values in panels (g) and (i) were calculated using one‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
ERCC1 forms a heterodimer with XPF, which is a structure‐specific endonuclease that is vital for DNA damage repair.[
34
] The wild‐type long ERCC1 gene structure consists of 10 exons and encodes 297 amino acids (Figure 4k). The wild‐type elongated ERCC1 protein is composed of five primary α‐helical structures, designated H1 to H5. The H1 and H2 helices collectively form the initial HhH motif, whereas the second HhH motif is composed of the H4 and H5 helices.[
15
] The two HhH motifs are XPF protein‐binding domains. ERCC1 residues Arg234 to Leu294 are considered indispensable binding sites for XPF.[
16
] Through the National Center for Biotechnology Information (NCBI), we found that exon 8 of the ERCC1 gene encodes residues Ser235 to Gly258, which are in the first HhH motif of the XPF protein‐binding domain (Figure 4k). A reasonable assumption is that the upregulated exon VIII‐deficient ERCC1 variant attenuates the excision repair capacity of ERCC1 and augments the susceptibility of cancer cells to cisplatin treatment.[
33
] As expected, SNRPA depletion markedly reduced the recruitment of XPF to ERCC1 (Figure 4l). Conversely, SNRPA overexpression promoted the interaction between XPF and ERCC1 (Figure 4m).
2.5
ERCC1‐E8(+) is Essential for SNRPA‐Modulated Cisplatin Resistance and DNA Repair
Cells employ the ERCC1–XPF complex to repair DNA damage.[
34
] Given that the amino acids encoded by exon 8 of ERCC1 are involved in the interaction between ERCC1 and XPF, we investigated whether SNRPA‐induced cisplatin resistance and DNA repair depended on exon 8. First, siRNAs targeting ERCC1‐E8(+) (siE8‐1 and siE8‐2) were transfected into cisplatin‐resistant cells. Through agarose gel electrophoresis assays, we confirmed the successful silencing of ERCC1‐E8(+) in A549/DDP and H1299/DDP cells (Figure S6a,b, Supporting Information). Next, we discovered that ERCC1‐E8(+) knockdown did not influence the proliferation and apoptosis of chemotherapy‐resistant LUAD cells without cisplatin treatment (Figure S6c,d, Supporting Information). The results of the CCK‐8 experiments assessing cisplatin cytotoxicity demonstrated a significant reversal of cisplatin resistance in A549/DDP and H1299/DDP cells upon the silencing of ERCC1‐E8(+) compared with that in the control group (Figure S7a, Supporting Information). The results of the colony formation (Figure S7b, Supporting Information) and EdU (Figure S7c,d, Supporting Information) assays indicated that the knockdown of ERCC1‐E8(+) significantly reduced the survival and proliferation of A549/DDP and H1299/DDP cells treated with cisplatin. The flow cytometry analysis of apoptosis and Western blot analysis of apoptotic markers determined that ERCC1‐E8(+) silencing promoted apoptosis (Figure S7e–g, Supporting Information). Consistently, Western blot analysis showed a significant increase in the expression levels of the DNA damage biomarker γ‐H2AX in cells after the knockdown of ERCC1‐E8(+) (Figure S7g, Supporting Information). These results underscore the crucial role of the ERCC1‐E8(+) isoform in promoting cisplatin resistance and DNA repair in LUAD cells.
The overexpression of SNRPA decreased the SOR of ERCC1‐E8 in A549 and H1299 cells (Figure
5a,b). Consistent with our expectations, the silencing of ERCC1‐E8(+) significantly reversed SNRPA‐enhanced cisplatin resistance, as evidenced by CCK‐8 (Figure 5c; Figure S8a, Supporting Information), colony formation (Figure 5d; Figure S8b, Supporting Information), EdU (Figure 5e; Figure S8c, Supporting Information) and apoptosis‐related (Figure 5f,g; Figure S8d, Supporting Information) assays. Additionally, SNRPA overexpression enhanced DNA repair, contributing to the reduced presence of γ‐H2AX foci, whereas the silencing of ERCC1‐E8(+) led to increased expression of γ‐H2AX (Figure 5h,i; Figure S8e, Supporting Information). These results underscore the crucial role of exon 8 of ERCC1 in SNRPA‐induced cisplatin resistance in LUAD cells.
Figure 5
ERCC1‐E8(+) is critical for SNRPA‐orchestrated cisplatin resistance and DNA damage repair. a,b) Agarose gel showing the SOR of ERCC1‐E8 in cisplatin‐resistant A549 and H1299 cells after ERCC1‐E8(+) knockdown (siExon 8 or siE8‐1, siE8‐2) and SNRPA overexpression. Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information. c) The viability of the designated cells was quantified utilizing a CCK‐8 assay following 48 h of exposure to cisplatin (n = 4). d) The specified cells were subjected to 14 days of treatment with 10 µM cisplatin, as assessed by a colony formation assay. Staining of the resultant colonies were stained with crystal violet (left panel), and a quantitative analysis of the colony frequency is presented in the bar graphs (right panel). e) An EdU incorporation assay was employed to measure the proliferation rates of the cells of interest under treatment with a 10 µM dose of cisplatin. f) The left panel displays representative images of Annexin V‐FITC/PI staining of the specified cells following 24 h of treatment with 10 µM cisplatin. g,h) Western blots showed the expression of apoptosis‐associated proteins and γ‐H2AX, a known indicator of DNA damage and repair; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPAs. i) Co‐localization of γ‐H2AX and DAPI immunofluorescence revealed γ‐H2AX nuclear foci. All data are presented as the mean ± SD (n ≥ 3). The p values in panels (a), (b), (d) and (e) were calculated using one‐way ANOVA. The p values in panels (c) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
2.6
The Stability of the SNRPA mRNA, which is Modified by m6A Methylation, is Regulated by the ELAVL1 Protein
m6A RNA methylation is considered to facilitate cisplatin resistance.[
35
] m6A dot plot assays demonstrated elevated levels of overall m6A methylation in the cisplatin‐resistant LUAD cell lines as opposed to their parental counterparts (Figure
6a). Furthermore, using the online bioinformatic tool MEME (https://meme‐suite.org/meme/tools/meme), the typical m6A motif DRACH (D = A, G or U; R = A or G; H = A, U or C) was identified in the SNRPA mRNA (Figure 6b). To test the prediction of the presence of the SNRPA mRNA m6A methylation, methylated RNA immunoprecipitation (MeRIP) for the enrichment of methylated SNRPA was performed (Figure S9a, Supporting Information). MeRIP‐qPCR experiments revealed that, compared with the IgG control, SNRPA exhibited significant m6A methylation in LUAD cells (Figure 6c). Using the RMVar (https://rmvar.renlab.org/), RM2target (http://rm2target.ca nceromics.org/), and RMbase (https://rna.sysu.edu.cn/rmbase/index.php) online databases, we predicted seven m6A regulators that might be involved in SNRPA mRNA m6A methylation (Figure 6d). Among them, ELAVL1 [also known as human antigen R (HuR)] was most significantly overexpressed in LUAD tissues and had the lowest p value compared with normal lung tissues (Figure 6e; Figure S9b,c, Supporting Information). Pearson's correlation analysis revealed that the most significant correlation was observed between the mRNA expression levels of SNRPA and ELAVL1 among the seven m6A regulators (R = 0.45; p < 0.001; Figure 6f; Figure S9d,e, Supporting Information).
Figure 6
SNRPA expression and mRNA stability are regulated by ELAVL1. a) Dot blot assays illustrated the global m6A level in the total RNA of cisplatin‐resistant LUAD cell lines and their corresponding parental counterparts. b) The typical m6A motif RRACH (D = A, G or U; R = A or G; H = A, U or C) is shown in the SNRPA mRNA using MEME. c) The bar graphs showed that the m6A modification occurred in SNRPA mRNA, as determined via MeRIP‐qPCR. Anti‐m6A: m6A antibody. d) Venn diagram illustrating the m6A regulators controlling SNRPA, which were predicted by RM2target, RMbase, and RMVar. e) The expression levels of the ELAVL1 gene in tumor and normal tissues obtained from TCGA‐LUAD database were depicted using a violin plot. f) Pearson's correlation analysis of ELAVL1 and SNRPA expression in TCGA‐LUAD dataset. g) The probability of ELAVL1 binding to SNRPA was predicted by RPISeq. RF: random forest algorithm. SVM: Support vector machine algorithm. h) RIP‐qPCR revealed that the SNRPA mRNA was enriched by the ELAVL1 antibody. i) RNA pulldown analysis revealed the immunoprecipitation of ELAVL1 and IGF2BP with biotin‐labeled SNRPA probes. Red arrows: ELAVL1; green arrows: IGF2BP1. j) Immunoprecipitation assays confirmed the molecular interaction between IGF2BP1 and ELAVL1. k,l) qRT‒PCR and Western blot analyses revealed that the depletion of ELAVL1 (shELAVL‐1 and shELAVL‐2) resulted in a subsequent decrease in SNRPA expression in A549/DDP and H1299/DDP cells. m) Using an actinomycin D (Act D) pulse‐chase experiment, the half‐lives of the SNRPA mRNA in the indicated cells treated with 5 µg mL−1 Act D were observed. n,o) Western blot was utilized to evaluate the protein expression levels of ELAVL1 and SNRPA in targeted cells treated with the lysosome inhibitor NH4Cl (left panel) or the proteasome inhibitor MG132 (right panel), each for a period of 8 h. p) The stability of the SNRPA protein displayed by Western blot in LUAD cells treated with CHX in different time gradients. All data are presented as the mean ± SD (n = 3). The p values in panels (c), (e) and (h) were calculated by Student's t‐test. The p values in panels (k) were calculated using one‐way ANOVA. The p values in panels (m) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
Recent studies have indicated that ELAVL1 can bind to mRNA, thereby increasing their stability.[
36
,
37
] The online tool RPISeq (http://pridb.gdcb.iastate.edu/RPISeq/) was used to predict potential interactions between the ELAVL1 protein and SNRPA mRNA (Figure 6g). RNA binding protein immunoprecipitation (RIP) and qRT‒PCR assays demonstrated that the SNRPA mRNA was greatly enriched by the ELAVL1 antibody (Figure 6h; Figure S10a, Supporting Information). IGF2BP proteins recognize m6A‐modified mRNAs, recruiting ELAVL1 as an RNA stabilizer to prevent mRNA degradation.[
38
] RNA‐binding protein pull‐down assay (RNA pull‐down) assays followed by Western blot analyses were conducted to further confirm the interaction between ELAVL1 and the SNRPA mRNA, as well as between IGF2BP and the SNRPA mRNA. The results revealed that the ELAVL1 and IGF2BP proteins were pulled down by the biotinylated SNRPA mRNA probes (Figure 6i). The association between ELAVL1 and IGF2BP was confirmed through IP assays. IGF2BP was identified in the complex that precipitated with ELAVL1, and reciprocally, ELAVL1 was subsequently detected in the IP of IGF2BP (Figure 6j).
qRT‒PCR and Western blot analyses revealed that ELAVL1 depletion (shELAVL1‐1 and shELAVL1‐2) resulted in a subsequent decrease in SNRPA expression (Figure 6k,l). Next, we wondered whether the ELAVL1‐mediated effect on SNRPA expression occurs at the mRNA or protein level. An actinomycin D (Act D) chase assay revealed that silencing ELAVL1 diminished the stability of the SNRPA mRNA in LUAD cells (Figure 6m). However, the proteasome inhibitor MG132 and the lysosome inhibitor NH4Cl did not reverse the reduction in the SNRPA protein level elicited by ELAVL1 silencing (Figure 6n,o). In addition, ELAVL1 knockdown did not affect the half‐life of SNRPA following treatment with actinomycin (CHX) (Figure 6p; Figure S11a, Supporting Information). Together, our data suggest that ELAVL1 binds to the SNRPA mRNA, promoting its stability through the m6A modification and increasing SNRPA expression.
2.7
SNRPA is Essential for the Increase in Cisplatin Resistance, DNA Repair, and ERCC1‐E8(+) Expression Mediated by ELAVL1
The roles and mechanisms of ELAVL1 in cisplatin resistance are unknown. Without cisplatin treatment, ELAVL1 knockdown inhibited cell viability but promoted apoptosis and DNA damage in chemotherapy‐resistant LUAD cells (Figure S10, Supporting Information). Cisplatin did not inhibit cell proliferation or induce apoptosis in chemotherapy‐resistant LUAD cells, however the combination of ELAVL1 silencing and DNA damage cisplatin showed a more significant inhibition of cell viability and a greater promotion of apoptosis and DNA damage than either cisplatin or shRNA‐ELAVL1 alone (Figure S10, Supporting Information).
As shown in Figures
7a and S12a (Supporting Information), upon ELAVL1 knockdown, SNRPA was overexpressed. Indeed, after ELAVL1 silencing, the resistance of A549/DDP and H1299/DDP cells was reversed (Figure S11b–e, Supporting Information), apoptotic cells were markedly increased with cisplatin treatment (Figure S11f,g, Supporting Information). Moreover, ELAVL1 depletion inhibited DNA repair (Figure 7g,h; Figure S12g, Supporting Information) and ERCC1‐E8(+) expression in the presence of cisplatin (Figure 7i; Figure S12i, Supporting Information). Consistent with our expectation, SNRPA overexpression effectively restored the ELAVL1 knockdown‐mediated reduction in cisplatin resistance and DNA repair and the downregulation of ERCC1‐E8(+) (Figure 7a–i; Figure S12, Supporting Information). To ascertain compelling in vivo evidence for the indispensability of SNRPA in ELAVL1‐mediated augmentation of cisplatin resistance, athymic mice were stratified into the following four cohorts: (I) NC, (II) shELAVL1, (III) shELAVL1+Vector, and (IV) shELAVL1+SNRPA. Relative to athymic mice inoculated with NC cells, those inoculated with shELAVL1 cells exhibited a reduced tumor burden (Figure 7j–n), intimating that ELAVL1 may potentiate cisplatin resistance. Compared with the third group (III), the fourth group (IV) exhibited an increased tumor size (Figure 7j–n). IHC assays revealed elevated levels of BAX and γ‐H2AX in group (II) than in group (I), whereas the expression of BCL2 was markedly diminished (Figure 7o). SNRPA overexpression counteracted the effects of ELAVL depletion on cisplatin sensitivity and DNA damage repair, as evidenced by decreased BAX and γ‐H2AX expression and increased BCL2 expression (Figure 7o). Our results support the conjecture that ELAVL1 may promote cisplatin resistance in an SNRPA‐dependent manner.
Figure 7
ELAVL1 promotes cisplatin resistance, DNA repair and ERCC1‐E8(+) expression in an SNRPA‐dependent manner. a) Western blot analysis was utilized to determine the levels of ELAVL1 and SNRPA expression in cisplatin‐resistant cell lines following the overexpression of SNRPA based on the knockdown of ELAVL1; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPA. b) Cell survival in the defined populations was quantified after 48 h of cisplatin exposure using CCK‐8 assays (n = 4). c) Colonies were visualized with crystal violet staining (left panel), and a quantitative assessment of colony numbers was presented in the accompanying bar graphs for statistical interpretation (right panel). d) EdU assays showing the proliferation of the indicated cells in a 10 µM cisplatin solution. e) The left panel presents micrographs illustrating the staining of the designated cells with Annexin V‐FITC/PI following exposure to 10 µM cisplatin for 24 h. f) The expression levels of proteins associated with apoptosis were analyzed using Western blot; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPA. g) Western blot was used to assess the expression of γ‐H2AX, a well‐known indicator of DNA damage and repair; ## represents exogenous Flag‐SNRPA, and # represents endogenous SNRPA. h) Immunofluorescence co‐localization of γ‐H2AX and DAPI in A549/DDP cells. i) Agarose gel showing that SNRPA overexpression effectively restored the ELAVL1 knockdown‐mediated downregulation of ERCC1‐E8(+). Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information. j‐n) Athymic nude mice were allocated into four experimental cohorts: the NC, shELAVL1 (shEL), shELAVL1+Vector (shEL+Vector), and shELAVL1+SNRPA‐Flag (shEL+SNRPA) cohorts. j,k) Bioluminescence images of xenograft tumors were shown. The bioluminescent signals were quantified and are presented in bar graphs. Additionally, at the culmination of the experiment, photographic documentation of the xenograft tumors from the 4 groups was performed (l). Subsequent statistical assessments were conducted to compare the mean tumor weights (m) and volumes (n) across the groups. o) IHC analyses were carried out to determine the protein expression levels within the tumors. All data are presented as the mean ± SD (n ≥ 3). The p values in panels (d), (i), (k), (m) and (n) were calculated using one‐way ANOVA. The p values in panels (b) were calculated using two‐way ANOVA. *p < 0.05; **p < 0.01; ***p < 0.001.
2.8
SNRPA Expression is Correlated with Therapeutic Resistance to Cisplatin in Clinical LUAD Specimens
To investigate the association between SNRPA expression and cisplatin resistance, LUAD specimens from patients who had received cisplatin treatment were selected. IHC results showed that cisplatin‐resistant patients (PFS < 6 months) exhibited higher SNRPA expression levels than cisplatin‐sensitive patients (PFS ≥ 6 months Figure
8a). A greater proportion of cisplatin‐resistant patients exhibited elevated levels of SNRPA expression in comparison to cisplatin‐sensitive patients (Figure 8b). Kaplan–Meier analysis based on 100 LUAD patients treated with cisplatin demonstrated that high SNRPA expression predicted poor overall survival (Figure 8c). The relationship of the intensity and distribution between SNRPA and ELAVL1 was positive, suggesting that ELAVL1 might upregulate SNRPA expression (Figure 8d). Using fresh frozen LUAD (T) and normal adjacent (N) tissue samples, we found that the expression of ELAVL1 was upregulated in LUAD tissue compared with the normal tissue (Figure 8e; Figure S13a, Supporting Information). Moreover, the spearman correlation suggests that there was a significant positive correlation between the protein expression of ELAVL1 and SNRPA (Figure 8f). Meanwhile, ERCC1‐E8 (+) expression were significantly higher in resistant patient tissues compared to sensitive tissues, as indicated by the lower spliced‐out ratio (SOR), i.e., ERCC1‐E8 (−)/ERCC1‐E8 (+), of ERCC Exon 8 (Figure 8g; Figure S13b, Supporting Information). These findings, combined with the experimental data derived from LUAD cell lines and xenograft models, reveal that m6A‐driven SNRPA can contribute to cisplatin resistance through SNRPA‐mediated apoptosis and DNA repair.
Figure 8
Correlations between the expression levels of SNRPA and ELAVL1 in tissues obtained from individuals diagnosed with LUAD. a,b) The left panel shows the results of IHC staining for SNRPA in LUAD tissues, which were categorized into cisplatin‐sensitive (PFS ≥ 6 months) and cisplatin‐resistant (PFS < 6 months) subgroups. The right panel quantifies the frequency of high (red bar) versus low (blue bar) SNRPA expression correlated with cisplatin responsiveness. b) This bar graph delineates the distribution of cisplatin‐resistant (red bar) versus cisplatin‐sensitive (blue bar) patients in the high‐ and low‐SNRPA expression cohorts. c) Kaplan–Meier survival plots delineating the OS stratification for 100 patients treated with cisplatin from Harbin Medical University Cancer Hospital. d) Consecutive LUAD tissue sections from two individuals display the IHC staining intensity for ELAVL1 and SNRPA. Case 1 correlates with high SNRPA expression, while Case 2 aligns with low SNRPA expression in LUAD tissues. e) Western blot revealed the protein expression of ELAVL1 using fresh frozen LUAD (n = 9) and normal adjacent tissues (n = 9), the others could be seen in Figure S13a (Supporting Information). f) Correlation between the expression of ELAVL1 and SNRPA based on Western blot results from nine pairs of fresh frozen tissues. g) Agarose gel electrophoresis showing the expression of ERCC1‐E8 (+) and ERCC1‐E8 (−) in fresh frozen cisplatin‐sensitive (S) (n = 9) and cisplatin‐resistant (R) (n = 9) tissue samples (left panel). Cropped blots are shown for the indicated ERCC1 isoforms or GAPDH. For uncropped blots, see Supporting Information. The bar graph shows the spliced‐out ratio (SOR) of ERCC Exon 8 (right panel). SOR: ERCC1‐E8 (−)/ERCC1‐E8 (+). Additional data can be seen in Figure S13b (Supporting Information). h) A schematic representation illustrating the molecular mechanism by which m6A‐modified SNRPA, which regulates the AS of ERCC1, contributes to the development of cisplatin resistance. All data are presented as the mean ± SD (n ≥ 3). The p values in panels (e) were calculated by Student's t‐test. The p values in panels (a), (b) and (d) were calculated by Chi‐square test. **p < 0.01; ***p < 0.001.
3
Discussion
The precise role and underlying mechanisms of AS in conferring resistance to cancer treatments, particularly cisplatin chemotherapy in LUAD, remain elusive. Our study demonstrated that SNRPA is upregulated in cisplatin‐resistant LUAD cells, which augments cellular chemoresistance to cisplatin by regulating the splicing of exon 8 of the ERCC1 gene. Specifically, the upregulation of ELAVL1 promotes reading of m6A methylation and increases SNRPA mRNA stability and expression. This increased SNRPA level then promotes the expression of ERCC1‐E8(+), which includes exon 8 and is responsible for coding the XPF protein‐binding domain. The resultant upsurge in the production of the ERCC1‐XPF complex leads to enhanced DNA repair and a subsequent enhancement of cisplatin resistance (Figure 8h).
In this study, SNRPA was identified for the first time as a factor related to sensitivity to platinum‐based drugs. The rapid advancement of next‐generation high‐throughput RNA sequencing technologies has resulted in the production of tremendous amounts of high‐dimensional “omics” data.[
39
] Owing to the vast and reliable data‐sharing efforts of research communities, such as those by TCGA, a wealth of diverse “omics” data, characterized by an unprecedented level of detail from adequately large cohorts, are accessible.[
40
] Survival analyses were conducted on both the treated and untreated groups using these public datasets to explore the associations between tumor‐related factors and therapeutic sensitivity to radiotherapy and chemotherapy.[
41
,
42
]
SNRPA (also known as U1A) is a core component of the U1 snRNP, which is one of the five essential snRNPs of the spliceosome.[
43
] The U1 snRNP facilitates the recognition and binding of the 5′ splice site of pre‐mRNA, a critical step in the splicing reaction.[
44
] The SNRPA protein is characterized by its RNA recognition motif (RRM), which is instrumental in mediating interactions with RNA substrates.[
45
] Given its central role in mRNA splicing, SNRPA has been implicated in numerous diseases, including cancer.[
46
,
47
] Elevated expression of SNRPA correlates with an unfavorable prognostic outcome in patients with LUAD,[
48
] hepatocellular carcinoma,[
49
] and cervical cancer.[
50
] SNRPA augments neoplastic cell proliferation in gastric carcinoma by modulating the expression of nerve growth factor.[
51
] SNRPA promotes the proliferation and migration while inhibiting the apoptosis of hepatocellular carcinoma cells.[
49
] The association of SNRPA with microvascular invasion is implicated in the promotion of metastatic progression in hepatocellular carcinoma via the activation of the NOTCH1/Snail signaling cascade.[
52
] In this study, we observed that SNRPA has no effect on the proliferation and apoptosis of LUAD cells without cisplatin. This discrepancy, unlike what is seen in gastric carcinoma and hepatocellular carcinoma, might be due to the unique molecular and genetic landscape of LUAD cells. LUAD is characterized by distinct oncogenic mutations (such as EGFR mutations) that co‐occur with many tumor suppressor gene alterations, which could alter the way SNRPA interacts with cellular machinery.[
53
] Additionally, the tumor microenvironment in LUAD, with its specific extracellular matrix components and signaling molecules, might also contribute to this differential regulation.[
54
]
In this study, SNRPA was first identified as a regulator of the ERCC1 exon 8 skipping event. The SNRPA protein, comprising 282 amino acids with an approximate molecular mass of 34 kilodaltons, is characterized by the presence of two RNA recognition motifs (RRM).[
55
] Indeed, SNRPA is capable of interacting with the loop region of the SL2 sequence AUUGCAC within the U1 RNA, facilitated by its N‐terminal RNA recognition motif.[
56
] SNRPA possesses the capacity to associate with the G‐quadruplex structure within the 5′ untranslated region of BAG‐1, and knockdown studies in colorectal cancer cell lines have indicated that SNRPA may exert a regulatory influence on the expression of BAG‐1.[
57
] The potential interaction between SNRPA and the ERCC1 pre‐mRNA, including determination of the specific RNA recognition motif of SNRPA, as well as the corresponding sequence within ERCC1, is interesting to be elucidated in the future.
In this study, SNRPA was shown to regulate the formation of the ERCC1 and XPF complex. SNRPA governs the alternative splicing of exon 8 within the ERCC1 gene, encoding amino acids from serine 235 to glycine 258. This region corresponds to the initial HhH motif in the ERCC1 protein, a crucial domain for binding to the XPF protein (Figure 4k).[
16
] The ERCC1‐XPF heterodimer functions as an essential endonuclease within the NER pathway, facilitating the excision of helix‐distorting DNA lesions.[
58
] ERCC1 serves primarily in damage recognition and repair complex assembly processes, whereas XPF provides the enzymatic activity required for excising the damaged DNA strand.[
59
] In addition to NER, the ERCC1‐XPF complex is involved in other DNA repair pathways, such as HR and ICL, highlighting the multifaceted role of this complex in maintaining genomic integrity.[
60
,
61
] Their coordinated actions are critical for maintaining genomic stability by ensuring the accurate and efficient repair of a broad spectrum of DNA lesions, including those induced by cisplatin.[
61
] Consistent with our findings, targeted inhibition of the structure‐specific endonuclease function of ERCC1‐XPF potentiated cisplatin resistance.[
62
,
63
] The overexpression of an exon VIII‐deficient variant of ERCC1 attenuated the NER capacity of ERCC1 and augmented the susceptibility of these cells to cisplatin in a dose‐dependent manner.[
33
]
ELAVL1, also known as HuR, is a pivotal RNA‐binding protein that modulates the posttranscriptional fate of a myriad of mRNAs bearing AU‐rich elements in untranslated regions (UTRs).[
64
] As an RNA stabilizer, ELAVL1, in conjunction with IGF2BP, which recognizes m6A modifications in transcripts, stabilizes these transcripts by preventing their degradation.[
38
] For instance, the recruitment of HuR to m6A‐modified sites is imperative for the stabilization of SOX2 messenger RNA mediated by methyltransferase‐like protein 3 (METTL3).[
65
] The m6A modification of the ZMYM1 mRNA by METTL3 augmented its stability through a mechanism dependent on ELAVL1.[
66
] In the context of cisplatin resistance, ELAVL1 is capable of interacting with the ARID1A mRNA to increase its stability, which leads to the repression of DNA double‐strand break accumulation and increased chemoresistance to cisplatin.[
67
,
68
] Our data suggest a similar mechanism by which ELAVL1 can bind to the SNRPA mRNA and increase its stability, contributing to DNA repair and cisplatin resistance. Moreover, it is fascinating to investigate the presence of other epigenetic modifications on SNRPA, including DNA methylation, histone modification, chromatin remodeling, non‐coding RNA interactions, and various types of RNA methylation, such as m1A, m5C, and m7G.
4
Conclusion
In summary, we identified SNRPA as a spliceosome‐related gene that may modulate resistance to platinum chemotherapy. Our study not only provides insights into the biological functions and mechanistic insights into the role of SNRPA in cisplatin resistance but also uncovers a novel regulatory function of ELAVL1 in modulating chemotherapy sensitivity. These findings contribute to paving the way for personalized therapy targeting alternative splicing and the m6A modification.
5
Experimental Section
Public Data Analysis and Clinical Tissue Samples
The Spliceosome database (http://spliceosomedb.ucsc.edu/) provides a synthesis of existing mass spectrometry data, revealing that 137 constituents of the spliceosome have been detected across various assembly stages of human spliceosomal complexes (Table S1, Supporting Information). Gene expression and clinical data were obtained from TCGA (https://portal.gdc.cancer.gov/) and the GEO(https://www.ncbi.nlm.nih.gov/geo/).
The screening criteria for differentially expressed genes (DEGs) were |log2FC| > 0.585 and p < 0.05. Differential expression heatmaps, survival analysis, and violin plots were generated using the R package. Independent prognostic factors were explored through Cox regression analyses and visualized in forest plots using the “survminer” R package. Additionally, the bioinformatics analysis workflow is illustrated in Figure S1 (Supporting Information).
Clinical tissue samples were collected from 100 LUAD patients who underwent surgery at Harbin Medical University Cancer Hospital.
Cell Culture and Reagents
LUAD cell lines resistant to cis‐diammine‐dichloroplatinum (II) (A549/DDP and H1299/DDP) and cisplatin‐sensitive cells (A549 and H1299) were preserved in the laboratory,[
12
,
69
] and the resistance stability of these two cisplatin‐resistant cell lines was reconfirmed at the beginning of the study. The culture conditions were as follows: RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS). Notably, A549/DDP and H1299/DDP cells were cultured with media containing 3 µM cisplatin (Biosharp, China).
The proteasome inhibitor MG132 (10 µM; HY‐13259, MedChemExpress) and the lysosome inhibitor NH4Cl (10 µM; 12125‐02‐9, Aladdin, China) were applied for 8 h in this study.
Establishment of Stable Cell Lines
Guide RNAs (sgRNAs) were designed and validated by HanBio Technology (Shanghai, China), as outlined in Table S4 (Supporting Information). The pHBLV‐U6‐gRNA‐EF1‐CAS9‐PURO plasmid was engineered for this purpose. Upon sequencing confirmation of the plasmids, the constructs were transfected into A549/DDP cells. Following a 48‐h transfection period, the cells were screened with puromycin‐containing culture media (2 µg mL−1, Solarbio, CAS 58‐58‐2). Western blot and qRT‒PCR were employed to confirm the SNRPA knockout clones.
Short hairpin RNA (shRNA) oligonucleotides, sourced from GeneChem (Shanghai, China) and listed in Table S4 (Supporting Information), were employed to downregulate SNRPA in H1299/DDP cells and ELAVL1 in both A549/DDP and H1299/DDP cells. Lentiviruses encoding SNRPA overexpression sequences (SNRPA‐Flag/SNRPA) were generated using the Ubi‐SNRPA‐3FLAG‐SV40‐puromycin vector (GeneChem, Shanghai, China) for transfection. Following transfection, these cell lines were subsequently subjected to puromycin selection (2 µg mL−1).
Transient Transfection
2 × 105 cells were seeded in six‐well plates. After 24 h of incubation to allow cell attachment, a mixture consisting of 200 µL of buffer, 4 µL of Polyplus (jetPRIME transfection reagent), and 5 µL of siRNA (RiboBio) (refer to Table S4, Supporting Information) was applied to the plates. The cells were incubated for 6 h following the instructions of the transfection reagent, after which the mixture was replaced with complete medium for further incubation.
Analysis of mRNA Expression
RNA was isolated using the Total RNA Kit I (R6834‐01; Omega Bio‐Tek). Subsequently, reverse transcription was carried out using the FastKing kit (KR118‐02; TIANGEN). qRT‒PCR was performed following the protocol provided with Talent qPCR Premix (FP209‐02, TIANGEN). A StepOne qRT‒PCR instrument (Applied Biosystems) was utilized to determine the expression levels of the target genes.
Splice variants were discriminated by electrophoresis. After the agarose gel was stained with GelRed dye (Biotium, 41003), the PCR products were added for electrophoresis. The signal was visualized with the FluorChem E system (ProteinSimple, CA, USA). The primer sequences are provided in Table S5 (Supporting Information).
Western Blot
Following cell or LUAD tissue collection, proteins were extracted by adding RIPA lysis buffer (AR0102, BOSTER) supplemented with protease inhibitors, including PMSF (ST506, Beyotime). Standard procedures were followed for gel electrophoresis and protein transfer onto polyvinylidene difluoride (PVDF) membranes. The primary antibodies used are listed in Table S6 (Supporting Information).
Resistance Assays
EdU incorporation, CCK‐8 and colony formation assays were used to assess viability, according to the previous studies.[
12
] LUAD cells were seeded in the indicated plates after trypsin digestion. Following 24 h of cell adherence, the cells were treated with 10 µM cisplatin for an additional 24 h. For EdU incorporation, subsequent experimental steps were conducted according to the instructions provided with the EdU Assay Kit (C10310‐1; RiboBio, China). Fluorescence images were captured immediately after DNA staining. For the CCK‐8 assay, cell viability was then quantified based on absorbance measurements using a kit (CK04, Dojindo, Japan). For the colony formation assay, the LUAD cells were kept in culture medium supplemented with 5 µM cisplatin for 14 days. These colonies were then stained with crystal violet and photographed.
RNA Sequencing (RNA‐seq) Analysis
An RNA‐seq transcriptome library was prepared using the Illumina Stranded mRNA Prep Ligation Kit (Illumina, USA). RNA transcriptome sequencing was employed to detect the differential expression of mRNAs between knockout (KO) and control (SNRPA‐NC) LUAD cells, with significance thresholds set at |log2FC| > 1 and a p value < 0.05. Data analysis, including alternative splice junctions, was conducted using the Majorbio Cloud Platform (Majorbio, Shanghai, China). An analysis of GO terms was performed using Apache ECharts (https://echarts.apache.org/zh/index.html).
RNA m6A Dot Blot Assay
After total RNA was extracted with TRIzol reagent, the mRNA products were denatured for 3 min (95 °C) and transferred onto Hybond‐N+ membranes (Amersham, GE Healthcare, USA). Following UV crosslinking of the membranes for 25 min, they were blocked for 1 h with 5% skim milk powder. The m6A antibody was then applied and incubated overnight at 4 °C. A luminescent working solution was subsequently added for exposure and photography. Afterward, the membrane was stained with 0.02% methylene blue (G1301, Solarbio, China).
Determining the m6A Regulators Targeting SNRPA
To identify potential m6A regulators involved in SNRPA mRNA methylation, the m6A WER Target Gene Database (RM2target, http://rm2target.canceromics.org/), the RNA Modification Base (RMBase, https://rna.sysu.edu.cn/rmbase/), and the Database of Functional Variants Involved in RNA Modifications (RMVAR, http://rmvar.renlab.org/) were utilized. The resulting dataset was analyzed for intersections using Venn diagrams. The relationships between SNRPA and m6A regulators were assessed by utilizing a bioinformatics website (http://www.bioinformatics.com.cn). Violin plots illustrating the expression of m6A regulators were generated using Hiplot (https://hiplot‐academic.com/basic/).
MeRIP
Total RNA was harvested from LUAD cells, and it subsequently utilized the riboMeRIP m6A Transcriptome Profiling Kit (C11051‐1; RiboBio, China). The m6A‐modified RNAs were then analyzed via qPCR. SRAMP (http://www.cuilab.cn/sramp) was employed to predict the MeRIP‐qPCR primers for SNRPA.
RIP
Cells were lysed on ice using RIP lysis buffer from an RNA immunoprecipitation kit (Bes5101, BersinBio, China). After centrifugation, the supernatant was collected and incubated with an anti‐IgG or m6A antibody. The immunoprecipitates were collected after the addition of magnetic beads, and the coprecipitated RNA was detected via qRT‒PCR.
RNA Pull‐Down
The experiments were conducted using an RNA pull‐down kit (Bes5102, BersinBio, China). The biotin‐labeled full‐length SNRPA mRNA was synthesized by GenePharma. Lysates from A549/DDP and H1299/DDP cells were isolated and then incubated with the biotinylated SNRPA mRNA at 4 °C. Subsequently, streptavidin‐labeled magnetic beads were added to capture the complexes, which were then analyzed by Western blot after elution.
RNA Stability Assay
The cells were treated with actinomycin D (5 µg mL−1) for 0, 3, or 6 h before being harvested and analyzed via qRT‒PCR. The data were normalized to the expression of GAPDH.
Animal Experiments
All animal experiments were approved by the Ethics Committee of Harbin Medical University (Grant No. KY2023‐75). A total of 1 × 107 tumor cells were injected subcutaneously into 4‐week‐old BALB/c mice (weighing 13–15 g). On the eighth day after injection, mice with detectable tumors (greater than or equal to 100 mm3) received cisplatin at a dosage of 5 mg kg−1 per mouse per administration. Injections were administered on days 8, 12, 16, 20, and 24, according to previous reports.[
70
,
71
] On the 28th day, in vivo animal fluorescence imaging was performed. After the mice were euthanized, the long and short diameters were determined using Vernier calipers and the tumor weights were measured.
Statistical Analysis
The statistical analyses were executed using GraphPad Prism and SPSS Statistics software. Data were presented as the mean ± SD of at least three independent experiments. Differences were tested using the Student's t‐test, one‐way ANOVA, two‐way ANOVA, Fisher's exact test and Chi‐square test, with significance indicated when p < 0.05. Correlations were determined using Pearson's correlation analysis.
Ethics
All patients in this study signed an informed consent form. This study was approved by the Ethics Committee of Harbin Medical University Cancer Hospital.
Public Data Analysis and Clinical Tissue Samples
The Spliceosome database (http://spliceosomedb.ucsc.edu/) provides a synthesis of existing mass spectrometry data, revealing that 137 constituents of the spliceosome have been detected across various assembly stages of human spliceosomal complexes (Table S1, Supporting Information). Gene expression and clinical data were obtained from TCGA (https://portal.gdc.cancer.gov/) and the GEO(https://www.ncbi.nlm.nih.gov/geo/).
The screening criteria for differentially expressed genes (DEGs) were |log2FC| > 0.585 and p < 0.05. Differential expression heatmaps, survival analysis, and violin plots were generated using the R package. Independent prognostic factors were explored through Cox regression analyses and visualized in forest plots using the “survminer” R package. Additionally, the bioinformatics analysis workflow is illustrated in Figure S1 (Supporting Information).
Clinical tissue samples were collected from 100 LUAD patients who underwent surgery at Harbin Medical University Cancer Hospital.
Cell Culture and Reagents
LUAD cell lines resistant to cis‐diammine‐dichloroplatinum (II) (A549/DDP and H1299/DDP) and cisplatin‐sensitive cells (A549 and H1299) were preserved in the laboratory,[
12
,
69
] and the resistance stability of these two cisplatin‐resistant cell lines was reconfirmed at the beginning of the study. The culture conditions were as follows: RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS). Notably, A549/DDP and H1299/DDP cells were cultured with media containing 3 µM cisplatin (Biosharp, China).
The proteasome inhibitor MG132 (10 µM; HY‐13259, MedChemExpress) and the lysosome inhibitor NH4Cl (10 µM; 12125‐02‐9, Aladdin, China) were applied for 8 h in this study.
Establishment of Stable Cell Lines
Guide RNAs (sgRNAs) were designed and validated by HanBio Technology (Shanghai, China), as outlined in Table S4 (Supporting Information). The pHBLV‐U6‐gRNA‐EF1‐CAS9‐PURO plasmid was engineered for this purpose. Upon sequencing confirmation of the plasmids, the constructs were transfected into A549/DDP cells. Following a 48‐h transfection period, the cells were screened with puromycin‐containing culture media (2 µg mL−1, Solarbio, CAS 58‐58‐2). Western blot and qRT‒PCR were employed to confirm the SNRPA knockout clones.
Short hairpin RNA (shRNA) oligonucleotides, sourced from GeneChem (Shanghai, China) and listed in Table S4 (Supporting Information), were employed to downregulate SNRPA in H1299/DDP cells and ELAVL1 in both A549/DDP and H1299/DDP cells. Lentiviruses encoding SNRPA overexpression sequences (SNRPA‐Flag/SNRPA) were generated using the Ubi‐SNRPA‐3FLAG‐SV40‐puromycin vector (GeneChem, Shanghai, China) for transfection. Following transfection, these cell lines were subsequently subjected to puromycin selection (2 µg mL−1).
Transient Transfection
2 × 105 cells were seeded in six‐well plates. After 24 h of incubation to allow cell attachment, a mixture consisting of 200 µL of buffer, 4 µL of Polyplus (jetPRIME transfection reagent), and 5 µL of siRNA (RiboBio) (refer to Table S4, Supporting Information) was applied to the plates. The cells were incubated for 6 h following the instructions of the transfection reagent, after which the mixture was replaced with complete medium for further incubation.
Analysis of mRNA Expression
RNA was isolated using the Total RNA Kit I (R6834‐01; Omega Bio‐Tek). Subsequently, reverse transcription was carried out using the FastKing kit (KR118‐02; TIANGEN). qRT‒PCR was performed following the protocol provided with Talent qPCR Premix (FP209‐02, TIANGEN). A StepOne qRT‒PCR instrument (Applied Biosystems) was utilized to determine the expression levels of the target genes.
Splice variants were discriminated by electrophoresis. After the agarose gel was stained with GelRed dye (Biotium, 41003), the PCR products were added for electrophoresis. The signal was visualized with the FluorChem E system (ProteinSimple, CA, USA). The primer sequences are provided in Table S5 (Supporting Information).
Western Blot
Following cell or LUAD tissue collection, proteins were extracted by adding RIPA lysis buffer (AR0102, BOSTER) supplemented with protease inhibitors, including PMSF (ST506, Beyotime). Standard procedures were followed for gel electrophoresis and protein transfer onto polyvinylidene difluoride (PVDF) membranes. The primary antibodies used are listed in Table S6 (Supporting Information).
Resistance Assays
EdU incorporation, CCK‐8 and colony formation assays were used to assess viability, according to the previous studies.[
12
] LUAD cells were seeded in the indicated plates after trypsin digestion. Following 24 h of cell adherence, the cells were treated with 10 µM cisplatin for an additional 24 h. For EdU incorporation, subsequent experimental steps were conducted according to the instructions provided with the EdU Assay Kit (C10310‐1; RiboBio, China). Fluorescence images were captured immediately after DNA staining. For the CCK‐8 assay, cell viability was then quantified based on absorbance measurements using a kit (CK04, Dojindo, Japan). For the colony formation assay, the LUAD cells were kept in culture medium supplemented with 5 µM cisplatin for 14 days. These colonies were then stained with crystal violet and photographed.
RNA Sequencing (RNA‐seq) Analysis
An RNA‐seq transcriptome library was prepared using the Illumina Stranded mRNA Prep Ligation Kit (Illumina, USA). RNA transcriptome sequencing was employed to detect the differential expression of mRNAs between knockout (KO) and control (SNRPA‐NC) LUAD cells, with significance thresholds set at |log2FC| > 1 and a p value < 0.05. Data analysis, including alternative splice junctions, was conducted using the Majorbio Cloud Platform (Majorbio, Shanghai, China). An analysis of GO terms was performed using Apache ECharts (https://echarts.apache.org/zh/index.html).
RNA m6A Dot Blot Assay
After total RNA was extracted with TRIzol reagent, the mRNA products were denatured for 3 min (95 °C) and transferred onto Hybond‐N+ membranes (Amersham, GE Healthcare, USA). Following UV crosslinking of the membranes for 25 min, they were blocked for 1 h with 5% skim milk powder. The m6A antibody was then applied and incubated overnight at 4 °C. A luminescent working solution was subsequently added for exposure and photography. Afterward, the membrane was stained with 0.02% methylene blue (G1301, Solarbio, China).
Determining the m6A Regulators Targeting SNRPA
To identify potential m6A regulators involved in SNRPA mRNA methylation, the m6A WER Target Gene Database (RM2target, http://rm2target.canceromics.org/), the RNA Modification Base (RMBase, https://rna.sysu.edu.cn/rmbase/), and the Database of Functional Variants Involved in RNA Modifications (RMVAR, http://rmvar.renlab.org/) were utilized. The resulting dataset was analyzed for intersections using Venn diagrams. The relationships between SNRPA and m6A regulators were assessed by utilizing a bioinformatics website (http://www.bioinformatics.com.cn). Violin plots illustrating the expression of m6A regulators were generated using Hiplot (https://hiplot‐academic.com/basic/).
MeRIP
Total RNA was harvested from LUAD cells, and it subsequently utilized the riboMeRIP m6A Transcriptome Profiling Kit (C11051‐1; RiboBio, China). The m6A‐modified RNAs were then analyzed via qPCR. SRAMP (http://www.cuilab.cn/sramp) was employed to predict the MeRIP‐qPCR primers for SNRPA.
RIP
Cells were lysed on ice using RIP lysis buffer from an RNA immunoprecipitation kit (Bes5101, BersinBio, China). After centrifugation, the supernatant was collected and incubated with an anti‐IgG or m6A antibody. The immunoprecipitates were collected after the addition of magnetic beads, and the coprecipitated RNA was detected via qRT‒PCR.
RNA Pull‐Down
The experiments were conducted using an RNA pull‐down kit (Bes5102, BersinBio, China). The biotin‐labeled full‐length SNRPA mRNA was synthesized by GenePharma. Lysates from A549/DDP and H1299/DDP cells were isolated and then incubated with the biotinylated SNRPA mRNA at 4 °C. Subsequently, streptavidin‐labeled magnetic beads were added to capture the complexes, which were then analyzed by Western blot after elution.
RNA Stability Assay
The cells were treated with actinomycin D (5 µg mL−1) for 0, 3, or 6 h before being harvested and analyzed via qRT‒PCR. The data were normalized to the expression of GAPDH.
Animal Experiments
All animal experiments were approved by the Ethics Committee of Harbin Medical University (Grant No. KY2023‐75). A total of 1 × 107 tumor cells were injected subcutaneously into 4‐week‐old BALB/c mice (weighing 13–15 g). On the eighth day after injection, mice with detectable tumors (greater than or equal to 100 mm3) received cisplatin at a dosage of 5 mg kg−1 per mouse per administration. Injections were administered on days 8, 12, 16, 20, and 24, according to previous reports.[
70
,
71
] On the 28th day, in vivo animal fluorescence imaging was performed. After the mice were euthanized, the long and short diameters were determined using Vernier calipers and the tumor weights were measured.
Statistical Analysis
The statistical analyses were executed using GraphPad Prism and SPSS Statistics software. Data were presented as the mean ± SD of at least three independent experiments. Differences were tested using the Student's t‐test, one‐way ANOVA, two‐way ANOVA, Fisher's exact test and Chi‐square test, with significance indicated when p < 0.05. Correlations were determined using Pearson's correlation analysis.
Ethics
All patients in this study signed an informed consent form. This study was approved by the Ethics Committee of Harbin Medical University Cancer Hospital.
Conflict of Interest
The authors declare no conflict of interest.
Author Contributions
W.F., J.H., and F.T. contributed equally to this work. W.F., L.C., and Y.X. designed the study. W.F., J.H., and F.T. carried development of methodology. X.H., K.Z., Y.Z., X.L., X.W., and X.W. supported analysis and interpretation of data. W.F. conducted experiments. W.F., J.H., F.T., L.C., and Y.X. wrote, reviewed, and edited the manuscript. All authors read and approved the final manuscript.
Supporting information
Supporting Information
Supplementary Tables |
Title: Associations Between Acute COVID-19 Symptom Profiles and Long COVID Prevalence: Population-Based Cross-Sectional Study | Body:
Introduction
Although SARS-CoV-2 will continue to circulate for the foreseeable future, we are now entering a new phase focused on the resulting health and social impacts of the COVID-19 pandemic. COVID-19 illness was devasting for many, resulting in over 1.1 million deaths in the United States alone [1]. Additionally, many COVID-19 survivors have not returned to their usual state of health for months or years after their COVID-19 illness. This prolonged period of recovery, commonly known as long COVID (also as post–COVID-19 condition), may affect 5% of the US adult population [2] with important implications for the health care system, workforce, and society more broadly.
There continues to be considerable uncertainty regarding long COVID, including its definition, risk factors, and mechanistic pathways. Long COVID is broadly defined as persistent new or worsening symptoms lasting for a minimum of 4 weeks (per the Centers for Disease Control and Prevention [3]) or 3 months (per the World Health Organization [4]) after a SARS-CoV-2 infection. Although there is not yet a comprehensive understanding of risk factors, a growing body of evidence reports higher long COVID prevalence among females [5-10] and individuals who had severe acute COVID-19 illness [561112].
Acute illness course may be indicative of mechanistic pathways that operate in the development of long COVID. Relatedly, specific acute symptoms may be an important and understudied predictor of long COVID. While several studies have explored associations between acute symptoms [13-15] and long COVID prevalence, there are over 200 documented symptoms of long COVID [16] across multiple organ systems [1718] complicating the investigatory process. Examining the clustering of acute symptoms, however, may provide more tangible information on the profiles of acute illness associated with long COVID.
We are not aware of any studies examining associations between acute symptoms and long COVID using a population-based sample. Therefore, our objectives are to examine (1) how acute COVID-19 symptoms cluster together; (2) sociodemographic and clinical predictors of symptom clusters; and (3) associations between symptom clusters and long COVID prevalence using a population-based probability survey of adults with polymerase chain reaction (PCR)–confirmed SARS-CoV-2. Findings can inform our understanding of how acute symptom profiles may predispose individuals to long COVID.
Methods
Study Population
Our data came from the Michigan COVID-19 Recovery Surveillance Study (MI CReSS), a population-based study of adults in Michigan with a PCR-confirmed SARS-CoV-2 infection in the Michigan Disease Surveillance System. We drew 14 sequential stratified samples of 1000 adults with COVID-19 onset between June 1, 2020, and May 31, 2022, from 13 geographic regions (6 public health preparedness regions [19], 6 counties in southeast Michigan, and Detroit). A base sample of 50 was drawn from each region, with the rest of the sample drawn proportionally to case numbers in each region during each time frame.
Eligible adults were alive at the time of sampling, not institutionalized, and had a valid phone number and zip code or county information (n=1,668,938). Of the 14,584 adults selected for the sample, 4628 provided consent and completed our survey over the phone with an interviewer in English, Spanish, or Arabic or online in English. All interviewers completed training on interviewing, the responsible conduct of research, and human subject protections prior to conducting interviews.
Of all eligible individuals in our sample, 32.2% completed the survey (American Association for Public Opinion Research response rate #6) [20]. Respondents completed the survey a median of 137 days (IQR 105‐176 days) after their COVID-19 onset date. The final sample of respondents was weighted to match the age and sex distribution within each region and the total sampling frame, simultaneously accounting for unequal probabilities of selection and nonresponse.
We excluded survey responses collected by proxy for reasons other than language translation or hearing assistance (n=19) as well as 21 phone interviews for which interviewers lacked confidence about data validity. Additionally, although there are some reports of long COVID among individuals with asymptomatic acute infection [13], we restricted our sample to individuals with acute symptoms since only 2 asymptomatic individuals out of 189 met our criteria for long COVID.
Acute Symptom Clusters
The MI CReSS survey collected information on 27 COVID-19 symptoms. For each symptom, respondents were asked “During your illness, did you ever experience [symptom]?” If yes, respondents were asked “Did you experience this symptom during the first two weeks of your illness?” We considered an acute symptom present if the respondent experienced it in the first 2 weeks of their illness [21].
Several symptoms were collapsed into single indicators, including fever (feeling feverish or having a fever of 100.4 °F [38 °C] or above); chills (chills or repeated shaking with chills); nasal congestion (nasal congestion or runny nose); gastrointestinal symptoms (nausea or vomiting, abdominal pain, or diarrhea); and weakness (muscle weakness or general weakness). We excluded a single symptom, trouble sleeping, due to its lack of specificity. The resulting 20 symptoms were as follows: fatigue; fever; chills; muscle aches; weakness; joint pain; shortness of breath; cough; nasal congestion; sore throat; gastrointestinal; loss of appetite; headache; lightheaded or dizzy; brain fog, memory loss, or disorientation; loss of sense of smell or taste; chest pain or tightness; heart rate or heart rhythm issues; hair loss; and rash or skin discoloration. For collapsed symptoms, we coded an acute symptom present if a respondent reported at least 1 contributing symptom in the first 2 weeks of their illness. We coded an acute symptom as absent if a respondent never experienced it, did not experience it in the first 2 weeks, or did not know whether they experienced it during their illness.
Long COVID Outcome
We defined long COVID as new or worsening symptoms lasting at least 90 days post COVID-19 onset to align with the 3-month threshold recommended by the World Health Organization [4]. We factored the time between COVID onset and the survey date into our definition since it varied across participants. Some of our respondents had less than 90 days between their COVID-19 onset and survey date. We used 2 methods (described below) to address this matter. All respondents were asked if they had recovered to their usual state of health at the time of the survey. We considered long COVID present if respondents had not recovered at least 90 days after COVID-19 onset. For respondents who had not recovered and were surveyed less than 90 days after their COVID onset, we set the long COVID variable to missing (n=56), since it is unclear what proportion of these individuals with persistent symptoms would recover by 90 days. The group identified as having long COVID remained constant in our analyses, as all of them, by definition, had 90+ days elapse between COVID-19 onset and the survey date. Alternatively, we used 2 approaches to define the group without long COVID. For the main analysis, we considered long COVID absent if respondents reported a recovery period of 90 days or less, including respondents who reported recovery who were surveyed less than 90 days after their COVID onset. We ran a sensitivity analysis excluding all individuals with less than 90 days between their COVID onset and survey date, regardless of their self-reported recovery status (n=610), to test the impact of these coding decisions.
Covariates
We included sociodemographic and clinical factors as potential confounders, including sex at birth (male and female); age group (18‐34, 35‐44, 45‐54, 55‐64, and 65+ years); race and ethnicity (Hispanic, Non-Hispanic White, Non-Hispanic Black, another race or ethnicity); annual household income (<US $35,000, US $35,000‐74,999, and US $75,000+); and BMI (underweight/healthy weight [BMI<25], overweight [BMI 25 to <30], moderately obese [BMI 30 to <35], and severely obese [BMI 35+]). We also included a binary indicator for the presence of any of the following pre-existing diagnosed conditions: chronic obstructive pulmonary disease, asthma, diabetes, heart disease, hypertension, liver disease, kidney disease, cerebrovascular disease, cancer, immunosuppressive conditions, autoimmune conditions, or a physical disability. We coded each pre-existing condition as absent if the respondent did not know if they had ever been diagnosed with the condition. We also coded the combined pre-existing condition indicator as “none” if a respondent had missing data for 1 condition but reported no previous diagnoses for the remaining 11 conditions. We included an additional indicator for a pre-existing diagnosed psychological condition separately because prior research has found an association between a pre-existing psychological condition and long COVID [71222].
Missing Data
Data missingness was generally minimal. However, income was missing for 10.9% of respondents. To account for this, we imputed income using the hot deck imputation method under the missing at random assumption [23]. Additionally, to account for missingness in race and ethnicity (4.3%) and BMI (2%), we included an “unknown” category for each variable. The remaining missingness (4% for long COVID outcome; ≤1% for additional covariates) was handled using listwise deletion.
Statistical Analysis
We used latent class analysis (LCA) to identify acute symptom clusters among symptomatic participants, accounting for the complex sampling design. Using the 3-step method [2425], we (1) estimated an unconditional LCA model for acute symptom clusters, (2) assigned respondents to classes using modal posterior probabilities and calculated classification uncertainties (ie, measurement errors), and (3) explored predictors of latent class membership while accounting for these measurement errors using multinomial logistic regression. To determine the adequate number of classes, we applied unconditional LCA iteratively, beginning with a 2-class model, and sequentially increased the number of latent classes while examining the fit statistics. We evaluated goodness of fit indices by considering the Bayesian information criterion, Vuong-Lo-Mendell-Rubin likelihood ratio test, and adjusted Vuong-Lo-Mendell-Rubin likelihood ratio test. All LCA analyses were conducted using MPLUS (version 8.6) [26] and used the full information maximum likelihood method to account for missing data in latent class indicator variables.
Finally, we examined associations between the acute symptom clusters and long COVID prevalence using modified Poisson regression, adjusting for the sociodemographic and clinical factors enumerated under covariates. Poisson regression analyses accounted for the complex survey design and were performed using Stata SE (version 17.0; StataCorp) [27].
Ethical Considerations
The MI CReSS study was deemed public health surveillance by the University of Michigan institutional review board. Respondents were mailed informed consent documents and subsequently provided consent either verbally over the phone or via the web-based survey tool.
This study, a secondary analysis of deidentified MI CReSS data, was deemed exempt by the University of Michigan institutional review board, which did not require additional consent from study subjects.
Study Population
Our data came from the Michigan COVID-19 Recovery Surveillance Study (MI CReSS), a population-based study of adults in Michigan with a PCR-confirmed SARS-CoV-2 infection in the Michigan Disease Surveillance System. We drew 14 sequential stratified samples of 1000 adults with COVID-19 onset between June 1, 2020, and May 31, 2022, from 13 geographic regions (6 public health preparedness regions [19], 6 counties in southeast Michigan, and Detroit). A base sample of 50 was drawn from each region, with the rest of the sample drawn proportionally to case numbers in each region during each time frame.
Eligible adults were alive at the time of sampling, not institutionalized, and had a valid phone number and zip code or county information (n=1,668,938). Of the 14,584 adults selected for the sample, 4628 provided consent and completed our survey over the phone with an interviewer in English, Spanish, or Arabic or online in English. All interviewers completed training on interviewing, the responsible conduct of research, and human subject protections prior to conducting interviews.
Of all eligible individuals in our sample, 32.2% completed the survey (American Association for Public Opinion Research response rate #6) [20]. Respondents completed the survey a median of 137 days (IQR 105‐176 days) after their COVID-19 onset date. The final sample of respondents was weighted to match the age and sex distribution within each region and the total sampling frame, simultaneously accounting for unequal probabilities of selection and nonresponse.
We excluded survey responses collected by proxy for reasons other than language translation or hearing assistance (n=19) as well as 21 phone interviews for which interviewers lacked confidence about data validity. Additionally, although there are some reports of long COVID among individuals with asymptomatic acute infection [13], we restricted our sample to individuals with acute symptoms since only 2 asymptomatic individuals out of 189 met our criteria for long COVID.
Acute Symptom Clusters
The MI CReSS survey collected information on 27 COVID-19 symptoms. For each symptom, respondents were asked “During your illness, did you ever experience [symptom]?” If yes, respondents were asked “Did you experience this symptom during the first two weeks of your illness?” We considered an acute symptom present if the respondent experienced it in the first 2 weeks of their illness [21].
Several symptoms were collapsed into single indicators, including fever (feeling feverish or having a fever of 100.4 °F [38 °C] or above); chills (chills or repeated shaking with chills); nasal congestion (nasal congestion or runny nose); gastrointestinal symptoms (nausea or vomiting, abdominal pain, or diarrhea); and weakness (muscle weakness or general weakness). We excluded a single symptom, trouble sleeping, due to its lack of specificity. The resulting 20 symptoms were as follows: fatigue; fever; chills; muscle aches; weakness; joint pain; shortness of breath; cough; nasal congestion; sore throat; gastrointestinal; loss of appetite; headache; lightheaded or dizzy; brain fog, memory loss, or disorientation; loss of sense of smell or taste; chest pain or tightness; heart rate or heart rhythm issues; hair loss; and rash or skin discoloration. For collapsed symptoms, we coded an acute symptom present if a respondent reported at least 1 contributing symptom in the first 2 weeks of their illness. We coded an acute symptom as absent if a respondent never experienced it, did not experience it in the first 2 weeks, or did not know whether they experienced it during their illness.
Long COVID Outcome
We defined long COVID as new or worsening symptoms lasting at least 90 days post COVID-19 onset to align with the 3-month threshold recommended by the World Health Organization [4]. We factored the time between COVID onset and the survey date into our definition since it varied across participants. Some of our respondents had less than 90 days between their COVID-19 onset and survey date. We used 2 methods (described below) to address this matter. All respondents were asked if they had recovered to their usual state of health at the time of the survey. We considered long COVID present if respondents had not recovered at least 90 days after COVID-19 onset. For respondents who had not recovered and were surveyed less than 90 days after their COVID onset, we set the long COVID variable to missing (n=56), since it is unclear what proportion of these individuals with persistent symptoms would recover by 90 days. The group identified as having long COVID remained constant in our analyses, as all of them, by definition, had 90+ days elapse between COVID-19 onset and the survey date. Alternatively, we used 2 approaches to define the group without long COVID. For the main analysis, we considered long COVID absent if respondents reported a recovery period of 90 days or less, including respondents who reported recovery who were surveyed less than 90 days after their COVID onset. We ran a sensitivity analysis excluding all individuals with less than 90 days between their COVID onset and survey date, regardless of their self-reported recovery status (n=610), to test the impact of these coding decisions.
Covariates
We included sociodemographic and clinical factors as potential confounders, including sex at birth (male and female); age group (18‐34, 35‐44, 45‐54, 55‐64, and 65+ years); race and ethnicity (Hispanic, Non-Hispanic White, Non-Hispanic Black, another race or ethnicity); annual household income (<US $35,000, US $35,000‐74,999, and US $75,000+); and BMI (underweight/healthy weight [BMI<25], overweight [BMI 25 to <30], moderately obese [BMI 30 to <35], and severely obese [BMI 35+]). We also included a binary indicator for the presence of any of the following pre-existing diagnosed conditions: chronic obstructive pulmonary disease, asthma, diabetes, heart disease, hypertension, liver disease, kidney disease, cerebrovascular disease, cancer, immunosuppressive conditions, autoimmune conditions, or a physical disability. We coded each pre-existing condition as absent if the respondent did not know if they had ever been diagnosed with the condition. We also coded the combined pre-existing condition indicator as “none” if a respondent had missing data for 1 condition but reported no previous diagnoses for the remaining 11 conditions. We included an additional indicator for a pre-existing diagnosed psychological condition separately because prior research has found an association between a pre-existing psychological condition and long COVID [71222].
Missing Data
Data missingness was generally minimal. However, income was missing for 10.9% of respondents. To account for this, we imputed income using the hot deck imputation method under the missing at random assumption [23]. Additionally, to account for missingness in race and ethnicity (4.3%) and BMI (2%), we included an “unknown” category for each variable. The remaining missingness (4% for long COVID outcome; ≤1% for additional covariates) was handled using listwise deletion.
Statistical Analysis
We used latent class analysis (LCA) to identify acute symptom clusters among symptomatic participants, accounting for the complex sampling design. Using the 3-step method [2425], we (1) estimated an unconditional LCA model for acute symptom clusters, (2) assigned respondents to classes using modal posterior probabilities and calculated classification uncertainties (ie, measurement errors), and (3) explored predictors of latent class membership while accounting for these measurement errors using multinomial logistic regression. To determine the adequate number of classes, we applied unconditional LCA iteratively, beginning with a 2-class model, and sequentially increased the number of latent classes while examining the fit statistics. We evaluated goodness of fit indices by considering the Bayesian information criterion, Vuong-Lo-Mendell-Rubin likelihood ratio test, and adjusted Vuong-Lo-Mendell-Rubin likelihood ratio test. All LCA analyses were conducted using MPLUS (version 8.6) [26] and used the full information maximum likelihood method to account for missing data in latent class indicator variables.
Finally, we examined associations between the acute symptom clusters and long COVID prevalence using modified Poisson regression, adjusting for the sociodemographic and clinical factors enumerated under covariates. Poisson regression analyses accounted for the complex survey design and were performed using Stata SE (version 17.0; StataCorp) [27].
Ethical Considerations
The MI CReSS study was deemed public health surveillance by the University of Michigan institutional review board. Respondents were mailed informed consent documents and subsequently provided consent either verbally over the phone or via the web-based survey tool.
This study, a secondary analysis of deidentified MI CReSS data, was deemed exempt by the University of Michigan institutional review board, which did not require additional consent from study subjects.
Results
Study Population
The analytic sample (n=4169) was predominantly female (n=2489, 54.6%) and non-Hispanic White (n=3059, 70.7%), with one-third (n=1384, 36.6%) between the ages of 18‐34 years (Table 1). Over 70% (n=2921) were overweight or obese and almost half (n=2091, 48.3%) had a pre-existing physical condition. Nearly 1 in 6 (n=693, 15.9%) had long COVID, defined as new or worsening symptoms lasting at least 90 days post SARS-CoV-2 infection.
Table 1.
Description of the analytic sample (n=4169) from the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022).
Characteristics
Weighted percentage (%)
N
Sex
Male
45.4
1680
Female
54.6
2489
Age group(years)
18‐34
36.6
1384
35‐44
18.5
741
45‐54
16.5
699
55‐64
16.0
711
65+
12.4
634
Raceandethnicity
Hispanic
6.6
243
Non-Hispanic Black
7.7
292
Another race or ethnicity
11.2
425
Non-Hispanic White
70.7
3059
Unknown
3.7
150
Annual household income(US $)
<35,000
28.7
1203
35,000-74,999
30.4
1272
75,000
40.9
1694
Current smoking prior to illness
9.3
402
BMI
Underweight/normal weight (BMI<25)
27.8
1194
Overweight (BMI 25 to <30)
31.4
1293
Moderately obese (BMI 30 to <35)
20.2
833
Severely obese (BMI 35+)
19.2
795
Unknown
1.4
54
Any pre-existing physical condition (excluding psychological)
48.3
2091
A pre-existing psychological condition
13.6
576
Long COVID
15.9
693
Phase
June 1, 2020, to September 30, 2020
13.2
535
October 1, 2020, to February 28, 2021
22.7
924
March 1, 2021, to May 31, 2021
13.4
534
June 1, 2021, to September 30, 2021
14.0
561
October 1, 2021, to February 28, 2022
19.6
795
March 1, 2022, to May 31, 2022
17.1
820
Survey mode
Phone
32.5
1336
Web-based
67.5
2833
Acute Symptom Clusters
We identified 6 mutually exclusive acute COVID-19 symptom clusters based on the Vuong-Lo-Mendell-Rubin likelihood ratio and adjusted Vuong-Lo-Mendell-Rubin likelihood ratio tests and the meaningfulness of the latent classes (Figure 1). While models with 7 classes yielded the lowest Bayesian information criterion values, the goodness-of-fit tests showed no significant difference in fit between models with 6 and 7 classes. We thus selected 6 classes based on interpretability.
Given the high probability of fatigue in 5 of the 6 classes, we did not consider it a distinguishing symptom in classes with a high probability of other symptoms. Cluster 1, representing 14.5% of the sample, was multi-symptomatic, with a high probability of flu-like (eg, fever, chills, muscle aches), pulmonary (eg, shortness of breath), gastrointestinal (eg, nausea, diarrhea), and neurological symptoms (eg, headache, dizziness). Cluster 2 (10% of the sample) had a predominance of shortness of breath. Cluster 3 (24.7% of the sample) was characterized by flu-like symptoms. Cluster 4 (10.8% of the sample) had a predominance of fatigue, without a high probability of additional symptoms. Cluster 5 (23.6% of the sample) was characterized by fever, with a lower probability of other flu-like symptoms than clusters 1 or 2. Cluster 6 (16.4% of the sample) had a low probability of all symptoms except nasal congestion.
The unadjusted (Table S1 in Multimedia Appendix 1) and adjusted results (Table 2) from the multinomial logistic regression models further characterize the acute symptom clusters. Compared with the nasal congestion cluster, females had greater odds of membership in the multi-symptomatic (adjusted odds ratio [aOR] 2.7, 95% CI 2.0‐3.8), flu-like (aOR 2.1, 95% CI 1.6‐2.8), and predominance of fatigue (aOR 1.8, 95% CI 1.2‐2.6) symptom clusters than males. Adults aged 65 years and older had lower odds of membership in the multi-symptomatic, predominance of shortness of breath, flu-like, and fever symptom clusters than adults aged 18‐34 years. However, adults aged 35‐54 years had greater odds of predominance of fatigue than adults aged 18‐34 years. Compared with Non-Hispanic White adults, adults identifying as Hispanic (aOR 2.0, 95% CI 1.1‐3.5) or another race or ethnicity (aOR 2.0, 95% CI 1.2‐3.2) had greater odds of membership in the multi-symptomatic cluster versus the nasal congestion cluster.
Figure 1.
Acute COVID-19 symptom clusters in the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022). Values for symptoms represent the probability of individuals in each cluster reporting each symptom. Values with a probability of 0.5 or greater have been highlighted in blue to aid visual interpretation of findings, with darker shades of blue reflecting higher probability values.
Table 2.
Adjusted multinomial logistic regression examining odds of belonging to specific acute COVID-19 symptom clusters (referent category: nasal congestion; n=4169) in the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022). All models mutually adjusted for variables listed in the table. Italicized values denote statistical significance at P<.05.
Multi-symptomatic
Predominance of shortness of breath
Flu-like
Predominance of fatigue
Fever
95% CI
P value
95% CI
P value
95% CI
P value
95% CI
P value
95% CI
P value
aORa
LBb
UBc
aOR
LB
UB
aOR
LB
UB
aOR
LB
UB
aOR
LB
UB
Sex
Male
1.0
—d
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Female
2.7
2.0
3.8
<.001
1.1
0.7
1.6
.64
2.1
1.6
2.8
<.001
1.8
1.2
2.6
.002
0.8
0.6
1.1
.23
Age group(years)
18‐34
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
35‐44
1.1
0.7
1.7
.63
0.9
0.5
1.5
.60
1.1
0.7
1.6
.69
2.5
1.4
4.4
.001
1.0
0.6
1.5
.95
45‐54
1.2
0.8
1.9
.43
0.9
0.5
1.6
.74
1.1
0.7
1.7
.64
1.9
1.0
3.6
.049
1.4
0.9
2.2
.19
55‐64
0.6
0.4
1.0
.07
0.7
0.4
1.3
.27
0.7
0.5
1.1
.09
1.7
1.0
3.1
.07
1.0
0.6
1.6
.99
65+
0.3
0.2
0.5
<.001
0.3
0.2
0.6
.001
0.3
0.2
0.5
<.001
1.6
0.9
2.8
.12
0.5
0.3
0.8
.003
Raceandethnicity
Hispanic
2.0
1.1
3.5
.019
0.7
0.3
1.6
.35
0.7
0.4
1.3
.21
1.0
0.5
2.0
.94
0.9
0.5
1.7
.68
Non-Hispanic Black
0.9
0.5
1.6
.76
0.9
0.5
1.8
.86
0.8
0.5
1.3
.38
0.5
0.2
1.1
.07
0.6
0.3
1.2
.14
Another race or ethnicity
2.0
1.2
3.2
.007
1.3
0.7
2.5
.35
1.1
0.7
1.8
.69
0.8
0.4
1.7
.59
1.2
0.7
2.0
.52
Non-Hispanic White
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Unknown
1.7
0.7
4.0
.20
1.3
0.5
3.4
.62
1.0
0.5
2.1
.98
1.1
0.4
3.0
.84
0.9
0.4
2.1
.82
Annual income(US $)
<35,000
2.2
0.7
4.0
<.001
2.5
1.5
4.1
<.001
1.3
0.9
1.9
.11
1.6
1.0
2.6
.046
1.3
0.9
1.9
.23
35,000-74,999
1.5
1.0
2.2
.03
1.5
0.9
2.5
.08
1.1
0.8
1.5
.62
1.3
0.8
2.0
.25
1.0
0.7
1.4
.83
75,000
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Smoking prior to illness
1.7
1.0
2.8
.04
0.9
0.4
1.9
.82
1.4
0.8
2.2
.23
1.3
0.7
2.5
.43
1.0
0.6
1.8
.97
BMI
Underweight/normal weight
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Overweight
1.2
0.8
1.9
.30
1.5
0.9
2.7
.12
1.6
1.1
2.3
.009
1.2
0.7
1.9
.46
1.2
0.8
1.8
.28
Moderately obese
1.6
1.0
2.5
.05
2.0
1.1
3.6
.02
1.5
1.0
2.3
.04
1.4
0.8
2.3
.21
1.0
0.7
1.6
.96
Severely obese
1.6
1.0
2.5
.05
1.6
0.9
2.9
.10
1.2
0.8
1.9
.33
1.1
0.6
1.8
.86
1.0
0.6
1.6
.93
Unknown
1.1
0.3
4.6
.87
2.0
0.4
8.9
.38
1.2
0.3
4.8
.81
1.4
0.3
6.5
.71
1.6
0.4
5.9
.51
Pre-existing physical condition
e
2.3
1.6
3.2
<.001
2.1
1.4
3.2
<.001
1.6
1.2
2.2
.003
1.6
1.1
2.4
.02
1.1
0.8
1.5
.77
Pre-existing psychological condition
3.3
2.1
5.2
<.001
1.6
0.9
3.0
.12
1.9
1.2
2.9
.007
1.4
0.7
2.6
.31
0.9
0.5
1.6
.69
Survey phase
June 1, 2020, to September 30, 2020
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
October 1, 2020, to February 28, 2021
1.1
0.7
1.9
.68
0.5
0.3
1.0
.04
1.5
0.9
2.3
.12
0.9
0.5
1.6
.64
0.9
0.5
1.5
.63
March 1, 2021, to May 31, 2021
1.7
0.9
3.0
.09
0.9
0.5
1.7
.66
1.5
0.9
2.7
.13
0.8
0.4
1.5
.44
1.0
0.6
1.9
.93
June 1, 2021, to September 30, 2021
1.2
0.6
2.2
.61
0.7
0.4
1.4
.36
1.5
0.9
2.5
.16
1.0
0.5
2.0
.94
1.4
0.8
2.5
.21
October 1, 2021, to February 28, 2022
1.5
0.9
2.5
.15
0.6
0.3
1.2
.14
1.4
0.9
2.3
.18
0.8
0.4
1.5
.52
1.2
0.7
2.1
.50
March 1, 2022, to May 31, 2022
0.8
0.4
1.5
.48
0.5
0.3
1.0
.06
1.3
0.8
2.2
.27
0.8
0.4
1.4
.39
1.6
1.0
2.7
.07
Survey mode
Phone
1.3
0.9
1.8
.14
1.0
0.7
1.6
.95
0.9
0.7
1.3
.66
1.1
0.8
1.7
.56
0.7
0.5
1.0
.07
Web-based
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
a
excluding psychological conditionsaOR: adjusted odds ratio.
b
LB: lower bound of 95% CI.
c
UB: upper bound of 95% CI.
d
Not applicable (referent value).
e
Excluding psychological conditions.
Annual household income and health status indicators, including BMI and pre-existing conditions, were strong predictors of symptom cluster membership. Compared with the nasal congestion cluster, adults with a household income less than US $35,000 (vs US $75,000+) had between 1.6‐2.5 times greater odds of membership in the multi-symptomatic, predominance of shortness of breath, and predominance of fatigue symptom clusters in adjusted models. In terms of health status, adults who were obese had greater odds of membership in the predominance of shortness of breath and flu-like symptom clusters. Additionally, having a pre-existing physical condition (vs none) was associated with greater odds of membership in the multi-symptomatic, predominance of shortness of breath, flu-like, and predominance of fatigue symptom clusters compared with the nasal congestion cluster. Lastly, adults with a pre-existing psychological condition (vs none) had greater odds of membership in the multi-symptomatic (aOR 3.3, 95% CI 2.1‐5.2) and flu-like symptom (aOR 1.9, 95% 1.2‐2.9) clusters.
Long COVID Outcome
Long COVID prevalence varied across the symptom clusters, from 39.7% in the multi-symptomatic cluster to 5.6% in the nasal congestion cluster (Figure 1). In adjusted models (Table 3), the multi-symptomatic cluster had the highest prevalence of long COVID compared with the nasal congestion cluster (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3‐8.7), followed by predominance of shortness of breath (aPR 3.7, 95% CI 2.5‐5.5), flu-like symptoms (aPR 2.8, 95% CI 1.9‐4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5‐3.3). The prevalence of long COVID was not statistically different between the clusters characterized by fever and nasal congestion (aPR 1.3, 95% CI 0.8‐1.9). Sociodemographic and health-related predictors of long COVID included female sex, older age, lower income, obesity, and pre-existing conditions. Responding via telephone (vs online) was also statistically associated with higher long COVID prevalence, likely due to associations between survey mode and respondent demographics (data not shown) and potential differences in symptoms reported based on mode.
Finally, results from the sensitivity analysis excluding all individuals with less than 90 days between their COVID onset and survey date closely aligned with the primary analysis, including the 6 acute symptom clusters identified (Table S2 in Multimedia Appendix 1) and their relationship with long COVID prevalence (Table S3 in Multimedia Appendix 1).
Table 3.
Modified Poisson regression examining prevalence of long COVID (n=4169) in the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022). Italicized values denote statistical significance at P<.05.
Unadjusted
Adjusteda
95% CI
P value
95% CI
P value
PRb
LBc
UBd
aPRe
LB
UB
Acute COVID-19 symptom clusters
Multi-symptomatic
7.1
5.0
10.2
<.001
6.1
4.3
8.7
<.001
Predominance of shortness of breath
4.0
2.7
6.0
<.001
3.7
2.5
5.5
<.001
Flu-like
2.8
2.0
4.1
<.001
2.8
1.9
4.0
<.001
Predominance of fatigue
2.6
1.7
3.9
<.001
2.2
1.5
3.3
<.001
Fever
1.2
0.8
1.8
.51
1.3
0.8
1.9
.27
Nasal congestion
1.0
—f
—
—
1.0
—
—
—
Sex
Male
1.0
—
—
—
1.0
—
—
—
Female
1.7
1.4
2.0
<.001
1.4
1.2
1.7
<.001
Age group(years)
18‐34
1.0
—
—
—
1.0
—
—
—
35‐44
1.8
1.4
2.3
<.001
1.7
1.4
2.2
<.001
45‐54
1.8
1.4
2.3
<.001
1.7
1.4
2.2
<.001
55‐64
1.9
1.5
2.4
<.001
2.1
1.7
2.6
<.001
65+
2.2
1.7
2.8
<.001
2.5
1.9
3.1
<.001
Raceandethnicity
Hispanic
1.5
1.1
1.9
.003
1.1
0.9
1.4
.44
Non-Hispanic Black
1.5
1.2
1.9
.002
1.1
0.9
1.5
.28
Another race or ethnicity
0.9
0.7
1.2
.36
0.8
0.6
1.1
.15
Non-Hispanic White
1.0
—
—
—
1.0
—
—
—
Unknown
1.0
0.6
1.5
.85
1.0
0.6
1.5
.88
Annual income(US $)
<35,000
1.7
1.4
2.1
<.001
1.3
1.0
1.5
.015
35,000-74,999
1.4
1.2
1.7
.001
1.2
1.0
1.5
.047
75,000
1.0
—
—
—
1.0
—
—
—
Smoking prior to illness
1.3
1.0
1.6
.05
1.1
0.8
1.3
.65
BMI
Underweight/normal weight
1.0
—
—
—
1.0
—
—
—
Overweight
1.3
1.0
1.6
.03
1.1
0.9
1.4
.33
Moderately obese
1.6
1.3
2.0
<.001
1.2
0.9
1.5
.16
Severely obese
2.0
1.6
2.5
<.001
1.3
1.1
1.7
.01
Unknown
1.2
0.6
2.5
.60
1.0
0.5
2.1
.94
Pre-existing physical conditiong
versusnone
1.8
1.5
2.1
<.001
1.2
1.0
1.4
.05
Pre-existing psychological conditionversusnone
1.5
1.2
1.8
<.001
1.1
0.9
1.3
.29
Survey phase
June 1, 2020, to September 30, 2020
1.0
—
—
—
1.0
—
—
—
October 1, 2020, to February 28, 2021
1.0
0.8
1.2
.78
0.9
0.7
1.1
.37
March 1, 2021, to May 31, 2021
0.9
0.7
1.1
.26
0.8
0.6
1.0
.025
June 1, 2021, to September 30, 2021
0.7
0.5
0.9
.005
0.7
0.5
0.9
.002
October 1, 2021, to February 28, 2022
0.8
0.6
1.0
.04
0.7
0.5
0.8
<.001
March 1, 2022, to May 31, 2022
0.3
0.2
0.4
<.001
0.4
0.3
0.5
<.001
Survey mode
Phone
1.6
1.4
1.9
<.001
1.3
1.1
1.5
.004
Web-based
1.0
—
—
—
1.0—
—
—
—
a
All models mutually adjusted for variables listed in table.
b
PR: prevalence ratio.excluding psychological conditions
c
LB: lower bound of 95% CI.
d
UB: upper bound of 95% CI.
e
aPR: adjusted prevalence ratio.
f
Not applicable (referent value).
g
Excluding psychological conditions.
Study Population
The analytic sample (n=4169) was predominantly female (n=2489, 54.6%) and non-Hispanic White (n=3059, 70.7%), with one-third (n=1384, 36.6%) between the ages of 18‐34 years (Table 1). Over 70% (n=2921) were overweight or obese and almost half (n=2091, 48.3%) had a pre-existing physical condition. Nearly 1 in 6 (n=693, 15.9%) had long COVID, defined as new or worsening symptoms lasting at least 90 days post SARS-CoV-2 infection.
Table 1.
Description of the analytic sample (n=4169) from the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022).
Characteristics
Weighted percentage (%)
N
Sex
Male
45.4
1680
Female
54.6
2489
Age group(years)
18‐34
36.6
1384
35‐44
18.5
741
45‐54
16.5
699
55‐64
16.0
711
65+
12.4
634
Raceandethnicity
Hispanic
6.6
243
Non-Hispanic Black
7.7
292
Another race or ethnicity
11.2
425
Non-Hispanic White
70.7
3059
Unknown
3.7
150
Annual household income(US $)
<35,000
28.7
1203
35,000-74,999
30.4
1272
75,000
40.9
1694
Current smoking prior to illness
9.3
402
BMI
Underweight/normal weight (BMI<25)
27.8
1194
Overweight (BMI 25 to <30)
31.4
1293
Moderately obese (BMI 30 to <35)
20.2
833
Severely obese (BMI 35+)
19.2
795
Unknown
1.4
54
Any pre-existing physical condition (excluding psychological)
48.3
2091
A pre-existing psychological condition
13.6
576
Long COVID
15.9
693
Phase
June 1, 2020, to September 30, 2020
13.2
535
October 1, 2020, to February 28, 2021
22.7
924
March 1, 2021, to May 31, 2021
13.4
534
June 1, 2021, to September 30, 2021
14.0
561
October 1, 2021, to February 28, 2022
19.6
795
March 1, 2022, to May 31, 2022
17.1
820
Survey mode
Phone
32.5
1336
Web-based
67.5
2833
Acute Symptom Clusters
We identified 6 mutually exclusive acute COVID-19 symptom clusters based on the Vuong-Lo-Mendell-Rubin likelihood ratio and adjusted Vuong-Lo-Mendell-Rubin likelihood ratio tests and the meaningfulness of the latent classes (Figure 1). While models with 7 classes yielded the lowest Bayesian information criterion values, the goodness-of-fit tests showed no significant difference in fit between models with 6 and 7 classes. We thus selected 6 classes based on interpretability.
Given the high probability of fatigue in 5 of the 6 classes, we did not consider it a distinguishing symptom in classes with a high probability of other symptoms. Cluster 1, representing 14.5% of the sample, was multi-symptomatic, with a high probability of flu-like (eg, fever, chills, muscle aches), pulmonary (eg, shortness of breath), gastrointestinal (eg, nausea, diarrhea), and neurological symptoms (eg, headache, dizziness). Cluster 2 (10% of the sample) had a predominance of shortness of breath. Cluster 3 (24.7% of the sample) was characterized by flu-like symptoms. Cluster 4 (10.8% of the sample) had a predominance of fatigue, without a high probability of additional symptoms. Cluster 5 (23.6% of the sample) was characterized by fever, with a lower probability of other flu-like symptoms than clusters 1 or 2. Cluster 6 (16.4% of the sample) had a low probability of all symptoms except nasal congestion.
The unadjusted (Table S1 in Multimedia Appendix 1) and adjusted results (Table 2) from the multinomial logistic regression models further characterize the acute symptom clusters. Compared with the nasal congestion cluster, females had greater odds of membership in the multi-symptomatic (adjusted odds ratio [aOR] 2.7, 95% CI 2.0‐3.8), flu-like (aOR 2.1, 95% CI 1.6‐2.8), and predominance of fatigue (aOR 1.8, 95% CI 1.2‐2.6) symptom clusters than males. Adults aged 65 years and older had lower odds of membership in the multi-symptomatic, predominance of shortness of breath, flu-like, and fever symptom clusters than adults aged 18‐34 years. However, adults aged 35‐54 years had greater odds of predominance of fatigue than adults aged 18‐34 years. Compared with Non-Hispanic White adults, adults identifying as Hispanic (aOR 2.0, 95% CI 1.1‐3.5) or another race or ethnicity (aOR 2.0, 95% CI 1.2‐3.2) had greater odds of membership in the multi-symptomatic cluster versus the nasal congestion cluster.
Figure 1.
Acute COVID-19 symptom clusters in the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022). Values for symptoms represent the probability of individuals in each cluster reporting each symptom. Values with a probability of 0.5 or greater have been highlighted in blue to aid visual interpretation of findings, with darker shades of blue reflecting higher probability values.
Table 2.
Adjusted multinomial logistic regression examining odds of belonging to specific acute COVID-19 symptom clusters (referent category: nasal congestion; n=4169) in the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022). All models mutually adjusted for variables listed in the table. Italicized values denote statistical significance at P<.05.
Multi-symptomatic
Predominance of shortness of breath
Flu-like
Predominance of fatigue
Fever
95% CI
P value
95% CI
P value
95% CI
P value
95% CI
P value
95% CI
P value
aORa
LBb
UBc
aOR
LB
UB
aOR
LB
UB
aOR
LB
UB
aOR
LB
UB
Sex
Male
1.0
—d
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Female
2.7
2.0
3.8
<.001
1.1
0.7
1.6
.64
2.1
1.6
2.8
<.001
1.8
1.2
2.6
.002
0.8
0.6
1.1
.23
Age group(years)
18‐34
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
35‐44
1.1
0.7
1.7
.63
0.9
0.5
1.5
.60
1.1
0.7
1.6
.69
2.5
1.4
4.4
.001
1.0
0.6
1.5
.95
45‐54
1.2
0.8
1.9
.43
0.9
0.5
1.6
.74
1.1
0.7
1.7
.64
1.9
1.0
3.6
.049
1.4
0.9
2.2
.19
55‐64
0.6
0.4
1.0
.07
0.7
0.4
1.3
.27
0.7
0.5
1.1
.09
1.7
1.0
3.1
.07
1.0
0.6
1.6
.99
65+
0.3
0.2
0.5
<.001
0.3
0.2
0.6
.001
0.3
0.2
0.5
<.001
1.6
0.9
2.8
.12
0.5
0.3
0.8
.003
Raceandethnicity
Hispanic
2.0
1.1
3.5
.019
0.7
0.3
1.6
.35
0.7
0.4
1.3
.21
1.0
0.5
2.0
.94
0.9
0.5
1.7
.68
Non-Hispanic Black
0.9
0.5
1.6
.76
0.9
0.5
1.8
.86
0.8
0.5
1.3
.38
0.5
0.2
1.1
.07
0.6
0.3
1.2
.14
Another race or ethnicity
2.0
1.2
3.2
.007
1.3
0.7
2.5
.35
1.1
0.7
1.8
.69
0.8
0.4
1.7
.59
1.2
0.7
2.0
.52
Non-Hispanic White
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Unknown
1.7
0.7
4.0
.20
1.3
0.5
3.4
.62
1.0
0.5
2.1
.98
1.1
0.4
3.0
.84
0.9
0.4
2.1
.82
Annual income(US $)
<35,000
2.2
0.7
4.0
<.001
2.5
1.5
4.1
<.001
1.3
0.9
1.9
.11
1.6
1.0
2.6
.046
1.3
0.9
1.9
.23
35,000-74,999
1.5
1.0
2.2
.03
1.5
0.9
2.5
.08
1.1
0.8
1.5
.62
1.3
0.8
2.0
.25
1.0
0.7
1.4
.83
75,000
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Smoking prior to illness
1.7
1.0
2.8
.04
0.9
0.4
1.9
.82
1.4
0.8
2.2
.23
1.3
0.7
2.5
.43
1.0
0.6
1.8
.97
BMI
Underweight/normal weight
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
Overweight
1.2
0.8
1.9
.30
1.5
0.9
2.7
.12
1.6
1.1
2.3
.009
1.2
0.7
1.9
.46
1.2
0.8
1.8
.28
Moderately obese
1.6
1.0
2.5
.05
2.0
1.1
3.6
.02
1.5
1.0
2.3
.04
1.4
0.8
2.3
.21
1.0
0.7
1.6
.96
Severely obese
1.6
1.0
2.5
.05
1.6
0.9
2.9
.10
1.2
0.8
1.9
.33
1.1
0.6
1.8
.86
1.0
0.6
1.6
.93
Unknown
1.1
0.3
4.6
.87
2.0
0.4
8.9
.38
1.2
0.3
4.8
.81
1.4
0.3
6.5
.71
1.6
0.4
5.9
.51
Pre-existing physical condition
e
2.3
1.6
3.2
<.001
2.1
1.4
3.2
<.001
1.6
1.2
2.2
.003
1.6
1.1
2.4
.02
1.1
0.8
1.5
.77
Pre-existing psychological condition
3.3
2.1
5.2
<.001
1.6
0.9
3.0
.12
1.9
1.2
2.9
.007
1.4
0.7
2.6
.31
0.9
0.5
1.6
.69
Survey phase
June 1, 2020, to September 30, 2020
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
October 1, 2020, to February 28, 2021
1.1
0.7
1.9
.68
0.5
0.3
1.0
.04
1.5
0.9
2.3
.12
0.9
0.5
1.6
.64
0.9
0.5
1.5
.63
March 1, 2021, to May 31, 2021
1.7
0.9
3.0
.09
0.9
0.5
1.7
.66
1.5
0.9
2.7
.13
0.8
0.4
1.5
.44
1.0
0.6
1.9
.93
June 1, 2021, to September 30, 2021
1.2
0.6
2.2
.61
0.7
0.4
1.4
.36
1.5
0.9
2.5
.16
1.0
0.5
2.0
.94
1.4
0.8
2.5
.21
October 1, 2021, to February 28, 2022
1.5
0.9
2.5
.15
0.6
0.3
1.2
.14
1.4
0.9
2.3
.18
0.8
0.4
1.5
.52
1.2
0.7
2.1
.50
March 1, 2022, to May 31, 2022
0.8
0.4
1.5
.48
0.5
0.3
1.0
.06
1.3
0.8
2.2
.27
0.8
0.4
1.4
.39
1.6
1.0
2.7
.07
Survey mode
Phone
1.3
0.9
1.8
.14
1.0
0.7
1.6
.95
0.9
0.7
1.3
.66
1.1
0.8
1.7
.56
0.7
0.5
1.0
.07
Web-based
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
1.0
—
—
—
a
excluding psychological conditionsaOR: adjusted odds ratio.
b
LB: lower bound of 95% CI.
c
UB: upper bound of 95% CI.
d
Not applicable (referent value).
e
Excluding psychological conditions.
Annual household income and health status indicators, including BMI and pre-existing conditions, were strong predictors of symptom cluster membership. Compared with the nasal congestion cluster, adults with a household income less than US $35,000 (vs US $75,000+) had between 1.6‐2.5 times greater odds of membership in the multi-symptomatic, predominance of shortness of breath, and predominance of fatigue symptom clusters in adjusted models. In terms of health status, adults who were obese had greater odds of membership in the predominance of shortness of breath and flu-like symptom clusters. Additionally, having a pre-existing physical condition (vs none) was associated with greater odds of membership in the multi-symptomatic, predominance of shortness of breath, flu-like, and predominance of fatigue symptom clusters compared with the nasal congestion cluster. Lastly, adults with a pre-existing psychological condition (vs none) had greater odds of membership in the multi-symptomatic (aOR 3.3, 95% CI 2.1‐5.2) and flu-like symptom (aOR 1.9, 95% 1.2‐2.9) clusters.
Long COVID Outcome
Long COVID prevalence varied across the symptom clusters, from 39.7% in the multi-symptomatic cluster to 5.6% in the nasal congestion cluster (Figure 1). In adjusted models (Table 3), the multi-symptomatic cluster had the highest prevalence of long COVID compared with the nasal congestion cluster (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3‐8.7), followed by predominance of shortness of breath (aPR 3.7, 95% CI 2.5‐5.5), flu-like symptoms (aPR 2.8, 95% CI 1.9‐4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5‐3.3). The prevalence of long COVID was not statistically different between the clusters characterized by fever and nasal congestion (aPR 1.3, 95% CI 0.8‐1.9). Sociodemographic and health-related predictors of long COVID included female sex, older age, lower income, obesity, and pre-existing conditions. Responding via telephone (vs online) was also statistically associated with higher long COVID prevalence, likely due to associations between survey mode and respondent demographics (data not shown) and potential differences in symptoms reported based on mode.
Finally, results from the sensitivity analysis excluding all individuals with less than 90 days between their COVID onset and survey date closely aligned with the primary analysis, including the 6 acute symptom clusters identified (Table S2 in Multimedia Appendix 1) and their relationship with long COVID prevalence (Table S3 in Multimedia Appendix 1).
Table 3.
Modified Poisson regression examining prevalence of long COVID (n=4169) in the Michigan COVID-19 Recovery Surveillance Study (June 1, 2020, to May 31, 2022). Italicized values denote statistical significance at P<.05.
Unadjusted
Adjusteda
95% CI
P value
95% CI
P value
PRb
LBc
UBd
aPRe
LB
UB
Acute COVID-19 symptom clusters
Multi-symptomatic
7.1
5.0
10.2
<.001
6.1
4.3
8.7
<.001
Predominance of shortness of breath
4.0
2.7
6.0
<.001
3.7
2.5
5.5
<.001
Flu-like
2.8
2.0
4.1
<.001
2.8
1.9
4.0
<.001
Predominance of fatigue
2.6
1.7
3.9
<.001
2.2
1.5
3.3
<.001
Fever
1.2
0.8
1.8
.51
1.3
0.8
1.9
.27
Nasal congestion
1.0
—f
—
—
1.0
—
—
—
Sex
Male
1.0
—
—
—
1.0
—
—
—
Female
1.7
1.4
2.0
<.001
1.4
1.2
1.7
<.001
Age group(years)
18‐34
1.0
—
—
—
1.0
—
—
—
35‐44
1.8
1.4
2.3
<.001
1.7
1.4
2.2
<.001
45‐54
1.8
1.4
2.3
<.001
1.7
1.4
2.2
<.001
55‐64
1.9
1.5
2.4
<.001
2.1
1.7
2.6
<.001
65+
2.2
1.7
2.8
<.001
2.5
1.9
3.1
<.001
Raceandethnicity
Hispanic
1.5
1.1
1.9
.003
1.1
0.9
1.4
.44
Non-Hispanic Black
1.5
1.2
1.9
.002
1.1
0.9
1.5
.28
Another race or ethnicity
0.9
0.7
1.2
.36
0.8
0.6
1.1
.15
Non-Hispanic White
1.0
—
—
—
1.0
—
—
—
Unknown
1.0
0.6
1.5
.85
1.0
0.6
1.5
.88
Annual income(US $)
<35,000
1.7
1.4
2.1
<.001
1.3
1.0
1.5
.015
35,000-74,999
1.4
1.2
1.7
.001
1.2
1.0
1.5
.047
75,000
1.0
—
—
—
1.0
—
—
—
Smoking prior to illness
1.3
1.0
1.6
.05
1.1
0.8
1.3
.65
BMI
Underweight/normal weight
1.0
—
—
—
1.0
—
—
—
Overweight
1.3
1.0
1.6
.03
1.1
0.9
1.4
.33
Moderately obese
1.6
1.3
2.0
<.001
1.2
0.9
1.5
.16
Severely obese
2.0
1.6
2.5
<.001
1.3
1.1
1.7
.01
Unknown
1.2
0.6
2.5
.60
1.0
0.5
2.1
.94
Pre-existing physical conditiong
versusnone
1.8
1.5
2.1
<.001
1.2
1.0
1.4
.05
Pre-existing psychological conditionversusnone
1.5
1.2
1.8
<.001
1.1
0.9
1.3
.29
Survey phase
June 1, 2020, to September 30, 2020
1.0
—
—
—
1.0
—
—
—
October 1, 2020, to February 28, 2021
1.0
0.8
1.2
.78
0.9
0.7
1.1
.37
March 1, 2021, to May 31, 2021
0.9
0.7
1.1
.26
0.8
0.6
1.0
.025
June 1, 2021, to September 30, 2021
0.7
0.5
0.9
.005
0.7
0.5
0.9
.002
October 1, 2021, to February 28, 2022
0.8
0.6
1.0
.04
0.7
0.5
0.8
<.001
March 1, 2022, to May 31, 2022
0.3
0.2
0.4
<.001
0.4
0.3
0.5
<.001
Survey mode
Phone
1.6
1.4
1.9
<.001
1.3
1.1
1.5
.004
Web-based
1.0
—
—
—
1.0—
—
—
—
a
All models mutually adjusted for variables listed in table.
b
PR: prevalence ratio.excluding psychological conditions
c
LB: lower bound of 95% CI.
d
UB: upper bound of 95% CI.
e
aPR: adjusted prevalence ratio.
f
Not applicable (referent value).
g
Excluding psychological conditions.
Discussion
Using a population-based sample of adults with PCR-confirmed SARS-CoV-2 in Michigan, we examined associations between acute COVID-19 symptom clusters and long COVID. Generally, we found that long COVID prevalence increased as the number of acute symptoms, or probability of severe symptoms, increased. Of the 6 acute symptom clusters we identified, long COVID prevalence was lowest in clusters with a low number of symptoms (nasal congestion, fever), and highest in clusters with more symptoms (multi-symptomatic) or a high probability of severe symptoms (shortness of breath). These findings are consistent with a growing body of research linking severe acute illness to long COVID [561112].
Long COVID prevalence was greater among respondents with multi-symptomatic acute illness with a high probability of fever, chills, and muscle aches. This is consistent with a study of patients at a hospital in Toronto, Canada, in which patients with acute constitutional (eg, fever, chills) or rheumatologic symptoms (eg, muscle aches) had greater odds of persistent symptoms 90 days after illness onset than patients without these acute symptoms [15]. Other studies have used a different approach, examining the progression of specific symptoms during and after COVID-19 illness [1628], rather than associations between acute symptoms and long COVID (defined as the presence of any symptom after a specified time period). For example, a study using an international convenience sample of individuals with persistent symptoms following COVID-19 illness reported 3 clusters of COVID-19 symptoms, including symptoms most prevalent during the acute phase (eg, diarrhea and fever), symptoms consistently present over time (eg, muscle aches, changes to sense of taste and smell, and fatigue), and symptoms increasingly prevalent over time (eg, brain fog and postexertional malaise) [16].
In terms of specific acute COVID-19 symptoms, our findings may provide clues to potential mechanisms leading to long COVID. The multi-symptomatic cluster had the highest probability of acute gastrointestinal symptoms. Although few studies have examined associations between acute symptoms and long COVID, one small study in India found an association between diarrhea during acute illness and long COVID [14]. There is also preliminary evidence that the SARS-CoV-2 virus persists in the gastrointestinal tract for months in a subset of infected individuals [29], potentially prolonging gastrointestinal symptoms or leading to symptoms outside the gastrointestinal tract [30].
The multi-symptomatic acute symptom cluster also had a high probability of neurological symptoms and, along with the shortness of breath cluster, respiratory symptoms. In a study of military health system beneficiaries, individuals with neurological and respiratory/systemic symptoms were more likely to report persistent symptoms 6 months following illness onset than participants with nasal symptoms [31]. Neurological and respiratory symptoms are common among individuals with long COVID [3233], suggesting the persistence of these symptoms over time. However, neurological symptoms associated with acute COVID-19 illness and long COVID may differ. While loss of taste and smell may persist from acute to chronic illness, other neurological long COVID symptoms, such as absent-mindedness, may have a delayed onset [163435], suggesting a separate mechanistic pathway.
We also identified sociodemographic disparities in acute COVID-19 symptom class membership. Since long COVID may be underdiagnosed among people who are from racial or ethnic minority groups or have low income [36], it is important to identify high-risk groups when attempting to quantify disparities in long COVID. Respondents who were female, younger, members of racial or ethnic minority groups, low income, and diagnosed with pre-existing physical or psychological conditions had greater odds of membership in the multi-symptomatic cluster, which had the highest long COVID prevalence (39.7%). Similarly, respondents who were younger, low income, and diagnosed with a pre-existing physical condition had greater odds of membership in the predominance of shortness of breath cluster, which had a long COVID prevalence of 22.4%. On the other hand, respondents who were female and middle-aged had greater odds of membership in the predominance of fatigue cluster, which had a long COVID prevalence of 14.5%.
There are also alternative explanations for these associations. First, residual confounding due to sociodemographic and health status differences may account for some of the association between the acute symptom clusters and long COVID. Second, we did not adjust for hospital admission since it likely mediates the relationship between acute symptoms and long COVID. However, post-hospital or post-ICU syndrome, whereby hospitalized individuals have prolonged recovery irrespective of admitting illness [3738], may partially explain associations between the more severe acute symptom clusters and long COVID.
Additionally, these findings should be interpreted within the context of several limitations. First, our sample is based on adults with PCR-confirmed SARS-CoV-2 in Michigan and therefore only representative of individuals who obtained a SARS-CoV-2 PCR test in Michigan. Given the limited availability of PCR testing early on, and the wide availability of at-home testing in later stages of the pandemic, our sample is not generalizable to all individuals with SARS-CoV-2 in Michigan during our study period. Second, our response rate (32.2%), although fairly high for a population-based survey, may be subject to nonresponse bias. However, population-based samples are more generalizable than clinical or convenience samples and we have previously shown that the impact of nonresponse on our study findings is likely minimal [39]. Third, our study relies on self-reported data, which is subject to recall and reporting biases. Fourth, we did not adjust for acute illness severity, including hospital admission, as we viewed these factors as mediators between acute symptoms and long COVID. Fifth, we did not examine the persistence of specific symptoms over time, an important avenue for future research.
A growing body of research links acute COVID-19 severity to long COVID. In our population-based sample of adults with PCR-confirmed SARS-CoV-2, we found that acute COVID-19 symptoms clustered into 6 classes ranging in symptom type and severity and that long COVID prevalence increased as the number of acute symptoms, or probability of severe symptoms, increased. Researchers and clinicians should consider acute COVID-19 symptom profiles when evaluating subsequent risk of long COVID, including potential mechanistic pathways in a research context, and proactively screen high-risk patients during the provision of clinical care.
supplementary material
10.2196/55697
Multimedia Appendix 1
Supplementary tables containing the unadjusted results from the multinomial logistic regression models and the results from the sensitivity analysis excluding all individuals with less than 90 days between their COVID onset and survey date. |
Title: Enhancement of Forskolin Production Using Aeroponic Cultivation of | Body:
1. Introduction
Coleus forskohlii (syn. C. barbatus var. barbatus) is an aromatic perennial plant belonging to the Lamiaceae family, native to tropical and subtropical regions including Brazil, East Africa, India, Myanmar, Nepal, Pakistan, and Sri Lanka [1,2]. In traditional Hindu and Ayurvedic medicine, C. forskohlii has been used to treat a variety of conditions such as heart disease, abdominal colic, respiratory disorders, insomnia, and convulsions [3].
The taxonomic classification of C. forskohlii has been ambiguous for decades and was only updated in 2019 [4] when the genus Plectranthus was divided into three distinct genera: Plectranthus sensu stricto, Coleus, and Equilabium, a decision further supported by the diversity of diterpenoids within these groups [5]. Studies between 1962 and 2019, which included the species Plectranthus barbatus as a synonym for C. forskohlii, should be reconsidered in light of this update [4].
Forskolin, discovered in the year 1974, is the main labdane diterpenoid identified in C. forskohlii. Its precursor, (13R)-manoyl oxide, was localized in the root cork, which explains why forskolin is mainly localized there [6,7]. Some studies, however, also mention the presence of forskolin in the aerial parts [8]. Forskolin is mainly known for its role as an activator of hormone-sensitive adenylate cyclase in intact cells or tissues, making it valuable for research on understanding cyclic AMP (adenosine monophosphate)-dependent physiological processes [6]. Forskolin is also used as a dietary supplement for fat loss as it increases cyclic AMP, enhances the use of body fat, and stimulates the basal metabolism and has been shown to be a lipolytic agent in topical application [9,10]. A large number of studies have investigated forskolin’s bioactivities and demonstrated anti-inflammatory properties in vitro and in vivo [11]. Forskolin could also be used as an anti-inflammatory agent to treat inflammation associated with obesity by inhibiting the expression of NFκB (nuclear factor-kappa B)-dependent genes [12].
Further studies on the root phytochemistry were carried out, and various diterpenoids, mainly forskolin derivatives, were identified [13]. One study compared the phytochemical profile of the genera Coleus and Plectranthus and revealed the predominant presence in the Coleus species of abietanes oxygenated at C-14, with formic acid or acetic acid as acyl groups, while in Plectranthus, isovaleric, senecioic, or hydroxybenzoic acid groups can also be found [5]. Other studies, focusing on the aerial parts of P. barbatus prior to the division of the genus, identified various diterpenoids [14,15] and some phenolic compounds with antioxidant properties [16]. However, characterization of the phytochemical profile of the aerial parts is very limited.
As forskolin and its derivatives are molecules of pharmaceutical, nutraceutical (sports nutrition, weight management supplement), and cosmetic (lipolytic agent) interest, the demand for Coleus forskohlii on the health market has increased, further enhanced by a growing consumer preference for natural products [10,17]. Therefore, the search for efficient methods for producing high-quality extract, rich in bioactive compounds, or powder of Coleus forskohlii is of great interest [18]. To meet growing market demand, the development of off-ground cultivation methods that provide easy access to the roots without plant sacrifice, thus allowing several harvests from the same plant, has emerged. While forskolin can be produced from hairy roots or callus cell cultures, these production methods, which require biological engineering, are considerably removed from the intact plant and can result in different metabolomic profiles [19,20,21].
The aim of this study was to develop aeroponic cultivation of C. forskohlii to provide a local, easy, and sustainable sourcing of C. forskohlii. In aeroponic conditions, plants grow in the air, out of the soil, giving easy access to the roots, and a nutrient solution is sprayed directly onto the roots to promote biomass development [22,23]. Aeroponic cultivation reduces the consumption of water, fertilizers, pesticides, and herbicides compared to hydroponics or geoponics, but as there is no solid support culture to absorb the excess of nutrients, developing the appropriate system parameters for plant growth is a challenge [24].
To achieve this objective, the French Horticultural Development Committee of the Centre-Val de Loire region (Comité de Développement Horticole de la Région Centre-Val de Loire; CDHRC) carried out, for the first time, different cultivation itineraries, involving different stresses or elicitations to assess their impact on plant biomass and metabolite production. Three different aeroponic culture itineraries, artificial lighting (LEDs), biostimulant, and hydric stress, in addition to the control itinerary were evaluated in this way. First, to assess the impact on the production of forskolin, as it is the main valuable compound targeted in C. forskohlii, root extracts were analyzed using HPLC-ELSD, and the forskolin content was determined using a calibration curve. Then, to gain greater insight into the plant composition under these growing conditions and therefore to produce a controlled quality extract, the phytochemical profile of the roots and aerial parts of C. forskohlii cultivated aeroponically in Région Centre-Val de Loire was investigated. Many health products (e.g., dietary supplements, cosmetics) on the market today contain C. forskohlii root extracts; therefore, it is important to have a good knowledge of the chemical profile of the extract to produce roots rich in forskolin and other bioactive compounds with interesting biological activities. Furthermore, as the aerial parts have been less explored than roots and are produced with aeroponic cultivation, this study could direct future valorization studies to transform this by-product into a co-product. The phytochemical profile was established using a molecular network that associates, in the same cluster, molecules presenting the same fragmentation pathway and that should belong to the same molecular family. This mapping associated with a database is very helpful for compound identification and extract comparison.
Lastly, to better assess the impact of artificial lighting (LEDs), which proved to be the most promising mode of forskolin production, on both forskolin and the other compounds, the roots and aerial extracts obtained with these conditions were further investigated using UHPLC-HRMS analysis with untargeted metabolomic analysis and were compared to the control modality. Statistical data treatments of the UHPLC-HRMS analysis of each part of the plant of the two modalities were performed to highlight potential differences between the two phytochemical profiles and identify compounds potentially affected by the growing conditions.
2. Results and Discussion
The main objectives of this study were to implement aeroponic culture conditions facilitating root harvesting, to develop a local and efficient sourcing of C. forskohlii and its main metabolite of interest, namely, forskolin, which is present mainly in the roots. To better qualify and valorize the cultivated plant and highlight the impact of the use of abiotic stresses during cultivation on the production of plant metabolites, the phytochemistry of the root and aerial parts was investigated.
2.1. Forskolin Quantification
The forskolin content in roots from the four cultivation modalities was quantified and compared. The ethanolic extract of C. forskohlii was obtained by ultrasonic extraction and then dried to be solubilized at a known concentration of 5 mg/mL. A calibration curve (Figure S1) of forskolin was prepared between 25 and 400 µg/mL. The samples and a calibration curve were analyzed using HPLC-ELSD, and the forskolin content was calculated. Figure 1 presents the forskolin content per gram of C. forskohlii roots according to the time (4 and 18 weeks) and cultivation modality (A: control; B: LED; C: biostimulant; D: hydric stress). After 4 weeks of cultivation, the control showed the best result in terms of forskolin content with 1.50 ± 0.04 mg/g of dry roots, while the LED, biostimulant, and hydric stress did not have any positive impact on the concentration of forskolin. The amount of forskolin in cultivated C. forskohlii is comparable to that obtained in plants collected in natural habitats. For instance, the forskolin content of plants obtained from different locations in the Vidarbha region (Maharashtra, India) showed between 0.66 and 10.24 mg/g of forskolin in the dried plant [25]. After a few weeks of cultivation, the content of forskolin decreased in the roots, especially for the control modality, with a decrease of 49% in forskolin production. A seasonal impact on the content in labdane diterpenes was also recorded for C. forskohlii growth in an experimental field [26]. After 18 weeks, group A reached the same level as group D, which, after 4 weeks, was the one with the lowest percentage of forskolin. The use of a biostimulant (group C) slightly limited the drop in forskolin content (minus 32% after 18 weeks) and led to a higher content than group D after 18 weeks. The itinerary using LEDs was the most promising one, as forskolin production dropped by only 12%. The forskolin content of 1.03 ± 0.03 mg/g of dry roots after 18 weeks of cultivation under lighting compared to 0.78 ± 0.08 mg/g without suggests that the drop in forskolin is linked to the photoperiod.
After 18 weeks of cultivation, the total root biomass was collected, weighed, and the total quantity of forskolin produced was calculated. Table 1 compares the quantity of root biomass harvested and the total amount of forskolin recovered relative to the biomass produced. Neither hydric stress nor the biostimulant improved the quantity of biomass and forskolin compared to the control, while LEDs helped to multiply the biomass production by a factor of two, with 296.0 g of dry biomass obtained for itinerary A (control) and 690.8 g obtained for itinerary B (LEDs). By combining the amount of forskolin per gram of biomass (Figure 1) with the quantity of biomass produced, the itinerary using LEDs showed a significantly higher forskolin production with 710.1 mg of forskolin recovered, while the control condition led to the production of 229.9 mg, which triples the final quantity. Figure 2 shows plants of the two itineraries after 18 weeks of cultivation, demonstrating the better root development using LEDs (Figure 2b). The application of supplementary lighting with a photoperiod of 16 h increased the biomass production after a few weeks of cultivation. The biomass collected at week 4 in itinerary B was smaller than the one collected in itinerary A. This could be related to the natural photoperiod, which was longer in July when harvesting occurred after 4 weeks, compared to October when harvesting occurred after 18 weeks. Therefore, adding LEDs extended the natural photoperiod with a higher impact in October than in July and thus contributed to the increase in root biomass. Light influences root productivity, as shown with Panax ginseng, a plant from the Araliaceae family, where the beneficial effect of white LED lighting in a hydroponic cultivation system was demonstrated [27].
Itinerary B, which used LEDs, increased the total quantity of forskolin recovered after 18 weeks of cultivation, but forskolin may not be the only metabolite impacted by the stress. Therefore, an untargeted metabolomic study of the phytochemical profile was undertaken not only on roots but also on the aerial parts. As they are directly subjected to the stress, their composition can also be impacted. Furthermore, most previous studies focused on forskolin and therefore investigated only roots, but an investigation of both the roots and aerial parts could be valuable for the overall valorization of the plant.
2.2. Metabolomic Investigation
2.2.1. Comparison of the Phytochemical Composition of Root and Aerial Parts
The aeroponic cultivation developed in this study under the control condition and LED lighting produced Coleus forskohlii with a forskolin content similar to that produced by C. forskohlii cultivated in the field in native countries.
As commercial products made from C. forskohlii are often root powders or extracts that contain many bioactive molecules and not only forskolin, it is important to determine the global phytochemical composition of the C. forskohlii produced in Région Centre-Val de Loire (France) using aeroponic cultivation. As C. forskohlii grew in soilless conditions without soil microorganisms and under different climate conditions, it is possible that the metabolomic composition was modified.
Molecular networking is based on the principle that molecules with similar structures tend to have similar mass fragmentation patterns. These patterns can be represented as a graph where nodes represent ions and where edges are drawn between nodes if the similarity score between them exceeds a predefined threshold. Associated nodes define a cluster that should represent molecules belonging to the same molecular family. In molecular networking, several analyses can be joined and therefore compared to help in characterizing the phytochemical profile. The roots of Coleus forskohlii have been mainly studied due to their high concentration of forskolin [28,29,30]. As the aerial parts have been less studied, building a molecular network using both samples may make it possible to associate molecules of both samples in the same cluster, thus helping in the identification of the aerial parts. Moreover, comparing the composition of the root and aerial parts can reveal common molecules or compound families, facilitating the annotation; it can also highlight the phytochemical specificity of each part of the plant. Figure 3 shows the molecular network obtained on GNPS (global natural products social molecular networking) from the UHPLC-HRMS/MS data in positive ionization mode for QC_R (Quality Control Roots: a mix of root samples from itineraries A and B) and QC_AP (Quality Control Aerial parts: a mix of aerial part samples from itineraries A and B) to be able to detect the main compounds produced in the two conditions. Each color corresponds to a part of the plant, with green representing the aerial parts and pink representing the roots. Nodes are divided into two colors when the ion is present in both samples. The size of the pie chart corresponds to the ion proportion in each sample, while the size of the node represents the sum of the ion intensities in the two samples. In the center of Figure 3, a global overview of the network is shown, with 490 nodes, including the singletons. Three clusters (square 1 on the left part of Figure 3) are dedicated to diterpenoids, with one cluster (1B) including as many molecules from roots as molecules from aerial parts. This cluster includes forskolin (feature 202), which is also present in the aerial parts but in a smaller amount. Although this cluster contains ions from both roots and aerial parts, most of the compounds are found either in the roots or in the aerial parts specifically. This, along with the fact that the other two clusters (1A and 1C) of diterpenes mainly consist of compounds extracted from the aerial parts, indicates that the aerial parts are a source of a wider diversity of diterpenes, with different skeletons. Additionally, a cluster dedicated to triterpenoids was also highlighted for the roots (square 2 on the right of Figure 3). A few phenolic compounds (square 3 on the right of Figure 3), including rosmarinic acid (feature 32), were identified, along with a small cluster of flavonoids containing apigenin-glucuronide (feature 28) (square 4 on the right of Figure 3) in the aerial parts. Phenolic compounds contribute to the antioxidant properties of the plant. Rosmarinic acid and scutellarein 4′-methyl ether 7-O-glucuronide, both identified in the aerial parts of P. barbatus, have been shown to be correlated with the antioxidant activity of a water extract [16].
The overall composition of the root and aerial parts of plants grown in an aeroponic system is close to that described in plants grown in more conventional systems, with diterpenoids representing a large part of the composition of C. forskohlii and the presence of a few phenolic compounds. Based on this molecular network, a deeper exploration of the phytochemistry and annotation of the top 25 compounds impacted by the use of LEDs during cultivation (Section 2.2.2) was performed.
2.2.2. Identification of Compounds Impacted by LEDs
In order to identify which metabolites were the most impacted by the addition of LEDs during cultivation, supervised multivariate analyses were employed. A PLS-DA (Partial Least Squares Discriminant Analysis) was performed using the peak list extracted from MetaboScape, and the top 25 Variables Important in Projection (VIP score > 1) were investigated.
Figure 4a presents the PLS-DA model based on the UHPLC-HRMS analyses in positive ionization mode of the root samples for control and LED conditions after 18 weeks of cultivation. Component 1 describes 95.4% of the variance, while component 2 represents 0.8%. The model achieved a quality prediction of R2 = 0.99 and a predictive ability of Q2 = 0.99. As shown in Figure 4a, two distinct groups can be easily distinguished in component 1, with itinerary A (control) on the right and itinerary B (LEDs) on the left. The samples of each group are separated along component 2 that describes only 0.8% of the variance, indicating the good similarity of samples belonging to the same modality. Figure 4b shows the top 25 VIPs associated with group separation on component 1, with 12 molecules being more intense in the LED cultivation itinerary and 13 more intense in the control condition.
In order to identify the VIPs, the molecular formula was first determined, and MS/MS data were employed to obtain fragmentation information.
Table 2 and Table 3 summarize the compound information for the roots and aerial parts, respectively, including chromatographic information, the itinerary in which each compound is more present, the calculated molecular formula, and a putative annotation when possible. To increase the confidence level of the proposed identification, information from HRMS/MS was included; molecular formulae were searched in the LOTUS database from both the Plectranthus and Coleus genera, and HRMS/MS data were computed using SIRIUS 5.8.
For root samples in Table 2, VIPs 1, 8, and 10 are all in-source fragments corresponding to compound R1 with a tR of 3.35 min and [M + H]+ at m/z 199.0598, suggesting a molecular formula of C9H10O5. The SIRIUS putative annotation suggested vanillylmandelic acid, a phenolic compound found in Lamiaceae [31]. The fragmentation spectrum shows ions at m/z 153.0543 and m/z 135.0434, corresponding to the losses of a carboxylic acidic function (46 u) and of a water molecule (18 u).
VIP 2 corresponding to compound R12 at 13.5 min with a [M + H]+ ion at m/z 331.1908 suggests the molecular formula C20H26O4. An abietane diterpenoid derivative, the 14-hydroxytaxodione corresponding to this molecular formula was identified in Plectranthus grandidentatus [32].
VIP 3, which is a [M + Na]+ adduct, corresponds to compound R17 at m/z 331.2612 with a tR of 15.00 min and indicates a molecular formula of C20H36O2, consistent with a fatty acid derivative. In the molecular network, the corresponding node is a singleton, which does not give any further information on the structure of compound R17.
VIPs 4, 5, and 7 are all associated with compound R3 at a tR of 5.62 min with a [M + H]+ at m/z 181.0494, which corresponds to the molecular formula C9H8O4. The fragmentation spectrum shows ions at m/z 163.0393 and 145.0282, suggesting the successive loss of H2O (18 u) due to hydroxyl functions, and m/z 135.0438 corresponding to the loss of a carboxylic acid function with another loss of the hydroxyl group with a fragment at m/z 117.0333. The molecular formula and fragmentation pattern suggest that this compound corresponds to caffeic acid, with a SIRIUS score of 100.00%. Caffeic acid is a phenolic compound commonly found in plants, including in the genus Coleus [33]. The node corresponding to R3 is located in cluster 3 (Figure 3) dedicated to phenolic compounds.
VIPs 6 and 17 correspond to compound R5 at a tR of 6.60 min with the [M + H]+ at m/z 313.0711, indicating a molecular formula of C17H12O6. No compound in the genus Plectranthus or Coleus corresponds to this molecular formula, but SIRUS suggested that the compound could be 4-(3,4-Dihydroxyphenyl)-6,7-dihydroxynaphthalene-2-carboxylic acid with a score of 90.26%.
VIP 9 corresponds to R9 with m/z 189.0659 and a tR of 7.85 min. No information from the mass spectrum enabled the molecular ion to be identified; therefore, no molecular formula or annotation can be suggested.
VIP 11 corresponds to compound R18 with m/z 355.1879 and a tR of 15.30 min. m/z 355.1879 had not been fragmented in MS/MS, which does not allow an annotation. Furthermore, no discriminating adduct that would have enabled the molecular formula to be calculated was found.
VIP 12 corresponds to R4 at 5.98 min with a [M + H]+ at m/z 183.1015 and [M + Na]+ at m/z 205.0835, suggesting a molecular formula of C10H14O3. The molecular formula corresponds to a small phenolic compound, which is consistent with the retention time, but there is no further information available to suggest an annotation.
VIP 13 corresponds to R10 with a [M + H]+ m/z 365.3171 and a tR of 11.00 min. The calculated molecular formula is C22H40N2O2, a nitrogen compound.
VIP 14 corresponds to compound R16 at m/z 301.2164 and at tR of 14.90 min, and the corresponding adduct [M + Na]+ was identified with m/z 323.1981, which indicates a molecular formula of C20H28O2. In the literature, the two diterpenoids sugiol and barbatusol, corresponding to this molecular formula, were found in the genus Plectranthus [34,35].
VIP 15 corresponds to R14 at a tR of 14.10 min with a [M + H]+ at m/z 313.1798, suggesting the molecular formula to be C20H24O3. The corresponding node is located in a cluster of four nodes mainly from roots, suggesting a skeleton specific to this part of the plant. R14 has a fragmentation pattern similar to that of diterpenoids and with a retention time consistent with other diterpenoid derivatives.
VIP 16 corresponds to compound R19 with m/z 317.2097 for [M + Na]+ and a tR of 15.40 min, which suggests a molecular formula of C18H30O3. According to the retention time and the fragmentation pattern, R19 may correspond to a fatty acid.
VIP 18 corresponds to compound R13 with a tR of 13.80 min and m/z 453.3370. The [M + H]+ and [M + NH4]+ at 489.3573 and 506.3840 suggested that 453.3370 m/z was [M − 2H2O + H]+. The molecular formula is C30H48O5, which corresponds to a triterpenoid; moreover, it is located in cluster 2, which is dedicated to triterpenoids. R13 is annotated as tormentic acid previously identified in the genus Coleus [36] and in accordance with SIRIUS with a score of 87.45%.
VIPs 20 and 24 correspond to compound R6 at a tR of 7.19 min with the [M − H2O + H]+ at m/z 312.1235 and an in-source fragment at m/z 177.0547. The molecular formula of R6 is C18H19NO5. SIRIUS suggested the compound to be N-feruloyloctopamine. Ion m/z 177.0540 corresponds to the ferulic derivative part of the molecule, while ion m/z 145.0279 is due to the loss of the methoxy group. Even with the nitrogen part, the node corresponding to R6 node is located in cluster 3 dedicated to phenolic acids (ferulic derivative).
VIP 21 corresponds to R2 at a tR of 3.87 min with [M + H]+ at m/z 183.1015. The fragmentation pattern is similar to that of R4 with the same molecular formula of C10H14O3.
VIP 22 corresponds to compound R8 at a tR of 7.65 min with a fragment at m/z 135.0440 and a parent ion [M + H]+ at m/z 361.0922 and its corresponding adduct [M + NH4]+ at m/z 378.1188, which corresponds to a molecular formula of C18H16O8. This molecular formula corresponds to rosmarinic acid, with a node located in cluster 3 (phenolic acids), which is in accordance with the SIRIUS annotation with a score of 99.21%. The molecule has already been found many times in the Lamiaceae family including in the genera Plectranthus [37] and Coleus [16,38,39].
VIP 23 corresponds to R7 with m/z 267.1177 and a tR of 7.34 min. As no information from the mass spectrum made it possible to identify the molecular ion, no molecular formula or annotation can be suggested.
VIP 25 corresponds to R15 at 14.30 min, and m/z 255.1353 corresponds to a [M + Na]+ adduct with the corresponding [M + H]+ at m/z 233.1533, suggesting a molecular formula of C15H20O2. The retention time and molecular formula are consistent with a terpenoid derivative, but as the [M + H]+ was not fragmented, no further information was available to suggest an annotation.
Compounds with a retention time in the first part of the chromatogram seem to be more positively impacted by the use of LEDs and vice versa in the second half. LEDs seem to improve the intensity of phenolic compounds such as vanillylmandelic acid (R1), caffeic acid (R3), or rosmarinic acid (R8) in the roots of C. forskohlii, whereas they seem to decrease the amount of the most non-polar compounds such as diterpenoids and triterpenoids. As for Ocimum basilicum, from the same family as C. forskohlii, the artificial LED lighting seems to enhance the plant’s phenolic content, as phenolic compounds have UV radiation protection properties [40]. Forskolin, previously quantified in the roots (Section 2.1) and positively influenced by LED lighting, does not appear here in the top 25 VIPs.
The same work was performed on the aerial parts of C. forskohlii to better understand the impact of LEDs on the whole plant as LEDs illuminate the aerial part. Figure 4a presents the PLS-DA model. Component 1 accounts for 83% of the variance and component 2 for 7.6%, with an R2 of 0.97 and a predictive ability Q2 of 0.98. In Figure 4c, two groups can be easily distinguished in component 1, with itinerary A (control) on the right and itinerary B (LEDs) on the left. Figure 4d presents the corresponding top 25 VIP scores, with 18 molecules being more intense for the cultivation itinerary using LEDs and 7 for the control itinerary.
VIPs 1, 4, 11, and 14 are related to AP10 at a tR of 12.81 min and correspond to [M + Na]+ at m/z 433.2202, [M − H2O + H]+ at m/z 375.2170, and two fragments, from the [M + H]+ at m/z 411.2377, m/z 315.1952 and m/z 297.1731. With a molecular formula of C22H34O7, AP10 can be identified as forskolin.
VIP 2 with m/z 320.2564 and a tR of 15.47 min, corresponds to compound AP20. The calculated molecular formula is C18H35NO2. The fragmentation spectrum of the [M + H]+ at m/z 298.2753 of AP20 indicates the loss of a molecule of water and the loss of the primary amide function with an ion fragment at m/z 245.2266 corresponding to fatty amide derivatives.
VIPs 3 and 12 correspond to AP7 at a tR of 7.55 min with a [M + H]+ at m/z 447.0930 and a molecular formula of C21H18O11. The ion at m/z 271.0604 corresponds to aglycone after the loss of the glucuronide moiety (176 u). AP7 can be annotated as apigenin-glucuronide with a SIRIUS score at 96.74%, which is supported by the GNPS data library. The AP7 node is located in cluster 4 with only three nodes, meaning that only two other similar flavonoid compounds present the same fragmentation pattern in the aerial parts of C. forskohlii.
VIPs 5 and 7 correspond to compound AP5 at a tR of 6.85 min with a [M + H]+ at m/z 291.0979, which suggests that the molecular formula of AP5 is C14H13N2O4. Fragment m/z 245.0921 corresponds to the loss of a carboxylic acid function (46 u), and fragment m/z 188.0702 corresponds to the loss of the N-malonyl part of the molecule, suggesting that compound AP5 is N-malonyl-D-tryptophan with a SIRIUS score of 94.46%.
VIP 6 corresponds to AP14 with a [M + H]+ at m/z 317.2092 and a molecular formula of C20H28O3 associated with a diterpenoid. Several abietane diterpenoids correspond to this formula, such as 11,20-dihydroxysugiol identified in the aerial parts of Plectranthus cyaneus, royleanone identified in the whole plant of Plectranthus grandidentatus, and 20-Deoxocarnosol isolated from Coleus barbatus [41,42,43].
VIP 8 corresponds to compound AP6 with a [M + H]+ at m/z 330.1339 and a tR at 7.19 min. Compound AP6 shows the same fragmentation pattern as R6, and both are located in cluster 3, with an ion source common fragment at m/z 177.0548. The molecular formula is C18H19NO5, and AP6 is annotated as N-feruloyloctopamine, a hydroxycinnamic amide.
VIPs 9 and 25 correspond to AP18 with [M + H]+ at m/z 389.1965 and a fragment at m/z 311.1646 at a tR of 15.25 min, which suggests a molecular formula of C22H28O6. The fragment at m/z 311.1646 indicates the loss of an acetate group together with the loss of a hydroxyl group (78 u). Seven different structures corresponding to abietane diterpenoids, especially abeoabietane diterpenoids or cycloabietane diterpenoids, were found in the genus Plectranthus using LOTUS [44,45,46,47,48]. The location of the corresponding node in cluster 1.A also supports this annotation.
VIPs 10 and 15, which correspond to compound AP2 at a tR of 5.71 min, are two fragments at m/z 227.1281 and 191.1065 from the [M + NH4]+ adduct at m/z 406.2071. The molecular formula of compound AP2 is C18H28O9. The fragment at m/z 227.1281 corresponds to the loss of a hexose moiety (162 u) with a molecular formula of C12H18O4. The aglycone is annotated as a hydroxyjasmonic acid, which implies that AP2 is a hydroxyjasmonic hexose, a compound already described in the Lamiaceae family [49].
VIP 13 corresponding to AP16 with a [M + H]+ at m/z 371.1857 and a tR of 15.21 min has a molecular formula of C22H26O5. The node from the molecular network is located in cluster 1.A, which suggests that the compound should be annotated as a diterpenoid.
VIP 16, corresponding to compound AP3, is an ion source fragment at m/z 206.0814, itself originating from the [M + H]+ at m/z 252.0866 with a tR of 6.37 min. The molecular formula of AP3 is C12H13NO5. The node corresponding to AP3 is located in the same cluster as AP5, suggesting a structural similarity between them. SIRIUS annotated AP3 with a score of 86.64% as a dipeptide as 2-[(2-carboxyacetyl)amino]-3-phenylpropanoic.
VIP 17 corresponds to compound AP4 with a tR of 6.82 min and m/z 312.1235; it is a nitrogen compound with the molecular formula C18H17NO4. The fragment m/z 177.0547 corresponds to the ferulic derivative part of the molecule as described previously for R6 and AP6. SIRIUS suggested that compound AP4 is a hydroxycinnamic acid amide.
VIP 18, which corresponds to compound AP1 at a tR of 4.85 min, is an in-source fragment ion from [M + H]+ at m/z 227.0551 with a molecular formula of C10H10O6. AP1 corresponds to a phenolic acid with a putative annotation from SIRIUS at 64.38% of 4,6-dimethoxyisophthalic acid.
VIP 19, corresponding to compound AP15, is a [M + Na]+ adduct with m/z 453.1889 and a tR of 14.86 min. The calculated formula of C24H30O7 corresponds to a diterpenoid. A cycloabietane, (2R,2′S,3′R,4′bS,7′R,8′aS,9′S,10′S)-3′,10′-Diacetoxy-4′b,5′,6′,7′,8′,8′a,9′,10′-octahydro-9′-hydroxy-2,4′b,7′-trimethyl-8′-methylidenspiro[cyclopropan-1,2′(1′H)-phenanthren]-1′,4′(3′H)-dion, corresponding to this molecular formula was previously identified [46].
VIP 20, corresponding to AP17 at m/z 437.1940, is a [M + Na]+ adduct of the [M + H]+ at m/z 415.2117 with a tR of 15.23 min. The fragmentation pattern of the [M + H]+ corresponds to a diterpenoid, with a node located in cluster 1.A. The molecular formula of C24H30O6 corresponds to an acetylated derivative of a 6,7-secoabietane diterpene previously identified in the genus Coleus [50].
VIP 21 corresponds to the [M + Na]+ adduct of the compound AP19 with a tR of 15.33 min and m/z 417.2250. The calculated molecular formula of C22H34O6 is associated with different labdane diterpenes previously identified in Plectranthus and Coleus, including a derivative from forskolin, 9-deoxyforskolin [13], a labdane diterpenoid, which is consistent with the molecular network as the node of AP19 is found in cluster 1.B (Figure 3) where forskolin is located.
VIP 22 corresponds to AP11 with a [M + H]+ at m/z 431.2069 corresponding to the molecular formula C24H30O7, identical to that of AP15, and a tR of 13.04 min. The node is located in cluster 1.A associated with different diterpenes. As mentioned for AP15, a cycloabietane corresponding to this molecular formula was previously identified [46].
VIP 23 at a tR of 13.87 min is a [M + Na]+ adduct with m/z 320.2564 corresponding to AP13. The calculated molecular formula is C18H35NO2. As for compound AP20, the fragmentation spectrum of the [M + H]+ at m/z 298.2753 indicates the loss of a hydroxyl function and of a primary amide function with an ion fragment at m/z 245.2266, which corresponds to a fatty amide derivative.
VIP 24 corresponds to the [M + Na]+ adduct of compound AP12 with a [M + H]+ at m/z 387.1810 and a molecular formula of C22H26O6. The fragmentation of the [M + H]+ corresponds to a cycloabietane diterpenoid, coleon Z, as previously reported in Coleus spp. [51].
Itinerary A, with no LED lighting, contains more dipeptide and nitrogen compounds such as cinnamic acid amides, while itinerary B increases the content of almost all listed diterpenes, including forskolin (Compounds AP10, AP11, AP12, AP16, AP17, AP18, and AP19), which indicates that LEDs seem to stimulate the biosynthesis of diterpenoids in the aerial parts.
To summarize the results obtained by comparing the control and LED modalities on metabolite amount, phenolic compounds appear to be concentrated in the root and aerial parts when using LEDs. This may be explained by the fact that they are generally UV protectors, and a higher light stress could improve their production in order to defend the plant. We also note that nitrogen compounds are more intense in the itineraries without LED stimulation. A possible explanation is that due to a higher biomass production with LED stimulation, amino acids are totally consumed for the production of primary metabolites and are hence less available for the production of other nitrogenous derivatives. Concerning diterpenes, one of the main molecular families of C. forskohlii, their concentration is higher in the leaves under LED stimulation, with, in particular, an increase in the forskolin content. The effect of LEDs on the roots is less clear, however. Some of the diterpenes are more concentrated in the roots from itinerary B, while others are more concentrated in the roots from itinerary A. Furthermore, some families of compounds, such as triterpenoids, previously identified in the molecular network, appear to be unaffected.
2.1. Forskolin Quantification
The forskolin content in roots from the four cultivation modalities was quantified and compared. The ethanolic extract of C. forskohlii was obtained by ultrasonic extraction and then dried to be solubilized at a known concentration of 5 mg/mL. A calibration curve (Figure S1) of forskolin was prepared between 25 and 400 µg/mL. The samples and a calibration curve were analyzed using HPLC-ELSD, and the forskolin content was calculated. Figure 1 presents the forskolin content per gram of C. forskohlii roots according to the time (4 and 18 weeks) and cultivation modality (A: control; B: LED; C: biostimulant; D: hydric stress). After 4 weeks of cultivation, the control showed the best result in terms of forskolin content with 1.50 ± 0.04 mg/g of dry roots, while the LED, biostimulant, and hydric stress did not have any positive impact on the concentration of forskolin. The amount of forskolin in cultivated C. forskohlii is comparable to that obtained in plants collected in natural habitats. For instance, the forskolin content of plants obtained from different locations in the Vidarbha region (Maharashtra, India) showed between 0.66 and 10.24 mg/g of forskolin in the dried plant [25]. After a few weeks of cultivation, the content of forskolin decreased in the roots, especially for the control modality, with a decrease of 49% in forskolin production. A seasonal impact on the content in labdane diterpenes was also recorded for C. forskohlii growth in an experimental field [26]. After 18 weeks, group A reached the same level as group D, which, after 4 weeks, was the one with the lowest percentage of forskolin. The use of a biostimulant (group C) slightly limited the drop in forskolin content (minus 32% after 18 weeks) and led to a higher content than group D after 18 weeks. The itinerary using LEDs was the most promising one, as forskolin production dropped by only 12%. The forskolin content of 1.03 ± 0.03 mg/g of dry roots after 18 weeks of cultivation under lighting compared to 0.78 ± 0.08 mg/g without suggests that the drop in forskolin is linked to the photoperiod.
After 18 weeks of cultivation, the total root biomass was collected, weighed, and the total quantity of forskolin produced was calculated. Table 1 compares the quantity of root biomass harvested and the total amount of forskolin recovered relative to the biomass produced. Neither hydric stress nor the biostimulant improved the quantity of biomass and forskolin compared to the control, while LEDs helped to multiply the biomass production by a factor of two, with 296.0 g of dry biomass obtained for itinerary A (control) and 690.8 g obtained for itinerary B (LEDs). By combining the amount of forskolin per gram of biomass (Figure 1) with the quantity of biomass produced, the itinerary using LEDs showed a significantly higher forskolin production with 710.1 mg of forskolin recovered, while the control condition led to the production of 229.9 mg, which triples the final quantity. Figure 2 shows plants of the two itineraries after 18 weeks of cultivation, demonstrating the better root development using LEDs (Figure 2b). The application of supplementary lighting with a photoperiod of 16 h increased the biomass production after a few weeks of cultivation. The biomass collected at week 4 in itinerary B was smaller than the one collected in itinerary A. This could be related to the natural photoperiod, which was longer in July when harvesting occurred after 4 weeks, compared to October when harvesting occurred after 18 weeks. Therefore, adding LEDs extended the natural photoperiod with a higher impact in October than in July and thus contributed to the increase in root biomass. Light influences root productivity, as shown with Panax ginseng, a plant from the Araliaceae family, where the beneficial effect of white LED lighting in a hydroponic cultivation system was demonstrated [27].
Itinerary B, which used LEDs, increased the total quantity of forskolin recovered after 18 weeks of cultivation, but forskolin may not be the only metabolite impacted by the stress. Therefore, an untargeted metabolomic study of the phytochemical profile was undertaken not only on roots but also on the aerial parts. As they are directly subjected to the stress, their composition can also be impacted. Furthermore, most previous studies focused on forskolin and therefore investigated only roots, but an investigation of both the roots and aerial parts could be valuable for the overall valorization of the plant.
2.2. Metabolomic Investigation
2.2.1. Comparison of the Phytochemical Composition of Root and Aerial Parts
The aeroponic cultivation developed in this study under the control condition and LED lighting produced Coleus forskohlii with a forskolin content similar to that produced by C. forskohlii cultivated in the field in native countries.
As commercial products made from C. forskohlii are often root powders or extracts that contain many bioactive molecules and not only forskolin, it is important to determine the global phytochemical composition of the C. forskohlii produced in Région Centre-Val de Loire (France) using aeroponic cultivation. As C. forskohlii grew in soilless conditions without soil microorganisms and under different climate conditions, it is possible that the metabolomic composition was modified.
Molecular networking is based on the principle that molecules with similar structures tend to have similar mass fragmentation patterns. These patterns can be represented as a graph where nodes represent ions and where edges are drawn between nodes if the similarity score between them exceeds a predefined threshold. Associated nodes define a cluster that should represent molecules belonging to the same molecular family. In molecular networking, several analyses can be joined and therefore compared to help in characterizing the phytochemical profile. The roots of Coleus forskohlii have been mainly studied due to their high concentration of forskolin [28,29,30]. As the aerial parts have been less studied, building a molecular network using both samples may make it possible to associate molecules of both samples in the same cluster, thus helping in the identification of the aerial parts. Moreover, comparing the composition of the root and aerial parts can reveal common molecules or compound families, facilitating the annotation; it can also highlight the phytochemical specificity of each part of the plant. Figure 3 shows the molecular network obtained on GNPS (global natural products social molecular networking) from the UHPLC-HRMS/MS data in positive ionization mode for QC_R (Quality Control Roots: a mix of root samples from itineraries A and B) and QC_AP (Quality Control Aerial parts: a mix of aerial part samples from itineraries A and B) to be able to detect the main compounds produced in the two conditions. Each color corresponds to a part of the plant, with green representing the aerial parts and pink representing the roots. Nodes are divided into two colors when the ion is present in both samples. The size of the pie chart corresponds to the ion proportion in each sample, while the size of the node represents the sum of the ion intensities in the two samples. In the center of Figure 3, a global overview of the network is shown, with 490 nodes, including the singletons. Three clusters (square 1 on the left part of Figure 3) are dedicated to diterpenoids, with one cluster (1B) including as many molecules from roots as molecules from aerial parts. This cluster includes forskolin (feature 202), which is also present in the aerial parts but in a smaller amount. Although this cluster contains ions from both roots and aerial parts, most of the compounds are found either in the roots or in the aerial parts specifically. This, along with the fact that the other two clusters (1A and 1C) of diterpenes mainly consist of compounds extracted from the aerial parts, indicates that the aerial parts are a source of a wider diversity of diterpenes, with different skeletons. Additionally, a cluster dedicated to triterpenoids was also highlighted for the roots (square 2 on the right of Figure 3). A few phenolic compounds (square 3 on the right of Figure 3), including rosmarinic acid (feature 32), were identified, along with a small cluster of flavonoids containing apigenin-glucuronide (feature 28) (square 4 on the right of Figure 3) in the aerial parts. Phenolic compounds contribute to the antioxidant properties of the plant. Rosmarinic acid and scutellarein 4′-methyl ether 7-O-glucuronide, both identified in the aerial parts of P. barbatus, have been shown to be correlated with the antioxidant activity of a water extract [16].
The overall composition of the root and aerial parts of plants grown in an aeroponic system is close to that described in plants grown in more conventional systems, with diterpenoids representing a large part of the composition of C. forskohlii and the presence of a few phenolic compounds. Based on this molecular network, a deeper exploration of the phytochemistry and annotation of the top 25 compounds impacted by the use of LEDs during cultivation (Section 2.2.2) was performed.
2.2.2. Identification of Compounds Impacted by LEDs
In order to identify which metabolites were the most impacted by the addition of LEDs during cultivation, supervised multivariate analyses were employed. A PLS-DA (Partial Least Squares Discriminant Analysis) was performed using the peak list extracted from MetaboScape, and the top 25 Variables Important in Projection (VIP score > 1) were investigated.
Figure 4a presents the PLS-DA model based on the UHPLC-HRMS analyses in positive ionization mode of the root samples for control and LED conditions after 18 weeks of cultivation. Component 1 describes 95.4% of the variance, while component 2 represents 0.8%. The model achieved a quality prediction of R2 = 0.99 and a predictive ability of Q2 = 0.99. As shown in Figure 4a, two distinct groups can be easily distinguished in component 1, with itinerary A (control) on the right and itinerary B (LEDs) on the left. The samples of each group are separated along component 2 that describes only 0.8% of the variance, indicating the good similarity of samples belonging to the same modality. Figure 4b shows the top 25 VIPs associated with group separation on component 1, with 12 molecules being more intense in the LED cultivation itinerary and 13 more intense in the control condition.
In order to identify the VIPs, the molecular formula was first determined, and MS/MS data were employed to obtain fragmentation information.
Table 2 and Table 3 summarize the compound information for the roots and aerial parts, respectively, including chromatographic information, the itinerary in which each compound is more present, the calculated molecular formula, and a putative annotation when possible. To increase the confidence level of the proposed identification, information from HRMS/MS was included; molecular formulae were searched in the LOTUS database from both the Plectranthus and Coleus genera, and HRMS/MS data were computed using SIRIUS 5.8.
For root samples in Table 2, VIPs 1, 8, and 10 are all in-source fragments corresponding to compound R1 with a tR of 3.35 min and [M + H]+ at m/z 199.0598, suggesting a molecular formula of C9H10O5. The SIRIUS putative annotation suggested vanillylmandelic acid, a phenolic compound found in Lamiaceae [31]. The fragmentation spectrum shows ions at m/z 153.0543 and m/z 135.0434, corresponding to the losses of a carboxylic acidic function (46 u) and of a water molecule (18 u).
VIP 2 corresponding to compound R12 at 13.5 min with a [M + H]+ ion at m/z 331.1908 suggests the molecular formula C20H26O4. An abietane diterpenoid derivative, the 14-hydroxytaxodione corresponding to this molecular formula was identified in Plectranthus grandidentatus [32].
VIP 3, which is a [M + Na]+ adduct, corresponds to compound R17 at m/z 331.2612 with a tR of 15.00 min and indicates a molecular formula of C20H36O2, consistent with a fatty acid derivative. In the molecular network, the corresponding node is a singleton, which does not give any further information on the structure of compound R17.
VIPs 4, 5, and 7 are all associated with compound R3 at a tR of 5.62 min with a [M + H]+ at m/z 181.0494, which corresponds to the molecular formula C9H8O4. The fragmentation spectrum shows ions at m/z 163.0393 and 145.0282, suggesting the successive loss of H2O (18 u) due to hydroxyl functions, and m/z 135.0438 corresponding to the loss of a carboxylic acid function with another loss of the hydroxyl group with a fragment at m/z 117.0333. The molecular formula and fragmentation pattern suggest that this compound corresponds to caffeic acid, with a SIRIUS score of 100.00%. Caffeic acid is a phenolic compound commonly found in plants, including in the genus Coleus [33]. The node corresponding to R3 is located in cluster 3 (Figure 3) dedicated to phenolic compounds.
VIPs 6 and 17 correspond to compound R5 at a tR of 6.60 min with the [M + H]+ at m/z 313.0711, indicating a molecular formula of C17H12O6. No compound in the genus Plectranthus or Coleus corresponds to this molecular formula, but SIRUS suggested that the compound could be 4-(3,4-Dihydroxyphenyl)-6,7-dihydroxynaphthalene-2-carboxylic acid with a score of 90.26%.
VIP 9 corresponds to R9 with m/z 189.0659 and a tR of 7.85 min. No information from the mass spectrum enabled the molecular ion to be identified; therefore, no molecular formula or annotation can be suggested.
VIP 11 corresponds to compound R18 with m/z 355.1879 and a tR of 15.30 min. m/z 355.1879 had not been fragmented in MS/MS, which does not allow an annotation. Furthermore, no discriminating adduct that would have enabled the molecular formula to be calculated was found.
VIP 12 corresponds to R4 at 5.98 min with a [M + H]+ at m/z 183.1015 and [M + Na]+ at m/z 205.0835, suggesting a molecular formula of C10H14O3. The molecular formula corresponds to a small phenolic compound, which is consistent with the retention time, but there is no further information available to suggest an annotation.
VIP 13 corresponds to R10 with a [M + H]+ m/z 365.3171 and a tR of 11.00 min. The calculated molecular formula is C22H40N2O2, a nitrogen compound.
VIP 14 corresponds to compound R16 at m/z 301.2164 and at tR of 14.90 min, and the corresponding adduct [M + Na]+ was identified with m/z 323.1981, which indicates a molecular formula of C20H28O2. In the literature, the two diterpenoids sugiol and barbatusol, corresponding to this molecular formula, were found in the genus Plectranthus [34,35].
VIP 15 corresponds to R14 at a tR of 14.10 min with a [M + H]+ at m/z 313.1798, suggesting the molecular formula to be C20H24O3. The corresponding node is located in a cluster of four nodes mainly from roots, suggesting a skeleton specific to this part of the plant. R14 has a fragmentation pattern similar to that of diterpenoids and with a retention time consistent with other diterpenoid derivatives.
VIP 16 corresponds to compound R19 with m/z 317.2097 for [M + Na]+ and a tR of 15.40 min, which suggests a molecular formula of C18H30O3. According to the retention time and the fragmentation pattern, R19 may correspond to a fatty acid.
VIP 18 corresponds to compound R13 with a tR of 13.80 min and m/z 453.3370. The [M + H]+ and [M + NH4]+ at 489.3573 and 506.3840 suggested that 453.3370 m/z was [M − 2H2O + H]+. The molecular formula is C30H48O5, which corresponds to a triterpenoid; moreover, it is located in cluster 2, which is dedicated to triterpenoids. R13 is annotated as tormentic acid previously identified in the genus Coleus [36] and in accordance with SIRIUS with a score of 87.45%.
VIPs 20 and 24 correspond to compound R6 at a tR of 7.19 min with the [M − H2O + H]+ at m/z 312.1235 and an in-source fragment at m/z 177.0547. The molecular formula of R6 is C18H19NO5. SIRIUS suggested the compound to be N-feruloyloctopamine. Ion m/z 177.0540 corresponds to the ferulic derivative part of the molecule, while ion m/z 145.0279 is due to the loss of the methoxy group. Even with the nitrogen part, the node corresponding to R6 node is located in cluster 3 dedicated to phenolic acids (ferulic derivative).
VIP 21 corresponds to R2 at a tR of 3.87 min with [M + H]+ at m/z 183.1015. The fragmentation pattern is similar to that of R4 with the same molecular formula of C10H14O3.
VIP 22 corresponds to compound R8 at a tR of 7.65 min with a fragment at m/z 135.0440 and a parent ion [M + H]+ at m/z 361.0922 and its corresponding adduct [M + NH4]+ at m/z 378.1188, which corresponds to a molecular formula of C18H16O8. This molecular formula corresponds to rosmarinic acid, with a node located in cluster 3 (phenolic acids), which is in accordance with the SIRIUS annotation with a score of 99.21%. The molecule has already been found many times in the Lamiaceae family including in the genera Plectranthus [37] and Coleus [16,38,39].
VIP 23 corresponds to R7 with m/z 267.1177 and a tR of 7.34 min. As no information from the mass spectrum made it possible to identify the molecular ion, no molecular formula or annotation can be suggested.
VIP 25 corresponds to R15 at 14.30 min, and m/z 255.1353 corresponds to a [M + Na]+ adduct with the corresponding [M + H]+ at m/z 233.1533, suggesting a molecular formula of C15H20O2. The retention time and molecular formula are consistent with a terpenoid derivative, but as the [M + H]+ was not fragmented, no further information was available to suggest an annotation.
Compounds with a retention time in the first part of the chromatogram seem to be more positively impacted by the use of LEDs and vice versa in the second half. LEDs seem to improve the intensity of phenolic compounds such as vanillylmandelic acid (R1), caffeic acid (R3), or rosmarinic acid (R8) in the roots of C. forskohlii, whereas they seem to decrease the amount of the most non-polar compounds such as diterpenoids and triterpenoids. As for Ocimum basilicum, from the same family as C. forskohlii, the artificial LED lighting seems to enhance the plant’s phenolic content, as phenolic compounds have UV radiation protection properties [40]. Forskolin, previously quantified in the roots (Section 2.1) and positively influenced by LED lighting, does not appear here in the top 25 VIPs.
The same work was performed on the aerial parts of C. forskohlii to better understand the impact of LEDs on the whole plant as LEDs illuminate the aerial part. Figure 4a presents the PLS-DA model. Component 1 accounts for 83% of the variance and component 2 for 7.6%, with an R2 of 0.97 and a predictive ability Q2 of 0.98. In Figure 4c, two groups can be easily distinguished in component 1, with itinerary A (control) on the right and itinerary B (LEDs) on the left. Figure 4d presents the corresponding top 25 VIP scores, with 18 molecules being more intense for the cultivation itinerary using LEDs and 7 for the control itinerary.
VIPs 1, 4, 11, and 14 are related to AP10 at a tR of 12.81 min and correspond to [M + Na]+ at m/z 433.2202, [M − H2O + H]+ at m/z 375.2170, and two fragments, from the [M + H]+ at m/z 411.2377, m/z 315.1952 and m/z 297.1731. With a molecular formula of C22H34O7, AP10 can be identified as forskolin.
VIP 2 with m/z 320.2564 and a tR of 15.47 min, corresponds to compound AP20. The calculated molecular formula is C18H35NO2. The fragmentation spectrum of the [M + H]+ at m/z 298.2753 of AP20 indicates the loss of a molecule of water and the loss of the primary amide function with an ion fragment at m/z 245.2266 corresponding to fatty amide derivatives.
VIPs 3 and 12 correspond to AP7 at a tR of 7.55 min with a [M + H]+ at m/z 447.0930 and a molecular formula of C21H18O11. The ion at m/z 271.0604 corresponds to aglycone after the loss of the glucuronide moiety (176 u). AP7 can be annotated as apigenin-glucuronide with a SIRIUS score at 96.74%, which is supported by the GNPS data library. The AP7 node is located in cluster 4 with only three nodes, meaning that only two other similar flavonoid compounds present the same fragmentation pattern in the aerial parts of C. forskohlii.
VIPs 5 and 7 correspond to compound AP5 at a tR of 6.85 min with a [M + H]+ at m/z 291.0979, which suggests that the molecular formula of AP5 is C14H13N2O4. Fragment m/z 245.0921 corresponds to the loss of a carboxylic acid function (46 u), and fragment m/z 188.0702 corresponds to the loss of the N-malonyl part of the molecule, suggesting that compound AP5 is N-malonyl-D-tryptophan with a SIRIUS score of 94.46%.
VIP 6 corresponds to AP14 with a [M + H]+ at m/z 317.2092 and a molecular formula of C20H28O3 associated with a diterpenoid. Several abietane diterpenoids correspond to this formula, such as 11,20-dihydroxysugiol identified in the aerial parts of Plectranthus cyaneus, royleanone identified in the whole plant of Plectranthus grandidentatus, and 20-Deoxocarnosol isolated from Coleus barbatus [41,42,43].
VIP 8 corresponds to compound AP6 with a [M + H]+ at m/z 330.1339 and a tR at 7.19 min. Compound AP6 shows the same fragmentation pattern as R6, and both are located in cluster 3, with an ion source common fragment at m/z 177.0548. The molecular formula is C18H19NO5, and AP6 is annotated as N-feruloyloctopamine, a hydroxycinnamic amide.
VIPs 9 and 25 correspond to AP18 with [M + H]+ at m/z 389.1965 and a fragment at m/z 311.1646 at a tR of 15.25 min, which suggests a molecular formula of C22H28O6. The fragment at m/z 311.1646 indicates the loss of an acetate group together with the loss of a hydroxyl group (78 u). Seven different structures corresponding to abietane diterpenoids, especially abeoabietane diterpenoids or cycloabietane diterpenoids, were found in the genus Plectranthus using LOTUS [44,45,46,47,48]. The location of the corresponding node in cluster 1.A also supports this annotation.
VIPs 10 and 15, which correspond to compound AP2 at a tR of 5.71 min, are two fragments at m/z 227.1281 and 191.1065 from the [M + NH4]+ adduct at m/z 406.2071. The molecular formula of compound AP2 is C18H28O9. The fragment at m/z 227.1281 corresponds to the loss of a hexose moiety (162 u) with a molecular formula of C12H18O4. The aglycone is annotated as a hydroxyjasmonic acid, which implies that AP2 is a hydroxyjasmonic hexose, a compound already described in the Lamiaceae family [49].
VIP 13 corresponding to AP16 with a [M + H]+ at m/z 371.1857 and a tR of 15.21 min has a molecular formula of C22H26O5. The node from the molecular network is located in cluster 1.A, which suggests that the compound should be annotated as a diterpenoid.
VIP 16, corresponding to compound AP3, is an ion source fragment at m/z 206.0814, itself originating from the [M + H]+ at m/z 252.0866 with a tR of 6.37 min. The molecular formula of AP3 is C12H13NO5. The node corresponding to AP3 is located in the same cluster as AP5, suggesting a structural similarity between them. SIRIUS annotated AP3 with a score of 86.64% as a dipeptide as 2-[(2-carboxyacetyl)amino]-3-phenylpropanoic.
VIP 17 corresponds to compound AP4 with a tR of 6.82 min and m/z 312.1235; it is a nitrogen compound with the molecular formula C18H17NO4. The fragment m/z 177.0547 corresponds to the ferulic derivative part of the molecule as described previously for R6 and AP6. SIRIUS suggested that compound AP4 is a hydroxycinnamic acid amide.
VIP 18, which corresponds to compound AP1 at a tR of 4.85 min, is an in-source fragment ion from [M + H]+ at m/z 227.0551 with a molecular formula of C10H10O6. AP1 corresponds to a phenolic acid with a putative annotation from SIRIUS at 64.38% of 4,6-dimethoxyisophthalic acid.
VIP 19, corresponding to compound AP15, is a [M + Na]+ adduct with m/z 453.1889 and a tR of 14.86 min. The calculated formula of C24H30O7 corresponds to a diterpenoid. A cycloabietane, (2R,2′S,3′R,4′bS,7′R,8′aS,9′S,10′S)-3′,10′-Diacetoxy-4′b,5′,6′,7′,8′,8′a,9′,10′-octahydro-9′-hydroxy-2,4′b,7′-trimethyl-8′-methylidenspiro[cyclopropan-1,2′(1′H)-phenanthren]-1′,4′(3′H)-dion, corresponding to this molecular formula was previously identified [46].
VIP 20, corresponding to AP17 at m/z 437.1940, is a [M + Na]+ adduct of the [M + H]+ at m/z 415.2117 with a tR of 15.23 min. The fragmentation pattern of the [M + H]+ corresponds to a diterpenoid, with a node located in cluster 1.A. The molecular formula of C24H30O6 corresponds to an acetylated derivative of a 6,7-secoabietane diterpene previously identified in the genus Coleus [50].
VIP 21 corresponds to the [M + Na]+ adduct of the compound AP19 with a tR of 15.33 min and m/z 417.2250. The calculated molecular formula of C22H34O6 is associated with different labdane diterpenes previously identified in Plectranthus and Coleus, including a derivative from forskolin, 9-deoxyforskolin [13], a labdane diterpenoid, which is consistent with the molecular network as the node of AP19 is found in cluster 1.B (Figure 3) where forskolin is located.
VIP 22 corresponds to AP11 with a [M + H]+ at m/z 431.2069 corresponding to the molecular formula C24H30O7, identical to that of AP15, and a tR of 13.04 min. The node is located in cluster 1.A associated with different diterpenes. As mentioned for AP15, a cycloabietane corresponding to this molecular formula was previously identified [46].
VIP 23 at a tR of 13.87 min is a [M + Na]+ adduct with m/z 320.2564 corresponding to AP13. The calculated molecular formula is C18H35NO2. As for compound AP20, the fragmentation spectrum of the [M + H]+ at m/z 298.2753 indicates the loss of a hydroxyl function and of a primary amide function with an ion fragment at m/z 245.2266, which corresponds to a fatty amide derivative.
VIP 24 corresponds to the [M + Na]+ adduct of compound AP12 with a [M + H]+ at m/z 387.1810 and a molecular formula of C22H26O6. The fragmentation of the [M + H]+ corresponds to a cycloabietane diterpenoid, coleon Z, as previously reported in Coleus spp. [51].
Itinerary A, with no LED lighting, contains more dipeptide and nitrogen compounds such as cinnamic acid amides, while itinerary B increases the content of almost all listed diterpenes, including forskolin (Compounds AP10, AP11, AP12, AP16, AP17, AP18, and AP19), which indicates that LEDs seem to stimulate the biosynthesis of diterpenoids in the aerial parts.
To summarize the results obtained by comparing the control and LED modalities on metabolite amount, phenolic compounds appear to be concentrated in the root and aerial parts when using LEDs. This may be explained by the fact that they are generally UV protectors, and a higher light stress could improve their production in order to defend the plant. We also note that nitrogen compounds are more intense in the itineraries without LED stimulation. A possible explanation is that due to a higher biomass production with LED stimulation, amino acids are totally consumed for the production of primary metabolites and are hence less available for the production of other nitrogenous derivatives. Concerning diterpenes, one of the main molecular families of C. forskohlii, their concentration is higher in the leaves under LED stimulation, with, in particular, an increase in the forskolin content. The effect of LEDs on the roots is less clear, however. Some of the diterpenes are more concentrated in the roots from itinerary B, while others are more concentrated in the roots from itinerary A. Furthermore, some families of compounds, such as triterpenoids, previously identified in the molecular network, appear to be unaffected.
2.2.1. Comparison of the Phytochemical Composition of Root and Aerial Parts
The aeroponic cultivation developed in this study under the control condition and LED lighting produced Coleus forskohlii with a forskolin content similar to that produced by C. forskohlii cultivated in the field in native countries.
As commercial products made from C. forskohlii are often root powders or extracts that contain many bioactive molecules and not only forskolin, it is important to determine the global phytochemical composition of the C. forskohlii produced in Région Centre-Val de Loire (France) using aeroponic cultivation. As C. forskohlii grew in soilless conditions without soil microorganisms and under different climate conditions, it is possible that the metabolomic composition was modified.
Molecular networking is based on the principle that molecules with similar structures tend to have similar mass fragmentation patterns. These patterns can be represented as a graph where nodes represent ions and where edges are drawn between nodes if the similarity score between them exceeds a predefined threshold. Associated nodes define a cluster that should represent molecules belonging to the same molecular family. In molecular networking, several analyses can be joined and therefore compared to help in characterizing the phytochemical profile. The roots of Coleus forskohlii have been mainly studied due to their high concentration of forskolin [28,29,30]. As the aerial parts have been less studied, building a molecular network using both samples may make it possible to associate molecules of both samples in the same cluster, thus helping in the identification of the aerial parts. Moreover, comparing the composition of the root and aerial parts can reveal common molecules or compound families, facilitating the annotation; it can also highlight the phytochemical specificity of each part of the plant. Figure 3 shows the molecular network obtained on GNPS (global natural products social molecular networking) from the UHPLC-HRMS/MS data in positive ionization mode for QC_R (Quality Control Roots: a mix of root samples from itineraries A and B) and QC_AP (Quality Control Aerial parts: a mix of aerial part samples from itineraries A and B) to be able to detect the main compounds produced in the two conditions. Each color corresponds to a part of the plant, with green representing the aerial parts and pink representing the roots. Nodes are divided into two colors when the ion is present in both samples. The size of the pie chart corresponds to the ion proportion in each sample, while the size of the node represents the sum of the ion intensities in the two samples. In the center of Figure 3, a global overview of the network is shown, with 490 nodes, including the singletons. Three clusters (square 1 on the left part of Figure 3) are dedicated to diterpenoids, with one cluster (1B) including as many molecules from roots as molecules from aerial parts. This cluster includes forskolin (feature 202), which is also present in the aerial parts but in a smaller amount. Although this cluster contains ions from both roots and aerial parts, most of the compounds are found either in the roots or in the aerial parts specifically. This, along with the fact that the other two clusters (1A and 1C) of diterpenes mainly consist of compounds extracted from the aerial parts, indicates that the aerial parts are a source of a wider diversity of diterpenes, with different skeletons. Additionally, a cluster dedicated to triterpenoids was also highlighted for the roots (square 2 on the right of Figure 3). A few phenolic compounds (square 3 on the right of Figure 3), including rosmarinic acid (feature 32), were identified, along with a small cluster of flavonoids containing apigenin-glucuronide (feature 28) (square 4 on the right of Figure 3) in the aerial parts. Phenolic compounds contribute to the antioxidant properties of the plant. Rosmarinic acid and scutellarein 4′-methyl ether 7-O-glucuronide, both identified in the aerial parts of P. barbatus, have been shown to be correlated with the antioxidant activity of a water extract [16].
The overall composition of the root and aerial parts of plants grown in an aeroponic system is close to that described in plants grown in more conventional systems, with diterpenoids representing a large part of the composition of C. forskohlii and the presence of a few phenolic compounds. Based on this molecular network, a deeper exploration of the phytochemistry and annotation of the top 25 compounds impacted by the use of LEDs during cultivation (Section 2.2.2) was performed.
2.2.2. Identification of Compounds Impacted by LEDs
In order to identify which metabolites were the most impacted by the addition of LEDs during cultivation, supervised multivariate analyses were employed. A PLS-DA (Partial Least Squares Discriminant Analysis) was performed using the peak list extracted from MetaboScape, and the top 25 Variables Important in Projection (VIP score > 1) were investigated.
Figure 4a presents the PLS-DA model based on the UHPLC-HRMS analyses in positive ionization mode of the root samples for control and LED conditions after 18 weeks of cultivation. Component 1 describes 95.4% of the variance, while component 2 represents 0.8%. The model achieved a quality prediction of R2 = 0.99 and a predictive ability of Q2 = 0.99. As shown in Figure 4a, two distinct groups can be easily distinguished in component 1, with itinerary A (control) on the right and itinerary B (LEDs) on the left. The samples of each group are separated along component 2 that describes only 0.8% of the variance, indicating the good similarity of samples belonging to the same modality. Figure 4b shows the top 25 VIPs associated with group separation on component 1, with 12 molecules being more intense in the LED cultivation itinerary and 13 more intense in the control condition.
In order to identify the VIPs, the molecular formula was first determined, and MS/MS data were employed to obtain fragmentation information.
Table 2 and Table 3 summarize the compound information for the roots and aerial parts, respectively, including chromatographic information, the itinerary in which each compound is more present, the calculated molecular formula, and a putative annotation when possible. To increase the confidence level of the proposed identification, information from HRMS/MS was included; molecular formulae were searched in the LOTUS database from both the Plectranthus and Coleus genera, and HRMS/MS data were computed using SIRIUS 5.8.
For root samples in Table 2, VIPs 1, 8, and 10 are all in-source fragments corresponding to compound R1 with a tR of 3.35 min and [M + H]+ at m/z 199.0598, suggesting a molecular formula of C9H10O5. The SIRIUS putative annotation suggested vanillylmandelic acid, a phenolic compound found in Lamiaceae [31]. The fragmentation spectrum shows ions at m/z 153.0543 and m/z 135.0434, corresponding to the losses of a carboxylic acidic function (46 u) and of a water molecule (18 u).
VIP 2 corresponding to compound R12 at 13.5 min with a [M + H]+ ion at m/z 331.1908 suggests the molecular formula C20H26O4. An abietane diterpenoid derivative, the 14-hydroxytaxodione corresponding to this molecular formula was identified in Plectranthus grandidentatus [32].
VIP 3, which is a [M + Na]+ adduct, corresponds to compound R17 at m/z 331.2612 with a tR of 15.00 min and indicates a molecular formula of C20H36O2, consistent with a fatty acid derivative. In the molecular network, the corresponding node is a singleton, which does not give any further information on the structure of compound R17.
VIPs 4, 5, and 7 are all associated with compound R3 at a tR of 5.62 min with a [M + H]+ at m/z 181.0494, which corresponds to the molecular formula C9H8O4. The fragmentation spectrum shows ions at m/z 163.0393 and 145.0282, suggesting the successive loss of H2O (18 u) due to hydroxyl functions, and m/z 135.0438 corresponding to the loss of a carboxylic acid function with another loss of the hydroxyl group with a fragment at m/z 117.0333. The molecular formula and fragmentation pattern suggest that this compound corresponds to caffeic acid, with a SIRIUS score of 100.00%. Caffeic acid is a phenolic compound commonly found in plants, including in the genus Coleus [33]. The node corresponding to R3 is located in cluster 3 (Figure 3) dedicated to phenolic compounds.
VIPs 6 and 17 correspond to compound R5 at a tR of 6.60 min with the [M + H]+ at m/z 313.0711, indicating a molecular formula of C17H12O6. No compound in the genus Plectranthus or Coleus corresponds to this molecular formula, but SIRUS suggested that the compound could be 4-(3,4-Dihydroxyphenyl)-6,7-dihydroxynaphthalene-2-carboxylic acid with a score of 90.26%.
VIP 9 corresponds to R9 with m/z 189.0659 and a tR of 7.85 min. No information from the mass spectrum enabled the molecular ion to be identified; therefore, no molecular formula or annotation can be suggested.
VIP 11 corresponds to compound R18 with m/z 355.1879 and a tR of 15.30 min. m/z 355.1879 had not been fragmented in MS/MS, which does not allow an annotation. Furthermore, no discriminating adduct that would have enabled the molecular formula to be calculated was found.
VIP 12 corresponds to R4 at 5.98 min with a [M + H]+ at m/z 183.1015 and [M + Na]+ at m/z 205.0835, suggesting a molecular formula of C10H14O3. The molecular formula corresponds to a small phenolic compound, which is consistent with the retention time, but there is no further information available to suggest an annotation.
VIP 13 corresponds to R10 with a [M + H]+ m/z 365.3171 and a tR of 11.00 min. The calculated molecular formula is C22H40N2O2, a nitrogen compound.
VIP 14 corresponds to compound R16 at m/z 301.2164 and at tR of 14.90 min, and the corresponding adduct [M + Na]+ was identified with m/z 323.1981, which indicates a molecular formula of C20H28O2. In the literature, the two diterpenoids sugiol and barbatusol, corresponding to this molecular formula, were found in the genus Plectranthus [34,35].
VIP 15 corresponds to R14 at a tR of 14.10 min with a [M + H]+ at m/z 313.1798, suggesting the molecular formula to be C20H24O3. The corresponding node is located in a cluster of four nodes mainly from roots, suggesting a skeleton specific to this part of the plant. R14 has a fragmentation pattern similar to that of diterpenoids and with a retention time consistent with other diterpenoid derivatives.
VIP 16 corresponds to compound R19 with m/z 317.2097 for [M + Na]+ and a tR of 15.40 min, which suggests a molecular formula of C18H30O3. According to the retention time and the fragmentation pattern, R19 may correspond to a fatty acid.
VIP 18 corresponds to compound R13 with a tR of 13.80 min and m/z 453.3370. The [M + H]+ and [M + NH4]+ at 489.3573 and 506.3840 suggested that 453.3370 m/z was [M − 2H2O + H]+. The molecular formula is C30H48O5, which corresponds to a triterpenoid; moreover, it is located in cluster 2, which is dedicated to triterpenoids. R13 is annotated as tormentic acid previously identified in the genus Coleus [36] and in accordance with SIRIUS with a score of 87.45%.
VIPs 20 and 24 correspond to compound R6 at a tR of 7.19 min with the [M − H2O + H]+ at m/z 312.1235 and an in-source fragment at m/z 177.0547. The molecular formula of R6 is C18H19NO5. SIRIUS suggested the compound to be N-feruloyloctopamine. Ion m/z 177.0540 corresponds to the ferulic derivative part of the molecule, while ion m/z 145.0279 is due to the loss of the methoxy group. Even with the nitrogen part, the node corresponding to R6 node is located in cluster 3 dedicated to phenolic acids (ferulic derivative).
VIP 21 corresponds to R2 at a tR of 3.87 min with [M + H]+ at m/z 183.1015. The fragmentation pattern is similar to that of R4 with the same molecular formula of C10H14O3.
VIP 22 corresponds to compound R8 at a tR of 7.65 min with a fragment at m/z 135.0440 and a parent ion [M + H]+ at m/z 361.0922 and its corresponding adduct [M + NH4]+ at m/z 378.1188, which corresponds to a molecular formula of C18H16O8. This molecular formula corresponds to rosmarinic acid, with a node located in cluster 3 (phenolic acids), which is in accordance with the SIRIUS annotation with a score of 99.21%. The molecule has already been found many times in the Lamiaceae family including in the genera Plectranthus [37] and Coleus [16,38,39].
VIP 23 corresponds to R7 with m/z 267.1177 and a tR of 7.34 min. As no information from the mass spectrum made it possible to identify the molecular ion, no molecular formula or annotation can be suggested.
VIP 25 corresponds to R15 at 14.30 min, and m/z 255.1353 corresponds to a [M + Na]+ adduct with the corresponding [M + H]+ at m/z 233.1533, suggesting a molecular formula of C15H20O2. The retention time and molecular formula are consistent with a terpenoid derivative, but as the [M + H]+ was not fragmented, no further information was available to suggest an annotation.
Compounds with a retention time in the first part of the chromatogram seem to be more positively impacted by the use of LEDs and vice versa in the second half. LEDs seem to improve the intensity of phenolic compounds such as vanillylmandelic acid (R1), caffeic acid (R3), or rosmarinic acid (R8) in the roots of C. forskohlii, whereas they seem to decrease the amount of the most non-polar compounds such as diterpenoids and triterpenoids. As for Ocimum basilicum, from the same family as C. forskohlii, the artificial LED lighting seems to enhance the plant’s phenolic content, as phenolic compounds have UV radiation protection properties [40]. Forskolin, previously quantified in the roots (Section 2.1) and positively influenced by LED lighting, does not appear here in the top 25 VIPs.
The same work was performed on the aerial parts of C. forskohlii to better understand the impact of LEDs on the whole plant as LEDs illuminate the aerial part. Figure 4a presents the PLS-DA model. Component 1 accounts for 83% of the variance and component 2 for 7.6%, with an R2 of 0.97 and a predictive ability Q2 of 0.98. In Figure 4c, two groups can be easily distinguished in component 1, with itinerary A (control) on the right and itinerary B (LEDs) on the left. Figure 4d presents the corresponding top 25 VIP scores, with 18 molecules being more intense for the cultivation itinerary using LEDs and 7 for the control itinerary.
VIPs 1, 4, 11, and 14 are related to AP10 at a tR of 12.81 min and correspond to [M + Na]+ at m/z 433.2202, [M − H2O + H]+ at m/z 375.2170, and two fragments, from the [M + H]+ at m/z 411.2377, m/z 315.1952 and m/z 297.1731. With a molecular formula of C22H34O7, AP10 can be identified as forskolin.
VIP 2 with m/z 320.2564 and a tR of 15.47 min, corresponds to compound AP20. The calculated molecular formula is C18H35NO2. The fragmentation spectrum of the [M + H]+ at m/z 298.2753 of AP20 indicates the loss of a molecule of water and the loss of the primary amide function with an ion fragment at m/z 245.2266 corresponding to fatty amide derivatives.
VIPs 3 and 12 correspond to AP7 at a tR of 7.55 min with a [M + H]+ at m/z 447.0930 and a molecular formula of C21H18O11. The ion at m/z 271.0604 corresponds to aglycone after the loss of the glucuronide moiety (176 u). AP7 can be annotated as apigenin-glucuronide with a SIRIUS score at 96.74%, which is supported by the GNPS data library. The AP7 node is located in cluster 4 with only three nodes, meaning that only two other similar flavonoid compounds present the same fragmentation pattern in the aerial parts of C. forskohlii.
VIPs 5 and 7 correspond to compound AP5 at a tR of 6.85 min with a [M + H]+ at m/z 291.0979, which suggests that the molecular formula of AP5 is C14H13N2O4. Fragment m/z 245.0921 corresponds to the loss of a carboxylic acid function (46 u), and fragment m/z 188.0702 corresponds to the loss of the N-malonyl part of the molecule, suggesting that compound AP5 is N-malonyl-D-tryptophan with a SIRIUS score of 94.46%.
VIP 6 corresponds to AP14 with a [M + H]+ at m/z 317.2092 and a molecular formula of C20H28O3 associated with a diterpenoid. Several abietane diterpenoids correspond to this formula, such as 11,20-dihydroxysugiol identified in the aerial parts of Plectranthus cyaneus, royleanone identified in the whole plant of Plectranthus grandidentatus, and 20-Deoxocarnosol isolated from Coleus barbatus [41,42,43].
VIP 8 corresponds to compound AP6 with a [M + H]+ at m/z 330.1339 and a tR at 7.19 min. Compound AP6 shows the same fragmentation pattern as R6, and both are located in cluster 3, with an ion source common fragment at m/z 177.0548. The molecular formula is C18H19NO5, and AP6 is annotated as N-feruloyloctopamine, a hydroxycinnamic amide.
VIPs 9 and 25 correspond to AP18 with [M + H]+ at m/z 389.1965 and a fragment at m/z 311.1646 at a tR of 15.25 min, which suggests a molecular formula of C22H28O6. The fragment at m/z 311.1646 indicates the loss of an acetate group together with the loss of a hydroxyl group (78 u). Seven different structures corresponding to abietane diterpenoids, especially abeoabietane diterpenoids or cycloabietane diterpenoids, were found in the genus Plectranthus using LOTUS [44,45,46,47,48]. The location of the corresponding node in cluster 1.A also supports this annotation.
VIPs 10 and 15, which correspond to compound AP2 at a tR of 5.71 min, are two fragments at m/z 227.1281 and 191.1065 from the [M + NH4]+ adduct at m/z 406.2071. The molecular formula of compound AP2 is C18H28O9. The fragment at m/z 227.1281 corresponds to the loss of a hexose moiety (162 u) with a molecular formula of C12H18O4. The aglycone is annotated as a hydroxyjasmonic acid, which implies that AP2 is a hydroxyjasmonic hexose, a compound already described in the Lamiaceae family [49].
VIP 13 corresponding to AP16 with a [M + H]+ at m/z 371.1857 and a tR of 15.21 min has a molecular formula of C22H26O5. The node from the molecular network is located in cluster 1.A, which suggests that the compound should be annotated as a diterpenoid.
VIP 16, corresponding to compound AP3, is an ion source fragment at m/z 206.0814, itself originating from the [M + H]+ at m/z 252.0866 with a tR of 6.37 min. The molecular formula of AP3 is C12H13NO5. The node corresponding to AP3 is located in the same cluster as AP5, suggesting a structural similarity between them. SIRIUS annotated AP3 with a score of 86.64% as a dipeptide as 2-[(2-carboxyacetyl)amino]-3-phenylpropanoic.
VIP 17 corresponds to compound AP4 with a tR of 6.82 min and m/z 312.1235; it is a nitrogen compound with the molecular formula C18H17NO4. The fragment m/z 177.0547 corresponds to the ferulic derivative part of the molecule as described previously for R6 and AP6. SIRIUS suggested that compound AP4 is a hydroxycinnamic acid amide.
VIP 18, which corresponds to compound AP1 at a tR of 4.85 min, is an in-source fragment ion from [M + H]+ at m/z 227.0551 with a molecular formula of C10H10O6. AP1 corresponds to a phenolic acid with a putative annotation from SIRIUS at 64.38% of 4,6-dimethoxyisophthalic acid.
VIP 19, corresponding to compound AP15, is a [M + Na]+ adduct with m/z 453.1889 and a tR of 14.86 min. The calculated formula of C24H30O7 corresponds to a diterpenoid. A cycloabietane, (2R,2′S,3′R,4′bS,7′R,8′aS,9′S,10′S)-3′,10′-Diacetoxy-4′b,5′,6′,7′,8′,8′a,9′,10′-octahydro-9′-hydroxy-2,4′b,7′-trimethyl-8′-methylidenspiro[cyclopropan-1,2′(1′H)-phenanthren]-1′,4′(3′H)-dion, corresponding to this molecular formula was previously identified [46].
VIP 20, corresponding to AP17 at m/z 437.1940, is a [M + Na]+ adduct of the [M + H]+ at m/z 415.2117 with a tR of 15.23 min. The fragmentation pattern of the [M + H]+ corresponds to a diterpenoid, with a node located in cluster 1.A. The molecular formula of C24H30O6 corresponds to an acetylated derivative of a 6,7-secoabietane diterpene previously identified in the genus Coleus [50].
VIP 21 corresponds to the [M + Na]+ adduct of the compound AP19 with a tR of 15.33 min and m/z 417.2250. The calculated molecular formula of C22H34O6 is associated with different labdane diterpenes previously identified in Plectranthus and Coleus, including a derivative from forskolin, 9-deoxyforskolin [13], a labdane diterpenoid, which is consistent with the molecular network as the node of AP19 is found in cluster 1.B (Figure 3) where forskolin is located.
VIP 22 corresponds to AP11 with a [M + H]+ at m/z 431.2069 corresponding to the molecular formula C24H30O7, identical to that of AP15, and a tR of 13.04 min. The node is located in cluster 1.A associated with different diterpenes. As mentioned for AP15, a cycloabietane corresponding to this molecular formula was previously identified [46].
VIP 23 at a tR of 13.87 min is a [M + Na]+ adduct with m/z 320.2564 corresponding to AP13. The calculated molecular formula is C18H35NO2. As for compound AP20, the fragmentation spectrum of the [M + H]+ at m/z 298.2753 indicates the loss of a hydroxyl function and of a primary amide function with an ion fragment at m/z 245.2266, which corresponds to a fatty amide derivative.
VIP 24 corresponds to the [M + Na]+ adduct of compound AP12 with a [M + H]+ at m/z 387.1810 and a molecular formula of C22H26O6. The fragmentation of the [M + H]+ corresponds to a cycloabietane diterpenoid, coleon Z, as previously reported in Coleus spp. [51].
Itinerary A, with no LED lighting, contains more dipeptide and nitrogen compounds such as cinnamic acid amides, while itinerary B increases the content of almost all listed diterpenes, including forskolin (Compounds AP10, AP11, AP12, AP16, AP17, AP18, and AP19), which indicates that LEDs seem to stimulate the biosynthesis of diterpenoids in the aerial parts.
To summarize the results obtained by comparing the control and LED modalities on metabolite amount, phenolic compounds appear to be concentrated in the root and aerial parts when using LEDs. This may be explained by the fact that they are generally UV protectors, and a higher light stress could improve their production in order to defend the plant. We also note that nitrogen compounds are more intense in the itineraries without LED stimulation. A possible explanation is that due to a higher biomass production with LED stimulation, amino acids are totally consumed for the production of primary metabolites and are hence less available for the production of other nitrogenous derivatives. Concerning diterpenes, one of the main molecular families of C. forskohlii, their concentration is higher in the leaves under LED stimulation, with, in particular, an increase in the forskolin content. The effect of LEDs on the roots is less clear, however. Some of the diterpenes are more concentrated in the roots from itinerary B, while others are more concentrated in the roots from itinerary A. Furthermore, some families of compounds, such as triterpenoids, previously identified in the molecular network, appear to be unaffected.
3. Materials and Methods
3.1. Plant Cultivation
3.1.1. Aeroponic System
The aeroponic cultivation of C. forskohlii presents several advantages such as a low consumption of water and easy access to the roots during plant growth. The aeroponic system used at the CDHRC consists of a 3 m long, 1.50 m wide cultivation tank. Polystyrene plates are placed on the tray to support the plants.
Cuttings are taken from mother plants grown at the CDHR in plastic pots under greenhouse conditions. Cuttings 5 to 8 cm high are made by cutting the base of the cutting above a node, starting from the stem apex of the aerial parts. Depending on plant development, between 1 and 3 cuttings can be made per stem. Care should be taken to ensure that leaves or leaflets are present at the top of the cutting. To prevent them from drying out, only 2 leaves are left on the cutting; the others are cut off with pruning shears. If the remaining leaves are too large, they are cut in half to prevent the cutting from dying.
The cuttings are then placed in the aeroponic system. Once the plants have roots of over 3 cm, they are placed in cultivation baskets filled with clay balls and then installed on the cultivation device. Each cultivation itinerary consists of 87 C. forskohlii plants, with a cultivation surface of 4.50 m2 for each itinerary, giving a cultivation density of 19.3 plants/m2. The system is housed in a glass greenhouse, which is kept frost-free, and a nutrient solution is diffused for 30 s every 5 min onto the roots using a pump system. A Coic-Lesaint mineral fertilization, based on a set conductivity of 1.5 mS/cm, is carried out. The system was set up on 7 June 2023 with a final collection on 17 October 2023.
3.1.2. Cultivation Itinerary
To improve biomass production or the production of compounds of interest, different itineraries were set up: (A) A control itinerary in which no abiotic stress was applied. (B) An itinerary integrating complementary lighting with a photoperiod of 16 h of daylight. The lighting is LED (83% red, 17% blue), with an intensity of 150 µmol/m2/s. (C) An itinerary with weekly application of a foliar biostimulant (Laminaveg) at a dose of 2 L/ha. (D) An itinerary with daily application of water stress. The pump is turned off for a period determined by the wilting state of the plant.
3.1.3. Harvesting and Drying
During harvesting, the roots of each plant were cut at 10 cm from their base and then dried in a greenhouse. The roots were harvested several times, after 4 weeks of cultivation and at the end after 18 weeks. The aerial parts of the plants were harvested (after 18 weeks) at the end of the cultivation and dried in an oven at 50 °C.
3.2. Phytochemicals
3.2.1. Chemicals
The absolute ethanol (>99.7%), acetonitrile (HPLC gradient grade), and formic acid (99–100%) used for extraction and HPLC-ELSD were all purchased from VWR (Fontenay-sous-Bois, France) and were HPLC gradient grade. Ultrapure water was produced with a Milli-Q EQ7000 system equipped with an LC-Pak filter from Merck (Darmstadt, Germany). UHPLC-HRMS analyses were performed with water and LC-MS grade acetonitrile from Honeywell (Seelze, Germany). Forskolin (>99%) was purchased from Extrasynthese (Genay, France), and a stock solution at 1 mg/mL was prepared in methanol.
3.2.2. Sample Preparation
An amount of 250 mg of powder of dried roots or aerial parts of C. forskohlii was extracted using ultrasound-assisted extraction with 12 mL of solvent during 1 h. The roots were extracted using 100% EtOH, while the aerial parts were extracted using a mixture of EtOH/water (80/20). The extract was then centrifuged during 10 min at 10,700× g, and the supernatant was filtered. The final extract was dried under nitrogen flow to obtain the dry crude extract. The dry extract was then dissolved with the same solvent used for extraction at a concentration of 5 mg/mL before further analysis.
All extractions for the quantification of forskolin in the roots were performed in triplicate (n = 3), while the extractions for the MS experiments, itineraries A and B of both the roots and aerial parts, were performed 5 times each (n = 5). For HRMS/MS, quality control samples were prepared: QC_AP, which is a mix of the supernatants from all aerial parts (itineraries A and B mixed), and QC_R, which is a mix of the supernatants from all roots (itineraries A and B mixed).
3.2.3. HPLC-ELSD Quantification
HPLC was performed with a Thermo Scientific Ultimate 3000 RSLC system using a binary pump, an automatic sampler, a thermostated column compartment, and an ELSD Sedex 100LT detector (Sedere, Olivet, France) in dynamic gain. The column was a Luna C18(2) (150 × 4.6 mm; 3 µm) (Phenomenex, Le Pecq, France). Gradient mode was used to separate the compounds. The mobile phase consisted of water (A) and acetonitrile (B), both acidified with 0.1% formic acid. The flow rate was fixed at 1 mL/min. Elution started with 10% B during 1.0 min. The amount of B increased up to 74% from 1 to 17.5 min and to 100% from 17.5 min to 18.5 min. The amount of B was maintained at 100% from 18.5 to 24.0 min and then decreased during 1.0 min to the initial conditions until 32 min.
A calibration range of forskolin was prepared in ethanol. Successive dilutions were made to obtain the 5 concentration solutions in the range 25–400 µg/mL (Figure S1).
All extractions for the quantification of forskolin in the roots were performed in triplicate (n = 3), and the data are presented as the mean ± standard deviation (SD). The statistical analysis was performed using XLSTAT (2017, Lumivero, Denver, CO, USA); differences between the samples were analyzed using one-way analysis of variance (ANOVA) and Tukey’s honestly significant difference with p ≤ 0.05.
3.2.4. UHPLC-HRMS and UHPLC-HRMS/MS Analyses
Chromatographic analyses were performed with a Thermo Scientific Ultimate 3000 RSLC system using a binary pump, an automatic sampler, a thermostated column compartment, and a DAD detector (200–800 nm) (Dionex, Germering, Germany).
The column was a Luna Omega C18 (150 × 2.1 mm; 1.6 µm) (Phenomenex, Le Pecq, France). Gradient mode was used to separate the compounds. The mobile phase consisted of water (A) and acetonitrile (B), both acidified with 0.1% formic acid. The flow rate was fixed at 0.5 mL/min. Elution started with 5% B during 0.3 min. The amount of B increased up to 85% from 0.5 to 15 min and to 100% from 15 min to 16 min. From 16 to 21 min, the amount of B was maintained and then decreased during 0.2 min to the initial conditions until 23 min.
The MS and MS/MS experiments were carried out on a maXis UHR-Q-TOF mass spectrometer (Bruker, Bremen, Germany) using the data-dependent acquisition mode (DDA) with an electrospray ion source (ESI) working in positive ionization mode. Nitrogen was used as the drying gas at a flow rate of 9 L/min heated at 200 °C and as the nebulizing gas at a pressure of 2 bar. The mass spectra were recorded in the m/z range 50–1650 at 1.6 Hz for MS and MS/MS. The capillary voltage was set at 4.5 kV. Two precursor ions were selected per cycle, fragmented at two collision energies (15–35 eV), and averaged into one MS/MS spectrum. All extraction replicates were analyzed using MS for statistical analysis, while the QC sample of the aerial parts (QC_AP) and the QC sample of the roots (QC_R) were analyzed using MS/MS experiments for identification of metabolites (Figures S2 and S3).
3.3. Analyses of Treatments
3.3.1. HRMS/MS Treatment
Analyses were converted to mzXML format using CompassXport (Bruker, Bremen, Germany). The mass spectrometry data were first processed with MZmine 3.8 [52]. Mass detections were fixed at 1.5 × 104 for the MS1 and 8.0 × 102 for the MS2 level. An ADAP chromatogram builder and resolver were employed, with a minimum of 4 consecutive scans at a threshold of 1.5 × 104. Alignment was performed based on a m/z tolerance of 0.005 or 8 ppm and a retention time tolerance of 0.8 min. Isotope 13C grouper, gapfilling, and filtering were then performed. A feature list comprising 490 features between 2 and 16 min was finally obtained. A molecular network was then generated using GNPS [53] with the Feature-Based Molecular Networking (FBMN) workflow [54]. A molecular network was created in which the edges were filtered to have a cosine score above 0.7 and more than 6 matched peaks. The molecular network was then visualized using Cytoscape 3.10.2 software [55].
The molecular formulae were generated using elemental composition with a mass accuracy ≤ 5 ppm. The mzXML format of the analysis was computed using SIRIUS 5.8 [56] in order to suggest an annotation. Manual interpretation together with the use of databases such as LOTUS [57] and GNPS [53] helped to perform the annotation.
3.3.2. Statistical Treatment of HRMS Data
All the MS data were processed using DataAnalysis 4.4 (Bruker, Bremen, Germany). For MS experiments, all extractions of root and aerial parts were performed 5 times (n = 5) for a total of 20 samples. MetaboScape 4.0 software (Bruker, Bremen, Germany) was used to build the feature table, a matrix containing the retention times, m/z, and intensity for each ion corresponding to the following parameters: intensity threshold was fixed at 10,000, mass range was fixed from m/z 50 to 1650, and time range between 2.5 and 16 min. The matrix generated contained 1760 variables for root analysis and 1702 variables for aerial part analysis. Then, the MetaboAnalyst 6.0 platform was employed for statistical analysis. A reduced matrix was obtained using MetaboAnalyst 6.0 by conserving only the significant variables (t-test); this treatment allowed the obtainment of a reduced matrix with 371 variables and 441 variables for the roots and aerial parts, respectively. Then, multivariate analysis was performed on the reduced matrix using PLS-DA, and VIP scores were obtained to determine the most discriminant features. The EICs (extracted ion chromatograms) of each of the Top 25 VIPs are reported (Tables S1 and S2) and were further investigated using HRMS/MS data (with itineraries A and B of each part of the plant mixed for HRMS/MS analysis) in order to identify them.
3.1. Plant Cultivation
3.1.1. Aeroponic System
The aeroponic cultivation of C. forskohlii presents several advantages such as a low consumption of water and easy access to the roots during plant growth. The aeroponic system used at the CDHRC consists of a 3 m long, 1.50 m wide cultivation tank. Polystyrene plates are placed on the tray to support the plants.
Cuttings are taken from mother plants grown at the CDHR in plastic pots under greenhouse conditions. Cuttings 5 to 8 cm high are made by cutting the base of the cutting above a node, starting from the stem apex of the aerial parts. Depending on plant development, between 1 and 3 cuttings can be made per stem. Care should be taken to ensure that leaves or leaflets are present at the top of the cutting. To prevent them from drying out, only 2 leaves are left on the cutting; the others are cut off with pruning shears. If the remaining leaves are too large, they are cut in half to prevent the cutting from dying.
The cuttings are then placed in the aeroponic system. Once the plants have roots of over 3 cm, they are placed in cultivation baskets filled with clay balls and then installed on the cultivation device. Each cultivation itinerary consists of 87 C. forskohlii plants, with a cultivation surface of 4.50 m2 for each itinerary, giving a cultivation density of 19.3 plants/m2. The system is housed in a glass greenhouse, which is kept frost-free, and a nutrient solution is diffused for 30 s every 5 min onto the roots using a pump system. A Coic-Lesaint mineral fertilization, based on a set conductivity of 1.5 mS/cm, is carried out. The system was set up on 7 June 2023 with a final collection on 17 October 2023.
3.1.2. Cultivation Itinerary
To improve biomass production or the production of compounds of interest, different itineraries were set up: (A) A control itinerary in which no abiotic stress was applied. (B) An itinerary integrating complementary lighting with a photoperiod of 16 h of daylight. The lighting is LED (83% red, 17% blue), with an intensity of 150 µmol/m2/s. (C) An itinerary with weekly application of a foliar biostimulant (Laminaveg) at a dose of 2 L/ha. (D) An itinerary with daily application of water stress. The pump is turned off for a period determined by the wilting state of the plant.
3.1.3. Harvesting and Drying
During harvesting, the roots of each plant were cut at 10 cm from their base and then dried in a greenhouse. The roots were harvested several times, after 4 weeks of cultivation and at the end after 18 weeks. The aerial parts of the plants were harvested (after 18 weeks) at the end of the cultivation and dried in an oven at 50 °C.
3.1.1. Aeroponic System
The aeroponic cultivation of C. forskohlii presents several advantages such as a low consumption of water and easy access to the roots during plant growth. The aeroponic system used at the CDHRC consists of a 3 m long, 1.50 m wide cultivation tank. Polystyrene plates are placed on the tray to support the plants.
Cuttings are taken from mother plants grown at the CDHR in plastic pots under greenhouse conditions. Cuttings 5 to 8 cm high are made by cutting the base of the cutting above a node, starting from the stem apex of the aerial parts. Depending on plant development, between 1 and 3 cuttings can be made per stem. Care should be taken to ensure that leaves or leaflets are present at the top of the cutting. To prevent them from drying out, only 2 leaves are left on the cutting; the others are cut off with pruning shears. If the remaining leaves are too large, they are cut in half to prevent the cutting from dying.
The cuttings are then placed in the aeroponic system. Once the plants have roots of over 3 cm, they are placed in cultivation baskets filled with clay balls and then installed on the cultivation device. Each cultivation itinerary consists of 87 C. forskohlii plants, with a cultivation surface of 4.50 m2 for each itinerary, giving a cultivation density of 19.3 plants/m2. The system is housed in a glass greenhouse, which is kept frost-free, and a nutrient solution is diffused for 30 s every 5 min onto the roots using a pump system. A Coic-Lesaint mineral fertilization, based on a set conductivity of 1.5 mS/cm, is carried out. The system was set up on 7 June 2023 with a final collection on 17 October 2023.
3.1.2. Cultivation Itinerary
To improve biomass production or the production of compounds of interest, different itineraries were set up: (A) A control itinerary in which no abiotic stress was applied. (B) An itinerary integrating complementary lighting with a photoperiod of 16 h of daylight. The lighting is LED (83% red, 17% blue), with an intensity of 150 µmol/m2/s. (C) An itinerary with weekly application of a foliar biostimulant (Laminaveg) at a dose of 2 L/ha. (D) An itinerary with daily application of water stress. The pump is turned off for a period determined by the wilting state of the plant.
3.1.3. Harvesting and Drying
During harvesting, the roots of each plant were cut at 10 cm from their base and then dried in a greenhouse. The roots were harvested several times, after 4 weeks of cultivation and at the end after 18 weeks. The aerial parts of the plants were harvested (after 18 weeks) at the end of the cultivation and dried in an oven at 50 °C.
3.2. Phytochemicals
3.2.1. Chemicals
The absolute ethanol (>99.7%), acetonitrile (HPLC gradient grade), and formic acid (99–100%) used for extraction and HPLC-ELSD were all purchased from VWR (Fontenay-sous-Bois, France) and were HPLC gradient grade. Ultrapure water was produced with a Milli-Q EQ7000 system equipped with an LC-Pak filter from Merck (Darmstadt, Germany). UHPLC-HRMS analyses were performed with water and LC-MS grade acetonitrile from Honeywell (Seelze, Germany). Forskolin (>99%) was purchased from Extrasynthese (Genay, France), and a stock solution at 1 mg/mL was prepared in methanol.
3.2.2. Sample Preparation
An amount of 250 mg of powder of dried roots or aerial parts of C. forskohlii was extracted using ultrasound-assisted extraction with 12 mL of solvent during 1 h. The roots were extracted using 100% EtOH, while the aerial parts were extracted using a mixture of EtOH/water (80/20). The extract was then centrifuged during 10 min at 10,700× g, and the supernatant was filtered. The final extract was dried under nitrogen flow to obtain the dry crude extract. The dry extract was then dissolved with the same solvent used for extraction at a concentration of 5 mg/mL before further analysis.
All extractions for the quantification of forskolin in the roots were performed in triplicate (n = 3), while the extractions for the MS experiments, itineraries A and B of both the roots and aerial parts, were performed 5 times each (n = 5). For HRMS/MS, quality control samples were prepared: QC_AP, which is a mix of the supernatants from all aerial parts (itineraries A and B mixed), and QC_R, which is a mix of the supernatants from all roots (itineraries A and B mixed).
3.2.3. HPLC-ELSD Quantification
HPLC was performed with a Thermo Scientific Ultimate 3000 RSLC system using a binary pump, an automatic sampler, a thermostated column compartment, and an ELSD Sedex 100LT detector (Sedere, Olivet, France) in dynamic gain. The column was a Luna C18(2) (150 × 4.6 mm; 3 µm) (Phenomenex, Le Pecq, France). Gradient mode was used to separate the compounds. The mobile phase consisted of water (A) and acetonitrile (B), both acidified with 0.1% formic acid. The flow rate was fixed at 1 mL/min. Elution started with 10% B during 1.0 min. The amount of B increased up to 74% from 1 to 17.5 min and to 100% from 17.5 min to 18.5 min. The amount of B was maintained at 100% from 18.5 to 24.0 min and then decreased during 1.0 min to the initial conditions until 32 min.
A calibration range of forskolin was prepared in ethanol. Successive dilutions were made to obtain the 5 concentration solutions in the range 25–400 µg/mL (Figure S1).
All extractions for the quantification of forskolin in the roots were performed in triplicate (n = 3), and the data are presented as the mean ± standard deviation (SD). The statistical analysis was performed using XLSTAT (2017, Lumivero, Denver, CO, USA); differences between the samples were analyzed using one-way analysis of variance (ANOVA) and Tukey’s honestly significant difference with p ≤ 0.05.
3.2.4. UHPLC-HRMS and UHPLC-HRMS/MS Analyses
Chromatographic analyses were performed with a Thermo Scientific Ultimate 3000 RSLC system using a binary pump, an automatic sampler, a thermostated column compartment, and a DAD detector (200–800 nm) (Dionex, Germering, Germany).
The column was a Luna Omega C18 (150 × 2.1 mm; 1.6 µm) (Phenomenex, Le Pecq, France). Gradient mode was used to separate the compounds. The mobile phase consisted of water (A) and acetonitrile (B), both acidified with 0.1% formic acid. The flow rate was fixed at 0.5 mL/min. Elution started with 5% B during 0.3 min. The amount of B increased up to 85% from 0.5 to 15 min and to 100% from 15 min to 16 min. From 16 to 21 min, the amount of B was maintained and then decreased during 0.2 min to the initial conditions until 23 min.
The MS and MS/MS experiments were carried out on a maXis UHR-Q-TOF mass spectrometer (Bruker, Bremen, Germany) using the data-dependent acquisition mode (DDA) with an electrospray ion source (ESI) working in positive ionization mode. Nitrogen was used as the drying gas at a flow rate of 9 L/min heated at 200 °C and as the nebulizing gas at a pressure of 2 bar. The mass spectra were recorded in the m/z range 50–1650 at 1.6 Hz for MS and MS/MS. The capillary voltage was set at 4.5 kV. Two precursor ions were selected per cycle, fragmented at two collision energies (15–35 eV), and averaged into one MS/MS spectrum. All extraction replicates were analyzed using MS for statistical analysis, while the QC sample of the aerial parts (QC_AP) and the QC sample of the roots (QC_R) were analyzed using MS/MS experiments for identification of metabolites (Figures S2 and S3).
3.2.1. Chemicals
The absolute ethanol (>99.7%), acetonitrile (HPLC gradient grade), and formic acid (99–100%) used for extraction and HPLC-ELSD were all purchased from VWR (Fontenay-sous-Bois, France) and were HPLC gradient grade. Ultrapure water was produced with a Milli-Q EQ7000 system equipped with an LC-Pak filter from Merck (Darmstadt, Germany). UHPLC-HRMS analyses were performed with water and LC-MS grade acetonitrile from Honeywell (Seelze, Germany). Forskolin (>99%) was purchased from Extrasynthese (Genay, France), and a stock solution at 1 mg/mL was prepared in methanol.
3.2.2. Sample Preparation
An amount of 250 mg of powder of dried roots or aerial parts of C. forskohlii was extracted using ultrasound-assisted extraction with 12 mL of solvent during 1 h. The roots were extracted using 100% EtOH, while the aerial parts were extracted using a mixture of EtOH/water (80/20). The extract was then centrifuged during 10 min at 10,700× g, and the supernatant was filtered. The final extract was dried under nitrogen flow to obtain the dry crude extract. The dry extract was then dissolved with the same solvent used for extraction at a concentration of 5 mg/mL before further analysis.
All extractions for the quantification of forskolin in the roots were performed in triplicate (n = 3), while the extractions for the MS experiments, itineraries A and B of both the roots and aerial parts, were performed 5 times each (n = 5). For HRMS/MS, quality control samples were prepared: QC_AP, which is a mix of the supernatants from all aerial parts (itineraries A and B mixed), and QC_R, which is a mix of the supernatants from all roots (itineraries A and B mixed).
3.2.3. HPLC-ELSD Quantification
HPLC was performed with a Thermo Scientific Ultimate 3000 RSLC system using a binary pump, an automatic sampler, a thermostated column compartment, and an ELSD Sedex 100LT detector (Sedere, Olivet, France) in dynamic gain. The column was a Luna C18(2) (150 × 4.6 mm; 3 µm) (Phenomenex, Le Pecq, France). Gradient mode was used to separate the compounds. The mobile phase consisted of water (A) and acetonitrile (B), both acidified with 0.1% formic acid. The flow rate was fixed at 1 mL/min. Elution started with 10% B during 1.0 min. The amount of B increased up to 74% from 1 to 17.5 min and to 100% from 17.5 min to 18.5 min. The amount of B was maintained at 100% from 18.5 to 24.0 min and then decreased during 1.0 min to the initial conditions until 32 min.
A calibration range of forskolin was prepared in ethanol. Successive dilutions were made to obtain the 5 concentration solutions in the range 25–400 µg/mL (Figure S1).
All extractions for the quantification of forskolin in the roots were performed in triplicate (n = 3), and the data are presented as the mean ± standard deviation (SD). The statistical analysis was performed using XLSTAT (2017, Lumivero, Denver, CO, USA); differences between the samples were analyzed using one-way analysis of variance (ANOVA) and Tukey’s honestly significant difference with p ≤ 0.05.
3.2.4. UHPLC-HRMS and UHPLC-HRMS/MS Analyses
Chromatographic analyses were performed with a Thermo Scientific Ultimate 3000 RSLC system using a binary pump, an automatic sampler, a thermostated column compartment, and a DAD detector (200–800 nm) (Dionex, Germering, Germany).
The column was a Luna Omega C18 (150 × 2.1 mm; 1.6 µm) (Phenomenex, Le Pecq, France). Gradient mode was used to separate the compounds. The mobile phase consisted of water (A) and acetonitrile (B), both acidified with 0.1% formic acid. The flow rate was fixed at 0.5 mL/min. Elution started with 5% B during 0.3 min. The amount of B increased up to 85% from 0.5 to 15 min and to 100% from 15 min to 16 min. From 16 to 21 min, the amount of B was maintained and then decreased during 0.2 min to the initial conditions until 23 min.
The MS and MS/MS experiments were carried out on a maXis UHR-Q-TOF mass spectrometer (Bruker, Bremen, Germany) using the data-dependent acquisition mode (DDA) with an electrospray ion source (ESI) working in positive ionization mode. Nitrogen was used as the drying gas at a flow rate of 9 L/min heated at 200 °C and as the nebulizing gas at a pressure of 2 bar. The mass spectra were recorded in the m/z range 50–1650 at 1.6 Hz for MS and MS/MS. The capillary voltage was set at 4.5 kV. Two precursor ions were selected per cycle, fragmented at two collision energies (15–35 eV), and averaged into one MS/MS spectrum. All extraction replicates were analyzed using MS for statistical analysis, while the QC sample of the aerial parts (QC_AP) and the QC sample of the roots (QC_R) were analyzed using MS/MS experiments for identification of metabolites (Figures S2 and S3).
3.3. Analyses of Treatments
3.3.1. HRMS/MS Treatment
Analyses were converted to mzXML format using CompassXport (Bruker, Bremen, Germany). The mass spectrometry data were first processed with MZmine 3.8 [52]. Mass detections were fixed at 1.5 × 104 for the MS1 and 8.0 × 102 for the MS2 level. An ADAP chromatogram builder and resolver were employed, with a minimum of 4 consecutive scans at a threshold of 1.5 × 104. Alignment was performed based on a m/z tolerance of 0.005 or 8 ppm and a retention time tolerance of 0.8 min. Isotope 13C grouper, gapfilling, and filtering were then performed. A feature list comprising 490 features between 2 and 16 min was finally obtained. A molecular network was then generated using GNPS [53] with the Feature-Based Molecular Networking (FBMN) workflow [54]. A molecular network was created in which the edges were filtered to have a cosine score above 0.7 and more than 6 matched peaks. The molecular network was then visualized using Cytoscape 3.10.2 software [55].
The molecular formulae were generated using elemental composition with a mass accuracy ≤ 5 ppm. The mzXML format of the analysis was computed using SIRIUS 5.8 [56] in order to suggest an annotation. Manual interpretation together with the use of databases such as LOTUS [57] and GNPS [53] helped to perform the annotation.
3.3.2. Statistical Treatment of HRMS Data
All the MS data were processed using DataAnalysis 4.4 (Bruker, Bremen, Germany). For MS experiments, all extractions of root and aerial parts were performed 5 times (n = 5) for a total of 20 samples. MetaboScape 4.0 software (Bruker, Bremen, Germany) was used to build the feature table, a matrix containing the retention times, m/z, and intensity for each ion corresponding to the following parameters: intensity threshold was fixed at 10,000, mass range was fixed from m/z 50 to 1650, and time range between 2.5 and 16 min. The matrix generated contained 1760 variables for root analysis and 1702 variables for aerial part analysis. Then, the MetaboAnalyst 6.0 platform was employed for statistical analysis. A reduced matrix was obtained using MetaboAnalyst 6.0 by conserving only the significant variables (t-test); this treatment allowed the obtainment of a reduced matrix with 371 variables and 441 variables for the roots and aerial parts, respectively. Then, multivariate analysis was performed on the reduced matrix using PLS-DA, and VIP scores were obtained to determine the most discriminant features. The EICs (extracted ion chromatograms) of each of the Top 25 VIPs are reported (Tables S1 and S2) and were further investigated using HRMS/MS data (with itineraries A and B of each part of the plant mixed for HRMS/MS analysis) in order to identify them.
3.3.1. HRMS/MS Treatment
Analyses were converted to mzXML format using CompassXport (Bruker, Bremen, Germany). The mass spectrometry data were first processed with MZmine 3.8 [52]. Mass detections were fixed at 1.5 × 104 for the MS1 and 8.0 × 102 for the MS2 level. An ADAP chromatogram builder and resolver were employed, with a minimum of 4 consecutive scans at a threshold of 1.5 × 104. Alignment was performed based on a m/z tolerance of 0.005 or 8 ppm and a retention time tolerance of 0.8 min. Isotope 13C grouper, gapfilling, and filtering were then performed. A feature list comprising 490 features between 2 and 16 min was finally obtained. A molecular network was then generated using GNPS [53] with the Feature-Based Molecular Networking (FBMN) workflow [54]. A molecular network was created in which the edges were filtered to have a cosine score above 0.7 and more than 6 matched peaks. The molecular network was then visualized using Cytoscape 3.10.2 software [55].
The molecular formulae were generated using elemental composition with a mass accuracy ≤ 5 ppm. The mzXML format of the analysis was computed using SIRIUS 5.8 [56] in order to suggest an annotation. Manual interpretation together with the use of databases such as LOTUS [57] and GNPS [53] helped to perform the annotation.
3.3.2. Statistical Treatment of HRMS Data
All the MS data were processed using DataAnalysis 4.4 (Bruker, Bremen, Germany). For MS experiments, all extractions of root and aerial parts were performed 5 times (n = 5) for a total of 20 samples. MetaboScape 4.0 software (Bruker, Bremen, Germany) was used to build the feature table, a matrix containing the retention times, m/z, and intensity for each ion corresponding to the following parameters: intensity threshold was fixed at 10,000, mass range was fixed from m/z 50 to 1650, and time range between 2.5 and 16 min. The matrix generated contained 1760 variables for root analysis and 1702 variables for aerial part analysis. Then, the MetaboAnalyst 6.0 platform was employed for statistical analysis. A reduced matrix was obtained using MetaboAnalyst 6.0 by conserving only the significant variables (t-test); this treatment allowed the obtainment of a reduced matrix with 371 variables and 441 variables for the roots and aerial parts, respectively. Then, multivariate analysis was performed on the reduced matrix using PLS-DA, and VIP scores were obtained to determine the most discriminant features. The EICs (extracted ion chromatograms) of each of the Top 25 VIPs are reported (Tables S1 and S2) and were further investigated using HRMS/MS data (with itineraries A and B of each part of the plant mixed for HRMS/MS analysis) in order to identify them.
4. Conclusions
This study is the first time that the cultivation of Coleus forskohlii using an aeroponic system is reported, allowing a local sustainable production of forskolin. Furthermore, harvesting the roots is easier and does not harm the survival of the plant. The addition of LED lighting during the cultivation process, up to October towards the end of the cultivation period when the natural photoperiod decreases, stimulates the root biomass production of C. forskohlii and positively impacts the amount of forskolin recovered, resulting in a forskolin production that is three times higher than under control conditions (710.1 ± 21.3 mg vs. 229.9 ± 17.7 mg).
The global phytochemistry profile of both parts of the plant was evaluated using UHPLC-HRMS/MS data and molecular networking. The results highlighted a high diversity of diterpenoid compounds in both parts of the plant, with forskolin and its derivatives being common. In the aerial parts, a wider variety of diterpenoids was highlighted, represented by several dedicated clusters. Additionally, triterpenoids, phenolic compounds and nitrogenous compounds were present. The overall composition of the roots is similar to the one described in the literature, suggesting that extracts produced from plants using an aeroponic cultivation system will likely have similar bioactivity and, therefore, be valuable in the health market. For aerial parts, which have been less studied than roots, this study provides a first overview of the phytochemical composition.
The aeroponic cultivation method with 16 h LED elicitation allows an increase in the production of Coleus forskohlii with an interesting yield of forskolin, making this method a very promising alternative for a local source of forskolin. Moreover, to identify the other metabolites affected by this stress, this study combined the statistical analysis of UHPLC-HRMS data (PLS-DA and VIP scores) with untargeted metabolomics using molecular networking and putative annotation obtained from GNPS, the LOTUS database, and SIRIUS computation of MS/MS data to support the annotation of the top 25 VIPs. These three databases, including experimental spectra, in silico fragmentation, and taxonomic information, improve the annotation of the molecules. The results showed that LEDs increase the phenolic compound composition in the roots, leading to higher amounts of rosmarinic acid or caffeic acid and the biosynthesis of diterpenes in the aerial parts such as forskolin, which are compounds of interest.
A study examining the influence of LEDs with varying parameters (such as duration, photoperiod, and LED color) would be valuable for better understanding their impact on the biosynthetic pathways of forskolin and other bioactive compounds. This research would provide insights into these pathways and help to evaluate the effects of different lighting conditions, ultimately optimizing their use for improved compound production. As the aerial parts of the plant are also rich in diterpenes and phenolic compounds with this culture method, future research could focus on this part to explore this chemical diversity and include bioactivity assays in order to also valorize this by-product. |
Title: M2 Tumor Associate Macrophage- (TAM-) Derived lncRNA HISLA Promotes EMT Potential in Bladder Cancer | Body:
1. Introduction
Bladder cancer (BC) causes nearly 170000 deaths worldwide annually, and it accounts for the first incidence of genitourinary cancer in China [1]. Currently, the treatment of bladder cancer is still mainly based on surgery, radiotherapy, and chemotherapy, and although the progress of surgery, chemotherapy, and molecular-targeted drugs has been developed, the prognosis of BC patients was not significantly improved which mainly due to the prone recurrence, invasion, and metastasis characteristics of BC [2, 3]. Therefore, investigating the mechanism of invasion and metastasis of bladder cancer is crucial for the treatment of bladder cancer.
Tumor-associated macrophages (TAMs) refer to the macrophages that infiltrate into the tumor microenvironment (TME) [4]. Typically, TAMs mainly polarized to M2-like macrophages which act as accomplice in malignant tumor progression [5]. For instance, CCL2-CCR2 axis was reported to induce immune evasion through PD-1 signaling in esophageal carcinogenesis via recruiting AMs [6]. TAMs contributed to ovarian cancer cell migration through secreting TGFBI and tenascin C [7]. TAM-derived CCL5 was shown to promote prostate cancer stem cells and metastasis via activating β-catenin/STAT3 signaling [8]. Furthermore, researches revealed that TAM-derived exosomes promoted the migration of gastric cancer cells by transfer of functional ApoE [9]. These findings suggested M2-TAMs as potential targets for the tumor treatment especially through regulating metastasis process.
Exosomes were secreted organelles with single membrane, which has diameter of ~100 nanometers and containing of great varieties of specific nucleic acids, lipids, and proteins [10]. As the most important vesicular transport structure between cells, exosomes have been found to play critical roles in cell communication as well as various physiological activities [11]. It has been proved that exosomes participated almost all the processes of tumor and serve essential roles in communication within the tumor microenvironment [12]. Long noncoding RNAs (lncRNAs) have been proved to be one of the most numerous communication media transmitted by exosomes and contribute to the regulation of cell proliferation, apoptosis, metastasis, autophagy, macrophage polarization, and other phenomenon of different kinds of cancers [13–15]. In bladder cancer, exosome-transmitted lncRNA PTENP1 was reported to suppress bladder cancer progression by competitively binding to microRNA-17 [16], exosomal lncRNA LNMAT2 illustrated to promote lymphatic metastasis in bladder cancer [17], and hypoxic exosomes were found to facilitate bladder tumor growth and development through transferring lncRNA UCA1 [18].
In the current manuscript, we revealed that lncRNA HISLA derived from TAMs contributed to enhance the EMT potential of BC by stabilizing β-catenin expression, and our findings suggested HISLA as a potential medical target for the bladder cancer treatment.
2. Materials and Methods
2.1. Cell Culture and M2-Like TAM Polarization
Human bladder cancer cell lines T24 and HTB-1 and human monocyte cell line THP-1 were all obtained from the Chinese Academy of Science (Shanghai, China). All cells were maintained in DMEM culture medium containing 10% FBS (Gibco), 100 U/ml penicillin, and 100 μg/ml streptomycin at 37°C supplied with 5% CO2 atmosphere. For M2-like TAM polarization, M0 THP-1 cells were incubated with IL-4 and IL-13 (R&D Systems) at 20 ng/ml concentration for 24 hours, and the markers of M2-TAMs including IL-10, CCL-18, CD206, CD163, Clever-1, and Arg-1 were detected.
2.2. Exosome Isolation and Identification
After reached 80% confluence, the polarized macrophages were incubated for 48 h in complete DMEM medium with 10% exosome-depleted FBS, and exosomes were isolated from the medium by differential centrifugation as described [19]. For identification of exosomes, transmission electron microscopy (TEM) and Nano-LC–MS/MS analysis were also performed as previously reported [9]. The purified exosomes were resuspended in PBS followed by cell treatment or RNA extraction. The PKH67 Green Fluorescent Cell Linker Kit (Sigma) was used for exosomes labelling according to the manufacturer's protocol. To examine the exosomal uptake into BC cells, the T24 cells were grown in 24-well plates and incubated with PKH67-labelled exosomes from polarized M2-like TAMs. DAPI was used to stain the nuclei, and fluorescence microscope (Zeiss, LSM700B, Germany) was used for visualization of immunofluorescence.
2.3. Wound Healing Assay
After treatment with si-Control or si-HISLA exosomes for 24 hours, BC cells were seeded into 6-well plates, and when cells were grown to 80–90% confluence, a scratch wound was made by using a 200 μl plastic pipette tip. The floating cells were washed three times by PBS. The scratches were photographed after 24 h by using microscopy.
2.4. Migration and Invasion Assays
Cell migration and invasion assays were conducted using Transwell Permeable Supports (Corning, NY, USA), and matrigel (Corning, NY, USA) was coated for invasion assay according to the manufacturer's instructions. The assays were performed as described [20].
2.5. Quantitative Real-Time PCR (qRT-PCR)
Total RNAs were extracted by using TRIZOL reagent according to the manufacturer's protocol (Invitrogen, Carlsbad, CA, USA). 2 μg RNAs were reversely transcribed by PrimeScriptTM RT reagent Kit (TaKaRa, Dalian, China). Quantitative PCR was performed by using SYBR Green PCR Master Mix (TaKaRa, Dalian, China) according to the manufacturer's instruction. GAPDH was used as an internal control, and 2^-ΔΔCT method was used for calculating the relative expression as described [21]. The primers used in the manuscript are shown in Table 1.
2.6. Western Blot Assay and Immunoprecipitation (IP)
Total protein was extracted from cells using RIPA Lysis Buffer (Thermo Scientific, USA) and quantified using BCA protein assay kit (Beyotime Biotechnology, China). Western blot and immunoprecipitation analysis were performed as described [22]. The antibodies for CD9 (sc-13118), CD81 (sc-166029), Clever-1 (sc-293254), β-actin (sc-8432), VEGF (sc-7269), and individual secondary antibodies were purchased from Santa Cruz (Santa Cruz Biotechnology, Santa Cruz, CA). The antibodies for HSP70 (#4873), Arg-1 (#93668), E-cadherin (#14472), N-cadherin (#13116), Vimentin (#5741), Snail (#3879), β-catenin (#8480), Ser33-phosphorylated β-catenin (#2009), c-Myc (#18583), GSK3β (#12456), and PCNA (#13110) were all obtained from Cell Signaling Technology (Cell Signaling Technology Inc, Beverly, USA). The working concentrations of antibodies were 1 : 1000 for all primary antibodies.
2.7. Animal Study and Immunofluorescence Experiment
Xenograft tumor experiments and in vivo lung metastasis models were approved by the Institutional Animal Care and Use Committee of Bengbu Medical College. The immunofluorescence experiments were performed as shown in previous study [23]. For lung metastasis model, 6 combined immunodeficient (SCID) mice at ~6 weeks old (Vital River, Beijing, China) were injected 1 × 106 T24 cells via the tail vein. The mice were treated with M0 or TAM exosomes for 4 weeks and followed by the examination by H&E staining. For xenograft models, T24 cells (5 × 106) were also subcutaneously injected into 5 mice followed by exosome treatment, and 4 weeks later, the mice were sacrificed and tumors were obtained.
2.8. Statistical Analysis
All data were presented as mean ± SD of three experiments. Two-tailed Student t-test was performed to determine the statistical significance, and p values < 0.05 were considered statistically significant.
2.1. Cell Culture and M2-Like TAM Polarization
Human bladder cancer cell lines T24 and HTB-1 and human monocyte cell line THP-1 were all obtained from the Chinese Academy of Science (Shanghai, China). All cells were maintained in DMEM culture medium containing 10% FBS (Gibco), 100 U/ml penicillin, and 100 μg/ml streptomycin at 37°C supplied with 5% CO2 atmosphere. For M2-like TAM polarization, M0 THP-1 cells were incubated with IL-4 and IL-13 (R&D Systems) at 20 ng/ml concentration for 24 hours, and the markers of M2-TAMs including IL-10, CCL-18, CD206, CD163, Clever-1, and Arg-1 were detected.
2.2. Exosome Isolation and Identification
After reached 80% confluence, the polarized macrophages were incubated for 48 h in complete DMEM medium with 10% exosome-depleted FBS, and exosomes were isolated from the medium by differential centrifugation as described [19]. For identification of exosomes, transmission electron microscopy (TEM) and Nano-LC–MS/MS analysis were also performed as previously reported [9]. The purified exosomes were resuspended in PBS followed by cell treatment or RNA extraction. The PKH67 Green Fluorescent Cell Linker Kit (Sigma) was used for exosomes labelling according to the manufacturer's protocol. To examine the exosomal uptake into BC cells, the T24 cells were grown in 24-well plates and incubated with PKH67-labelled exosomes from polarized M2-like TAMs. DAPI was used to stain the nuclei, and fluorescence microscope (Zeiss, LSM700B, Germany) was used for visualization of immunofluorescence.
2.3. Wound Healing Assay
After treatment with si-Control or si-HISLA exosomes for 24 hours, BC cells were seeded into 6-well plates, and when cells were grown to 80–90% confluence, a scratch wound was made by using a 200 μl plastic pipette tip. The floating cells were washed three times by PBS. The scratches were photographed after 24 h by using microscopy.
2.4. Migration and Invasion Assays
Cell migration and invasion assays were conducted using Transwell Permeable Supports (Corning, NY, USA), and matrigel (Corning, NY, USA) was coated for invasion assay according to the manufacturer's instructions. The assays were performed as described [20].
2.5. Quantitative Real-Time PCR (qRT-PCR)
Total RNAs were extracted by using TRIZOL reagent according to the manufacturer's protocol (Invitrogen, Carlsbad, CA, USA). 2 μg RNAs were reversely transcribed by PrimeScriptTM RT reagent Kit (TaKaRa, Dalian, China). Quantitative PCR was performed by using SYBR Green PCR Master Mix (TaKaRa, Dalian, China) according to the manufacturer's instruction. GAPDH was used as an internal control, and 2^-ΔΔCT method was used for calculating the relative expression as described [21]. The primers used in the manuscript are shown in Table 1.
2.6. Western Blot Assay and Immunoprecipitation (IP)
Total protein was extracted from cells using RIPA Lysis Buffer (Thermo Scientific, USA) and quantified using BCA protein assay kit (Beyotime Biotechnology, China). Western blot and immunoprecipitation analysis were performed as described [22]. The antibodies for CD9 (sc-13118), CD81 (sc-166029), Clever-1 (sc-293254), β-actin (sc-8432), VEGF (sc-7269), and individual secondary antibodies were purchased from Santa Cruz (Santa Cruz Biotechnology, Santa Cruz, CA). The antibodies for HSP70 (#4873), Arg-1 (#93668), E-cadherin (#14472), N-cadherin (#13116), Vimentin (#5741), Snail (#3879), β-catenin (#8480), Ser33-phosphorylated β-catenin (#2009), c-Myc (#18583), GSK3β (#12456), and PCNA (#13110) were all obtained from Cell Signaling Technology (Cell Signaling Technology Inc, Beverly, USA). The working concentrations of antibodies were 1 : 1000 for all primary antibodies.
2.7. Animal Study and Immunofluorescence Experiment
Xenograft tumor experiments and in vivo lung metastasis models were approved by the Institutional Animal Care and Use Committee of Bengbu Medical College. The immunofluorescence experiments were performed as shown in previous study [23]. For lung metastasis model, 6 combined immunodeficient (SCID) mice at ~6 weeks old (Vital River, Beijing, China) were injected 1 × 106 T24 cells via the tail vein. The mice were treated with M0 or TAM exosomes for 4 weeks and followed by the examination by H&E staining. For xenograft models, T24 cells (5 × 106) were also subcutaneously injected into 5 mice followed by exosome treatment, and 4 weeks later, the mice were sacrificed and tumors were obtained.
2.8. Statistical Analysis
All data were presented as mean ± SD of three experiments. Two-tailed Student t-test was performed to determine the statistical significance, and p values < 0.05 were considered statistically significant.
3. Results
3.1. Identification of M2-Like TAMs from THP-1 and TAM-Derived Exosomes
To illustrate the effect of TAM-derived exosomal lncRNA, we incubated M0 THP-1 cells with IL-4 and IL-13 to promote M2 polarization at first. The phenotype of M2-like TAMs was confirmed by qRT-PCR. As shown in Figure 1(a), the markers of M2-TAMs including IL-10, CCL-18, CD206, and CD163 were all increased by IL-4 and IL-13 administration. Consistently, protein levels of TAM markers Arg-1 and Clever-1 were also increased in IL-4 and IL-13 stimulated M0 THP-1 cells (Figure 1(b)). Moreover, we isolated exosomes from M0 and TAMs, and the identification was performed by transmission electron microscopy examination (Figure 1(c)), nanoparticle-tracking analysis (NTA) (Figure 1(d)), and western blot analysis of exosome marker proteins (Figure 1(e)). These results indicated that the THP-1 macrophages were polarized from M0- to M2-like TAM phenomenon, and the exosomes were purified from M0 and TAMs successfully.
3.2. TAM-Derived Exosome Promotes Migration, Invasion, and EMT Process of Bladder Cancer Cells
Polarized TAMs were cultured, and exosomes were isolated from the conditioned medium. To confirm that the exosomes from TAMs could be internalized by BC cells, T24 BC cells were incubated with PKH67-labelled exosomes derived from M2-TAMs. As shown in Figure 2(a), pKH67-labelled exosomes could be taken up by T24 BC cells. Due to the metastatic prone nature of BC, we examined the effect of TAM-exo in the metastasis and invasion processes. We found that TAM-exo administration obviously promotes T24 and HTB-1 cell metastasis abilities compared with M0-exo treatment (Figures 2(b) and 2(c)). Consistently, transwell assay results showed the enhanced metastasis effect of T24 cells and HTB-1 cells (Figures 2(d) and 2(e)). Furthermore, we found that the invasion abilities of T24 cells and HTB-1 cells were also aggravated by TAM-exo treatment (Figures 2(f) and 2(g)). In addition, we detected the markers of EMT process, and we found that in both T24 and HTB-1 cells, E-cadherin expression was decreased in TAM-exo-treated cells, and the expression of N-cadherin, Vimentin, VEGF, and Snail was increased upon TAM-exo administration compared with M0-exo treatment (Figure 2(h)). These results indicated that TAM-derived exosomes could be taken up by BC cells and subsequently promoted the metastasis, invasion, and EMT processes of BC cells.
3.3. lncRNA HISLA Expression Is Upregulated in TAM-Derived Exosomes
In order to investigate the role of lncRNA HISLA in BC, we detected the expression of lncRNA HISLA in T24, HTB-1, M0, and TAMs, and we found that lncRNA HISLA was significantly increased in M2-like TAMs (Figure 3(a)). Furthermore, we detected lncRNA HISLA levels in exosomes from M0 and TAMs, and we found that the expression of lncRNA HISLA was upregulated in TAM exosomes compared with M0 exosomes (Figure 3(b)). In addition, to confirm whether exosomal lncRNA HISLA derived from TAMs could be taken up by BC cells, we examined HISLA expression in TAMs or M0 exosome-treated T24 or HTB-1 cells, and we observed that both of these cells could internalized lncRNA HISLA therefore leading to the increased HISLA expression in BC cells (Figure 3(c)). These findings suggested that lncRNA HISLA expression is increased in TAM-derived exosomes, and lncRNA HISLA could be transferred from TAMs to BC cells.
3.4. Silencing of lncRNA-HISLA in TAM-exo Suppresses Migration, Invasion, and EMT Process of Bladder Cells
Since we know the overexpression of lncRNA HISLA in exosomes from TAMs, we used siRNA-targeted HISLA to silence its expression and evaluate the precise effect of exosomal HISLA in BC cells. As shown in Figure 4(a), the efficiency of HISLA siRNA in TAM-exo was confirmed by qPCR. Moreover, T24 or HTB-1 cells were incubated with TAM exosomes transfected with control siRNA or HISLA siRNA, and the abilities of metastasis were significantly suppressed in si-HISLA exosome-treated BC cells compared with the si-Control groups (Figures 4(b) and 4(c)). Consistent with the wound healing assay results, transwell data also indicated that metastasis ability was inhibited in BC cells incubated with HISLA-silenced TAM exosomes (Figures 4(d) and 4(e)). Similar results of invasion ability were observed in BC cells after HISLA siRNA exosome treatment (Figures 4(f) and 4(g)). In addition, lncRNA HISLA siRNA-treated exosomes led to the increased expression of E-cadherin and downregulation of N-cadherin, Vimentin, and VEGF, which meant that silencing of HISLA in TAM-exo suppressed the EMT potential of BC cells (Figure 4(h)). Furthermore, we performed in vivo metastatic and xenograft experiments, and we found that silencing of HISLA significantly alleviated lung metastasis of tumor cells and suppressed tumor development (Figures 4(i) and 4(j)).
3.5. Exosomal lncRNA-HISLA from TAMs Promotes Wnt/β-Catenin Signaling Activation in Bladder Cells
Wnt/β-catenin signaling pathway has been proved to play crucial roles in the regulation of bladder cancer progression and contributes to EMT process [24–26]; therefore, we investigated whether TAM exosomal lncRNA HISLA could regulate EMT ability of BC cells through Wnt/β-catenin signaling. As shown in Figures 5(a) and 5(b), silencing of HISLA in TAM-exo led to the decreased expression of total β-catenin, nuclei β-catenin, and downstream c-Myc in both T24 and HTB-1 cells. Moreover, the levels of Ser33-phosphorylated β-catenin which lead to the proteasomal degradation of β-catenin were significantly increased in BC cells treated with HISLA-silenced TAMs-exo, while the expression of GSK3β which is responsible for Ser33-phosphorylation of β-catenin was not affected during si-HISLA or si-Control TAM-exo-treated BC cells (Figures 5(a) and 5(b)). Consistent results of HISLA overexpressed TAM-exo-treated BC cells were observed as shown in Figures 5(c) and 5(d) that overexpression of HISLA in exosomes derived from TAMs conferred to the increased β-catenin expression and increased activation of β-catenin. These findings indicated that TAM-derived exosomal lncRNA HISLA promoted the activation of Wnt/β-catenin signaling pathway.
3.6. Exosomal lncRNA-HISLA Derived from TAMs Stabilized β-Catenin in BC Cells through Preventing Interaction between GSK3β and β-Catenin
Cycloheximide (CHX) was always used for the analysis of protein half-life; therefore, we used CHX to examine the effect of HISLA on β-catenin stabilization. We found that silencing of HISLA in TAM-exo obviously enhanced the degradation of β-catenin (Figures 6(a) and 6(b)). GSK3β was well-known to interact with β-catenin and promote the phosphorylation of β-catenin at Ser33, Ser37, and Thr41, therefore to promote the proteasomal degradation of β-catenin [27]. Therefore, we investigated whether lncRNA HISLA contributed to the interaction between GSK3β and β-catenin. Interestingly, we found that overexpression of HISLA in TAM-exo significantly interrupted the interaction between GSK3β and β-catenin (Figure 6(c)), while silencing of HISLA in exosome derived from TAMs enhanced the interaction among GSK3β and β-catenin (Figure 6(d)) with the existence of mg132 which was a widely used proteasome inhibitor. Furthermore, we performed immunofluorescence experiment, and we found that overexpression of HISLA significantly inhibited the interaction between GSK3β and β-catenin (Figure 6(e)). By using rescue experiment, we confirmed that HISLA regulated EMT potential mainly through β-catenin signaling (Figure 6(f)). These data revealed that exosomal lncRNA derived from TAMs inhibited the interaction between GSK3β and β-catenin, therefore suppressed the phosphorylation of β-catenin, and the stabilized β-catenin led to the abnormal activation of EMT process and promotion of metastasis and invasion of BC cells.
3.1. Identification of M2-Like TAMs from THP-1 and TAM-Derived Exosomes
To illustrate the effect of TAM-derived exosomal lncRNA, we incubated M0 THP-1 cells with IL-4 and IL-13 to promote M2 polarization at first. The phenotype of M2-like TAMs was confirmed by qRT-PCR. As shown in Figure 1(a), the markers of M2-TAMs including IL-10, CCL-18, CD206, and CD163 were all increased by IL-4 and IL-13 administration. Consistently, protein levels of TAM markers Arg-1 and Clever-1 were also increased in IL-4 and IL-13 stimulated M0 THP-1 cells (Figure 1(b)). Moreover, we isolated exosomes from M0 and TAMs, and the identification was performed by transmission electron microscopy examination (Figure 1(c)), nanoparticle-tracking analysis (NTA) (Figure 1(d)), and western blot analysis of exosome marker proteins (Figure 1(e)). These results indicated that the THP-1 macrophages were polarized from M0- to M2-like TAM phenomenon, and the exosomes were purified from M0 and TAMs successfully.
3.2. TAM-Derived Exosome Promotes Migration, Invasion, and EMT Process of Bladder Cancer Cells
Polarized TAMs were cultured, and exosomes were isolated from the conditioned medium. To confirm that the exosomes from TAMs could be internalized by BC cells, T24 BC cells were incubated with PKH67-labelled exosomes derived from M2-TAMs. As shown in Figure 2(a), pKH67-labelled exosomes could be taken up by T24 BC cells. Due to the metastatic prone nature of BC, we examined the effect of TAM-exo in the metastasis and invasion processes. We found that TAM-exo administration obviously promotes T24 and HTB-1 cell metastasis abilities compared with M0-exo treatment (Figures 2(b) and 2(c)). Consistently, transwell assay results showed the enhanced metastasis effect of T24 cells and HTB-1 cells (Figures 2(d) and 2(e)). Furthermore, we found that the invasion abilities of T24 cells and HTB-1 cells were also aggravated by TAM-exo treatment (Figures 2(f) and 2(g)). In addition, we detected the markers of EMT process, and we found that in both T24 and HTB-1 cells, E-cadherin expression was decreased in TAM-exo-treated cells, and the expression of N-cadherin, Vimentin, VEGF, and Snail was increased upon TAM-exo administration compared with M0-exo treatment (Figure 2(h)). These results indicated that TAM-derived exosomes could be taken up by BC cells and subsequently promoted the metastasis, invasion, and EMT processes of BC cells.
3.3. lncRNA HISLA Expression Is Upregulated in TAM-Derived Exosomes
In order to investigate the role of lncRNA HISLA in BC, we detected the expression of lncRNA HISLA in T24, HTB-1, M0, and TAMs, and we found that lncRNA HISLA was significantly increased in M2-like TAMs (Figure 3(a)). Furthermore, we detected lncRNA HISLA levels in exosomes from M0 and TAMs, and we found that the expression of lncRNA HISLA was upregulated in TAM exosomes compared with M0 exosomes (Figure 3(b)). In addition, to confirm whether exosomal lncRNA HISLA derived from TAMs could be taken up by BC cells, we examined HISLA expression in TAMs or M0 exosome-treated T24 or HTB-1 cells, and we observed that both of these cells could internalized lncRNA HISLA therefore leading to the increased HISLA expression in BC cells (Figure 3(c)). These findings suggested that lncRNA HISLA expression is increased in TAM-derived exosomes, and lncRNA HISLA could be transferred from TAMs to BC cells.
3.4. Silencing of lncRNA-HISLA in TAM-exo Suppresses Migration, Invasion, and EMT Process of Bladder Cells
Since we know the overexpression of lncRNA HISLA in exosomes from TAMs, we used siRNA-targeted HISLA to silence its expression and evaluate the precise effect of exosomal HISLA in BC cells. As shown in Figure 4(a), the efficiency of HISLA siRNA in TAM-exo was confirmed by qPCR. Moreover, T24 or HTB-1 cells were incubated with TAM exosomes transfected with control siRNA or HISLA siRNA, and the abilities of metastasis were significantly suppressed in si-HISLA exosome-treated BC cells compared with the si-Control groups (Figures 4(b) and 4(c)). Consistent with the wound healing assay results, transwell data also indicated that metastasis ability was inhibited in BC cells incubated with HISLA-silenced TAM exosomes (Figures 4(d) and 4(e)). Similar results of invasion ability were observed in BC cells after HISLA siRNA exosome treatment (Figures 4(f) and 4(g)). In addition, lncRNA HISLA siRNA-treated exosomes led to the increased expression of E-cadherin and downregulation of N-cadherin, Vimentin, and VEGF, which meant that silencing of HISLA in TAM-exo suppressed the EMT potential of BC cells (Figure 4(h)). Furthermore, we performed in vivo metastatic and xenograft experiments, and we found that silencing of HISLA significantly alleviated lung metastasis of tumor cells and suppressed tumor development (Figures 4(i) and 4(j)).
3.5. Exosomal lncRNA-HISLA from TAMs Promotes Wnt/β-Catenin Signaling Activation in Bladder Cells
Wnt/β-catenin signaling pathway has been proved to play crucial roles in the regulation of bladder cancer progression and contributes to EMT process [24–26]; therefore, we investigated whether TAM exosomal lncRNA HISLA could regulate EMT ability of BC cells through Wnt/β-catenin signaling. As shown in Figures 5(a) and 5(b), silencing of HISLA in TAM-exo led to the decreased expression of total β-catenin, nuclei β-catenin, and downstream c-Myc in both T24 and HTB-1 cells. Moreover, the levels of Ser33-phosphorylated β-catenin which lead to the proteasomal degradation of β-catenin were significantly increased in BC cells treated with HISLA-silenced TAMs-exo, while the expression of GSK3β which is responsible for Ser33-phosphorylation of β-catenin was not affected during si-HISLA or si-Control TAM-exo-treated BC cells (Figures 5(a) and 5(b)). Consistent results of HISLA overexpressed TAM-exo-treated BC cells were observed as shown in Figures 5(c) and 5(d) that overexpression of HISLA in exosomes derived from TAMs conferred to the increased β-catenin expression and increased activation of β-catenin. These findings indicated that TAM-derived exosomal lncRNA HISLA promoted the activation of Wnt/β-catenin signaling pathway.
3.6. Exosomal lncRNA-HISLA Derived from TAMs Stabilized β-Catenin in BC Cells through Preventing Interaction between GSK3β and β-Catenin
Cycloheximide (CHX) was always used for the analysis of protein half-life; therefore, we used CHX to examine the effect of HISLA on β-catenin stabilization. We found that silencing of HISLA in TAM-exo obviously enhanced the degradation of β-catenin (Figures 6(a) and 6(b)). GSK3β was well-known to interact with β-catenin and promote the phosphorylation of β-catenin at Ser33, Ser37, and Thr41, therefore to promote the proteasomal degradation of β-catenin [27]. Therefore, we investigated whether lncRNA HISLA contributed to the interaction between GSK3β and β-catenin. Interestingly, we found that overexpression of HISLA in TAM-exo significantly interrupted the interaction between GSK3β and β-catenin (Figure 6(c)), while silencing of HISLA in exosome derived from TAMs enhanced the interaction among GSK3β and β-catenin (Figure 6(d)) with the existence of mg132 which was a widely used proteasome inhibitor. Furthermore, we performed immunofluorescence experiment, and we found that overexpression of HISLA significantly inhibited the interaction between GSK3β and β-catenin (Figure 6(e)). By using rescue experiment, we confirmed that HISLA regulated EMT potential mainly through β-catenin signaling (Figure 6(f)). These data revealed that exosomal lncRNA derived from TAMs inhibited the interaction between GSK3β and β-catenin, therefore suppressed the phosphorylation of β-catenin, and the stabilized β-catenin led to the abnormal activation of EMT process and promotion of metastasis and invasion of BC cells.
4. Discussion
In the current study, we illustrated the role of TAM exosome-derived lncRNA HISLA in metastasis, invasion abilities, and EMT potential of BC cells. To the best of our knowledge, this manuscript is the first publication about the relationship between TAM-derived exosomal lncRNA HISLA and bladder cancer.
Tumor associated macrophages (TAMs) are macrophages infiltrating into tumor tissue and an important component of tumor microenvironment (TME), and the abundance of TAMs in tumor tissues is always correlated with poor prognosis of solid tumors usually [28]. In bladder cancer, TAM has been found to be closely related to the progression of bladder cancer. For instance, M2-like TAMs driven by specific genomic alterations were reported to be associated with prognosis in bladder cancer [29]. M2-like TAM infiltration in TME also observed to increase the expression of CXCL8, which promoted the cancer progression by affecting migration and invasion processes of bladder cells [30]. In turn, tumor cells will also enhance their own development by promoting M2 polarization. For example, previous study showed that BMP4 ligands of bladder cells induced M2-like TAM polarization and favored bladder cancer progression [31]. Bladder cancer cells could also secrete exosomal miR-21 to promote cancer development by promoting activation of STAT3-induced M2 polarization [32]. LINC01140 in bladder cells were found to modulate macrophage M2-TAM polarization and aggravated aggressiveness of bladder cancer cell [33]. In the current study, we investigated the effect of TAM-derived exosomes in bladder cancer progression. The M0 THP-1 cells were incubated with IL-4 and IL-13 to induce M2-TAM phenotype, and the expression of markers of M2-TAMs such as IL-10, CCL-18, CD206, CD163, Clever-1, and Arg-1 was detected and confirmed. Furthermore, we found that TAM-exo treatment significantly promoted the migration, invasion, and EMT potential of bladder cells compared with M0 exosome administration, which indicated that TAM infiltration may play crucial roles in bladder cell progression.
Long noncoding RNA (lncRNA) often refers to noncoding RNA with a length of more than 200 nucleotides which has been proved to be closely correlated with tumorigenesis and development [34–36]. At present, amounts of lncRNAs have been shown to contribute to nearly all the processes of bladder cancer. For instance, lncRNA-RMRP sponged miR-206 to promote proliferation, migration, and invasion of bladder cancer [37]. lncRNA CASC11 promoted bladder cancer cell proliferation through miRNA-150 [38]. lncRNA GClnc1 was found to promote proliferation and invasion of bladder cancer via activation of Myc [39]. lncRNA-SLC16A1-AS1 has been shown to induce bladder cancer metabolic reprogramming as target and coactivator of E2F1 [40]. lncRNA HIF-1α-stabilizing long noncoding RNA (HISLA) has been reported to suppress the interaction of PHD2 and HIF-1α to inhibit the hydroxylation and degradation of HIF-1α, leading to the regulation of aerobic glycolysis of breast cancer cells [41]. Serum HISLA expression in breast cancer patients was significantly increased compared to the healthy controls, which suggested lncRNA HISLA as a potential biomarker for breast cancer diagnosis and prognosis [42]. However, the role of lncRNA HILSA in bladder cancer still remains largely unknown. In the current research, we revealed that lncRNA HISLA from TAM-exo could be taken up by bladder cancer cells, and silencing of HILSA in TAM-exo alleviated the metastasis, invasion, and EMT marker expression of bladder cancer cells, which indicated that exosomal lncRNA HISLA derived from TAMs promoted EMT potential of bladder cancer cell.
Epithelial-mesenchymal transition (EMT) refers to the biological process of epithelial cells transforming into motile mesenchymal cells with interstitial phenotype through specific procedures, and it plays critical roles in embryonic development, chronic inflammation, tissue reconstruction, and cancer metastasis [43]. Wnt/β-catenin is well-known signaling pathway regulating EMT process and has been proved to be essential for the migration and invasion of various kinds of cancer cells [44]. During want/β-catenin-induced activation of EMT, the regulation of β-catenin expression is one of the most important limiting steps to control the procedure. β-Catenin is successively phosphorylated by CKI and GSK-3β, followed by reorganization by β-Trcp and ubiquitination by an E3 ubiquitin ligase complex, which lead to the proteasomal degradation of β-catenin [45]; therefore, the regulation of GSK-3β-induced β-catenin phosphorylation is crucial for the EMT activation. In this research, we found that exosomal lncRNA HISLA positive correlated with the expression of β-catenin, and negative correlation was found between HISLA expression and β-catenin Ser33 phosphorylation in BC cells. In addition, exosomal lncRNA HISLA derived from M2-like TAMs was found to stabilize β-catenin through interrupting the interaction between GSK3β and β-catenin and finally led to the overactivation of EMT abilities of BC cells.
5. Conclusion
In the current study, we revealed the bladder cancer promoting effect of exosomal lncRNA HISLA derived from M2-like TAMs by regulating β-catenin-induced EMT activation and suggested lncRNA HISLA from TAM exosomes as a potential medical target for the bladder cancer treatment. |
Title: Mitochondria and Acute Leukemia: A Clinician’s Perspective | Body:
1. Introduction
Acute leukemia, a fast-growing cancer of immature blood cells, is of two main types: ALL and AML. Although acute leukemia can affect individuals of all ages, it exhibits specific prevalence patterns, with ALL being more common in children and AML being more common in adults [1]. Biological manifestations range from relatively well-differentiated forms to those with pronounced dysregulation and a lack of differentiation [2,3]. In ALL, malignant transformation occurs at various stages of lymphoid progenitor development, commonly affecting either the B- or the T-cell lineage. Conversely, AML originates from myeloid progenitor cells, leading to the accumulation of immature myeloblasts in the bone marrow and peripheral blood and rarely in soft tissues or skin. This unrestrained proliferation disrupts normal hematopoiesis, resulting in anemia, thrombocytopenia, and neutropenia.
The biology of acute leukemia involves a complex interplay between genetic and epigenetic alterations [4,5]. Mutations in genes that regulate the cell cycle, apoptosis, and differentiation, such as TP53, FLT3, and NPM1 in AML and NOTCH1 and TEL-AML1 in ALL, are frequently observed. These genetic abnormalities can lead to uncontrolled cell proliferation and resistance to apoptosis. Additionally, chromosomal translocations, such as the t(9;22) Philadelphia chromosome in ALL and t(15;17) in acute promyelocytic leukemia (a subtype of AML), are pivotal in leukemogenesis.
The prognosis of acute leukemia has significantly improved owing to sequential refinement and developments in chemotherapy, targeted therapies, and hematopoietic stem cell transplantation. However, outcomes vary considerably and are influenced by factors such as patient age, genetic mutations, and response to therapy. While pediatric ALL represents a success in cancer therapy with excellent outcomes, adult AML remains problematic with lower long-term survival rates. Continued research is vital for developing novel therapies and enhancing survival outcomes in patients with acute leukemia. Simultaneously, efforts must be made to minimize the long-term adverse effects of treatment, especially in pediatric patients.
2. Mitochondria and Acute Leukemia
Mitochondrial dysfunction is a feature of numerous malignancies, including acute leukemia. This malfunction results in altered energy production within cells, unregulated cell death, and increased survival of cancer cells [6,7]. Acute leukemia presents with several mitochondrial irregularities that facilitate cancer progression and resistance to treatment (Figure 1). Multiple mechanisms of mitochondrial dysregulation have been reported to cause the proliferation of leukemia clones, including higher mtDNA content, lower mitophagy, evasion of apoptosis, and metabolic shifts from the Krebs cycle (TCA) to fatty acid oxidation (FAO).
Concepts of Mitochondrial Apoptosis
Apoptosis, also known as programmed cell death, is an essential biological process by which cells undergo systematic and controlled elimination. Unlike necrosis, which results from acute injury, apoptosis is energy-dependent and intricately regulated, and thereby maintains the cellular balance. This process involves cell shrinkage, nuclear fragmentation, chromatin condensation, and the formation of apoptotic bodies, which are subsequently engulfed by neighboring cells.
Two primary pathways govern apoptosis: extrinsic (death receptor), and intrinsic (mitochondrial). The extrinsic pathway is initiated by the binding of death ligands to surface receptors (FADD/TRADD), forming the death-inducing signalling complex (DISC) and activating caspase-8 (Figure 2). Caspase 8 cleaves caspase 3/7 to induce apoptosis directly, or can also cleave pro-apoptotic BID, leading to mitochondrial outer membrane permeabilization (MOMP) by oligomerization of BAK and BAX and linking the extrinsic pathway to the intrinsic apoptosis pathway [8]. The intrinsic pathway is mediated by a delicate balance between anti-apoptotic and pro-apoptotic BCL-2 family proteins. BCL-2 anti-apoptotic proteins (such as BCL-2, BCL-XL, and MCL-1) sequester pro-apoptotic activators (BIM, BID, and PUMA) and sensitizers (BAD, NOXA, and HRK) to inhibit oligomerization and activation of pro-apoptotic effectors (BAK and BAX). Triggered by internal stress (such as DNA damage or removal of growth factors), intrinsic apoptosis shifts the apoptosis pendulum towards pro-apoptotic sensitizers, which bind to anti-apoptotic BCL-2 proteins, thereby releasing activators. The pro-apoptotic activators bind to effectors, leading to their oligomerization, which leads to MOMP and release of cytochrome c into the cytoplasm, facilitating apoptosome formation and caspase-9 activation [9].
Concepts of Mitochondrial Apoptosis
Apoptosis, also known as programmed cell death, is an essential biological process by which cells undergo systematic and controlled elimination. Unlike necrosis, which results from acute injury, apoptosis is energy-dependent and intricately regulated, and thereby maintains the cellular balance. This process involves cell shrinkage, nuclear fragmentation, chromatin condensation, and the formation of apoptotic bodies, which are subsequently engulfed by neighboring cells.
Two primary pathways govern apoptosis: extrinsic (death receptor), and intrinsic (mitochondrial). The extrinsic pathway is initiated by the binding of death ligands to surface receptors (FADD/TRADD), forming the death-inducing signalling complex (DISC) and activating caspase-8 (Figure 2). Caspase 8 cleaves caspase 3/7 to induce apoptosis directly, or can also cleave pro-apoptotic BID, leading to mitochondrial outer membrane permeabilization (MOMP) by oligomerization of BAK and BAX and linking the extrinsic pathway to the intrinsic apoptosis pathway [8]. The intrinsic pathway is mediated by a delicate balance between anti-apoptotic and pro-apoptotic BCL-2 family proteins. BCL-2 anti-apoptotic proteins (such as BCL-2, BCL-XL, and MCL-1) sequester pro-apoptotic activators (BIM, BID, and PUMA) and sensitizers (BAD, NOXA, and HRK) to inhibit oligomerization and activation of pro-apoptotic effectors (BAK and BAX). Triggered by internal stress (such as DNA damage or removal of growth factors), intrinsic apoptosis shifts the apoptosis pendulum towards pro-apoptotic sensitizers, which bind to anti-apoptotic BCL-2 proteins, thereby releasing activators. The pro-apoptotic activators bind to effectors, leading to their oligomerization, which leads to MOMP and release of cytochrome c into the cytoplasm, facilitating apoptosome formation and caspase-9 activation [9].
3. Relevance of Mitochondrial Apoptosis in Acute Leukemia
In acute leukemia, the disruption of apoptosis is pivotal in disease development and progression. Mitochondrial dysfunction and the altered expression of Bcl-2 family proteins are central to this process. Acute leukemias often exhibit higher levels of anti-apoptotic proteins, such as Bcl-2, Bcl-XL, MCL-1 and others, which stabilize the mitochondrial membranes and prevent MOMP. This confers a survival advantage to leukemia cells and promotes resistance to conventional therapies [10]. Apoptotic priming, which is measured by the concentration of exogenous pro-apoptotic peptide required to release cytochrome c, is an important factor in determining tumor response to drugs. Myeloblasts exhibit greater susceptibility to apoptotic priming than normal hematopoietic stem cells when exposed to chemotherapeutic agents, including daunorubicin, etoposide, and mitoxantrone [11]. The correlation between the drug response and apoptotic priming was significantly stronger than that between cell proliferation and BAX expression. Subcellular heterogeneity in cytochrome c release in cancer cell lines dictates the heterogeneity in response to the chemotherapeutic drug etoposide and the reduction in apoptotic priming in myeloblasts after first-line chemotherapy, potentially leading to drug resistance [12].
Targeting mitochondrial apoptosis offers a potential therapeutic strategy for acute leukemia. BH3 mimetics and inhibitors of anti-apoptotic proteins, such as Venetoclax, aim to restore apoptotic balance and promote cell death in leukemic cells. These therapies provide hope for overcoming resistance mechanisms and improving patient outcomes [13].
In summary, apoptosis, particularly through the mitochondrial pathway, is crucial for cellular homeostasis and cancer prevention. In addition to mitochondrial apoptotic priming, dysregulation of other mitochondrial functions also plays an important role in acute leukemia.
3.1. Mitochondrial Membrane Potential
The mitochondrial membrane potential (Δψm) is a critical component of ATP generation through oxidative phosphorylation and the induction of apoptosis. In leukemia cells, alterations in Δψm can contribute to resistance to apoptosis. Specifically, elevated Δψm, as noted in acute promyelocytic leukemia, can result in an increased threshold for apoptotic signalling, making cells less likely to undergo programmed cell death. This alteration can be attributed to modifications in mitochondrial ion channels and transporters, which influence Δψm and, ultimately, cell survival [14].
3.2. Mitochondrial Metabolism and Bioenergetics
Leukemia cells undergo metabolic reprogramming to fulfil their energy and biosynthetic requirements. Reprogramming often involves a shift from oxidative phosphorylation to glycolysis even in the presence of oxygen, which is known as the Warburg effect. Consequently, leukemia cells reduce their reliance on mitochondrial ATP production and diminish the impact of mitochondrial dysfunction on energy production. However, leukemia cells retain functional mitochondria to produce critical metabolites and regulate reactive oxygen species (ROS) production [15]. The anti-apoptotic proteins not only regulate the apoptosis pathway but also have been shown to possess non-canonical functions mediating cell survival [16].
3.3. Mitochondrial Biogenesis and Dynamics
Mitochondrial biogenesis and dynamics, including fission, fusion, and mitophagy, are crucial for maintaining proper mitochondrial function and structure. In acute leukemia, there is evidence of disrupted mitochondrial dynamics, which can lead to an imbalance in cellular energy levels and impaired management of reactive oxygen species (ROS). Proteins involved in these processes, such as DRP1 (Dynamin-related protein 1) for fission and mitofusins for fusion, often show altered expression or activity in leukemia cells and can be potential targets for therapy [17]. Furthermore, impaired mitophagy can result in the accumulation of damaged mitochondria, exacerbating oxidative stress and promoting the development of leukemia [18].
3.4. Molecular Mechanisms and Genetic Mutations
3.4.1. Common Genetic Mutations Affecting Mitochondrial Function
Genetic alterations that affect mitochondrial function are widespread in patients with acute leukemia. Mutations in genes, including NOTCH1, TP53 and FLT3, not only promote oncogenesis but also affect mitochondrial pathways [19,20,21]. For instance, TP53 mutations, which are prevalent in various malignancies and acute leukemia, impair mitochondrial permeability and apoptosis regulation, resulting in increased cell survival [22]. FLT3 mutations, particularly FLT3-ITD mutations, are known to enhance cellular proliferation and metabolic reprogramming, partly through mitochondrial pathways [19].
3.4.2. Molecular Pathways Influenced by These Mutations
Genetic mutations in acute leukemia affect multiple molecular pathways, including those that regulate the mitochondrial function. The PI3K/AKT/mTOR pathway, which is often activated in leukemia, promotes cell growth and survival by modulating mitochondrial metabolism and biogenesis [23]. Additionally, mutations in epigenetic regulators, such as DNMT3A and TET2, can alter gene expression profiles, which may indirectly affect mitochondrial function and resilience [24,25].
In summary, mitochondrial dysfunction in acute leukemia includes alterations in the apoptotic pathways, metabolic reprogramming, and genetic mutations. Understanding these factors will offer valuable insights into potential therapeutic approaches targeting mitochondrial vulnerabilities in leukemia cells, ultimately improving the outcomes of patients with acute leukemia.
3.1. Mitochondrial Membrane Potential
The mitochondrial membrane potential (Δψm) is a critical component of ATP generation through oxidative phosphorylation and the induction of apoptosis. In leukemia cells, alterations in Δψm can contribute to resistance to apoptosis. Specifically, elevated Δψm, as noted in acute promyelocytic leukemia, can result in an increased threshold for apoptotic signalling, making cells less likely to undergo programmed cell death. This alteration can be attributed to modifications in mitochondrial ion channels and transporters, which influence Δψm and, ultimately, cell survival [14].
3.2. Mitochondrial Metabolism and Bioenergetics
Leukemia cells undergo metabolic reprogramming to fulfil their energy and biosynthetic requirements. Reprogramming often involves a shift from oxidative phosphorylation to glycolysis even in the presence of oxygen, which is known as the Warburg effect. Consequently, leukemia cells reduce their reliance on mitochondrial ATP production and diminish the impact of mitochondrial dysfunction on energy production. However, leukemia cells retain functional mitochondria to produce critical metabolites and regulate reactive oxygen species (ROS) production [15]. The anti-apoptotic proteins not only regulate the apoptosis pathway but also have been shown to possess non-canonical functions mediating cell survival [16].
3.3. Mitochondrial Biogenesis and Dynamics
Mitochondrial biogenesis and dynamics, including fission, fusion, and mitophagy, are crucial for maintaining proper mitochondrial function and structure. In acute leukemia, there is evidence of disrupted mitochondrial dynamics, which can lead to an imbalance in cellular energy levels and impaired management of reactive oxygen species (ROS). Proteins involved in these processes, such as DRP1 (Dynamin-related protein 1) for fission and mitofusins for fusion, often show altered expression or activity in leukemia cells and can be potential targets for therapy [17]. Furthermore, impaired mitophagy can result in the accumulation of damaged mitochondria, exacerbating oxidative stress and promoting the development of leukemia [18].
3.4. Molecular Mechanisms and Genetic Mutations
3.4.1. Common Genetic Mutations Affecting Mitochondrial Function
Genetic alterations that affect mitochondrial function are widespread in patients with acute leukemia. Mutations in genes, including NOTCH1, TP53 and FLT3, not only promote oncogenesis but also affect mitochondrial pathways [19,20,21]. For instance, TP53 mutations, which are prevalent in various malignancies and acute leukemia, impair mitochondrial permeability and apoptosis regulation, resulting in increased cell survival [22]. FLT3 mutations, particularly FLT3-ITD mutations, are known to enhance cellular proliferation and metabolic reprogramming, partly through mitochondrial pathways [19].
3.4.2. Molecular Pathways Influenced by These Mutations
Genetic mutations in acute leukemia affect multiple molecular pathways, including those that regulate the mitochondrial function. The PI3K/AKT/mTOR pathway, which is often activated in leukemia, promotes cell growth and survival by modulating mitochondrial metabolism and biogenesis [23]. Additionally, mutations in epigenetic regulators, such as DNMT3A and TET2, can alter gene expression profiles, which may indirectly affect mitochondrial function and resilience [24,25].
In summary, mitochondrial dysfunction in acute leukemia includes alterations in the apoptotic pathways, metabolic reprogramming, and genetic mutations. Understanding these factors will offer valuable insights into potential therapeutic approaches targeting mitochondrial vulnerabilities in leukemia cells, ultimately improving the outcomes of patients with acute leukemia.
3.4.1. Common Genetic Mutations Affecting Mitochondrial Function
Genetic alterations that affect mitochondrial function are widespread in patients with acute leukemia. Mutations in genes, including NOTCH1, TP53 and FLT3, not only promote oncogenesis but also affect mitochondrial pathways [19,20,21]. For instance, TP53 mutations, which are prevalent in various malignancies and acute leukemia, impair mitochondrial permeability and apoptosis regulation, resulting in increased cell survival [22]. FLT3 mutations, particularly FLT3-ITD mutations, are known to enhance cellular proliferation and metabolic reprogramming, partly through mitochondrial pathways [19].
3.4.2. Molecular Pathways Influenced by These Mutations
Genetic mutations in acute leukemia affect multiple molecular pathways, including those that regulate the mitochondrial function. The PI3K/AKT/mTOR pathway, which is often activated in leukemia, promotes cell growth and survival by modulating mitochondrial metabolism and biogenesis [23]. Additionally, mutations in epigenetic regulators, such as DNMT3A and TET2, can alter gene expression profiles, which may indirectly affect mitochondrial function and resilience [24,25].
In summary, mitochondrial dysfunction in acute leukemia includes alterations in the apoptotic pathways, metabolic reprogramming, and genetic mutations. Understanding these factors will offer valuable insights into potential therapeutic approaches targeting mitochondrial vulnerabilities in leukemia cells, ultimately improving the outcomes of patients with acute leukemia.
4. Role of Mitochondrial Apoptosis in Leukemia Initiation and Progression
The impact of mitochondrial apoptosis on leukemia initiation and progression is significant as it influences leukemia cell survival and therapeutic responses.
4.1. Early Apoptotic Changes
Mitochondrial apoptosis involves a sequence of molecular events that occurs in the outer mitochondrial membrane. Cellular stress or damage signals trigger disruption of the mitochondrial membrane, which is commonly mediated by pro-apoptotic proteins, such as Bax and Bak. This disruption causes the release of cytochrome c into the cytoplasm, triggering apoptosome formation and caspase activation, ultimately leading to cell death.
In the early stages of leukemia, mutations in certain oncogenes and tumor suppressor genes often support cell survival, despite damage that typically induces apoptosis. For example, overexpression of anti-apoptotic proteins such as Bcl-2 can inhibit mitochondrial membrane permeabilization, enabling leukemia cells to avoid apoptosis [26]. Resistance to early apoptotic signals is crucial for clonal expansion of leukemia cells, paving the way for disease progression.
4.2. Influence on Leukemic Stem Cells (LSCs)
LSCs are a subpopulation of leukemia cells capable of self-renewal and sustaining leukemia clones. These cells are known for their resistance to standard treatments, largely because of their quiescent nature and strong antiapoptotic defense. Mitochondrial pathways significantly contribute to the survival and maintenance of LSCs. LSCs often exhibit altered mitochondrial dynamics, including changes in fission and fusion and shifts in metabolic functions that favor survival [27]. Additionally, LSCs use mitochondrial protective mechanisms, such as the overexpression of Bcl-2 family proteins, to prevent the release of apoptotic factors [28]. This mitochondrial reinforcement aids the survival of LSCs and their resistance to oxidative stress, which is common in chemotherapeutic treatments, making mitochondrial apoptosis a critical therapeutic target for eliminating the root cause of leukemia proliferation.
4.3. Drug Resistance Mechanisms
Drug resistance poses a significant obstacle to effective treatment of leukemia, often leading to relapse and poor outcomes.
4.4. Evasion of Apoptosis in Resistant Leukemia
Leukemia cells often develop resistance to therapy by evading apoptosis, specifically via the intrinsic mitochondrial pathway. They achieve this by upregulating anti-apoptotic proteins, such as Bcl-2, Bcl-XL, and Mcl-1, which inhibit pro-apoptotic proteins and maintain mitochondrial integrity. These proteins prevent mitochondrial outer membrane permeabilization, blocking cytochrome c release and subsequent caspase activation and thereby averting cell death. Mutations in apoptotic regulators such as p53 can also contribute to resistance. Normally, p53 enhances apoptosis in response to DNA damage by promoting the transcription of pro-apoptotic proteins, such as PUMA and NOXA. When mutated, p53 fails to perform this function, leading to an impaired apoptotic response and allowing leukemia cells to survive despite chemotherapy [21].
4.5. Role of Mitochondria in Drug Resistance
The mitochondria are centrally involved in mediating drug resistance in leukemia. In addition to regulating apoptotic proteins, the mitochondria undergo functional and dynamic alterations that aid cell survival under therapeutic stress. For example, a metabolic shift towards oxidative phosphorylation (OXPHOS) over glycolysis can provide a survival advantage to leukemia cells. This metabolic reprogramming supports energy production and reduces the levels of reactive oxygen species (ROS), which are typically cytotoxic and induce apoptosis [29].
Mitochondrial dynamics, particularly fission and fusion processes, are also altered in drug-resistant leukemia cells. Proteins such as Drp1 drive mitochondrial fission, facilitating the removal of damaged mitochondria via mitophagy, which enhances cell survival [30].
In summary, early apoptotic changes involving mitochondrial membrane permeabilization and anti-apoptotic defenses are key to leukemia transformation and clonal expansion. LSCs exploit mitochondrial mechanisms to ensure survival and resistance to therapies, posing a major challenge in achieving lasting remission. Drug resistance in leukemia is intricately linked to mitochondrial function and apoptosis (Figure 3). Understanding these mitochondrial pathways will provide the foundation for the development of novel therapeutic strategies. Targeting mitochondrial apoptotic pathways, either through the direct induction of apoptosis or the modulation of mitochondrial dynamics and bioenergetics, holds promise for overcoming resistance and improving outcomes in patients with leukemia.
4.1. Early Apoptotic Changes
Mitochondrial apoptosis involves a sequence of molecular events that occurs in the outer mitochondrial membrane. Cellular stress or damage signals trigger disruption of the mitochondrial membrane, which is commonly mediated by pro-apoptotic proteins, such as Bax and Bak. This disruption causes the release of cytochrome c into the cytoplasm, triggering apoptosome formation and caspase activation, ultimately leading to cell death.
In the early stages of leukemia, mutations in certain oncogenes and tumor suppressor genes often support cell survival, despite damage that typically induces apoptosis. For example, overexpression of anti-apoptotic proteins such as Bcl-2 can inhibit mitochondrial membrane permeabilization, enabling leukemia cells to avoid apoptosis [26]. Resistance to early apoptotic signals is crucial for clonal expansion of leukemia cells, paving the way for disease progression.
4.2. Influence on Leukemic Stem Cells (LSCs)
LSCs are a subpopulation of leukemia cells capable of self-renewal and sustaining leukemia clones. These cells are known for their resistance to standard treatments, largely because of their quiescent nature and strong antiapoptotic defense. Mitochondrial pathways significantly contribute to the survival and maintenance of LSCs. LSCs often exhibit altered mitochondrial dynamics, including changes in fission and fusion and shifts in metabolic functions that favor survival [27]. Additionally, LSCs use mitochondrial protective mechanisms, such as the overexpression of Bcl-2 family proteins, to prevent the release of apoptotic factors [28]. This mitochondrial reinforcement aids the survival of LSCs and their resistance to oxidative stress, which is common in chemotherapeutic treatments, making mitochondrial apoptosis a critical therapeutic target for eliminating the root cause of leukemia proliferation.
4.3. Drug Resistance Mechanisms
Drug resistance poses a significant obstacle to effective treatment of leukemia, often leading to relapse and poor outcomes.
4.4. Evasion of Apoptosis in Resistant Leukemia
Leukemia cells often develop resistance to therapy by evading apoptosis, specifically via the intrinsic mitochondrial pathway. They achieve this by upregulating anti-apoptotic proteins, such as Bcl-2, Bcl-XL, and Mcl-1, which inhibit pro-apoptotic proteins and maintain mitochondrial integrity. These proteins prevent mitochondrial outer membrane permeabilization, blocking cytochrome c release and subsequent caspase activation and thereby averting cell death. Mutations in apoptotic regulators such as p53 can also contribute to resistance. Normally, p53 enhances apoptosis in response to DNA damage by promoting the transcription of pro-apoptotic proteins, such as PUMA and NOXA. When mutated, p53 fails to perform this function, leading to an impaired apoptotic response and allowing leukemia cells to survive despite chemotherapy [21].
4.5. Role of Mitochondria in Drug Resistance
The mitochondria are centrally involved in mediating drug resistance in leukemia. In addition to regulating apoptotic proteins, the mitochondria undergo functional and dynamic alterations that aid cell survival under therapeutic stress. For example, a metabolic shift towards oxidative phosphorylation (OXPHOS) over glycolysis can provide a survival advantage to leukemia cells. This metabolic reprogramming supports energy production and reduces the levels of reactive oxygen species (ROS), which are typically cytotoxic and induce apoptosis [29].
Mitochondrial dynamics, particularly fission and fusion processes, are also altered in drug-resistant leukemia cells. Proteins such as Drp1 drive mitochondrial fission, facilitating the removal of damaged mitochondria via mitophagy, which enhances cell survival [30].
In summary, early apoptotic changes involving mitochondrial membrane permeabilization and anti-apoptotic defenses are key to leukemia transformation and clonal expansion. LSCs exploit mitochondrial mechanisms to ensure survival and resistance to therapies, posing a major challenge in achieving lasting remission. Drug resistance in leukemia is intricately linked to mitochondrial function and apoptosis (Figure 3). Understanding these mitochondrial pathways will provide the foundation for the development of novel therapeutic strategies. Targeting mitochondrial apoptotic pathways, either through the direct induction of apoptosis or the modulation of mitochondrial dynamics and bioenergetics, holds promise for overcoming resistance and improving outcomes in patients with leukemia.
5. Mitochondria-Targeted Therapies in Acute Leukemia
Mitochondria-targeted therapies are gaining prominence in the treatment of both acute myeloid and acute lymphoblastic acute leukemia [31,32]. Their efficacy depends on targeting of apoptotic mechanisms and mitochondrial function, which are critical for cell survival. BH3 mimetics such as venetoclax, navitoclax, and other apoptotic modulators play crucial roles in these therapies. Combining these treatments with conventional chemotherapy or novel agents is a promising approach [33,34,35,36,37,38,39]. Here, we discuss the mechanisms, combination strategies, and clinical outcomes of these innovative therapies.
5.1. BH3 Mimetics
BH3 mimetics are a class of drugs that mimic the function of BH3-only proteins and are crucial for promoting apoptosis by antagonizing the anti-apoptotic Bcl-2 family proteins [40]. Venetoclax stands out for its role in inhibiting Bcl-2, a protein that typically prevents apoptosis and is overexpressed in many patients with leukemia. By binding to Bcl-2, venetoclax frees pro-apoptotic factors such as Bax and Bak, triggering cell death through the intrinsic mitochondrial pathway [41].
Venetoclax has shown significant efficacy in AML, especially in older patients or in those who are unable to endure intensive chemotherapy. When combined with hypomethylating agents, such as azacitidine or decitabine, venetoclax results in high response rates and extended survival by creating a pro-apoptotic environment that sensitizes leukemia cells [42]. Similarly, venetoclax in combination with chemotherapy has shown promising results in ALL [32].
The dual targeting of Bcl-2 and Bcl-XL appears to be promising for ALL treatment [37]. Mcl-1 inhibitors appear interesting and target Mcl-1, which is frequently upregulated in leukemia, thereby contributing to resistance to chemotherapy. Agents such as S63845 and AMG397 disrupt Mcl-1 interactions and promote mitochondria-mediated apoptosis [43].
5.2. Other Apoptotic Modulators
In addition to BH3 mimetics, other apoptotic modulators have been investigated. SMAC mimetics belong to another class of potential drugs [44]. These compounds neutralize the inhibitors of apoptosis proteins (IAPs), which normally prevent caspase activation and apoptosis. SMAC mimetics boost apoptotic signalling by deactivating IAPs, potentially enhancing the effectiveness of other apoptotic drugs.
5.3. Combination Therapies
5.3.1. Synergistic Effects with Chemotherapy
Combining mitochondria-targeted therapies with traditional chemotherapy has demonstrated synergistic effects that enhance the treatment efficacy [45,46]. Chemotherapy induces cellular stress and DNA damage by priming leukemia cells to induce apoptosis. BH3 mimetics and other apoptotic modulators increase this effect by inhibiting anti-apoptotic proteins, thereby promoting cancer cell death through the mitochondrial pathways.
One notable combination involves venetoclax with cytotoxic agents such as cytarabine or anthracycline compounds [47]. Chemotherapy-induced DNA damage increases the expression of pro-apoptotic proteins, thereby creating an optimal environment for BH3 mimetics to maximize their pro-apoptotic effects. Clinical trials have shown that such combinations yield higher complete remission and longer survival rates in patients with AML than chemotherapy alone. The VIALE-C trial is a prime example demonstrating the enhanced efficacy of venetoclax combined with low-dose cytarabine (LDAC) in older or unfit AML patients [48]. Similarly, the combination of venetoclax with fludarabine, cytarabine, and anthracycline-based chemotherapy in patients with newly diagnosed AML showed better rates of deeper remission and successful bridging to allogeneic bone marrow transplantation without an increase in therapy-related toxicity [46].
5.3.2. Potential in Combination with Novel Agents
The potential of mitochondria-targeted therapies when combined with novel targeted agents is another promising area. Such combinations can affect multiple survival mechanisms in leukemia cells, potentially overcoming resistance pathways and improving therapeutic outcomes. For instance, combining venetoclax with FLT3 inhibitors (such as gilteritinib or midostaurin) can be particularly effective in AML patients with FLT3 mutations [49,50]. FLT3 inhibitors disrupt critical signalling pathways that drive leukemia cell proliferation. Pairing these drugs with venetoclax, which induces apoptosis by targeting Bcl-2, provides an approach for concurrently inhibiting cell growth and promoting cell death.
Combining venetoclax with IDH inhibitors (such as ivosidenib or enasidenib) targets another subset of AML patients with IDH mutations [51]. These mutations hinder cellular differentiation and contribute to leukemogenesis. IDH inhibitors rectify these metabolic anomalies, whereas venetoclax enhances apoptosis, addressing multiple facets of leukemia cell survival and potentially yielding superior treatment results.
5.1. BH3 Mimetics
BH3 mimetics are a class of drugs that mimic the function of BH3-only proteins and are crucial for promoting apoptosis by antagonizing the anti-apoptotic Bcl-2 family proteins [40]. Venetoclax stands out for its role in inhibiting Bcl-2, a protein that typically prevents apoptosis and is overexpressed in many patients with leukemia. By binding to Bcl-2, venetoclax frees pro-apoptotic factors such as Bax and Bak, triggering cell death through the intrinsic mitochondrial pathway [41].
Venetoclax has shown significant efficacy in AML, especially in older patients or in those who are unable to endure intensive chemotherapy. When combined with hypomethylating agents, such as azacitidine or decitabine, venetoclax results in high response rates and extended survival by creating a pro-apoptotic environment that sensitizes leukemia cells [42]. Similarly, venetoclax in combination with chemotherapy has shown promising results in ALL [32].
The dual targeting of Bcl-2 and Bcl-XL appears to be promising for ALL treatment [37]. Mcl-1 inhibitors appear interesting and target Mcl-1, which is frequently upregulated in leukemia, thereby contributing to resistance to chemotherapy. Agents such as S63845 and AMG397 disrupt Mcl-1 interactions and promote mitochondria-mediated apoptosis [43].
5.2. Other Apoptotic Modulators
In addition to BH3 mimetics, other apoptotic modulators have been investigated. SMAC mimetics belong to another class of potential drugs [44]. These compounds neutralize the inhibitors of apoptosis proteins (IAPs), which normally prevent caspase activation and apoptosis. SMAC mimetics boost apoptotic signalling by deactivating IAPs, potentially enhancing the effectiveness of other apoptotic drugs.
5.3. Combination Therapies
5.3.1. Synergistic Effects with Chemotherapy
Combining mitochondria-targeted therapies with traditional chemotherapy has demonstrated synergistic effects that enhance the treatment efficacy [45,46]. Chemotherapy induces cellular stress and DNA damage by priming leukemia cells to induce apoptosis. BH3 mimetics and other apoptotic modulators increase this effect by inhibiting anti-apoptotic proteins, thereby promoting cancer cell death through the mitochondrial pathways.
One notable combination involves venetoclax with cytotoxic agents such as cytarabine or anthracycline compounds [47]. Chemotherapy-induced DNA damage increases the expression of pro-apoptotic proteins, thereby creating an optimal environment for BH3 mimetics to maximize their pro-apoptotic effects. Clinical trials have shown that such combinations yield higher complete remission and longer survival rates in patients with AML than chemotherapy alone. The VIALE-C trial is a prime example demonstrating the enhanced efficacy of venetoclax combined with low-dose cytarabine (LDAC) in older or unfit AML patients [48]. Similarly, the combination of venetoclax with fludarabine, cytarabine, and anthracycline-based chemotherapy in patients with newly diagnosed AML showed better rates of deeper remission and successful bridging to allogeneic bone marrow transplantation without an increase in therapy-related toxicity [46].
5.3.2. Potential in Combination with Novel Agents
The potential of mitochondria-targeted therapies when combined with novel targeted agents is another promising area. Such combinations can affect multiple survival mechanisms in leukemia cells, potentially overcoming resistance pathways and improving therapeutic outcomes. For instance, combining venetoclax with FLT3 inhibitors (such as gilteritinib or midostaurin) can be particularly effective in AML patients with FLT3 mutations [49,50]. FLT3 inhibitors disrupt critical signalling pathways that drive leukemia cell proliferation. Pairing these drugs with venetoclax, which induces apoptosis by targeting Bcl-2, provides an approach for concurrently inhibiting cell growth and promoting cell death.
Combining venetoclax with IDH inhibitors (such as ivosidenib or enasidenib) targets another subset of AML patients with IDH mutations [51]. These mutations hinder cellular differentiation and contribute to leukemogenesis. IDH inhibitors rectify these metabolic anomalies, whereas venetoclax enhances apoptosis, addressing multiple facets of leukemia cell survival and potentially yielding superior treatment results.
5.3.1. Synergistic Effects with Chemotherapy
Combining mitochondria-targeted therapies with traditional chemotherapy has demonstrated synergistic effects that enhance the treatment efficacy [45,46]. Chemotherapy induces cellular stress and DNA damage by priming leukemia cells to induce apoptosis. BH3 mimetics and other apoptotic modulators increase this effect by inhibiting anti-apoptotic proteins, thereby promoting cancer cell death through the mitochondrial pathways.
One notable combination involves venetoclax with cytotoxic agents such as cytarabine or anthracycline compounds [47]. Chemotherapy-induced DNA damage increases the expression of pro-apoptotic proteins, thereby creating an optimal environment for BH3 mimetics to maximize their pro-apoptotic effects. Clinical trials have shown that such combinations yield higher complete remission and longer survival rates in patients with AML than chemotherapy alone. The VIALE-C trial is a prime example demonstrating the enhanced efficacy of venetoclax combined with low-dose cytarabine (LDAC) in older or unfit AML patients [48]. Similarly, the combination of venetoclax with fludarabine, cytarabine, and anthracycline-based chemotherapy in patients with newly diagnosed AML showed better rates of deeper remission and successful bridging to allogeneic bone marrow transplantation without an increase in therapy-related toxicity [46].
5.3.2. Potential in Combination with Novel Agents
The potential of mitochondria-targeted therapies when combined with novel targeted agents is another promising area. Such combinations can affect multiple survival mechanisms in leukemia cells, potentially overcoming resistance pathways and improving therapeutic outcomes. For instance, combining venetoclax with FLT3 inhibitors (such as gilteritinib or midostaurin) can be particularly effective in AML patients with FLT3 mutations [49,50]. FLT3 inhibitors disrupt critical signalling pathways that drive leukemia cell proliferation. Pairing these drugs with venetoclax, which induces apoptosis by targeting Bcl-2, provides an approach for concurrently inhibiting cell growth and promoting cell death.
Combining venetoclax with IDH inhibitors (such as ivosidenib or enasidenib) targets another subset of AML patients with IDH mutations [51]. These mutations hinder cellular differentiation and contribute to leukemogenesis. IDH inhibitors rectify these metabolic anomalies, whereas venetoclax enhances apoptosis, addressing multiple facets of leukemia cell survival and potentially yielding superior treatment results.
6. Clinical Trials and Outcomes
Overview of Ongoing and Completed Clinical Trials
Numerous clinical trials have explored the efficacy and safety of mitochondria-targeted therapies for acute leukemia, with venetoclax being central to many pivotal studies. These trials typically investigated drugs in various combinations with chemotherapy and novel agents to expand their therapeutic reach.
6.1. VIALE-A Trial
The VIALE-A trial (NCT02993523) is a landmark study that evaluated the combination of venetoclax and azacitidine versus azacitidine alone in treatment-naïve patients with AML who were ineligible for intensive chemotherapy. The results showed significantly improved overall survival and higher complete remission rates in the combination therapy group, leading to FDA approval of venetoclax for treating newly diagnosed AML [52].
6.2. VIALE-C Trial
The VIALE-C trial (NCT03069352) studied the combination of venetoclax and low-dose cytarabine (LDAC) in a similar cohort. The study also reported better outcomes with the combination therapy, although the overall survival benefit was less pronounced than in VIALE-A. However, the response rates were notably higher, underscoring the potential of venetoclax to enhance the efficacy of low-dose chemotherapy regimens. The other trials that are currently recruiting patients are summarized in Table 1.
6.3. Combinations with Intensive Chemotherapies and Novel Agents
Multiple trials have reported outcomes with the use of venetoclax in combination with more intensive chemotherapy regimens such as the ‘7 + 3’ regimen (cytarabine and daunorubicin), primarily targeting younger and better-fit patients with newly diagnosed AML [53,54,55,56]. These results indicate that integrating venetoclax into these protocols can be safely performed with promising response rates. Ongoing trials are also examining venetoclax with novel agents such as FLT3 and IDH inhibitors [57,58].
In summary, venetoclax has significantly altered the landscape of AML treatment, not only in older patients but also in those unsuitable for intensive chemotherapy. The VIALE-A and VIALE-C trials have highlighted the benefits of venetoclax in combination with hypomethylating agents and low-dose cytarabine. The improved survival and higher remission rates in these trials have established venetoclax as a critical component of AML therapy. Additionally, promising results from trials combining venetoclax with intensive chemotherapy regimens and novel targeted agents underscore the broad applicability and versatility of BH3 mimetics. These successes highlight the potential of mitochondria-targeted therapies to transform the existing treatment modalities and improve patient outcomes.
However, mitochondria-targeted therapies present significant challenges, particularly in terms of drug resistance and safety. Resistance to Bcl-2 inhibition, especially when used alone, can emerge through multiple mechanisms, including the upregulation of other anti-apoptotic proteins such as Mcl-1 and Bcl-XL, or mutations in the Bcl-2 gene [59]. Addressing these issues requires novel strategies to prevent the onset of resistance or overcome established resistance mechanisms.
The long-term safety implications associated with these medications will persist, given that they are novel, and our understanding of their interactions and potential toxicities is evolving with their increased usage. Additionally, other mitochondria-targeted agents, such as Mcl-1 inhibitors, face obstacles in clinical development, and some studies have been terminated owing to safety concerns. The narrow therapeutic window and potential off-target effects of these inhibitors underscore the challenge of precisely targeting Bcl-2 family proteins, while preserving normal cell function [60]. However, other agents are currently undergoing early phase trials [61,62].
6.1. VIALE-A Trial
The VIALE-A trial (NCT02993523) is a landmark study that evaluated the combination of venetoclax and azacitidine versus azacitidine alone in treatment-naïve patients with AML who were ineligible for intensive chemotherapy. The results showed significantly improved overall survival and higher complete remission rates in the combination therapy group, leading to FDA approval of venetoclax for treating newly diagnosed AML [52].
6.2. VIALE-C Trial
The VIALE-C trial (NCT03069352) studied the combination of venetoclax and low-dose cytarabine (LDAC) in a similar cohort. The study also reported better outcomes with the combination therapy, although the overall survival benefit was less pronounced than in VIALE-A. However, the response rates were notably higher, underscoring the potential of venetoclax to enhance the efficacy of low-dose chemotherapy regimens. The other trials that are currently recruiting patients are summarized in Table 1.
6.3. Combinations with Intensive Chemotherapies and Novel Agents
Multiple trials have reported outcomes with the use of venetoclax in combination with more intensive chemotherapy regimens such as the ‘7 + 3’ regimen (cytarabine and daunorubicin), primarily targeting younger and better-fit patients with newly diagnosed AML [53,54,55,56]. These results indicate that integrating venetoclax into these protocols can be safely performed with promising response rates. Ongoing trials are also examining venetoclax with novel agents such as FLT3 and IDH inhibitors [57,58].
In summary, venetoclax has significantly altered the landscape of AML treatment, not only in older patients but also in those unsuitable for intensive chemotherapy. The VIALE-A and VIALE-C trials have highlighted the benefits of venetoclax in combination with hypomethylating agents and low-dose cytarabine. The improved survival and higher remission rates in these trials have established venetoclax as a critical component of AML therapy. Additionally, promising results from trials combining venetoclax with intensive chemotherapy regimens and novel targeted agents underscore the broad applicability and versatility of BH3 mimetics. These successes highlight the potential of mitochondria-targeted therapies to transform the existing treatment modalities and improve patient outcomes.
However, mitochondria-targeted therapies present significant challenges, particularly in terms of drug resistance and safety. Resistance to Bcl-2 inhibition, especially when used alone, can emerge through multiple mechanisms, including the upregulation of other anti-apoptotic proteins such as Mcl-1 and Bcl-XL, or mutations in the Bcl-2 gene [59]. Addressing these issues requires novel strategies to prevent the onset of resistance or overcome established resistance mechanisms.
The long-term safety implications associated with these medications will persist, given that they are novel, and our understanding of their interactions and potential toxicities is evolving with their increased usage. Additionally, other mitochondria-targeted agents, such as Mcl-1 inhibitors, face obstacles in clinical development, and some studies have been terminated owing to safety concerns. The narrow therapeutic window and potential off-target effects of these inhibitors underscore the challenge of precisely targeting Bcl-2 family proteins, while preserving normal cell function [60]. However, other agents are currently undergoing early phase trials [61,62].
7. Future Directions and Conclusions
To advance and enhance current therapeutic approaches, mitochondria-targeted therapies must be leveraged by building upon recent advancements and incorporating these new agents in an evolving medical landscape.
7.1. Development of Next-Generation Inhibitors
Dual Inhibitors: Ongoing research has focused on developing compounds that can simultaneously inhibit multiple anti-apoptotic proteins (e.g., dual Bcl-2/Bcl-XL inhibitors) to prevent resistance and promote sustained apoptosis in leukemia cells [63].
Improved Mcl-1 Inhibitors: Efforts are ongoing to optimize Mcl-1 inhibitors with improved efficacy and safety profiles, potentially through the development of selective inhibitors that minimize off-target effects [64].
7.2. Combination Therapies
Broadening Combinations: Expanding the range of combinations with other targeted therapies, immunotherapies, and novel agents can address multiple survival mechanisms in leukemia cells. For instance, combining BH3 mimetics with novel immune checkpoint inhibitors may enhance anti-tumor immune responses [65].
7.3. Overcoming Resistance Mechanisms
To combat acquired resistance, research strategies should focus on understanding and targeting the underlying mechanisms. One approach is to develop inhibitors that target Bcl-2, along with other pro-survival pathways, such as Mcl-1 [66].
7.4. Other Optimization Strategies
Incorporating diverse patient populations into clinical trials to collect real-world evidence on the efficacy and safety of mitochondria-targeted therapies will refine patient selection and enhance therapeutic strategies. Patient-derived xenografts (PDXs) and organoids, as advanced models, can more accurately represent leukemia heterogeneity and provide a more relevant context for novel therapy testing [67]. Using systems biology to map complex cellular signalling and apoptosis regulation networks in leukemia can inform the creation of multi-targeted strategies, incorporating functional and genetic methods for personalized treatments [68]. Identifying biomarkers is crucial for predicting and monitoring responses to mitochondria-targeted therapies, enabling personalized treatment regimens and early detection of resistance or relapse [69,70].
In summary, it is likely that in the future, we will utilize genomic, proteomic, and transcriptomic data to customize treatment combinations based on the molecular and functional profiles of individual patients with leukemia to ensure tailored and effective therapies under the umbrella of new trials that include diverse populations.
7.1. Development of Next-Generation Inhibitors
Dual Inhibitors: Ongoing research has focused on developing compounds that can simultaneously inhibit multiple anti-apoptotic proteins (e.g., dual Bcl-2/Bcl-XL inhibitors) to prevent resistance and promote sustained apoptosis in leukemia cells [63].
Improved Mcl-1 Inhibitors: Efforts are ongoing to optimize Mcl-1 inhibitors with improved efficacy and safety profiles, potentially through the development of selective inhibitors that minimize off-target effects [64].
7.2. Combination Therapies
Broadening Combinations: Expanding the range of combinations with other targeted therapies, immunotherapies, and novel agents can address multiple survival mechanisms in leukemia cells. For instance, combining BH3 mimetics with novel immune checkpoint inhibitors may enhance anti-tumor immune responses [65].
7.3. Overcoming Resistance Mechanisms
To combat acquired resistance, research strategies should focus on understanding and targeting the underlying mechanisms. One approach is to develop inhibitors that target Bcl-2, along with other pro-survival pathways, such as Mcl-1 [66].
7.4. Other Optimization Strategies
Incorporating diverse patient populations into clinical trials to collect real-world evidence on the efficacy and safety of mitochondria-targeted therapies will refine patient selection and enhance therapeutic strategies. Patient-derived xenografts (PDXs) and organoids, as advanced models, can more accurately represent leukemia heterogeneity and provide a more relevant context for novel therapy testing [67]. Using systems biology to map complex cellular signalling and apoptosis regulation networks in leukemia can inform the creation of multi-targeted strategies, incorporating functional and genetic methods for personalized treatments [68]. Identifying biomarkers is crucial for predicting and monitoring responses to mitochondria-targeted therapies, enabling personalized treatment regimens and early detection of resistance or relapse [69,70].
In summary, it is likely that in the future, we will utilize genomic, proteomic, and transcriptomic data to customize treatment combinations based on the molecular and functional profiles of individual patients with leukemia to ensure tailored and effective therapies under the umbrella of new trials that include diverse populations.
8. Conclusions
Mitochondria-targeted therapies have altered the paradigm for the treatment of acute leukemia. Recent successes with BH3 mimetics such as venetoclax, especially when used in combination, have led to better patient outcomes. However, challenges remain, such as treatment resistance and safety concerns. Overcoming these hurdles through innovative approaches, including combination therapies, personalized medicine, and new clinical trials, is crucial to the progress of these treatments. Utilizing the latest research and incorporating new therapeutic methods, we can look forward to developing more effective and tailored treatments that improve the quality of life and survival rates of patients with acute leukemia. |
Title: miR-198 targets | Body:
1
Introduction
Oral squamous cell carcinoma (OSCC) is responsible for incidence of 3,77,713 new cases and 1,77,757 deaths in both sexes worldwide (1). The situation is worse for India as it ranks first in the rate of incidence of lip and oral cavity cancers across countries worldwide (2). Despite easy access of tumors in the oral cavity, patients present with advanced tumors due to lack of awareness, leading to late detection. In the case of early diagnosis, the 5-year survival rate can be more than 80%, but most patients are diagnosed at advanced stages with low (39%) 5-year survival rate (3). Drawbacks in the current treatment regimens, despite several technological advancements, are majorly responsible for the lack of improvement in the 5-year survival rates during the past few decades (4). It thus creates an urgent need to identify novel therapeutic modalities for OSCC, which not only improve prognosis but also diagnosis and patient survival. Increasing evidence reveals a new class of small non-coding RNAs (ncRNAs), the miRNAs as therapeutic targets in numerous diseases including cancers (5). Since their discovery, the role of miRNA dysregulation in disease progression has widely been studied which highlights the potential that miRNAs hold in the treatment of these diseases. The understanding of such miRNA-gene target axis could open new avenues for personalized cancer therapy.
miRNAs post-transcriptionally regulate the expression of their target genes by either inducing degradation or by inhibiting translation (5). Reports from the last few decades have underlined the pivotal role miRNAs play in the maintenance of homeostasis by regulating several cellular processes (6, 7). Their abnormal expression has been linked to development of various diseases including OSCC (8, 9). miRNAs such as miR-137, miR-126a, miR-99a, miR-375, miR-6741-3p and miR-617 are reported to be downregulated and potentially tumor suppressive, whereas miR-21, miR-27a, miR-181b, miR-131, miR-155 and miR-130a are reported to be upregulated and oncogenic in OSCC (10–14). Restoration of tumor suppressive miRNAs and knockdown of oncogenic miRNAs can be used as novel therapy for cancer treatment. Interestingly in cancer, miRNAs expression profiles present with more miRNA subsets with lower expression levels compared with the normal tissue, indicative of tumor suppressive functions (15). Also, miRNA genes are located in the cancer-associated genomic region, which are frequently deleted in cancers (16). The exact mechanisms for regulation of miRNA expression remain less understood, with epigenetic silencing, defective miRNA biogenesis machinery, and genetic changes being some of the players (17).
Epigenetic modifications seem to be the major mechanism responsible for miRNA expression disruptions in cancer (18). Silencing or downregulation of tumor suppressor miRNAs by aberrant methylation of CpG sites located in their promoters is demonstrated by several studies and found to be associated with prognosis of the disease and response to therapy (19–22). Further, ~50% miRNAs are found to be embedded in CpG islands (23). Studies demonstrate that ectopic unmasking of epigenetically silenced miR-127 and miR-124a restored their expressions in bladder cancer and colorectal cancer cells respectively, highlighting a method for identification of epigenetically silenced tumor suppressor miRNAs (24, 25). Kozaki et al. (26) have identified for the first time miR-137 and miR-193a as epigenetically silenced miRs due to DNA methylation in OSCC.
A previous study from our laboratory analyzed the miRNA microarray expression profile of 5-Azacytidine treated cells from an OSCC cell line SCC131 to identify novel tumor suppressor miRNAs. The analysis yielded 50 upregulated microRNAs, including miR-198 (13). Various oncogenes and signaling pathways have been identified as targets for miR-198, elucidating its role as tumor suppressor in prostate cancer, gastric cancer, lung adenocarcinoma, hepatocellular carcinoma, breast cancer, colorectal cancer, head and neck squamous cell carcinoma, thyroid cancer, osteosarcoma and pancreatic cancer (27). Some of the targets of miR-198 include well known oncogenes like livin (BIRC7), mindbomb E3 ubiquitin protein ligase 1 (MIB1), fibroblast growth factor receptor 1 (FGFR1), MET Proto-Oncogene, receptor tyrosine kinase (MET), ADAM metallopeptidase domain 28 (ADAM 28), and fucosyl transferase 8 (FUT8). The dysregulation of miR-198 in these diseases indicates its key role in the maintenance of normal tissue homeostasis and highlights the potential of miR-198 replacement therapy for cancer treatment. Despite being widely studied in several diseased conditions including cancers (27), not much is known about the role of miR-198 in OSCC, except with a single report by Kang et al. (4), where they have elucidated the role of miR-198 in OSCC pathogenesis via its gene target CDK4.
Here, we report the role of miR-198 as a tumor suppressor in OSCC and its novel target TOPORS. We elucidated the mechanism for upregulation of miR-198 following 5-Azacytidine treatment of cells from an OSCC cell line SCC131. Further, we have also investigated if the restoration of miR-198 level by a synthetic miR-198 mimic can have an antitumor effect in vivo.
2
Materials and methods
2.1
OSCC cell lines and patient samples
The cells from human OSCC cell lines, UPCI: SCC084 and UPCI: SCC131, were grown in DMEM supplemented with 10% fetal bovine serum and 1x antibiotic/antimycotic solution (Sigma-Aldrich, St. Louis, MO) in a humidified chamber with 5% CO2 at 37°C. These cell lines were a kind gift from Prof. Susanne Gollin, University of Pittsburgh, Pittsburgh, PA (28). These cell lines were generated following Institutional Review Board guidelines from consenting patients undergoing surgery for squamous cell carcinoma of the oral cavity at the University of Pittsburgh Medical Centre (28). SCC131 cells were derived from a T2N2bM0 lesion on the floor of the mouth of a 73-year-old Caucasian male, and SCC084 cells were derived from a T2N2M0 lesion of the retromolar trigone of a 52-year-old Caucasian male (28). A total of 39 matched OSCC tumor and their adjacent normal oral tissues from the patients were obtained at the Kidwai Memorial Institute of Oncology (KMIO), Bangalore from July 13, 2018 to November 14, 2018. The samples were obtained as surgically resected tissues from oral cancerous lesions and adjacent normal tissues (taken from the farthest margin of surgical resection) in the RNALater (Sigma-Aldrich, St. Louis, MO) and transferred to -80°C until further use (13, 14). The clinicopathological information for the 39 patients is given in
Supplementary Tables S1
,
S2
. Tumors were classified according to tumor, node, and metastasis criteria (29).
2.2
Transient transfections and reporter assays
For the overexpression studies, SCC131 or SCC084 cells were seeded at 2×106 cells/well in a 6-well plate and transfected with an appropriate construct or a combination of different constructs using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA) according to the manufacturer’s instructions. For the dual-luciferase reporter assay, 5 × 104 cells/well were transfected in 24-well plates with different constructs. The dual-luciferase reporter assays were performed post 48 h of transfection using the Dual-Luciferase Reporter Assay System (Promega, Madison, WI) according to the manufacturer’s protocol. The pRL-TK control vector (Promega, Madison, WI) was co-transfected in the cells for normalizing the transfection efficiency (10).
2.3
Total RNA isolation and cDNA preparation
Total RNA from tissues and cells was isolated using TRI-Reagent (Sigma-Aldrich, St. Louis, MO) and quantitated using a NanoDrop 1000 spectrophotometer (Thermo Fischer Scientific, Waltham, MA). The first-strand cDNA was synthesized from 2 µg of total RNA using a Verso cDNA Synthesis Kit (Thermo Fischer Scientific, Waltham, MA).
2.4
qRT-PCR analysis
The expression level of miR-198 was determined by qRT-PCR using the miR-Q technique by Sharbati-Tehrani et al. (30). The details of the primers are given in
Supplementary Table S3
. The RT-qPCR analysis was performed using the DyNAmo ColorFlash SYBR Green qPCR Kit in StepOne Plus Real-Time PCR and QuantStudio 3 PCR Systems (Thermo Fischer Scientific, Waltham, MA). GAPDH and 5S rRNA were used as normalizing controls (13). The following equation ΔCtgene = Ctgene-Ctnormalizing control, was used to calculate the fold change. Ct represents cycle threshold value, and ΔCt represents the gene expression normalized to GAPDH or 5S rRNA.
2.5
Plasmid constructs
The miR-198 (pmiR-198) construct was generated in pcDNA3-EGFP, using human genomic DNA as a template and gene specific primers (
Supplementary Table S4
) by a standard laboratory method. The TOPORS-ORF construct (pTOPORS) in the pcDNA 3.1(+) vector (Invitrogen, Waltham, MA, USA) was obtained from the GenScript (cat # OHu18284C, ORF size- 3138 bp, Piscataway, NJ). For the validation of a direct interaction between miR-198 and the 3’UTR of TOPORS by the dual-luciferase reporter assay, we cloned the 3’UTR of TOPORS in a sense orientation in the pMIR-REPORT miRNA Expression Reporter Vector System (Thermo Fischer Scientific, Waltham, MA) downstream to the luciferase (Luc) gene using human genomic DNA as a template. Briefly, the TOPORS sequences were obtained from the UCSC Genome browser (https://genome.ucsc.edu/) and the 769 bp long fragment was amplified from human genomic DNA using specific primers (
Supplementary Table S4
). The fragment was first cloned in a cloning vector pBluescript II KS (+) [pBSKS (+)] and the construct was named as pBSKS-TOPORS-3’UTR-S. From this vector, we excised the 3’UTR fragment with Pme I and Mlu I and ligated it to the pMIR-REPORT vector, and this construct was named as pMIR-REPORT-TOPORS-3’UTR-S. To validate the sequence specificity of the interaction between miR-198 and the TOPORS 3’UTR, the target site for miR-198 present in the 3’UTR of TOPORS was abrogated by site-directed mutagenesis (31). For this, the construct pBSKS-TOPORS-3’UTR-M was generated using specific primers (
Supplementary Table S4
) and pBSKS-TOPORS-3’UTR-S (carrying the TOPORS 3’UTR in a sense orientation) as the template. The mutated TOPORS 3’UTR was excised from this construct with Pme I and Mlu I and ligated in the pMIR-REPORT vector (also digested with the same enzymes) to generate the pMIR-REPORT-TOPORS-3’UTR-M construct. To determine if the 3’UTR of TOPORS is responsible for miR-198-mediated regulation of TOPORS, the pTOPORS-3′UTR-S, and pTOPORS-3′UTR-M constructs were generated by cloning the relevant 3′UTR sequences downstream to the TOPORS-ORF at EcoR V and Not I restriction sites in the pTOPORS construct (
Supplementary Table S4
). All constructs used in the study were validated by restriction enzyme digestion and Sanger sequencing on a 3730xl DNA analyser (Thermo Fisher Scientific, Waltham, MA, USA).
2.6
Western hybridization
Protein lysates from cells were prepared using the CelLytic M Cell Lysis Reagent (Sigma-Aldrich, St. Louis, MO). The proteins were then resolved on an SDS-PAGE and then transferred on a PVDF membrane (Pall Corp., Port Washington, NY). The membrane was blocked using 5% fat-free milk powder in 1× PBST (1xX Phosphate-Buffered Saline, 0.1% Tween 20 Detergent), and the signal was visualized using an appropriate antibody and the Immobilon Western Chemiluminescent HRP substrate (Milipore, Billerica, MA). The following antibodies were used: anti-rabbit-TOPORS (1:1,000 dilution; cat# ab86383, Abcam, Cambridge, CAMBS, UK), anti-mouse β-actin (1:10,000 dilution; cat# A5441, Sigma-Aldrich, St. Louis, MO), p53 wild-type (1: 1,000 dilution; cat# AH00152, Thermo Fisher Scientific, Waltham, MA), p21 Waf1/Cip1 (1:2,500 dilution; cat#2946, CST, Danvers, MA) and secondary HRP-conjugated goat anti-rabbit/HRP-conjugated goat anti-mouse (1.5:5,000 dilution; Bangalore Genei, Bangalore, India) antibodies.
2.7
Cell proliferation assay
To analyze the rate of cell proliferation, a trypan blue dye exclusion assay was employed (13, 32). Briefly, 2x106 cells were seeded in 6-well plates. After 16 hr, cells were transfected with the desired construct(s) as described above. Post 24 h of transfection, they were trypsinized, counted and reseeded at the density of 30,000 cells/well in triplicates in four 24-well plates (one plate for each day; total 4 days). This is day 0. Starting from day 1 (24 h after reseeding), cells from each well were trypsinized. The cells were then diluted 1:10 with 0.4% trypan blue dye (Sigma-Aldrich, St. Louis, MO) prepared in 1XPBS, allowed to stain for 10 min at room temperature and counted using a hemacytometer. This was done for all 4 days. The number of viable cells/mL was calculated using the following formula: number of viable cells/mL = the average count per square (unstained/live cells) x dilution factor x 104 (13).
2.8
Detection of caspase-3 activation
The CaspGLOW Fluorescein Active Caspase-3 Staining Kit (Biovision, Mountain View, CA) was used to analyze the rate of apoptosis in cells transfected with the appropriate constructs (10). After transfection, FITC-DEVD label was added to cells, and the rest of the steps were followed as per the manufacturer’s protocol. The fluorescence percentage was measured, using FACSCalibur Flow cytometer (BD Biosciences, New Jersey, NJ), using the FL-1 channel and the BD Accuri C6 software.
2.9
Soft agar colony forming assay
The anchorage-independent growth is one of the hallmarks of cancer, where the cancer cells have the ability to grow independently on a solid surface and was assessed by the number of colonies formed in soft agar (10, 13, 14). After transfection of cells with different constructs, 6,000 cells were plated in 1 ml of 0.35% Difco Noble Agar (Difco, Mumbai, India) diluted with DMEM culture media in a 35 mm dish. After 20-25 days, colonies were stained with 0.01% crystal violet (Sigma-Aldrich, St. Louis, MO), counted and imaged using a Leica Inverted Microscope DMi1 (Leica Microsystems, Wetzlar, Germany).
2.10
Identification of the MIR198 promoter
Although Duan et al. (33) have earlier identified an independent promoter for miR-198, a thorough analysis of the primers used by them for the promoter sequence revealed the presence of the promoter in another gene, homogentisate 1,2-dioxygenase (HGD; NC_000003.12-120628172-120682239, complement) and not in the FSTL1 gene (NC_000003.12-120392293-120450992, complement), which harbours the MIR198 gene. Therefore, we used the following databases to predict the putative MIR198 gene promoter: DBTSS (http://dbtss.hgc.jp/), MatInspector (https://www.genomatix.de) and Switchgear (https://switchgeargenomics.com). The predicted DNA sequence for the MIR198 promoter was then cloned upstream to the luciferase ORF in the pGL3-Basic vector (Promega, Madison, OH), using a standard laboratory method and specific primers (
Supplementary Table S5
). The resulting promoter construct, pMIR198-prom was then used for the promoter reporter assay using the Dual-Luciferase Reporter Assay system (Promega, Madison, WI, USA), according to manufacturer’s instructions.
2.11
Genomic bisulfite sequencing
For demethylation experiments, SCC131 cells were grown in T-75 flasks till 80% confluency and treated with a freshly prepared 5-Azacytidine at 5 μM final concentration (Sigma-Aldrich, St. Lous, MO) for 5 days with the drug and the medium being replaced every 24 h. DMSO (Sigma-Aldrich, St. Lous, MO) was used as a vehicle control. Following treatment, cells were harvested, and the genomic DNA was extracted using a Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA), according to the manufacturer’s instructions. An aliquot of 2 µg of total genomic DNA from each treatment was treated with sodium bisulphite (10, 14). Using sodium bisulphite treated genomic DNA from control (DMSO) and 5-Azacytidine treated SCC131 cells as templates, the MIR198 promoter-specific fragment was amplified (
Supplementary Table S6
) using methylation specific primers and cloned in the pTZ57R TA cloning vector (Thermo Fisher Scientific, Waltham, MA). Ten random TA clones were selected for each treatment and Sanger sequenced as described above. Methylation-specific primers were designed using the MethPrimer database (http://www.urogene.org/methprimer/).
2.12
In silicoanalysis of gene targets for miR-198
A consensus approach was employed to predict the gene targets for miR-198 using following mRNA target prediction algorithms like miRDB (http://mirdb.org), TargetScanHuman (https://www.targetscan.org/vert_80/), PicTar (https://pictar.mdc-berlin.de/cgi-bin/PicTar_vertebrate.cgi) and CoMeTa (https://cometa.tigem.it/) (
Supplementary Table S7
).
2.13
In vivo study in nude mice
To analyze the effect of miR-198-mediated targeting of TOPORS on tumor growth, 2×106 SCC131 cells were transfected with 900 nM of a synthetic miR-198 mimic or 900 nM of a mock control (scrambled oligos) separately. After 24 h of transfection, cells from both groups were suspended separately in 150 μl of incomplete DMEM and then injected into the right flank of a female BALB/c athymic 6-week-old nude mouse subcutaneously (10, 13, 14). Tumors were allowed to grow in animals of the two experimental sets, and tumor volumes were measured using a Vernier’s calliper every alternate day till the termination of the experiment. At the end of the study, animals were euthanized in CO2 atmosphere under sterile conditions by cervical dislocation. Tumor volumes were calculated using the formula: V = L×W2×0.5, where L and W represent the length and width of the tumor, respectively (10, 13, 14). The animals were photographed, and the tumor xenografts were harvested, photographed and weighed at the end of the study by trained personnel. Mice were maintained on a 12:12 h light/dark cycle in proper cages with sufficient food and water. Efforts were taken to alleviate suffering of the animals, and they were handled and treated only by trained personnel. Animals were consistently monitored for general health and behaviour. miRIDIAN microRNA hsa-miR-198 Hairpin mimic (cat# C-300532-05-0020) and miRIDIAN microRNA Negative Control #1/Mock (cat# CN-001000-01-XX) were purchased from Dharmacon (Lafayette, CO).
2.14
Statistical analysis
The statistical significance of the comparison between any two experimental data sets was calculated using the student’s t test in the GraphPad Prism 8 software (Boston, MA, USA). The statistical significance of comparisons between multiple data sets was calculated by ANOVA (one-way analysis of variance). Comparisons in data sets were considered significant when p-values were ≤0.05 (*), <0.01 (**), <0.001 (***), <0.0001 (****) or non-significant (ns) when the p-value was >0.05.
2.1
OSCC cell lines and patient samples
The cells from human OSCC cell lines, UPCI: SCC084 and UPCI: SCC131, were grown in DMEM supplemented with 10% fetal bovine serum and 1x antibiotic/antimycotic solution (Sigma-Aldrich, St. Louis, MO) in a humidified chamber with 5% CO2 at 37°C. These cell lines were a kind gift from Prof. Susanne Gollin, University of Pittsburgh, Pittsburgh, PA (28). These cell lines were generated following Institutional Review Board guidelines from consenting patients undergoing surgery for squamous cell carcinoma of the oral cavity at the University of Pittsburgh Medical Centre (28). SCC131 cells were derived from a T2N2bM0 lesion on the floor of the mouth of a 73-year-old Caucasian male, and SCC084 cells were derived from a T2N2M0 lesion of the retromolar trigone of a 52-year-old Caucasian male (28). A total of 39 matched OSCC tumor and their adjacent normal oral tissues from the patients were obtained at the Kidwai Memorial Institute of Oncology (KMIO), Bangalore from July 13, 2018 to November 14, 2018. The samples were obtained as surgically resected tissues from oral cancerous lesions and adjacent normal tissues (taken from the farthest margin of surgical resection) in the RNALater (Sigma-Aldrich, St. Louis, MO) and transferred to -80°C until further use (13, 14). The clinicopathological information for the 39 patients is given in
Supplementary Tables S1
,
S2
. Tumors were classified according to tumor, node, and metastasis criteria (29).
2.2
Transient transfections and reporter assays
For the overexpression studies, SCC131 or SCC084 cells were seeded at 2×106 cells/well in a 6-well plate and transfected with an appropriate construct or a combination of different constructs using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA) according to the manufacturer’s instructions. For the dual-luciferase reporter assay, 5 × 104 cells/well were transfected in 24-well plates with different constructs. The dual-luciferase reporter assays were performed post 48 h of transfection using the Dual-Luciferase Reporter Assay System (Promega, Madison, WI) according to the manufacturer’s protocol. The pRL-TK control vector (Promega, Madison, WI) was co-transfected in the cells for normalizing the transfection efficiency (10).
2.3
Total RNA isolation and cDNA preparation
Total RNA from tissues and cells was isolated using TRI-Reagent (Sigma-Aldrich, St. Louis, MO) and quantitated using a NanoDrop 1000 spectrophotometer (Thermo Fischer Scientific, Waltham, MA). The first-strand cDNA was synthesized from 2 µg of total RNA using a Verso cDNA Synthesis Kit (Thermo Fischer Scientific, Waltham, MA).
2.4
qRT-PCR analysis
The expression level of miR-198 was determined by qRT-PCR using the miR-Q technique by Sharbati-Tehrani et al. (30). The details of the primers are given in
Supplementary Table S3
. The RT-qPCR analysis was performed using the DyNAmo ColorFlash SYBR Green qPCR Kit in StepOne Plus Real-Time PCR and QuantStudio 3 PCR Systems (Thermo Fischer Scientific, Waltham, MA). GAPDH and 5S rRNA were used as normalizing controls (13). The following equation ΔCtgene = Ctgene-Ctnormalizing control, was used to calculate the fold change. Ct represents cycle threshold value, and ΔCt represents the gene expression normalized to GAPDH or 5S rRNA.
2.5
Plasmid constructs
The miR-198 (pmiR-198) construct was generated in pcDNA3-EGFP, using human genomic DNA as a template and gene specific primers (
Supplementary Table S4
) by a standard laboratory method. The TOPORS-ORF construct (pTOPORS) in the pcDNA 3.1(+) vector (Invitrogen, Waltham, MA, USA) was obtained from the GenScript (cat # OHu18284C, ORF size- 3138 bp, Piscataway, NJ). For the validation of a direct interaction between miR-198 and the 3’UTR of TOPORS by the dual-luciferase reporter assay, we cloned the 3’UTR of TOPORS in a sense orientation in the pMIR-REPORT miRNA Expression Reporter Vector System (Thermo Fischer Scientific, Waltham, MA) downstream to the luciferase (Luc) gene using human genomic DNA as a template. Briefly, the TOPORS sequences were obtained from the UCSC Genome browser (https://genome.ucsc.edu/) and the 769 bp long fragment was amplified from human genomic DNA using specific primers (
Supplementary Table S4
). The fragment was first cloned in a cloning vector pBluescript II KS (+) [pBSKS (+)] and the construct was named as pBSKS-TOPORS-3’UTR-S. From this vector, we excised the 3’UTR fragment with Pme I and Mlu I and ligated it to the pMIR-REPORT vector, and this construct was named as pMIR-REPORT-TOPORS-3’UTR-S. To validate the sequence specificity of the interaction between miR-198 and the TOPORS 3’UTR, the target site for miR-198 present in the 3’UTR of TOPORS was abrogated by site-directed mutagenesis (31). For this, the construct pBSKS-TOPORS-3’UTR-M was generated using specific primers (
Supplementary Table S4
) and pBSKS-TOPORS-3’UTR-S (carrying the TOPORS 3’UTR in a sense orientation) as the template. The mutated TOPORS 3’UTR was excised from this construct with Pme I and Mlu I and ligated in the pMIR-REPORT vector (also digested with the same enzymes) to generate the pMIR-REPORT-TOPORS-3’UTR-M construct. To determine if the 3’UTR of TOPORS is responsible for miR-198-mediated regulation of TOPORS, the pTOPORS-3′UTR-S, and pTOPORS-3′UTR-M constructs were generated by cloning the relevant 3′UTR sequences downstream to the TOPORS-ORF at EcoR V and Not I restriction sites in the pTOPORS construct (
Supplementary Table S4
). All constructs used in the study were validated by restriction enzyme digestion and Sanger sequencing on a 3730xl DNA analyser (Thermo Fisher Scientific, Waltham, MA, USA).
2.6
Western hybridization
Protein lysates from cells were prepared using the CelLytic M Cell Lysis Reagent (Sigma-Aldrich, St. Louis, MO). The proteins were then resolved on an SDS-PAGE and then transferred on a PVDF membrane (Pall Corp., Port Washington, NY). The membrane was blocked using 5% fat-free milk powder in 1× PBST (1xX Phosphate-Buffered Saline, 0.1% Tween 20 Detergent), and the signal was visualized using an appropriate antibody and the Immobilon Western Chemiluminescent HRP substrate (Milipore, Billerica, MA). The following antibodies were used: anti-rabbit-TOPORS (1:1,000 dilution; cat# ab86383, Abcam, Cambridge, CAMBS, UK), anti-mouse β-actin (1:10,000 dilution; cat# A5441, Sigma-Aldrich, St. Louis, MO), p53 wild-type (1: 1,000 dilution; cat# AH00152, Thermo Fisher Scientific, Waltham, MA), p21 Waf1/Cip1 (1:2,500 dilution; cat#2946, CST, Danvers, MA) and secondary HRP-conjugated goat anti-rabbit/HRP-conjugated goat anti-mouse (1.5:5,000 dilution; Bangalore Genei, Bangalore, India) antibodies.
2.7
Cell proliferation assay
To analyze the rate of cell proliferation, a trypan blue dye exclusion assay was employed (13, 32). Briefly, 2x106 cells were seeded in 6-well plates. After 16 hr, cells were transfected with the desired construct(s) as described above. Post 24 h of transfection, they were trypsinized, counted and reseeded at the density of 30,000 cells/well in triplicates in four 24-well plates (one plate for each day; total 4 days). This is day 0. Starting from day 1 (24 h after reseeding), cells from each well were trypsinized. The cells were then diluted 1:10 with 0.4% trypan blue dye (Sigma-Aldrich, St. Louis, MO) prepared in 1XPBS, allowed to stain for 10 min at room temperature and counted using a hemacytometer. This was done for all 4 days. The number of viable cells/mL was calculated using the following formula: number of viable cells/mL = the average count per square (unstained/live cells) x dilution factor x 104 (13).
2.8
Detection of caspase-3 activation
The CaspGLOW Fluorescein Active Caspase-3 Staining Kit (Biovision, Mountain View, CA) was used to analyze the rate of apoptosis in cells transfected with the appropriate constructs (10). After transfection, FITC-DEVD label was added to cells, and the rest of the steps were followed as per the manufacturer’s protocol. The fluorescence percentage was measured, using FACSCalibur Flow cytometer (BD Biosciences, New Jersey, NJ), using the FL-1 channel and the BD Accuri C6 software.
2.9
Soft agar colony forming assay
The anchorage-independent growth is one of the hallmarks of cancer, where the cancer cells have the ability to grow independently on a solid surface and was assessed by the number of colonies formed in soft agar (10, 13, 14). After transfection of cells with different constructs, 6,000 cells were plated in 1 ml of 0.35% Difco Noble Agar (Difco, Mumbai, India) diluted with DMEM culture media in a 35 mm dish. After 20-25 days, colonies were stained with 0.01% crystal violet (Sigma-Aldrich, St. Louis, MO), counted and imaged using a Leica Inverted Microscope DMi1 (Leica Microsystems, Wetzlar, Germany).
2.10
Identification of the MIR198 promoter
Although Duan et al. (33) have earlier identified an independent promoter for miR-198, a thorough analysis of the primers used by them for the promoter sequence revealed the presence of the promoter in another gene, homogentisate 1,2-dioxygenase (HGD; NC_000003.12-120628172-120682239, complement) and not in the FSTL1 gene (NC_000003.12-120392293-120450992, complement), which harbours the MIR198 gene. Therefore, we used the following databases to predict the putative MIR198 gene promoter: DBTSS (http://dbtss.hgc.jp/), MatInspector (https://www.genomatix.de) and Switchgear (https://switchgeargenomics.com). The predicted DNA sequence for the MIR198 promoter was then cloned upstream to the luciferase ORF in the pGL3-Basic vector (Promega, Madison, OH), using a standard laboratory method and specific primers (
Supplementary Table S5
). The resulting promoter construct, pMIR198-prom was then used for the promoter reporter assay using the Dual-Luciferase Reporter Assay system (Promega, Madison, WI, USA), according to manufacturer’s instructions.
2.11
Genomic bisulfite sequencing
For demethylation experiments, SCC131 cells were grown in T-75 flasks till 80% confluency and treated with a freshly prepared 5-Azacytidine at 5 μM final concentration (Sigma-Aldrich, St. Lous, MO) for 5 days with the drug and the medium being replaced every 24 h. DMSO (Sigma-Aldrich, St. Lous, MO) was used as a vehicle control. Following treatment, cells were harvested, and the genomic DNA was extracted using a Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA), according to the manufacturer’s instructions. An aliquot of 2 µg of total genomic DNA from each treatment was treated with sodium bisulphite (10, 14). Using sodium bisulphite treated genomic DNA from control (DMSO) and 5-Azacytidine treated SCC131 cells as templates, the MIR198 promoter-specific fragment was amplified (
Supplementary Table S6
) using methylation specific primers and cloned in the pTZ57R TA cloning vector (Thermo Fisher Scientific, Waltham, MA). Ten random TA clones were selected for each treatment and Sanger sequenced as described above. Methylation-specific primers were designed using the MethPrimer database (http://www.urogene.org/methprimer/).
2.12
In silicoanalysis of gene targets for miR-198
A consensus approach was employed to predict the gene targets for miR-198 using following mRNA target prediction algorithms like miRDB (http://mirdb.org), TargetScanHuman (https://www.targetscan.org/vert_80/), PicTar (https://pictar.mdc-berlin.de/cgi-bin/PicTar_vertebrate.cgi) and CoMeTa (https://cometa.tigem.it/) (
Supplementary Table S7
).
2.13
In vivo study in nude mice
To analyze the effect of miR-198-mediated targeting of TOPORS on tumor growth, 2×106 SCC131 cells were transfected with 900 nM of a synthetic miR-198 mimic or 900 nM of a mock control (scrambled oligos) separately. After 24 h of transfection, cells from both groups were suspended separately in 150 μl of incomplete DMEM and then injected into the right flank of a female BALB/c athymic 6-week-old nude mouse subcutaneously (10, 13, 14). Tumors were allowed to grow in animals of the two experimental sets, and tumor volumes were measured using a Vernier’s calliper every alternate day till the termination of the experiment. At the end of the study, animals were euthanized in CO2 atmosphere under sterile conditions by cervical dislocation. Tumor volumes were calculated using the formula: V = L×W2×0.5, where L and W represent the length and width of the tumor, respectively (10, 13, 14). The animals were photographed, and the tumor xenografts were harvested, photographed and weighed at the end of the study by trained personnel. Mice were maintained on a 12:12 h light/dark cycle in proper cages with sufficient food and water. Efforts were taken to alleviate suffering of the animals, and they were handled and treated only by trained personnel. Animals were consistently monitored for general health and behaviour. miRIDIAN microRNA hsa-miR-198 Hairpin mimic (cat# C-300532-05-0020) and miRIDIAN microRNA Negative Control #1/Mock (cat# CN-001000-01-XX) were purchased from Dharmacon (Lafayette, CO).
2.14
Statistical analysis
The statistical significance of the comparison between any two experimental data sets was calculated using the student’s t test in the GraphPad Prism 8 software (Boston, MA, USA). The statistical significance of comparisons between multiple data sets was calculated by ANOVA (one-way analysis of variance). Comparisons in data sets were considered significant when p-values were ≤0.05 (*), <0.01 (**), <0.001 (***), <0.0001 (****) or non-significant (ns) when the p-value was >0.05.
3
Results
3.1
Validation of 5-Azacytidine induced upregulation of miR-198 and its anti-proliferative role in OSCC cells
We first validated the upregulation of miR-198 in 5-Azacytidine treated SCC131 cells, using RT6-198 and short-miR-198 primers specific to miR-198 (
Supplementary Table S3
). A significant upregulation in the miR-198 level was observed in 5-Azacytidine treated cells compared with the vehicle (DMSO) treated cells, thus validating the human miRNA microarray data (
Figure 1A
).
Figure 1
Validation of upregulation of miR-198 in 5-Azacytidine treated cells and its antiproliferative role. (A) 5-Azacytidine upregulates miR-198 in SCC131 cells compared to those treated with the control (DMSO). (B) MiR-198 ectopic overexpression suppresses the proliferation of SCC131 and SCC084 cells. The trypan blue dye exclusion assay was used to analyze transient overexpression of miR-198 on cell proliferation, using the pmiR-198 construct and cloning vector pcDNA3-EGFP. Note, a significant reduction in proliferation of both SCC131 and SCC084 cells transfected with the pmiR-198 construct compared to those transfected with the vector control. Each data point is an average of 3 biological replicates.
DNA hypermethylation is one of the key players for the reduced expression of tumor suppressor miRNAs in cancer. As 5-Azacytidine is a global hypomethylating agent which can reactivate the transcription of tumor suppressor miRNAs, and miR-198 was observed to be upregulated post 5-Azacytidine treatment of SCC131 cells, we hypothesized that miR-198 could be a tumor suppressor miRNA. We, therefore, wanted to explore if overexpression of miR-198 in OSCC cells has an anti-proliferative role in OSCC cells. We then transfected SCC131 and SCC084 cells with pcDNA3-EGFP (vector control) and pmiR-198 separately and performed the trypan blue dye exclusion cell counting assay. The results showed that miR-198 significantly reduced the rate of proliferation of OSCC cells compared with those transfected the vector control, suggesting that it negatively regulates cell proliferation and functions as a tumor suppressor in OSCC cells (
Figure 1B
).
3.2
Mechanism for miR-198 upregulation following 5-Azacytidine treatment of SCC131 cells
To understand the underlying mechanism for the upregulation of miR-198 in 5-Azacytidine treated SCC131 cells, we hypothesized that the demethylation of the MIR198 promoter can cause upregulation of miR-198 levels. Since the promoter of miR-198 is unknown, we first retrieved the putative MIR198 promoter sequence from the DBTSS, Switchgear and Matinspector databases (
Supplementary Figure S1A
). The 839 bp sequence (
Supplementary Figure S1A
) was cloned in the pGL3-Basic vector. The MIR198 promoter construct (pMIR198-prom;
Supplementary Figure S1B
) along with the positive control pGL3-Control and the negative control pGL3-Basic were then transfected separately in SCC131 and SCC084 cells and the dual-luciferase reporter assay was performed. The results showed a significant promoter activity for the MIR198 promoter construct in both SCC131 and SCC084 cells compared with the pGL3-Basic vector (
Supplementary Figure S1C
), suggesting that the putative MIR198 promoter sequence indeed represents an independent promoter for the MIR198 gene.
After characterizing the MIR198 promoter, we analysed the methylation status of the MIR198 promoter, using bisulphite sequencing PCR (BSP). We have designed BSP specific primers (
Supplementary Table S6
) from nucleotide positions -213 to +190, which harbours 3 CpG sites (
Figure 2A
). Using sodium bisulphite treated genomic DNA from control and 5-Azacytidine treated SCC131 cells as templates, we amplified the MIR198 promoter-specific fragment with BSP-specific primers (
Supplementary Table S6
), cloned them in a TA cloning vector, and Sanger sequenced 10 random clones for each of the control and 5-Azacytidine treated SCC131 cells. The results showed that the percentage of methylation at the MIR198 promoter region reduced from 76.66% in the control-treated SCC131 cells to 50% in 5-Azacytidine treated cells (
Figure 2B
), suggesting that demethylation of the MIR198 promoter is responsible for upregulation of miR-198 following 5-Azacytidine treatment of SCC131 cells.
Figure 2
Demethylation of the MIR198 promoter following 5-Azacytidine treatment of SCC131 cells. (A) The schematic diagram of the MIR198 promoter region. The blue portion was analyzed for bisulphite sequencing and the cloned sequence contains 3 CpG sites, highlighted as red triangles. (B) Methylation status of 3 CpG sites in the MIR198 promoter fragment from control (left panel) and 5-Azacytidine treated SCC131 cells (right panel). Each row represents methylation status of CpGs from a single TA clone.
3.3
Identification of a gene target for miR-198
MiRNAs are known to regulate gene expression by binding to their cognate mRNA targets. As we have observed miR-198 to have an anti-proliferative role in OSCC cells, we wanted to elucidate its target gene(s) and the mechanism through which it exerts its effect. To this end, we have used a consensus approach by employing four different mRNA target prediction algorithms (e.g., TargetScanHuman, miRDB, PicTar and CoMeTa) to identify gene target(s) of miR-198. We found TOPORS (topoisomerase I binding, arginine/serine-rich, E3 ubiquitin protein ligase), NRIP1 (nuclear receptor interacting protein 1), PBX1 (pre-B-cell leukemia homeobox 1), PDCD1LG2 (programmed cell death 1 ligand 2), SLC2A1 [solute carrier family 2 (facilitated glucose transporter), member 1], PUM2 (pumilio RNA-binding family member 2), MET (met proto-oncogene), VCP (Valosin Containing Protein) and FUT8 [(fucosyltransferase 8 (alpha (1, 6) fucosyltransferase)] as potential gene targets of miR-198 predicted by all the four algorithms (
Supplementary Table S7
). Among these FUT8, VCP, PUM2, PBX1 and MET are already explored targets of miR-198. A thorough literature search was performed for the remaining targets considering their impact in cancer, known role, implication in pathways etc. TOPORS is known to directly interact and regulate the expression of p53 (34). The role of TP53 in cancer progression is well established. Also, other pathways (MAPK, PI3K/AKT, HGF/MET, JAK/STAT and FGFR1) are already explored for miR-198 (27). However, none of the studies investigated the regulation of p53 after ectopic expression of miR-198. Thus, we decided to pursue TOPORS as a gene target for miR-198.
To determine if miR-198 targets and regulates TOPORS, we transfected pmiR-198 and pcDNA3-EGFP separately in SCC131 cells and assessed the levels of TOPORS transcript and protein (
Figure 3A
). The results showed that miR-198 reduces the levels of TOPORS transcript and protein (
Figure 3A
), suggesting miR-198-mediated regulation of TOPORS. To determine if there is a dose-dependent regulation of TOPORS by miR-198, we transfected different quantities of the pmiR-198 construct in SCC131 cells. The results showed that miR-198 reduces the levels of TOPORS transcript and protein in a dose-dependent manner (
Figure 3B
). We next looked for the effect of 5-Azacytidine treatment on the expression of both TOPORS transcript and protein in SCC131 cells. The results showed a concomitant reduction in the levels of TOPORS transcript and protein with an increased level of miR-198 in 5-Azacytine treated cells compared with the control-treated cells (
Figure 3C
). These observations further strengthen the significance of miR-198-mediated regulation of TOPORS.
Figure 3
Identification of TOPORS as a gene target of miR-198. (A) Transfection of pmiR-198 in SCC131 cells shows decreased TOPORS transcript and protein levels compared with those transfected with the vector control. (B) A dose-dependent regulation of TOPORS by miR-198 in SCC131 cells. (C) The 5-Azacytidine treatment of SCC131 cells shows an increased level of miR-198 with concomitant decreased levels of TOPORS transcript and the protein. For qRT-PCR data,each bar is an average of 2 technical replicates.
3.4
Confirmation of a direct interaction between miR-198 and the 3’UTR of TOPORS
The target prediction programs showed one putative target site (TS) in the 3’UTR of TOPORS from nucleotide positions 543-549 for binding of the miR-198 seed region (SD) (
Figure 4A
). The CLUSTALW alignment (https://www.genome.jp/tools-bin/clustalw) showed that this TS is conserved across species (
Figure 4A
). To validate the direct interaction between miR-198 and the 3’UTR of TOPORS, we employed the use of a gold standard assay, the dual-luciferase reporter (DLR) assay. The DLR assay was performed using various constructs illustrated in
Figure 4B
. Using the site-directed mutagenesis, we generated a negative control construct, pMIR-REPORT-TOPORS-3’UTR-M, by abrogating the TS for miR-198 in the 3’UTR of TOPORS. If miRNA binds to the 3’UTR of the target gene TOPORS, the luciferase activity significantly reduces compared with control transfected cells. Therefore, we co-transfected SCC131 cells with pMIR-REPORT-TOPORS-3’UTR-S containing the intact 3’UTR of TOPORS in a sense orientation and pmiR-198 or pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP (vector control) and quantified the luciferase reporter activity. We observed a significant reduction in the luciferase activity in cells co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pmiR-198 compared with those co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP (
Figure 4C
). As expected, cells co-transfected with pmiR-198 and pMIR-REPORT-TOPORS-3’UTR-M showed the luciferase activity comparable to those co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP. This is attributed to the lack of binding of miR-198 to the TOPORS 3’UTR, as the TS is abrogated in the pMIR-REPORT-TOPORS-3’UTR-M construct (
Figures 4B, C
). We also analysed the binding of miR-198 to its known gene target FUT8 as a positive control. We observed that the cells co-transfected with pmiR-198 and pMIR-REPORT-FUT8-3’UTR-S showed a significantly reduced luciferase activity compared with those co-transfected with pMIR-REPORT-FUT8-3’UTR-S and pcDNA3-EGFP, confirming the binding of miR-198 to the 3’UTR of FUT8. The above observations confirm that miR-198 binds directly to the TS in the 3’UTR of TOPORS in a sequence-specific manner.
Figure 4
Confirmation of the binding of miR-198 to the 3’UTR of TOPORS by the dual-luciferase reporter assay in SCC131 cells. (A) Diagram showing conservation of the putative target site (TS) for the miR-198 seed sequence (SD) in the 3’UTR of TOPORS across species. The numbers denote nucleotide positions. (B) Schematic diagrams depicting various constructs used in the dual-luciferase reporter assay. The ‘X’ indicates abrogated TS region. (C) The results of dual-luciferase reporter assay. Note, a significantly reduced RLU in cells co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pmiR-198 compared to those transfected with pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP, confirming the binding of miR-198 to 3’UTR of TOPORS. Each bar is an average of 3 biological replicates.
3.5
Expression analysis of TOPORS and miR-198 in OSCC patient samples
To understand the biological relevance of the interaction between miR-198 and TOPORS, we determined the levels of miR-198 and TOPORS transcripts in 39 matched normal oral tissue and OSCC patient samples, using qRT-PCR (
Figure 5
). We found miR-198 to be significantly downregulated in 29/39 (74.35%) OSCC samples compared with their matched normal oral tissue samples (viz., patient no. 63, 3, 8, 33, 47, 49, 52, 56, 62, 64, 70, 76, 80, 10, 14, 15, 17, 24, 32, 43, 44, 45, 48, 51, 59, 61, 65, 66 and 67) and significantly upregulated in tumor samples compared with their matched normal oral tissue samples in 5/39 (12.8%) (viz., patient no. 68, 55, 50, 57 and 60) (
Figure 5A
). Moreover, no significant change in its levels was observed in 5/39 (12.8%) OSCC samples compared with their normal tissue counterparts (viz., patient no. 54, 25, 46, 53 and 6). Further, we found TOPORS to be significantly upregulated in 19/39 (48.7%) OSCC samples compared with their matched normal oral tissue samples (viz., patient no. 68, 8, 33, 47, 49, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 50, 60, 65 and 66) (
Figure 5B
) and significantly downregulated in 17/39 (43.58%) OSCC samples compared with their matched normal oral tissues (viz., patient no 54, 63, 3, 46, 52, 53, 55, 56, 62, 6, 32, 45, 51, 57, 59, 61 and 67) (
Figure 5
). Moreover, no significant change was reported in its levels in 3/39 (7.6%) (viz., patient no. 25, 80 and 17) OSCC samples compared with their matched normal tissue samples (
Figure 5B
). In patient numbers 8, 33, 47, 49, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 65, and 66 the miR-198 expression is significantly downregulated compared with their matched normal oral tissue samples and concomitantly the TOPORS expression is significantly upregulated compared with their matched normal oral tissue samples. In patient numbers 55 and 57 miR-198 is significantly upregulated compared with their matched normal oral tissue samples and concomitantly TOPORS is significantly downregulated compared with their matched normal oral tissue samples. Overall, an inverse relation of such kind was observed between the levels of miR-198 and TOPORS in 18/39 (46.15%) (viz., patient no. 8, 33, 47, 49, 55, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 57, 65 and 66) OSCC patient samples (
Figure 5
), suggesting the biological relevance of their interaction.
Figure 5
The qRT-PCR analysis of (A) miR-198 and (B)
TOPORS in OSCC patient samples. The T2, T3 and T4 represent the different stages of tumors, and the numbers along X-axis denote different patient numbers. Each qRT-PCR data is an average of 2 technical replicates.
3.6
TOPORS overexpression enhances OSCC cell proliferation
MiRNAs are known to negatively regulate their target genes by binding to their 3’UTRs. As we have observed miR-198 to be a tumor suppressor miRNA, which targets TOPORS, it is interesting to study the role of TOPORS in OSCC cells. To this end, we transfected SCC131 and SCC084 cells with vector control and pTOPORS separately and checked for cell proliferation by trypan blue dye exclusion assay. We observed a significant increase in proliferation of cells transfected with pTOPORS compared with those transfected with the vector control, suggesting that TOPORS exerts a positive regulation on proliferation of OSCC cells from both cell lines (
Supplementary Figure S2
).
3.7
Expression of TOPORS depends on the presence or absence of its 3’UTR
Since miR-198 binds directly to the 3’UTR of TOPORS, we wanted to investigate if the function of TOPORS depends on the presence of its 3’UTR. To this end, we utilized the following different TOPORS overexpression constructs: pTOPORS harbouring a complete TOPORS ORF, pTOPORS-3’UTR-S with the 3’UTR of TOPORS cloned downstream to the TOPORS ORF in the pTOPORS construct in a sense orientation and pTOPORS-3’UTR-M with the abrogated TS in the 3’UTR of TOPORS cloned downstream to the TOPORS ORF in the pTOPORS construct. We then transfected the vector control [pcDNA3.1(+)] only or co-transfected vector control and pmiR-198 or pmiR-198 with pTOPORS, pTOPORS-3’UTR-S or pTOPORS-3’UTR-M in SCC084 and SCC131 cells and performed Western blot analysis to assess TOPORS levels (
Supplementary Figure S3
). The results showed that the TOPORS levels is reduced in cells co-transfected with the vector and pmiR-198 compared with those transfected with the vector control only, because of miR-198 targeting the endogenous TOPORS (
Supplementary Figure S3
). Also, an increased level of TOPORS was observed in cells co-transfected with pmiR-198 and pTOPORS as compared to those co-transfected with the vector and pmiR-198 (
Supplementary Figure S3
). Further, we observed a reduced level of TOPORS in cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 as compared with those co-transfected with pTOPORS and pmiR-198, due to the presence of a functional TS in the 3’UTR of pTOPORS-3’UTR-S (
Supplementary Figure S3
). Further, an increased level of TOPORS was observed in cells co-transfected with pTOPORS-3’UTR-M and pmiR-198 as compared with those co-transfected with pTOPORS-3’UTR-S and pmiR-198, because of the absence of TS in the 3’UTR of pTOPORS-3’UTR-M (
Supplementary Figure S3
). These observations suggest that TOPORS expression depends on the presence or absence of its 3’UTR and is, in part, regulated by miR-198.
3.8
miR-198 regulates proliferation, apoptosis and anchorage-independent growth of OSCC cells, in part, via targeting the 3’UTR of TOPORS
We then used the above constructs to assess the effect of miR-198 mediated regulation of TOPORS on a few hallmarks of cancer such as cell proliferation, apoptosis, and anchorage-independent growth. To this end, we co-transfected pTOPORS, pTOPORS-3’UTR-S and pTOPORS-3’UTR-M constructs separately with pmiR-198 in SCC131 and SCC084 cells and assessed cell proliferation using the trypan blue dye exclusion assay. As expected, we observed a decreased rate of cell proliferation in cells co-transfected with pmiR-198 and vector compared with those transfected with the vector only (
Figure 6A
), due to targeting of endogenous TOPORS by miR-198. Further, we observed a decreased rate of cell proliferation in cells co-transfected with pmiR-198 and pTOPORS-3’UTR-S compared with those co-transfected with pmiR-198 and pTOPORS, due to the presence of a functional TS in the 3’UTR of pTOPORS-3’UTR-S (
Figure 6A
). As expected, we observed no difference in the rate of cell proliferation in cells co-transfected with pmiR-198 and pTOPORS compared with those co-transfected with pmiR-198 and pTOPORS-3’UTR-M, due to the absence of a functional TS in 3’UTR of pTOPORS-3’UTR-M (
Figure 6A
). A similar observation was made in cells from both cell lines SCC131 and SCC084 (
Figure 6A
). These observations highlighted the negative regulation of cell proliferation by miR-198, in part, via targeting the 3’UTR of TOPORS.
Figure 6
Regulation of cell proliferation and apoptosis by miR-198, in part, via targeting the 3’UTR of TOPORS. (A) The analysis of cell proliferation by the trypan blue dye exclusion assay in SCC131 and SCC084 cells co-transfected with pmiR-198 and different TOPORS overexpression constructs or vector control. Note, a significantly decreased rate of proliferation of cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 compared to those co-transfected with pTOPORS and pmiR-198 or pTOPORS-3’UTR-M and pmiR-198. (B) The quantitative analysis of apoptosis rate as assessed by the caspase-3 assay in SCC131 and SCC084 cells co-transfected with pmiR-198 and different TOPORS overexpression constructs or vector control. Note, a significant increase in the rate of apoptosis on overexpression of pmiR-198 compared with the vector control in both SCC131 and SCC084 cells. Also, there is a significant increase in the rate of apoptosis of cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 in comparison to those co-transfected with pTOPORS and pmiR-198 or pTOPORS-3’UTR-M and pmiR-198. Each bar is an average of 3 biological replicates.
To determine if miR-198 regulates apoptosis by targeting the 3’UTR of TOPORS, we again used the above constructs and performed caspase-3 assay and analysed the percentage of caspase-3 positive cells in SCC131 and SCC084 cells. We observed a significant increase in the rate of apoptosis in cells co-transfected with pmiR-198 and vector control compared with those transfected with vector control only (
Figure 6B
). Also, the percentage of apoptotic cells was significantly higher in cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 compared with those co-transfected with pTOPORS-3’UTR-M or pTOPORS and pmiR-198, due to binding of the miR-198 to the TS in pTOPORS-3’UTR-S (
Figure 6B
). The percentage of caspase-3 positive cells was significantly higher in cells transfected pmiR-198 and pcDNA3.1(+) compared with those co-transfected with pTOPORS and pmiR-198, due to ectopic expression of oncogenic TOPORS-ORF (
Figure 6B
). These observations suggested that miR-198 positively regulates cellular apoptosis in SCC131 and SCC084 cells, in part, by directly targeting the 3’UTR of TOPORS.
To investigate the effect of miR-198-mediated regulation of TOPORS on anchorage-independent growth of OSCC cells, we co-transfected pTOPORS, pTOPORS-3’UTR-S and pTOPORS-3’UTR-M separately with pmiR-198 in SCC084 and SCC131 cells by soft agar colony forming assay (
Figure 7
). As expected, we observed a reduced number of colonies in cells co-transfected with pmiR-198 and the vector compared with those transfected with the vector only (
Figure 7
). Further, a reduced number of colonies was observed in cells co-transfected with pmiR-198 and pTOPORS-3’UTR-S compared with those co-transfected with pTOPORS and pmiR-198 (
Figure 7
), due to binding of miR-198 to the TS in the pTOPORS-3’UTR-S. Further, no difference in the number of colonies was observed in cells co-transfected with pmiR-198 and pTOPORS compared with those co-transfected with pmiR-198 and pTOPORS-3’UTR-M, due to the absence of a functional TS in pTOPORS-3’UTR-M (
Figure 7
). These results clearly suggested a negative regulation of miR-198 on the anchorage-independent growth, in part, by targeting the 3’UTR of TOPORS.
Figure 7
Regulation of anchorage-independent growth of OSCC cells by miR-198, in part, via targeting the 3’UTR of TOPORS. Quantitative assessment of the anchorage independent growth capabilities and representative microphotographs of the colonies for (A) SCC131 and (B) SCC084 cells co-transfected with pmiR-198 and different TOPORS overexpression constructs or vector control by soft agar colony forming assay. Each bar is an average of 4 biological replicates.
3.9
Restoration of miR-198 levels by a synthetic mimic suppresses tumor growth in vivo
Since we had observed an anti-proliferative role of miR-198, we hypothesized that the restoration of the expression level of the miR-198 by a synthetic miR-198 mimic could reduce the level of oncogenic TOPORS in OSCC cells, which might have an anti-tumor effect in vivo. To this end, we used in vivo pre-treated OSCC xenograft nude mouse model. We first optimized the dosage of a synthetic miR-198 mimic in SCC131 cells and found 900 nM of the mimic to be an optimum dose to reduce TOPORS at both the transcript and protein levels (
Supplementary Figure S4
). We then injected equal numbers of pre-transfected SCC131 cells with 900 nM of miR-198 mimic (M900) or 900 nM of mimic control (MC900) separately into the right flanks of female nude mice (6 weeks of age). The mice were monitored for OSCC xenograft (tumor) growth for 40 days. As expected, there was a reduction in both tumor volume and weight in nude mice injected with miR-198 mimic treated cells compared to that of the mock control (
Figure 8
), suggesting that miR-198 functions as tumor suppressor in OSCC.
Figure 8
Restoration of miR-198 level suppresses tumorigenesis in vivo. (A) Photographs of nude mice showing tumor growth on day 40 of injection. The numbers denote mice numbers. The corresponding excised xenograft on day 40 is shown below each mouse. Each group had 6 mice. One mouse (#6) in the mimic control group died early. Dotted red circles mark the xenografts in mice. Note, no tumor development in mice # 5 in the mimic control group. (B) Effect of miR-198 mimic on the volume of xenografts during a time course of 40 days. (C) The weight of xenografts on day 40.
3.10
Effect of miR-198-mediated regulation of TOPORS on the p53/p21 signaling in OSCC cells
TOPORS, an E3 ubiquitin and SUMO ligase, is known to affect the expression of several proteins, including the guardian of the genome, p53 (34–38). The p53 pathway is well known for induction of apoptosis and cell cycle arrest (39). The effect of miR-198 on the p53 signaling has not been explored. We, therefore, wanted to understand if miR-198-mediated regulation of TOPORS has any impact on the p53 pathway in OSCC (
Figure 9A
). To this end, we determined the levels of p53 and its downstream target p21 (WAF1/CIP1/CDKN1A) in OSCC cells transfected with pmiR-198 or pTOPORS, using the Western blot analysis (
Figures 9B, C
). The results showed that the cells transfected with pmiR-198 had increased levels of p53 and p21 compared with those transfected with the vector control in both SCC131 and SCC084 cells (
Figure 9B
). As expected, overexpression of miR-198 led to a reduced level of TOPORS in both SCC131 and SCC084 cells (
Figure 9B
). In contrast, cells transfected with pTOPORS showed reduced levels of p53 and p21 compared with those transfected with the vector control in both SCC131 and SCC084 cells (
Figure 9C
). These results highlighted the significant roles miR-198 and TOPORS in the p53 pathway in OSCC. These observations also indicated that miR-198 enhances signaling of p53/p21, in part, by regulating TOPORS.
Figure 9
miR-198 upregulates p53/p21 signaling, in part, by regulating TOPORS. (A) A diagrammatic representation of the miR-198/TOPORS/p53/p21 axis. (B) Western blot analysis following overexpression of miR-198 in SCC131 and SCC084 cells. (C) Western blot analysis following overexpression of TOPORS in SCC131 and SCC084 cells. β-actin was used as a loading control.
3.11
Effect of miR-198-mediated regulation of TOPORS and p53/p21 signalling in OSCC patient samples
Since the p53/p21 pathway is observed to be directly correlated with miR-198-mediated regulation of TOPORS (
Figure 9
), we wanted to explore the levels of TP53 transcript in the OSCC patient samples which showed an inverse relation of miR-198 and TOPORS levels (viz., patient no. 8, 33, 47, 49, 55, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 57, 65 and 66) by qRT-PCR using TP53 specific primers (
Supplementary Table S3
). The results showed that TP53 was significantly downregulated in 7/18 OSCC samples (viz., patient no. 47, 49, 55, 76, 14, 43 and 66) and upregulated in 5/18 OSCC samples (viz., patient no. 8, 64, 10, 48 and 57) compared with their matched normal tissue samples (
Figure 10
). Since TP53 is the most mutated gene in cancer, we checked the CDKN1A transcript level using CDK1NA (P21) specific primer (
Supplementary Table S3
) as mutations in CDKN1A are less frequent and CDKN1A is a direct target and a downstream effector for p53. The results showed a significant downregulation of the CDKN1A level in 9/18 OSCC samples (viz., patient no. 8, 33, 47, 49, 76, 43, 44, 48 and 66) and its upregulation in 4/18 OSCC samples (viz., patient no. 55, 10, 57 and 65) compared to matched normal oral tissue samples (
Figure 10
). No significant change between their levels was observed in 6/18 OSCC samples for TP53 (viz., patient no. 33, 70, 15, 24, 44 and 65) and 5/18 OSCC samples for CDKN1A (viz., patient no. 64, 70, 14, 15 and 24) compared with matched normal oral samples (
Figure 10
). We then analyzed the expression pattern of TP53 and CDKN1A with respect to miR-198 expression in these inversely related patient samples. Interestingly, we observed miR-198 levels to be positively correlated with CDKN1A levels in 11/18 OSCC samples compared with their matched normal oral tissue samples (viz., patient no. 8, 33, 47, 49, 55, 76, 43, 44, 48, 57 and 66) (
Figure 10
). For example, if miR-198 expression is observed to be downregulated in the tumor sample from patient no. 47, the expression of CDKN1A (downstream effector of p53) and TP53 was also downregulated and the expression of TOPORS was upregulated in this tumor sample compared with its matched normal tissue sample (
Figure 10
). The Pearson correlation analysis indicate an inverse correlation between miR-198 and TOPORS (r = -0.08; p value=0.614) whereas the analysis of miR-198 and CDKN1A (r = 0.422; p value=0.080) indicate a positive correlation (
Supplementary Figure S5
) but the correlation was not significant. The small sample size, tumor heterogeneity and the presence of other underlying alternate mechanisms are possible reasons. Nonetheless, these results highlighted the biological relevance of this interaction between miR-198 and TOPORS and its role in the p53/p21 pathway.
Figure 10
The qRT-PCR analysis of TP53 and CDKN1A in OSCC patient samples. The transcript levels of (A)
TP53 and (B)
CDKN1A in 18 matched normal oral tissue and OSCC tissue samples from patients who showed an inverse correlation in the levels of miR-198 and TOPORS. T3 and T4 represent the different stages of tumors, and the numbers along X-axis denote different patient numbers. Each qRT-PCR data is an average of 2 technical replicates.
3.1
Validation of 5-Azacytidine induced upregulation of miR-198 and its anti-proliferative role in OSCC cells
We first validated the upregulation of miR-198 in 5-Azacytidine treated SCC131 cells, using RT6-198 and short-miR-198 primers specific to miR-198 (
Supplementary Table S3
). A significant upregulation in the miR-198 level was observed in 5-Azacytidine treated cells compared with the vehicle (DMSO) treated cells, thus validating the human miRNA microarray data (
Figure 1A
).
Figure 1
Validation of upregulation of miR-198 in 5-Azacytidine treated cells and its antiproliferative role. (A) 5-Azacytidine upregulates miR-198 in SCC131 cells compared to those treated with the control (DMSO). (B) MiR-198 ectopic overexpression suppresses the proliferation of SCC131 and SCC084 cells. The trypan blue dye exclusion assay was used to analyze transient overexpression of miR-198 on cell proliferation, using the pmiR-198 construct and cloning vector pcDNA3-EGFP. Note, a significant reduction in proliferation of both SCC131 and SCC084 cells transfected with the pmiR-198 construct compared to those transfected with the vector control. Each data point is an average of 3 biological replicates.
DNA hypermethylation is one of the key players for the reduced expression of tumor suppressor miRNAs in cancer. As 5-Azacytidine is a global hypomethylating agent which can reactivate the transcription of tumor suppressor miRNAs, and miR-198 was observed to be upregulated post 5-Azacytidine treatment of SCC131 cells, we hypothesized that miR-198 could be a tumor suppressor miRNA. We, therefore, wanted to explore if overexpression of miR-198 in OSCC cells has an anti-proliferative role in OSCC cells. We then transfected SCC131 and SCC084 cells with pcDNA3-EGFP (vector control) and pmiR-198 separately and performed the trypan blue dye exclusion cell counting assay. The results showed that miR-198 significantly reduced the rate of proliferation of OSCC cells compared with those transfected the vector control, suggesting that it negatively regulates cell proliferation and functions as a tumor suppressor in OSCC cells (
Figure 1B
).
3.2
Mechanism for miR-198 upregulation following 5-Azacytidine treatment of SCC131 cells
To understand the underlying mechanism for the upregulation of miR-198 in 5-Azacytidine treated SCC131 cells, we hypothesized that the demethylation of the MIR198 promoter can cause upregulation of miR-198 levels. Since the promoter of miR-198 is unknown, we first retrieved the putative MIR198 promoter sequence from the DBTSS, Switchgear and Matinspector databases (
Supplementary Figure S1A
). The 839 bp sequence (
Supplementary Figure S1A
) was cloned in the pGL3-Basic vector. The MIR198 promoter construct (pMIR198-prom;
Supplementary Figure S1B
) along with the positive control pGL3-Control and the negative control pGL3-Basic were then transfected separately in SCC131 and SCC084 cells and the dual-luciferase reporter assay was performed. The results showed a significant promoter activity for the MIR198 promoter construct in both SCC131 and SCC084 cells compared with the pGL3-Basic vector (
Supplementary Figure S1C
), suggesting that the putative MIR198 promoter sequence indeed represents an independent promoter for the MIR198 gene.
After characterizing the MIR198 promoter, we analysed the methylation status of the MIR198 promoter, using bisulphite sequencing PCR (BSP). We have designed BSP specific primers (
Supplementary Table S6
) from nucleotide positions -213 to +190, which harbours 3 CpG sites (
Figure 2A
). Using sodium bisulphite treated genomic DNA from control and 5-Azacytidine treated SCC131 cells as templates, we amplified the MIR198 promoter-specific fragment with BSP-specific primers (
Supplementary Table S6
), cloned them in a TA cloning vector, and Sanger sequenced 10 random clones for each of the control and 5-Azacytidine treated SCC131 cells. The results showed that the percentage of methylation at the MIR198 promoter region reduced from 76.66% in the control-treated SCC131 cells to 50% in 5-Azacytidine treated cells (
Figure 2B
), suggesting that demethylation of the MIR198 promoter is responsible for upregulation of miR-198 following 5-Azacytidine treatment of SCC131 cells.
Figure 2
Demethylation of the MIR198 promoter following 5-Azacytidine treatment of SCC131 cells. (A) The schematic diagram of the MIR198 promoter region. The blue portion was analyzed for bisulphite sequencing and the cloned sequence contains 3 CpG sites, highlighted as red triangles. (B) Methylation status of 3 CpG sites in the MIR198 promoter fragment from control (left panel) and 5-Azacytidine treated SCC131 cells (right panel). Each row represents methylation status of CpGs from a single TA clone.
3.3
Identification of a gene target for miR-198
MiRNAs are known to regulate gene expression by binding to their cognate mRNA targets. As we have observed miR-198 to have an anti-proliferative role in OSCC cells, we wanted to elucidate its target gene(s) and the mechanism through which it exerts its effect. To this end, we have used a consensus approach by employing four different mRNA target prediction algorithms (e.g., TargetScanHuman, miRDB, PicTar and CoMeTa) to identify gene target(s) of miR-198. We found TOPORS (topoisomerase I binding, arginine/serine-rich, E3 ubiquitin protein ligase), NRIP1 (nuclear receptor interacting protein 1), PBX1 (pre-B-cell leukemia homeobox 1), PDCD1LG2 (programmed cell death 1 ligand 2), SLC2A1 [solute carrier family 2 (facilitated glucose transporter), member 1], PUM2 (pumilio RNA-binding family member 2), MET (met proto-oncogene), VCP (Valosin Containing Protein) and FUT8 [(fucosyltransferase 8 (alpha (1, 6) fucosyltransferase)] as potential gene targets of miR-198 predicted by all the four algorithms (
Supplementary Table S7
). Among these FUT8, VCP, PUM2, PBX1 and MET are already explored targets of miR-198. A thorough literature search was performed for the remaining targets considering their impact in cancer, known role, implication in pathways etc. TOPORS is known to directly interact and regulate the expression of p53 (34). The role of TP53 in cancer progression is well established. Also, other pathways (MAPK, PI3K/AKT, HGF/MET, JAK/STAT and FGFR1) are already explored for miR-198 (27). However, none of the studies investigated the regulation of p53 after ectopic expression of miR-198. Thus, we decided to pursue TOPORS as a gene target for miR-198.
To determine if miR-198 targets and regulates TOPORS, we transfected pmiR-198 and pcDNA3-EGFP separately in SCC131 cells and assessed the levels of TOPORS transcript and protein (
Figure 3A
). The results showed that miR-198 reduces the levels of TOPORS transcript and protein (
Figure 3A
), suggesting miR-198-mediated regulation of TOPORS. To determine if there is a dose-dependent regulation of TOPORS by miR-198, we transfected different quantities of the pmiR-198 construct in SCC131 cells. The results showed that miR-198 reduces the levels of TOPORS transcript and protein in a dose-dependent manner (
Figure 3B
). We next looked for the effect of 5-Azacytidine treatment on the expression of both TOPORS transcript and protein in SCC131 cells. The results showed a concomitant reduction in the levels of TOPORS transcript and protein with an increased level of miR-198 in 5-Azacytine treated cells compared with the control-treated cells (
Figure 3C
). These observations further strengthen the significance of miR-198-mediated regulation of TOPORS.
Figure 3
Identification of TOPORS as a gene target of miR-198. (A) Transfection of pmiR-198 in SCC131 cells shows decreased TOPORS transcript and protein levels compared with those transfected with the vector control. (B) A dose-dependent regulation of TOPORS by miR-198 in SCC131 cells. (C) The 5-Azacytidine treatment of SCC131 cells shows an increased level of miR-198 with concomitant decreased levels of TOPORS transcript and the protein. For qRT-PCR data,each bar is an average of 2 technical replicates.
3.4
Confirmation of a direct interaction between miR-198 and the 3’UTR of TOPORS
The target prediction programs showed one putative target site (TS) in the 3’UTR of TOPORS from nucleotide positions 543-549 for binding of the miR-198 seed region (SD) (
Figure 4A
). The CLUSTALW alignment (https://www.genome.jp/tools-bin/clustalw) showed that this TS is conserved across species (
Figure 4A
). To validate the direct interaction between miR-198 and the 3’UTR of TOPORS, we employed the use of a gold standard assay, the dual-luciferase reporter (DLR) assay. The DLR assay was performed using various constructs illustrated in
Figure 4B
. Using the site-directed mutagenesis, we generated a negative control construct, pMIR-REPORT-TOPORS-3’UTR-M, by abrogating the TS for miR-198 in the 3’UTR of TOPORS. If miRNA binds to the 3’UTR of the target gene TOPORS, the luciferase activity significantly reduces compared with control transfected cells. Therefore, we co-transfected SCC131 cells with pMIR-REPORT-TOPORS-3’UTR-S containing the intact 3’UTR of TOPORS in a sense orientation and pmiR-198 or pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP (vector control) and quantified the luciferase reporter activity. We observed a significant reduction in the luciferase activity in cells co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pmiR-198 compared with those co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP (
Figure 4C
). As expected, cells co-transfected with pmiR-198 and pMIR-REPORT-TOPORS-3’UTR-M showed the luciferase activity comparable to those co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP. This is attributed to the lack of binding of miR-198 to the TOPORS 3’UTR, as the TS is abrogated in the pMIR-REPORT-TOPORS-3’UTR-M construct (
Figures 4B, C
). We also analysed the binding of miR-198 to its known gene target FUT8 as a positive control. We observed that the cells co-transfected with pmiR-198 and pMIR-REPORT-FUT8-3’UTR-S showed a significantly reduced luciferase activity compared with those co-transfected with pMIR-REPORT-FUT8-3’UTR-S and pcDNA3-EGFP, confirming the binding of miR-198 to the 3’UTR of FUT8. The above observations confirm that miR-198 binds directly to the TS in the 3’UTR of TOPORS in a sequence-specific manner.
Figure 4
Confirmation of the binding of miR-198 to the 3’UTR of TOPORS by the dual-luciferase reporter assay in SCC131 cells. (A) Diagram showing conservation of the putative target site (TS) for the miR-198 seed sequence (SD) in the 3’UTR of TOPORS across species. The numbers denote nucleotide positions. (B) Schematic diagrams depicting various constructs used in the dual-luciferase reporter assay. The ‘X’ indicates abrogated TS region. (C) The results of dual-luciferase reporter assay. Note, a significantly reduced RLU in cells co-transfected with pMIR-REPORT-TOPORS-3’UTR-S and pmiR-198 compared to those transfected with pMIR-REPORT-TOPORS-3’UTR-S and pcDNA3-EGFP, confirming the binding of miR-198 to 3’UTR of TOPORS. Each bar is an average of 3 biological replicates.
3.5
Expression analysis of TOPORS and miR-198 in OSCC patient samples
To understand the biological relevance of the interaction between miR-198 and TOPORS, we determined the levels of miR-198 and TOPORS transcripts in 39 matched normal oral tissue and OSCC patient samples, using qRT-PCR (
Figure 5
). We found miR-198 to be significantly downregulated in 29/39 (74.35%) OSCC samples compared with their matched normal oral tissue samples (viz., patient no. 63, 3, 8, 33, 47, 49, 52, 56, 62, 64, 70, 76, 80, 10, 14, 15, 17, 24, 32, 43, 44, 45, 48, 51, 59, 61, 65, 66 and 67) and significantly upregulated in tumor samples compared with their matched normal oral tissue samples in 5/39 (12.8%) (viz., patient no. 68, 55, 50, 57 and 60) (
Figure 5A
). Moreover, no significant change in its levels was observed in 5/39 (12.8%) OSCC samples compared with their normal tissue counterparts (viz., patient no. 54, 25, 46, 53 and 6). Further, we found TOPORS to be significantly upregulated in 19/39 (48.7%) OSCC samples compared with their matched normal oral tissue samples (viz., patient no. 68, 8, 33, 47, 49, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 50, 60, 65 and 66) (
Figure 5B
) and significantly downregulated in 17/39 (43.58%) OSCC samples compared with their matched normal oral tissues (viz., patient no 54, 63, 3, 46, 52, 53, 55, 56, 62, 6, 32, 45, 51, 57, 59, 61 and 67) (
Figure 5
). Moreover, no significant change was reported in its levels in 3/39 (7.6%) (viz., patient no. 25, 80 and 17) OSCC samples compared with their matched normal tissue samples (
Figure 5B
). In patient numbers 8, 33, 47, 49, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 65, and 66 the miR-198 expression is significantly downregulated compared with their matched normal oral tissue samples and concomitantly the TOPORS expression is significantly upregulated compared with their matched normal oral tissue samples. In patient numbers 55 and 57 miR-198 is significantly upregulated compared with their matched normal oral tissue samples and concomitantly TOPORS is significantly downregulated compared with their matched normal oral tissue samples. Overall, an inverse relation of such kind was observed between the levels of miR-198 and TOPORS in 18/39 (46.15%) (viz., patient no. 8, 33, 47, 49, 55, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 57, 65 and 66) OSCC patient samples (
Figure 5
), suggesting the biological relevance of their interaction.
Figure 5
The qRT-PCR analysis of (A) miR-198 and (B)
TOPORS in OSCC patient samples. The T2, T3 and T4 represent the different stages of tumors, and the numbers along X-axis denote different patient numbers. Each qRT-PCR data is an average of 2 technical replicates.
3.6
TOPORS overexpression enhances OSCC cell proliferation
MiRNAs are known to negatively regulate their target genes by binding to their 3’UTRs. As we have observed miR-198 to be a tumor suppressor miRNA, which targets TOPORS, it is interesting to study the role of TOPORS in OSCC cells. To this end, we transfected SCC131 and SCC084 cells with vector control and pTOPORS separately and checked for cell proliferation by trypan blue dye exclusion assay. We observed a significant increase in proliferation of cells transfected with pTOPORS compared with those transfected with the vector control, suggesting that TOPORS exerts a positive regulation on proliferation of OSCC cells from both cell lines (
Supplementary Figure S2
).
3.7
Expression of TOPORS depends on the presence or absence of its 3’UTR
Since miR-198 binds directly to the 3’UTR of TOPORS, we wanted to investigate if the function of TOPORS depends on the presence of its 3’UTR. To this end, we utilized the following different TOPORS overexpression constructs: pTOPORS harbouring a complete TOPORS ORF, pTOPORS-3’UTR-S with the 3’UTR of TOPORS cloned downstream to the TOPORS ORF in the pTOPORS construct in a sense orientation and pTOPORS-3’UTR-M with the abrogated TS in the 3’UTR of TOPORS cloned downstream to the TOPORS ORF in the pTOPORS construct. We then transfected the vector control [pcDNA3.1(+)] only or co-transfected vector control and pmiR-198 or pmiR-198 with pTOPORS, pTOPORS-3’UTR-S or pTOPORS-3’UTR-M in SCC084 and SCC131 cells and performed Western blot analysis to assess TOPORS levels (
Supplementary Figure S3
). The results showed that the TOPORS levels is reduced in cells co-transfected with the vector and pmiR-198 compared with those transfected with the vector control only, because of miR-198 targeting the endogenous TOPORS (
Supplementary Figure S3
). Also, an increased level of TOPORS was observed in cells co-transfected with pmiR-198 and pTOPORS as compared to those co-transfected with the vector and pmiR-198 (
Supplementary Figure S3
). Further, we observed a reduced level of TOPORS in cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 as compared with those co-transfected with pTOPORS and pmiR-198, due to the presence of a functional TS in the 3’UTR of pTOPORS-3’UTR-S (
Supplementary Figure S3
). Further, an increased level of TOPORS was observed in cells co-transfected with pTOPORS-3’UTR-M and pmiR-198 as compared with those co-transfected with pTOPORS-3’UTR-S and pmiR-198, because of the absence of TS in the 3’UTR of pTOPORS-3’UTR-M (
Supplementary Figure S3
). These observations suggest that TOPORS expression depends on the presence or absence of its 3’UTR and is, in part, regulated by miR-198.
3.8
miR-198 regulates proliferation, apoptosis and anchorage-independent growth of OSCC cells, in part, via targeting the 3’UTR of TOPORS
We then used the above constructs to assess the effect of miR-198 mediated regulation of TOPORS on a few hallmarks of cancer such as cell proliferation, apoptosis, and anchorage-independent growth. To this end, we co-transfected pTOPORS, pTOPORS-3’UTR-S and pTOPORS-3’UTR-M constructs separately with pmiR-198 in SCC131 and SCC084 cells and assessed cell proliferation using the trypan blue dye exclusion assay. As expected, we observed a decreased rate of cell proliferation in cells co-transfected with pmiR-198 and vector compared with those transfected with the vector only (
Figure 6A
), due to targeting of endogenous TOPORS by miR-198. Further, we observed a decreased rate of cell proliferation in cells co-transfected with pmiR-198 and pTOPORS-3’UTR-S compared with those co-transfected with pmiR-198 and pTOPORS, due to the presence of a functional TS in the 3’UTR of pTOPORS-3’UTR-S (
Figure 6A
). As expected, we observed no difference in the rate of cell proliferation in cells co-transfected with pmiR-198 and pTOPORS compared with those co-transfected with pmiR-198 and pTOPORS-3’UTR-M, due to the absence of a functional TS in 3’UTR of pTOPORS-3’UTR-M (
Figure 6A
). A similar observation was made in cells from both cell lines SCC131 and SCC084 (
Figure 6A
). These observations highlighted the negative regulation of cell proliferation by miR-198, in part, via targeting the 3’UTR of TOPORS.
Figure 6
Regulation of cell proliferation and apoptosis by miR-198, in part, via targeting the 3’UTR of TOPORS. (A) The analysis of cell proliferation by the trypan blue dye exclusion assay in SCC131 and SCC084 cells co-transfected with pmiR-198 and different TOPORS overexpression constructs or vector control. Note, a significantly decreased rate of proliferation of cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 compared to those co-transfected with pTOPORS and pmiR-198 or pTOPORS-3’UTR-M and pmiR-198. (B) The quantitative analysis of apoptosis rate as assessed by the caspase-3 assay in SCC131 and SCC084 cells co-transfected with pmiR-198 and different TOPORS overexpression constructs or vector control. Note, a significant increase in the rate of apoptosis on overexpression of pmiR-198 compared with the vector control in both SCC131 and SCC084 cells. Also, there is a significant increase in the rate of apoptosis of cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 in comparison to those co-transfected with pTOPORS and pmiR-198 or pTOPORS-3’UTR-M and pmiR-198. Each bar is an average of 3 biological replicates.
To determine if miR-198 regulates apoptosis by targeting the 3’UTR of TOPORS, we again used the above constructs and performed caspase-3 assay and analysed the percentage of caspase-3 positive cells in SCC131 and SCC084 cells. We observed a significant increase in the rate of apoptosis in cells co-transfected with pmiR-198 and vector control compared with those transfected with vector control only (
Figure 6B
). Also, the percentage of apoptotic cells was significantly higher in cells co-transfected with pTOPORS-3’UTR-S and pmiR-198 compared with those co-transfected with pTOPORS-3’UTR-M or pTOPORS and pmiR-198, due to binding of the miR-198 to the TS in pTOPORS-3’UTR-S (
Figure 6B
). The percentage of caspase-3 positive cells was significantly higher in cells transfected pmiR-198 and pcDNA3.1(+) compared with those co-transfected with pTOPORS and pmiR-198, due to ectopic expression of oncogenic TOPORS-ORF (
Figure 6B
). These observations suggested that miR-198 positively regulates cellular apoptosis in SCC131 and SCC084 cells, in part, by directly targeting the 3’UTR of TOPORS.
To investigate the effect of miR-198-mediated regulation of TOPORS on anchorage-independent growth of OSCC cells, we co-transfected pTOPORS, pTOPORS-3’UTR-S and pTOPORS-3’UTR-M separately with pmiR-198 in SCC084 and SCC131 cells by soft agar colony forming assay (
Figure 7
). As expected, we observed a reduced number of colonies in cells co-transfected with pmiR-198 and the vector compared with those transfected with the vector only (
Figure 7
). Further, a reduced number of colonies was observed in cells co-transfected with pmiR-198 and pTOPORS-3’UTR-S compared with those co-transfected with pTOPORS and pmiR-198 (
Figure 7
), due to binding of miR-198 to the TS in the pTOPORS-3’UTR-S. Further, no difference in the number of colonies was observed in cells co-transfected with pmiR-198 and pTOPORS compared with those co-transfected with pmiR-198 and pTOPORS-3’UTR-M, due to the absence of a functional TS in pTOPORS-3’UTR-M (
Figure 7
). These results clearly suggested a negative regulation of miR-198 on the anchorage-independent growth, in part, by targeting the 3’UTR of TOPORS.
Figure 7
Regulation of anchorage-independent growth of OSCC cells by miR-198, in part, via targeting the 3’UTR of TOPORS. Quantitative assessment of the anchorage independent growth capabilities and representative microphotographs of the colonies for (A) SCC131 and (B) SCC084 cells co-transfected with pmiR-198 and different TOPORS overexpression constructs or vector control by soft agar colony forming assay. Each bar is an average of 4 biological replicates.
3.9
Restoration of miR-198 levels by a synthetic mimic suppresses tumor growth in vivo
Since we had observed an anti-proliferative role of miR-198, we hypothesized that the restoration of the expression level of the miR-198 by a synthetic miR-198 mimic could reduce the level of oncogenic TOPORS in OSCC cells, which might have an anti-tumor effect in vivo. To this end, we used in vivo pre-treated OSCC xenograft nude mouse model. We first optimized the dosage of a synthetic miR-198 mimic in SCC131 cells and found 900 nM of the mimic to be an optimum dose to reduce TOPORS at both the transcript and protein levels (
Supplementary Figure S4
). We then injected equal numbers of pre-transfected SCC131 cells with 900 nM of miR-198 mimic (M900) or 900 nM of mimic control (MC900) separately into the right flanks of female nude mice (6 weeks of age). The mice were monitored for OSCC xenograft (tumor) growth for 40 days. As expected, there was a reduction in both tumor volume and weight in nude mice injected with miR-198 mimic treated cells compared to that of the mock control (
Figure 8
), suggesting that miR-198 functions as tumor suppressor in OSCC.
Figure 8
Restoration of miR-198 level suppresses tumorigenesis in vivo. (A) Photographs of nude mice showing tumor growth on day 40 of injection. The numbers denote mice numbers. The corresponding excised xenograft on day 40 is shown below each mouse. Each group had 6 mice. One mouse (#6) in the mimic control group died early. Dotted red circles mark the xenografts in mice. Note, no tumor development in mice # 5 in the mimic control group. (B) Effect of miR-198 mimic on the volume of xenografts during a time course of 40 days. (C) The weight of xenografts on day 40.
3.10
Effect of miR-198-mediated regulation of TOPORS on the p53/p21 signaling in OSCC cells
TOPORS, an E3 ubiquitin and SUMO ligase, is known to affect the expression of several proteins, including the guardian of the genome, p53 (34–38). The p53 pathway is well known for induction of apoptosis and cell cycle arrest (39). The effect of miR-198 on the p53 signaling has not been explored. We, therefore, wanted to understand if miR-198-mediated regulation of TOPORS has any impact on the p53 pathway in OSCC (
Figure 9A
). To this end, we determined the levels of p53 and its downstream target p21 (WAF1/CIP1/CDKN1A) in OSCC cells transfected with pmiR-198 or pTOPORS, using the Western blot analysis (
Figures 9B, C
). The results showed that the cells transfected with pmiR-198 had increased levels of p53 and p21 compared with those transfected with the vector control in both SCC131 and SCC084 cells (
Figure 9B
). As expected, overexpression of miR-198 led to a reduced level of TOPORS in both SCC131 and SCC084 cells (
Figure 9B
). In contrast, cells transfected with pTOPORS showed reduced levels of p53 and p21 compared with those transfected with the vector control in both SCC131 and SCC084 cells (
Figure 9C
). These results highlighted the significant roles miR-198 and TOPORS in the p53 pathway in OSCC. These observations also indicated that miR-198 enhances signaling of p53/p21, in part, by regulating TOPORS.
Figure 9
miR-198 upregulates p53/p21 signaling, in part, by regulating TOPORS. (A) A diagrammatic representation of the miR-198/TOPORS/p53/p21 axis. (B) Western blot analysis following overexpression of miR-198 in SCC131 and SCC084 cells. (C) Western blot analysis following overexpression of TOPORS in SCC131 and SCC084 cells. β-actin was used as a loading control.
3.11
Effect of miR-198-mediated regulation of TOPORS and p53/p21 signalling in OSCC patient samples
Since the p53/p21 pathway is observed to be directly correlated with miR-198-mediated regulation of TOPORS (
Figure 9
), we wanted to explore the levels of TP53 transcript in the OSCC patient samples which showed an inverse relation of miR-198 and TOPORS levels (viz., patient no. 8, 33, 47, 49, 55, 64, 70, 76, 10, 14, 15, 24, 43, 44, 48, 57, 65 and 66) by qRT-PCR using TP53 specific primers (
Supplementary Table S3
). The results showed that TP53 was significantly downregulated in 7/18 OSCC samples (viz., patient no. 47, 49, 55, 76, 14, 43 and 66) and upregulated in 5/18 OSCC samples (viz., patient no. 8, 64, 10, 48 and 57) compared with their matched normal tissue samples (
Figure 10
). Since TP53 is the most mutated gene in cancer, we checked the CDKN1A transcript level using CDK1NA (P21) specific primer (
Supplementary Table S3
) as mutations in CDKN1A are less frequent and CDKN1A is a direct target and a downstream effector for p53. The results showed a significant downregulation of the CDKN1A level in 9/18 OSCC samples (viz., patient no. 8, 33, 47, 49, 76, 43, 44, 48 and 66) and its upregulation in 4/18 OSCC samples (viz., patient no. 55, 10, 57 and 65) compared to matched normal oral tissue samples (
Figure 10
). No significant change between their levels was observed in 6/18 OSCC samples for TP53 (viz., patient no. 33, 70, 15, 24, 44 and 65) and 5/18 OSCC samples for CDKN1A (viz., patient no. 64, 70, 14, 15 and 24) compared with matched normal oral samples (
Figure 10
). We then analyzed the expression pattern of TP53 and CDKN1A with respect to miR-198 expression in these inversely related patient samples. Interestingly, we observed miR-198 levels to be positively correlated with CDKN1A levels in 11/18 OSCC samples compared with their matched normal oral tissue samples (viz., patient no. 8, 33, 47, 49, 55, 76, 43, 44, 48, 57 and 66) (
Figure 10
). For example, if miR-198 expression is observed to be downregulated in the tumor sample from patient no. 47, the expression of CDKN1A (downstream effector of p53) and TP53 was also downregulated and the expression of TOPORS was upregulated in this tumor sample compared with its matched normal tissue sample (
Figure 10
). The Pearson correlation analysis indicate an inverse correlation between miR-198 and TOPORS (r = -0.08; p value=0.614) whereas the analysis of miR-198 and CDKN1A (r = 0.422; p value=0.080) indicate a positive correlation (
Supplementary Figure S5
) but the correlation was not significant. The small sample size, tumor heterogeneity and the presence of other underlying alternate mechanisms are possible reasons. Nonetheless, these results highlighted the biological relevance of this interaction between miR-198 and TOPORS and its role in the p53/p21 pathway.
Figure 10
The qRT-PCR analysis of TP53 and CDKN1A in OSCC patient samples. The transcript levels of (A)
TP53 and (B)
CDKN1A in 18 matched normal oral tissue and OSCC tissue samples from patients who showed an inverse correlation in the levels of miR-198 and TOPORS. T3 and T4 represent the different stages of tumors, and the numbers along X-axis denote different patient numbers. Each qRT-PCR data is an average of 2 technical replicates.
4
Discussion
Epigenetic silencing is one of the major mechanisms that regulate tumor suppressor miRNAs and induce tumor progression (40). To our knowledge, very few miRNAs (e.g., miR-137, miR-127, miR-375, miR-34b, miR-193a, miR-200/miR-205, miR-6741-3p, and miR-617) are known to be regulated by promoter hypermethylation in OSCC (13, 14, 26, 41). Here we report for the first time that miR-198 is an epigenetically silenced tumor suppressor miRNA in OSCC. We identified an independent promoter for miR-198 and using BSP, we established that miR-198 expression increases following 5-Azacytidine (hypomethylating drug) treatment due to demethylation at the MIR198 promoter in OSCC (
Figures 1A
,
2B
). Contrary to our observations, Sundaram et al. (42) identified a bi-directional switch FSTL1/miR-198 as a mechanism for transcriptional regulation of miR-198. The regulation of exonic miRNAs such as miR-198 is not well understood and elucidated as they are relatively low in number compared with intronic miRNAs (43). Some miRNAs are also known to be under dual regulation of their host gene promoters and their independent promoters (44–46). To make matters even more complex, miR-198 is known to be regulated by several non-coding RNAs (27), highlighting the varied ways that miRNAs are regulated and how we still require additional studies to understand this complex interaction.
miRNAs regulate gene expression by modulating the expression of their target genes. We showed for the first time that the overexpression of miR-198 reduces the expression of its target TOPORS at both transcript and protein levels in OSCC cells by directly binding to its 3’UTR in a sequence-specific manner (
Figure 4
). Though 45 gene targets for miR-198 are reported across various cancers, the role of only miR-198 mediated regulation of CDK4 is reported in OSCC (4, 27). It is interesting to note that, till date no other miRNA is known to target TOPORS. Also, we show here for the first time the oncogenic role of TOPORS in OSCC.
miR-198, an exonic miRNA located in the last non-coding exon 11 of the FSTL1 (Follistatin Like 1) gene on chromosome 3q13.33, acts as a tumor suppressor in several cancers (27). Wang et al. (47) observed that downregulation of miR-198 is significantly associated with tumor size, lymph node metastasis, and tumor node metastasis (TNM) stage in patients with non-small cell lung cancer (NSCLC). TOPORS is a multifunctional protein belonging to the RING family of proteins and functions as both E3 ubiquitin ligase and E3 SUMO ligase. It is involved in several cellular processes such as maintenance of retinal homeostasis, cell division, maintenance of genomic stability, chromatin organization, double strand break repair and base excision repair (34–38). In accordance with other studies showing tumor suppressive function of miR-198, we show that TOPORS is upregulated and miR-198 is downregulated in a majority of OSCC tumors compared to their normal counterparts (
Figure 5
). However, we did not observe any correlation in 21/39 OSCC samples, suggesting the involvement of alternate mechanisms such as tumor heterogeneity and heterogenous genetic composition of each patient (14, 48). An inverse correlation between the expression levels of miR-198 and TOPORS in a majority of OSCC samples suggests the biological significance of their interaction.
Previous studies have explored the involvement of miR-198 in MAPK and PI3K/AKT, HGF/MET, JAK/STAT and FGFR1 pathways (27). Further, TOPORS is known to directly interact and regulate the expression of p53 (38). However, none of the studies have investigated a link between miR-198 and p53. Thus, we decided to focus on this pathway and investigated the levels of p53 and its downstream effector target p21 (CDKN1A/CIP1/WAF1) following miR-198 and TOPORS overexpression in OSCC cells. The miR-198 overexpression should reduce TOPORS level. A reduction in TOPORS expression is expected to overcome p53 inhibition, subsequently increasing p53 and p21 levels. As expected, we observed reduced TOPORS levels and enhanced p53 and p21 levels in miR-198 overexpressed cells from both OSCC cell lines and vice-versa for TOPORS overexpressed cells (
Figure 9
). In the OSCC patient samples which showed an inverse correlation between miR-198 and TOPORS expression (18 patients) (
Figure 5
), we also observed the miR-198 levels to be positively correlated with CDKN1A (p21) levels in 11/18 tumor samples (viz., patient no. 8, 33, 43, 44, 47, 48, 49, 55, 57, 66 and 76) compared with their matched normal oral samples (
Figures 5
,
10
), highlighting the biological relevance of miR-198-mediated TOPORS regulation on the p53-p21 signalling pathway.
miR-198 deregulation has been linked to modulation of various essential cellular properties, including proliferation, apoptosis, colony-forming ability, and invasion in a wide range of cancers such as prostate, gastric, lung, hepatocellular, and breast cancers (27). Interestingly, as it is known that a single miRNA can target many genes and a gene can also be targeted by many miRNAs (49), we therefore wanted to confirm that miR-198-mediated regulation of TOPORS is due the interaction between the 3′UTR of TOPORS and miR-198. To achieve this goal, we have transfected pTOPORS, pTOPORS-3′UTR-S, and pTOPORS-3′UTR-M constructs separately with pmiR-198 in OSCC cells. As expected, we observed a significant decrease in cell proliferation (
Figure 6A
) and anchorage-independent growth (
Figure 7
), and a significant increase in apoptosis (
Figure 6B
) in OSCC cells co-transfected with pTOPORS-3′UTR-S and pmiR-198 as compared to those co-transfected with pTOPORS or pTOPORS-3′UTR-M and pmiR-198, confirming that miR-198 regulates various hallmarks of cancers, in part, by directly targeting the 3′UTR of TOPORS.
Several studies reported reduced tumor size and volume in nude mice injected with miR-198 synthetic mimics compared with control group (50, 51). Kang et al. (4) injected athymic mice with OSCC cells (Cal-27-tongue cancer cell line) transfected with a synthetic miR-198 mimic and reported a significant reduction in tumor size and volume compared with mice injected with a mimic control. In our study, the mice injected with a miR-198 synthetic mimic treated OSCC cells showed reduced tumor weight and volume compared to mock treated cells, which reveals the tumor suppressor function of miR-198 (
Figure 8
). Taken together, these results suggested that miR-198 exerts its tumor suppressor functions in OSCC at least, in part, by regulating TOPORS (
Figure 11
), and therefore synthetic miR-198 mimics could be used as a therapeutic target in oral cancer However, the limitations of such study include the lack of a more diverse patient cohort for miR-198 and TOPORS expression analysis, the need for protein interaction studies between TOPORS and p53 in patient samples where a correlation was observed, the exploration of the combinatorial effect of miR-198 and its other known targets with TOPORS in OSCC, and the investigation of the significance of individual CpG sites in the MIR198 promoter that contribute to epigenetic silencing. Further research is required to deepen our understanding of miR-198 regulation in these contexts.
Figure 11
A diagrammatical representation of the effect of 5-Azacytidine induced upregulation of miR-198 in SCC131 cells.The independent promoter for miR-198 is represented by three CpG sites. The treatment of 5-Azacytidine of SCC131 cells leads to demethylation of the MIR198 promoter. This causes an increase in the expression of primary miR-198 (Pri-miR-198) due to an increased transcription of miR-198. The Pri-miR-198 is processed by Drosha/DGCR8 complex to form the preliminary miR-198 (Pre-miR-198). The Pre-miR-198 is then exported out of the nucleus to the cytoplasm by Exportin-5. In the cytoplasm, it is processed for maturation via the Dicer/TRBP complex. The mature miR-198 is loaded on the RISC. This miR-198-RISC then binds to the TS on the 3’UTR of TOPORS mRNA, causing reduced expression of TOPORS. Reduced expression of TOPORS in turn increases p53 expression, causing apoptosis. The p53 also enhances the expression of p21 and subsequently increases cell cycle arrest. RISC, RNA-induced silencing complex; TS, target site; TSS, transcription start site.
Despite their small size, miRNAs play a pivotal role in regulating numerous cellular processes. Tumor suppressor and oncogenic miRNAs, along with their various targets influencing multiple pathways, present new opportunities for therapeutic interventions (52). Therapies may focus on restoring tumor suppressor miRNAs using miRNA mimics or inhibiting oncogenic miRNAs with miRNA inhibitors. Currently, several pharmaceutical companies are competing to develop effective delivery systems, with miRNAs such as miR-155, miR-10b, and miR-16 undergoing clinical trials for T-cell lymphoma, glioma, and lung cancer, respectively (52). These promising advancements could pave the way for personalized cancer treatments in future.
5
Conclusions
Our study has identified that miR-198 is an epigenetically silenced tumor suppressor, which directly targets and represses TOPORS and thereby activates the p53/p21 signaling in OSCC cells. Further, our observations have shown that miR-198 suppresses cell proliferation, anchorage-independent growth and enhances apoptosis of OSCC cells, in part, by targeting the 3’UTR of TOPORS. Moreover, our observations in nude mice highlight the therapeutic potential of miR-198 and TOPORS in OSCC. An in-depth understanding of the mechanisms by which miR-198 affects cancer progression could pave the way for miR-198 to serve as a therapeutic target for not just oral cancer but other cancers as well. |
Title: Biochemical Characterization and Disease Control Efficacy of | Body:
1. Introduction
Chitin (Ct) is one of the most abundant biopolymers in nature [1,2], and it can be found in different organisms as a supportive and protective component of the exoskeleton of arthropods, fungal cell walls, and some algae [3]. Chitin is traditionally extracted from crustacean shells, squid skeletons, and the cuticles of insects [4,5,6]. Currently, commercial Ct and its deacetylated derivate, chitosan (Cs), are produced from shrimp and crab shells as by-products of the sea-food industry [1,2,7]. In addition, beside the textile and paper industries, Ct and Cs are also used in the food industry, agriculture, wastewater treatment, tissue engineering, biomedical, and biotechnological fields [8,9]. The chemical structures of Ct and Cs consist of 2-amino-2-deoxy-D-glucopyranose and 2-acetamide-2-deoxy-D-glucopyranose units, respectively, linked together by various amounts of glycosidic β-1,4 bonds (Figure 1A,B) [10]. The conversion from Ct to Cs involves the removal of the acetyl group (-CH3CO) from the acetyl glucosamine monomer within the Ct chain. This action releases the amino group (-NH2), converting it into a glucosamine monomer and ultimately yielding Cs (Figure S1).
The traditional extraction of Ct from crustacean shells requires strong alkali, high temperature treatment, and has seasonal supply as well as geographical limitations [11,12]. Since the late 1970s, after White et al. [11] introduced a laboratory-scale method for extracting chitosan from Mucor rouxii mycelia, numerous protocols have been devised to use fungal biomass as an alternative source for chitosan production, instead of crustacean shells [11]. It is well-known that fungal cell walls are rich in neutral polysaccharides, glycoproteins with minor amounts of galactosamine polymers, polyuronides, melanin, lipids, and chitin which give rigidity to the cell wall [10,13,14,15]. The taxonomic groups Zygomycetes, Ascomycetes, Basidiomycetes, and Mitosporic fungi are known to contain Ct in their cell walls. In contrast, Oomycetes, which were previously classified as fungi, are characterized by the presence of cellulose instead of Ct [14]. Therefore, the extraction of Ct from fungi has attracted significant importance recently. Although insect-derived chitin/chitosan has properties similar to those from crustaceans, fungal Ct/Cs provides distinct advantages, including superior particle size uniformity and lower molecular weight, compared to that derived from both insects and crustaceans [16,17].
Some edible fungi have recently attracted attention for their nutritional and medicinal properties [18]. Additionally, mushrooms are significant sources of biologically active compounds, which have important effects on the immune system and have antimicrobial activities [19]. Among the most important edible fungi, genus Pleurotus has been extensively investigated for its biological properties due to the presence of some bioactive compounds such as polysaccharides, enzymes, and proteins [20,21,22,23]. In particular, Pleurotus eryngii (DC.) Quél., also known as the king oyster mushroom, is native to the Mediterranean basin of Europe and the Middle East [24], and known in Italy as “Cardoncello”. This fungus is rich in vitamins and minerals, and has a low content of carbohydrates, cholesterol, and other bioactive substances that can improve the immune response [25,26]. Several studies have explored the pharmaceutical applications of Ct derived from edible mushrooms, particularly the Pleurotus species [27]. However, to our knowledge, there has been no significant research investigating the potential use of Ct and/or Cs extracted from P. eryngii for plant disease control.
The main objectives of the current study are to: (i) physiochemically characterize the obtained Cs from P. eryngii (Cs-Pe); (ii) evaluate the in vitro antimicrobial activity of bleached and unbleached Cs-Pe against various phytopathogenic bacteria and fungi compared to commercial chitosan (C.Cs); (iii) assess the phytotoxic and cytotoxic effects of the studied Cs-Pe; and (iv) evaluate the in vivo effects of the studied Cs-Pe on plums’ quality and their potential biocontrol activity against Monilinia laxa, the causal agent of brown-rot disease.
2. Results
2.1. Biomass Quantification
Table 1 summarizes the key findings, including the weight of produced fungal biomass in three different nutrient media: (i) potato dextrose broth supplemented with 50 g of wheat powder (PDBW); (ii) malt extract broth (MEB); and (iii) nutrient broth (NB). Table 1 also illustrates the dry weight and yield percentage of extracted Ct and deacetylated Cs, both bleached and unbleached. In particular, the highest significant biomass production was achieved with PDBW, with 65.5 g of fresh weight (F.Wt) and 31.6 g of dry weight (DW). MEB produced 12.6 g of FW and 4.4 g of DW, while NB produced 22.3 g of FW and 9.4 g of DW of biomass. On the other hand, the produced biomass from PDBW was used for the extraction of chitin and the deacetylation process to obtain the chitosan. The unbleached chitin (U.Ct.Pe) and bleached chitin (B.Ct.Pe) yielded 13.3 g DW (42.1% relative to fungal biomass) and 2.6 g (8.2% relative to fungal biomass), respectively. In contrast, unbleached chitosan (U.Cs.Pe) and bleached chitosan (B.Cs.Pe) produced 6.4 g DW (48.1% relative to Ct) and 1.9 g (73.1% relative to Ct), respectively (Figure 2).
2.2. Physicochemical Characteristics of Chitin and Chitosan
2.2.1. FT-IR Analysis
The study’s findings, which are shown in Figure 3, showed the existence of many absorption bands in the 4000–400 cm−1 region. The FT-IR spectrum is of B.Cs.Pe, U.Cs.Pe, C.Cs, and Ct. After the depolymerization process, the Cs structure stays stable, as evidenced by the IR spectrum, which was quite close to native Cs [28,29]. Table 2 illustrates the FT-IR spectra of studied Cs.Pe compared to C.Cs and Ct. In particular, a band cantered around 3450 cm−1 signifies the existence of an OH group [30,31]. The band around 2887 cm−1 is attributed to C-H stretching. In addition, the presence of the C=O group is shown by a band at around 1649 cm−1. The intensity of this peak was decreased and a shift occurred compared to C.Cs due to the decrease for the acetyl group in B.Cs.Pe and U.Cs.Pe [32]. The presence of a band around 1570 cm−1 corresponds to the N-H bending group in the B.Cs.Pe and shifted at 1558, 1573, and 1560 cm−1 at U.Cs.Pe, C.Cs, and Ct, respectively. The bands around 1150 cm−1 and 1075 cm−1 were attributed to C-C and C-N groups, respectively [33,34]. Compared to the FT-IR spectra of Ct, there was a decrease in the shift and also intensity peak of C=O in the case of the extracted Cs due to the decrease of acetyl content because of the deacetylation process performed in Ct.
2.2.2. Molecular Weight and Viscosity
The viscosity of B.Cs.Pe and U.Cs.Pe resulted in 1.21 and 1.02 centipoise (cP), whereas, their molecular weights (M.Wt) were equal to 20.5 KDa and 17.3 KDa, respectively. In fact, high M.Wt Cs had a higher viscosity than a low M.Wt one. Omogbai and Ikenebomeh [35] reported that the viscosity of Cs is an important factor which determines its applications and significantly affects its antimicrobial activities.
2.2.3. Degree of Deacetylation
The degree of deacetylation (DD%) in Ct and Cs, which is essential for determining their physicochemical properties, can be analyzed using infrared spectroscopy. Infrared spectra of Ct and Cs from P. eryngii and C.Cs were used to measure the DD%, which was found to be 72.47, 81.12, 80.01, and 62.57% for B.Cs.Pe, U.Cs.Pe, C.Cs, and Ct, respectively.
2.3. Antimicrobial Activity
Antibacterial. The results of the antibacterial assay demonstrated the potent efficacy of U.Cs.Pe and B.Cs.Pe against all tested pathogenic bacteria (Figure S2). Notably, the U.Cs.Pe exhibited the lowest minimum inhibitory concentration (MIC) value (0.375 mg/mL) for Clavibacter michiganensis compared to B.Cs.Pe (0.75 mg/mL) and C.Cs (1.5 mg/mL). Regarding Escherichia coli, the lowest MIC value (1.5 mg/mL) was observed in the case of U.Cs.Pe and B.Cs.Pe, compared to C.Cs (3.0 mg/mL). Regarding Xanthomonas campestris, the lowest MIC value (1.5 mg/mL) was observed in the case of U.Cs.Pe, compared to both B.Cs.Pe and C.Cs (3.0 mg/mL) (Table 3). The positive control showed MIC values ranged between 50 µg/mL in the case of C. michiganensis and P. fluorescence, and 100 µg/mL in the case of E. coli and X. campestris.
Concerning the bactericidal results, the minimum bactericidal concentration (MBC) of U.Cs.Pe were 6.0, 3.0, 0.75, and 3.0 mg/mL for E. coli, X. campestris, C. michiganensis, and Pseudomonas fluorescence, respectively. In comparison, B.Cs.Pe exhibited MBC values of 6.0, 6.0, 1.5, and >6.0 mg/mL against the same bacteria (Table 4).
Antifungal. The results of the antifungal assay demonstrated that the studied Cs has a potent efficacy against all tested pathogenic fungi (Figure S3). In particular, the lowest MIC values exhibited by the U.Cs.Pe were 1.5, 0.375, and 0.75 mg/mL compared to B.Cs.Pe ≥3.0, 1.5, and 1.5 mg/mL against Penicillium expansum, Botrytis cinerea, and Monilinia laxa, respectively (Table 5). Whereas, the MIC values for C.Cs displayed 1.5 mg/mL against all tested fungi. The positive control showed a MIC value equal to 100 µg/mL for all tested fungi.
Concerning the fungicidal results, the minimum fungicidal concentration (MFC) of U.Cs.Pe demonstrated lower values of 6.0, 3.0, and 0.75 mg/mL compared to 6.0, 6.0, and 1.5 mg/mL in the case of both B.Cs.Pe and C.Cs, against P. expansum, B. cinerea, and M. laxa, respectively (Table 6).
2.4. Phytotoxicity
The phytotoxic effects of the studied chitosan were evaluated against three sensitive plants (Lepidium sativum, Lactuca sativa, and Solanum lycopersicum) at concentrations of 3000, 1500, and 750 ppm. The results are presented in Table 7. Specifically, for L. sativum, C.Cs exhibited the highest significant germination index (G.I.) of 53.4% at the lowest tested concentration of 750 ppm, compared to other tested Cs. For L. sativa, U.Cs.Pe demonstrated the highest significant G.I. of 50.6% at 750 ppm, which is very close to the control. In the case of S. lycopersicum, both B.Cs.Pe and U.Cs.Pe showed the highest significant G.I. (63.5%) and (54.4%), respectively, which closely match the control. All tested Cs exhibited very low significant G.I. at the highest concentration of 3000 ppm. The notable phytotoxic activity of the Cs extracted from P. eryngii suggests its potential use as a natural herbicidal agent against harmful weeds.
2.5. Cytotoxicity
The cytotoxic effect of the C.Cs and Cs.Pe on the mitotic index (MI), phase index (PI), and total abnormalities (Tab) of Vicia faba root tips was illustrated in Table 8. The highest significant MI was found in the case of U.Cs.Pe (16.92%) followed by B.Cs.Pe (16.87%), while the lowest MI was recorded in the case of C.Cs (12.67%) compared to control cells (14.87%).
The PI included mitotic index and abnormality percentages for each cell division stage, except the interphase, as illustrated in Table 8. In particular, the highest MI at the prophase stage was 15.98%, presented in C.Cs, whereas the lowest MI at the prophase was found in U.Cs.Pe with a value of 13.88%, compared to the control (16.98%). The highest MI % at the metaphase was 57.87% for C.Cs, while the highest values at the anaphase (23.87%) and telophase (22.02%) were recorded in U.Cs.Pe. The highest significant values in abnormality percentages at the prophase, metaphase, anaphase, and telophase stages were observed in the case of C.Cs, with values of 3.87, 23.67, 7.45, and 3.54%, respectively.
The Tab % of mitosis was found in C.Cs (38.53%), while the lowest values were 17.08 and 18.65% in the case of B.Cs.Pe and U.Cs.Pe, respectively, compared to the control (24.64%). The Tab % at the interphase was presented in C.Cs with a value of 1.87%, compared to the control 0.87%, whereas there were no any abnormalities at the interphase stage for other treatments.
Different types of chromosomal abnormality at different stages of V. faba root tip cell division were recorded in Figure 4. The micronucleus at the interphase was found in C.Cs (Figure 4A,B). At the metaphase stage, the common abnormalities were presented as stickiness (Figure 4C), disturbed (Figure 4D), non-congression (Figure 4E), and oblique (Figure 4F,G). At the anaphase, they were disturbed (Figure 4H,I), bridges (Figure 4J), late separation (Figure 4K), and laggard (Figure 4L,M). At the telophase, the common types of abnormality were disturbed (Figure 4N,O), late separation (Figure 4P,Q), laggard (Figure 4R), bridge (Figure 4S), and diagonal telophase (Figure 4T).
2.6. In Vivo Fruit-Coating Assay
2.6.1. Quality Parameters
The selected U.Cs.Pe showed a strong ability to form a protective coating-layer on fruit surfaces. On the other hand, there was not significant effect on the quality parameters of control fruitss treated with 1% acetic acid and/or glycerol.
(I)
Weight loss
The influence of different coatings on weight loss percentages is depicted in Figure 5. In particular, the highest significant weight loss (27%) was observed in the case of the positive control (C + ve), i.e., fruits infected with M. laxa (Figure 6), whereas, the lowest weight loss (4%) was observed in the case of fruits treated with U.Cs.Pe, followed by fruits treated with U.Cs.Pe and infected with M. laxa (7%) (Figure 6).
(II)
Peel color change
The obtained results of the influence of different coatings on peel color are illustrated in Figure 7. The treatment with U.Cs.Pe did not show significant variation in peel-color, even 7 DAT (Figure S4). In particular, fruits treated with U.Cs.Pe, whether infected or not, exhibited moderate changes in peel color with ΔE values ranging between 1.5 and 2.4 relative to the negative control (C − ve) (7 DAT). On the other hand, the highest significant change in peel color was observed in the case of C + ve, with a Δ E 3.76 (7 DAT). In addition, C.Cs-treated fruits showed the least change in peel color with a ΔE value of 1.17.
(III)
Firmness
The results of the studied coating treatments on fruit firmness 7 DAT are illustrated in Figure 8. Firmness is a crucial visual quality parameter for fresh market fruits as it significantly influences post-harvest quality and shelf life. Treated fruits with U.Cs.Pe showed a low, insignificant reduction in firmness compared to C − ve and those treated with C.Cs. Notably, infected fruits treated with U.Cs.Pe exhibited only a slight significant decrease in firmness, with a value of 1.83 kg/cm2, compared to those infected and treated with C.Cs, which showed a significant firmness reduction to 1.15 kg/cm2.
(IV) Sugar content
Specifically, the results from treated fruits with U.Cs.Pe, either infected or not, showed a slight decrease in sugar content even at 7 DAT (13.70 and 14.67 Brix, respectively), compared to untreated controls (15.40 Brix) (Figure 9), whereas, fruits treated with C.Cs, whether infected or not, exhibited sugar contents of 10.70 and 12.33 Brix, respectively.
2.6.2. Decay Evaluation
After 5 days of storage, untreated fruits, or those treated with C.Cs and infected with M. laxa, exhibited brown-rot symptoms. In contrast, fruits treated with U.Cs.Pe showed a reduction in disease symptoms, with decay percentages of 46.4 and 52.6% for preventive and curative treatments, respectively (Figure 10). The C + ve showed the highest decay percentage ranged between 94.4 and 100% with regard to prevention and curative treatment, respectively, whereas, C.Cs showed a moderate decay percentage of 55.6 and 61.7% in the case of prevention or curative treatment, respectively (Figure S5). On the other hand, the decay percentage of acetic acid (1%), either for prevention or curative treatments, were 61.1 and 77.8%, respectively, excluding any effect on the Cs efficacy.
2.1. Biomass Quantification
Table 1 summarizes the key findings, including the weight of produced fungal biomass in three different nutrient media: (i) potato dextrose broth supplemented with 50 g of wheat powder (PDBW); (ii) malt extract broth (MEB); and (iii) nutrient broth (NB). Table 1 also illustrates the dry weight and yield percentage of extracted Ct and deacetylated Cs, both bleached and unbleached. In particular, the highest significant biomass production was achieved with PDBW, with 65.5 g of fresh weight (F.Wt) and 31.6 g of dry weight (DW). MEB produced 12.6 g of FW and 4.4 g of DW, while NB produced 22.3 g of FW and 9.4 g of DW of biomass. On the other hand, the produced biomass from PDBW was used for the extraction of chitin and the deacetylation process to obtain the chitosan. The unbleached chitin (U.Ct.Pe) and bleached chitin (B.Ct.Pe) yielded 13.3 g DW (42.1% relative to fungal biomass) and 2.6 g (8.2% relative to fungal biomass), respectively. In contrast, unbleached chitosan (U.Cs.Pe) and bleached chitosan (B.Cs.Pe) produced 6.4 g DW (48.1% relative to Ct) and 1.9 g (73.1% relative to Ct), respectively (Figure 2).
2.2. Physicochemical Characteristics of Chitin and Chitosan
2.2.1. FT-IR Analysis
The study’s findings, which are shown in Figure 3, showed the existence of many absorption bands in the 4000–400 cm−1 region. The FT-IR spectrum is of B.Cs.Pe, U.Cs.Pe, C.Cs, and Ct. After the depolymerization process, the Cs structure stays stable, as evidenced by the IR spectrum, which was quite close to native Cs [28,29]. Table 2 illustrates the FT-IR spectra of studied Cs.Pe compared to C.Cs and Ct. In particular, a band cantered around 3450 cm−1 signifies the existence of an OH group [30,31]. The band around 2887 cm−1 is attributed to C-H stretching. In addition, the presence of the C=O group is shown by a band at around 1649 cm−1. The intensity of this peak was decreased and a shift occurred compared to C.Cs due to the decrease for the acetyl group in B.Cs.Pe and U.Cs.Pe [32]. The presence of a band around 1570 cm−1 corresponds to the N-H bending group in the B.Cs.Pe and shifted at 1558, 1573, and 1560 cm−1 at U.Cs.Pe, C.Cs, and Ct, respectively. The bands around 1150 cm−1 and 1075 cm−1 were attributed to C-C and C-N groups, respectively [33,34]. Compared to the FT-IR spectra of Ct, there was a decrease in the shift and also intensity peak of C=O in the case of the extracted Cs due to the decrease of acetyl content because of the deacetylation process performed in Ct.
2.2.2. Molecular Weight and Viscosity
The viscosity of B.Cs.Pe and U.Cs.Pe resulted in 1.21 and 1.02 centipoise (cP), whereas, their molecular weights (M.Wt) were equal to 20.5 KDa and 17.3 KDa, respectively. In fact, high M.Wt Cs had a higher viscosity than a low M.Wt one. Omogbai and Ikenebomeh [35] reported that the viscosity of Cs is an important factor which determines its applications and significantly affects its antimicrobial activities.
2.2.3. Degree of Deacetylation
The degree of deacetylation (DD%) in Ct and Cs, which is essential for determining their physicochemical properties, can be analyzed using infrared spectroscopy. Infrared spectra of Ct and Cs from P. eryngii and C.Cs were used to measure the DD%, which was found to be 72.47, 81.12, 80.01, and 62.57% for B.Cs.Pe, U.Cs.Pe, C.Cs, and Ct, respectively.
2.2.1. FT-IR Analysis
The study’s findings, which are shown in Figure 3, showed the existence of many absorption bands in the 4000–400 cm−1 region. The FT-IR spectrum is of B.Cs.Pe, U.Cs.Pe, C.Cs, and Ct. After the depolymerization process, the Cs structure stays stable, as evidenced by the IR spectrum, which was quite close to native Cs [28,29]. Table 2 illustrates the FT-IR spectra of studied Cs.Pe compared to C.Cs and Ct. In particular, a band cantered around 3450 cm−1 signifies the existence of an OH group [30,31]. The band around 2887 cm−1 is attributed to C-H stretching. In addition, the presence of the C=O group is shown by a band at around 1649 cm−1. The intensity of this peak was decreased and a shift occurred compared to C.Cs due to the decrease for the acetyl group in B.Cs.Pe and U.Cs.Pe [32]. The presence of a band around 1570 cm−1 corresponds to the N-H bending group in the B.Cs.Pe and shifted at 1558, 1573, and 1560 cm−1 at U.Cs.Pe, C.Cs, and Ct, respectively. The bands around 1150 cm−1 and 1075 cm−1 were attributed to C-C and C-N groups, respectively [33,34]. Compared to the FT-IR spectra of Ct, there was a decrease in the shift and also intensity peak of C=O in the case of the extracted Cs due to the decrease of acetyl content because of the deacetylation process performed in Ct.
2.2.2. Molecular Weight and Viscosity
The viscosity of B.Cs.Pe and U.Cs.Pe resulted in 1.21 and 1.02 centipoise (cP), whereas, their molecular weights (M.Wt) were equal to 20.5 KDa and 17.3 KDa, respectively. In fact, high M.Wt Cs had a higher viscosity than a low M.Wt one. Omogbai and Ikenebomeh [35] reported that the viscosity of Cs is an important factor which determines its applications and significantly affects its antimicrobial activities.
2.2.3. Degree of Deacetylation
The degree of deacetylation (DD%) in Ct and Cs, which is essential for determining their physicochemical properties, can be analyzed using infrared spectroscopy. Infrared spectra of Ct and Cs from P. eryngii and C.Cs were used to measure the DD%, which was found to be 72.47, 81.12, 80.01, and 62.57% for B.Cs.Pe, U.Cs.Pe, C.Cs, and Ct, respectively.
2.3. Antimicrobial Activity
Antibacterial. The results of the antibacterial assay demonstrated the potent efficacy of U.Cs.Pe and B.Cs.Pe against all tested pathogenic bacteria (Figure S2). Notably, the U.Cs.Pe exhibited the lowest minimum inhibitory concentration (MIC) value (0.375 mg/mL) for Clavibacter michiganensis compared to B.Cs.Pe (0.75 mg/mL) and C.Cs (1.5 mg/mL). Regarding Escherichia coli, the lowest MIC value (1.5 mg/mL) was observed in the case of U.Cs.Pe and B.Cs.Pe, compared to C.Cs (3.0 mg/mL). Regarding Xanthomonas campestris, the lowest MIC value (1.5 mg/mL) was observed in the case of U.Cs.Pe, compared to both B.Cs.Pe and C.Cs (3.0 mg/mL) (Table 3). The positive control showed MIC values ranged between 50 µg/mL in the case of C. michiganensis and P. fluorescence, and 100 µg/mL in the case of E. coli and X. campestris.
Concerning the bactericidal results, the minimum bactericidal concentration (MBC) of U.Cs.Pe were 6.0, 3.0, 0.75, and 3.0 mg/mL for E. coli, X. campestris, C. michiganensis, and Pseudomonas fluorescence, respectively. In comparison, B.Cs.Pe exhibited MBC values of 6.0, 6.0, 1.5, and >6.0 mg/mL against the same bacteria (Table 4).
Antifungal. The results of the antifungal assay demonstrated that the studied Cs has a potent efficacy against all tested pathogenic fungi (Figure S3). In particular, the lowest MIC values exhibited by the U.Cs.Pe were 1.5, 0.375, and 0.75 mg/mL compared to B.Cs.Pe ≥3.0, 1.5, and 1.5 mg/mL against Penicillium expansum, Botrytis cinerea, and Monilinia laxa, respectively (Table 5). Whereas, the MIC values for C.Cs displayed 1.5 mg/mL against all tested fungi. The positive control showed a MIC value equal to 100 µg/mL for all tested fungi.
Concerning the fungicidal results, the minimum fungicidal concentration (MFC) of U.Cs.Pe demonstrated lower values of 6.0, 3.0, and 0.75 mg/mL compared to 6.0, 6.0, and 1.5 mg/mL in the case of both B.Cs.Pe and C.Cs, against P. expansum, B. cinerea, and M. laxa, respectively (Table 6).
2.4. Phytotoxicity
The phytotoxic effects of the studied chitosan were evaluated against three sensitive plants (Lepidium sativum, Lactuca sativa, and Solanum lycopersicum) at concentrations of 3000, 1500, and 750 ppm. The results are presented in Table 7. Specifically, for L. sativum, C.Cs exhibited the highest significant germination index (G.I.) of 53.4% at the lowest tested concentration of 750 ppm, compared to other tested Cs. For L. sativa, U.Cs.Pe demonstrated the highest significant G.I. of 50.6% at 750 ppm, which is very close to the control. In the case of S. lycopersicum, both B.Cs.Pe and U.Cs.Pe showed the highest significant G.I. (63.5%) and (54.4%), respectively, which closely match the control. All tested Cs exhibited very low significant G.I. at the highest concentration of 3000 ppm. The notable phytotoxic activity of the Cs extracted from P. eryngii suggests its potential use as a natural herbicidal agent against harmful weeds.
2.5. Cytotoxicity
The cytotoxic effect of the C.Cs and Cs.Pe on the mitotic index (MI), phase index (PI), and total abnormalities (Tab) of Vicia faba root tips was illustrated in Table 8. The highest significant MI was found in the case of U.Cs.Pe (16.92%) followed by B.Cs.Pe (16.87%), while the lowest MI was recorded in the case of C.Cs (12.67%) compared to control cells (14.87%).
The PI included mitotic index and abnormality percentages for each cell division stage, except the interphase, as illustrated in Table 8. In particular, the highest MI at the prophase stage was 15.98%, presented in C.Cs, whereas the lowest MI at the prophase was found in U.Cs.Pe with a value of 13.88%, compared to the control (16.98%). The highest MI % at the metaphase was 57.87% for C.Cs, while the highest values at the anaphase (23.87%) and telophase (22.02%) were recorded in U.Cs.Pe. The highest significant values in abnormality percentages at the prophase, metaphase, anaphase, and telophase stages were observed in the case of C.Cs, with values of 3.87, 23.67, 7.45, and 3.54%, respectively.
The Tab % of mitosis was found in C.Cs (38.53%), while the lowest values were 17.08 and 18.65% in the case of B.Cs.Pe and U.Cs.Pe, respectively, compared to the control (24.64%). The Tab % at the interphase was presented in C.Cs with a value of 1.87%, compared to the control 0.87%, whereas there were no any abnormalities at the interphase stage for other treatments.
Different types of chromosomal abnormality at different stages of V. faba root tip cell division were recorded in Figure 4. The micronucleus at the interphase was found in C.Cs (Figure 4A,B). At the metaphase stage, the common abnormalities were presented as stickiness (Figure 4C), disturbed (Figure 4D), non-congression (Figure 4E), and oblique (Figure 4F,G). At the anaphase, they were disturbed (Figure 4H,I), bridges (Figure 4J), late separation (Figure 4K), and laggard (Figure 4L,M). At the telophase, the common types of abnormality were disturbed (Figure 4N,O), late separation (Figure 4P,Q), laggard (Figure 4R), bridge (Figure 4S), and diagonal telophase (Figure 4T).
2.6. In Vivo Fruit-Coating Assay
2.6.1. Quality Parameters
The selected U.Cs.Pe showed a strong ability to form a protective coating-layer on fruit surfaces. On the other hand, there was not significant effect on the quality parameters of control fruitss treated with 1% acetic acid and/or glycerol.
(I)
Weight loss
The influence of different coatings on weight loss percentages is depicted in Figure 5. In particular, the highest significant weight loss (27%) was observed in the case of the positive control (C + ve), i.e., fruits infected with M. laxa (Figure 6), whereas, the lowest weight loss (4%) was observed in the case of fruits treated with U.Cs.Pe, followed by fruits treated with U.Cs.Pe and infected with M. laxa (7%) (Figure 6).
(II)
Peel color change
The obtained results of the influence of different coatings on peel color are illustrated in Figure 7. The treatment with U.Cs.Pe did not show significant variation in peel-color, even 7 DAT (Figure S4). In particular, fruits treated with U.Cs.Pe, whether infected or not, exhibited moderate changes in peel color with ΔE values ranging between 1.5 and 2.4 relative to the negative control (C − ve) (7 DAT). On the other hand, the highest significant change in peel color was observed in the case of C + ve, with a Δ E 3.76 (7 DAT). In addition, C.Cs-treated fruits showed the least change in peel color with a ΔE value of 1.17.
(III)
Firmness
The results of the studied coating treatments on fruit firmness 7 DAT are illustrated in Figure 8. Firmness is a crucial visual quality parameter for fresh market fruits as it significantly influences post-harvest quality and shelf life. Treated fruits with U.Cs.Pe showed a low, insignificant reduction in firmness compared to C − ve and those treated with C.Cs. Notably, infected fruits treated with U.Cs.Pe exhibited only a slight significant decrease in firmness, with a value of 1.83 kg/cm2, compared to those infected and treated with C.Cs, which showed a significant firmness reduction to 1.15 kg/cm2.
(IV) Sugar content
Specifically, the results from treated fruits with U.Cs.Pe, either infected or not, showed a slight decrease in sugar content even at 7 DAT (13.70 and 14.67 Brix, respectively), compared to untreated controls (15.40 Brix) (Figure 9), whereas, fruits treated with C.Cs, whether infected or not, exhibited sugar contents of 10.70 and 12.33 Brix, respectively.
2.6.2. Decay Evaluation
After 5 days of storage, untreated fruits, or those treated with C.Cs and infected with M. laxa, exhibited brown-rot symptoms. In contrast, fruits treated with U.Cs.Pe showed a reduction in disease symptoms, with decay percentages of 46.4 and 52.6% for preventive and curative treatments, respectively (Figure 10). The C + ve showed the highest decay percentage ranged between 94.4 and 100% with regard to prevention and curative treatment, respectively, whereas, C.Cs showed a moderate decay percentage of 55.6 and 61.7% in the case of prevention or curative treatment, respectively (Figure S5). On the other hand, the decay percentage of acetic acid (1%), either for prevention or curative treatments, were 61.1 and 77.8%, respectively, excluding any effect on the Cs efficacy.
2.6.1. Quality Parameters
The selected U.Cs.Pe showed a strong ability to form a protective coating-layer on fruit surfaces. On the other hand, there was not significant effect on the quality parameters of control fruitss treated with 1% acetic acid and/or glycerol.
(I)
Weight loss
The influence of different coatings on weight loss percentages is depicted in Figure 5. In particular, the highest significant weight loss (27%) was observed in the case of the positive control (C + ve), i.e., fruits infected with M. laxa (Figure 6), whereas, the lowest weight loss (4%) was observed in the case of fruits treated with U.Cs.Pe, followed by fruits treated with U.Cs.Pe and infected with M. laxa (7%) (Figure 6).
(II)
Peel color change
The obtained results of the influence of different coatings on peel color are illustrated in Figure 7. The treatment with U.Cs.Pe did not show significant variation in peel-color, even 7 DAT (Figure S4). In particular, fruits treated with U.Cs.Pe, whether infected or not, exhibited moderate changes in peel color with ΔE values ranging between 1.5 and 2.4 relative to the negative control (C − ve) (7 DAT). On the other hand, the highest significant change in peel color was observed in the case of C + ve, with a Δ E 3.76 (7 DAT). In addition, C.Cs-treated fruits showed the least change in peel color with a ΔE value of 1.17.
(III)
Firmness
The results of the studied coating treatments on fruit firmness 7 DAT are illustrated in Figure 8. Firmness is a crucial visual quality parameter for fresh market fruits as it significantly influences post-harvest quality and shelf life. Treated fruits with U.Cs.Pe showed a low, insignificant reduction in firmness compared to C − ve and those treated with C.Cs. Notably, infected fruits treated with U.Cs.Pe exhibited only a slight significant decrease in firmness, with a value of 1.83 kg/cm2, compared to those infected and treated with C.Cs, which showed a significant firmness reduction to 1.15 kg/cm2.
(IV) Sugar content
Specifically, the results from treated fruits with U.Cs.Pe, either infected or not, showed a slight decrease in sugar content even at 7 DAT (13.70 and 14.67 Brix, respectively), compared to untreated controls (15.40 Brix) (Figure 9), whereas, fruits treated with C.Cs, whether infected or not, exhibited sugar contents of 10.70 and 12.33 Brix, respectively.
2.6.2. Decay Evaluation
After 5 days of storage, untreated fruits, or those treated with C.Cs and infected with M. laxa, exhibited brown-rot symptoms. In contrast, fruits treated with U.Cs.Pe showed a reduction in disease symptoms, with decay percentages of 46.4 and 52.6% for preventive and curative treatments, respectively (Figure 10). The C + ve showed the highest decay percentage ranged between 94.4 and 100% with regard to prevention and curative treatment, respectively, whereas, C.Cs showed a moderate decay percentage of 55.6 and 61.7% in the case of prevention or curative treatment, respectively (Figure S5). On the other hand, the decay percentage of acetic acid (1%), either for prevention or curative treatments, were 61.1 and 77.8%, respectively, excluding any effect on the Cs efficacy.
3. Discussion
In our study, Ct was extracted from the cell wall of P. eryngii, and subsequently underwent a deacetylation process to obtain Cs, which is soluble in acidic solutions. Chitin’s insolubility in water and many organic solvents is due to its primary structure, comprising nanofibrils encapsulated within a protein matrix [36]. These nanofibrils, typically forming crystallites approximately 3 nm in diameter, are stabilized by hydrogen bonds between the amine and carbonyl groups, as reported by Ibrahim et al. [37].
The M.Wt and viscosity of the Cs obtained in our study were similar to the findings of Pochanavanich and Suntornsuck [38]. They reported that the viscosity of Cs extracted from Aspergillus niger, Rhizopus oryzae, Lentinus edodes, and P. sajo-caju ranged between 3.1 and 6.2 cP, corresponding to a M.Wt between 27 and 190 KDa. This is significantly lower than the viscosity of commercial crab shell Cs, which is 372.7 cP. Furthermore, our results closely match those of Khalaf [39], who found that the viscosity of fungal Cs extracted from A. niger, P. citrinum, Fusarium oxysporum, and R. oryzae ranged between 2.7 and 6.8 cP, corresponding to a M.Wt between 45.54 and 113.71 KDa. On other hand, our results are slightly higher than the results obtained by Ugochukwu et al. [40], who reported that the extracted Cs from P. ostreatus exhibited viscosity ranging between 0.38 and 0.37 cP, corresponding to M.Wt ranging between 6.48 and 6.25 KDa. It is important to emphasize that low molecular weight (LMw) Cs has a high charge density and excellent solubility, making it highly effective and widely applicable in the pharmaceutical, biomedical, and food industries [41]. Additionally, LMw Cs is characterized by high permeability due to its low viscosity, a property that is crucial for biological applications and enhances its antimicrobial activity [42]. Many previous studies have reported that Ct and Cs extracted from insects showed, generally, properties similar to those extracted from crustaceans [15,17], whereas, the Ct/Cs extracted from fungi are characterized by higher particle size uniformity and LMw, compared to those extracted from insect and crustaceans [16]. In addition, the Ct/Cs derived from shellfish exhibited evenly distributed acetyl groups, whereas fungal Cs tended to exhibit a clustering pattern in its acetylation [17]. It is important to highlight that chitosan, with a low molecular weight, has been reported to reduce the tensile strength and elongation of chitosan membranes, while enhancing their permeability, as reported by Rong and Horng [42].
On the other hand, the results of the degree of acetylation were similar to those reported by Pochanavanich and Suntornsuk [38] who reported that the Cs produced from different fungal genera, among them, Aspergillus, Rhizopus, Pleurotus, etc., had a degree of deacetylation ranging between 84 and 90%. The DD% is linked to the molecule’s positive charge density, enhancing its bioactivity in various applications such as antimicrobial and coagulation agents, as reported by Khalaf [39].
The significant antimicrobial activity of the studied Cs, especially U.Cs.Pe, may be attributed to its ready solubility in organic solvents, enhanced by the presence of free amino groups. These amino groups are responsible for conferring the polymer’s basic behaviour and cationic properties, thereby increasing its reactivity [43]. In addition, the presence of amino and hydroxyl groups in the polymer structure of Cs allows them to penetrate the microbial cell membranes, rupturing their integrity and causing cell death [43]. This characteristic, in turn, raises Cs solubility in water and subsequently improves its adhesion to the negatively charged microbial cell wall surfaces [44]. On the other hand, numerous studies have reported that the antimicrobial activity of Cs is closely related to its M.Wt. Specifically, Cs with different M.Wt has been observed to exhibit varying levels of effectiveness against different species of bacteria [45]. Some studies indicate that increasing the M.Wt of Cs is associated with reduced activity, whereas other research suggests that Cs with a high Mw exhibits greater activity compared to chitosan with a low Mw [46]. Hosseinnejad and Jafari [47] reported that the components of Cs are absorbed into the surface of microbial cells, where they are thought to exert antimicrobial activity. This interaction makes the lipid cell membrane more permeable, allowing essential substances to leak out of the cell, ultimately leading to cell death. However, the relationship between Cs’s molecular weight and its antimicrobial activity can vary depending on specific conditions and the pathogen under study. Further research is required to fully elucidate this relationship and optimize the use of chitosan for antimicrobial applications.
Regarding the in vivo trial, the notable efficacy of U.Cs.Pe was evident in its ability to form a protective coating layer on the surface of fruits. This coating can alter gas permeation, lower the respiration rate, and reduce water loss, leading to better weight maintenance and enhanced firmness retention in the fruits [48,49]. In particular, the monitored quality parameters, such as weight loss, has a notable impact on the perceived flavour and freshness of fruits, as reported by Ribeiro and de Freitas [50]. In particular, the significant weight loss observed during storage is primarily due to increased water loss through evaporation and transpiration. This water loss reduces the turgor pressure within the fruits’ cells, leading to tissue shrinkage and overall weight reduction [51]. Our findings revealed that fruits treated with Cs.Pe exhibited minimal weight loss percentages in agreement with the findings of Li et al. [52], who studied the effect of coating treatment with Cs on the quality parameters of plums during storage. Their conclusion highlighted that the Cs treatment acted as a physical barrier, effectively inhibiting moisture loss and reducing water flow from the fruits’ tissues, and consequently delayed the shrinkage and dehydration of the plums [52]. The low reduction in fruits’ firmness after treatment with Cs.Pe could be due to the coated film which protects the fruits from the transpiration rate, and hence delays the over-softening of plums, as reported by Zhang et al. [53].
On the other hand, our results showed that treatment with Cs.Pe had no effect on the sugar content of treated fruits during storage. In another study conducted by Kambhampati and Kotra [54], it was reported that there was a rapid decline in the titratable acidity of kiwifruit samples during storage, compared to the Cs-coated, fresh-cut kiwifruit samples. In the latter study, the authors explained also that the Cs-coating treatment could make an effective film and reduce metabolic activities which inhibit the metabolic rate of the titratable acids, maintaining higher acidity during post-harvest. Consequently, the results of in vivo trials suggested that Cs derived from natural sources, such P. eryngii, might be used to extend the shelf life of fruits.
The results obtained from the application of Cs.Pe to control fungal disease demonstrated a promising effect in lowering the percentage of plums’ decay caused by M. laxa infection. A related study by Brulé et al. [55] reported that Cs treatment is an efficient way to manage plant diseases either pre- or post-harvest, including various fungal diseases that affect grapevines, like powdery mildew, grey mould, and downy mildew. On the other hand, Zeng et al. [56] reported that the treatment of navel oranges with 2% Cs can significantly reduce the disease incidence and lesion diameter caused by P. italicum and P. digitatum, inducing disease resistance by regulating H2O2 levels, antioxidant enzymes, and the ascorbate–glutathione cycle.
The beneficial biological effects of Cs have been reported in various agricultural applications. For instance, Cs used in seed-coating technology can trigger an innate immune response in developing roots, effectively targeting parasitic cyst nematodes while preserving beneficial organisms [57]. Moreover, Cs can serve as a natural seed treatment and plant growth promoter, acting as an eco-friendly biopesticide that enhances plants’ innate defence mechanisms against fungal infections. This approach reduces reliance on synthetic fungicides and contributes to environmental protection.
Fungal-derived chitosan presents several advantages over crustacean-derived chitosan in agriculture. Notably, fungal chitosan is more sustainable because it can be produced continuously through bio-fermentation, making it a more renewable resource. Additionally, chitin and chitosan extracted from fungi is characterized by its greater particle size uniformity and lower molecular weight compared to those extracted from crustaceans, which enhances their solubility and, consequently, their bioactivity.
4. Materials and Methods
4.1. Tested Fungi
The tested P. eryngii (collection number 1544), stored at 4 °C in the fungal collection of the Department of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, Potenza (Italy) was sub-cultured on Potato Dextrose Agar (PDA) nutrient media for 96 h at 24 °C.
4.2. Biomass Quantification
To determine the most effective nutrient medium for maximizing biomass production, three different nutrient media were prepared: (i) PDBW; (ii) MEB; and (iii) NB. Three Erlenmeyer flasks (1 L) containing 800 mL for each tested media were prepared, autoclaved, inoculated with two agar pieces (Ø 0.5 cm2) of fresh fungal mycelium, and incubated in agitation (180 rpm) for 10 days at 24 °C in a rotary-incubator (Heidolph WB 2000, Labexchange, Germany) [58]. The incubated broths were collected and centrifuged at 20,000× g for 15 min. The biomasses were harvested, washed twice with sterile distilled water (SDW), and dried under laminar flow overnight. The biomass weights were measured following the dried-weight method as reported by Álvarez et al. [59], and the highest biomass quantity was chosen for subsequent analysis.
4.3. Chitin Extraction and Deacetylation
The Ct extraction was carried out as described by Hassainia et al. [60]. In particular, the extraction method of Ct and transformation to Cs were performed through two main steps: (i) deproteinization of fungal biomass; (ii) heterogeneous deacetylation of Ct.
Deproteinization. Fungal biomass was dried in an oven at 60 °C for 48 h, and then ground into powder using liquid nitrogen. The biomass was demineralized using 0.5 M formic acid (CH2O2) for 1 h at room temperature while stirring. Subsequently, the sample was washed with SDW to restore a neutral pH (7.0). The demineralized biomass was then stirred with 2 M NaOH for 2 h at 80 °C to remove proteins. Following deproteinization, half of the Ct sample underwent a bleaching procedure with 5% hydrogen peroxide (H2O2) for 1 h at 90 °C, while the other half remained untreated as unbleached. The bleaching process of Ct removes pigments, proteins, and impurities, resulting in a purer product. This enhances its color, material properties, biocompatibility, and functionality, making it suitable for biomedical, pharmaceutical, food, and cosmetic applications. Thus, bleaching ensures Ct meets the necessary standards for these uses.
Heterogeneous deacetylation. The Cs was obtained by heterogeneous deacetylation of both bleached and unbleached Ct [61]. As briefly reported: 50 mL of 12 M NaOH were added to 40–80 mg of dried Ct and incubated for 2 h at 100 °C. Flakes of Cs were obtained by thoroughly rinsing the solid residue with SDW. The collected Cs was dissolved in 1% acetic acid (CH₃COOH) at 40 °C and the pH was adjusted to 7.0. Acetic acid (1%) is used to protonate the amino groups, converting them into ammonium ions, which enhances the solubility of Cs.
The yield percentages of U.Ct.Pe, B.Ct.Pe, U.Cs.Pe, and B.Cs-Pe were assessed according to the following Equation (1):(1)Yield%=Dryweightofchitinorchitosan(g)Dryweightoffungalbiomassg×100
4.4. Physicochemical Characterization of Chitin and Chitosan
4.4.1. Fourier Transform Infrared Spectroscopy (FT-IR) Analysis
The FT-IR analysis was applied to samples with better crystallinity to confirm the presence of functional groups of extracted Ct and Cs. The studied Ct and Cs samples were mixed with potassium bromide (KBr) in the range from 4000–400 cm−1, and a Shimadzu FT-IR 460 (Shimadzu 8400S, Tokyo, Japan) apparatus was used to record the spectra.
4.4.2. Determination of Molecular Weight and Viscosity
Molecular weight is one of the most fundamental parameters in characterizing polymers and determining their activity. The M.Wt of Cs-Pe was determined by measuring its viscosity using the Mark–Houwink–Sakurada (MHS) Equation (2) because the viscosity of a polymer is directly related to its M.Wt [62,63].
M.Wta = K/η(2)
where: (M.Wt) molecular weight; (η) intrinsic viscosity; (K) and (a) constants (K = 0.078; a = 0.76) for given solute–solvent system and temperature.
The determination of constants (K) and (a) from the intrinsic viscosity data requires either a series of monodisperse polymers of known M.Wt or a series of polydisperse polymer samples with known viscosity-average M.Wts. The intrinsic viscosity of Cs-Pe has been determined experimentally by capillary viscometry using an Ostwald viscometer (Model AMV-200, Paar Physica Edison, NJ, USA). The method is based on the measurement of the flow (t0) of the same volume of solvent (1% acetic acid) or Cs solution (t) contained between the two points (lines) marked on the viscometer using a chronometer with an inclination angle of 15 degrees and a capillary diameter of 0.9 mm. These parameters were chosen in order to minimize adjustments for kinetic energy and shear, combined with the usage of solution concentrations of less than 1% (w/v) [62,63]. The viscosity (η) has been calculated following Equation (3).
η = (t − t0)∕t0(3)
where: (η) intrinsic viscosity; (t0) the flow time of 1% acetic acid; (t) the flow time of chitosan solution contained between the two points (lines).
4.4.3. Determination of Degree of Deacetylation (DD %)
According to Stamford [64], vibrational spectra in the infrared region was used to determine the degree of chitosan deacetylation by the ratio between the absorbance at wavelengths 1655 and 3450 cm−1, and the DD % was calculated following Equation (4), as proposed by Domzy and Roberts [65]:DD (%) = 100 − [(A1655/A3450) × 115](4)
where: A1655 is the absorbance of wavelength 1655 cm−1, and A3450 is the absorption band at wavelength 3450 cm−1. The number “115” is the value of A1655/A3450 found in pure chitosan.
4.5. Antimicrobial Activity Assay
Tested bacteria. The tested bacteria were E. coli Migula (strain number ITM 103), X. campestris (Pammel) Dowson (strain number NCPPB 3035), C. michiganensis Smith (strain number C3718), and P. fluorescens (Flügge) Migula (strain number NCPPB 1796).
Tested fungi. The tested fungi were P. expansum Link (collection number 1152 isolated from orange), B. cinerea Pers. (collection number 1931 isolated from strawberry), and M. laxa (Aderh. and Ruhland) Honey (collection number 1518 isolated from plum). All tested fungi were conserved in a refrigerator at 4 °C and recultivated on PDA media at 24 °C for 96 h.
All tested bacterial and fungal strains were conserved in the collection located in the Department of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, Potenza (Italy), and recultivated on King’s B (KB) medium [66] for bacteria, or PDA for fungi.
The MICs of studied U.Cs.Pe and B.Cs.Pe samples were carried out against all the above-tested phytopathogens, compared to C.Cs, using a 96-well microplate (Nunc MaxiSorp®, Vedbaek, Denmark) following the micro-dilution method [67]. Commercial chitosan derived from crustacean shells with molecular weight 100,000–300,000 Da (Thermo Scientific, Fair Lawn, NJ, USA) was used as a control.
Stock solutions of U.Cs.Pe, B.Cs.Pe, and C.Cs at 6.0 mg/mL were dissolved in acetic acid (1%). The prepared solutions were stirred, filtered (0.22 μm), and conserved in a refrigerator at 4 °C. Six tested concentrations, labeled C1 to C6 (6.0, 3.0, 1.5, 0.75, 0.375, and 0.187 mg/mL), were prepared in SDW. One hundred microliters (µL) of each prepared concentration were added to the microplate wells, which had previously been supplemented with 50 µL of microbial suspension per well. The bacterial suspensions were adjusted to 108 colony-forming units (CFU)/mL, while the fungal suspensions were adjusted to 106 spores/mL, based on optical density measurements at 590 nm. All plates were incubated at 37 °C for 24 h in the case of bacteria, and 24 °C for 48 h in the case of fungi. The absorbance was measured using a microplate reader instrument (DAS s.r.l., Rome, Italy) at λ = 540 nm. Wells only filled with broth KB or PDB supplemented with acetic acid (1%) were considered as a control negative (C − ve). Two positive controls, Tetracycline (50 and 100 μg.mL−1) and Cycloheximide (100 and 500 μg.mL−1), for bacteria and fungi, respectively, were utilized mainly to ensure that the assay was carried out correctly, and also to validate the obtained results from different experimental samples.
The MICs values for each tested pathogen were determined by monitoring the lowest tested concentration, which caused a significant reduction in microbial growth (values close to negative control). To determine the eventual bactericidal/fungicidal or bacteriostatic/fungistatic effects of each tested dose, the MBC and MFC were identified by re-culturing the tested micro-organisms from the lowest dose of each treatment that showed no visible growth. The MBC and MFC were determined to differentiate between the ability to completely kill the micro-organisms (bactericidal/fungicidal) or just inhibit the growth (bacteriostatic/fungistatic).
4.6. Phytotoxicity Assay
A bioassay based on seed germination and radical elongation (SG-RE) was carried out to evaluate the eventual phytotoxic effect of U.Cs.Pe, B.Cs.Pe, and C.Cs on L. sativum L. (garden cress), S. lycopersicum L. (tomatoes), and L. sativa L. (lettuce) [68]. The seeds were sterilized in 3% hydrogen peroxide for 1 min, rinsed twice with deionized SDW, and then immersed in each studied sample at 3.0, 1.5, and 0.75 mg/mL under shaking conditions (180 rpm/60 min). Seeds immersed only in SDW were considered as C − ve. Ten seeds for each tested plant species were transferred into glass Petri dishes (Ø 180 mm) containing two sterile filter papers (Whatman No. 1), pre-moistened with 2 mL of SDW or each single treatment, and then sealed with Parafilm. All petri dishes were incubated in a growth chamber at 30 °C with 85% relative humidity with 12-h photoperiod for 5 days. The experiment was carried out in triplicates. The seed germination (%) and the radical elongation (cm) were measured. The G.I. was calculated following Equation (5).
G.I. (%) = [(S.Gt × R.Et)∕(S.Gc × R.Ec)] × 100(5)
where: G.I. is the germination index; SGt is the average number of germinated and treated seeds; REt is the average radical elongation of treated seeds; SGc is the average number of germinated seeds of negative control; REc is the average radical elongation of negative control.
4.7. Cytotoxicity Assay
The cytotoxicity of the studied Cs was assessed using a chromosome aberrations assay as follows: root tips of Vicia faba L. (broad bean) seeds cv. Skha1 were exposed to different Cs samples for 24 h. The root tips were then immersed in Carnoy’s solution (glacial acetic acid and ethanol, 1:3 v/v) and chilled for 48 h. The samples were then soaked for 5 min in SDW, followed by a hydrolysis in 1N HCl at 60 °C for 8 min. The root tips were rinsed with SDW, dyed with aceto-orcein for 4 h, treated with a drop of 45% acetic acid, and used for microscopic examination. The eventual chromosomal aberrations observed in the treated V. faba root tips are illustrated in the diagrammatic scheme (Figure S6).
4.8. In Vivo Fruit Treatment Assay
4.8.1. Chitosan Treatment
The Cs solution was prepared by dissolving the studied chitosan sample, which showed a higher efficacy in vitro trial, in acetic acid (1%) at 6 mg/mL. Glycerol was utilized as a plasticizer at 750 µL/g chitosan [69]. The fruits of the Prunus domestica L. (plum) cultivar ‘Angeleno’, before treatment, were firstly surface-sterilized with sodium hypochlorite (0.5%) and then rinsed with SDW. The disinfected fruits were allowed to dry, and randomly divided into nine groups treated with: (i) acetic acid (1%); (ii) acetic acid (1%)+ glycerol (750 µL/g); (iii) acetic acid (1%)+ M. laxa; (iv) Cs.Pe; (v) Cs.Pe + M. laxa; (vi) C.Cs; (vii) C.Cs + M. laxa; (viii) C − ve (SDW); and (ix) C + ve (fruits infected with M. laxa). The treatment was carried out by spraying the fruits singularly with each prepared solution followed by a drying period of 2 h at room temperature (Figure S7).
4.8.2. Quality Parameters Evaluation
The quality parameters (weight loss, firmness, color change, and sugar content) were measured to evaluate the effects of the two types of chitosan on the fruits, compared to C.Cs.
(I)
Weight loss
The fruit weight loss (%) for every treatment was measured at T0, T2, and T7 days after treatment (DAT) at room temperature using Equation (6) [70].
Weight loss % = (A – B)/(A × 100)(6)
where: A indicates the fruit weight at the time (0), and B indicates the fruit weight after 2 or 7 days.
(II)
Peel color change
The eventual change in fruit color was measured on two sides of each treated fruit using a Colorimeter Color Analyzer (Minolta CR 400 ChromaMeter, Minolta Corp., Tokyo, Japan) at T0, T2, and T7 DAT, following Equation (7) [71].
(7)ΔE=(ΔL)2+(Δa)2+(Δb)2
where ΔE: the overall changes in color indices: L index refers to black-to-white color; a index refers to green-to-red color; b index refers blue-to-yellow color.
(III)
Firmness
The firmness of the treated fruits was measured using a Fruit Pressure Tester (model FT 327, Alfonsine, Italy) with a penetrating cylinder (Ø = 8 mm). The cylinder was inserted to a constant depth of 3–5 mm into the fruit pulp at a steady speed of 2 mm per second. Firmness was determined after T7 DAT on 3 fruits per treatment, and expressed in Newtons (N), which is equivalent to kg/s2, compared to the negative control [72].
(IV)
Sugar content
The soluble sugars in the treated fruits, primarily glucose, fructose, and sucrose, were measured using refractometry (HI96800, Hanna, Villafranca Padovana, Italy) T7 DAT on 3 fruits per treatment [73]. The values were recorded in degrees Brix, which is equivalent to 1 g sucrose/100 g liquid.
4.8.3. Artificial Fungal Infection and Decay Evaluation
For the fungal infection, M. laxa isolated from plum, previously identified based on morphological features and molecular analysis, was used.
The sequences of the studied isolate were deposited in the NCBI GenBank with accession numbers HF678387. The studied isolate was conserved in the fungal collection of DAFE, University of Basilicata, Potenza, (Italy). A fungal suspension (107 spores/mL) was prepared for fruit inoculation. After 48 h of treatment, a sterile needle was used to puncture the fruits, and 10 µL of the fungal suspension was applied to the wounded area [74]. The fruits were subsequently kept moist at room temperature in polyethylene plastic bags. The decay percentage of brown-rot disease was calculated using Equation (8), as reported by Wang et al. [75], with minor modification after 5 days of inoculation following the infection scale, as specified below. The experiment was carried out twice, with three replicates each time.
(8)Decay%=100×∑(0×N0+1×N1+2×N2)/(2×N)
where: N0 = no decay; N1 = slight decay (≤25% of surface decay); N2 = moderate-to-high decay (more than 25% of surface decay); N = total number of fruits.
4.9. Statistical Analysis
The obtained results of the bioassays have been statistically analyzed by applying the ANOVA test to assess overall differences among group means using the Statistical Package for the Social Sciences (SPSS), version 13.0 (Prentice Hall: Chicago, IL, USA, 2004). After that, to identify specific differences between groups, a post hoc test was performed to evaluate the significance level, with a probability of p < 0.05.
4.1. Tested Fungi
The tested P. eryngii (collection number 1544), stored at 4 °C in the fungal collection of the Department of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, Potenza (Italy) was sub-cultured on Potato Dextrose Agar (PDA) nutrient media for 96 h at 24 °C.
4.2. Biomass Quantification
To determine the most effective nutrient medium for maximizing biomass production, three different nutrient media were prepared: (i) PDBW; (ii) MEB; and (iii) NB. Three Erlenmeyer flasks (1 L) containing 800 mL for each tested media were prepared, autoclaved, inoculated with two agar pieces (Ø 0.5 cm2) of fresh fungal mycelium, and incubated in agitation (180 rpm) for 10 days at 24 °C in a rotary-incubator (Heidolph WB 2000, Labexchange, Germany) [58]. The incubated broths were collected and centrifuged at 20,000× g for 15 min. The biomasses were harvested, washed twice with sterile distilled water (SDW), and dried under laminar flow overnight. The biomass weights were measured following the dried-weight method as reported by Álvarez et al. [59], and the highest biomass quantity was chosen for subsequent analysis.
4.3. Chitin Extraction and Deacetylation
The Ct extraction was carried out as described by Hassainia et al. [60]. In particular, the extraction method of Ct and transformation to Cs were performed through two main steps: (i) deproteinization of fungal biomass; (ii) heterogeneous deacetylation of Ct.
Deproteinization. Fungal biomass was dried in an oven at 60 °C for 48 h, and then ground into powder using liquid nitrogen. The biomass was demineralized using 0.5 M formic acid (CH2O2) for 1 h at room temperature while stirring. Subsequently, the sample was washed with SDW to restore a neutral pH (7.0). The demineralized biomass was then stirred with 2 M NaOH for 2 h at 80 °C to remove proteins. Following deproteinization, half of the Ct sample underwent a bleaching procedure with 5% hydrogen peroxide (H2O2) for 1 h at 90 °C, while the other half remained untreated as unbleached. The bleaching process of Ct removes pigments, proteins, and impurities, resulting in a purer product. This enhances its color, material properties, biocompatibility, and functionality, making it suitable for biomedical, pharmaceutical, food, and cosmetic applications. Thus, bleaching ensures Ct meets the necessary standards for these uses.
Heterogeneous deacetylation. The Cs was obtained by heterogeneous deacetylation of both bleached and unbleached Ct [61]. As briefly reported: 50 mL of 12 M NaOH were added to 40–80 mg of dried Ct and incubated for 2 h at 100 °C. Flakes of Cs were obtained by thoroughly rinsing the solid residue with SDW. The collected Cs was dissolved in 1% acetic acid (CH₃COOH) at 40 °C and the pH was adjusted to 7.0. Acetic acid (1%) is used to protonate the amino groups, converting them into ammonium ions, which enhances the solubility of Cs.
The yield percentages of U.Ct.Pe, B.Ct.Pe, U.Cs.Pe, and B.Cs-Pe were assessed according to the following Equation (1):(1)Yield%=Dryweightofchitinorchitosan(g)Dryweightoffungalbiomassg×100
4.4. Physicochemical Characterization of Chitin and Chitosan
4.4.1. Fourier Transform Infrared Spectroscopy (FT-IR) Analysis
The FT-IR analysis was applied to samples with better crystallinity to confirm the presence of functional groups of extracted Ct and Cs. The studied Ct and Cs samples were mixed with potassium bromide (KBr) in the range from 4000–400 cm−1, and a Shimadzu FT-IR 460 (Shimadzu 8400S, Tokyo, Japan) apparatus was used to record the spectra.
4.4.2. Determination of Molecular Weight and Viscosity
Molecular weight is one of the most fundamental parameters in characterizing polymers and determining their activity. The M.Wt of Cs-Pe was determined by measuring its viscosity using the Mark–Houwink–Sakurada (MHS) Equation (2) because the viscosity of a polymer is directly related to its M.Wt [62,63].
M.Wta = K/η(2)
where: (M.Wt) molecular weight; (η) intrinsic viscosity; (K) and (a) constants (K = 0.078; a = 0.76) for given solute–solvent system and temperature.
The determination of constants (K) and (a) from the intrinsic viscosity data requires either a series of monodisperse polymers of known M.Wt or a series of polydisperse polymer samples with known viscosity-average M.Wts. The intrinsic viscosity of Cs-Pe has been determined experimentally by capillary viscometry using an Ostwald viscometer (Model AMV-200, Paar Physica Edison, NJ, USA). The method is based on the measurement of the flow (t0) of the same volume of solvent (1% acetic acid) or Cs solution (t) contained between the two points (lines) marked on the viscometer using a chronometer with an inclination angle of 15 degrees and a capillary diameter of 0.9 mm. These parameters were chosen in order to minimize adjustments for kinetic energy and shear, combined with the usage of solution concentrations of less than 1% (w/v) [62,63]. The viscosity (η) has been calculated following Equation (3).
η = (t − t0)∕t0(3)
where: (η) intrinsic viscosity; (t0) the flow time of 1% acetic acid; (t) the flow time of chitosan solution contained between the two points (lines).
4.4.3. Determination of Degree of Deacetylation (DD %)
According to Stamford [64], vibrational spectra in the infrared region was used to determine the degree of chitosan deacetylation by the ratio between the absorbance at wavelengths 1655 and 3450 cm−1, and the DD % was calculated following Equation (4), as proposed by Domzy and Roberts [65]:DD (%) = 100 − [(A1655/A3450) × 115](4)
where: A1655 is the absorbance of wavelength 1655 cm−1, and A3450 is the absorption band at wavelength 3450 cm−1. The number “115” is the value of A1655/A3450 found in pure chitosan.
4.4.1. Fourier Transform Infrared Spectroscopy (FT-IR) Analysis
The FT-IR analysis was applied to samples with better crystallinity to confirm the presence of functional groups of extracted Ct and Cs. The studied Ct and Cs samples were mixed with potassium bromide (KBr) in the range from 4000–400 cm−1, and a Shimadzu FT-IR 460 (Shimadzu 8400S, Tokyo, Japan) apparatus was used to record the spectra.
4.4.2. Determination of Molecular Weight and Viscosity
Molecular weight is one of the most fundamental parameters in characterizing polymers and determining their activity. The M.Wt of Cs-Pe was determined by measuring its viscosity using the Mark–Houwink–Sakurada (MHS) Equation (2) because the viscosity of a polymer is directly related to its M.Wt [62,63].
M.Wta = K/η(2)
where: (M.Wt) molecular weight; (η) intrinsic viscosity; (K) and (a) constants (K = 0.078; a = 0.76) for given solute–solvent system and temperature.
The determination of constants (K) and (a) from the intrinsic viscosity data requires either a series of monodisperse polymers of known M.Wt or a series of polydisperse polymer samples with known viscosity-average M.Wts. The intrinsic viscosity of Cs-Pe has been determined experimentally by capillary viscometry using an Ostwald viscometer (Model AMV-200, Paar Physica Edison, NJ, USA). The method is based on the measurement of the flow (t0) of the same volume of solvent (1% acetic acid) or Cs solution (t) contained between the two points (lines) marked on the viscometer using a chronometer with an inclination angle of 15 degrees and a capillary diameter of 0.9 mm. These parameters were chosen in order to minimize adjustments for kinetic energy and shear, combined with the usage of solution concentrations of less than 1% (w/v) [62,63]. The viscosity (η) has been calculated following Equation (3).
η = (t − t0)∕t0(3)
where: (η) intrinsic viscosity; (t0) the flow time of 1% acetic acid; (t) the flow time of chitosan solution contained between the two points (lines).
4.4.3. Determination of Degree of Deacetylation (DD %)
According to Stamford [64], vibrational spectra in the infrared region was used to determine the degree of chitosan deacetylation by the ratio between the absorbance at wavelengths 1655 and 3450 cm−1, and the DD % was calculated following Equation (4), as proposed by Domzy and Roberts [65]:DD (%) = 100 − [(A1655/A3450) × 115](4)
where: A1655 is the absorbance of wavelength 1655 cm−1, and A3450 is the absorption band at wavelength 3450 cm−1. The number “115” is the value of A1655/A3450 found in pure chitosan.
4.5. Antimicrobial Activity Assay
Tested bacteria. The tested bacteria were E. coli Migula (strain number ITM 103), X. campestris (Pammel) Dowson (strain number NCPPB 3035), C. michiganensis Smith (strain number C3718), and P. fluorescens (Flügge) Migula (strain number NCPPB 1796).
Tested fungi. The tested fungi were P. expansum Link (collection number 1152 isolated from orange), B. cinerea Pers. (collection number 1931 isolated from strawberry), and M. laxa (Aderh. and Ruhland) Honey (collection number 1518 isolated from plum). All tested fungi were conserved in a refrigerator at 4 °C and recultivated on PDA media at 24 °C for 96 h.
All tested bacterial and fungal strains were conserved in the collection located in the Department of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, Potenza (Italy), and recultivated on King’s B (KB) medium [66] for bacteria, or PDA for fungi.
The MICs of studied U.Cs.Pe and B.Cs.Pe samples were carried out against all the above-tested phytopathogens, compared to C.Cs, using a 96-well microplate (Nunc MaxiSorp®, Vedbaek, Denmark) following the micro-dilution method [67]. Commercial chitosan derived from crustacean shells with molecular weight 100,000–300,000 Da (Thermo Scientific, Fair Lawn, NJ, USA) was used as a control.
Stock solutions of U.Cs.Pe, B.Cs.Pe, and C.Cs at 6.0 mg/mL were dissolved in acetic acid (1%). The prepared solutions were stirred, filtered (0.22 μm), and conserved in a refrigerator at 4 °C. Six tested concentrations, labeled C1 to C6 (6.0, 3.0, 1.5, 0.75, 0.375, and 0.187 mg/mL), were prepared in SDW. One hundred microliters (µL) of each prepared concentration were added to the microplate wells, which had previously been supplemented with 50 µL of microbial suspension per well. The bacterial suspensions were adjusted to 108 colony-forming units (CFU)/mL, while the fungal suspensions were adjusted to 106 spores/mL, based on optical density measurements at 590 nm. All plates were incubated at 37 °C for 24 h in the case of bacteria, and 24 °C for 48 h in the case of fungi. The absorbance was measured using a microplate reader instrument (DAS s.r.l., Rome, Italy) at λ = 540 nm. Wells only filled with broth KB or PDB supplemented with acetic acid (1%) were considered as a control negative (C − ve). Two positive controls, Tetracycline (50 and 100 μg.mL−1) and Cycloheximide (100 and 500 μg.mL−1), for bacteria and fungi, respectively, were utilized mainly to ensure that the assay was carried out correctly, and also to validate the obtained results from different experimental samples.
The MICs values for each tested pathogen were determined by monitoring the lowest tested concentration, which caused a significant reduction in microbial growth (values close to negative control). To determine the eventual bactericidal/fungicidal or bacteriostatic/fungistatic effects of each tested dose, the MBC and MFC were identified by re-culturing the tested micro-organisms from the lowest dose of each treatment that showed no visible growth. The MBC and MFC were determined to differentiate between the ability to completely kill the micro-organisms (bactericidal/fungicidal) or just inhibit the growth (bacteriostatic/fungistatic).
4.6. Phytotoxicity Assay
A bioassay based on seed germination and radical elongation (SG-RE) was carried out to evaluate the eventual phytotoxic effect of U.Cs.Pe, B.Cs.Pe, and C.Cs on L. sativum L. (garden cress), S. lycopersicum L. (tomatoes), and L. sativa L. (lettuce) [68]. The seeds were sterilized in 3% hydrogen peroxide for 1 min, rinsed twice with deionized SDW, and then immersed in each studied sample at 3.0, 1.5, and 0.75 mg/mL under shaking conditions (180 rpm/60 min). Seeds immersed only in SDW were considered as C − ve. Ten seeds for each tested plant species were transferred into glass Petri dishes (Ø 180 mm) containing two sterile filter papers (Whatman No. 1), pre-moistened with 2 mL of SDW or each single treatment, and then sealed with Parafilm. All petri dishes were incubated in a growth chamber at 30 °C with 85% relative humidity with 12-h photoperiod for 5 days. The experiment was carried out in triplicates. The seed germination (%) and the radical elongation (cm) were measured. The G.I. was calculated following Equation (5).
G.I. (%) = [(S.Gt × R.Et)∕(S.Gc × R.Ec)] × 100(5)
where: G.I. is the germination index; SGt is the average number of germinated and treated seeds; REt is the average radical elongation of treated seeds; SGc is the average number of germinated seeds of negative control; REc is the average radical elongation of negative control.
4.7. Cytotoxicity Assay
The cytotoxicity of the studied Cs was assessed using a chromosome aberrations assay as follows: root tips of Vicia faba L. (broad bean) seeds cv. Skha1 were exposed to different Cs samples for 24 h. The root tips were then immersed in Carnoy’s solution (glacial acetic acid and ethanol, 1:3 v/v) and chilled for 48 h. The samples were then soaked for 5 min in SDW, followed by a hydrolysis in 1N HCl at 60 °C for 8 min. The root tips were rinsed with SDW, dyed with aceto-orcein for 4 h, treated with a drop of 45% acetic acid, and used for microscopic examination. The eventual chromosomal aberrations observed in the treated V. faba root tips are illustrated in the diagrammatic scheme (Figure S6).
4.8. In Vivo Fruit Treatment Assay
4.8.1. Chitosan Treatment
The Cs solution was prepared by dissolving the studied chitosan sample, which showed a higher efficacy in vitro trial, in acetic acid (1%) at 6 mg/mL. Glycerol was utilized as a plasticizer at 750 µL/g chitosan [69]. The fruits of the Prunus domestica L. (plum) cultivar ‘Angeleno’, before treatment, were firstly surface-sterilized with sodium hypochlorite (0.5%) and then rinsed with SDW. The disinfected fruits were allowed to dry, and randomly divided into nine groups treated with: (i) acetic acid (1%); (ii) acetic acid (1%)+ glycerol (750 µL/g); (iii) acetic acid (1%)+ M. laxa; (iv) Cs.Pe; (v) Cs.Pe + M. laxa; (vi) C.Cs; (vii) C.Cs + M. laxa; (viii) C − ve (SDW); and (ix) C + ve (fruits infected with M. laxa). The treatment was carried out by spraying the fruits singularly with each prepared solution followed by a drying period of 2 h at room temperature (Figure S7).
4.8.2. Quality Parameters Evaluation
The quality parameters (weight loss, firmness, color change, and sugar content) were measured to evaluate the effects of the two types of chitosan on the fruits, compared to C.Cs.
(I)
Weight loss
The fruit weight loss (%) for every treatment was measured at T0, T2, and T7 days after treatment (DAT) at room temperature using Equation (6) [70].
Weight loss % = (A – B)/(A × 100)(6)
where: A indicates the fruit weight at the time (0), and B indicates the fruit weight after 2 or 7 days.
(II)
Peel color change
The eventual change in fruit color was measured on two sides of each treated fruit using a Colorimeter Color Analyzer (Minolta CR 400 ChromaMeter, Minolta Corp., Tokyo, Japan) at T0, T2, and T7 DAT, following Equation (7) [71].
(7)ΔE=(ΔL)2+(Δa)2+(Δb)2
where ΔE: the overall changes in color indices: L index refers to black-to-white color; a index refers to green-to-red color; b index refers blue-to-yellow color.
(III)
Firmness
The firmness of the treated fruits was measured using a Fruit Pressure Tester (model FT 327, Alfonsine, Italy) with a penetrating cylinder (Ø = 8 mm). The cylinder was inserted to a constant depth of 3–5 mm into the fruit pulp at a steady speed of 2 mm per second. Firmness was determined after T7 DAT on 3 fruits per treatment, and expressed in Newtons (N), which is equivalent to kg/s2, compared to the negative control [72].
(IV)
Sugar content
The soluble sugars in the treated fruits, primarily glucose, fructose, and sucrose, were measured using refractometry (HI96800, Hanna, Villafranca Padovana, Italy) T7 DAT on 3 fruits per treatment [73]. The values were recorded in degrees Brix, which is equivalent to 1 g sucrose/100 g liquid.
4.8.3. Artificial Fungal Infection and Decay Evaluation
For the fungal infection, M. laxa isolated from plum, previously identified based on morphological features and molecular analysis, was used.
The sequences of the studied isolate were deposited in the NCBI GenBank with accession numbers HF678387. The studied isolate was conserved in the fungal collection of DAFE, University of Basilicata, Potenza, (Italy). A fungal suspension (107 spores/mL) was prepared for fruit inoculation. After 48 h of treatment, a sterile needle was used to puncture the fruits, and 10 µL of the fungal suspension was applied to the wounded area [74]. The fruits were subsequently kept moist at room temperature in polyethylene plastic bags. The decay percentage of brown-rot disease was calculated using Equation (8), as reported by Wang et al. [75], with minor modification after 5 days of inoculation following the infection scale, as specified below. The experiment was carried out twice, with three replicates each time.
(8)Decay%=100×∑(0×N0+1×N1+2×N2)/(2×N)
where: N0 = no decay; N1 = slight decay (≤25% of surface decay); N2 = moderate-to-high decay (more than 25% of surface decay); N = total number of fruits.
4.8.1. Chitosan Treatment
The Cs solution was prepared by dissolving the studied chitosan sample, which showed a higher efficacy in vitro trial, in acetic acid (1%) at 6 mg/mL. Glycerol was utilized as a plasticizer at 750 µL/g chitosan [69]. The fruits of the Prunus domestica L. (plum) cultivar ‘Angeleno’, before treatment, were firstly surface-sterilized with sodium hypochlorite (0.5%) and then rinsed with SDW. The disinfected fruits were allowed to dry, and randomly divided into nine groups treated with: (i) acetic acid (1%); (ii) acetic acid (1%)+ glycerol (750 µL/g); (iii) acetic acid (1%)+ M. laxa; (iv) Cs.Pe; (v) Cs.Pe + M. laxa; (vi) C.Cs; (vii) C.Cs + M. laxa; (viii) C − ve (SDW); and (ix) C + ve (fruits infected with M. laxa). The treatment was carried out by spraying the fruits singularly with each prepared solution followed by a drying period of 2 h at room temperature (Figure S7).
4.8.2. Quality Parameters Evaluation
The quality parameters (weight loss, firmness, color change, and sugar content) were measured to evaluate the effects of the two types of chitosan on the fruits, compared to C.Cs.
(I)
Weight loss
The fruit weight loss (%) for every treatment was measured at T0, T2, and T7 days after treatment (DAT) at room temperature using Equation (6) [70].
Weight loss % = (A – B)/(A × 100)(6)
where: A indicates the fruit weight at the time (0), and B indicates the fruit weight after 2 or 7 days.
(II)
Peel color change
The eventual change in fruit color was measured on two sides of each treated fruit using a Colorimeter Color Analyzer (Minolta CR 400 ChromaMeter, Minolta Corp., Tokyo, Japan) at T0, T2, and T7 DAT, following Equation (7) [71].
(7)ΔE=(ΔL)2+(Δa)2+(Δb)2
where ΔE: the overall changes in color indices: L index refers to black-to-white color; a index refers to green-to-red color; b index refers blue-to-yellow color.
(III)
Firmness
The firmness of the treated fruits was measured using a Fruit Pressure Tester (model FT 327, Alfonsine, Italy) with a penetrating cylinder (Ø = 8 mm). The cylinder was inserted to a constant depth of 3–5 mm into the fruit pulp at a steady speed of 2 mm per second. Firmness was determined after T7 DAT on 3 fruits per treatment, and expressed in Newtons (N), which is equivalent to kg/s2, compared to the negative control [72].
(IV)
Sugar content
The soluble sugars in the treated fruits, primarily glucose, fructose, and sucrose, were measured using refractometry (HI96800, Hanna, Villafranca Padovana, Italy) T7 DAT on 3 fruits per treatment [73]. The values were recorded in degrees Brix, which is equivalent to 1 g sucrose/100 g liquid.
4.8.3. Artificial Fungal Infection and Decay Evaluation
For the fungal infection, M. laxa isolated from plum, previously identified based on morphological features and molecular analysis, was used.
The sequences of the studied isolate were deposited in the NCBI GenBank with accession numbers HF678387. The studied isolate was conserved in the fungal collection of DAFE, University of Basilicata, Potenza, (Italy). A fungal suspension (107 spores/mL) was prepared for fruit inoculation. After 48 h of treatment, a sterile needle was used to puncture the fruits, and 10 µL of the fungal suspension was applied to the wounded area [74]. The fruits were subsequently kept moist at room temperature in polyethylene plastic bags. The decay percentage of brown-rot disease was calculated using Equation (8), as reported by Wang et al. [75], with minor modification after 5 days of inoculation following the infection scale, as specified below. The experiment was carried out twice, with three replicates each time.
(8)Decay%=100×∑(0×N0+1×N1+2×N2)/(2×N)
where: N0 = no decay; N1 = slight decay (≤25% of surface decay); N2 = moderate-to-high decay (more than 25% of surface decay); N = total number of fruits.
4.9. Statistical Analysis
The obtained results of the bioassays have been statistically analyzed by applying the ANOVA test to assess overall differences among group means using the Statistical Package for the Social Sciences (SPSS), version 13.0 (Prentice Hall: Chicago, IL, USA, 2004). After that, to identify specific differences between groups, a post hoc test was performed to evaluate the significance level, with a probability of p < 0.05.
5. Conclusions
The characteristics of chitosan extracted from P. eryngii contribute significantly to its potent antimicrobial activity. The presence of amino and hydroxyl groups in chitosan’s polymer structure enables it to penetrate microbial cell membranes, disrupting their integrity and leading to cell death. Moreover, studies have shown that chitosan effectively inhibits the growth of M. laxa, a highly damaging fungal pathogen affecting stone fruits. The antimicrobial efficacy of chitosan has sparked interest in developing formulations for diverse applications in agriculture, medicine, and industry. The findings of the current research are expected to gain more attention in the future, as fungal-derived chitosan presents a promising natural alternative for controlling serious phytopathogens, especially post-harvest, when few chemical pesticides are permitted for use in commercial formulations, either as plant-growth promoters or for crop protection. |
Title: Clinical features of Tibetan adolescent tuberculosis at high altitudes: a retrospective study | Body:
Introduction
Adolescence has been characterized by a substantial increase in the incidence of TB since the early twentieth century, especially in some low- or middle-income countries or regions [1]. Yearly, 850,000 adolescents (aged 10–19 years) have become infected with tuberculosis (TB) globally [2, 3] and in 2020, 226,000 children and young aolescents (aged 10–14 years) died due to TB [4]. A 6 years of experience at a hospital in Argentina found that 48% of individuals diagnosed with TB disease were adolescents [5]. Given substantially increased incidence of TB in adolescence [3], this population requires more attention. In TB surveillance systems, individuals aged < 15 years are grouped with children, and those aged ≥ 15 years are grouped with adults [3, 6]. Adolescents have not been considered a distinct population in TB policy or treatment services [3]. They have received little attention in TB research, and their vulnerability to the disease has been largely forgotten [1]. Few studies have explored the characteristics of tuberculosis in this age group (10–19 years old) [1.5]. Thus, adolescent TB is considered inadequately recognized and underreported [2].
Mycobacterium tuberculosis (MTB) is a facultative aerobic bacillus and low oxygen pressures inhibit the ability of MTB to survive and multiply. Its growth slows when MTB is exposed to hypoxia [7]. Previous studies showed that 10–20% of adolescents with TB disease have pleural TB, 10–20% have extrathoracic TB, and approximately 25% have lung cavitation [3, 8–11]. While few studies have investigated adolescent TB in high-altitude hypoxia so far. To identify the clinical features of adolescent TB in plateaus, we performed a comprehensive and retrospective analysis in this study.
Materials and methods
Study subjects
We conducted an observational and retrospective study in the department of infectious diseases of ** People’s Hospital, where the average elevation is 3000 m above sea level. Patients who were treated in the hospital between July 2019 to July 2021 were included. The presence of symptoms was evaluated by TB nurse specialists and physicians during hospitalization. Detailed information, including demographics, medical history, presenting symptoms, laboratory results, and radiological results, was collected by reviewing medical records. All study staff were trained on the study protocol.
The inclusion criteria for this study were as follows: (1) patients aged ≥ 10 years, (2) living in the Tibet Autonomous Region for more than 10 years, and (3) newly diagnosis of pulmonary TB during the study period. Exclusion criteria were as follows: (1) patients without lung computed tomography (CT) scans during the hospitalization, (2) patients whose medical records were found to be incomplete or were not accessible for analysis, (3) patients receiving immunosuppressive therapy, (4) patients complicated with HIV, diabetes mellitus and cancer, and (5) patients complicated with other infectious diseases.
Pulmonary TB was defined according to the National Health and Family Planning Commission of PRC diagnostic criteria launched in 2017 [12]. In brief, it was disease due to MTB that caused radiologic or microbiological abnormalities consistent with active TB, with or without TB-related clinical symptoms. Active pulmonary TB was referred to symptomatic pulmonary TB. Subclinical pulmonary TB was referred to disease caused by MTB without any symptoms but with detectable radiologic or microbiological abnormalities that could be used to diagnose active pulmonary TB [13–15]. The symptoms included but not limited to clinical TB-related symptoms such as cough, fever, night sweats, weight loss, or chest pain, regardless of symptoms duration. Extrapulmonary TB (EPTB) refers to TB involving organs other than the lungs (e.g., the lymph nodes, abdomen, joints and bone, genitourinary tract, skin, or meninges). Rifampicin-resistant TB was defined as resistance to at least rifampicin. Patients aged 10–19 years were classified into the adolescent group; those aged ≥ 19 years composed the adult group. The criteria of exposure to TB were individuals who had recent contact with someone with active tuberculosis and the cumulative duration of exposure was greater than eight hours in a confined space [4].
Sputum was collected for nucleic acid tests. When spontaneously expectorated sputum could not be obtained, induced sputum was collected for testing with the patient’s consent. Sputum induction was performed by TB nurse specialists in a isolation ward during hospitalization as previously described [16].
The study was designed in accordance with the Helsinki Declaration. The protocol and instruments were approved by the Ethics Committee of ** People’s Hospital (LZRY.NO.20210315 [001]). As this was a retrospective study and the data were anonymous, the requirement for informed consent was therefore waived.
Statistical analysis
Statistical evaluation was performed via SPSS software (IBM SPSS Statistics 21; SPSS Inc., Chicago, IL, USA). All the data were presented as the means ± standard deviations (SDs), as applicable. A t test was used to compare means between two independent groups, and the χ2 test was used for enumeration data comparison. Probability values (P) < 0.05 were considered statistically significant.
Study subjects
We conducted an observational and retrospective study in the department of infectious diseases of ** People’s Hospital, where the average elevation is 3000 m above sea level. Patients who were treated in the hospital between July 2019 to July 2021 were included. The presence of symptoms was evaluated by TB nurse specialists and physicians during hospitalization. Detailed information, including demographics, medical history, presenting symptoms, laboratory results, and radiological results, was collected by reviewing medical records. All study staff were trained on the study protocol.
The inclusion criteria for this study were as follows: (1) patients aged ≥ 10 years, (2) living in the Tibet Autonomous Region for more than 10 years, and (3) newly diagnosis of pulmonary TB during the study period. Exclusion criteria were as follows: (1) patients without lung computed tomography (CT) scans during the hospitalization, (2) patients whose medical records were found to be incomplete or were not accessible for analysis, (3) patients receiving immunosuppressive therapy, (4) patients complicated with HIV, diabetes mellitus and cancer, and (5) patients complicated with other infectious diseases.
Pulmonary TB was defined according to the National Health and Family Planning Commission of PRC diagnostic criteria launched in 2017 [12]. In brief, it was disease due to MTB that caused radiologic or microbiological abnormalities consistent with active TB, with or without TB-related clinical symptoms. Active pulmonary TB was referred to symptomatic pulmonary TB. Subclinical pulmonary TB was referred to disease caused by MTB without any symptoms but with detectable radiologic or microbiological abnormalities that could be used to diagnose active pulmonary TB [13–15]. The symptoms included but not limited to clinical TB-related symptoms such as cough, fever, night sweats, weight loss, or chest pain, regardless of symptoms duration. Extrapulmonary TB (EPTB) refers to TB involving organs other than the lungs (e.g., the lymph nodes, abdomen, joints and bone, genitourinary tract, skin, or meninges). Rifampicin-resistant TB was defined as resistance to at least rifampicin. Patients aged 10–19 years were classified into the adolescent group; those aged ≥ 19 years composed the adult group. The criteria of exposure to TB were individuals who had recent contact with someone with active tuberculosis and the cumulative duration of exposure was greater than eight hours in a confined space [4].
Sputum was collected for nucleic acid tests. When spontaneously expectorated sputum could not be obtained, induced sputum was collected for testing with the patient’s consent. Sputum induction was performed by TB nurse specialists in a isolation ward during hospitalization as previously described [16].
The study was designed in accordance with the Helsinki Declaration. The protocol and instruments were approved by the Ethics Committee of ** People’s Hospital (LZRY.NO.20210315 [001]). As this was a retrospective study and the data were anonymous, the requirement for informed consent was therefore waived.
Statistical analysis
Statistical evaluation was performed via SPSS software (IBM SPSS Statistics 21; SPSS Inc., Chicago, IL, USA). All the data were presented as the means ± standard deviations (SDs), as applicable. A t test was used to compare means between two independent groups, and the χ2 test was used for enumeration data comparison. Probability values (P) < 0.05 were considered statistically significant.
Results
Baseline information
In total, 255 patients (63 adolescents and 192 adults) were recruited. The mean age was 14.4 ± 2.4 years in the adolescent group and 39.9 ± 16.9 years in the adult group. Males accounted for 47.6% and 56.8% of the patients in the two groups, respectively (P > 0.05). The mean body mass index (BMI) was 18.94 ± 2.83 kg/m2 in the adolescent group and 21.09 ± 3.26 kg/m2 in the adult group (P < 0.01). The adolescent group were students from primary and secondary schools. The main occupations of the patients in the adult group were farmers (124, 64.6%) or college students (30, 15.6%). There were 11 (17.5%) EPTB patients in the adolescent group and 20 (10.4%) in the adult group, respectively (P > 0.05).
Clinical manifestation of adolescent patients with pulmonary TB
Patient symptoms were recorded. Compared with those in the adult group, typical TB-related manifestations such as sputum, weakness, chest pain and back pain were significantly less common in the adolescent group (all P < 0.05) (Table 1). All subclinical pulmonary TB patients were those who manifested chest X-ray abnormalities during active surveillance programme or health check-up. The chest X-ray abnormalities make them go to hospital for tuberculosis screening. Compared with patients in the adult group, there were significantly more patients with subclinical pulmonary TB in the adolescent group (23.8% vs. 8.3%, P = 0.001, χ2 = 10.639). Compared with the adult patients, the adolescent patients had a higher TB exposure rate (38.1% vs. 8.3%, χ2 = 31.769) (P < 0.001) (Table 1).
Table 1Clinical manifestation of adolescent patients with pulmonary TBClinical manifestationAdolescent group (n = 63)Adult group (n = 192)χ2P valueHistory of TB exposure24 (38.1%)16 (8.3%)31.769 < 0.001Subclinical pulmonary TB15 (23.8%)16 (8.3%)10.639 < 0.001Active pulmonary TB48 (76.2%)176 (91.7%)10.639 < 0.001Cough41 (65.1%)136 (70.8%)0.7400.390Sputum31 (49.2%)124 (64.6%)4.7050.030Chest pain17 (27.0%)83 (43.2%)5.2520.022Back pain12 (19.0%)70 (36.5%)6.5910.010Fever22 (34.9%)66 (34.4%)0.0600.0937Weakness12 (19.0%)64 (33.3%)4.6270.031Shortness of breath14 (22.2%)59 (30.7%)1.6800.195Night sweat13 (20.6%)48 (25.0%)0.4970.481Headache7 (11.1%)24 (12.5%)0.8600.770Hemoptysis4 (6.3%)21 (10.9%)1.1290.288Abdominal pain8 (12.7%)19 (9.9%)0.3940.530Lumbago1 (1.6%)4 (2.1%)0.0770.781Diarrhea0 (0.0%)4 (2.1%)0.3250.568weight loss0 (0.0%)3 (1.6%)0.1060.745Abbreviation: TB tuberculosis
Blood laboratory examination of adolescent patients with pulmonary TB
Clinical laboratory data, including the white blood cell (WBC) count, percentage of neutrophils (N%), C-reactive protein (CRP), procalcitonin (PCT), and erythrocyte sedimentation rate (ESR), were collected. The ESR was significantly greater in the adolescent group than in the adult group (P = 0.026) (Table 2). There was no significant difference between the groups in CRP, PCT, N% or WBC count. In addition, Routine blood sugar test was done during hospitalization.There were no diabetes in the adolescent group.
Table 2Blood laboratory examination of adolescent patients with pulmonary TBBlood laboratory examinationAdolescent group (n = 63)Adult group (n = 192)tP valueCRP (mg/L)44.9 ± 58.242.4 ± 52.20.3200.749PCT (ng/mL)0.3 ± 0.90.5 ± 3.5-0.4380.662ESR (mm/h)47.3 ± 24.838.6 ± 26.72.2370.026WBC (× 109/L)7.2 ± 2.87.9 ± 5.1-1.0420.299Neutrophil %65.7 ± 12.467.0 ± 12.5-0.6990.485Abbreviations: CRP C-reactive protein, PCT procalcitonin, ESR erythrocyte sedimentation rate, WBC white blood cell, N% percentage of neutrophils
Lung CT of adolescent patients with pulmonary TB
Pulmonary computed tomography (CT) images were included in this study. Compared with adult patients, adolescent patients presented a lower rate of pleural thickening (P < 0.01) (Table 3). There was no significant difference in the location of the lesions or the pulmonary cavity between the two groups.
Table 3Lung CT images of adolescent patients with pulmonary TBLung CTAdolescent group (n = 63)Adult group (n = 192)χ2P valuePleural thickening10 (15.9%)75 (39.1%)12.2430.002Pulmonary cavity1.6910.194 No46 (73.0%)155 (80.7%) Yes17 (27.0%)37 (19.3%)Location of lesions3.5190.172 Left15 (23.8%)30 (15.6%) Right15 (23.8%)37 (19.3%) Bilateral33 (52.4%)125 (65.1%)Abbreviation: CT computed tomography
Results of the GeneXpert MTB/RIF assay of adolescent patients with pulmonary TB
Samples from 29 adolescent and 95 adult patients were tested via GeneXpert MTB/RIF. No significant difference was found in the results of GeneXpert MTB/RIF between the two groups (P > 0.05) (Table 4). In terms of rifampicin-resistant TB, there was no significant difference between the two groups (3.2% vs 7.3%) (P > 0.05).
Table 4Results of the GeneXpert MTB/RIF assay of adolescent patients with pulmonary TBGeneXpertAdolescent group (n = 63)Adult group (n = 192)χ2P valuePositive14 (22.2%)33 (24.3%)0.8000.371Negative15 (23.8%)62 (45.6%)1.6190.203No34 (54.0%)97 (30.1%)0.2260.635rifampicin-resistant TB2 (3.2%)14 (7.3%)0.7570.384
Clinical features of adolescent patients with subclinical pulmonary TB
There were 15 subclinical pulmonary TB patients and 48 active patients in the adolescent group. Compared with active adolescent patients, more subclinical patients were female (12/15 vs 27/48) (Table 5). Induced sputum was obtained from 9 of the 15 adolescent subclinical pulmonary TB patients and 20 of the 48 active patients via the GeneXpert MTB/RIF assay. The positive rate of the sputum GeneXpert test was higher in subclinical TB patients than in active TB patients (6/15 vs 8/48) (Table 5).
Table 5Clinical features of adolescent patients with subclinical pulmonary TBClinical featuresSubclinical pulmonary TB (n = 15)Active pulmonary TB (n = 48)Gender Male321 Female1227GeneXpert Positive68 Negative312 NO628Abbreviation: TB tuberculosis
Baseline information
In total, 255 patients (63 adolescents and 192 adults) were recruited. The mean age was 14.4 ± 2.4 years in the adolescent group and 39.9 ± 16.9 years in the adult group. Males accounted for 47.6% and 56.8% of the patients in the two groups, respectively (P > 0.05). The mean body mass index (BMI) was 18.94 ± 2.83 kg/m2 in the adolescent group and 21.09 ± 3.26 kg/m2 in the adult group (P < 0.01). The adolescent group were students from primary and secondary schools. The main occupations of the patients in the adult group were farmers (124, 64.6%) or college students (30, 15.6%). There were 11 (17.5%) EPTB patients in the adolescent group and 20 (10.4%) in the adult group, respectively (P > 0.05).
Clinical manifestation of adolescent patients with pulmonary TB
Patient symptoms were recorded. Compared with those in the adult group, typical TB-related manifestations such as sputum, weakness, chest pain and back pain were significantly less common in the adolescent group (all P < 0.05) (Table 1). All subclinical pulmonary TB patients were those who manifested chest X-ray abnormalities during active surveillance programme or health check-up. The chest X-ray abnormalities make them go to hospital for tuberculosis screening. Compared with patients in the adult group, there were significantly more patients with subclinical pulmonary TB in the adolescent group (23.8% vs. 8.3%, P = 0.001, χ2 = 10.639). Compared with the adult patients, the adolescent patients had a higher TB exposure rate (38.1% vs. 8.3%, χ2 = 31.769) (P < 0.001) (Table 1).
Table 1Clinical manifestation of adolescent patients with pulmonary TBClinical manifestationAdolescent group (n = 63)Adult group (n = 192)χ2P valueHistory of TB exposure24 (38.1%)16 (8.3%)31.769 < 0.001Subclinical pulmonary TB15 (23.8%)16 (8.3%)10.639 < 0.001Active pulmonary TB48 (76.2%)176 (91.7%)10.639 < 0.001Cough41 (65.1%)136 (70.8%)0.7400.390Sputum31 (49.2%)124 (64.6%)4.7050.030Chest pain17 (27.0%)83 (43.2%)5.2520.022Back pain12 (19.0%)70 (36.5%)6.5910.010Fever22 (34.9%)66 (34.4%)0.0600.0937Weakness12 (19.0%)64 (33.3%)4.6270.031Shortness of breath14 (22.2%)59 (30.7%)1.6800.195Night sweat13 (20.6%)48 (25.0%)0.4970.481Headache7 (11.1%)24 (12.5%)0.8600.770Hemoptysis4 (6.3%)21 (10.9%)1.1290.288Abdominal pain8 (12.7%)19 (9.9%)0.3940.530Lumbago1 (1.6%)4 (2.1%)0.0770.781Diarrhea0 (0.0%)4 (2.1%)0.3250.568weight loss0 (0.0%)3 (1.6%)0.1060.745Abbreviation: TB tuberculosis
Blood laboratory examination of adolescent patients with pulmonary TB
Clinical laboratory data, including the white blood cell (WBC) count, percentage of neutrophils (N%), C-reactive protein (CRP), procalcitonin (PCT), and erythrocyte sedimentation rate (ESR), were collected. The ESR was significantly greater in the adolescent group than in the adult group (P = 0.026) (Table 2). There was no significant difference between the groups in CRP, PCT, N% or WBC count. In addition, Routine blood sugar test was done during hospitalization.There were no diabetes in the adolescent group.
Table 2Blood laboratory examination of adolescent patients with pulmonary TBBlood laboratory examinationAdolescent group (n = 63)Adult group (n = 192)tP valueCRP (mg/L)44.9 ± 58.242.4 ± 52.20.3200.749PCT (ng/mL)0.3 ± 0.90.5 ± 3.5-0.4380.662ESR (mm/h)47.3 ± 24.838.6 ± 26.72.2370.026WBC (× 109/L)7.2 ± 2.87.9 ± 5.1-1.0420.299Neutrophil %65.7 ± 12.467.0 ± 12.5-0.6990.485Abbreviations: CRP C-reactive protein, PCT procalcitonin, ESR erythrocyte sedimentation rate, WBC white blood cell, N% percentage of neutrophils
Lung CT of adolescent patients with pulmonary TB
Pulmonary computed tomography (CT) images were included in this study. Compared with adult patients, adolescent patients presented a lower rate of pleural thickening (P < 0.01) (Table 3). There was no significant difference in the location of the lesions or the pulmonary cavity between the two groups.
Table 3Lung CT images of adolescent patients with pulmonary TBLung CTAdolescent group (n = 63)Adult group (n = 192)χ2P valuePleural thickening10 (15.9%)75 (39.1%)12.2430.002Pulmonary cavity1.6910.194 No46 (73.0%)155 (80.7%) Yes17 (27.0%)37 (19.3%)Location of lesions3.5190.172 Left15 (23.8%)30 (15.6%) Right15 (23.8%)37 (19.3%) Bilateral33 (52.4%)125 (65.1%)Abbreviation: CT computed tomography
Results of the GeneXpert MTB/RIF assay of adolescent patients with pulmonary TB
Samples from 29 adolescent and 95 adult patients were tested via GeneXpert MTB/RIF. No significant difference was found in the results of GeneXpert MTB/RIF between the two groups (P > 0.05) (Table 4). In terms of rifampicin-resistant TB, there was no significant difference between the two groups (3.2% vs 7.3%) (P > 0.05).
Table 4Results of the GeneXpert MTB/RIF assay of adolescent patients with pulmonary TBGeneXpertAdolescent group (n = 63)Adult group (n = 192)χ2P valuePositive14 (22.2%)33 (24.3%)0.8000.371Negative15 (23.8%)62 (45.6%)1.6190.203No34 (54.0%)97 (30.1%)0.2260.635rifampicin-resistant TB2 (3.2%)14 (7.3%)0.7570.384
Clinical features of adolescent patients with subclinical pulmonary TB
There were 15 subclinical pulmonary TB patients and 48 active patients in the adolescent group. Compared with active adolescent patients, more subclinical patients were female (12/15 vs 27/48) (Table 5). Induced sputum was obtained from 9 of the 15 adolescent subclinical pulmonary TB patients and 20 of the 48 active patients via the GeneXpert MTB/RIF assay. The positive rate of the sputum GeneXpert test was higher in subclinical TB patients than in active TB patients (6/15 vs 8/48) (Table 5).
Table 5Clinical features of adolescent patients with subclinical pulmonary TBClinical featuresSubclinical pulmonary TB (n = 15)Active pulmonary TB (n = 48)Gender Male321 Female1227GeneXpert Positive68 Negative312 NO628Abbreviation: TB tuberculosis
Discussion
Human MTB infection is a continuous process involving metabolic bacterial activity and antagonistic immunological responses [14]. The natural progression of TB includes initial exposure, latent infection, incipient TB, subclinical TB and active TB [14]. In this study we found that adolescent pulmonary TB at high altitudes were prone to manifest as subclinical TB. According to Carril SS, tuberculosis in adolescents may present characteristics similar to those of adults, with a higher frequency of severe pulmonary disease leading to pulmonary sequelae [5]. While our results revealed significantly more subclinical pulmonary TB in Tibetan adolescents than in adults. Typical TB-related manifestations, such as sputum, weakness, chest pain and back pain, were less common in adolescents. Studies have shown that culture-positive TB can last up to 18 months before passive case presentation [17, 18], whereas symptoms are often reported only 2–3 months before diagnosis [19]. In a recent TB prevalence survey in Asia [20], symptoms were not reported by up to 80% of individuals with microbiologically confirmed infectious TB. As access to start the protocols for identifying active TB relied mostly on positive symptom screening (e.g., cough, fever, night sweats, weight loss), subclinical TB was usually missed [8]. Subclinical TB patients who persistently or intermittently carry lower bacillary burdens in the lungs may be sources of transmission in the community [18]. Previous study also showed that smear-positive subclinical tuberculosis contributes to MTB transmission [21]. Thus, as is shown in a modeling study, subclinical TB plays an important role in the transmission of TB and needs to be diagnosed and treated [22]. Diagnostic pathways only based on TB-related symptoms will delay diagnosis of subclinical adolescent TB at high altitudes. Paying attention to adolescent individuals with subclinical TB who have not yet sought health care could provide opportunities for therapeutic interventions to prevent progression to active TB disease and transmission of TB bacilli [14, 23, 24] in schools. In this study, there were no diabetes or receiving immunosuppressive therapy patients in the adolescent group. We also excluded patients with combination of HIV and cancer. Chinese adolescents spending most of their time at home and school, made it easier to perform active surveillance or health check-up. This might be probable mechanism of higher cases of subclinical TB in adolescents in this study.
There were more female adolescent subclinical TB patients in this study. This finding was consistent with previous research, which revealed that TB without cough was more common among women [25]. The maturation of the cough reflex is the result of structural and functional changes caused by endogenous and exogenous factors [26]. According to Plevkova et al., sex hormones are the main endogenous factors that impact the maturation of the cough reflex [26]. The cough threshold was similar between the sexes before puberty. It becomes lower at mid-puberty and significantly greater at late puberty in girls [27]. Our finding that there were more females among adolescent subclinical TB patients might be partly related to the changes in sex hormones in adolescence.
In this study, adolescent patients had a higher TB exposure rate than did adult controls. Chinese adolescents spend most of their time at home and school, making it easier to identify TB contacts. Given that a large proportion of transmission may occur outside household exposure [28], we consider that the high TB exposure of adolescents was related partly to accommodations at schools [29–31].
GeneXpert MTB/RIF assay was also performed for TB diagnosis in this study. Although there were more subclinical patients in the adolescent group than in the control group, the positive rate of the sputum GeneXpert MTB/RIF assay in the adolescent group was similar to that in the adult group. Our study was consistent with a recent meta-analysis, which revealed that approximately a quarter of pulmonary TB patients who did not report any cough had positive sputum smears [25]. Considering the bacillary burdens, molecular detection of MTB was important for adolescents with subclinical pulmonary TB. Our study also showed a higher number of subclinical cases with GeneXpert positive results as compared to active adolescent TB cases. We think that the different dependence on microbiological abnormalities in diagnosing pulmonary TB might lead to the dicrepancy.
ESR is a sensitive marker of the inflammatory response and is part of a well-established routine investigation for TB. A previous research showed that compared to healthy controls, ESR values were significantly increased in tuberculosis patients. To measure ESR is a simple and cost-effective method to predict TB [32]. A systematic review and meta-analysis also showed that elevated ESR in newly diagnosed TB patients helps physicians in early diagnosis [33]. In this study, the ESR was significantly higher in the adolescent group. Therefore, the ESR cannot be neglected when screening for adolescent TB.
In this study, adolescent patients presented a lower rate of pleural thickening than did adult patients. This finding was inconsistent with those of previous studies, which showed that cavitation and pleural effusion were more common in adolescent TB patients [3, 14]. The fact that those studies included recurrent TB patients and our study included newly diagnosed asymptomatic patients might be the reason for the difference.
There are some limitations in our study. First, the limited cases limited the representativeness in this study. According to the latest China population census report, there were 3 137 901 Tibetan resident in the Tibet Autonomous Region. The mean annual incidence of TB is 101.98 per 100 000 population and we estimated that the total number of TB patients in the Tibet Autonomous Region was about 3200. Thus we think that the 255 TB patients included in the study, which accounting for about 7.9% of the total number of TB patients, might have a certain representativeness. Second, as patients have seldom been performed DST in this study, we did not include these data in our analyses. Due to the limitation of testing technology, all patients did not conducted IGRAs. Low oxygen pressures inhibit the ability of MTB to survive and multiply in high-altitude. Due to the limitation of MTB culture technology, all patients did not conducted sputum culture. Third, The nearly 1:3 ratio between the two groups introduces a potential source of bias, particularly selection bias, which may affect the comparability of the results. The larger group could disproportionately influence the findings, potentially leading to an over- or underestimation of the effect size. Although statistical adjustments were not applied to balance the groups (such as propensity score matching or weighting methods), we performed multivariable analyses to control for key confounding factors. However, the risk of residual confounding remains, and the unequal group sizes may limit the generalizability of our findings. Future studies with more balanced group sizes or the application of advanced statistical methods would be beneficial to validate and strengthen the conclusions.
Our study included only hospitalized adolescent TB patients. Future research is needed to investigate the clinical features of adolescent TB patients in the community and develop alternative inexpensive, sputum-free tests for diagnosis of subclinical TB.
Conclusions
Adolescent patients with pulmonary TB at high altitudes were prone to subclinical TB and have a higher previous TB exposure rate. Paying attention to adolescent individuals with subclinical TB who have not yet sought health care could provide opportunities for early diagnosis at high altitudes. Sputum GeneXpert test and ESR were important for the diagnosis of subclinical pulmonary TB. |
Title: 6′-Sialyllactose Alleviates Muscle Fatigue through Reduced Blood Lactate Level after Treadmill Exercise in Mice | Body:
1. Introduction
During exercise with limited oxygen supply, lactate production increases through anaerobic metabolism in muscles; thus, lactate serves as a marker of fatigue [1,2]. Lactate clearance post-exercise is essential for recovery and performance enhancement [1]. Optimizing lactate clearance can improve athletic performance and overall fitness [3]. Recovery strategies may involve increasing mitochondrial density, enhancing lactate transport, and maintaining glycolytic capacity in muscle fibers [4,5,6]. Dietary interventions and exercise training can induce these adaptations [7,8]. However, long-term safety and potential adverse effects, such as gastrointestinal distress and drug interactions, need consideration [9,10]. Moreover, some supplements lack sufficient evidence regarding their athletic performance enhancement [11]. Addressing these concerns requires safe and accessible treatments.
Skeletal muscle has two main fiber types: slow-twitch and fast-twitch. Slow-twitch fibers are fatigue-resistant, mitochondria-rich, and mainly use aerobic metabolism, specifically the oxidative phosphorylation (OXPHOS) pathway, for ATP production [12,13]. This pathway includes enzymes and protein complexes such as NADH dehydrogenase, succinate ubiquinone oxidoreductase, ubiquinol cytochrome c oxidoreductase, cytochrome c oxidoreductase, and ATP synthase. They facilitate electron transport and ATP synthesis within the mitochondria [14]. Slow-twitch fibers are essential for endurance activities such as endurance exercise [15]. Fast-twitch fibers mainly rely on anaerobic metabolism for quick ATP production, crucial for power and speed but can lead to fatigue due to by-products such as lactate [15,16].
6′-Sialyllactose (6′-SL), a human milk oligosaccharide (HMO) found abundantly in breast milk, is involved in several bioactive functions such as enhancing immune function, maintaining gut microbiota balance, and promoting brain development; it also serves as a soluble decoy receptor for viruses, bacteria, and protozoan parasites [17,18]. Moreover, 6′-SL has been shown to improve symptoms of UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase myopathy in vivo (mouse) and in clinical settings [19,20]. 6’-SL improves endurance exercise performance in Caenorhabditis elegans by increasing clustered morphology of mitochondria, decreasing fatty acid levels, and increasing glycogen use during exercise [21]. Increased mitochondrial function can produce more ATP efficiently using the OXPHOS pathway, thus relying less on anaerobic glycolysis, which is the primary source of lactate production [22].
Based on the above findings, 6’-SL may also be beneficial against muscle fatigue during exercise by affecting muscle characteristics and mitochondrial function. To test this hypothesis, in the present study, we aimed to investigate the effects of 6’-SL on lactate production, a marker of fatigue during exercise, muscle fiber type, and OXPHOS complex protein expression in mouse.
2. Materials and Methods
2.1. Animal and Treatments
All animal experiments were performed according to the guidelines of the Pohang Technopark Animal Ethics Committee (ABCC 2022009; Pohang, Republic of Korea). This study was performed with 4-week-old male C57BL/6J mice, weighing 19–22 g, from Daehan Biolink (Seoul, Republic of Korea). The mice were individually housed in a controlled environment (22–23 °C, 12-:12-h light/dark cycle) and fed a standard diet; they had free access to water. Animal health was monitored by animal activity, panting, fur condition, body weight, body weight gain, and food intake. Animal activity, panting, and fur condition were monitored once daily, five days per week. Body weight, body weight gain, and food intake were measured weekly. After three weeks of acclimatization to the experimental facility, the mice (n = 20) were randomly divided into two groups, control (n = 10) and 6′-SL groups (n = 10), based on body weight measurements taken by investigators. Once a day, five days per week, the control group mice were orally administered water, and 6′-SL group mice were administered 100 mg/kg 6′-SL for twelve weeks. Weight gain was calculated as the difference in body weight between weeks. Physical characteristics (body weight, body weight gain, and food intake), serum biomarker measurements, blood lactate and blood glucose measurements, and western blotting were assessed in this study. 6′-SL was produced using the one pot reaction system invented by GeneChem, Inc. (Daejeon, Republic of Korea) and dissolved in water. Briefly, substrates and enzymes (cytidylate kinase (CMK), polyphospate kinase (PPK), CMP-NeuAc synthetase (NEU), N-acetyl-D-glucosamine-2-epimerase (NANE), NeuAc aldolase (NAN), and α2,6-sialyltranferase (α2,6STN)) were mixed and reacted in a one-pot reactor. These enzymes were obtained from a strain of beta-D-galactosidase deficient E. coli BW25113 that originated from non-pathogenic E. coli K-12. All procedures were approved by the institutional ethics committee for the care and use of animals.
2.2. Serum Analysis
After humanely euthanizing the mice, blood samples were collected. The samples were then centrifuged at 6000 rpm for 10 min at 4 °C to obtain serum, which was then stored at −80 °C until further analysis. The serum was used for the analysis of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), T-bilirubin, albumin (ALB), total protein, blood urea nitrogen (BUN), creatinine, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), and total cholesterol (TC) levels using an autoanalyzer (TBA-40FR; Toshiba, Tochigi, Japan).
2.3. Treadmill Exercise
At 12 weeks after 6′-SL administration, the mice were made to perform treadmill exercise (Mouse Treadmill, Ugo basile, Gemonio, Italy). The mice were first acclimated to a speed of 16.67 m/min for 3 min, and then, the speed was increased by 1.67 m/min every 3 min; the total running time was set to 20 min.
2.4. Blood Lactate and Glucose Measurements
At 12 weeks after 6′-SL administration, blood samples were collected from the tail tip at the basal level (rest) and 0, 5, and 10 min after the 20-min treadmill exercise session to assess the blood glucose and lactate levels using a handheld glucometer (ACCU-check, Seoul, Republic of Korea) and a lactate test meter (Lactate Pro 2, Kyoto, Japan), respectively.
2.5. Western Blotting
After humanely euthanizing the mice, gastrocnemius (GAS) muscles were harvested from the mice. The GAS muscle was frozen in liquid nitrogen for protein extraction. Total protein from the GAS muscle was extracted via homogenization using T-PERTM Tissue Protein Extraction Reagent (78510; Thermo Fisher, Rockford, IL, USA) and centrifuged at 15,000 rpm for 15 min at 4 °C. The supernatant was added to 5× SDS sample buffer and boiled for 5 min at 100 °C or 1 h at 37 °C. The supernatant was then collected, and protein levels were measured using the Quick StartTM Bradford Protein Assay Kit (5000202; Bio-Rad Laboratories, Hercules, CA, USA). Equal amounts of samples (20 μg) were loaded onto 4–20% Mini-PROTEAN® TGXTM Precast Protein Gels (4561096; Bio-Rad Laboratories, Hercules, CA, USA) and resolved. Thereafter, the resolved proteins were transferred onto polyvinylidene difluoride membranes using the Trans-Blot Turbo Transfer System (25 V for 7 min) (1704156; Bio-Rad Laboratories). The membranes were blocked with casein blocking buffer (IBS-BC052; iNtRON Biotechnology, Boston, MA, USA) for 30 min at room temperature (25 °C ± 1) and incubated overnight at 4 °C with primary antibodies: anti-slow MHC (1:250, M8421; Sigma-Aldrich, St. Louis, MO, USA), anti-fast MHC (1:1000, ab91506; Abcam, Cambridge, UK), anti-OXPHOS (1:1000, ab110413; Abcam), and anti-α-tubulin (1:1000, sc-5286; Santa Cruz Biotechnology, Dallas, TX, USA). The following day, the membranes were washed with Tris-buffered saline containing 0.1% Tween (TBST) three times for 10 min and incubated with HRP-conjugated goat anti-rabbit or anti-mouse IgG secondary antibodies (1:10,000, LF-SA8001 and LF-SA8002; Abfrontier, Seoul, Republic of Korea) at room temperature (25 °C ± 1) for 2 h. After washing three times with TBST, the protein bands were detected with ECL reagent (1705061; Bio-Rad Laboratories) using a ChemiDoc XRS Imaging System (Universal Hood II, Bio-Rad Laboratories) with the Image Lab 6.0 software (Bio-Rad Laboratories).
2.6. Statistical Analysis
Data are presented as mean ± SD. Two-tailed unpaired t-test or two-way analysis of variance followed by Tukey’s multiple comparisons test were used to evaluate the differences between the groups. The significance level was set at p < 0.05. For all statistical analyses, GraphPad Prism 10.1.0 (GraphPad Software, Inc., San Diego, CA, USA) was used. The sample size for each experiment was selected to calculate significant differences. The number of animals was adjusted between the control and 6′-SL treatment groups. For physical characteristics, serum biomarker measurements, and blood lactate and blood glucose measurements, data from eight mice were included for significance calculation. For Western blotting, data from three mice were included for significance calculation. Animals were excluded if they died prematurely or if tissue samples were lost during autopsy.
2.1. Animal and Treatments
All animal experiments were performed according to the guidelines of the Pohang Technopark Animal Ethics Committee (ABCC 2022009; Pohang, Republic of Korea). This study was performed with 4-week-old male C57BL/6J mice, weighing 19–22 g, from Daehan Biolink (Seoul, Republic of Korea). The mice were individually housed in a controlled environment (22–23 °C, 12-:12-h light/dark cycle) and fed a standard diet; they had free access to water. Animal health was monitored by animal activity, panting, fur condition, body weight, body weight gain, and food intake. Animal activity, panting, and fur condition were monitored once daily, five days per week. Body weight, body weight gain, and food intake were measured weekly. After three weeks of acclimatization to the experimental facility, the mice (n = 20) were randomly divided into two groups, control (n = 10) and 6′-SL groups (n = 10), based on body weight measurements taken by investigators. Once a day, five days per week, the control group mice were orally administered water, and 6′-SL group mice were administered 100 mg/kg 6′-SL for twelve weeks. Weight gain was calculated as the difference in body weight between weeks. Physical characteristics (body weight, body weight gain, and food intake), serum biomarker measurements, blood lactate and blood glucose measurements, and western blotting were assessed in this study. 6′-SL was produced using the one pot reaction system invented by GeneChem, Inc. (Daejeon, Republic of Korea) and dissolved in water. Briefly, substrates and enzymes (cytidylate kinase (CMK), polyphospate kinase (PPK), CMP-NeuAc synthetase (NEU), N-acetyl-D-glucosamine-2-epimerase (NANE), NeuAc aldolase (NAN), and α2,6-sialyltranferase (α2,6STN)) were mixed and reacted in a one-pot reactor. These enzymes were obtained from a strain of beta-D-galactosidase deficient E. coli BW25113 that originated from non-pathogenic E. coli K-12. All procedures were approved by the institutional ethics committee for the care and use of animals.
2.2. Serum Analysis
After humanely euthanizing the mice, blood samples were collected. The samples were then centrifuged at 6000 rpm for 10 min at 4 °C to obtain serum, which was then stored at −80 °C until further analysis. The serum was used for the analysis of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), T-bilirubin, albumin (ALB), total protein, blood urea nitrogen (BUN), creatinine, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), and total cholesterol (TC) levels using an autoanalyzer (TBA-40FR; Toshiba, Tochigi, Japan).
2.3. Treadmill Exercise
At 12 weeks after 6′-SL administration, the mice were made to perform treadmill exercise (Mouse Treadmill, Ugo basile, Gemonio, Italy). The mice were first acclimated to a speed of 16.67 m/min for 3 min, and then, the speed was increased by 1.67 m/min every 3 min; the total running time was set to 20 min.
2.4. Blood Lactate and Glucose Measurements
At 12 weeks after 6′-SL administration, blood samples were collected from the tail tip at the basal level (rest) and 0, 5, and 10 min after the 20-min treadmill exercise session to assess the blood glucose and lactate levels using a handheld glucometer (ACCU-check, Seoul, Republic of Korea) and a lactate test meter (Lactate Pro 2, Kyoto, Japan), respectively.
2.5. Western Blotting
After humanely euthanizing the mice, gastrocnemius (GAS) muscles were harvested from the mice. The GAS muscle was frozen in liquid nitrogen for protein extraction. Total protein from the GAS muscle was extracted via homogenization using T-PERTM Tissue Protein Extraction Reagent (78510; Thermo Fisher, Rockford, IL, USA) and centrifuged at 15,000 rpm for 15 min at 4 °C. The supernatant was added to 5× SDS sample buffer and boiled for 5 min at 100 °C or 1 h at 37 °C. The supernatant was then collected, and protein levels were measured using the Quick StartTM Bradford Protein Assay Kit (5000202; Bio-Rad Laboratories, Hercules, CA, USA). Equal amounts of samples (20 μg) were loaded onto 4–20% Mini-PROTEAN® TGXTM Precast Protein Gels (4561096; Bio-Rad Laboratories, Hercules, CA, USA) and resolved. Thereafter, the resolved proteins were transferred onto polyvinylidene difluoride membranes using the Trans-Blot Turbo Transfer System (25 V for 7 min) (1704156; Bio-Rad Laboratories). The membranes were blocked with casein blocking buffer (IBS-BC052; iNtRON Biotechnology, Boston, MA, USA) for 30 min at room temperature (25 °C ± 1) and incubated overnight at 4 °C with primary antibodies: anti-slow MHC (1:250, M8421; Sigma-Aldrich, St. Louis, MO, USA), anti-fast MHC (1:1000, ab91506; Abcam, Cambridge, UK), anti-OXPHOS (1:1000, ab110413; Abcam), and anti-α-tubulin (1:1000, sc-5286; Santa Cruz Biotechnology, Dallas, TX, USA). The following day, the membranes were washed with Tris-buffered saline containing 0.1% Tween (TBST) three times for 10 min and incubated with HRP-conjugated goat anti-rabbit or anti-mouse IgG secondary antibodies (1:10,000, LF-SA8001 and LF-SA8002; Abfrontier, Seoul, Republic of Korea) at room temperature (25 °C ± 1) for 2 h. After washing three times with TBST, the protein bands were detected with ECL reagent (1705061; Bio-Rad Laboratories) using a ChemiDoc XRS Imaging System (Universal Hood II, Bio-Rad Laboratories) with the Image Lab 6.0 software (Bio-Rad Laboratories).
2.6. Statistical Analysis
Data are presented as mean ± SD. Two-tailed unpaired t-test or two-way analysis of variance followed by Tukey’s multiple comparisons test were used to evaluate the differences between the groups. The significance level was set at p < 0.05. For all statistical analyses, GraphPad Prism 10.1.0 (GraphPad Software, Inc., San Diego, CA, USA) was used. The sample size for each experiment was selected to calculate significant differences. The number of animals was adjusted between the control and 6′-SL treatment groups. For physical characteristics, serum biomarker measurements, and blood lactate and blood glucose measurements, data from eight mice were included for significance calculation. For Western blotting, data from three mice were included for significance calculation. Animals were excluded if they died prematurely or if tissue samples were lost during autopsy.
3. Results
3.1. Effects of 6′-SL on Physiological Characteristics
To confirm the effects of 6′-SL on physiological characteristics in mice, body weight, weight gain, food intake, and serum biomarker levels were measured. 6′-SL increased body weight at 7, 9, 10, and 12 weeks, and weight gain was observed at 1 and 7 weeks (Figure 1A,B). Food intake remained unchanged for 12 weeks (Figure 1C). In the 6’-SL group, the serum BUN level decreased, but the liver and kidney biomarker levels, as well as lipid profiles, showed no significant differences compared with those in the control group (Table 1). These results indicate that 6′-SL is safe to administer for up to 12 weeks without causing any organ damage.
3.2. 6′-SL Decreases the Level of Fatigue-Related Factors during Exercise
We confirmed the effects of 6′-SL on blood lactate level, which is a useful biological marker of fatigue in the exercising muscles.
The lactate level of the 6′-SL treated group (3.0 ± 0.35 mmol/L) was lower than that of the control group (4.3 ± 0.35 mmol/L) at rest. Immediately after the treadmill exercise (0 min), lactate levels in the 6′-SL treated group (5.5 ± 0.76 mmol/L) decreased significantly compared to that in the control group (10.8 ± 3.47 mmol/L) and remained around the same level (6.3 ± 0.96–7.3 ± 2.06 mmol/L) for 10 min (Figure 2A). On the contrary, the control group showed an increase in the lactate level immediately after the treadmill exercise (0 min) (10.8 ± 3.47 mmol/L), and the level remained high for 10 min (9.4 ± 2.49–10.9 ± 3.13 mmol/L) (Figure 2A). Moreover, the level of Area Under the Curve (AUC) also showed decreased blood lactate in the 6′-SL treated group (87.47) compared to the control group (142.7) (Figure 2A).
The glucose level was lower in the 6′-SL group compared with that in the control group at rest; it increased at 5 min and significantly increased at 10 min after the treadmill exercise (p > 0.01) (Figure 2B). However, the level of AUC was similar between two groups (Control: 2200, 6′-SL: 2235) (Figure 2B).
Furthermore, the serum BUN level, a fatigue-related factor, decreased in the 6′-SL group compared with that in the control group (control: 23.25 ± 3.24 mg/dL, 6′-SL: 19.75 ± 2.92 mg/dL) (Table 1). These results suggest that 6′-SL decreases blood lactate level and promotes blood glucose production during exercise.
3.3. 6′-SL Increases Energy Metabolism in the GAS Muscle
To examine the effect of 6’-SL on MHC isoform, protein expression of slow and fast MHC was measured in the GAS muscle. 6′-SL notably elevated slow MHC protein expression (p < 0.05) but did not affect fast MHC expression (Figure 3A). Slow MHC is rich in mitochondria and efficiently produces ATP through aerobic metabolism. Here, 6’-SL increased the expression of slow MHC, and thus efficient energy source metabolism through an aerobic mechanism was expected to occur.
To confirm this, we examined the changes in protein expression of the OXPHOS complex, which is involved in aerobic metabolism. As shown in Figure 3B, the expression of complexes I (p < 0.01), III (p < 0.05), IV (p < 0.05), and V (p < 0.01) increased in the 6′-SL group compared with that in the control group. These results suggest that 6′-SL increases energy metabolism by increasing slow MHC expression in the GAS muscles.
3.1. Effects of 6′-SL on Physiological Characteristics
To confirm the effects of 6′-SL on physiological characteristics in mice, body weight, weight gain, food intake, and serum biomarker levels were measured. 6′-SL increased body weight at 7, 9, 10, and 12 weeks, and weight gain was observed at 1 and 7 weeks (Figure 1A,B). Food intake remained unchanged for 12 weeks (Figure 1C). In the 6’-SL group, the serum BUN level decreased, but the liver and kidney biomarker levels, as well as lipid profiles, showed no significant differences compared with those in the control group (Table 1). These results indicate that 6′-SL is safe to administer for up to 12 weeks without causing any organ damage.
3.2. 6′-SL Decreases the Level of Fatigue-Related Factors during Exercise
We confirmed the effects of 6′-SL on blood lactate level, which is a useful biological marker of fatigue in the exercising muscles.
The lactate level of the 6′-SL treated group (3.0 ± 0.35 mmol/L) was lower than that of the control group (4.3 ± 0.35 mmol/L) at rest. Immediately after the treadmill exercise (0 min), lactate levels in the 6′-SL treated group (5.5 ± 0.76 mmol/L) decreased significantly compared to that in the control group (10.8 ± 3.47 mmol/L) and remained around the same level (6.3 ± 0.96–7.3 ± 2.06 mmol/L) for 10 min (Figure 2A). On the contrary, the control group showed an increase in the lactate level immediately after the treadmill exercise (0 min) (10.8 ± 3.47 mmol/L), and the level remained high for 10 min (9.4 ± 2.49–10.9 ± 3.13 mmol/L) (Figure 2A). Moreover, the level of Area Under the Curve (AUC) also showed decreased blood lactate in the 6′-SL treated group (87.47) compared to the control group (142.7) (Figure 2A).
The glucose level was lower in the 6′-SL group compared with that in the control group at rest; it increased at 5 min and significantly increased at 10 min after the treadmill exercise (p > 0.01) (Figure 2B). However, the level of AUC was similar between two groups (Control: 2200, 6′-SL: 2235) (Figure 2B).
Furthermore, the serum BUN level, a fatigue-related factor, decreased in the 6′-SL group compared with that in the control group (control: 23.25 ± 3.24 mg/dL, 6′-SL: 19.75 ± 2.92 mg/dL) (Table 1). These results suggest that 6′-SL decreases blood lactate level and promotes blood glucose production during exercise.
3.3. 6′-SL Increases Energy Metabolism in the GAS Muscle
To examine the effect of 6’-SL on MHC isoform, protein expression of slow and fast MHC was measured in the GAS muscle. 6′-SL notably elevated slow MHC protein expression (p < 0.05) but did not affect fast MHC expression (Figure 3A). Slow MHC is rich in mitochondria and efficiently produces ATP through aerobic metabolism. Here, 6’-SL increased the expression of slow MHC, and thus efficient energy source metabolism through an aerobic mechanism was expected to occur.
To confirm this, we examined the changes in protein expression of the OXPHOS complex, which is involved in aerobic metabolism. As shown in Figure 3B, the expression of complexes I (p < 0.01), III (p < 0.05), IV (p < 0.05), and V (p < 0.01) increased in the 6′-SL group compared with that in the control group. These results suggest that 6′-SL increases energy metabolism by increasing slow MHC expression in the GAS muscles.
4. Discussion
The present study demonstrated that 6′-SL increased protein expression of slow MHC and OXPHOS complexes in the GAS muscle and blood glucose level, and decreased blood lactate level. Furthermore, 6′-SL significantly increased the expression of slow MHC, which is found in slow-twitch muscle fibers.
Muscle fiber type is associated with exercise performance capacity. The ratio of slow-twitch to fast-twitch muscle fibers plays an important role in contractile events, energy homeostasis, muscle force, and fatigue [23,24]. Slow-twitch muscle fibers are crucial for slow but continuous ATP synthesis and contractile events [24,25]. Therefore, muscles with increased amounts of slow-twitch muscle fibers are more resistant to fatigue and perform better during prolonged exercise [24,25]. In contrast, fast-twitch muscle fibers facilitate rapid ATP production, support muscle speed and force, and are less fatigue-resistant [24,25].
Slow-twitch muscle fibers contain a large number of mitochondria, allowing them to obtain ATP via oxidative metabolism [26]. Moreover, the number of slow-twitch muscle fibers correlates with muscle fitness [26]. For example, a reduced number of slow-twitch muscle fibers diminishes energy metabolism, consequently leading to several diseases such as metabolic disorders, heart failure, and muscular dystrophy [26]. Improvement in muscle function by exercise training is induced by an increase in the proportion of slow-twitch muscle fibers [26]. Moreover, endurance exercise is known to increase respiratory capacity and the ratio of slow-twitch muscle fibers [27,28]. Myricetin enhances anti-fatigue ability by switching fast- to slow-fiber type [29].
These reports support the idea that 6′-SL-indcued protein expression of slow MHC will enhance anti-fatigue in muscle (Figure 3A). However, the precise mechanism of 6’-SL-induced slow-MHC protein expression is not yet clear. It will be necessary to investigate the muscle fiber switching through evaluating the expression of each muscle fiber type (type I, type IIa, type IIb, and type IIx) using western blotting and immunofluorescence, as well as the expression of myogenic regulatory factors, myostatin, paired box family, and forkhead box transcription factors, which are known to control myofiber development and differentiation [30].
In muscle cells, OXPHOS complex protein expression is upregulated during exercise to increase energy supply, and this process involves OXPHOS-mediated lactate oxidation [31,32]. Moreover, mitochondrial dysfunction is known to result in excess fatigue and other symptoms associated with chronic disease and muscle deterioration [33]. Clinical trials have shown that oral dietary supplements such as L-carnitine, alpha-lipoic acid, coenzyme Q10, reduced nicotinamide adenine dinucleotide (NADH), and membrane phospholipids can restore mitochondrial function and reduce fatigue [33]. Platycodon grandiflorum-derived saponins enhance exercise performance by increasing the expression of OXPHOS-III and -IV complex proteins [34].
6′-SL increased protein expression of OXPHOS complexes in the GAS muscle in our study (Figure 3B). Further study will be needed to evaluate molecular mechanisms involved in effects of 6’-SL on OXPHOS complex proteins. The relevance of protein kinases such as cAMP-dependent protein kinase A, PTEN-induced putative kinase 1, cyclin-dependent kinase 11, SRC, and 5′-AMP-activated protein kinase, which are known to regulate the OXPHOS system, could improve the quality of this study [35].
Several factors cause muscle weakness and fatigue. For example, aggregation of metabolites, such as lactic acid, ammonia, BUN, and inorganic phosphorus, that induce intracellular acidosis has been shown to cause muscle fatigue [36]. Efforts to eliminate these metabolic products and to recover energy production can reverse fatigue. For example, Moringa oleifera extracts increase swimming endurance by reducing the accumulation of lactate, BUN, and ammonia [37]. Glossogyne tenuifolia extracts increase grip strength and endurance swimming time by decreasing lactate level [36].
Endurance exercise demands high energy levels and consumes glucose from tissue glycogen in the liver and muscle [38]; reduced blood glucose levels have been shown to cause fatigue [39]. Moreover, for contraction of working muscles, glucose is used as a source of energy to produce ATP, which is the immediate fuel source of the body.
In our study, 6′-SL decreased blood lactate levels and increased blood glucose levels after the treadmill exercise (Figure 2). Therefore, we speculate that 6′-SL may enhance exercise performance by decreasing lactate and BUN levels and enhancing glucose supply in the blood. Further studies to investigate how 6’-SL regulate lactate metabolism during exercise would be interesting. For example, changes in glycolysis (glycolysis hexokinase), tricarboxylic acid cycle enzymes (citrate synthase, succinate dehydrogenase, malate dehydrogenase, pyruvate dehydrogenase), oxygen consumption, and ATP production may be worth investigating. Moreover, measuring the level of lactate in muscle and liver and assessing the relevance of Cori cycle will more precisely explain how blood lactate levels decline.
Plasma fatty acids and intramuscular triglycerides are energy sources used during exercise [40]. To verify this, we assessed the effects of 6′-SL on the serum levels of LDL, HDL, TG, and TC. The findings showed no alterations in the levels of these parameters, suggesting that fats were not used as an energy source in this study (Table 1).
To confirm the safety of 6′-SL, we assessed its effects on liver and kidney tissue damage. Assessment of ALT, AST, ALP, T-bilirubin, ALB, total protein, and creatine, the key markers of tissue damage, revealed that 6′-SL did not change their levels, suggesting no organ damage during exercise performance (Table 1). After 6′-SL administration for 12 weeks, increased body weight was observed (Figure 1). As this increase was within the range of normal average body weight change, we suggest that it does not have considerable significance. However, it cannot be ruled out that the weight of muscles or bones related to exercise increased with 6′-SL administration for 12 weeks. However, food intake and body weight gain were not significantly different between the groups after 12 weeks (Figure 1). These results indicate that the mice grew at a steady rate during treatment. The safety of 6′-SL in healthy adults has also been reported [41].
This study has several limitations. First, a single dose of 6′-SL (100 mg/kg) was administered for a single duration (12 weeks) in this study. It is necessary to optimize the effect of 6′-SL by conducting experiments under various conditions, such as the concentration at which there is no effect, concentration at which there is the best effect, and minimum period for which the effect appears in the future. Second, only one type of exercise was tested to measure blood lactate and glucose levels. Treadmill running is physically demanding and is an effective exercise to burn energy and induce fatigue; therefore, this type of exercise was chosen in this study to measure blood lactate and blood glucose levels after inducing fatigue. However, in the future, another type of exercise, such as swimming, should be conducted to obtain more detailed results on the effectiveness of 6’-SL in reducing lactate levels. Moreover, exhausted exercise performance such as treadmill running, swimming, and rotarod test should be performed to confirm whether the reduction in blood lactate level is indeed associated with improved exercise performance in the future. Third, the efficacy of 6’-SL in reducing muscle fatigue is more robust when compared to other nutritional supplements known to be effective in removing lactate from the blood, such as omega-3, CoQ10, magnesium, and acetyl-L-carnitine. To support these findings and develop 6’-SL as a dietary supplement to alleviate muscle fatigue, exploring clinical relevance will be required.
5. Conclusions
In conclusion, this study demonstrated that 6′-SL increased muscle content, especially slow MHC, which contains abundant mitochondria. Therefore, 6′-SL increased the expression of OXPHOS complexes to produce energy using glucose as an energy source, resulting in decreased lactate levels during treadmill exercise. Moreover, serum biomarker levels were not changed. These results suggest that 6’-SL is beneficial and safe in reducing muscle fatigue during exercise, indicating future utility in athletic and clinical settings after further validation. |
Title: Antidepressant treatment initiation among children and adolescents with acute versus long COVID: a large retrospective cohort study | Body:
Introduction
The coronavirus disease (COVID) affected more than 651 million people worldwide, with an estimated 65 million struggling with post-acute sequelae, or long COVID [1]. Long COVID is a protracted illness lasting beyond 4–12 weeks following acute COVID infection [2]. Long COVID is an evolving condition that encompasses more than 200 complex and heterogeneous symptoms (i.e., fatigue, neurocognitive problems, mood disturbance, respiratory problems) that present beyond the acute COVID infection [1, 2]. The United States Centers for Disease Control and Prevention defines long COVID as ongoing symptoms lasting for three months or more, while the United Kingdom National Institute for Health and Care Excellence defines it as post-acute symptoms after four weeks [3]. In the absence of a standard, agreed-upon definition, [3] we considered all available evidence and adopted a definition as a protracted illness lasting 4–12 weeks after the initial acute COVID infection. The estimated global prevalence of long COVID varies widely from 4–66% [4]. The Centers for Disease Control and Prevention estimated that 1.3% of children and adolescents in the United States (US) reported ever having experienced long COVID in 2022 [5].
Long COVID may be associated with a greater burden of psychiatric conditions and a higher risk of developing mental health problems [6–8]. US adults with long COVID had a higher risk of mental health–related medical encounters for at least 6 months after COVID infection than before COVID infection [8]. An electronic health record study of 236 379 adults with long COVID showed that those with long COVID had a higher rate of psychiatric diagnoses (33.6%, of which 12.8% were first-time diagnoses) in the six months after the acute infection than those who had influenza or other respiratory tract infections [7]. Although adults with long COVID and COVID experience depression and anxiety symptoms, these symptoms are more common among those who had long COVID [6]. Most of the aforementioned studies have been in adults, thus there is a need for more research on the impacts of long COVID on children and adolescents.
Despite the low prevalence of long COVID in children and adolescents, it can have a debilitating effect, [2] as noted in the US President’s Memorandum on Addressing the Long-Term Effects of COVID-19 [9]. The COVID-19 pandemic had a profound impact on child and adolescent mental health [10]. Globally, depression, anxiety, and antidepressant use among children and adolescents increased during the COVID-19 pandemic and remained higher than pre-pandemic levels [11–16]. The increase was most notable among females and countries in North America and Europe [11, 13]. Far less is known about the impact of long COVID on child and adolescent mental health. While there is no effective treatment for long COVID, antidepressant (an important tool in the treatment of depression) might be useful in the management of psychiatric sequelae of long COVID [17]. Long COVID symptoms include depression, anxiety, and mood disorders, which can be managed by antidepressant treatment. In addition, emerging evidence shows that selective serotonin reuptake inhibitors can reduce severity and mortality of COVID infection, [18] although this has not been confirmed yet [19]. Antidepressant prescribing can provide complementary information regarding the burden and the receipt of care for psychiatric symptoms among children and adolescents with long COVID. This study aimed to examine antidepressant initiation following long COVID. We compared antidepressant initiation among children and adolescents aged 3–17 years old at first evidence of long COVID to those with COVID without evidence of long COVID. We hypothesized that children and adolescents with long COVID were at a higher risk of antidepressant initiation.
Methods
Study design and data sources
We conducted a retrospective cohort study using Komodo’s Healthcare Map™ database [20]. Komodo’s Healthcare Map™ is an administrative database of healthcare encounters, which is age and geographic representative of the US population with either privately or publicly funded health insurance. In any given year, there are, on average, 200 million insured individuals across the age continuum, primarily employer-based insurance and a smaller proportion of publicly funded programs, and over 80 million with full-adjudicated medical and prescription claims. The study was approved by the Institutional Review Board of the University of Maryland Baltimore. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
Study population
The study population was US children and adolescents 3–17 years old. We limited the study cohort to those with the first evidence of a medical claim with a COVID or long COVID diagnosis on or after October 1, 2021, i.e., the date when the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code for long COVID (U09.9) became effective and thus identifiable in the data. We identified long COVID with this code, as this was used in clinical practice soon after its release to identify individuals with long COVID patients [21]. The index date for the start of follow-up was the earliest date of a medical claim associated with a long COVID or COVID diagnosis during the cohort accrual period from October 1, 2021, through April 4, 2022. The index date was required to be at least 12 months prior to the study end date (April 4, 2023) to allow for a 12-month follow-up for all subjects. We required continuous insurance enrollment six months before the index date (i.e., baseline period) and 12 months after the index date (i.e., outcome assessment period). We excluded those with missing information for year of birth and sex or with any antidepressant prescription claims in the six-month baseline period. For the COVID group, we also excluded children and adolescents with diagnosis codes of a long COVID or history of COVID (ICD-10-CM code Z86.16) within the six-month baseline period (Fig. 1).
Fig. 1Attrition diagram and sample selection of the study cohort
Exposure
Exposure was the first evidence of COVID and long COVID status. Children and adolescents were designated to the long COVID cohort if they had at least one medical claim associated with ICD-10-CM code U09.9 in the cohort accrual period. We designated children and adolescents as COVID if they had at least one medical claim associated with an ICD-10-CM code U07.1 or U07.2. The COVID group could not have any medical claims with a long COVID diagnosis during the baseline or cohort accrual period.
We did not include the ‘no-exposure’ group, i.e., those who did not have claims of a COVID and long COVID diagnosis, as this would dilute the study’s focus from the impact of the COVID-19 infection to the more general impact of the pandemic. The effect of the quarantine measures on youth’s mental health are largely unmeasurable in any data source. It is known that the COVID-19 pandemic affected physical activities, access to entertainment, positive familial relationships, and social support due to different social restriction measures, leading to worsened mental health outcomes in adolescents [22, 23]. Additionally, it is potential that there are undetected COVID and long COVID cases in our data. We considered these issues and felt that it would introduce bias that we would not be able to adjust for in the analytic models. On the other hand, the COVID and long COVID cohorts were nested in a population that was exposed to the quarantine measures of the pandemic and the COVID infection. Thus, by comparing long COVID with COVID cohorts, we could isolate the effect of the COVID infection, since all other factors would be held equal.
Outcome
The study outcome was antidepressant initiation, defined as having at least one pharmacy claim for antidepressant medication within 12 months after the index date. Antidepressant medications included selective serotonin reuptake inhibitors, serotonin norepinephrine reuptake inhibitors, tricyclic antidepressants, and other antidepressants (Additional File 1 Table S1).
Covariates
Covariates were assessed in the six-month baseline period. These included age, sex, and other mental health diagnoses (i.e., anxiety, depression, bipolar disorder, adjustment disorder, attention-deficit/hyperactivity disorder (ADHD), seizure, and schizophrenia), psychotropic medications (i.e., anxiolytics, antipsychotics, mood stabilizers, sedatives, and ADHD medications), medical conditions (i.e., cancer, diabetes, immunocompromised conditions, gastrointestinal conditions and respiratory conditions), and healthcare utilization (i.e., hospitalization and emergency department visits). Additional File 1 Table S1 lists the ICD-10 codes and generic drug names used to identify baseline covariates.
Statistical analysis plan
Due to the large number of COVID relative to long COVID cases, we randomly selected two COVID cases for every long COVID case to create the analytic comparator cohort. Descriptive measures of baseline demographics and clinical covariates assessed differences between long COVID and COVID groups. We used propensity score (PS) methods to control for confounding due to imbalances in the baseline covariates. Logistic regression estimated the PS as the probability of long COVID status given the measured baseline covariates. We implemented PS using a 1-to-1 greedy matching without replacement and a 0.15 caliper [24]. The absolute standardized difference (ASD) evaluated covariate balance before and after PS matching, with an ASD < 0.1 as evidence of good balance. In the PS-matched sample, log-binomial models estimated the risk ratio of antidepressant initiation among long COVID relative to COVID groups.
Sensitivity analyses tested the robustness of our findings to several assumptions. To address potential misclassification of long COVID cases, we excluded those with COVID who developed long COVID in the 12-month outcome assessment period. To minimize the potential inclusion of prevalent long COVID cases, we excluded those who had unofficial long COVID diagnosis codes (i.e., suggested codes before official COVID-19 ICD-10 codes were available) before October 1, 2021. To account for the COVID infection waves associated with different variants, we included the month/year of diagnosis date in our outcome model. Finally, since US states differed in the COVID prevalence and pandemic response, [25] we generated robust standard errors, using a multilevel log-binomial model, clustered at the state level. We determined statistical significance using the 95% confidence interval (CI) or p < 0.05 (2-sided). All analyses were performed using SAS® version 9.4.
Study design and data sources
We conducted a retrospective cohort study using Komodo’s Healthcare Map™ database [20]. Komodo’s Healthcare Map™ is an administrative database of healthcare encounters, which is age and geographic representative of the US population with either privately or publicly funded health insurance. In any given year, there are, on average, 200 million insured individuals across the age continuum, primarily employer-based insurance and a smaller proportion of publicly funded programs, and over 80 million with full-adjudicated medical and prescription claims. The study was approved by the Institutional Review Board of the University of Maryland Baltimore. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
Study population
The study population was US children and adolescents 3–17 years old. We limited the study cohort to those with the first evidence of a medical claim with a COVID or long COVID diagnosis on or after October 1, 2021, i.e., the date when the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code for long COVID (U09.9) became effective and thus identifiable in the data. We identified long COVID with this code, as this was used in clinical practice soon after its release to identify individuals with long COVID patients [21]. The index date for the start of follow-up was the earliest date of a medical claim associated with a long COVID or COVID diagnosis during the cohort accrual period from October 1, 2021, through April 4, 2022. The index date was required to be at least 12 months prior to the study end date (April 4, 2023) to allow for a 12-month follow-up for all subjects. We required continuous insurance enrollment six months before the index date (i.e., baseline period) and 12 months after the index date (i.e., outcome assessment period). We excluded those with missing information for year of birth and sex or with any antidepressant prescription claims in the six-month baseline period. For the COVID group, we also excluded children and adolescents with diagnosis codes of a long COVID or history of COVID (ICD-10-CM code Z86.16) within the six-month baseline period (Fig. 1).
Fig. 1Attrition diagram and sample selection of the study cohort
Exposure
Exposure was the first evidence of COVID and long COVID status. Children and adolescents were designated to the long COVID cohort if they had at least one medical claim associated with ICD-10-CM code U09.9 in the cohort accrual period. We designated children and adolescents as COVID if they had at least one medical claim associated with an ICD-10-CM code U07.1 or U07.2. The COVID group could not have any medical claims with a long COVID diagnosis during the baseline or cohort accrual period.
We did not include the ‘no-exposure’ group, i.e., those who did not have claims of a COVID and long COVID diagnosis, as this would dilute the study’s focus from the impact of the COVID-19 infection to the more general impact of the pandemic. The effect of the quarantine measures on youth’s mental health are largely unmeasurable in any data source. It is known that the COVID-19 pandemic affected physical activities, access to entertainment, positive familial relationships, and social support due to different social restriction measures, leading to worsened mental health outcomes in adolescents [22, 23]. Additionally, it is potential that there are undetected COVID and long COVID cases in our data. We considered these issues and felt that it would introduce bias that we would not be able to adjust for in the analytic models. On the other hand, the COVID and long COVID cohorts were nested in a population that was exposed to the quarantine measures of the pandemic and the COVID infection. Thus, by comparing long COVID with COVID cohorts, we could isolate the effect of the COVID infection, since all other factors would be held equal.
Outcome
The study outcome was antidepressant initiation, defined as having at least one pharmacy claim for antidepressant medication within 12 months after the index date. Antidepressant medications included selective serotonin reuptake inhibitors, serotonin norepinephrine reuptake inhibitors, tricyclic antidepressants, and other antidepressants (Additional File 1 Table S1).
Covariates
Covariates were assessed in the six-month baseline period. These included age, sex, and other mental health diagnoses (i.e., anxiety, depression, bipolar disorder, adjustment disorder, attention-deficit/hyperactivity disorder (ADHD), seizure, and schizophrenia), psychotropic medications (i.e., anxiolytics, antipsychotics, mood stabilizers, sedatives, and ADHD medications), medical conditions (i.e., cancer, diabetes, immunocompromised conditions, gastrointestinal conditions and respiratory conditions), and healthcare utilization (i.e., hospitalization and emergency department visits). Additional File 1 Table S1 lists the ICD-10 codes and generic drug names used to identify baseline covariates.
Statistical analysis plan
Due to the large number of COVID relative to long COVID cases, we randomly selected two COVID cases for every long COVID case to create the analytic comparator cohort. Descriptive measures of baseline demographics and clinical covariates assessed differences between long COVID and COVID groups. We used propensity score (PS) methods to control for confounding due to imbalances in the baseline covariates. Logistic regression estimated the PS as the probability of long COVID status given the measured baseline covariates. We implemented PS using a 1-to-1 greedy matching without replacement and a 0.15 caliper [24]. The absolute standardized difference (ASD) evaluated covariate balance before and after PS matching, with an ASD < 0.1 as evidence of good balance. In the PS-matched sample, log-binomial models estimated the risk ratio of antidepressant initiation among long COVID relative to COVID groups.
Sensitivity analyses tested the robustness of our findings to several assumptions. To address potential misclassification of long COVID cases, we excluded those with COVID who developed long COVID in the 12-month outcome assessment period. To minimize the potential inclusion of prevalent long COVID cases, we excluded those who had unofficial long COVID diagnosis codes (i.e., suggested codes before official COVID-19 ICD-10 codes were available) before October 1, 2021. To account for the COVID infection waves associated with different variants, we included the month/year of diagnosis date in our outcome model. Finally, since US states differed in the COVID prevalence and pandemic response, [25] we generated robust standard errors, using a multilevel log-binomial model, clustered at the state level. We determined statistical significance using the 95% confidence interval (CI) or p < 0.05 (2-sided). All analyses were performed using SAS® version 9.4.
Results
Characteristics of the study cohort
We identified 893 198 COVID and 9137 long COVID children and adolescents from October 1, 2021 through April 4, 2022 (Fig. 1). Before PS matching, the analytic sample of two COVID for every long COVID case included 18 274 COVID and 9137 long COVID children and adolescents. Compared with the COVID group, a larger proportion of the long COVID group were aged 14–17 years old (40.5% versus 26.2%), had immunocompromised conditions (1.1% versus 0.5%), respiratory conditions (17.7% versus 9.3%), and gastrointestinal conditions (19.0% versus 10.1%), and had emergency department visits (19.0% vs. 13.0%) and hospitalization encounters (5.4% vs. 0.8%) in the six-month baseline period (Table 1). There were statistically significant differences between the long COVID and COVID groups, respectively, in terms of depression (2.6% versus 1.4%), anxiety (7.2% versus 3.1%), and adjustment disorder (4.8% versus 2.9%), and psychotropic medication use other than antidepressants, such as anxiolytics (2.0% versus 1.3%), mood stabilizers (0.6% versus 0.4%), sedatives (0.3% versus 0.1%) and ADHD medications (5.6% versus 4.7%).
Table 1Baseline characteristics of COVID and long COVID children and adolescents before and after PS matchingBefore matchingAfter matchingCOVID (n = 18 274)Frequency (%)Long COVID (n = 9137)Frequency (%)ASDap valuebCOVID(n = 8443)Frequency (%)Long COVID(n = 8443)Frequency (%)ASDa
Demographics at baseline
Age as of index date
c
3–4 years old2196 (12.0)668 (7.3)
0.32
< 0.001
647 (7.7)660 (7.8)0.025–9 years old5926 (32.4)2155 (23.6)2043 (24.2)2043 (24.2)10–13 years old5357 (29.3)2613 (28.6)2480 (29.4)2495 (29.6)14–17 years old4795 (26.2)3701 (40.5)3273 (38.8)3245 (38.4)
Sex
Female8908 (48.8)4491 (49.2)
0.16
0.534143 (49.1)4059 (48.1)0.01Male9366 (51.3)4646 (50.9)4300 (50.9)4384 (51.9)
Psychiatric disorders at baseline
Depression256 (1.4)241 (2.6)0.09
< 0.001
188 (2.2)213 (2.5)0.02Anxiety569 (3.1)660 (7.2)
0.19
< 0.001
497 (5.9)410 (4.9)0.05ADHDd955 (5.2)589 (6.5)0.05
< 0.001
550 (6.5)540 (6.4)0.01Adjustment disorder530 (2.9)434 (4.8)
0.10
< 0.001
365 (4.3)380 (4.5)0.01Bipolar disorder28 (0.2)18 (0.2)0.010.4015 (0.2)18 (0.2)0.01Seizure204 (1.1)184 (2.0)0.07
< 0.001
132 (1.6)136 (1.6)0.00Schizophrenia17 (0.1)10 (0.1)0.010.68< 10< 100.00
Psychotropic use at baseline
Anxiolytics240 (1.3)179 (2.0)0.05
< 0.001
137 (1.6)157 (1.9)0.02Antipsychotics102 (0.6)50 (0.6)0.000.9142 (0.5)48 (0.6)0.01Mood stabilizers65 (0.4)51 (0.6)0.03
0.01
45 (0.5)47 (0.6)0.00Sedatives22 (0.1)26 (0.3)0.04
0.002
17 (0.2)21 (0.3)0.01ADHD medications855 (4.7)515 (5.6)0.04
< 0.001
480 (5.7)474 (5.6)0.00
Medical comorbidities at baseline
Cancer44 (0.2)30 (0.3)0.020.1923 (0.3)28 (0.3)0.01Diabetes99 (0.5)60 (0.7)0.010.2453 (0.6)53 (0.6)0.00Immunocompromised conditions91 (0.5)100 (1.1)0.07
< 0.001
65 (0.8)78 (0.9)0.02Respiratory conditions1690 (9.3)1617 (17.7)
0.25
< 0.001
1324 (15.7)1440 (17.1)0.04Gastrointestinal conditions1837 (10.1)1734 (19.0)
0.26
< 0.001
1312 (15.5)1336 (15.8)0.01
Healthcare utilization at baseline
Any emergency department visits2382 (13.0)1735 (19.0)
0.16
< 0.001
1348 (16.0)1350 (16.0)0.00Any hospitalizations151 (0.8)489 (5.4)
0.26
< 0.001
151 (1.8)167 (2.0)0.01aAbsolute standardized difference, in bold ASD > 0.1; bp-value from a chi-square test, in bold p-value < 0.05cPercentages may not add up to 100% due to rounding; dAttention-deficit/hyperactivity disorder
After PS matching, the sample had 8443 children and adolescents in each group. The baseline covariates were well-balanced, as evidenced by an ASD < 0.10 (Table 1). Characteristics of those excluded from the PS-matched COVID and long COVID groups are in Additional File 1 Table S2.
Antidepressant initiation
The distributions of antidepressant therapeutic subclass initiation among COVID-19 and long COVID children and adolescents are in Table 2, with selective serotonin reuptake inhibitors being the most common antidepressant in both groups.
Table 2Antidepressant therapeutic subclass initiation among COVID-19 and long COVID children and adolescentsAntidepressant drug classCOVID-19n (%)Long COVIDn (%)Selective serotonin reuptake inhibitors357 (2.0)314 (3.4)Serotonin and norepinephrine reuptake inhibitors< 1015 (0.2)Tricyclic antidepressants23 (0.1)102 (1.1)Other antidepressants45 (0.3)33 (0.4)Total in each cohort18 2749137
The risk ratios for antidepressant initiation, before and after PS matching, are in Table 3. After PS matching, 3.4% (283/8443) of COVID and 4.7% (395/8443) of long COVID children and adolescents initiated an antidepressant. In the PS-matched analysis, the long COVID group had statistically significant higher risks of initiating antidepressants (RR = 1.40, 95% CI = 1.20, 1.62, p < 0.0001) relative to the COVID group (Table 3).
Table 3Risk of antidepressant initiation among long COVID compared with COVID children and adolescentsExposureTotalAntidepressant initiation (n, %)RR (95%CI)p valueBefore PS matching COVID18 274434 (2.4)Ref Long COVID9137464 (5.1)
2.20 (1.93–2.51)
< 0.001
After PS matching COVID8443283 (3.4)Ref Long COVID8443395 (4.7)
1.40 (1.20–1.62)
< 0.001
In bold, p < 0.05
All sensitivity analyses produced risk estimates consistent with the primary analysis (Table 4).
Table 4Sensitivity analyses of the primary analysis of the risk of antidepressant initiationSensitivity analysesRR (95%CI)p value1. Excluded COVID cases who developed long COVID during following-up time1.64 (1.41–1.91)< 0.0012. Adjusted for the month of diagnosis in the outcome model1.42 (1.23–1.66)< 0.0013. Adjusted for the clustering effect of states1.51 (1.32–1.72)< 0.0014. Excluded long COVID cases who had medical claims with unofficial long COVID codes during the baseline period1.43 (1.23–1.66)< 0.001
Characteristics of the study cohort
We identified 893 198 COVID and 9137 long COVID children and adolescents from October 1, 2021 through April 4, 2022 (Fig. 1). Before PS matching, the analytic sample of two COVID for every long COVID case included 18 274 COVID and 9137 long COVID children and adolescents. Compared with the COVID group, a larger proportion of the long COVID group were aged 14–17 years old (40.5% versus 26.2%), had immunocompromised conditions (1.1% versus 0.5%), respiratory conditions (17.7% versus 9.3%), and gastrointestinal conditions (19.0% versus 10.1%), and had emergency department visits (19.0% vs. 13.0%) and hospitalization encounters (5.4% vs. 0.8%) in the six-month baseline period (Table 1). There were statistically significant differences between the long COVID and COVID groups, respectively, in terms of depression (2.6% versus 1.4%), anxiety (7.2% versus 3.1%), and adjustment disorder (4.8% versus 2.9%), and psychotropic medication use other than antidepressants, such as anxiolytics (2.0% versus 1.3%), mood stabilizers (0.6% versus 0.4%), sedatives (0.3% versus 0.1%) and ADHD medications (5.6% versus 4.7%).
Table 1Baseline characteristics of COVID and long COVID children and adolescents before and after PS matchingBefore matchingAfter matchingCOVID (n = 18 274)Frequency (%)Long COVID (n = 9137)Frequency (%)ASDap valuebCOVID(n = 8443)Frequency (%)Long COVID(n = 8443)Frequency (%)ASDa
Demographics at baseline
Age as of index date
c
3–4 years old2196 (12.0)668 (7.3)
0.32
< 0.001
647 (7.7)660 (7.8)0.025–9 years old5926 (32.4)2155 (23.6)2043 (24.2)2043 (24.2)10–13 years old5357 (29.3)2613 (28.6)2480 (29.4)2495 (29.6)14–17 years old4795 (26.2)3701 (40.5)3273 (38.8)3245 (38.4)
Sex
Female8908 (48.8)4491 (49.2)
0.16
0.534143 (49.1)4059 (48.1)0.01Male9366 (51.3)4646 (50.9)4300 (50.9)4384 (51.9)
Psychiatric disorders at baseline
Depression256 (1.4)241 (2.6)0.09
< 0.001
188 (2.2)213 (2.5)0.02Anxiety569 (3.1)660 (7.2)
0.19
< 0.001
497 (5.9)410 (4.9)0.05ADHDd955 (5.2)589 (6.5)0.05
< 0.001
550 (6.5)540 (6.4)0.01Adjustment disorder530 (2.9)434 (4.8)
0.10
< 0.001
365 (4.3)380 (4.5)0.01Bipolar disorder28 (0.2)18 (0.2)0.010.4015 (0.2)18 (0.2)0.01Seizure204 (1.1)184 (2.0)0.07
< 0.001
132 (1.6)136 (1.6)0.00Schizophrenia17 (0.1)10 (0.1)0.010.68< 10< 100.00
Psychotropic use at baseline
Anxiolytics240 (1.3)179 (2.0)0.05
< 0.001
137 (1.6)157 (1.9)0.02Antipsychotics102 (0.6)50 (0.6)0.000.9142 (0.5)48 (0.6)0.01Mood stabilizers65 (0.4)51 (0.6)0.03
0.01
45 (0.5)47 (0.6)0.00Sedatives22 (0.1)26 (0.3)0.04
0.002
17 (0.2)21 (0.3)0.01ADHD medications855 (4.7)515 (5.6)0.04
< 0.001
480 (5.7)474 (5.6)0.00
Medical comorbidities at baseline
Cancer44 (0.2)30 (0.3)0.020.1923 (0.3)28 (0.3)0.01Diabetes99 (0.5)60 (0.7)0.010.2453 (0.6)53 (0.6)0.00Immunocompromised conditions91 (0.5)100 (1.1)0.07
< 0.001
65 (0.8)78 (0.9)0.02Respiratory conditions1690 (9.3)1617 (17.7)
0.25
< 0.001
1324 (15.7)1440 (17.1)0.04Gastrointestinal conditions1837 (10.1)1734 (19.0)
0.26
< 0.001
1312 (15.5)1336 (15.8)0.01
Healthcare utilization at baseline
Any emergency department visits2382 (13.0)1735 (19.0)
0.16
< 0.001
1348 (16.0)1350 (16.0)0.00Any hospitalizations151 (0.8)489 (5.4)
0.26
< 0.001
151 (1.8)167 (2.0)0.01aAbsolute standardized difference, in bold ASD > 0.1; bp-value from a chi-square test, in bold p-value < 0.05cPercentages may not add up to 100% due to rounding; dAttention-deficit/hyperactivity disorder
After PS matching, the sample had 8443 children and adolescents in each group. The baseline covariates were well-balanced, as evidenced by an ASD < 0.10 (Table 1). Characteristics of those excluded from the PS-matched COVID and long COVID groups are in Additional File 1 Table S2.
Antidepressant initiation
The distributions of antidepressant therapeutic subclass initiation among COVID-19 and long COVID children and adolescents are in Table 2, with selective serotonin reuptake inhibitors being the most common antidepressant in both groups.
Table 2Antidepressant therapeutic subclass initiation among COVID-19 and long COVID children and adolescentsAntidepressant drug classCOVID-19n (%)Long COVIDn (%)Selective serotonin reuptake inhibitors357 (2.0)314 (3.4)Serotonin and norepinephrine reuptake inhibitors< 1015 (0.2)Tricyclic antidepressants23 (0.1)102 (1.1)Other antidepressants45 (0.3)33 (0.4)Total in each cohort18 2749137
The risk ratios for antidepressant initiation, before and after PS matching, are in Table 3. After PS matching, 3.4% (283/8443) of COVID and 4.7% (395/8443) of long COVID children and adolescents initiated an antidepressant. In the PS-matched analysis, the long COVID group had statistically significant higher risks of initiating antidepressants (RR = 1.40, 95% CI = 1.20, 1.62, p < 0.0001) relative to the COVID group (Table 3).
Table 3Risk of antidepressant initiation among long COVID compared with COVID children and adolescentsExposureTotalAntidepressant initiation (n, %)RR (95%CI)p valueBefore PS matching COVID18 274434 (2.4)Ref Long COVID9137464 (5.1)
2.20 (1.93–2.51)
< 0.001
After PS matching COVID8443283 (3.4)Ref Long COVID8443395 (4.7)
1.40 (1.20–1.62)
< 0.001
In bold, p < 0.05
All sensitivity analyses produced risk estimates consistent with the primary analysis (Table 4).
Table 4Sensitivity analyses of the primary analysis of the risk of antidepressant initiationSensitivity analysesRR (95%CI)p value1. Excluded COVID cases who developed long COVID during following-up time1.64 (1.41–1.91)< 0.0012. Adjusted for the month of diagnosis in the outcome model1.42 (1.23–1.66)< 0.0013. Adjusted for the clustering effect of states1.51 (1.32–1.72)< 0.0014. Excluded long COVID cases who had medical claims with unofficial long COVID codes during the baseline period1.43 (1.23–1.66)< 0.001
Discussion
In this cohort study of US children and adolescents aged 3–17, we found that long COVID was associated with a higher risk of antidepressant initiation relative to COVID. Relative to the COVID group, a higher proportion of the long COVID group in this study had medical and mental health conditions and had used healthcare services. This suggests a higher burden of baseline illness among children and adolescents in the long COVID group compared with the COVID group.
The higher risk of antidepressant initiation among children and adolescents with long COVID was observed even after adjusting for imbalances in baseline medical and mental health disorders. Previous studies report that individuals with long COVID have a greater burden of psychiatric conditions [6, 26]. Research shows that children and adolescents with long COVID experience symptoms, including mood (17%), fatigue (10%), sleep disorders (8%), and headache (8%) [2]. The progression of pre-existing psychiatric conditions and chronic conditions among long COVID individuals might also contribute to the higher risk of antidepressant treatment initiation. A study using electronic health records from nine US children’s hospitals showed that, compared to children and adolescents with COVID who did not develop long COVID, those with long COVID were more likely to be hospitalized during the acute COVID infection phase or have complex chronic conditions [27]. A systematic review in adult populations suggests that comorbid anxiety and depression are associated with increased risk of long COVID, [28] but there is very little research on long COVID in children and adolescents. Research in adults with long COVID shows increased social stigma, mental health stress, and medical misdiagnosis [29, 30]. Adults with long COVID have higher rates of depression, anxiety, and suicidality compared to those with COVID [6]. Their rates of depression, anxiety, and suicidality are similar to individuals with chronic and debilitating conditions, such as cancer or diabetes [6]. A new study suggests that serotonin reduction among long COVID individuals may explain the mechanism of psychiatric symptoms, [31] which may also explain the increased risk of antidepressant treatment initiation. Extrapolating from adults, it is possible that our finding of a higher risk of antidepressant initiation among those with long COVID may be due to a greater psychiatric burden subsequent to long COVID.
Emerging evidence suggests a protective effect of antidepressants against COVID infection [18, 32, 33]. This may encourage providers to initiate antidepressant medication. Because our study could not ascertain the indications for antidepressant initiation, the underlying factors that drive antidepressant initiation among children and adolescents with long COVID are unclear. Studies on the increased risk of antidepressant treatment among children and adolescents with long COVID, especially those exploring the indications for antidepressant initiation, are warranted.
This study has several strengths. This study leveraged the Komodo’s Healthcare Map,™ which is a large database representative of the US. The database contained more than 1 million children and adolescents with COVID, enabling a large sample for this study. Our study minimized prevalent user bias by restricting the cohort to new evidence of COVID. We used an active comparator cohort, comparing long COVID with COVID, in order to minimize potential healthy user bias. Despite these strengths, we acknowledge several limitations of our study. First, long COVID symptoms are difficult to distinguish from symptoms of depression, such as anxiety, mood disorders, or chronic fatigue. This could have led to underreporting of long COVID. Our sensitivity analysis attempted to adjust for misclassification. Second, we were only able to detect COVID and long COVID cases that received medical attention, and it is possible that there were many un-detected cases in the data. To mitigate this potential misclassification bias owing to un-detectable cases in the data, we used an active comparator design rather than an un-infected comparator. Third, the use of the ICD-10-CM U09.9 code to identify long COVID may have led to the underrepresentation of males, Hispanic ethnicity, and people of low socioeconomic status. Prior studies report that the U09.9 code was most commonly used for females, Whites, and people living in areas with low poverty and low unemployment [34]. Fourth, it was not possible to identify the timing of COVID infection in relation to the development of long COVID, especially with the lack of a standard definition of long COVID. Finally, our data did not have information on the severity of the COVID infection, vaccination status, race/ethnicity or socioeconomic status, and thus, we were unable to evaluate potential confounding by these factors. Our population primarily had private commercial insurance, which limited generalizability to those with public insurance or the uninsured. Lower socioeconomic status is associated with higher levels of psychiatric problems in those with long COVID, [6] thus our study may have under-represented antidepressant initiation.
Long COVID is likely to have a substantial and multi-faceted public health impact [35]. Emerging evidence reveals new symptoms of long COVID and its debilitating impact on physical and mental health [1, 6, 27, 35]. Long COVID is now recognized as a disability under Title II (state and local government) and III (public accommodations) of the Americans with Disabilities Act, Sect. 504 of the Rehabilitation Act of 1973, and Sect. 1557 of the Patient Protection and Affordable Care Act [36]. Despite the burden of mental health issues among children and adolescents with long COVID, four years after the COVID-19 pandemic, the underlying biological mechanism, the complete list of symptoms, and the effective treatment of long COVID remain unresolved. There is growing US federal investment in research to tackle the long-term consequences of COVID infection [9, 37]. The National Institutes for Health has invested more than $1.5 billion into the Researching COVID to Enhance Recovery Initiative [37]. Our study is the first, to our knowledge, to report on an increased risk of antidepressant initiation among children and adolescents with long COVID. This contributes to the existing literature on the impact of long COVID on child and adolescent health. Our findings inform the need for treatment and management of mental health and well-being of children and adolescents with long COVID. This raises awareness of the mental health needs of children and adolescents and their caregivers, including access to healthcare for screening and treatment of mental health conditions. For those most affected and requiring antidepressant treatment, ongoing monitoring should be in place. With the growing burden of long COVID coupled with the long-term negative effect of COVID-19 pandemic on the development and mental health of children, [38] it is critical to prioritize the mental health and well-being of children and adolescents as this will have impacts for generations to come.
Conclusion
Our study found that children and adolescents with long COVID had a higher risk of antidepressant initiation relative to those with COVID. The results highlight a need for symptom monitoring and mental health management among children and adolescents who develop long COVID. Future studies evaluating the changes in treatment prescriptions and dosages among youth initiating or using antidepressants and their potential effectiveness in reducing severity and psychiatric symptoms for long COVID patients are needed. A better understanding of the manifestation and impact of long COVID in the pediatric population could better inform approaches to address their mental health and well-being.
Electronic supplementary material
Supplementary Material 1
Electronic supplementary material
Supplementary Material 1 |
Title: Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM‑related gene prediction model and independent external validation | Body:
Introduction
Primary liver cancer is among the six most widespread malignancies worldwide, and has the third highest mortality rate globally (1–3). Liver cancer is closely associated with chronic liver disease in >90% of cases, and causes of cirrhosis are important risk factors for liver cancer. Alcohol consumption, diabetes, obesity-induced non-alcoholic steatohepatitis and hepatitis B and V viruses are all critical risk elements for liver cancer, in addition to biliary cirrhosis and hemochromatosis (4,5). Currently, the primary treatment options for liver cancer are radical resection or liver transplantation. However, for patients with advanced, recurrent liver cancer or those who are not suitable for surgery, the prognosis remains unsatisfactory. Despite some advances in the diagnosis, treatment and management of liver cancer, its overall survival remains poor due to the high rates of relapse, vascular invasion or distant metastasis (6). Therefore, it is urgently necessary to explore effective and representative biomarkers and new predictive tools.
Circulating tumor cells (CTCs) are tumor cells that have been shed from a primary or metastatic lesion into the bloodstream, which are rare in healthy individuals (7–9). CTCs exist as single cells or multicellular aggregates known as circulating tumour microemboli (CTMs) (10). Studies in mouse models have confirmed that CTMs are more metastatic than individual CTCs, with results suggesting that the injection of clusters of aggregated cancer cells significantly increases the formation of tumours compared to the injection of the same number of individual cancer cells into mice (11–13). Heterotopic CTMs contain many helper cells, such as red blood cells, fibroblasts and immune cells, which contribute to the metastatic survival of CTMs, rather than just an aggregation of individual cancer cells (14). As an essential component of liquid biopsy technology, CTCs play an essential role in the diagnosis and treatment of cancer, carrying heterogeneous information about the primary tumor and serving as an effective biomarker and modeling tool. Researchers have found that CTCs serve a key role in the metastatic process of tumors. Therefore, the isolation and identification of CTCs with non-invasive biopsy can be widely applied for the early diagnosis, real-time efficacy monitoring and prognosis evaluation of tumors (12,15–18). In general, it has been shown that higher levels of CTCs are associated with a worse outcome in patients with tumors. For example, in two studies of patients with liver cancer, the duration of survival was significantly shorter and associated with poor clinical features in the CTC-positive cohort (19,20). Similarly, Sun et al (21) found that the risk of tumor recurrence increased in patients with liver cancer when the preoperative CTC count was ≥2/7.5 ml, particularly at a-fetoprotein levels of ≤400 ng/ml. With advances in technology, and the genomic, transcriptomic and proteomic analysis of CTCs at the single-cell level, as well as the refinement of CTC in vitro models, our understanding of the critical role of CTCs in cancer has been improved (22,23). Nevertheless, the biological functions of CTCs in tumors at the molecular level have not been fully elucidated. Therefore, the present study aimed to identify the CTC/CTM-related genes (CRGs) in liver cancer and explore their clinical significance.
In the present study, a comprehensive analysis of the transcriptomic data and clinical information of liver cancer in The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases was performed. Analysis of these data in combination with mRNA data associated with liver cancer from the GSE117623 dataset led to the identification of 258 CRGs. Subsequently, a prognostic model and risk subgroups for patients with liver cancer were constructed based on five CRGs, and the associations between different subgroups of patients and immune markers such as immune infiltration, immune checkpoints and tumor mutation burden (TMB) were analyzed. Finally, the detection efficacy and clinical value of the model were evaluated, and chemotherapeutic agents with potential therapeutic value were screened.
Materials and methods
Data sources
The transcriptome profiles and the corresponding clinicopathological data of patients with liver cancer were obtained from TCGA database (https://portal.gdc.cancer.gov/) as the training cohort. In addition, RNA-sequencing (RNA-seq) data and clinical trait information from patients with liver cancer were downloaded from the ICGC database (LIRI-JP dataset; http://icgc.org/) and Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/) for validation. Specifically, 12,518 CRGs in the GSE117623 dataset were downloaded from the GEO database (24). Transcriptomic and matched clinical data from the IMvigor210 cohort of patients treated with anti-PD-L1 were collected (research-pub.gene.com/IMvigor210CoreBiologies) to explore the value of model genes in assessing response to immunotherapy (25).
Identification of candidate genes
To acquire the differentially expressed genes (DEGs) associated with CTCs/CTMs, the limma R package (version 2.7, bioinf.wehi.edu.au/limma) was used to process the RNA-seq data using a false discovery rate (FDR) <0.05 and |log2 (fold change)|>2 as the cutoff criteria. A Venn diagram was then constructed using a Venn webtool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to illustrate the intersection among TCGA-DEGs, ICGC-DEGs and genes from the GSE117623 dataset. These intersected genes were considered to be the CRGs.
Pathway enrichment and protein-protein interaction (PPI) network analysis
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles and pathways associated with the CRGs using the clusterProfiler R package (version 3.19) (26). The cut-offs were set as P<0.05 and FDR <0.05. Gene set enrichment analysis (GSEA) was performed to investigate the common biological pathways (27) using cp.kegg.v7.1.symbols.gmt as a reference gene set with a threshold of P<0.05, to screen for key enriched pathways in different risk groups. In addition, interactions among the CRGs were illustrated by the construction of a PPI network using the STRING database (https://string-db.org/), with an interaction score >0.7 being considered significant. Moreover, Cytoscape software was utilized to visually represent the PPI network. Specifically, the Cytoscape plug-in Molecular Complex Detection (MCODE) (version 2.0.3) was utilized to identify the highly interconnected modules of the PPI network with the following criteria: Degree cut-off, 2; node score cut-off, 0.2; k-core, 2; and max. depth, 100 (28). In addition, another Cytoscape plug-in, cytoHubba (version 0.1), was used to rank the nodes in the network according to their network functionality (29). The gene set variation analysis (GSVA) package (version 3.19) was used to explore the signaling pathways between high- and low-risk groups (30).
Construction and validation of the risk prognostic model
Univariate Cox regression analysis was performed to determine the prognostic CRGs and the CRGs associated with survival time, with P<0.01 considered to be statistically significant. Then, least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was performed to further filter prognostic CRGs associated with the overall survival (OS) of patients with liver cancer (31). Subsequently, a risk signature was developed via stepwise multivariate Cox proportional hazards regression analysis. Prognostic gene signatures were constructed based on linear combinations of regression coefficients derived by multiplying the LASSO Cox regression model coefficients by their mRNA expression levels (32): Risk score=∑ (βmRNA × mRNA)n, where β represents the regression coefficient for the mRNA, mRNA represents the expression level of the mRNA, and n represents the specific gene. Receiver operating characteristic (ROC) curves and Kaplan-Meier curves were constructed to evaluate the predictive performance of the prognostic model in TCGA cohort. pheatmap R package (version 1.0.12; cran.r-project.org/web/packages/pheatmap/index.html) to plot images describing gene expression heatmaps, risk scores and OS for high and low risk groups. Data from the ICGC and GEO databases were used as external validation data to test the predictive capability of the model.
Identification of liver cancer subtypes
A non-negative matrix factorization (NMF) clustering algorithm was utilized to analyze the five signature genes in the risk score model, and determine the subtypes of CRGs in liver cancer using the NMF R package (version 0.27) (33). Using conformal, scatter and silhouette features, the optimal number of clusters with n=2 was determined.
Establishing the predictive nomogram
Nomograms are widely used as a tools for the prognostic analysis of patients with tumors (34). A simplified liver cancer nomogram was constructed for each dataset based on the CRG model and its predictive performance was evaluated by plotting calibration curves.
Bioinformatics analysis of the prognostic signature
The association between the low- and high-risk groups and clinical characteristics were explored using Chi-square tests, and the results were displayed as a heatmap. In addition, the associations between the signature genes and immune cell infiltration were analyzed. Six algorithms, namely CIBERSORT-ABS (35), TIMER (36) (https://cistrome.shinyapps.io/timer/), QUANTISEQ (37), MCPCOUNTER (38), XCELL (39) and EPIC (40,41), were used to evaluate the differences in the immune microenvironment between the two risk groups. Tumor-associated immune comprehensive score was assessed via ImmunoPhenoScore in R package IOBR (42) (version 0.99.9, http://github.com/IOBR/IOBR). Waterfall plots for the two risk groups were produced using the maftools (github.com/PoisonAlien/maftools) R package (version 3.19). Differences in the expression of major histocompatibility complex (MHC) molecules, human leukocyte antigen (HLA) signature, chemokines and potential immune checkpoints were also compared between the two groups. To investigate the association between signature genes and immune subtypes, ‘Subtypes’ module of the TISIDB database (http://cis.hku.hk/TISIDB/index.php). Pearson correlation coefficients of the signature genes expression with the immune checkpoints (PD-1, PD-L1 and CTLA4) were calculated using R language to assess the correlation. In this study, the OCLR algorithm and the Primary Cell Biology Consortium (PCBC, http://progenitorcells.org/) stemness score model were used to calculate the mRNAsi of cells in the TCGA-LIHC dataset and to assess the correlation between the stemness index and the risk score (43). Pearson correlation coefficients of the signature genes expression with the immune checkpoints were calculated using the R language to assess the correlation.
Screening potential therapeutic small molecule drugs for liver cancer
To identify small molecule compounds that may be suitable for the treatment of liver cancer, the pRRophetic (genemed.uchicago.edu/~pgeeleher/pRRophetic/) R package (version 3.19) was used to calculate the half-maximal inhibitory concentration (IC50) based on data from the Genomics of Drug Sensitivity in Cancer database (44).
Cell culture and transfection
HepG2 and MHCC97H human liver cancer cells (cat. nos. CTCC-001-0014 and CTCC-400-0192, respectively) were obtained from the Meisen Chinese Tissue Culture Collections. The cell lines were authenticated by short tandem repeat testing. Both cell lines were cultivated in high-glucose Dulbecco's modified Eagle's medium (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.) at 37°C in 5% CO2. Two small interfering RNA (siRNAs) targeting tumor protein p53 inducible protein 3 (TP53I3), namely si-TP53I3-1 and si-TP53I3-2, and an siRNA negative control were synthesized by and purchased from Sangon Biotech Co., Ltd. The sequences of siRNAs are listed in Table SI. Cell transfection was conducted in 6-well plates when cell confluence was 60–70%, with a final siRNA concentration of 50 nM per well. Transfection of the liver cancer cells was performed using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) following the manufacturer's instructions. Transfection was performed for 6–8 h at 37°C in 5% CO2. The cells were harvested at 24 h post-transfection for reverse transcription-quantitative PCR (RT-qPCR) analysis and at 48 h post-transfection for western blot and in vitro functional assessment.
Western blot analysis
Cells were lysed on ice with RIPA buffer (Wuhan Boster Biological Technology, Ltd.) containing protease inhibitor cocktail (MedChemExpress) for 20 min. The protein contents of the cell lysates were quantified using a BCA protein assay kit (Beyotime Institute of Biotechnology). Then, 30 µg protein/lane was separated by 10% SDS-PAGE (Boster Biological Technology) and transferred to PVDF membranes (EMD Millipore). The membranes were blocked with 5% defatted milk at room temperature for 2 h, then incubated with anti-TP53I3 (#14828-1-AP; 1:1,000; Proteintech Group, Inc.) and anti-ACTB (#AC006; 1:3,000; ABclonal Biotech Co., Ltd.). primary antibodies at 4°C for 12–16 h, followed by HRP-conjugated Affinipure goat anti-rabbit IgG (H+L) (SA00001-2; 1:5,000; Proteintech) secondary antibodies at room temperature for 2 h, and the signal was detected using Pierce® ECL Western Blotting Substrate (Thermo Fisher Scientific, Inc.). Finally, the bands were detected and analyzed using ChemiDoc™ XRS+ with Image Lab™ software (version 6.0, Bio-Rad Laboratories, Inc.).
RT-qPCR
Total RNA was extracted from cells using FreeZol reagent (Vazyme Biotech Co., Ltd.) and synthesized into cDNA using PrimeScript™ RT Master Mix (Takara Bio, Inc.), according to the manufacturer's instructions. qPCR was then carried out using the CFX96 Real-Time PCR System (Bio-Rad Laboratories, Inc.) with the SYBR Green PCR kit (Thermo Fisher Scientific, Inc.) according to the standard protocol. The thermocycling conditions used were as follows: 95°C for 30 sec pre-cycling, and then 40 cycles of 95°C for 10 sec and 60°C for 30 sec. The primer pairs were synthesized by Sangon Biotech Co., Ltd. and their sequences are presented in Table SII. The relative expression of TP53I3 was calculated using the formula 2−ΔΔCq with GAPDH as the reference gene (34).
Cell proliferation assay
Cell Counting Kit 8 (CCK-8) assay (ABclonal Biotech Co., Ltd.) was utilized to assess the proliferation ability of the cells. Cells (3,000/well) were plated in a 96-well plate and incubated overnight at 37°C to allow adhesion. At 24, 48 and 74 h, 100 µl 10% CCK-8 solution was added to each well and the cells were cultured in a cell incubator for 2 h, after which absorbance was measured at 450 nm using a microplate reader (Thermo Fisher Scientific, Inc.).
Colony formation assay
Cells were seeded into 6-well plates at a concentration of 1,000 cells/well. The cells were cultured at 37°C with 5% CO2 in fresh medium and allowed to grow for 14 days. The colonies were then fixed for 15 min at room temperature in 4% paraformaldehyde (Wuhan Servicebio Technology Co., Ltd.), and stained with crystal violet (0.5% wt./vol.) at room temperature for 15 min. Finally photographs of the plates were taken and the colonies were quantified using an inverted microscope (Guangzhou Micro-shot Technology Co., Ltd.). The number of colonies was counted manually. Each independently counted colony refers to a cell cluster of ≥50 cells. The experiment was repeated three times.
5-Ethynyl-2′-deoxyuridine (EdU) detection
The BeyoClick™ EdU-555 Cell Proliferation Kit (Beyotime Institute of Biotechnology) was employed to investigate the proliferation rate of the human liver cancer cells according to the manufacturer's protocols. Briefly, after incubation with 1X EdU (10 µM) solution for 2 h at 37°C, cells were fixed with paraformaldehyde (4%) for 30 min at room temperature, then permeabilized with 0.3% Triton X-100 for 15 min and finally stained with Hoechst 33342 and 4′,6-diamidino-2-phenylindole in the absence of light for 30 min at room temperature. Finally, the cells were imaged by fluorescence microscopy.
Statistical analysis
Bioinformatics analysis and mapping were accomplished using R software. Survival rates were compared using Kaplan-Meier analysis with the calculation of P-values using log-rank tests, or the 2-stage test in the plot with late-stage crossover (cran.r-project.org/web/packages/TSHRC/TSHRC.pdf). In addition, the Chi-square test was used for comparisons between categorical variables, and unpaired Student's t-test was utilized to evaluate the discrepancies between the two risk groups. Correlations between variables were assessed using Spearman's correlation test. The cell groups were compared by one-way ANOVA followed by Dunnett's post hoc tests. For each statistical analysis, P<0.05 was considered to indicate a statistically significant result.
Data sources
The transcriptome profiles and the corresponding clinicopathological data of patients with liver cancer were obtained from TCGA database (https://portal.gdc.cancer.gov/) as the training cohort. In addition, RNA-sequencing (RNA-seq) data and clinical trait information from patients with liver cancer were downloaded from the ICGC database (LIRI-JP dataset; http://icgc.org/) and Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/) for validation. Specifically, 12,518 CRGs in the GSE117623 dataset were downloaded from the GEO database (24). Transcriptomic and matched clinical data from the IMvigor210 cohort of patients treated with anti-PD-L1 were collected (research-pub.gene.com/IMvigor210CoreBiologies) to explore the value of model genes in assessing response to immunotherapy (25).
Identification of candidate genes
To acquire the differentially expressed genes (DEGs) associated with CTCs/CTMs, the limma R package (version 2.7, bioinf.wehi.edu.au/limma) was used to process the RNA-seq data using a false discovery rate (FDR) <0.05 and |log2 (fold change)|>2 as the cutoff criteria. A Venn diagram was then constructed using a Venn webtool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to illustrate the intersection among TCGA-DEGs, ICGC-DEGs and genes from the GSE117623 dataset. These intersected genes were considered to be the CRGs.
Pathway enrichment and protein-protein interaction (PPI) network analysis
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles and pathways associated with the CRGs using the clusterProfiler R package (version 3.19) (26). The cut-offs were set as P<0.05 and FDR <0.05. Gene set enrichment analysis (GSEA) was performed to investigate the common biological pathways (27) using cp.kegg.v7.1.symbols.gmt as a reference gene set with a threshold of P<0.05, to screen for key enriched pathways in different risk groups. In addition, interactions among the CRGs were illustrated by the construction of a PPI network using the STRING database (https://string-db.org/), with an interaction score >0.7 being considered significant. Moreover, Cytoscape software was utilized to visually represent the PPI network. Specifically, the Cytoscape plug-in Molecular Complex Detection (MCODE) (version 2.0.3) was utilized to identify the highly interconnected modules of the PPI network with the following criteria: Degree cut-off, 2; node score cut-off, 0.2; k-core, 2; and max. depth, 100 (28). In addition, another Cytoscape plug-in, cytoHubba (version 0.1), was used to rank the nodes in the network according to their network functionality (29). The gene set variation analysis (GSVA) package (version 3.19) was used to explore the signaling pathways between high- and low-risk groups (30).
Construction and validation of the risk prognostic model
Univariate Cox regression analysis was performed to determine the prognostic CRGs and the CRGs associated with survival time, with P<0.01 considered to be statistically significant. Then, least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was performed to further filter prognostic CRGs associated with the overall survival (OS) of patients with liver cancer (31). Subsequently, a risk signature was developed via stepwise multivariate Cox proportional hazards regression analysis. Prognostic gene signatures were constructed based on linear combinations of regression coefficients derived by multiplying the LASSO Cox regression model coefficients by their mRNA expression levels (32): Risk score=∑ (βmRNA × mRNA)n, where β represents the regression coefficient for the mRNA, mRNA represents the expression level of the mRNA, and n represents the specific gene. Receiver operating characteristic (ROC) curves and Kaplan-Meier curves were constructed to evaluate the predictive performance of the prognostic model in TCGA cohort. pheatmap R package (version 1.0.12; cran.r-project.org/web/packages/pheatmap/index.html) to plot images describing gene expression heatmaps, risk scores and OS for high and low risk groups. Data from the ICGC and GEO databases were used as external validation data to test the predictive capability of the model.
Identification of liver cancer subtypes
A non-negative matrix factorization (NMF) clustering algorithm was utilized to analyze the five signature genes in the risk score model, and determine the subtypes of CRGs in liver cancer using the NMF R package (version 0.27) (33). Using conformal, scatter and silhouette features, the optimal number of clusters with n=2 was determined.
Establishing the predictive nomogram
Nomograms are widely used as a tools for the prognostic analysis of patients with tumors (34). A simplified liver cancer nomogram was constructed for each dataset based on the CRG model and its predictive performance was evaluated by plotting calibration curves.
Bioinformatics analysis of the prognostic signature
The association between the low- and high-risk groups and clinical characteristics were explored using Chi-square tests, and the results were displayed as a heatmap. In addition, the associations between the signature genes and immune cell infiltration were analyzed. Six algorithms, namely CIBERSORT-ABS (35), TIMER (36) (https://cistrome.shinyapps.io/timer/), QUANTISEQ (37), MCPCOUNTER (38), XCELL (39) and EPIC (40,41), were used to evaluate the differences in the immune microenvironment between the two risk groups. Tumor-associated immune comprehensive score was assessed via ImmunoPhenoScore in R package IOBR (42) (version 0.99.9, http://github.com/IOBR/IOBR). Waterfall plots for the two risk groups were produced using the maftools (github.com/PoisonAlien/maftools) R package (version 3.19). Differences in the expression of major histocompatibility complex (MHC) molecules, human leukocyte antigen (HLA) signature, chemokines and potential immune checkpoints were also compared between the two groups. To investigate the association between signature genes and immune subtypes, ‘Subtypes’ module of the TISIDB database (http://cis.hku.hk/TISIDB/index.php). Pearson correlation coefficients of the signature genes expression with the immune checkpoints (PD-1, PD-L1 and CTLA4) were calculated using R language to assess the correlation. In this study, the OCLR algorithm and the Primary Cell Biology Consortium (PCBC, http://progenitorcells.org/) stemness score model were used to calculate the mRNAsi of cells in the TCGA-LIHC dataset and to assess the correlation between the stemness index and the risk score (43). Pearson correlation coefficients of the signature genes expression with the immune checkpoints were calculated using the R language to assess the correlation.
Screening potential therapeutic small molecule drugs for liver cancer
To identify small molecule compounds that may be suitable for the treatment of liver cancer, the pRRophetic (genemed.uchicago.edu/~pgeeleher/pRRophetic/) R package (version 3.19) was used to calculate the half-maximal inhibitory concentration (IC50) based on data from the Genomics of Drug Sensitivity in Cancer database (44).
Cell culture and transfection
HepG2 and MHCC97H human liver cancer cells (cat. nos. CTCC-001-0014 and CTCC-400-0192, respectively) were obtained from the Meisen Chinese Tissue Culture Collections. The cell lines were authenticated by short tandem repeat testing. Both cell lines were cultivated in high-glucose Dulbecco's modified Eagle's medium (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.) at 37°C in 5% CO2. Two small interfering RNA (siRNAs) targeting tumor protein p53 inducible protein 3 (TP53I3), namely si-TP53I3-1 and si-TP53I3-2, and an siRNA negative control were synthesized by and purchased from Sangon Biotech Co., Ltd. The sequences of siRNAs are listed in Table SI. Cell transfection was conducted in 6-well plates when cell confluence was 60–70%, with a final siRNA concentration of 50 nM per well. Transfection of the liver cancer cells was performed using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) following the manufacturer's instructions. Transfection was performed for 6–8 h at 37°C in 5% CO2. The cells were harvested at 24 h post-transfection for reverse transcription-quantitative PCR (RT-qPCR) analysis and at 48 h post-transfection for western blot and in vitro functional assessment.
Western blot analysis
Cells were lysed on ice with RIPA buffer (Wuhan Boster Biological Technology, Ltd.) containing protease inhibitor cocktail (MedChemExpress) for 20 min. The protein contents of the cell lysates were quantified using a BCA protein assay kit (Beyotime Institute of Biotechnology). Then, 30 µg protein/lane was separated by 10% SDS-PAGE (Boster Biological Technology) and transferred to PVDF membranes (EMD Millipore). The membranes were blocked with 5% defatted milk at room temperature for 2 h, then incubated with anti-TP53I3 (#14828-1-AP; 1:1,000; Proteintech Group, Inc.) and anti-ACTB (#AC006; 1:3,000; ABclonal Biotech Co., Ltd.). primary antibodies at 4°C for 12–16 h, followed by HRP-conjugated Affinipure goat anti-rabbit IgG (H+L) (SA00001-2; 1:5,000; Proteintech) secondary antibodies at room temperature for 2 h, and the signal was detected using Pierce® ECL Western Blotting Substrate (Thermo Fisher Scientific, Inc.). Finally, the bands were detected and analyzed using ChemiDoc™ XRS+ with Image Lab™ software (version 6.0, Bio-Rad Laboratories, Inc.).
RT-qPCR
Total RNA was extracted from cells using FreeZol reagent (Vazyme Biotech Co., Ltd.) and synthesized into cDNA using PrimeScript™ RT Master Mix (Takara Bio, Inc.), according to the manufacturer's instructions. qPCR was then carried out using the CFX96 Real-Time PCR System (Bio-Rad Laboratories, Inc.) with the SYBR Green PCR kit (Thermo Fisher Scientific, Inc.) according to the standard protocol. The thermocycling conditions used were as follows: 95°C for 30 sec pre-cycling, and then 40 cycles of 95°C for 10 sec and 60°C for 30 sec. The primer pairs were synthesized by Sangon Biotech Co., Ltd. and their sequences are presented in Table SII. The relative expression of TP53I3 was calculated using the formula 2−ΔΔCq with GAPDH as the reference gene (34).
Cell proliferation assay
Cell Counting Kit 8 (CCK-8) assay (ABclonal Biotech Co., Ltd.) was utilized to assess the proliferation ability of the cells. Cells (3,000/well) were plated in a 96-well plate and incubated overnight at 37°C to allow adhesion. At 24, 48 and 74 h, 100 µl 10% CCK-8 solution was added to each well and the cells were cultured in a cell incubator for 2 h, after which absorbance was measured at 450 nm using a microplate reader (Thermo Fisher Scientific, Inc.).
Colony formation assay
Cells were seeded into 6-well plates at a concentration of 1,000 cells/well. The cells were cultured at 37°C with 5% CO2 in fresh medium and allowed to grow for 14 days. The colonies were then fixed for 15 min at room temperature in 4% paraformaldehyde (Wuhan Servicebio Technology Co., Ltd.), and stained with crystal violet (0.5% wt./vol.) at room temperature for 15 min. Finally photographs of the plates were taken and the colonies were quantified using an inverted microscope (Guangzhou Micro-shot Technology Co., Ltd.). The number of colonies was counted manually. Each independently counted colony refers to a cell cluster of ≥50 cells. The experiment was repeated three times.
5-Ethynyl-2′-deoxyuridine (EdU) detection
The BeyoClick™ EdU-555 Cell Proliferation Kit (Beyotime Institute of Biotechnology) was employed to investigate the proliferation rate of the human liver cancer cells according to the manufacturer's protocols. Briefly, after incubation with 1X EdU (10 µM) solution for 2 h at 37°C, cells were fixed with paraformaldehyde (4%) for 30 min at room temperature, then permeabilized with 0.3% Triton X-100 for 15 min and finally stained with Hoechst 33342 and 4′,6-diamidino-2-phenylindole in the absence of light for 30 min at room temperature. Finally, the cells were imaged by fluorescence microscopy.
Statistical analysis
Bioinformatics analysis and mapping were accomplished using R software. Survival rates were compared using Kaplan-Meier analysis with the calculation of P-values using log-rank tests, or the 2-stage test in the plot with late-stage crossover (cran.r-project.org/web/packages/TSHRC/TSHRC.pdf). In addition, the Chi-square test was used for comparisons between categorical variables, and unpaired Student's t-test was utilized to evaluate the discrepancies between the two risk groups. Correlations between variables were assessed using Spearman's correlation test. The cell groups were compared by one-way ANOVA followed by Dunnett's post hoc tests. For each statistical analysis, P<0.05 was considered to indicate a statistically significant result.
Results
Differentially expressed CRGs
The liver cancer (liver hepatocellular carcinoma) gene expression profiles were downloaded from TCGA and ICGC portals and 1,622 and 628 DEGs, respectively, were screened out using the limma R package. The DEGs from TCGA and ICGC databases are shown as volcano plots in Fig. 1A and B, respectively. A Venn diagram was then constructed to filter out the differentially expressed CRGs (Fig. 1C). The intersection of the DEGs from TCGA and ICGC databases with the 12,518 CRGs from GSE117623 yielded a total of 258 differentially expressed CRGs (Table SIII).
PPI network construction
To investigate the interrelationship of the differentially expressed CRGs and identify hub genes, a PPI network was constructed and module analysis performed to determine co-expression networks. Firstly, the 258 differentially expressed CRGs were uploaded to the STRING database, and the minimum required interaction score was set to 0.7, which indicates a strong interaction between the CRGs. The STRING interactions were then analyzed using Cytoscape and the resulting co-expression network, which contained 155 nodes and 2,731 edges, is shown in Fig. 2A. In addition, modules with >50 genes were identified using the MCODE plug-in and 10 hub genes in that module, namely topoisomerase IIa, cyclin B2, cell division cycle associated 8 (CDCA8), BIRC5, aurora kinase B, cyclin B1, BUB1 mitotic checkpoint serine/threonine kinase (BUB1), BUB1B, kinesin family member 20A and TTK protein kinase, were characterized using the cytoHubba plug-in (Fig. 2B). This included 57 nodes and 1,497 edges. These potential hub genes may be instrumental in the biological progression of liver cancer.
GO and KEGG enrichment analyses
To explore the biological categories and biological processes associated with the differentially expressed CRGs, GO and KEGG enrichment analyses were conducted using R software, and the enrichment results are shown in bubble charts (Fig. 2C and D). The GO enrichment analysis revealed that the differentially expressed CRGs were principally concentrated in the biological process terms ‘nuclear division’, ‘organelle fission’, ‘chromosome segregation’ and ‘mitotic nuclear division’. In addition, the main cellular component terms included ‘chromosomal region’, ‘chromosome, centromeric region’, ‘spindle’ and ‘kinetochore’. Moreover, the molecular function terms associated with the CRGs were ‘tubulin binding’, ‘microtubule-binding’, ‘ATPase activity’ and ‘motor activity’ (Fig. 2C). Regarding the KEGG analysis, the primary terms are shown in Fig. 2D, which reveals that the differentially expressed CRGs were particularly enriched in ‘cell cycle’, ‘microRNAs in cancer’, ‘p53 signaling pathway’ and ‘cellular senescence’.
Construction of a prognostic model and validation of the model in the ICGC cohort
Univariate Cox regression analysis demonstrated that 88 CRGs were strongly associated with survival in patients with liver cancer (P<0.01), all of which were prognostic risk factors (Fig. 3A). Then, the 88 CRGs were regression penalized using LASSO Cox regression to exclude relatively insignificant parameters (Fig. 3B and C). Stepwise multivariate Cox regression was subsequently employed to construct a predictive signature for patients with liver cancer in TCGA cohort (Fig. 3D). The five genes in the signature were CDCA8, TP53I3, hepatitis A virus cellular receptor 1 (HAVCR1), MYCN proto-oncogene (MYCN) and thioredoxin reductase 1 (TXNRD1). The formula for risk score calculation was as follows: Risk score=(0.0826 × expression level of CDCA8) + (0.0112 × expression level of TP53I3) + (0.0824 × expression level of MYCN) + (0.0376 × expression level of HAVCR1) + (0.0120 × expression level of TXNRD1) (Table SIV). Patients in TCGA cohort were classified into high- and low-risk groups using the median predictive index as the cut-off point. As Fig. 4A shows, the low-risk group was significantly associated with improved survival (P<0.05). To evaluate the predictive ability of this prognostic signature, an ROC analysis of the risk score was conducted. The area under the curve (AUC) values predicted from the ROC curves for 1-, 3- and 5-year OS were 0.804, 0.736 and 0.707, respectively (Fig. 4B). In Fig. 4C, the upper panel shows the expression heat map of the five prognostic model genes in the high- and low subgroups, the middle panel reveals that the risk of patients with liver cancer increases as risk score increases, and the lower panel demonstrates the poor OS of the patients in the high-risk group compared with those in the low-risk group. To validate the predictive power of the signature, the same formula was used to analyze the risk score of each patient in the ICGC dataset, for independent external validation. The Kaplan-Meier curves also displayed a poor prognosis of patients in the high-risk group in this dataset (P<0.05; Fig. 4D). In addition, the ROC curve showed the strong predictive ability of the risk-score signature for prognosis, with AUCs for the prediction of 1-, 3- and 5-year OS of 0.714, 0.730 and 0.726, respectively (Fig. 4E). Also, the expression of the five CRGs and the mortality of the patients increased as the risk scores increased (Fig. 4F).
Independent prognostic role of the gene signature
To investigate whether the CTC/CTM-associated 5-gene signature could be an independent prognostic factor for patients with liver cancer, the prognostic value of this signature was compared with that of several clinicopathological factors, including age, sex, grade and American Joint Committee on Cancer (AJCC) stage in both cohorts using univariate and multivariate Cox regression analyses. For TCGA cohort, 365 valid patients were included, 182 in the high-risk group and 183 in the low-risk group. Univariate Cox analysis indicated that risk score [P<0.001; hazard ratio (HR), 1.258; 95% confidence interval (95% CI), 1.183–1.338)], AJCC stage (P<0.001; HR, 1.658; 95% CI, 1.340–2.053) and T status (P<0.001; HR, 1.633; 95% CI, 1.332–2.003) were candidate factors. Further multivariate Cox regression analysis emphasized that risk score was an independent risk factor for patients with liver cancer (P<0.001; HR, 1.227; 95%CI, 1.142–1.318) (Fig. 4G). For the ICGC cohort, 229 patients were included, 59 in the high-risk group and 170 in the low-risk group. Univariate Cox regression analysis demonstrated that sex (P=0.033; HR, 0.505; 95% CI, 0.270–0.946), risk score (P=0.010; HR, 1.076; 95% CI, 1.017–1.137) and tumor stage (P<0.001; HR, 2.252; 95% CI, 1.543–3.287) were potential risk factors. Multivariate Cox regression analysis also indicated that sex (P=0.004; HR, 0.389; 95% CI, 0.204–0.740), risk score (P=0.030; HR, 1.070; 95% CI, 1.006–1.138 and tumor stage (P<0.001; HR, 2.330; 95% CI, 1.601–3.391) were independent predictors for patients in the ICGC cohort (Fig. 4H). In conclusion, these findings indicated that the 5-CRG risk signature was closely associated with the clinical characteristics of patients with liver cancer, had a fine predictive capacity and has potential as a prognostic indicator for these patients.
Validation of the signature in the GEO cohort
To further verify the predictive power of the prognostic signature, the GSE14520 dataset was analyzed. In this dataset, 221 valid patients were included, with 100 in the high-risk group and 121 in the low-risk group. As the results in Fig. 5A illustrate, patients in the low-risk group had improved survival outcomes compared with those in the high-risk group (P<0.05). In the GEO cohort, the AUC for 5-year OS was 0.684 (Fig. 5B). The expression of model genes, risk score distribution and survival status for each patient in this validation cohort are shown in Fig. 5C. Following univariate Cox regression analysis (Fig. 5D), the results of independent prognostic analysis revealed that risk score (P<0.001; HR=1.280; 95% CI, 1.145–1.432), as well as AJCC stage (P<0.001; HR, 2.017; 95% CI, 1.518–2.681) (Fig. 5E) were independent risk factors in this cohort. These findings indicate that the prognostic model is promising as a predictive signature.
Identification of CTC/CTM-related molecular subtypes
Patients were clustered into different subtypes based on the expression levels of the five prognostic signature genes using the NMF algorithm. To ensure the robustness of the clustering results, the coefficient of correlation was used to determine the optimal number of clusters, and when the number of clusters was 2, clear boundaries were observed for both subtypes. This indicated the stable and reliable clustering of the liver cancer samples (Fig. 6A). The OS of patients in cluster 1 (C1) was significantly improved compared with that of C2 (P=0.002; Fig. 6B). Most immune checkpoints were upregulated in the C2 group compared with the C1 group (Fig. 6C). In addition, it was also found that the level of immune infiltration in the tumor microenvironment was also distinct in the two groups, with immune score, stromal score and ESTIMATE score of the C1 group being significantly lower than those of the C2 group (P<0.05; Fig. 6D). This suggests that C1 molecular subtype tends to present ‘cold tumors’, whereas the C2 molecular subtype tends to present ‘hot tumors’. It was also noted that the level of neutrophil infiltration was higher in the C1 group than in the C2 group, whereas the levels of monocytic lineage and fibroblast infiltration were lower in the C1 group than in the C2 group (P<0.05; Fig. 6E).
Identification of potentially therapeutic small molecule drugs
The sensitivity of the high- and low-risk groups to various chemotherapeutic agents was compared to evaluate drugs for potential use in liver cancer. The findings indicate that the low-risk group was associated with a higher IC50 for chemotherapeutic compounds including ABT.888 (veliparib), AS601245 (an ATP-competitive JNK inhibitor), AG.014699 (rucaparib), A.443654 (a pan-Akt inhibitor), ATRA (tretinoin) and AUY922 (luminespib). By contrast, axitinib, A.770041 (an LCK inhibitor), AZD.0530 (saracatinib), AMG.706 (motesanib), AKT.inhibitor.VIII and AICAR (acadesine) had a higher IC50 in the high-risk group, indicating that patients in the low-risk group may benefit more from treatment with these compounds (P<0.05; Fig. 7). The sensitivity of the two liver cancer subtypes to various chemotherapeutic drugs was also evaluated. The results suggested that patients with the C1 subtype might be more sensitive to metformin, lapatinib, elesclomol, docetaxel, camptothecin, bosutinib, axitinib and vinblastine, while patients in group C2 would likely benefit by treatment with cisplatin, bortezomib, bleomycin, bicalutamide, mitomycin C, imatinib, etoposide and gemcitabine (Fig. S1).
Pathway analysis by GSEA and GSVA
To further explore the molecular mechanism associated with the signature genes and the prognostic module, GSEA was performed in TCGA liver cancer cohort. Fig. 8A-E reveals the KEGG pathways of the five signature genes, namely CDCA8, HAVCR1, MYCN, TP53I3 and TXNRD1, showing the five most upregulated and downregulated pathways for each gene. The signature genes are mainly concentrated in KEGG pathways including ‘cell cycle’, ‘p53 signaling pathway’, ‘complement and coagulation cascades’ and ‘drug metabolism cytochrome p450’. In addition, GSEA was used to compare the high- and low-risk groups based on the risk scores. The KEGG pathways enriched in the high and low risk groups are shown in Fig. 8F.
GSVA was also utilized to analyze the differences in biological behavior between the high- and low-risk groups. The results demonstrated that pathways associated with tumor progression, such as ‘cell cycle’, ‘DNA replication’, ‘RNA degradation’, ‘mTOR signaling pathway’ and ‘P53 signaling pathway’, were mainly concentrated in the high-risk group. By contrast, metabolism-related pathways, including ‘fatty acid metabolism’, ‘propanoate metabolism’, ‘butanoate metabolism’ and ‘tyrosine metabolism’, were mainly present in the low-risk group of patients (Fig. 8G).
Differentiation of immune infiltration between the two risk subgroups
In view of the important role of immune checkpoints in tumor immunotherapy, the differential expression of immune checkpoint genes was analyzed between risk subgroups. The results revealed that common immune checkpoint genes, including cytotoxic T-lymphocyte associated protein 4 (CTLA4), CD274, programmed cell death 1, and T-cell immunoreceptor with Ig and ITIM domains were upregulated in the high-risk group compared with the low-risk group (P<0.05; Fig. 9A). This suggests that the poor prognosis of high-risk patients with liver cancer may at least partially be attributed to an immunosuppressive microenvironment. Chemokines and their receptors are necessary for the targeted migration of immune cells and the initiation and execution of the immune response (45,46). Therefore, the differential expression of chemokines and their receptors was analyzed in the two risk subgroups, which revealed higher levels of expression for the majority of these chemokines and receptors in patients in the high-risk group (P<0.05; Fig. 9B and C). An association between risk score and HLA-associated gene expression was also observed. As shown in Fig. 9D, the abundance of HLA-related genes was higher in patients at high risk than those in the low-risk group (P<0.05). The results of algorithms were visualized using heat maps, including assessment of immune cell infiltration in the two risk subgroups, and the results suggest that the high-risk group has more abundant immune cell infiltration (Fig. 9E). In addition, further exploration of the association between risk score and immune pathway activity revealed that cytolytic activity, type I IFN response and type II IFN response scores were higher in the low-risk group, and conversely, the MHC class I score was higher in the high-risk group (P<0.05; Fig. 10F). These results demonstrate that patients in the high-risk group are more likely to benefit from immunotherapy.
Clinicopathological parameter correlation analysis
To investigate the prognostic value of the CRG signature in patients with different clinical features, a heat map was drawn to reveal whether there was a potential association with clinicopathological features in the high- and low-risk subgroups (Fig. 10A). The expression levels of CDCA8 and TXNRD1 were higher in the high-risk group than in the low-risk group. In addition, the results revealed that the high-risk score was closely associated with a higher T stage (P<0.001), higher grade (P<0.001), higher tumor stage (P<0.001) and poor patient survival status (P<0.001).
Analysis of the immunological value of the CRG signature
Since TMB and tumor immune dysfunction and exclusion (TIDE) are good indicators of the response to immunotherapy, sample scores were calculated for each patient with liver cancer and variability between the high- and low-risk subgroups was assessed. The results revealed that the high-risk group had a higher TMB and lower TIDE index, which further demonstrates that patients in the high-risk group should be more responsive to immunotherapy (P<0.05; Fig. 10B and C). The mRNA expression-based stemness score revealed a positive correlation between liver cancer tumor stemness and the risk score, indicating that tumors in the high-risk group are more likely to undergo malignant progression and thus lose their differentiated phenotype (P<0.05; Fig. 10D). In addition, the maftools R package was used to visualize the differences in somatic mutation distribution between the high- and low-risk groups. The results demonstrated that the high-risk group had a higher mutation frequency compared with the low-risk group (91.38 vs. 77.84%, respectively). The most mutated gene in the low-risk group was catenin b1 (23%) and the most mutated gene in the high-risk group was TP53 (43%) (Fig. 10E).
Correlation analysis of risk signature genes and immune checkpoints
Immune checkpoints have an important role in immune regulation, and immune checkpoint inhibitors are used in cancer therapy. Therefore, the associations between the signature genes and the expression of immune checkpoint genes, namely programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1) and CTLA4, were investigated. The results in Fig. 11A indicate that the expression of CDCA8 was positively correlated with that of the three immune checkpoints, PD-1 (R=0.3; P=4.9×10−9), PD-L1 (R=0.32; P=3.8×10−10) and CTLA4 (R=0.32; P=3.9×10−10). In addition, the expression of HAVCR1 was positively correlated with CTLA4 expression (R=0.32; P=1.3×10−10). TISIDB portal was used to analyze the expression of signature genes in different immune subtypes, specifically: C1, wound healing; C2, IFN-g dominant; C3, inflammatory; C4, lymphocyte depleted; C5, immunologically quiet; and C6, TGF-b dominant (47). The results indicated that the roles of these five genes differ among the different immune subtypes, with CACA8, MYCN and TXNRD1 being differentially expressed among the immune subtypes. Specifically, the TISIDB analysis revealed that CDCA8 was highly expressed in the C1 and C2 types, MYCN was highly expressed in the C1 type, and TXNRD1 was mainly expressed in the C2 and C4 types (Fig. 11B). In addition, the IMvigor dataset was used to predict the responsiveness of the five signature genes to atelelizumab treatment. Notably, consistent with the previous findings, the analysis suggested that patients with high expression of CDCA8 and TXNRD1 may obtain improved treatment outcomes (Fig. 11C). The correlations between tumor immune infiltration by CD4+ T cells, CD8+ T cells, B cells, neutrophils, macrophages and dendritic cells, and the expression of the five signature genes were also investigated (Fig. S2). In this analysis, correlation coefficients >0.3 and P<0.05 were considered as distinctive; partial.cor denotes partial correlation, indicating the correlation of gene expression with immune cell infiltration in the TIMER database. The results show that CDCA8 expression is positively correlated with the infiltration of six types of immune cells: B cells (partial.cor, 0.441; P=9.08×10−18), CD8+ T cells (partial.cor, 0.303; P=1.03×10−8), CD4+ T cells (partial.cor, 0.359; P=6.74×10−12), macrophages (partial.cor, 0.439; P=1.70×10−17), neutrophils (partial.cor, 0.368; P=1.63×10−12) and dendritic cells (partial.cor, 0.465; P=1.22×10−19). Similarly, HAVCR1 expression was found to be positively correlated with the infiltration of B cells (partial.cor, 0.302; P=1.14×10−8), macrophages (partial.cor, 0.302; P=1.34×10−8), neutrophils (partial.cor, 0.392; P=4.00×10−14) and dendritic cells (partial.cor, 0.317; P=2.18×10−9), and TXNRD1 expression was positively associated with neutrophil infiltration (partial.cor, 0.322; P=8.67×10−10).
Comparison of the CRG signature with external prognostic models
To better assess the predictive efficacy of the CRG prognostic model, the risk signature was compared with six published liver cancer prognostic models. The signature of Du et al (48) was a m6A-based gene signature; the signature of Fu and Song (49) was a pyroptosis-related gene signature; the signature of Guo et al (50) was a signature containing nine genes; Lei et al (51) devised a starvation-based nine-mRNA signature; Tian et al (52) proposed a five-gene prognostic signature for liver cancer; and the signature of Zheng et al (53) comprised five pyroptosis-related genes. When the accuracy of these models and the current model were compared, it was found that the C-index and restricted mean survival of the CRG signature were higher than those of the other six models, which indicates that the present model is optimal (Fig. 12A and B). Additionally, the AUCs of the CRG model for 1-, 3- and 5-year OS were 0.807, 0.711 and 0.667, respectively, which were higher than those of the other signatures, which validates the previous results (Fig. 12C).
Establishment and validation of a predictive nomogram
To forecast the survivability of patients with liver cancer, a nomogram including factors such as age, sex, stage and risk score was created to predict probability of OS at 1, 3, and 5 years in the TCGA cohort. In addition, calibration plots were constructed to evaluate the predictive power of the nomogram (Fig. 12D). Similarly, two nomograms were also constructed for the ICGC and GSE14520 cohorts (Fig. S3). These all indicate the good predictive power of the model.
Downregulation of TP53I3 inhibits liver cancer cell proliferation
Among the five signature genes, CDCA8, MYCN, HAVCR1 and TXNRD1 have previously been demonstrated to have a biological regulatory function in liver cancer (vide infra), but TP53I3 has been poorly studied in liver cancer. Therefore, the role of TP53I3 in liver cancer cells was evaluated using cellular experiments. TP53I3 was knocked down in HepG2 and MHCC97H cells using siRNA, and the transfection efficiency was verified by western blot analysis and RT-qPCR (Fig. 13A and B). To explore the impact of TP53I3 on the proliferation of liver cancer cells in vitro, CCK-8, EdU and colony formation analyses were performed. The results showed that the proliferation ability and colony formation of the liver cancer cells was significantly suppressed after TP53I3 depletion (P<0.05; Fig. 13C-E), which indicates that TP53I3 promotes the proliferation of liver cancer cells.
Differentially expressed CRGs
The liver cancer (liver hepatocellular carcinoma) gene expression profiles were downloaded from TCGA and ICGC portals and 1,622 and 628 DEGs, respectively, were screened out using the limma R package. The DEGs from TCGA and ICGC databases are shown as volcano plots in Fig. 1A and B, respectively. A Venn diagram was then constructed to filter out the differentially expressed CRGs (Fig. 1C). The intersection of the DEGs from TCGA and ICGC databases with the 12,518 CRGs from GSE117623 yielded a total of 258 differentially expressed CRGs (Table SIII).
PPI network construction
To investigate the interrelationship of the differentially expressed CRGs and identify hub genes, a PPI network was constructed and module analysis performed to determine co-expression networks. Firstly, the 258 differentially expressed CRGs were uploaded to the STRING database, and the minimum required interaction score was set to 0.7, which indicates a strong interaction between the CRGs. The STRING interactions were then analyzed using Cytoscape and the resulting co-expression network, which contained 155 nodes and 2,731 edges, is shown in Fig. 2A. In addition, modules with >50 genes were identified using the MCODE plug-in and 10 hub genes in that module, namely topoisomerase IIa, cyclin B2, cell division cycle associated 8 (CDCA8), BIRC5, aurora kinase B, cyclin B1, BUB1 mitotic checkpoint serine/threonine kinase (BUB1), BUB1B, kinesin family member 20A and TTK protein kinase, were characterized using the cytoHubba plug-in (Fig. 2B). This included 57 nodes and 1,497 edges. These potential hub genes may be instrumental in the biological progression of liver cancer.
GO and KEGG enrichment analyses
To explore the biological categories and biological processes associated with the differentially expressed CRGs, GO and KEGG enrichment analyses were conducted using R software, and the enrichment results are shown in bubble charts (Fig. 2C and D). The GO enrichment analysis revealed that the differentially expressed CRGs were principally concentrated in the biological process terms ‘nuclear division’, ‘organelle fission’, ‘chromosome segregation’ and ‘mitotic nuclear division’. In addition, the main cellular component terms included ‘chromosomal region’, ‘chromosome, centromeric region’, ‘spindle’ and ‘kinetochore’. Moreover, the molecular function terms associated with the CRGs were ‘tubulin binding’, ‘microtubule-binding’, ‘ATPase activity’ and ‘motor activity’ (Fig. 2C). Regarding the KEGG analysis, the primary terms are shown in Fig. 2D, which reveals that the differentially expressed CRGs were particularly enriched in ‘cell cycle’, ‘microRNAs in cancer’, ‘p53 signaling pathway’ and ‘cellular senescence’.
Construction of a prognostic model and validation of the model in the ICGC cohort
Univariate Cox regression analysis demonstrated that 88 CRGs were strongly associated with survival in patients with liver cancer (P<0.01), all of which were prognostic risk factors (Fig. 3A). Then, the 88 CRGs were regression penalized using LASSO Cox regression to exclude relatively insignificant parameters (Fig. 3B and C). Stepwise multivariate Cox regression was subsequently employed to construct a predictive signature for patients with liver cancer in TCGA cohort (Fig. 3D). The five genes in the signature were CDCA8, TP53I3, hepatitis A virus cellular receptor 1 (HAVCR1), MYCN proto-oncogene (MYCN) and thioredoxin reductase 1 (TXNRD1). The formula for risk score calculation was as follows: Risk score=(0.0826 × expression level of CDCA8) + (0.0112 × expression level of TP53I3) + (0.0824 × expression level of MYCN) + (0.0376 × expression level of HAVCR1) + (0.0120 × expression level of TXNRD1) (Table SIV). Patients in TCGA cohort were classified into high- and low-risk groups using the median predictive index as the cut-off point. As Fig. 4A shows, the low-risk group was significantly associated with improved survival (P<0.05). To evaluate the predictive ability of this prognostic signature, an ROC analysis of the risk score was conducted. The area under the curve (AUC) values predicted from the ROC curves for 1-, 3- and 5-year OS were 0.804, 0.736 and 0.707, respectively (Fig. 4B). In Fig. 4C, the upper panel shows the expression heat map of the five prognostic model genes in the high- and low subgroups, the middle panel reveals that the risk of patients with liver cancer increases as risk score increases, and the lower panel demonstrates the poor OS of the patients in the high-risk group compared with those in the low-risk group. To validate the predictive power of the signature, the same formula was used to analyze the risk score of each patient in the ICGC dataset, for independent external validation. The Kaplan-Meier curves also displayed a poor prognosis of patients in the high-risk group in this dataset (P<0.05; Fig. 4D). In addition, the ROC curve showed the strong predictive ability of the risk-score signature for prognosis, with AUCs for the prediction of 1-, 3- and 5-year OS of 0.714, 0.730 and 0.726, respectively (Fig. 4E). Also, the expression of the five CRGs and the mortality of the patients increased as the risk scores increased (Fig. 4F).
Independent prognostic role of the gene signature
To investigate whether the CTC/CTM-associated 5-gene signature could be an independent prognostic factor for patients with liver cancer, the prognostic value of this signature was compared with that of several clinicopathological factors, including age, sex, grade and American Joint Committee on Cancer (AJCC) stage in both cohorts using univariate and multivariate Cox regression analyses. For TCGA cohort, 365 valid patients were included, 182 in the high-risk group and 183 in the low-risk group. Univariate Cox analysis indicated that risk score [P<0.001; hazard ratio (HR), 1.258; 95% confidence interval (95% CI), 1.183–1.338)], AJCC stage (P<0.001; HR, 1.658; 95% CI, 1.340–2.053) and T status (P<0.001; HR, 1.633; 95% CI, 1.332–2.003) were candidate factors. Further multivariate Cox regression analysis emphasized that risk score was an independent risk factor for patients with liver cancer (P<0.001; HR, 1.227; 95%CI, 1.142–1.318) (Fig. 4G). For the ICGC cohort, 229 patients were included, 59 in the high-risk group and 170 in the low-risk group. Univariate Cox regression analysis demonstrated that sex (P=0.033; HR, 0.505; 95% CI, 0.270–0.946), risk score (P=0.010; HR, 1.076; 95% CI, 1.017–1.137) and tumor stage (P<0.001; HR, 2.252; 95% CI, 1.543–3.287) were potential risk factors. Multivariate Cox regression analysis also indicated that sex (P=0.004; HR, 0.389; 95% CI, 0.204–0.740), risk score (P=0.030; HR, 1.070; 95% CI, 1.006–1.138 and tumor stage (P<0.001; HR, 2.330; 95% CI, 1.601–3.391) were independent predictors for patients in the ICGC cohort (Fig. 4H). In conclusion, these findings indicated that the 5-CRG risk signature was closely associated with the clinical characteristics of patients with liver cancer, had a fine predictive capacity and has potential as a prognostic indicator for these patients.
Validation of the signature in the GEO cohort
To further verify the predictive power of the prognostic signature, the GSE14520 dataset was analyzed. In this dataset, 221 valid patients were included, with 100 in the high-risk group and 121 in the low-risk group. As the results in Fig. 5A illustrate, patients in the low-risk group had improved survival outcomes compared with those in the high-risk group (P<0.05). In the GEO cohort, the AUC for 5-year OS was 0.684 (Fig. 5B). The expression of model genes, risk score distribution and survival status for each patient in this validation cohort are shown in Fig. 5C. Following univariate Cox regression analysis (Fig. 5D), the results of independent prognostic analysis revealed that risk score (P<0.001; HR=1.280; 95% CI, 1.145–1.432), as well as AJCC stage (P<0.001; HR, 2.017; 95% CI, 1.518–2.681) (Fig. 5E) were independent risk factors in this cohort. These findings indicate that the prognostic model is promising as a predictive signature.
Identification of CTC/CTM-related molecular subtypes
Patients were clustered into different subtypes based on the expression levels of the five prognostic signature genes using the NMF algorithm. To ensure the robustness of the clustering results, the coefficient of correlation was used to determine the optimal number of clusters, and when the number of clusters was 2, clear boundaries were observed for both subtypes. This indicated the stable and reliable clustering of the liver cancer samples (Fig. 6A). The OS of patients in cluster 1 (C1) was significantly improved compared with that of C2 (P=0.002; Fig. 6B). Most immune checkpoints were upregulated in the C2 group compared with the C1 group (Fig. 6C). In addition, it was also found that the level of immune infiltration in the tumor microenvironment was also distinct in the two groups, with immune score, stromal score and ESTIMATE score of the C1 group being significantly lower than those of the C2 group (P<0.05; Fig. 6D). This suggests that C1 molecular subtype tends to present ‘cold tumors’, whereas the C2 molecular subtype tends to present ‘hot tumors’. It was also noted that the level of neutrophil infiltration was higher in the C1 group than in the C2 group, whereas the levels of monocytic lineage and fibroblast infiltration were lower in the C1 group than in the C2 group (P<0.05; Fig. 6E).
Identification of potentially therapeutic small molecule drugs
The sensitivity of the high- and low-risk groups to various chemotherapeutic agents was compared to evaluate drugs for potential use in liver cancer. The findings indicate that the low-risk group was associated with a higher IC50 for chemotherapeutic compounds including ABT.888 (veliparib), AS601245 (an ATP-competitive JNK inhibitor), AG.014699 (rucaparib), A.443654 (a pan-Akt inhibitor), ATRA (tretinoin) and AUY922 (luminespib). By contrast, axitinib, A.770041 (an LCK inhibitor), AZD.0530 (saracatinib), AMG.706 (motesanib), AKT.inhibitor.VIII and AICAR (acadesine) had a higher IC50 in the high-risk group, indicating that patients in the low-risk group may benefit more from treatment with these compounds (P<0.05; Fig. 7). The sensitivity of the two liver cancer subtypes to various chemotherapeutic drugs was also evaluated. The results suggested that patients with the C1 subtype might be more sensitive to metformin, lapatinib, elesclomol, docetaxel, camptothecin, bosutinib, axitinib and vinblastine, while patients in group C2 would likely benefit by treatment with cisplatin, bortezomib, bleomycin, bicalutamide, mitomycin C, imatinib, etoposide and gemcitabine (Fig. S1).
Pathway analysis by GSEA and GSVA
To further explore the molecular mechanism associated with the signature genes and the prognostic module, GSEA was performed in TCGA liver cancer cohort. Fig. 8A-E reveals the KEGG pathways of the five signature genes, namely CDCA8, HAVCR1, MYCN, TP53I3 and TXNRD1, showing the five most upregulated and downregulated pathways for each gene. The signature genes are mainly concentrated in KEGG pathways including ‘cell cycle’, ‘p53 signaling pathway’, ‘complement and coagulation cascades’ and ‘drug metabolism cytochrome p450’. In addition, GSEA was used to compare the high- and low-risk groups based on the risk scores. The KEGG pathways enriched in the high and low risk groups are shown in Fig. 8F.
GSVA was also utilized to analyze the differences in biological behavior between the high- and low-risk groups. The results demonstrated that pathways associated with tumor progression, such as ‘cell cycle’, ‘DNA replication’, ‘RNA degradation’, ‘mTOR signaling pathway’ and ‘P53 signaling pathway’, were mainly concentrated in the high-risk group. By contrast, metabolism-related pathways, including ‘fatty acid metabolism’, ‘propanoate metabolism’, ‘butanoate metabolism’ and ‘tyrosine metabolism’, were mainly present in the low-risk group of patients (Fig. 8G).
Differentiation of immune infiltration between the two risk subgroups
In view of the important role of immune checkpoints in tumor immunotherapy, the differential expression of immune checkpoint genes was analyzed between risk subgroups. The results revealed that common immune checkpoint genes, including cytotoxic T-lymphocyte associated protein 4 (CTLA4), CD274, programmed cell death 1, and T-cell immunoreceptor with Ig and ITIM domains were upregulated in the high-risk group compared with the low-risk group (P<0.05; Fig. 9A). This suggests that the poor prognosis of high-risk patients with liver cancer may at least partially be attributed to an immunosuppressive microenvironment. Chemokines and their receptors are necessary for the targeted migration of immune cells and the initiation and execution of the immune response (45,46). Therefore, the differential expression of chemokines and their receptors was analyzed in the two risk subgroups, which revealed higher levels of expression for the majority of these chemokines and receptors in patients in the high-risk group (P<0.05; Fig. 9B and C). An association between risk score and HLA-associated gene expression was also observed. As shown in Fig. 9D, the abundance of HLA-related genes was higher in patients at high risk than those in the low-risk group (P<0.05). The results of algorithms were visualized using heat maps, including assessment of immune cell infiltration in the two risk subgroups, and the results suggest that the high-risk group has more abundant immune cell infiltration (Fig. 9E). In addition, further exploration of the association between risk score and immune pathway activity revealed that cytolytic activity, type I IFN response and type II IFN response scores were higher in the low-risk group, and conversely, the MHC class I score was higher in the high-risk group (P<0.05; Fig. 10F). These results demonstrate that patients in the high-risk group are more likely to benefit from immunotherapy.
Clinicopathological parameter correlation analysis
To investigate the prognostic value of the CRG signature in patients with different clinical features, a heat map was drawn to reveal whether there was a potential association with clinicopathological features in the high- and low-risk subgroups (Fig. 10A). The expression levels of CDCA8 and TXNRD1 were higher in the high-risk group than in the low-risk group. In addition, the results revealed that the high-risk score was closely associated with a higher T stage (P<0.001), higher grade (P<0.001), higher tumor stage (P<0.001) and poor patient survival status (P<0.001).
Analysis of the immunological value of the CRG signature
Since TMB and tumor immune dysfunction and exclusion (TIDE) are good indicators of the response to immunotherapy, sample scores were calculated for each patient with liver cancer and variability between the high- and low-risk subgroups was assessed. The results revealed that the high-risk group had a higher TMB and lower TIDE index, which further demonstrates that patients in the high-risk group should be more responsive to immunotherapy (P<0.05; Fig. 10B and C). The mRNA expression-based stemness score revealed a positive correlation between liver cancer tumor stemness and the risk score, indicating that tumors in the high-risk group are more likely to undergo malignant progression and thus lose their differentiated phenotype (P<0.05; Fig. 10D). In addition, the maftools R package was used to visualize the differences in somatic mutation distribution between the high- and low-risk groups. The results demonstrated that the high-risk group had a higher mutation frequency compared with the low-risk group (91.38 vs. 77.84%, respectively). The most mutated gene in the low-risk group was catenin b1 (23%) and the most mutated gene in the high-risk group was TP53 (43%) (Fig. 10E).
Correlation analysis of risk signature genes and immune checkpoints
Immune checkpoints have an important role in immune regulation, and immune checkpoint inhibitors are used in cancer therapy. Therefore, the associations between the signature genes and the expression of immune checkpoint genes, namely programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1) and CTLA4, were investigated. The results in Fig. 11A indicate that the expression of CDCA8 was positively correlated with that of the three immune checkpoints, PD-1 (R=0.3; P=4.9×10−9), PD-L1 (R=0.32; P=3.8×10−10) and CTLA4 (R=0.32; P=3.9×10−10). In addition, the expression of HAVCR1 was positively correlated with CTLA4 expression (R=0.32; P=1.3×10−10). TISIDB portal was used to analyze the expression of signature genes in different immune subtypes, specifically: C1, wound healing; C2, IFN-g dominant; C3, inflammatory; C4, lymphocyte depleted; C5, immunologically quiet; and C6, TGF-b dominant (47). The results indicated that the roles of these five genes differ among the different immune subtypes, with CACA8, MYCN and TXNRD1 being differentially expressed among the immune subtypes. Specifically, the TISIDB analysis revealed that CDCA8 was highly expressed in the C1 and C2 types, MYCN was highly expressed in the C1 type, and TXNRD1 was mainly expressed in the C2 and C4 types (Fig. 11B). In addition, the IMvigor dataset was used to predict the responsiveness of the five signature genes to atelelizumab treatment. Notably, consistent with the previous findings, the analysis suggested that patients with high expression of CDCA8 and TXNRD1 may obtain improved treatment outcomes (Fig. 11C). The correlations between tumor immune infiltration by CD4+ T cells, CD8+ T cells, B cells, neutrophils, macrophages and dendritic cells, and the expression of the five signature genes were also investigated (Fig. S2). In this analysis, correlation coefficients >0.3 and P<0.05 were considered as distinctive; partial.cor denotes partial correlation, indicating the correlation of gene expression with immune cell infiltration in the TIMER database. The results show that CDCA8 expression is positively correlated with the infiltration of six types of immune cells: B cells (partial.cor, 0.441; P=9.08×10−18), CD8+ T cells (partial.cor, 0.303; P=1.03×10−8), CD4+ T cells (partial.cor, 0.359; P=6.74×10−12), macrophages (partial.cor, 0.439; P=1.70×10−17), neutrophils (partial.cor, 0.368; P=1.63×10−12) and dendritic cells (partial.cor, 0.465; P=1.22×10−19). Similarly, HAVCR1 expression was found to be positively correlated with the infiltration of B cells (partial.cor, 0.302; P=1.14×10−8), macrophages (partial.cor, 0.302; P=1.34×10−8), neutrophils (partial.cor, 0.392; P=4.00×10−14) and dendritic cells (partial.cor, 0.317; P=2.18×10−9), and TXNRD1 expression was positively associated with neutrophil infiltration (partial.cor, 0.322; P=8.67×10−10).
Comparison of the CRG signature with external prognostic models
To better assess the predictive efficacy of the CRG prognostic model, the risk signature was compared with six published liver cancer prognostic models. The signature of Du et al (48) was a m6A-based gene signature; the signature of Fu and Song (49) was a pyroptosis-related gene signature; the signature of Guo et al (50) was a signature containing nine genes; Lei et al (51) devised a starvation-based nine-mRNA signature; Tian et al (52) proposed a five-gene prognostic signature for liver cancer; and the signature of Zheng et al (53) comprised five pyroptosis-related genes. When the accuracy of these models and the current model were compared, it was found that the C-index and restricted mean survival of the CRG signature were higher than those of the other six models, which indicates that the present model is optimal (Fig. 12A and B). Additionally, the AUCs of the CRG model for 1-, 3- and 5-year OS were 0.807, 0.711 and 0.667, respectively, which were higher than those of the other signatures, which validates the previous results (Fig. 12C).
Establishment and validation of a predictive nomogram
To forecast the survivability of patients with liver cancer, a nomogram including factors such as age, sex, stage and risk score was created to predict probability of OS at 1, 3, and 5 years in the TCGA cohort. In addition, calibration plots were constructed to evaluate the predictive power of the nomogram (Fig. 12D). Similarly, two nomograms were also constructed for the ICGC and GSE14520 cohorts (Fig. S3). These all indicate the good predictive power of the model.
Downregulation of TP53I3 inhibits liver cancer cell proliferation
Among the five signature genes, CDCA8, MYCN, HAVCR1 and TXNRD1 have previously been demonstrated to have a biological regulatory function in liver cancer (vide infra), but TP53I3 has been poorly studied in liver cancer. Therefore, the role of TP53I3 in liver cancer cells was evaluated using cellular experiments. TP53I3 was knocked down in HepG2 and MHCC97H cells using siRNA, and the transfection efficiency was verified by western blot analysis and RT-qPCR (Fig. 13A and B). To explore the impact of TP53I3 on the proliferation of liver cancer cells in vitro, CCK-8, EdU and colony formation analyses were performed. The results showed that the proliferation ability and colony formation of the liver cancer cells was significantly suppressed after TP53I3 depletion (P<0.05; Fig. 13C-E), which indicates that TP53I3 promotes the proliferation of liver cancer cells.
Discussion
Liver cancer remains a significant challenge to human health, with high rates of incidence and recurrence, even after surgical resection. Numerous studies have demonstrated that CTCs are tightly associated with the metastasis, epithelial-mesenchymal transition and recurrence of malignant tumors, including liver cancer (54–58). Therefore, it is critical to screen molecules associated with CTCs to identify biomarkers for the prediction of liver cancer. In the present study, a reliable prognostic signature based on CRGs was constructed and its clinical application in patients with liver cancer was explored. The results showed that the CRG prognostic model accurately predicted the prognosis and immunotherapy sensitivity of patients with liver cancer.
In the present study, 258 CRGs were identified by systematically analyzing the DEGs in TCGA and ICGC databases and the CTC expression profiles of liver cancer. These genes were then screened to construct a five-CRG signature in the TCGA cohort. Kaplan-Meier survival and ROC analyses were performed to confirm the prognostic value of the signature, and the results were validated in ICGC and GEO cohorts. Univariate and multifactorial Cox analyses further confirmed that the risk signature was able to serve as an independent prognostic factor. In addition, nomograms for all three cohorts showed the good predictive power of the model. The genes in the prognostic signature were CDCA8, HAVCR1, TP53I3, MYCN and TXNRD1, all of which have the potential to be used as liver cancer prognostic risk genes. Previous studies have demonstrated the ability of CDCA8 to promote cancer cell proliferation and migration in several tumors, including esophageal squamous cell carcinoma (59), thyroid cancer (60), malignant glioma and cutaneous melanoma (61). In addition, Jeon et al (62) demonstrated that silencing CDCA8 effectively suppressed liver cancer growth and stemness, implying that CDCA8 may be a CTC-related gene. HAVCR1 is highly expressed in a variety of tumors, including colorectal cancer, non-small-cell lung cancer, clear cell renal cell carcinoma and liver cancer, and is an independent prognostic factor (63–66). Moreover, Ye et al (66) found that T-cell immunoglobulin mucin-1+ (HAVCR1+) regulatory B cell infiltration was significantly higher in liver tumor tissues compared with paraneoplastic tissues in patients with liver cancer and promoted the immune escape of liver cancer cells, implying that it could be used as an immune therapeutic target. TP53I3, also known as p53-inducible gene 3, is involved in the apoptosis process and DNA damage response. Previous studies have revealed that TP53I3 promotes the invasion and metastasis of lung cancer cells and that silencing TP53I3 increases the chemosensitivity of non-small cell lung cancer cells to docetaxel (67,68). Notably, the present study also demonstrated that the knockdown of TP53I3 inhibited the proliferation ability of liver cancer cells in cellular experiments. These findings may indicate a novel strategy for the treatment of liver cancer. Qin et al (69,70) highlighted that MYCN, a member of the MYC proto-oncogene family, may be a stem cell-like marker for liver cancer and is potentially a therapeutic target of acyclic retinoid for liver cancer. TXNRD1 is an antioxidant enzyme that has been reported to be overexpressed in liver cancer. Lee et al (71) observed that the inhibition of TXNRD1 suppressed liver cancer cell proliferation, promoted apoptosis and induced oxidative stress, suggesting that it could be used as a therapeutic target for liver cancer. In conclusion, these previous studies suggest that the five signature genes have an important role in the development of liver cancer and may have potential as therapeutic targets.
As indicated by KEGG analysis, CRGs may promote the development, metastasis and recurrence of liver cancer via the cell cycle and p53 signaling pathway. GSEA analysis of the five signature genes and the high-risk group in the prognostic model identified various oncogenesis-associated features, including the terms ‘cell cycle’, ‘p53 signaling pathway’, ‘WNT signaling pathway’ and ‘DNA replication’. In addition, GSVA results showed that tumor progression-related pathways, such as ‘cell cycle’, ‘DNA replication’, ‘mTOR signaling pathway’ and ‘P53 signaling pathway’, were mainly concentrated in the high-risk group, which was generally consistent with the GSEA results. On the basis of this, a number of potential therapeutic agents were also evaluated, with veliparib (72), ATRA (73,74) and AUY922 (75) exhibiting high drug sensitivity in the high-risk group, suggesting that these agents are likely to be therapeutic candidates.
Immunotherapy is playing an increasingly important role in liver cancer. Therefore, the relevance of the present model to immune infiltration and immunotherapy was also analyzed in the present study. Immune cell infiltration analysis demonstrated that CDCA8 and HAVCR1 correlated with the infiltration abundance of several immune cells, including B cells, CD8+ T cells, macrophages, neutrophils and dendritic cells. In addition, immune checkpoint expression, TMB scores and immune cell infiltration levels were strongly associated with patients in the high-risk subgroup. The analysis of somatic mutation rates also indicated that patients in the high-risk group had an elevated frequency of mutations and greater occurrence of TP53 mutations. It has been proposed that TIDE scores may be used by oncologists to assist in the selection of suitable patients for immune checkpoint inhibition therapy (76). Consistent with this, the present study found that patients in the high-risk group had lower TIDE scores, while those in the low-risk group had higher TIDE scores, indicating that the high-risk patients may benefit more from immunotherapy. All these findings confirm that the present model has good risk stratification capabilities and is suitable for selecting the patients who may benefit from immunotherapy.
Notably, this five-risk gene signature was also used to identify liver cancer subgroups C1 and C2, of which C2 as a high-risk subgroup showed a worse prognosis. Compared with group C1, group C2 had a higher immune checkpoint expression and higher stromal, immune and ESTIMATE scores for each sample, which also suggested that patients in group C2 were more suitable for immunotherapy. More importantly, several chemotherapeutic agents to which C2 patients should be sensitive were also identified. These were cisplatin (77), bortezomib (78), bleomycin, bicalutamide, mitomycin C, imatinib, etoposide and gemcitabine (79), which could improve the prognosis of patients in the C2 group. In conclusion, the findings of this analysis are helpful, but future studies are necessary to verify this.
However, the study has some limitations. For example, the regulatory role of these five CRGs in liver cancer were not further investigated experimentally. Other external validation of the model is lacking and must to be conducted in clinical samples in the future. In addition, chemotherapy were not analyzed. Therefore, additional studies and more evidence are required to refine the present model in the future.
Supplementary Material
Supporting Data
Supporting Data |
Title: Trifunctional Graphene Quantum Dot@LDH Integrated Nanoprobes for Visualization Therapy of Gastric Cancer | Body:
1
Introduction
Cancer is a complex disease that threatens human health.[
1
,
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,
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] To combat this, effective cancer treatments, such as chemotherapy and radiation therapy, were developed after extensive research. However, both chemotherapy and radiation therapy have significant adverse effects on the human body. In recent years, nanotechnology has provided a new perspective for cancer treatment.[
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,
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] With the development of nanomolecular imaging, the difficulty of diagnosis was also gradually reduced. However, these studies are often conducted separately for diagnosis and treatment.[
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,
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,
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,
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] The feedback of treatment effect is not timely, leading to the delay of the disease. Thus, an integrated approach of visualization and precise treatment has become a significant trend in tumor treatment.[
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,
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] Multifunctional nanomaterials have become an ideal solution, owing to their multidimensional characteristics.[
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,
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] Different diagnostic media such as magnetic imaging, nuclear imaging, optical imaging, and therapeutic agents can be loaded in one package, and targeted delivery into specific sites can be achieved.[
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,
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,
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,
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,
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] Another advantage is that the distribution and efficacy of nanomaterials can be observed in real time.[
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] Although there are many excellent studies, achieving good biocompatibility and tumor‐targeted aggregation is still one of the problems.
Layered double hydroxide (LDHs), a biocompatible and biodegradable 2D nanomaterial, has been proven to be an excellent carrier for drug delivery,[
20
,
21
] ATP delivery,[
22
] and DNA delivery.[
23
,
24
] LDH exhibits highly sensitive acidity‐induced dissolution properties, which are conducive to pH‐responsive drug release. Due to the shape effect of the plate‐like structure, LDH can be more efficiently taken via endocytosis and can linger in lysosomes for a longer amount of time.[
25
] Unfortunately, LDH itself does not have imaging capability. Interestingly, LDH can stably coexist with some functional nanoparticles, such as CuS dots for photodynamic therapy,[
14
] Mn and Fe3O4 for MR imaging.[
13
,
26
] Owing to these advantages, LDH is ideal for constructing multifunctional nanoplatforms.
Compared with MR or CT imaging, optical imaging is usually much quicker and less expensive, and its spatial resolution is the highest.[
27
] Quantum dots (QDs), such as CdTe,[
28
] Ag2S,[
29
] and graphene QDs, are the most important components in nano fluorescence imaging. GQDs are a type of nanomaterial with excellent biocompatibility and photostability and are widely used as fluorescent probes in biological imaging[
30
,
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,
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,
33
,
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,
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,
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] and tumor therapy.[
37
,
38
] GQDs alone are still difficult to target for delivery to tumor sites. Moreover, the drug loading method of electrostatic binding[
39
] or chemical bond connection[
40
] is generally adopted on the surface of GQDs. The disadvantage is that the drugs are exposed to the biological environment without protection, making it easy to escape to non‐target tissues during transportation, and cause toxic side effects. To overcome these difficulties, the construction of multifunctional nanomaterials is the best solution.
Recently, LDH and GQDs have been used in energy batteries,[
41
] drug delivery,[
42
] and sensors[
43
,
44
] in several studies, but only a few of these were able to simultaneously achieve biological imaging and treatment. In our previous work, sulfur‐doped GQDs (SGQDs) with good biocompatibility were successfully prepared and exhibited intense blue fluorescence,[
30
] which is suitable for bioimaging. To integrate visualization and tumor targeting, composite LDH@SGQD was synthesized by coprecipitation. The design of an integrated probe for gastric cancer treatment was completed using the traditional chemotherapy drug etoposide (VP16). Owing to the weak acidity of the tumor microenvironment,[
45
] LDH@SGQD‐VP16 can be aggregated more easily. The long‐term stable fluorescence of the tumor site gives it the ability of biological imaging and effect tracking. Enhanced therapeutic effects were observed in vitro and in vivo. The results of the relative characterization and biological experiments proved that LDH@SGQD‐VP16 is a good multifunctional nanocomposite for precise visualization therapy of gastric cancer and provides an experimental basis for clinical applications.
2
Results and Discussion
2.1
Synthesis and Characterization
Figure
1
shows the design of the trifunctional nanoprobes LDH@SGQD‐VP16 for visual detection of cancer, tumor targeting and precise treatment. Briefly, LDH@SGQDs were synthesized using the coprecipitation method and could launch green fluorescence with a blue laser for visual imaging, and the proportion of SGQDs was ≈4.3%. In the synthesis process, drug‐loading nanocomposite system can be obtained by adding VP16 to the reaction system. The drug‐loading rate was ≈28.1%. LDH@SGQD‐VP16 is enriched by blood circulation onto the surface of tumor cells due to the acidic microenvironment, and then the nanocomplex is engulfed by the cells. VP16 is released into the cytoplasm by degradation of lysosome, causing apoptosis, for achieving cancer therapy.
Figure 1
Design of trifunctional nanoprobes LDH@SGQD‐VP16 for cancer treatment. LDH@SGQD‐VP16 is composed of LDH for tumor cells targeting by pH‐sensitive properties, SGQD for fluorescence imaging, and VP16 for cancer therapy.
Figure
2a–c shows the TEM characterization of LDH and LDH@SGQD. The structures of LDH and LDH@SGQD were both hexagonal, the SEM characterization shows the same conclusion (Figure S1, Supporting Information). However, in contrast to LDH, there are many small black points on the surface of LDH@SGQD, which are the SGQDs present on the surface of LDH through electrostatic binding, as shown in the inset Figure 2b. The particle sizes of SGQD are ≈3.0–5.0 nm, and the distribution of SGQDs on the surface of the LDH is even without agglomeration. The lattice structure of SGQDs can be clearly observed by HRTEM in Figure 2c. The results of the dynamic light scattering showed that the size of most particles of LDH@SGQD was ≈100 nm and ≈30 nm smaller than that of the LDH@SGQD‐VP16 particles (Figure 2d). To investigate the thickness changes of LDH@SGQD materials before and after VP16 loading, an atomic force microscope was used (Figure S2, Supporting Information). The thickness of LDH@SGQD (Figure S2a, Supporting Information) was found to be 33.73 nm through the layer height analysis, whereas that of LDH@SGQD‐VP16 increased to 50.74 nm (Figure S2b, Supporting Information). The loading of VP16 increased the spacing between layers, leading to an increase in the thickness of the composite material, which proves that VP16 loading was successfully carried out. The presence of the sulfur elemental analysis by TEM‐EDX (Figure S3, Supporting Information) and SEM‐EDAX (Figure S4, Supporting Information) also confirmed the successful synthesis of the composite.
Figure 2
TEM images of a) LDH, b) LDH@SGQD. Inset in (b): SGQD on LDH@SGQD. HRTEM of the SGQD on c) LDH@SGQD. d) Diameter distribution of LDH@SGQD and LDH@SGQD‐VP16. e) XRD analysis of LDH, LDH@SGQD, and LDH@SGQD‐VP16. f) XPS spectrum, g) high‐resolution C 1s, and h) S 2p of LDH@SGQD and LDH@SGQD‐VP16. i) Photograph under UV light at 365 nm and PL spectra of SGQD, LDH@SGQD, and LDH@SGQD‐VP16.
The zeta potential detection in Figure S5 in the Supporting Information shows that the potential of LDH is positive at ≈+40 mV, while that of SGQD is negative at ≈‐20 mV. In theory, SGQDs can be combined on the LDH surface via electrostatic binding. The potential of LDH@SGQD confirms the above conjecture, which is ≈+20 mV, proving that LDH and SGQD are indeed bound together by electrostatic action. In addition, the potential of VP16 was almost 0 mV, so the potential of LDH@SGQD‐VP16 is the same as that of LDH@SGQD, around +15 mV.
In terms of FTIR, as shown in Figure S6a in the Supporting Information, the characteristic peaks of LDH at 3450 cm–1 and 1647 cm–1 correspond to the telescopic and flexural vibration peaks of O─H, respectively, and the 1387 cm–1 peak corresponds to the telescopic vibration peak of N─O. In addition to the O─H peaks of SGQD, the characteristic absorption peak of SGQD is the stretching vibration peak of C─O at 1121 cm–1, which was not observed for LDH. The characteristic peaks of LDH@SGQD contain both N─O and C─O stretching vibration peaks, indicating that SGQD and LDH were combined to form new composite materials. Figure S6b in the Supporting Information compares the variation of characteristic peaks before and after the VP16 drug loading and shows that the characteristic absorption peaks of LDH@SGQD and VP16 were also included in the characteristic absorption peaks of LDH@SGQD‐VP16, indicating that the drug VP16 was successfully carried on the nanomaterials.
The XRD diagram in Figure 2e shows that the position and intensity of the diffraction peaks of LDH@SGQD are the same as those of LDH, indicating that the addition of SGQD does not affect the crystal structure of the composites. After carrying VP16, the position of the diffraction peak shifted slightly to the left, and the strength of the LDH@SGQD‐VP16 peak decreased significantly, indicating that the crystallinity was decreased and that VP16 had been successfully loaded. According to the Bragg equation, with a decrease in the two‐theta of the (003) diffraction peak, the layer spacing increases (Figure S7, Supporting Information). This indicated VP16 was loaded into the LDH layer.
The combustion characteristics of LDH@SGQD and LDH@SGQD‐VP16 were studied by means of thermogravimetry (Figure S8, Supporting Information). The weight loss before 200 °C is owing to the water between the layers. From 200 to 700 °C, the main reason is the crystallization water of Mg‐OH and Al‐OH. The difference between LDH@SGQD and LDH@SGQD‐VP16 is that the weight loss rate after drug‐loading is higher, being 46.5% and 64.291%. This indicated VP16 was loaded successfully.
The XPS characterizations of LDH@SGQD and LDH@SGQD‐VP16 are shown in Figure 2f–h. In the full spectrum analysis, five elements of the composite can be found: C1s (285 eV), O1s (531 eV), Mg1s (1303 eV), Al2p (74), and S2p (168). After carrying VP16, the proportions of C and O were higher than those before. Since the basic structure of VP16 is composed of two elements, C and O, the extra C and O may have been from the drug. This proves that the drug‐loading system has been successfully constructed. As shown in Figure 2g, C1s peak can be divided into three peaks corresponding to C─C (284.8 eV), C─O (286.8 eV), and C═O (288.8 eV). The C─O intensity of LDH@SGQD‐VP16 was much higher, which also provides evidence for formation of the drug‐loading system. The fine spectrum of S2p in Figure 2h, can also be divided into 168.1 and 169.4 eV peaks, which correspond to SO3/2p3/2 and SO3/2p1/2, respectively. As SGQD is the only source of S, it is also proved that SGQDs and LDHs constitute a composite structure. The high‐resolution O 1s, Mg 1s, and Al 2p of LDH@SGQD and LDH@SGQD‐VP16 are similar (Figure S9, Supporting Information).
Figure 2i shows the photoluminescence capability of SGQD, LDH@SGQD, and LDH@SGQD‐VP16. They were found to have good water solubility without agglomeration. Under the UV lamp, SGQD presented a bluish fluorescence, which was consistent with the fluorescence excitation/emission spectrum. The maximum emission peak was ≈480 nm, and the maximum excitation peak was ≈380 nm (Figure S10, Supporting Information). The fluorescence of LDH@SGQD under the UV lamp was cyan, indicating that its emission spectrum had a redshift. The maximum emission peak of LDH@SGQD was ≈560 nm, and the maximum excitation peak was ≈470 nm. After the drug VP16 was applied, the fluorescence spectrum was consistent with that of LDH@SGQD. Compared with SGQD, its fluorescence spectrum also shows a redshift, but the range is lower than that of LDH@SGQD, with the maximum emission peak at ≈540 nm and the maximum excitation peak at ≈440 nm. Theoretically, a redshift of fluorescence is more favorable for biological imaging.[
46
]
Figure S11 in the Supporting Information shows the sustained release of LDH@SGQD‐VP16 in PBS with different pH values (4.8, 5.8, and 7.0). The release of VP16 within 24 h showed an increasing trend, indicating that the drug could be released from the compound structure. Approximately 50% of VP16 was released in 3 h, indicating a sudden release, after which the release rate decreased significantly, until the amount released reached 90% within 48 h at pH 4.8. With the increase in pH, the total amount of sustained‐release decreased gradually, indicating that LDH@SGQD‐VP16 has the ability of pH‐sensitive release. The results provide a theoretical basis for acid microenvironment‐sensitive targeting and toxicity reduction.
2.2
Fluorescence Imaging Capability In Vitro and In Vivo
Figure
3a shows the fluorescence imaging of HGC‐27 cells incubated with LDH@SGQD‐VP16 for 24 h at 488 nm excitation light, and cell membrane was stained with Dil at 543 nm. Different from the reported SGQDs,[
30
] LDH@SGQD‐VP16 mainly gathered on the surface of the cell membrane with a small proportion of it entering the cytoplasm. This indicates that the method by which the composite enters cells is different from that of SGQD. Usually, the material released after cell phagocytosis enters lysosomes, in which an acidic environment is conducive to the disintegration of LDH@SGQD‐VP16 to release VP16. The lysosome co‐localization experiment confirmed this hypothesis in HGC‐27 cells (Figure 3b). The lysosomes were marked with a red fluorescent probe, Lyso‐Tracker Red, and the fluorescence of merged images turned orange with the combination of green fluorescence from LDH@SGQD‐VP16, which was transferred to the lysosome after being swallowed. Figure S12 in the Supporting Information shows the fluorescence imaging of LDH@SGQD‐VP16 (80 mg L–1) incubated with SGC7901 cells for 1, 6, 12, and 24 h. It was found that the fluorescent signal could be detected on the cells when incubated for 1 h, but the signal was weak and punctuated. With time, the fluorescence intensity of cells becomes stronger. When incubated for 12 h, the outline structure of the cells could be observed, but the boundary was not clear enough. After 24 h, the cell boundaries could be distinguished. This shows that the enrichment of the material in the cell is positively correlated to time. In contrast to HGC‐27 cells, the enrichment of LDH@SGQD‐VP16 in SGC7901 cells was more localized to the periphery of cells, which may affect the therapeutic effect. In summary, the accumulation of LDH@SGQD‐VP16 on the surface of human gastric cancer cells was time‐dependent. This suggests that the concentration of drugs in cells can be determined by detecting the fluorescence intensity of LDH@SGQD‐VP16, which can be used to trace the drug via spontaneous fluorescence, providing a basis for precise visualization therapy.
Figure 3
CLSM images of HGC‐27 cells treated with LDH@SGQD‐VP16 (40 mg L–1) for 3 and 24 h at 488 nm. a) Cell membrane dyed by Dil at 543 nm. Scale: 20 µm. b) Lysosomal co‐localization of LDH@SGQD and LDH@SGQD‐VP16 at 24 h. Scale: 20 µm. In vivo fluorescence images at 1, 3, 6, 24, 48, and 72 h; organs (heart, liver, spleen, lung, kidney, and tumor) at 72 h, c) post intravenous injection with LDH@SGQD (0.1 mL, 9.2 g L–1) visualized at 475 nm and fluorescence quantification using ImageJ. The data in the figures represent the mean ± SD (n = 3). Significant differences: ***p < 0.001.
To investigate the imaging capability in vivo, first, a nude mouse model of HGC‐27 gastric cancer was established. As shown in Figure 3c, 0.1 mL (9.2 g L–1) LDH@SGQD was injected into the mouse by intravenous injection. In vivo imaging was performed at 1, 3, 6, 24, 48, and 72 h, and visceral imaging was performed at 72 h, at 475 nm. The results show that fluorescence can be found at the tumor site after 1 h of injection, indicating that LDH@SGQD can target tumor tissue rapidly via blood circulation. With the extension of the injection time, the fluorescence at the tumor site gradually increased, and the retention time at the tumor site was very long. At 72 h after injection, fluorescence emission was still observed. After dissection, fluorescence was observed in the liver, kidney, and tumor, suggesting that LDH@SGQD is metabolized by the liver and kidney. The tumor site can be distinguished by observation, and the material is enriched by, and visualized due to, targeting accumulation and fluorescence intensity. It was demonstrated that LDH@SGQD can be used as a fluorescent probe for tumor visualization therapy.
2.3
Safety and Cytotoxicity Analysis
Figure
4a shows the safety of the materials using NIH 3T3 cells. Almost all cells survived after incubation with LDH, SGQD, or LDH@SGQD (20, 40, 80, 160 and 320 mg L–1) for 24 h, indicating no toxicity to NIH 3T3 cells. Even when the co‐incubation time was extended to 48 h, as shown in Figure S13a in the Supporting Information, the cell viability was still close to 100%. Cytotoxicity analysis was carried out using HGC‐27 and SGC7901 cells. Figure 4b shows the toxicity of LDH@SGQD‐VP16, LDH@SGQD, and VP16 (10, 20, 40 and 80 mg L–1) in NIH 3T3 cells for 24 h. At 40 mg L–1, the cell viability of the LDH@SGQD‐VP16 was close to 100%, whereas that of the VP16 group was less than 60%. These results indicate that LDH@SGQD can reduce the toxic side effects of VP16 on normal cells. The same result was observed at 48 h (Figure S13b, Supporting Information). When the concentration was increased to 80 mg L–1, the cell viability decreased to ≈65%. For safety purposes, the maximum concentration was 40 mg L–1 in the HGC‐27 cytotoxicity test. LDH@SGQD‐VP16, LDH@SGQD and VP16 (10, 15, 20, 25, 30, 35 and 40 mg L–1) were incubated with HGC‐27 cells for 24 h. At 40 mg L–1, the cytotoxicity of LDH@SGQD‐VP16 was ≈1.6 times higher than that of VP16 (Figure 4c). This indicates that LDH@SGQD‐VP16 can increase the cytotoxic effects on HGC‐27, which means that the complex will be suitable for gastric cancer treatment. Similar results were obtained in another gastric cancer cell line, SGC7901, where the cell viability of the LDH@SGQD‐VP16 group was lower than that of the VP16 group at 40 mg L–1 concentration, being 14% at 24 h, and 13% at 48 h, respectively (Figure S14, Supporting Information). At 80 mg L–1, the survival rate of SGC7901 cells incubated with LDH@SGQD‐VP16 was less than 50%; thus, the concentration of LDH@SGQD‐VP16 in the follow‐up experiment using SGC7901 was set to 80 mg L–1. The cytotoxicity test showed that LDH@SGQD‐VP16 had a protective effect on normal cells, and the cytotoxic effects on tumor cells increased. This indicates that LDH@SGQD‐VP16 can serve as a gastric cancer therapeutic agent, with better biocompatibility and higher efficacy compared to existing treatments. Figure 4d shows the apoptosis of HGC‐27 cells treated with VP16 and LDH@SGQD‐VP16 (20 and 40 mg L–1, respectively) for 24 h. At 20 mg L–1, the apoptotic rates of VP16 and LDH@SGQD‐VP16 were similar, with a difference of only 8%. However, at 40 mg L–1, the apoptotic rate of the LDH@SQGD‐VP16 group increased substantially, becoming ≈2.7 times that of the VP16 group. VP16 alone can induce apoptosis in HGC‐27 cells, but the proportion is low and independent of drug concentration. Unlike VP16, LDH@SGQD‐VP16 promoted the apoptosis of HGC‐27 cells in a concentration‐dependent manner. Therefore, LDH@SGQD‐VP16 can improve the therapeutic efficiency of gastric cancer treatment, by promoting apoptosis in gastric cancer cells, as demonstrated in the HGC‐27 cell line.
Figure 4
Growth inhibition assay. a) NIH 3T3 cells were incubated with LDH, SGQD, and LDH@SGQD at different concentration (20, 40, 80, 160, and 320 mg L–1) (n = 4); b) LDH@SGQD‐VP16, LDH@SGQD, and VP16 at different concentration (10, 20, 40, and 80 mg L–1) (n = 4) ; c) HGC‐27 cells were incubated with LDH@SGQD‐VP16, LDH@SGQD, and VP16 at different concentration (10, 15, 20, 25, 30, 35, and 40 mg L–1) (n = 4) for 24 h. Cell apoptosis assay by flow cytometry. d) HGC‐27 cells were incubated with LDH@SGQD‐VP16 and VP16 at different concentrations (20 and 40 mg L–1) and control for 24 h. e) Scheme of in vivo experiment; f) Photograph of HGC‐27 tumors for each tested group (LDH@SGQD‐VP16, LDH@SGQD, VP16, PBS) at the 18th‐day post intravenous injection; In vivo antigastric carcinoma effect: g) Volume change of tumor and h) weight variety of mice (n = 4). i) Cell proliferation analysis (Ki67) and cell apoptosis analysis (TUNEL) of four groups (LDH@SGQD‐VP16, LDH@SGQD, PBS, and VP16) by Ki67/TUNEL (Green), DAPI(Blue). Scale: 100 µm. j) Fluorescence area analysis by ImageJ (n = 3). All the data of figures represent the mean ± SD. Significant differences: ***p < 0.001 or *p < 0.05.
2.4
Therapeutic Effect on Gastric Cancer In Vivo
To evaluate the therapeutic effect of LDH@SGQD‐VP16 on gastric cancer in vivo, mice were divided into four groups: PBS was the control; the experimental groups were LDH@SGQD, LDH@SGQD‐VP16, and VP16. As shown in Figure 4e, the experiment involved intraperitoneal injection for 18 days, and all the materials were injected every alternate day. The weight of the mice and tumor volume were recorded manually. Figure 4f shows the tumor tissues obtained after the experiment was completed. It can be observed that the tumor masses from the LDH@SGQD‐VP16 group were smaller than those from the other groups. This indicates that the therapeutic effect of VP16 was improved after the nanomaterials were loaded. From the quantitative analysis of the volume size (Figure 4g), it was found that the tumor inhibition rate of LDH@SGQD‐VP16 was higher than that of VP16. This result is consistent with the cytotoxicity test, which means that LDH@SGQD‐VP16 improved tumor treatment. In contrast to VP16, the average weight of the LDH@SGQD‐VP16 group mice continued to increase (Figure 4h). This indicates that the side effects of VP16 are harmful to the body, while LDH@SGQD‐VP16 treatment achieves biosafety. Immunohistochemical analysis of the tumor tissue was performed using paraffin‐embedded sections (Figure 4i). Cell proliferation was detected using the Ki67 labeling method. Green fluorescence indicates cells with positive proliferation, while blue fluorescence indicates nuclei stained by DAPI. Combined with the fluorescence statistical results in Figure 4j, cell proliferation of the LDH@SGQD‐VP16 group was the lowest, 35% less than that of the VP16 group. It has been shown that LDH@SGQD‐VP16 can reduce cell proliferation and inhibit tumor growth. The results of the terminal deoxynucleotidyl transferase dUTP nick‐end labeling (TUNEL) assay showed that the apoptosis rate of the LDH@SGQD‐VP16 group was 35% higher than that of the VP16 group. It has been shown that LDH@SGQD‐VP16 can promote apoptosis, which is consistent with the trend observed in the in vitro apoptosis experiment. Similar results were observed from the hematoxylin and eosin (H&E) staining of tumor tissues (Figure
5
). The number of tumor cells in the LDH@SGQD‐VP16 group was the lowest, followed by the that of the VP16 group. Serum analysis of liver and kidney function indexes γ‐glutamyltransferase (γ‐GT), creatinine (CR), and blood urea nitrogen (BUN) were carried out (Table
1
). All indices were within a safe range. Tissue safety and pathology analyses were performed by routine tissue H&E staining (Figure 5). In the VP16 group, some of the hepatocytes were stained dark red, indicating that VP16 had done some damage to hepatocytes. However, LDH@SGQD‐VP16 did not cause any significant damage to the organs. These results indicated that biosafety can be improved upon LDH@SGQD‐VP16 usage.
Figure 5
H&E staining images of tumor tissues and other organs (heart, liver, spleen, lung, and kidney) after mice were sacrificed at the 18th‐day post the intraperitoneal injection with various formulas including PBS, VP16, LDH@SGQD, and LDH@SGQD‐VP16. Scale: 100 µm.
Table 1
The liver parameter (γ‐GT) and the kidney parameter (CR, BUN) of mice treated with PBS, VP16, LDH@SGQD, and LDH@SGQD‐VP16
Index
γ‐GT [U L–1]
CR [µmol L–1]
BUN [mg dL–1]
PBS
5.852
28.608
11.965
LDH@SGQD
4.028
32.694
13.676
LDH@SGQD‐VP16
6.345
36.782
11.79
VP16
7.4
37.804
13.702
John Wiley & Sons, Ltd.
2.1
Synthesis and Characterization
Figure
1
shows the design of the trifunctional nanoprobes LDH@SGQD‐VP16 for visual detection of cancer, tumor targeting and precise treatment. Briefly, LDH@SGQDs were synthesized using the coprecipitation method and could launch green fluorescence with a blue laser for visual imaging, and the proportion of SGQDs was ≈4.3%. In the synthesis process, drug‐loading nanocomposite system can be obtained by adding VP16 to the reaction system. The drug‐loading rate was ≈28.1%. LDH@SGQD‐VP16 is enriched by blood circulation onto the surface of tumor cells due to the acidic microenvironment, and then the nanocomplex is engulfed by the cells. VP16 is released into the cytoplasm by degradation of lysosome, causing apoptosis, for achieving cancer therapy.
Figure 1
Design of trifunctional nanoprobes LDH@SGQD‐VP16 for cancer treatment. LDH@SGQD‐VP16 is composed of LDH for tumor cells targeting by pH‐sensitive properties, SGQD for fluorescence imaging, and VP16 for cancer therapy.
Figure
2a–c shows the TEM characterization of LDH and LDH@SGQD. The structures of LDH and LDH@SGQD were both hexagonal, the SEM characterization shows the same conclusion (Figure S1, Supporting Information). However, in contrast to LDH, there are many small black points on the surface of LDH@SGQD, which are the SGQDs present on the surface of LDH through electrostatic binding, as shown in the inset Figure 2b. The particle sizes of SGQD are ≈3.0–5.0 nm, and the distribution of SGQDs on the surface of the LDH is even without agglomeration. The lattice structure of SGQDs can be clearly observed by HRTEM in Figure 2c. The results of the dynamic light scattering showed that the size of most particles of LDH@SGQD was ≈100 nm and ≈30 nm smaller than that of the LDH@SGQD‐VP16 particles (Figure 2d). To investigate the thickness changes of LDH@SGQD materials before and after VP16 loading, an atomic force microscope was used (Figure S2, Supporting Information). The thickness of LDH@SGQD (Figure S2a, Supporting Information) was found to be 33.73 nm through the layer height analysis, whereas that of LDH@SGQD‐VP16 increased to 50.74 nm (Figure S2b, Supporting Information). The loading of VP16 increased the spacing between layers, leading to an increase in the thickness of the composite material, which proves that VP16 loading was successfully carried out. The presence of the sulfur elemental analysis by TEM‐EDX (Figure S3, Supporting Information) and SEM‐EDAX (Figure S4, Supporting Information) also confirmed the successful synthesis of the composite.
Figure 2
TEM images of a) LDH, b) LDH@SGQD. Inset in (b): SGQD on LDH@SGQD. HRTEM of the SGQD on c) LDH@SGQD. d) Diameter distribution of LDH@SGQD and LDH@SGQD‐VP16. e) XRD analysis of LDH, LDH@SGQD, and LDH@SGQD‐VP16. f) XPS spectrum, g) high‐resolution C 1s, and h) S 2p of LDH@SGQD and LDH@SGQD‐VP16. i) Photograph under UV light at 365 nm and PL spectra of SGQD, LDH@SGQD, and LDH@SGQD‐VP16.
The zeta potential detection in Figure S5 in the Supporting Information shows that the potential of LDH is positive at ≈+40 mV, while that of SGQD is negative at ≈‐20 mV. In theory, SGQDs can be combined on the LDH surface via electrostatic binding. The potential of LDH@SGQD confirms the above conjecture, which is ≈+20 mV, proving that LDH and SGQD are indeed bound together by electrostatic action. In addition, the potential of VP16 was almost 0 mV, so the potential of LDH@SGQD‐VP16 is the same as that of LDH@SGQD, around +15 mV.
In terms of FTIR, as shown in Figure S6a in the Supporting Information, the characteristic peaks of LDH at 3450 cm–1 and 1647 cm–1 correspond to the telescopic and flexural vibration peaks of O─H, respectively, and the 1387 cm–1 peak corresponds to the telescopic vibration peak of N─O. In addition to the O─H peaks of SGQD, the characteristic absorption peak of SGQD is the stretching vibration peak of C─O at 1121 cm–1, which was not observed for LDH. The characteristic peaks of LDH@SGQD contain both N─O and C─O stretching vibration peaks, indicating that SGQD and LDH were combined to form new composite materials. Figure S6b in the Supporting Information compares the variation of characteristic peaks before and after the VP16 drug loading and shows that the characteristic absorption peaks of LDH@SGQD and VP16 were also included in the characteristic absorption peaks of LDH@SGQD‐VP16, indicating that the drug VP16 was successfully carried on the nanomaterials.
The XRD diagram in Figure 2e shows that the position and intensity of the diffraction peaks of LDH@SGQD are the same as those of LDH, indicating that the addition of SGQD does not affect the crystal structure of the composites. After carrying VP16, the position of the diffraction peak shifted slightly to the left, and the strength of the LDH@SGQD‐VP16 peak decreased significantly, indicating that the crystallinity was decreased and that VP16 had been successfully loaded. According to the Bragg equation, with a decrease in the two‐theta of the (003) diffraction peak, the layer spacing increases (Figure S7, Supporting Information). This indicated VP16 was loaded into the LDH layer.
The combustion characteristics of LDH@SGQD and LDH@SGQD‐VP16 were studied by means of thermogravimetry (Figure S8, Supporting Information). The weight loss before 200 °C is owing to the water between the layers. From 200 to 700 °C, the main reason is the crystallization water of Mg‐OH and Al‐OH. The difference between LDH@SGQD and LDH@SGQD‐VP16 is that the weight loss rate after drug‐loading is higher, being 46.5% and 64.291%. This indicated VP16 was loaded successfully.
The XPS characterizations of LDH@SGQD and LDH@SGQD‐VP16 are shown in Figure 2f–h. In the full spectrum analysis, five elements of the composite can be found: C1s (285 eV), O1s (531 eV), Mg1s (1303 eV), Al2p (74), and S2p (168). After carrying VP16, the proportions of C and O were higher than those before. Since the basic structure of VP16 is composed of two elements, C and O, the extra C and O may have been from the drug. This proves that the drug‐loading system has been successfully constructed. As shown in Figure 2g, C1s peak can be divided into three peaks corresponding to C─C (284.8 eV), C─O (286.8 eV), and C═O (288.8 eV). The C─O intensity of LDH@SGQD‐VP16 was much higher, which also provides evidence for formation of the drug‐loading system. The fine spectrum of S2p in Figure 2h, can also be divided into 168.1 and 169.4 eV peaks, which correspond to SO3/2p3/2 and SO3/2p1/2, respectively. As SGQD is the only source of S, it is also proved that SGQDs and LDHs constitute a composite structure. The high‐resolution O 1s, Mg 1s, and Al 2p of LDH@SGQD and LDH@SGQD‐VP16 are similar (Figure S9, Supporting Information).
Figure 2i shows the photoluminescence capability of SGQD, LDH@SGQD, and LDH@SGQD‐VP16. They were found to have good water solubility without agglomeration. Under the UV lamp, SGQD presented a bluish fluorescence, which was consistent with the fluorescence excitation/emission spectrum. The maximum emission peak was ≈480 nm, and the maximum excitation peak was ≈380 nm (Figure S10, Supporting Information). The fluorescence of LDH@SGQD under the UV lamp was cyan, indicating that its emission spectrum had a redshift. The maximum emission peak of LDH@SGQD was ≈560 nm, and the maximum excitation peak was ≈470 nm. After the drug VP16 was applied, the fluorescence spectrum was consistent with that of LDH@SGQD. Compared with SGQD, its fluorescence spectrum also shows a redshift, but the range is lower than that of LDH@SGQD, with the maximum emission peak at ≈540 nm and the maximum excitation peak at ≈440 nm. Theoretically, a redshift of fluorescence is more favorable for biological imaging.[
46
]
Figure S11 in the Supporting Information shows the sustained release of LDH@SGQD‐VP16 in PBS with different pH values (4.8, 5.8, and 7.0). The release of VP16 within 24 h showed an increasing trend, indicating that the drug could be released from the compound structure. Approximately 50% of VP16 was released in 3 h, indicating a sudden release, after which the release rate decreased significantly, until the amount released reached 90% within 48 h at pH 4.8. With the increase in pH, the total amount of sustained‐release decreased gradually, indicating that LDH@SGQD‐VP16 has the ability of pH‐sensitive release. The results provide a theoretical basis for acid microenvironment‐sensitive targeting and toxicity reduction.
2.2
Fluorescence Imaging Capability In Vitro and In Vivo
Figure
3a shows the fluorescence imaging of HGC‐27 cells incubated with LDH@SGQD‐VP16 for 24 h at 488 nm excitation light, and cell membrane was stained with Dil at 543 nm. Different from the reported SGQDs,[
30
] LDH@SGQD‐VP16 mainly gathered on the surface of the cell membrane with a small proportion of it entering the cytoplasm. This indicates that the method by which the composite enters cells is different from that of SGQD. Usually, the material released after cell phagocytosis enters lysosomes, in which an acidic environment is conducive to the disintegration of LDH@SGQD‐VP16 to release VP16. The lysosome co‐localization experiment confirmed this hypothesis in HGC‐27 cells (Figure 3b). The lysosomes were marked with a red fluorescent probe, Lyso‐Tracker Red, and the fluorescence of merged images turned orange with the combination of green fluorescence from LDH@SGQD‐VP16, which was transferred to the lysosome after being swallowed. Figure S12 in the Supporting Information shows the fluorescence imaging of LDH@SGQD‐VP16 (80 mg L–1) incubated with SGC7901 cells for 1, 6, 12, and 24 h. It was found that the fluorescent signal could be detected on the cells when incubated for 1 h, but the signal was weak and punctuated. With time, the fluorescence intensity of cells becomes stronger. When incubated for 12 h, the outline structure of the cells could be observed, but the boundary was not clear enough. After 24 h, the cell boundaries could be distinguished. This shows that the enrichment of the material in the cell is positively correlated to time. In contrast to HGC‐27 cells, the enrichment of LDH@SGQD‐VP16 in SGC7901 cells was more localized to the periphery of cells, which may affect the therapeutic effect. In summary, the accumulation of LDH@SGQD‐VP16 on the surface of human gastric cancer cells was time‐dependent. This suggests that the concentration of drugs in cells can be determined by detecting the fluorescence intensity of LDH@SGQD‐VP16, which can be used to trace the drug via spontaneous fluorescence, providing a basis for precise visualization therapy.
Figure 3
CLSM images of HGC‐27 cells treated with LDH@SGQD‐VP16 (40 mg L–1) for 3 and 24 h at 488 nm. a) Cell membrane dyed by Dil at 543 nm. Scale: 20 µm. b) Lysosomal co‐localization of LDH@SGQD and LDH@SGQD‐VP16 at 24 h. Scale: 20 µm. In vivo fluorescence images at 1, 3, 6, 24, 48, and 72 h; organs (heart, liver, spleen, lung, kidney, and tumor) at 72 h, c) post intravenous injection with LDH@SGQD (0.1 mL, 9.2 g L–1) visualized at 475 nm and fluorescence quantification using ImageJ. The data in the figures represent the mean ± SD (n = 3). Significant differences: ***p < 0.001.
To investigate the imaging capability in vivo, first, a nude mouse model of HGC‐27 gastric cancer was established. As shown in Figure 3c, 0.1 mL (9.2 g L–1) LDH@SGQD was injected into the mouse by intravenous injection. In vivo imaging was performed at 1, 3, 6, 24, 48, and 72 h, and visceral imaging was performed at 72 h, at 475 nm. The results show that fluorescence can be found at the tumor site after 1 h of injection, indicating that LDH@SGQD can target tumor tissue rapidly via blood circulation. With the extension of the injection time, the fluorescence at the tumor site gradually increased, and the retention time at the tumor site was very long. At 72 h after injection, fluorescence emission was still observed. After dissection, fluorescence was observed in the liver, kidney, and tumor, suggesting that LDH@SGQD is metabolized by the liver and kidney. The tumor site can be distinguished by observation, and the material is enriched by, and visualized due to, targeting accumulation and fluorescence intensity. It was demonstrated that LDH@SGQD can be used as a fluorescent probe for tumor visualization therapy.
2.3
Safety and Cytotoxicity Analysis
Figure
4a shows the safety of the materials using NIH 3T3 cells. Almost all cells survived after incubation with LDH, SGQD, or LDH@SGQD (20, 40, 80, 160 and 320 mg L–1) for 24 h, indicating no toxicity to NIH 3T3 cells. Even when the co‐incubation time was extended to 48 h, as shown in Figure S13a in the Supporting Information, the cell viability was still close to 100%. Cytotoxicity analysis was carried out using HGC‐27 and SGC7901 cells. Figure 4b shows the toxicity of LDH@SGQD‐VP16, LDH@SGQD, and VP16 (10, 20, 40 and 80 mg L–1) in NIH 3T3 cells for 24 h. At 40 mg L–1, the cell viability of the LDH@SGQD‐VP16 was close to 100%, whereas that of the VP16 group was less than 60%. These results indicate that LDH@SGQD can reduce the toxic side effects of VP16 on normal cells. The same result was observed at 48 h (Figure S13b, Supporting Information). When the concentration was increased to 80 mg L–1, the cell viability decreased to ≈65%. For safety purposes, the maximum concentration was 40 mg L–1 in the HGC‐27 cytotoxicity test. LDH@SGQD‐VP16, LDH@SGQD and VP16 (10, 15, 20, 25, 30, 35 and 40 mg L–1) were incubated with HGC‐27 cells for 24 h. At 40 mg L–1, the cytotoxicity of LDH@SGQD‐VP16 was ≈1.6 times higher than that of VP16 (Figure 4c). This indicates that LDH@SGQD‐VP16 can increase the cytotoxic effects on HGC‐27, which means that the complex will be suitable for gastric cancer treatment. Similar results were obtained in another gastric cancer cell line, SGC7901, where the cell viability of the LDH@SGQD‐VP16 group was lower than that of the VP16 group at 40 mg L–1 concentration, being 14% at 24 h, and 13% at 48 h, respectively (Figure S14, Supporting Information). At 80 mg L–1, the survival rate of SGC7901 cells incubated with LDH@SGQD‐VP16 was less than 50%; thus, the concentration of LDH@SGQD‐VP16 in the follow‐up experiment using SGC7901 was set to 80 mg L–1. The cytotoxicity test showed that LDH@SGQD‐VP16 had a protective effect on normal cells, and the cytotoxic effects on tumor cells increased. This indicates that LDH@SGQD‐VP16 can serve as a gastric cancer therapeutic agent, with better biocompatibility and higher efficacy compared to existing treatments. Figure 4d shows the apoptosis of HGC‐27 cells treated with VP16 and LDH@SGQD‐VP16 (20 and 40 mg L–1, respectively) for 24 h. At 20 mg L–1, the apoptotic rates of VP16 and LDH@SGQD‐VP16 were similar, with a difference of only 8%. However, at 40 mg L–1, the apoptotic rate of the LDH@SQGD‐VP16 group increased substantially, becoming ≈2.7 times that of the VP16 group. VP16 alone can induce apoptosis in HGC‐27 cells, but the proportion is low and independent of drug concentration. Unlike VP16, LDH@SGQD‐VP16 promoted the apoptosis of HGC‐27 cells in a concentration‐dependent manner. Therefore, LDH@SGQD‐VP16 can improve the therapeutic efficiency of gastric cancer treatment, by promoting apoptosis in gastric cancer cells, as demonstrated in the HGC‐27 cell line.
Figure 4
Growth inhibition assay. a) NIH 3T3 cells were incubated with LDH, SGQD, and LDH@SGQD at different concentration (20, 40, 80, 160, and 320 mg L–1) (n = 4); b) LDH@SGQD‐VP16, LDH@SGQD, and VP16 at different concentration (10, 20, 40, and 80 mg L–1) (n = 4) ; c) HGC‐27 cells were incubated with LDH@SGQD‐VP16, LDH@SGQD, and VP16 at different concentration (10, 15, 20, 25, 30, 35, and 40 mg L–1) (n = 4) for 24 h. Cell apoptosis assay by flow cytometry. d) HGC‐27 cells were incubated with LDH@SGQD‐VP16 and VP16 at different concentrations (20 and 40 mg L–1) and control for 24 h. e) Scheme of in vivo experiment; f) Photograph of HGC‐27 tumors for each tested group (LDH@SGQD‐VP16, LDH@SGQD, VP16, PBS) at the 18th‐day post intravenous injection; In vivo antigastric carcinoma effect: g) Volume change of tumor and h) weight variety of mice (n = 4). i) Cell proliferation analysis (Ki67) and cell apoptosis analysis (TUNEL) of four groups (LDH@SGQD‐VP16, LDH@SGQD, PBS, and VP16) by Ki67/TUNEL (Green), DAPI(Blue). Scale: 100 µm. j) Fluorescence area analysis by ImageJ (n = 3). All the data of figures represent the mean ± SD. Significant differences: ***p < 0.001 or *p < 0.05.
2.4
Therapeutic Effect on Gastric Cancer In Vivo
To evaluate the therapeutic effect of LDH@SGQD‐VP16 on gastric cancer in vivo, mice were divided into four groups: PBS was the control; the experimental groups were LDH@SGQD, LDH@SGQD‐VP16, and VP16. As shown in Figure 4e, the experiment involved intraperitoneal injection for 18 days, and all the materials were injected every alternate day. The weight of the mice and tumor volume were recorded manually. Figure 4f shows the tumor tissues obtained after the experiment was completed. It can be observed that the tumor masses from the LDH@SGQD‐VP16 group were smaller than those from the other groups. This indicates that the therapeutic effect of VP16 was improved after the nanomaterials were loaded. From the quantitative analysis of the volume size (Figure 4g), it was found that the tumor inhibition rate of LDH@SGQD‐VP16 was higher than that of VP16. This result is consistent with the cytotoxicity test, which means that LDH@SGQD‐VP16 improved tumor treatment. In contrast to VP16, the average weight of the LDH@SGQD‐VP16 group mice continued to increase (Figure 4h). This indicates that the side effects of VP16 are harmful to the body, while LDH@SGQD‐VP16 treatment achieves biosafety. Immunohistochemical analysis of the tumor tissue was performed using paraffin‐embedded sections (Figure 4i). Cell proliferation was detected using the Ki67 labeling method. Green fluorescence indicates cells with positive proliferation, while blue fluorescence indicates nuclei stained by DAPI. Combined with the fluorescence statistical results in Figure 4j, cell proliferation of the LDH@SGQD‐VP16 group was the lowest, 35% less than that of the VP16 group. It has been shown that LDH@SGQD‐VP16 can reduce cell proliferation and inhibit tumor growth. The results of the terminal deoxynucleotidyl transferase dUTP nick‐end labeling (TUNEL) assay showed that the apoptosis rate of the LDH@SGQD‐VP16 group was 35% higher than that of the VP16 group. It has been shown that LDH@SGQD‐VP16 can promote apoptosis, which is consistent with the trend observed in the in vitro apoptosis experiment. Similar results were observed from the hematoxylin and eosin (H&E) staining of tumor tissues (Figure
5
). The number of tumor cells in the LDH@SGQD‐VP16 group was the lowest, followed by the that of the VP16 group. Serum analysis of liver and kidney function indexes γ‐glutamyltransferase (γ‐GT), creatinine (CR), and blood urea nitrogen (BUN) were carried out (Table
1
). All indices were within a safe range. Tissue safety and pathology analyses were performed by routine tissue H&E staining (Figure 5). In the VP16 group, some of the hepatocytes were stained dark red, indicating that VP16 had done some damage to hepatocytes. However, LDH@SGQD‐VP16 did not cause any significant damage to the organs. These results indicated that biosafety can be improved upon LDH@SGQD‐VP16 usage.
Figure 5
H&E staining images of tumor tissues and other organs (heart, liver, spleen, lung, and kidney) after mice were sacrificed at the 18th‐day post the intraperitoneal injection with various formulas including PBS, VP16, LDH@SGQD, and LDH@SGQD‐VP16. Scale: 100 µm.
Table 1
The liver parameter (γ‐GT) and the kidney parameter (CR, BUN) of mice treated with PBS, VP16, LDH@SGQD, and LDH@SGQD‐VP16
Index
γ‐GT [U L–1]
CR [µmol L–1]
BUN [mg dL–1]
PBS
5.852
28.608
11.965
LDH@SGQD
4.028
32.694
13.676
LDH@SGQD‐VP16
6.345
36.782
11.79
VP16
7.4
37.804
13.702
John Wiley & Sons, Ltd.
3
Conclusion
For the visualization therapy of cancer, we designed a trifunctional integrated nanoprobe, LDH@SGQD‐VP16, which combines the fluorescence imaging capability of SGQDs, the tumor‐targeting properties of LDH in an acidic environment, and the enhanced therapeutic effect of the drug VP16. Chemical characterization proved that LDH and SGQD were combined through electrostatic interactions and had a particle size of ≈100 nm. Compared with SGQD, fluorescence emission has a redshift, which is more conducive to fluorescence imaging of cells and animals. VP16 was successfully loaded with a carrying rate of 28.1%, as confirmed by the FTIR and XRD characteristics. The experimental results of in vitro cells and tumor‐bearing mice show that LDH@SGQD‐VP16 can improve the therapeutic effect of VP16 on the cells in vitro, protect normal cells from damage, inhibit tumor growth in vivo, promote apoptosis, and achieve targeted imaging of the tumor tissue. Compared with other complexes of GQDs and LDH, such as N‐GQDs/CoFe2O4/LDH or PANI/N‐GQD/MO/LDH for breast cancer therapy,[
26
,
42
] LDH@SGQD‐VP16 is much better in biocompatibility, easier to prepare, and has the advantage of fluorescence visualization for tumor location, which is suitable for effect tracking in real‐time. In addition, the fluorescence intensity of a material can be used to represent drug enrichment. Therefore, the system has achieved the integration of tumor detection, tumor treatment and drug tracking. The ability of LDH@SGQD‐VP16 to accumulate in the tumor microenvironment, in addition to its fluorescence imaging characteristics, makes it an ideal biocompatible nanoparticle for diagnostic and therapeutic vectors. This study provides an experimental basis for LDH@SGQD‐VP16 as a new strategy for the visualization therapy of tumors.
4
Experimental Section
Materials
Etoposide (≥98%), NaOH, Na2SO3, Mg(NO3)2·6H2O, and Al(NO3)3·9H2O were purchased from Sinopharm Chemical Reagent Co. Ltd. Phosphate‐buffered saline (PBS), DMEM, and fetal bovine serum (FBS) were obtained from Hyclone. CCK8, Dil, Annexin V‐APC/7‐AAD and LysoTracker Red were purchased from Keygen Biotech. Anti‐Ki67 antibody was purchased from Santa Cruz Biotechnology, and TUNEL Apoptosis Assay Kit was purchased from Beyotime Biotechnology. All the materials were used as received.
Cell Lines and Animals
Fibroblast NIH 3T3 cells, and gastric cancer HGC‐27 and SGC7901 cells were purchased from the National Collection of Authenticated Cell Cultures. Female nude mice (6–8 weeks old) were purchased from Shanghai Sippr‐BK Laboratory Animal Co. Ltd.
Synthesis of SGQDs
SGQDs were synthesized according to the previous report.[
30
] Briefly, 1,3,6‐trinitropyrene was stirred with Na2SO3 solution, transferred to an autoclave, and heated at 130 °C for 12 h via an industrial‐scale procedure.
Preparation of LDH@SGQD‐VP16
800 mL of deionized water (ddH2O) was boiled for 30 min to remove CO2. NaOH (0.544 g) was placed in a bottle with ddH2O (80 mL) and then placed in a water bath at 60 °C, while stirring with nitrogen (N2) at a speed of 400 rpm. After continuous stirring for 5 min, SGQD (1 mg) and VP16 (600 mg) powder were added. Mg(NO3)2·6H2O (1.538 g) and Al(NO3)3·9H2O (0.75 g) were added to ddH2O (20 mL); this solution was added to the NaOH solution, drop‐wise, and the mixture was stirred for 3 h. After the reaction, the pellet was collected after centrifugating the mixture at 7000 rpm for 15 min; then, the pellet was dispersed in 70 mL ddH2O and placed in a reactor at a constant temperature of 100 °C for 20 h. After the reaction was complete, LHD@SGQD or LHD@SGQD‐VP16 was washed three times to remove impurities, and preserved at 4 °C. The SGQD content and drug‐loading rate were determined by UV‐analysis at 375 and 285 nm, respectively.
Characterization of LDH@SGQD and LDH@SGQD‐VP16
The size, morphology, and elemental analysis were investigated using Transmission electron microscopy (TEM, JEM 2010F, JEOL Ltd., Japan), scanning electron microscopy (SEM, Sigma 300, Zeiss, GER) and atomic force microscopy (AFM, SPM‐9600, Shimadzu, Japan). The Zeta‐potential values, as well as the hydrodynamic particle sizes were recorded using dynamic light scattering (DLS, Nano‐HT, Zetasizer, Malvern Panalytical, Malvern, UK). X‐ray diffraction (XRD, D/max 2550, Rigaku, GER) patterns were obtained using an X‐ray diffractometer with Cu/Kα radiation. FTIR was used to detect changes in functional groups using FTIR spectrometer (FTIR, FTS165, Bio‐Rad, USA). UV absorption and fluorescence spectroscopy were performed using a spectrophotometer (UV, 3100, Hitachi, Japan) and a fluorescence spectrophotometer (7000, Hitachi, Japan) respectively. An X‐ray photoelectron spectrometer (XPS, AXIS ULTRA DLD, Kratos, UK) was used to collect the XPS spectra. Thermogravimetric analysis was obtained using a thermal Gravimetric Analyzer (TG, TGA5500, TA, USA).
Sustained Release of LDH@SGQD‐VP16
LDH@SGQD‐VP16 (VP16: 15 mg) was dispersed in ddH2O (3 mL). One milliliter of the solution was placed in a 3.5 KD dialysis bag. Three bottles of 250 mL of PBS with different pH values (pH 4.8, pH 5.8, and pH 7.0) were prepared. The samples were divided into three groups, and the dialysis bags were placed in different bottles in a constant temperature shaker at a speed of 150 rpm and a temperature of 37 °C. The absorbance of VP16 in aqueous solution was detected at 0, 5, 15, and 45 min and at 2, 3, 5, 7, 24, and 48 h using a microplate reader (SpectraMax M5, Molecular Devices, USA) and the release amount was calculated using a standard curve.
In Vitro and In Vivo Optical Imaging
Approximately 2×105 cells were seeded in a glass‐bottom dish (Cellvis, Mountain View, CA, USA) with 2 mL of culture medium. After 24 h of culture, the LDH@SGQD‐VP16 (VP16: 40 mg L–1) were added into the dish. The cell membrane was stained with Dil for 10 min at 37 °C. Lysosomes were stained with LysoRed for 1 h at 37 °C. After incubation, the cells were examined under a confocal microscope (TCS SP5, Leica, GER) using 488 and 543 nm lasers. To investigate the in vivo optical imaging capacity, a nude mouse model of HGC‐27 gastric cancer was established (Female, 6–8 weeks old) and intravenously injected with LDH@SGQD (0.1 mL, 9.2 g L–1) through the tail vein. At 1, 3, 6, 24, 48, and 72 h after injection, and organs (heart, liver, spleen, lung, kidney, and tumor) was collected at 72 h, an in vivo imaging system (NightOWL LB983, Berthold, GER) was used to get the results at 475 nm. The fluorescence quantification was obtained using ImageJ (n = 3). All experiments were performed in compliance with the relevant laws and institutional guidelines and were approved by the Institutional Animal Care and Use Committee at the Shanghai Institute of Materia Medica, Chinese Academy of Sciences, and the Experimental Animal Center of Tongji University (No: TJAB03821101).
Cytotoxicity Test
NIH 3T3 cells were cultured in DMEM medium with 1% penicillin‐streptomycin and 10% fetal bovine serum (FBS, Hyclone, USA) at 37 °C in a Thermo cell incubator with 5% CO2. SGC7901 cells were cultured in RPMI‐1640 medium supplemented with 1% penicillin‐streptomycin and 10% fetal bovine serum. HGC‐27 cells were cultured in RPMI‐1640 medium supplemented with 1% penicillin‐streptomycin and 20% fetal bovine serum. The CCK8 assay was used to estimate cytotoxicity. Briefly, cells were seeded in 96‐well plates, and ≈5000 cells were seeded in each well (n = 4). Different concentrations of materials were added to each group. After 24 and 48 h in culture, CCK8 was added and incubated at 37 °C for 2 h. A microplate reader (ELX800, BIO‐TEK, USA) was used to obtain the experimental data. HGC‐27 cells were cultured in 6‐well plates with 2 mL culture medium for apoptosis analysis. After incubation with LDH@SGQD‐VP16, VP16, and PBS (VP16 concentration of 20 and 40 mg L–1) for 24 h, V‐APC and 7‐AAD were used for staining, and the results were obtained using a flow cytometry (FCM, FACS Aria II, BD, USA).
Treatment of Tumor‐Bearing Mice
A model of the HGC‐27 (1 × 106 cells per mouse, 0.1 mL of saline) subcutaneous tumor was established (Female, 6–8 weeks old). After 10 days, upon tumor growth to a volume of about ≈100 mm3, a tumor‐bearing model was established. According to the standard VP16 injection concentration of 15 mg kg–1, the corresponding material concentration was allocated, and the PBS containing LDH@SGQD, LDH@SGQD‐VP16, VP16, and PBS were injected intraperitoneally (n = 4, Female, 6–8 weeks old). The injection frequency was 2 days, and injections were administered for 18 days; the weight and tumor volume were recorded.
Immunohistochemical Analysis of Mice
The viscera (heart, liver, spleen, lung, and kidney) and HGC‐27 gastric cancer tissues of the mice were soaked in 4% paraformaldehyde, embedded in paraffin and sliced to a thickness of ≈3 µm. Conventional hematoxylin–Iran Hematoxylin and Eosin (H&E staining) were used to perform routine histopathological analysis. Anti‐Ki67 antibody staining was used for Ki67 proliferation detection using the following steps: incubation at 4 °C overnight, followed by washing with PBS, incubation with the secondary antibody (green fluorescence) for 2 h, re‐staining with DAPI, washing with PBS, and sealing with glycerol. TUNEL apoptosis was detected using a TUNEL Apoptosis Assay Kit. Each section was dripped with 50 µL of detection liquid, incubated at 37 °C for 60 min, and washed with PBS. Serum analysis of liver and kidney function indexes γ‐glutamyltransferase (γ‐GT), creatinine (CR), and blood urea nitrogen (BUN) were carried out using a biochemical analyzer (Chemray 240, Rayto, China).
Statistical Analyses
Baseline calibration and curve translation of the data of XRD was adjusted using Jade 5. The data of PL/PLE was normalized and plotted using OriginPro 2017. Baseline calibration of the data of FTIR was adjusted using Omnic 7.3. All of the data were presented as the mean ± standard deviation (SD). Statistical analysis of fluorescence quantification of in vivo fluorescence images was performed using Analysis of variance (ANOVA), and others was performed using Student's t‐test using GraphPad Prism 7 software (*p < 0.05, **p < 0.01, and ***p < 0.001).
Materials
Etoposide (≥98%), NaOH, Na2SO3, Mg(NO3)2·6H2O, and Al(NO3)3·9H2O were purchased from Sinopharm Chemical Reagent Co. Ltd. Phosphate‐buffered saline (PBS), DMEM, and fetal bovine serum (FBS) were obtained from Hyclone. CCK8, Dil, Annexin V‐APC/7‐AAD and LysoTracker Red were purchased from Keygen Biotech. Anti‐Ki67 antibody was purchased from Santa Cruz Biotechnology, and TUNEL Apoptosis Assay Kit was purchased from Beyotime Biotechnology. All the materials were used as received.
Cell Lines and Animals
Fibroblast NIH 3T3 cells, and gastric cancer HGC‐27 and SGC7901 cells were purchased from the National Collection of Authenticated Cell Cultures. Female nude mice (6–8 weeks old) were purchased from Shanghai Sippr‐BK Laboratory Animal Co. Ltd.
Synthesis of SGQDs
SGQDs were synthesized according to the previous report.[
30
] Briefly, 1,3,6‐trinitropyrene was stirred with Na2SO3 solution, transferred to an autoclave, and heated at 130 °C for 12 h via an industrial‐scale procedure.
Preparation of LDH@SGQD‐VP16
800 mL of deionized water (ddH2O) was boiled for 30 min to remove CO2. NaOH (0.544 g) was placed in a bottle with ddH2O (80 mL) and then placed in a water bath at 60 °C, while stirring with nitrogen (N2) at a speed of 400 rpm. After continuous stirring for 5 min, SGQD (1 mg) and VP16 (600 mg) powder were added. Mg(NO3)2·6H2O (1.538 g) and Al(NO3)3·9H2O (0.75 g) were added to ddH2O (20 mL); this solution was added to the NaOH solution, drop‐wise, and the mixture was stirred for 3 h. After the reaction, the pellet was collected after centrifugating the mixture at 7000 rpm for 15 min; then, the pellet was dispersed in 70 mL ddH2O and placed in a reactor at a constant temperature of 100 °C for 20 h. After the reaction was complete, LHD@SGQD or LHD@SGQD‐VP16 was washed three times to remove impurities, and preserved at 4 °C. The SGQD content and drug‐loading rate were determined by UV‐analysis at 375 and 285 nm, respectively.
Characterization of LDH@SGQD and LDH@SGQD‐VP16
The size, morphology, and elemental analysis were investigated using Transmission electron microscopy (TEM, JEM 2010F, JEOL Ltd., Japan), scanning electron microscopy (SEM, Sigma 300, Zeiss, GER) and atomic force microscopy (AFM, SPM‐9600, Shimadzu, Japan). The Zeta‐potential values, as well as the hydrodynamic particle sizes were recorded using dynamic light scattering (DLS, Nano‐HT, Zetasizer, Malvern Panalytical, Malvern, UK). X‐ray diffraction (XRD, D/max 2550, Rigaku, GER) patterns were obtained using an X‐ray diffractometer with Cu/Kα radiation. FTIR was used to detect changes in functional groups using FTIR spectrometer (FTIR, FTS165, Bio‐Rad, USA). UV absorption and fluorescence spectroscopy were performed using a spectrophotometer (UV, 3100, Hitachi, Japan) and a fluorescence spectrophotometer (7000, Hitachi, Japan) respectively. An X‐ray photoelectron spectrometer (XPS, AXIS ULTRA DLD, Kratos, UK) was used to collect the XPS spectra. Thermogravimetric analysis was obtained using a thermal Gravimetric Analyzer (TG, TGA5500, TA, USA).
Sustained Release of LDH@SGQD‐VP16
LDH@SGQD‐VP16 (VP16: 15 mg) was dispersed in ddH2O (3 mL). One milliliter of the solution was placed in a 3.5 KD dialysis bag. Three bottles of 250 mL of PBS with different pH values (pH 4.8, pH 5.8, and pH 7.0) were prepared. The samples were divided into three groups, and the dialysis bags were placed in different bottles in a constant temperature shaker at a speed of 150 rpm and a temperature of 37 °C. The absorbance of VP16 in aqueous solution was detected at 0, 5, 15, and 45 min and at 2, 3, 5, 7, 24, and 48 h using a microplate reader (SpectraMax M5, Molecular Devices, USA) and the release amount was calculated using a standard curve.
In Vitro and In Vivo Optical Imaging
Approximately 2×105 cells were seeded in a glass‐bottom dish (Cellvis, Mountain View, CA, USA) with 2 mL of culture medium. After 24 h of culture, the LDH@SGQD‐VP16 (VP16: 40 mg L–1) were added into the dish. The cell membrane was stained with Dil for 10 min at 37 °C. Lysosomes were stained with LysoRed for 1 h at 37 °C. After incubation, the cells were examined under a confocal microscope (TCS SP5, Leica, GER) using 488 and 543 nm lasers. To investigate the in vivo optical imaging capacity, a nude mouse model of HGC‐27 gastric cancer was established (Female, 6–8 weeks old) and intravenously injected with LDH@SGQD (0.1 mL, 9.2 g L–1) through the tail vein. At 1, 3, 6, 24, 48, and 72 h after injection, and organs (heart, liver, spleen, lung, kidney, and tumor) was collected at 72 h, an in vivo imaging system (NightOWL LB983, Berthold, GER) was used to get the results at 475 nm. The fluorescence quantification was obtained using ImageJ (n = 3). All experiments were performed in compliance with the relevant laws and institutional guidelines and were approved by the Institutional Animal Care and Use Committee at the Shanghai Institute of Materia Medica, Chinese Academy of Sciences, and the Experimental Animal Center of Tongji University (No: TJAB03821101).
Cytotoxicity Test
NIH 3T3 cells were cultured in DMEM medium with 1% penicillin‐streptomycin and 10% fetal bovine serum (FBS, Hyclone, USA) at 37 °C in a Thermo cell incubator with 5% CO2. SGC7901 cells were cultured in RPMI‐1640 medium supplemented with 1% penicillin‐streptomycin and 10% fetal bovine serum. HGC‐27 cells were cultured in RPMI‐1640 medium supplemented with 1% penicillin‐streptomycin and 20% fetal bovine serum. The CCK8 assay was used to estimate cytotoxicity. Briefly, cells were seeded in 96‐well plates, and ≈5000 cells were seeded in each well (n = 4). Different concentrations of materials were added to each group. After 24 and 48 h in culture, CCK8 was added and incubated at 37 °C for 2 h. A microplate reader (ELX800, BIO‐TEK, USA) was used to obtain the experimental data. HGC‐27 cells were cultured in 6‐well plates with 2 mL culture medium for apoptosis analysis. After incubation with LDH@SGQD‐VP16, VP16, and PBS (VP16 concentration of 20 and 40 mg L–1) for 24 h, V‐APC and 7‐AAD were used for staining, and the results were obtained using a flow cytometry (FCM, FACS Aria II, BD, USA).
Treatment of Tumor‐Bearing Mice
A model of the HGC‐27 (1 × 106 cells per mouse, 0.1 mL of saline) subcutaneous tumor was established (Female, 6–8 weeks old). After 10 days, upon tumor growth to a volume of about ≈100 mm3, a tumor‐bearing model was established. According to the standard VP16 injection concentration of 15 mg kg–1, the corresponding material concentration was allocated, and the PBS containing LDH@SGQD, LDH@SGQD‐VP16, VP16, and PBS were injected intraperitoneally (n = 4, Female, 6–8 weeks old). The injection frequency was 2 days, and injections were administered for 18 days; the weight and tumor volume were recorded.
Immunohistochemical Analysis of Mice
The viscera (heart, liver, spleen, lung, and kidney) and HGC‐27 gastric cancer tissues of the mice were soaked in 4% paraformaldehyde, embedded in paraffin and sliced to a thickness of ≈3 µm. Conventional hematoxylin–Iran Hematoxylin and Eosin (H&E staining) were used to perform routine histopathological analysis. Anti‐Ki67 antibody staining was used for Ki67 proliferation detection using the following steps: incubation at 4 °C overnight, followed by washing with PBS, incubation with the secondary antibody (green fluorescence) for 2 h, re‐staining with DAPI, washing with PBS, and sealing with glycerol. TUNEL apoptosis was detected using a TUNEL Apoptosis Assay Kit. Each section was dripped with 50 µL of detection liquid, incubated at 37 °C for 60 min, and washed with PBS. Serum analysis of liver and kidney function indexes γ‐glutamyltransferase (γ‐GT), creatinine (CR), and blood urea nitrogen (BUN) were carried out using a biochemical analyzer (Chemray 240, Rayto, China).
Statistical Analyses
Baseline calibration and curve translation of the data of XRD was adjusted using Jade 5. The data of PL/PLE was normalized and plotted using OriginPro 2017. Baseline calibration of the data of FTIR was adjusted using Omnic 7.3. All of the data were presented as the mean ± standard deviation (SD). Statistical analysis of fluorescence quantification of in vivo fluorescence images was performed using Analysis of variance (ANOVA), and others was performed using Student's t‐test using GraphPad Prism 7 software (*p < 0.05, **p < 0.01, and ***p < 0.001).
Conflict of Interest
The authors declare no conflict of interest.
Supporting information
Supporting Information |
Title: Prognostic Value of Transthoracic Impedance Cardiography, Amino-Terminal Pro-B-Type Natriuretic Peptide Levels, The Six-Minute Walk Test, and Chest X-Ray in Elderly Patients with Chronic Heart Failure: A Comparative Study in Lithuania | Body:
Introduction
Chronic heart failure (CHF) is a complex clinical syndrome that develops as a result of structural and/or functional ventricular damage and is characterized by complaints and symptoms caused by left ventricular (LV) and/or right ventricular dysfunction [1,2]. In Europe in 2019, the median incidence of CHF in the general population was 3.2 (quartile 1 [Q1] 2.66-quartile 3 [Q3] 4.17) cases per 1000 person-years, while in Lithuania, the median incidence was 4.67 cases per 1000 person-years [3]. The incidence of CHF increases with age, with about 20% of people over 75 years of age having the syndrome [2].
The etiology of CHF varies in different regions of the globe and depends on geography and cultural aspects. In the developed and Western world, leading etiologies are coronary artery disease and hypertension [1]. Chronic heart failure is associated with frequent, recurrent, and long-term hospitalizations, which results in a significant economic burden for healthcare systems. The overall 1-year survival rate of CHF patients is 75.9%, the 5-year survival rate is 45.5%, the 10-year survival rate is 24.5%, and the 15-year survival rate is only 12.7% [4]. The hospitalization of a CHF patient is an event with a clear negative prognostic value [5]. Patients hospitalized for CHF decompensation have an overall 5-year survival rate of only 25% regardless of left ventricular ejection fraction (LVEF) [5]. In patients diagnosed with CHF during hospitalization, the median survival is 2.4 years shorter than in those undergoing out-patient treatment (2.9 versus 5.3 years) [4]. In women, the survival is poorer than in men, and older patients also have a higher risk of death compared to younger patients [4].
The diagnosis of CHF can be made based on characteristic complaints, symptoms, and objective signs of cardiac dysfunction. However, medical history and objective examination alone are not sufficient, and additional diagnostic tests are needed to rule out other diseases with similar symptoms [1]. As of most recent European Society of Cardiology guidelines the following tests are recommended for the assessment of patients with suspected CHF: electrocardiogram, natriuretic peptide (NP) plasma levels, basic blood workup for comorbidities, transthoracic echocardiography (TTE), and chest X-ray [1].
Natriuretic peptides such as B-type (or brain) natriuretic peptide (BNP) and amino-terminal pro-B-type natriuretic peptide (NT-proBNP) – are indispensable biomarkers used for the diagnosis and prognosis of CHF [6]. The presence of CHF increases serum NP levels [7], which is significantly associated with a more severe disease course and a worse prognosis in elderly CHF patients [8]. Natriuretic peptides have a high negative predictive value and may exclude the diagnosis of CHF. On the other hand, the positive predictive value of the NP test is low, and thus the test does not definitively confirm the diagnosis of CHF [1].
Echocardiography is recommended as the key imagery test to assess cardiac structure and function. During TTE clinicians may determine one of key parameters in CHF which is LVEF, as well as other parameters such as chamber size, type of LV hypertrophy, regional wall motion abnormalities, right ventricular function, signs of pulmonary hypertension, valvular function, and markers of diastolic function [1]. A chest X-ray is recommended to differentiate the suspected CHF from other causes of dyspnea. Additional findings such as pulmonary congestion or enlarged heart may also provide additional diagnostic benefits in the diagnosis of CHF [1].
Transthoracic impedance cardiography (ICG) is a safe, non-invasive, cheap, simple test, that does not require special training of the personnel, and it is a reliable method for evaluating the hemodynamics of the cardiovascular (CV) system, which is also valuable in the diagnosis of CHF [9]. This method measures the impedance of the tissues through which a low-strength current passes [10]. Areas of the body with higher amounts of blood or fluid will have lower impedance, while areas with denser tissue will have higher impedance [9]. This helps to identify one of the main pathological processes of CHF-fluid accumulation in the chest and assign patients to a group with a poor prognosis [10].
Even though the six-minute walk test (6MWT) is not a routine test, it is a widely available, inexpensive, easy-to-perform, and well-tolerated method of assessing the functional capacity of CHF patients [11]. Studies have shown that patients over 65 years of age walk a significantly shorter distance during the 6MWT than younger patients do [12,13]. Reduced 6-minute walk distance (6MWD) in CHF patients is associated with increased mortality, non-fatal CV events, and hospitalizations [11]. A 6MWD ≤300 m indicates a poor prognosis, while <200 m indicates a significantly increased risk of death. A stable 6MWT score over 12 months indicates improved survival in patients with CHF [11]. Studies have also shown a statistically significant correlation between a higher degree of LV systolic or diastolic dysfunction and a lower 6MWD [14]. The combination of the 6MWT results with stroke volume (SV) and cardiac output (CO) determined by ICG testing may help more accurately predict a patient’s functional capacity than 6MWT alone [15]. In the comparison of the 6MWT and NP plasma levels, 6MWT reflects functional capacity, disease severity, and quality of life, while NP is more related to cardiac function and reflects CHF symptoms, LVEF, and the New York Heart Association (NYHA) class [16]. The higher the NYHA class and serum NT-proBNP concentrations, the poorer the echocardiographic indices of cardiac chamber size and wall thickness [17], thus these tests should be considered together rather than in isolation to better predict disease prognosis. It can be argued that the combined use of NP and ICG testing increases specificity and sensitivity, as the systolic time ratio (STR) index complements, rather than replicates, the NP results, which helps to identify high-risk patients, who are less likely to survive and are more likely to experience CHF-related deterioration [18]. Other investigators have found a significant association between LVEF determined by TTE and pre-ejection period (PEP), LV ejection time (LVET), and STR determined by ICG [19].
Currently, interventional hemodynamic, radiological, and nuclear medicine tests are considered the gold standard for confirming etiologic diagnosis of CHF, selecting the appropriate treatment, and assessing the prognosis [20–23]. These testing methods are highly accurate in diagnosing etiology of CHF and predicting the disease outcome for the patients. However, they have some important drawbacks, such as high cost, invasiveness, and limited availability [20–23]. Thus, the search for new non-invasive, low-cost, and widely available diagnostic and prognostic tests for CHF is also driven by economic considerations, among other factors.
In this study, we hypothesized that low-cost, widely available, and rapidly performed non-invasive tests such as 6MWT, NT-proBNP, TTE, ICG, and chest X-ray would have an aggregate prognostic value in predicting poor outcomes in the elderly CHF patients.
Therefore, this study from a single center in Lithuania aimed to evaluate outcomes in 87 elderly patients hospitalized with CHF. The methods used included comparing ICG, NT-proBNP serum levels, the 6MWT, and chest X-ray.
Material and Methods
Studied Sample
An observational prospective study was conducted in Lithuania, in 2019–2022. All participants of the study provided written informed consent. The study was approved by the local Biomedical Research Ethics Committee (No: BE-2-17). The study included 87 patients (49 men and 38 women) hospitalized for CHF who underwent 6MWT and ICG tests in addition to the standard CHF diagnostic tests such as TTE, NT-proBNP, chest X-ray. The inclusion criterion was a diagnosis of CHF confirmed according to the European Society of Cardiology guidelines in force at the time [1,24]. The exclusion criteria were age <65 years, severe aortic valve regurgitation, severe aortic valve stenosis, acute myocardial infarction, acute heart failure, heart rate ≥180 bpm, mean arterial blood pressure >130 mmHg, an implanted cardioverter-defibrillator, or an implanted intrathoracic impedance monitor.
The primary endpoint of the study was CV death during in-patient treatment and the secondary endpoint was CV death during the whole follow-up of the study. Data about CV deaths after discharge from the hospital were gathered from the National Medical Record Database. These data were used to calculate Kaplan-Meier curves.
Methods of Examination
In this study, we analyzed data from serum NT-proBNP, TTE, chest X-ray, 6MWT, and ICG tests. All these tests were performed on the day of enrollment, and NT-proBNP, 6MWT, and ICG tests were repeated on the day of discharge.
The six-minute walking test was performed in accordance with the most recent 6MWT guidelines [25] and methodological recommendations [11]. TTE was performed with an Aloka Prosound α6 (Hitachi Aloka Medical, Ltd., Tokyo, Japan) using a 3 MHz transducer UST-5299. All measurements were performed by an experienced cardiologist, and all of them were performed by the same doctor. The researcher was blinded to the patient’s data. All measurements were performed based on the recommendations of the American Society of Echocardiography [26] and the European Association for Cardiac Imaging [27]. For ICG measurements, the Niccomo® (MEDIS Medizinische Messtechnik GmbH, Ilmenau, Germany) ICG monitor was used, which uses a modification of the 8-electrode setup: 4 electrodes are attached to either side of the neck, at the level of the great vessels, and a further 4 electrodes are attached to either side of the thorax, at the midline of the axilla and at the level of the xiphoid process. The following parameters were recorded during the ICG test: SV, stroke index (SI), thoracic fluid content (TFC), TFC index (TFCI), CO, STR, PEP, LVET, left cardiac work (LCW), and LCW index (LCWI) [28,29]. The dilatation of the right pulmonary artery on chest X-ray (dRPAcXR) was identified when interlobar artery at the hilum on the right side was >15 mm in women and >16 mm in men.
Statistical Analysis
The IBM Statistical Package for Social Sciences Statistics for Windows, version 29.0 (IBM Corporation, New York, United States) was used for data analysis. All the continuous data were not distributed normally. Therefore, for descriptive statistics, all continuous data are presented as the median and Q1–Q3, and non-parametric tests were used to test statistical hypotheses. For categorical variables, presented as numbers (%), the chi-square test was used for comparisons between the groups. Two independent variables were compared using the Mann–Whitney U test. Spearman’s correlation coefficient (r) was used to assess relationships between all non-invasive CHF diagnostic tests. Receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracy of the test to identify lethal patient outcomes, and the area under the curve was evaluated. The sensitivity and specificity of the main diagnostic tests were calculated. Cut-off values were selected so that Youden’s index had the highest value possible. The Kaplan-Meier method, log-rank test and univariate and multivariate Cox regression models (controlling for all main covariates such as age, gender, body mass index [BMI], and comorbidities) were used to calculate the survival rates, differences in survival curves, CV death risk and survival chances. Hazard ratios (HRs) were estimated using the Cox proportional hazards model. Results were considered statistically significant when the P value was <0.05.
Studied Sample
An observational prospective study was conducted in Lithuania, in 2019–2022. All participants of the study provided written informed consent. The study was approved by the local Biomedical Research Ethics Committee (No: BE-2-17). The study included 87 patients (49 men and 38 women) hospitalized for CHF who underwent 6MWT and ICG tests in addition to the standard CHF diagnostic tests such as TTE, NT-proBNP, chest X-ray. The inclusion criterion was a diagnosis of CHF confirmed according to the European Society of Cardiology guidelines in force at the time [1,24]. The exclusion criteria were age <65 years, severe aortic valve regurgitation, severe aortic valve stenosis, acute myocardial infarction, acute heart failure, heart rate ≥180 bpm, mean arterial blood pressure >130 mmHg, an implanted cardioverter-defibrillator, or an implanted intrathoracic impedance monitor.
The primary endpoint of the study was CV death during in-patient treatment and the secondary endpoint was CV death during the whole follow-up of the study. Data about CV deaths after discharge from the hospital were gathered from the National Medical Record Database. These data were used to calculate Kaplan-Meier curves.
Methods of Examination
In this study, we analyzed data from serum NT-proBNP, TTE, chest X-ray, 6MWT, and ICG tests. All these tests were performed on the day of enrollment, and NT-proBNP, 6MWT, and ICG tests were repeated on the day of discharge.
The six-minute walking test was performed in accordance with the most recent 6MWT guidelines [25] and methodological recommendations [11]. TTE was performed with an Aloka Prosound α6 (Hitachi Aloka Medical, Ltd., Tokyo, Japan) using a 3 MHz transducer UST-5299. All measurements were performed by an experienced cardiologist, and all of them were performed by the same doctor. The researcher was blinded to the patient’s data. All measurements were performed based on the recommendations of the American Society of Echocardiography [26] and the European Association for Cardiac Imaging [27]. For ICG measurements, the Niccomo® (MEDIS Medizinische Messtechnik GmbH, Ilmenau, Germany) ICG monitor was used, which uses a modification of the 8-electrode setup: 4 electrodes are attached to either side of the neck, at the level of the great vessels, and a further 4 electrodes are attached to either side of the thorax, at the midline of the axilla and at the level of the xiphoid process. The following parameters were recorded during the ICG test: SV, stroke index (SI), thoracic fluid content (TFC), TFC index (TFCI), CO, STR, PEP, LVET, left cardiac work (LCW), and LCW index (LCWI) [28,29]. The dilatation of the right pulmonary artery on chest X-ray (dRPAcXR) was identified when interlobar artery at the hilum on the right side was >15 mm in women and >16 mm in men.
Statistical Analysis
The IBM Statistical Package for Social Sciences Statistics for Windows, version 29.0 (IBM Corporation, New York, United States) was used for data analysis. All the continuous data were not distributed normally. Therefore, for descriptive statistics, all continuous data are presented as the median and Q1–Q3, and non-parametric tests were used to test statistical hypotheses. For categorical variables, presented as numbers (%), the chi-square test was used for comparisons between the groups. Two independent variables were compared using the Mann–Whitney U test. Spearman’s correlation coefficient (r) was used to assess relationships between all non-invasive CHF diagnostic tests. Receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracy of the test to identify lethal patient outcomes, and the area under the curve was evaluated. The sensitivity and specificity of the main diagnostic tests were calculated. Cut-off values were selected so that Youden’s index had the highest value possible. The Kaplan-Meier method, log-rank test and univariate and multivariate Cox regression models (controlling for all main covariates such as age, gender, body mass index [BMI], and comorbidities) were used to calculate the survival rates, differences in survival curves, CV death risk and survival chances. Hazard ratios (HRs) were estimated using the Cox proportional hazards model. Results were considered statistically significant when the P value was <0.05.
Results
Descriptive Statistics of the Studied Sample
The median age of the study participants was 75 years (70.0–83.5). The descriptive statistics of the patients are presented in Table 1. The patients were distributed into 3 groups according to NYHA functional capacity classes: NYHA class II – 17 patients (19.5%), NYHA class III – 55 patients (63.2%), and NYHA class IV – 15 patients (17.2%). According to the cause of CHF, the patients were divided into 6 groups. In 56 patients (64.4%), the cause of CHF was found to be ischemic heart disease (IHD), in 19 patients (21.8%), arterial hypertension, in 6 patients (6.9%), atrial fibrillation or atrial flutter, in 2 patients (2.3%), cardiomyopathy, in 2 patients (2.3%), chronic obstructive pulmonary disease (COPD), and in 1 patient (1.1%), valvular heart disease.
Associations Between Key Chronic Heart Failure Markers
In our study, we looked for associations between key ICG parameters, 6MWD, and other diagnostic markers of CHF. The analysis revealed weak to moderate correlations between key ICG parameters and other CHF markers. The strongest correlations were found between TFC/TFCI and NT-proBNP (r=0.358/r=0.408, P<0.001), LVEF (r=−0.479/r=−0.326, P<0.001/P=0.002), left atrial dimension in the parasternal long axis view (LAD) (r=0.488/r=0.446, P<0.001), and NYHA class (r=0.471/r=0.434, P<0.001) (Table 2).
In addition to that, we also looked for associations between the NYHA functional class and other diagnostic markers of CHF and between 6MWD and other diagnostic markers of CHF. The strongest correlations were found between NYHA class and TTE indices LVEF (r=−0.550, P<0.001), LAD (r=0.423, P<0.001), and NT-proBNP (r=0.398, P<0.001) (Table 3). The strongest correlations were found between 6MWD and left ventricular mass (LVM) index (r=−0.353, P<0.001), NT-proBNP (r=−0.251, P=0.019) and left ventricular end-diastolic dimension index (LVEDDI) (r=−0.550, P=0.019) (Table 4).
Outcome Analysis
The median duration of the follow-up of the patients included in the study was 23 months (the shortest duration being 2 months, and the longest – 36 months). By the end of the study, 58 of the subjects (66.7%) survived, and 29 (33.3%) died because of a major CV event. The results of the study were analyzed by comparing the objective examination, laboratory, and instrumental data between the surviving and the deceased groups (statistically significant results are presented in Table 5). There were statistically significant differences between the surviving and the deceased groups in respiratory rate, NYHA class, NT-proBNP, glomerular filtration rate, 6MWD, ICG parameters (except for LCW and LCWI), and TTE parameters (except for the LVM).
Diagnostic accuracy to detect CV death during the whole study period was assessed for all non-invasive CHF diagnostic tests and other clinical and laboratory data. ROC curves were selected to assess the diagnostic accuracy of these tests. Baseline parameters, recorded at enrollment, were analyzed. Cut-off values were selected so that Youden’s index had the highest value possible. These cut-off values were then used in univariate and multivariate Cox regression models (statistically significant results are presented in Table 6). First, the influence of all baseline demographic, clinical, laboratory, TTE, 6MWT, and ICG parameters on the survival of patients was evaluated using a univariate Cox regression model. Next, statistically significant variables from univariate Cox regression model were included in the multivariate Cox regression model. The final Cox model of multivariate regression analysis of statistically significant variables was created using the forward stepwise procedure. The final model revealed that the following baseline variables were independently and significantly associated with CV death (all P<0.05): TFC ≥41.1 1/kΩ (HR 32.354, 95% confidence interval [CI] 2.758–379.488), NT-proBNP ≥332.0 pmol/L (HR 4.739, 95% CI 1.656–13.559), 6MWD ≤203.5 m (HR 3.975, 95% CI 1.002–15.770), and dRPAcXR (HR 5.555, 95% CI 1.714–18.005). To assess the association between the patients’ poor outcome and laboratory and instrumental findings, survival curves were calculated using the Kaplan-Meier method. Cut-off values were determined by the ROC curve analysis, data is presented in Figures 1–4.
Descriptive Statistics of the Studied Sample
The median age of the study participants was 75 years (70.0–83.5). The descriptive statistics of the patients are presented in Table 1. The patients were distributed into 3 groups according to NYHA functional capacity classes: NYHA class II – 17 patients (19.5%), NYHA class III – 55 patients (63.2%), and NYHA class IV – 15 patients (17.2%). According to the cause of CHF, the patients were divided into 6 groups. In 56 patients (64.4%), the cause of CHF was found to be ischemic heart disease (IHD), in 19 patients (21.8%), arterial hypertension, in 6 patients (6.9%), atrial fibrillation or atrial flutter, in 2 patients (2.3%), cardiomyopathy, in 2 patients (2.3%), chronic obstructive pulmonary disease (COPD), and in 1 patient (1.1%), valvular heart disease.
Associations Between Key Chronic Heart Failure Markers
In our study, we looked for associations between key ICG parameters, 6MWD, and other diagnostic markers of CHF. The analysis revealed weak to moderate correlations between key ICG parameters and other CHF markers. The strongest correlations were found between TFC/TFCI and NT-proBNP (r=0.358/r=0.408, P<0.001), LVEF (r=−0.479/r=−0.326, P<0.001/P=0.002), left atrial dimension in the parasternal long axis view (LAD) (r=0.488/r=0.446, P<0.001), and NYHA class (r=0.471/r=0.434, P<0.001) (Table 2).
In addition to that, we also looked for associations between the NYHA functional class and other diagnostic markers of CHF and between 6MWD and other diagnostic markers of CHF. The strongest correlations were found between NYHA class and TTE indices LVEF (r=−0.550, P<0.001), LAD (r=0.423, P<0.001), and NT-proBNP (r=0.398, P<0.001) (Table 3). The strongest correlations were found between 6MWD and left ventricular mass (LVM) index (r=−0.353, P<0.001), NT-proBNP (r=−0.251, P=0.019) and left ventricular end-diastolic dimension index (LVEDDI) (r=−0.550, P=0.019) (Table 4).
Outcome Analysis
The median duration of the follow-up of the patients included in the study was 23 months (the shortest duration being 2 months, and the longest – 36 months). By the end of the study, 58 of the subjects (66.7%) survived, and 29 (33.3%) died because of a major CV event. The results of the study were analyzed by comparing the objective examination, laboratory, and instrumental data between the surviving and the deceased groups (statistically significant results are presented in Table 5). There were statistically significant differences between the surviving and the deceased groups in respiratory rate, NYHA class, NT-proBNP, glomerular filtration rate, 6MWD, ICG parameters (except for LCW and LCWI), and TTE parameters (except for the LVM).
Diagnostic accuracy to detect CV death during the whole study period was assessed for all non-invasive CHF diagnostic tests and other clinical and laboratory data. ROC curves were selected to assess the diagnostic accuracy of these tests. Baseline parameters, recorded at enrollment, were analyzed. Cut-off values were selected so that Youden’s index had the highest value possible. These cut-off values were then used in univariate and multivariate Cox regression models (statistically significant results are presented in Table 6). First, the influence of all baseline demographic, clinical, laboratory, TTE, 6MWT, and ICG parameters on the survival of patients was evaluated using a univariate Cox regression model. Next, statistically significant variables from univariate Cox regression model were included in the multivariate Cox regression model. The final Cox model of multivariate regression analysis of statistically significant variables was created using the forward stepwise procedure. The final model revealed that the following baseline variables were independently and significantly associated with CV death (all P<0.05): TFC ≥41.1 1/kΩ (HR 32.354, 95% confidence interval [CI] 2.758–379.488), NT-proBNP ≥332.0 pmol/L (HR 4.739, 95% CI 1.656–13.559), 6MWD ≤203.5 m (HR 3.975, 95% CI 1.002–15.770), and dRPAcXR (HR 5.555, 95% CI 1.714–18.005). To assess the association between the patients’ poor outcome and laboratory and instrumental findings, survival curves were calculated using the Kaplan-Meier method. Cut-off values were determined by the ROC curve analysis, data is presented in Figures 1–4.
Discussion
The major finding of the present study was that parameters such as TFC ≥41.1 1/kΩ, NT-proBNP ≥332.0 pmol/L, 6MWD ≤203.5 m, and dRPAcXR were independently and statistically significantly (all P<0.05) associated with a poorer prognosis in CHF patients. Chronic heart failure patients with TFC ≥41.1 1/kΩ had a 32.4-fold higher risk of CV death, compared to patients with TFC <41.1 1/kΩ. Patients with NT-proBNP ≥332.0 pmol/L had a 4.7-fold higher risk of CV death than patients with NT-proBNP <332.0 pmol/L did. Patients with the total 6MWD ≤203.5 m had a 3.9-fold higher risk of CV death compared to patients with the total 6MWD >203.5 m. The dRPAcXR was associated with a 5.6-fold increase in the risk of CV death.
Similar to our study, Giannitsi et al found that in patients with mild-to-moderate (NYHA functional class II–III) heart failure with reduced ejection fraction (HFrEF), reduced 6MWD was one of the strongest independent prognostic indicators associated with an increased risk of mortality, non-fatal CV events, and hospitalization [11]. Ferreira et al found that 6MWD was directly proportional to disease progression, i.e., for every 50-m reduction in 6MWD, there was an 8% higher risk of hospitalization or death and a 14% higher risk of death due to CHF [12]. Patients with a 6MWD of 241–360 m had a 1.44-fold increase in the probability of a poor outcome (95% CI 1.14–1.80, P=0.002), while patients with a 6MWD of <240 m had a 1.73-fold increase in that probability (95% CI 1.38–2.18, P<0.001) [12], which is consistent with the results of our study.
Grundtvig et al also obtained similar results: the strongest independent risk factors for lethal outcome in patients with CHF were older age (HR 1.036, 95% CI 1.025–1.047, P<0.001), lower 6MWD (HR 0.979, 95% CI 0.972–0.986, P<0.001), higher NT-proBNP (HR 1.861, 95% CI 1.450–2.389, P<0.001), and a diagnosis of cancer within the last 5 years (HR 3.179, 95% CI 2.574–3.926, P<0.001). In the group of patients with a 6MWD ≤360 m, mortality was 25%, while in groups with 6MWD 361–479 m or ≥480 m, poor outcomes were observed in 9% and 4% of patients, respectively [30].
Malfatto et al found that higher BNP and TFC values were independent predictors of a lethal outcome. In patients with BNP <450 pmol/L and TFC <40 1/kΩ, the 4-year mortality rate was only 2.1%, whereas in patients with BNP ≥450 pmol/L and TFC ≥40 1/kΩ, the 4-year mortality rate was 46.5% (P=0.001) [31]. In our study, the maximum follow-up was shorter (3 years), which may explain the lower mortality rate, with a third of the patients dying during the entire follow-up period.
Sadauskas et al found similar parameters that were independently and statistically significantly associated with a poorer prognosis in patients with CHF. The parameters that significantly increased the probability of a lethal outcome were BNP ≥350 pmol/L (odds ratio [OR] 4.4, 95% CI 1.3–15.7, P<0.05), TFC ≥34 1/kΩ (OR 4.3, 95% CI 1.0–19.7, P<0.05), and STR ≥0.55 (OR 2.9, 95% CI 1.1–7.7 P<0.05) [29]. However, similarly to our study, there was no statistically significant association between male sex, NYHA class III–IV, TFCI, SV, CO, or LCWI and the risk of death [29].
Scardovi et al. found that older age (HR 1.162, 95% CI 1.043–1.294, P=0.004), male sex (HR 2.115, 95% CI 1.257–3.560, P=0.005), grade 3 renal failure (HR 1.991, 95% CI 1.324–2.996, P=0.001), a non-obese phenotype (HR 1.905, 95% CI 1.070–3.390, P=0.028), IHD in CHF etiology (HR 1.833, 95% CI 1.264–2.660, P=0.001), NYHA class III (HR 1.958, 95% CI 1.343–2.855, P<0.001), NT-proBNP >125 pmol/L (HR 2.144, 95% CI 1.403–3.276, P=0.001), and 6MWD ≤360 m (HR 1.923, 95% CI 1.195–3.096, P=0.007) were independent risk factors in predicting patient death [32]. In our study, we did not find any association of poor outcome with age, sex, or BMI, but this could be explained by the fact that more than a half of all the participants of our study were 75 or older. The results for the NYHA functional class, NT-proBNP, and 6MWD were consistent with our findings, except for the lower NYHA class and NT-proBNP margins and the higher 6MWD margin, but this may be explained by the fact that the study by Scardovi et al. analyzed the data of outpatients with stable heart failure.
The present study found a weak to moderate correlation between the ICG data, 6MWD, and other non-invasive tests for CHF. The strongest correlations were found between: NT-proBNP and TFCI; 6MWD and PEP; LVEF and STR and PEP; LAD and SRT and PEP; NYHA class and STR, TFC, TFCI and SV. However, no statistically significant correlations were found between ICG parameters and LVEDDI, while associations with other TTE parameters were weak. The associations of the NYHA functional class with the results of the other analyzed non-invasive diagnostic tests for CHF were found to be weak to moderate, with the strongest associations found between the NYHA functional class and LVEF, LAD, serum NT-proBNP levels, and LVEDDI.
The findings of researchers who have evaluated the association of ICG data and 6MWT data with other invasive and non-invasive tests for the diagnosis of CHF are contradictory. Some find moderate to very strong associations, others do not find statistically significant associations at all. For example, Sadauskas et al [29,33] and Galas et al [28] also found that TFC correlates best with serum NP concentration, with correlation coefficients of 0.425 (P=0.001), 0.4 (P<0.001), and 0.46 (P=0.000001), respectively. Ling Ding et al [34] also found that 6MWD correlated best with CO (r=0.494, P=0.023), SI (r=0.633, P=0.002), LCW (r=0.615, P=0.003), and LCWI (r=0.491, P=0.024). Fang Liu et al [15] also found that 6MWD correlated best with SI (r=0.42, P<0.05) and CO (r=0.66, P<0.01). Parot et al [35] also found that the change in LVEF determined by TTE during patient treatment correlated well with the change in STR (r=−0.73). Sadauskas et al found a similar association between LVEF and STR: r=−0.31, P=0.001 [29]; r=−0.4, P=0.002 [33]. However, one study did not find even a satisfactory association between CO determined by thermodilution and CO determined by ICG [36]. In one study, a weak association between the 2 parameters (area under the curve 0.537) was found, predicting a 15% change in CO measured by thermodilution, whereas the ICG method had a sensitivity of only 55.56%, a specificity of 33.33% and a diagnostic accuracy of 46.67% [36]. In summary, the associations between ICG data and 6MWD and other non-invasive diagnostic tests examined in this study were consistent with those of other investigators, but the associations were not strong, possibly influenced by the more severe condition of the patients in the study.
This study has several limitations. First, the study included patients from only one hospital, and thus the specifics of the hospital might have influenced the results. Second, the study included elderly patients with a median age of 75 years, thus the results of this study may only be applicable to older patients. Third, the study included 87 patients, thus a larger sample size would be required to validate our study results. Fourth, only hospitalized patients with a previously established diagnosis of CHF were included in the study, while outpatients were excluded, which might have led to a poorer prognosis for the patients. Fifth, most of the study participants belonged to NYHA functional class III and IV, and therefore the results of this study should not be generalized to patients with mild and stable CHF. Sixth, ICG is not used routinely in many in-patient and out-patient clinics, so, it is also a certain limitation for the implementation of the results of this study in the clinical practice. Seventh, the major limitation of the study is the lack of control group. And last, 6MWT is not without limitations as well. Its results can be affected by myriad factors with no direct relation to cardiopulmonary status, including age, sex, height, and weight. It can also be affected by comorbidities such as cognitive impairment, peripheral arterial disease, musculoskeletal disorders. Therefore, the authors acknowledge that a larger multicenter study with control group should be performed to confirm the results of this study.
Conclusions
In this study, TFC ≥41.1 1/kΩ, NT-proBNP ≥332.0 pmol/L, 6MWD ≤203.5 m, and dRPAcXR had a combined prognostic value in predicting cardiovascular death in patients with CHF. Therefore, these parameters may be of value in assessment of the diagnosis and prognosis in this patient cohort. |
Title: Novel Therapeutic Approach for Obesity: Seaweeds as an Alternative Medicine with the Latest Conventional Therapy | Body:
1. Introduction
A World Health Organization (WHO) report indicates that more than 600 million people are now affected by obesity globally [1]. The report on obesity in Korea shows that the male obesity rate decreases with age (especially over 70 years), while the female obesity rate increases rapidly with age [2]. It has been observed that obesity is the result of the imbalance between intake and expenditure of energy in the body, and it is a state of chronic, proinflammatory process that initiates from the surplus adipose tissue [3]. Obesity is also defined as excess accumulation of intra-abdominal body fat, which may be divided into two categories: subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). VAT may be a stronger predictor of obesity connected with inflammation and metabolic disorders [4]. Adipocytes store excess fats and triacylglycerols, which serve as energy reserves. When the body needs energy, these reserves are broken down, releasing free fatty acids that can be used for fuel. The storage of triacylglycerols also forms adipokines, a key cytokine of adipose tissue involved in inflammation, immunity, and energy metabolism. The adipokines affect glucose metabolism in the liver, muscles, and fat tissues [5]. Additionally, obesity induces chronic low-grade inflammation, which can negatively impact the quality of life and cast an additional burden on healthcare expenses.
The association between obesity and colorectal cancer was evaluated in the last decade but, in 2022, obesity’s association with breast cancer and lung cancer has also been observed [6,7]. Allopathic medicine, which is being used for the reduction in obesity, also has side effects and produces metabolic disorders. Therefore, it is necessary to find an alternative medicine along with conventional therapy for better treatment of obesity. In the latest study in 2024, the effect of various kinds of exercise and yoga on obesity was explained [1]. Now, in the path of alternative medicine, seaweeds might play an important role and provide a new direction as supplementary materials in treating obesity. In the current review, the significance of seaweeds in treating obesity is discussed with the latest conventional therapy.
Seaweeds are autotrophic organisms found in coastal areas and are an excellent source of protein, vitamins, essential fat, fibers, and omega-3 and omega-6 polyunsaturated fatty acids [8]. There are several types of seaweeds; green and red seaweeds contain higher protein than brown–green seaweeds, while polyphenols are higher in brown seaweeds as compared to red seaweeds [9]. Seaweeds can be classified taxonomically into three major groups: Chlorophyta (green algae), Phaeophyceae (brown algae), and Rhodophyta (red algae). Seaweed is the traditional food of many countries, including China, Japan, Korea, Mexico, and other countries [9]. Fiber is a copious important element for healthy food and it can be classified into soluble and insoluble fiber. Soluble fibers are responsible for gel formation in contact with water, in the intestine, while insoluble fiber is not capable of making gel. Insoluble fiber is capable of retaining water, acts as a laxative in the digestive tract, and accelerates fecal intestinal movement [10]. Soluble fiber decreases blood cholesterol and glucose concentration and promotes the growth of intestinal microbiota [11]. Seaweeds contain primary and secondary metabolites; primary metabolites like carbohydrates and fiber help reduce cardiovascular disease and obesity, while secondary metabolites fight a stressful situation. Secondary metabolites such as polyphenols and fucoxanthin have been seen to suppress hunger and reduce obesity.
The major diseases or disorders related to obesity are metabolic syndrome, high blood pressure, dyslipidemia, myocardial infarction, stroke, and specific cancers. Obesity patients are simply frustrated throughout their life and feel anxiety, which reflects on their faces. In the present review, the impact of various anti-obesity seaweed and bioactive ingredients food on obesity has been studied.
2. Seaweeds as Anti-Obesity Weapons
Seaweed is a good source of fiber and highly unsaturated fatty acids (HUFA) such as alpha-linolenic acid (18:3n-3) and LA (linoleic acid, 18:2n-6). These fatty acids are called essential fatty acids as they are necessary for our health development but are not synthesized in our body [12,13]. In our body, these essential fatty acids change into long-chain fatty acids. These long-chain fatty acids, such as arachidonic acid, are important for brain development and reduce obesity. The latest research in 2022 shows the reduced level of inflammatory markers and cytokines in obese mice [14]. Sourav Roy et al. provided arachidonic acid supplementation to high-fat-diet-induced mice and observed that cytokine gene expression reduced in obese mice, although the study shows no significant changes in the level of triglyceride. In Palmaria palmata (red algae), essential fatty acid changes into eicosatetraenoic, while, in Gracilaria gracilis, it changes into Arachidonic acid. Additionally, seaweeds also contain various bioactive compounds such as pigments and minerals, which may reduce pathological conditions of diseases [15]. Epidemiologic studies showed that intake of seaweeds reduces the causes of the onset of lipidemia, cancer, and type 2 diabetes [16].
Fucoxanthin is one of the pigments obtained from seaweeds that reduce the absorption of triglyceride in GIT and reduce obesity [17]. Alginates, another cluster of algal compounds, possess inhibitory activity against exocrine gland enzymes. The repressing activity of alginates might vary in different kinds of algae and also depends on the composition of various alginates [18]. El Khoury and colleagues investigated how adding sodium alginate to chocolate milk affected blood glucose and insulin levels. They found that 2.5% alginate-added milk reduced the 6% glucose peak and 46% insulin peak at 30 min compared to chocolate milk [19].
2.1. Green Algae Carotenoid Siphonoxanthin and Neoxanthin
Siphonoxanthin has been identified as a bioactive compound in green algae such as Caulerpa lentillifera. The anti-obesity effect of Siphonoxanthin in 3T3 cells has been evaluated and it was found that it prevents lipid accumulation and adipogenesis. Siphonoxanthin, a compound found in green algae, can stimulate energy expenditure and decrease fat storage in white adipose tissue (WAT) by altering gene expression, which can inhibit lipid synthesis in WAT and potentially prevent obesity. Apart from the anti-obesity effect, it also contains anti-cancerous activity, as it enhances the antiangiogenic impact on cancer cells [20]. It also inhibits kinase B phosphorylation in 3T3-L1 cells and reduces the gene expression of CCAAT-enhancer-binding protein α (C/EBPα), peroxisome proliferator-activated receptor γ (PPARγ), fatty-acid-binding protein 4 (Fabp4), and stearoyl-CoA desaturase 1 (SCD1) [21].
Another carotenoid present in green algae and plants is Neoxanthin. It is important xanthophylls are found in algae along with terrestrial plants. Neoxanthin is found in two isomeric forms; one is 9′-cis-neoxanthin and another is trans-neoxanthin. 9′-cis-neoxanthin is found in green leaves abundantly where photosynthesis occurs, while trans-neoxanthin is found in fruits and flower petals [22]. An in vitro study on 3T3 cells reveals that Neoxanthin inhibits lipid accumulation like Siphonoxanthin and decreases gene expression of C/EBPα and PPARγ [23].
2.2. Brown Seaweed Phlorotannin
Phlorotannin is a polyphenol that inhibits the lipid digestive enzyme, pancreatic lipase; as a result, lipid absorption is hampered and lipids are excreted as undigested food from the body. Phlorotannin not only reduces obesity but also contains anticancer and antiviral activity [24].
Brown seaweeds are a good source of Phlorotannin, a natural bioactive compound. The lipase enzyme is responsible for lipid digestion secreted in pancreatic juice, which digests lipids into fatty acids and glycerol and helps for absorption of lipids in the duodenum and jejunum (small intestine) [25]. Seaweeds can be considered potential therapeutic agents due to their rich source of bioactive compounds. Algae bioactive compounds are known for the management of digestion and have the potential capacity for obesity treatments [26].
2.3. Brown Seaweed Fucoxanthin
Fucoxanthin, a marine carotenoid predominantly found in brown seaweeds and diatoms, constitutes the majority of naturally occurring carotenoids. It is particularly abundant in edible seaweeds like Undaria pinnatifida, Laminaria digitata, and Hijikia fusiformis, as well as marine microalgae. Fucoxanthin’s potent inhibitory properties make it a promising candidate for functional food applications [27]. The anti-obesity property of fucoxanthin depends upon the fact that it modulates the transcription factors/regulators that play a crucial role in adipocyte differentiation and development [28].
In adipocytes, various nuclear protein receptors are expressed during early development, which regulate adipogenesis. Sterol regulatory-element-binding protein1c (SREBP-1c) is one of the nuclear protein receptors that regulate fatty acid and cholesterol synthesis [29]. In another pathway, fucoxanthin upregulates the uncoupling protein1 in brown adipose tissue. The uncoupling protein1 increases fat oxidation in mitochondria in adipose tissue and enhances thermogenesis. The mechanism of the effect of seaweeds on obesity is illustrated in Figure 1.
An in vitro study by Maeda et al. analyzed the anti-obesity effect of fucoxanthin in 3T3-L1 cells and found that fucoxanthin prevented the deposition of lipids inside the 3T3-L1 and stopped the conversion of fucoxanthin to fucoxanthinol. The 3T3-L1 cell line was created in 1962 by modifying the original 3T3 Swiss albino cell line. It is used to investigate diseases and abnormalities associated with adipose tissue. Fucoxanthin also reduces lipid differentiation and development in 3T3-L1 cells [30]. The effect of fucoxanthin on body weight is still unclear. Some studies have shown that fucoxanthin can help mice and rats gain weight when they are fed a high-fat diet, while other studies have found no significant difference. The study was investigated to see the potential benefits of fucoxanthin-rich wakame lipids (68% glycoloipid + 10% fucoxanthin) in combating obesity in mice. Mice were provided a high-fat diet and compared with a normal-fat diet for 10 weeks. The study found that fucoxanthin reduces body weight along with the weight of white adipose tissue [31]. The study is also supported by another study carried out in 2015 by Maria et al. [32].
2.4. Brown Seaweed Alginates
Alginate or alginic acid is a naturally edible polysaccharide known for its anti-obesity effects. Alginate’s derivatives of algin copolymerize with β-1,4-d-mannuronic acid and α-1,4-l-guluronic acid to form the structural part of brown seaweeds. The role of sodium alginate, a dietary fiber derived from brown seaweed, in weight loss has been observed [33]. However, the exact mechanism of how alginate reduces body weight is still not clear. In one study, when alginate was provided to overweight people (15 g/day) for 14 weeks, it reduced body weight in comparison to maltodextrin [34]. Alginate forms gel in the stomach due to its viscous nature, giving the feeling of satiety and reducing appetite [35]. Eating alginate bread was associated with lower levels of circulating triacylglycerols compared to a control diet. Alginate may help reduce lipid absorption by inhibiting pancreatic lipase, an enzyme that is involved in fat breakdown [36]. Alginate may change the gene expression related to lipid metabolism and show the weight loss effect. The latest research indicates the role of microbiota in maintaining health and reducing obesity and related disorders. It is believed that alginate can help prevent weight gain by improving gut microbiota [37].
2.5. Brown Seaweed Fucoidans
It is an extremely sulfated polysaccharide found in the cell wall and extracellular space between the cells in brown seaweeds. The major sugar present in fucoidans is fucose, which is found in pyranose form. Like the other polysaccharides, it is not linked with α-[1,4] or α-[1,6] glycosidic bonds, although it is α-(1–3)-linked. Fucoidans have anti-inflammatory and wound-healing properties [38]. Few studies validate that fucoidans may inhibit obesity. The antiadipogenic activity of fucoidans has been seen in Undaria pinnatifida brown seaweed. Moreover, fucoidans prevent the accumulation of lipids and reactive oxygen species (ROS) in adipocytes. An in vitro study revealed that fucoidans were additionally found to inhibit lipid deposition in cell line 3T3-L1 cells [39,40]. Fucoidans downregulate the gene expression related to adipogenesis in the 3T3-L1 cell line. The study indicates that fucoidans may reduce obesity by either preventing the deposition of fat or promoting lipolysis.
2.6. Red Seaweed
Campylaephora hypnaeoides J. Agardh (C. hypnaeoides) is a very famous red seaweed in Japan. It is also used as a local dish known as Egonori in Japan and contains high polysaccharides. It is found near the coast of the Japan Sea. This study reveals that it prevents postprandial hyperglycemia. In the mice study, it was observed that, when the red seaweed was given to HF-diet-induced obese mice for 13 weeks, the HF diet supplemented with 2% C. hypnaeoides and 6% C. hypnaeoides reduced body weight, decreased insulin resistance, decreased adiponectin, and increased malondialdehyde (MDA), tumor necrosis factor α (TNF-α), and monocyte chemoattractant protein-1 (MCP-1) [41]. It also decreased glutathione and superoxide dismutase, which increase oxidative stress. In this way, it suppressed inflammatory response and increased antioxidant response. This study reveals that red seaweeds have anti-obesity therapeutic potential; however, more human trials are required to ascertain the facts.
2.7. Astaxanthin (ASX)
It is xanthophyll carotenoids containing red pigment that have antioxidant properties [42]. It is the most potent antioxidant, exhibiting 100 times more oxygen-scavenging ability than vitamin E. It inhibits cholesterol synthesis and reduces the size of adipocytes and body weight [43]. The study demonstrates that, when astaxanthin extracted from green microalga Haematococcus pluvialis is taken as a supplement, it reduces body weight and prevents lipid storage in the liver, which enhances liver functions [44]. Gut microbiota analysis revealed that astaxanthin alleviated high-fat-diet-induced gut dysbiosis by restoring the Firmicutes-to-Bacteroides ratio. Additionally, astaxanthin reduces the number of obesity-associated pathogenic bacteria and induces the growth of beneficial microbiota associated with carbohydrate and lipid metabolism. Studies conducted on Caenorhabditis elegans have shown that astaxanthin, when combined with other compounds like anthocyanin (in nanoparticle form), can reduce lipid levels and exhibit antioxidant properties, suggesting potential benefits of astaxanthin in addressing obesity. In high-sugar-diet-induced high-fat Caenorhabditis elegans, these nanoparticle supplementations reduce lipid accumulation and increase life span by improving antioxidants [45]. The antioxidant property of astaxanthin is higher than other carotenoids due to the presence of two additional oxygenated groups. Astaxanthin, a potent antioxidant, neutralizes reactive oxygen species (ROS) and reactive nitrogen species (RNS), reduces the low-grade inflammation in adipose tissue, and reduces obesity [46]. The possible mechanism of astaxanthin is illustrated in Figure 2. Humans acquire astaxanthin primarily through seafood consumption or dietary supplements. Healthy adults can safely consume up to 6 milligrams of astaxanthin daily. Despite astaxanthin having several beneficial effects, few studies have produced inconsistent results regarding the actual health benefits attributed to astaxanthin.
2.1. Green Algae Carotenoid Siphonoxanthin and Neoxanthin
Siphonoxanthin has been identified as a bioactive compound in green algae such as Caulerpa lentillifera. The anti-obesity effect of Siphonoxanthin in 3T3 cells has been evaluated and it was found that it prevents lipid accumulation and adipogenesis. Siphonoxanthin, a compound found in green algae, can stimulate energy expenditure and decrease fat storage in white adipose tissue (WAT) by altering gene expression, which can inhibit lipid synthesis in WAT and potentially prevent obesity. Apart from the anti-obesity effect, it also contains anti-cancerous activity, as it enhances the antiangiogenic impact on cancer cells [20]. It also inhibits kinase B phosphorylation in 3T3-L1 cells and reduces the gene expression of CCAAT-enhancer-binding protein α (C/EBPα), peroxisome proliferator-activated receptor γ (PPARγ), fatty-acid-binding protein 4 (Fabp4), and stearoyl-CoA desaturase 1 (SCD1) [21].
Another carotenoid present in green algae and plants is Neoxanthin. It is important xanthophylls are found in algae along with terrestrial plants. Neoxanthin is found in two isomeric forms; one is 9′-cis-neoxanthin and another is trans-neoxanthin. 9′-cis-neoxanthin is found in green leaves abundantly where photosynthesis occurs, while trans-neoxanthin is found in fruits and flower petals [22]. An in vitro study on 3T3 cells reveals that Neoxanthin inhibits lipid accumulation like Siphonoxanthin and decreases gene expression of C/EBPα and PPARγ [23].
2.2. Brown Seaweed Phlorotannin
Phlorotannin is a polyphenol that inhibits the lipid digestive enzyme, pancreatic lipase; as a result, lipid absorption is hampered and lipids are excreted as undigested food from the body. Phlorotannin not only reduces obesity but also contains anticancer and antiviral activity [24].
Brown seaweeds are a good source of Phlorotannin, a natural bioactive compound. The lipase enzyme is responsible for lipid digestion secreted in pancreatic juice, which digests lipids into fatty acids and glycerol and helps for absorption of lipids in the duodenum and jejunum (small intestine) [25]. Seaweeds can be considered potential therapeutic agents due to their rich source of bioactive compounds. Algae bioactive compounds are known for the management of digestion and have the potential capacity for obesity treatments [26].
2.3. Brown Seaweed Fucoxanthin
Fucoxanthin, a marine carotenoid predominantly found in brown seaweeds and diatoms, constitutes the majority of naturally occurring carotenoids. It is particularly abundant in edible seaweeds like Undaria pinnatifida, Laminaria digitata, and Hijikia fusiformis, as well as marine microalgae. Fucoxanthin’s potent inhibitory properties make it a promising candidate for functional food applications [27]. The anti-obesity property of fucoxanthin depends upon the fact that it modulates the transcription factors/regulators that play a crucial role in adipocyte differentiation and development [28].
In adipocytes, various nuclear protein receptors are expressed during early development, which regulate adipogenesis. Sterol regulatory-element-binding protein1c (SREBP-1c) is one of the nuclear protein receptors that regulate fatty acid and cholesterol synthesis [29]. In another pathway, fucoxanthin upregulates the uncoupling protein1 in brown adipose tissue. The uncoupling protein1 increases fat oxidation in mitochondria in adipose tissue and enhances thermogenesis. The mechanism of the effect of seaweeds on obesity is illustrated in Figure 1.
An in vitro study by Maeda et al. analyzed the anti-obesity effect of fucoxanthin in 3T3-L1 cells and found that fucoxanthin prevented the deposition of lipids inside the 3T3-L1 and stopped the conversion of fucoxanthin to fucoxanthinol. The 3T3-L1 cell line was created in 1962 by modifying the original 3T3 Swiss albino cell line. It is used to investigate diseases and abnormalities associated with adipose tissue. Fucoxanthin also reduces lipid differentiation and development in 3T3-L1 cells [30]. The effect of fucoxanthin on body weight is still unclear. Some studies have shown that fucoxanthin can help mice and rats gain weight when they are fed a high-fat diet, while other studies have found no significant difference. The study was investigated to see the potential benefits of fucoxanthin-rich wakame lipids (68% glycoloipid + 10% fucoxanthin) in combating obesity in mice. Mice were provided a high-fat diet and compared with a normal-fat diet for 10 weeks. The study found that fucoxanthin reduces body weight along with the weight of white adipose tissue [31]. The study is also supported by another study carried out in 2015 by Maria et al. [32].
2.4. Brown Seaweed Alginates
Alginate or alginic acid is a naturally edible polysaccharide known for its anti-obesity effects. Alginate’s derivatives of algin copolymerize with β-1,4-d-mannuronic acid and α-1,4-l-guluronic acid to form the structural part of brown seaweeds. The role of sodium alginate, a dietary fiber derived from brown seaweed, in weight loss has been observed [33]. However, the exact mechanism of how alginate reduces body weight is still not clear. In one study, when alginate was provided to overweight people (15 g/day) for 14 weeks, it reduced body weight in comparison to maltodextrin [34]. Alginate forms gel in the stomach due to its viscous nature, giving the feeling of satiety and reducing appetite [35]. Eating alginate bread was associated with lower levels of circulating triacylglycerols compared to a control diet. Alginate may help reduce lipid absorption by inhibiting pancreatic lipase, an enzyme that is involved in fat breakdown [36]. Alginate may change the gene expression related to lipid metabolism and show the weight loss effect. The latest research indicates the role of microbiota in maintaining health and reducing obesity and related disorders. It is believed that alginate can help prevent weight gain by improving gut microbiota [37].
2.5. Brown Seaweed Fucoidans
It is an extremely sulfated polysaccharide found in the cell wall and extracellular space between the cells in brown seaweeds. The major sugar present in fucoidans is fucose, which is found in pyranose form. Like the other polysaccharides, it is not linked with α-[1,4] or α-[1,6] glycosidic bonds, although it is α-(1–3)-linked. Fucoidans have anti-inflammatory and wound-healing properties [38]. Few studies validate that fucoidans may inhibit obesity. The antiadipogenic activity of fucoidans has been seen in Undaria pinnatifida brown seaweed. Moreover, fucoidans prevent the accumulation of lipids and reactive oxygen species (ROS) in adipocytes. An in vitro study revealed that fucoidans were additionally found to inhibit lipid deposition in cell line 3T3-L1 cells [39,40]. Fucoidans downregulate the gene expression related to adipogenesis in the 3T3-L1 cell line. The study indicates that fucoidans may reduce obesity by either preventing the deposition of fat or promoting lipolysis.
2.6. Red Seaweed
Campylaephora hypnaeoides J. Agardh (C. hypnaeoides) is a very famous red seaweed in Japan. It is also used as a local dish known as Egonori in Japan and contains high polysaccharides. It is found near the coast of the Japan Sea. This study reveals that it prevents postprandial hyperglycemia. In the mice study, it was observed that, when the red seaweed was given to HF-diet-induced obese mice for 13 weeks, the HF diet supplemented with 2% C. hypnaeoides and 6% C. hypnaeoides reduced body weight, decreased insulin resistance, decreased adiponectin, and increased malondialdehyde (MDA), tumor necrosis factor α (TNF-α), and monocyte chemoattractant protein-1 (MCP-1) [41]. It also decreased glutathione and superoxide dismutase, which increase oxidative stress. In this way, it suppressed inflammatory response and increased antioxidant response. This study reveals that red seaweeds have anti-obesity therapeutic potential; however, more human trials are required to ascertain the facts.
2.7. Astaxanthin (ASX)
It is xanthophyll carotenoids containing red pigment that have antioxidant properties [42]. It is the most potent antioxidant, exhibiting 100 times more oxygen-scavenging ability than vitamin E. It inhibits cholesterol synthesis and reduces the size of adipocytes and body weight [43]. The study demonstrates that, when astaxanthin extracted from green microalga Haematococcus pluvialis is taken as a supplement, it reduces body weight and prevents lipid storage in the liver, which enhances liver functions [44]. Gut microbiota analysis revealed that astaxanthin alleviated high-fat-diet-induced gut dysbiosis by restoring the Firmicutes-to-Bacteroides ratio. Additionally, astaxanthin reduces the number of obesity-associated pathogenic bacteria and induces the growth of beneficial microbiota associated with carbohydrate and lipid metabolism. Studies conducted on Caenorhabditis elegans have shown that astaxanthin, when combined with other compounds like anthocyanin (in nanoparticle form), can reduce lipid levels and exhibit antioxidant properties, suggesting potential benefits of astaxanthin in addressing obesity. In high-sugar-diet-induced high-fat Caenorhabditis elegans, these nanoparticle supplementations reduce lipid accumulation and increase life span by improving antioxidants [45]. The antioxidant property of astaxanthin is higher than other carotenoids due to the presence of two additional oxygenated groups. Astaxanthin, a potent antioxidant, neutralizes reactive oxygen species (ROS) and reactive nitrogen species (RNS), reduces the low-grade inflammation in adipose tissue, and reduces obesity [46]. The possible mechanism of astaxanthin is illustrated in Figure 2. Humans acquire astaxanthin primarily through seafood consumption or dietary supplements. Healthy adults can safely consume up to 6 milligrams of astaxanthin daily. Despite astaxanthin having several beneficial effects, few studies have produced inconsistent results regarding the actual health benefits attributed to astaxanthin.
3. Seaweed Edible Product
Milk products like milk dessert, yoghurt, and smoked cheese are currently being used to mix brown algae Laminaria. Smoked cheese is a cheese that has been preserved using smoke-curing. There are two main methods of smoke-curing: cold-smoking, which involves exposing the food to smoke at temperatures between 20° and 30 °C, and hot-smoking, which partially or fully cooks the food while treating it with smoke at temperatures ranging from 40° to 90 °C. Alginate oligosaccharides of Laminaria hyperborean are being used as supplements in yogurt [47]. The nutritional value of milk products can be enhanced by adding seaweed. The brown algae Ascophyllum nodosum and Fucus vesiculosus have been used as antioxidants to enhance the life of milk and quality [48]. Saccharina latissimi use as iodine supplementation has been observed in cheese. Cardiovascular diseases such as hypertension and others can be prevented by adding seaweeds to food content as their application in food reduces sodium content [49]. Due to acting as an antioxidant and having Angiotensin-I-converting enzyme inhibitory activity, Palmaria palmate and Saccharina longicruris are also used as flakes. P. palmata is a very good source of protein and carbohydrate contents, while S. longicruris has an excellent source of fiber and mineral contents, which makes it a very promising food [50]. Undaria pinnatifida and Laminaria ochroleuca are being used to develop gluten-free products [51]. The seaweeds and their edible products with their beneficial effects are illustrated in Figure 3.
3.1. Japanese Famous Edible Seaweed
In Japan, seaweeds like Mozuku, Hijiki, Wakame, and Nori are highly prized for their nutritional value and health benefits. These seaweeds are popular choices due to their potential to lower blood pressure, reduce the risk of cardiovascular diseases, and aid in weight management and metabolic health. The Japanese seaweeds are illustrated in Figure 4.
Mozuku
Mozuku, a type of seaweed, is primarily farmed in Okinawa, where it is known for its chewy texture and slightly slimy consistency. Residents of Okinawa Prefecture exhibited significantly higher consumption of Mozuku compared to those residing outside the prefecture [52]. The average Japanese adult consumes approximately 14.3 g of seaweed per day [53].
In contrast, Itomozuku, found in the Hokuriku region, is significantly slimmer with finer strands. Mozuku contains fucoidan, a soluble dietary fiber that can be easily absorbed when added to miso or other soups. Consuming Mozuku with vinegar and soy sauce can enhance calcium absorption due to the citric acid in the vinegar. This nutritious seaweed offers a refreshing and smooth texture, making it a popular side dish.
Hijiki
Hijiki, a type of seaweed, is typically sold in two forms: soft, bud-like mehijiki and chewy, long-stemmed nagahjiki. While both fresh and dried hijiki are available, the majority of hijiki sold in Japan is imported. Dried hijiki is often rehydrated and cooked as nimono, a simmered dish that often includes carrots, aburaage (deep-fried tofu), soybeans, and shiitake mushrooms. Hijiki can be incorporated into various Japanese dishes, such as rice dishes, salads, tamagoyaki (rolled omelet), tempura, shiraae (mashed tofu salad), sunomono, and miso soup. Renowned for its high calcium content, hijiki is a valuable food for those seeking to address calcium deficiencies. Hijiki algae extract showed the weakest antimicrobial activity [54]. Hijiki seaweed contained the highest amount of calcium, with a concentration of 8853.82 milligrams per kilogram [55]. The study also indicated that cobalt is present in hijiki and kombu. Hijiki fusiformis demonstrated the strongest antioxidant activity, reaching 65% [56].
Wakame
Wakame, a type of seaweed, is a popular ingredient in Japanese cuisine. It is prized for its nutritional value, low-calorie content, and ability to aid digestion. Wakame is often found in dried form but it can also be purchased fresh, cooked, salted, or charcoal-dried. While most wakame available in Japan is farmed, the town of Fukaura in Aomori specializes in natural wakame harvested from the ocean. They use a traditional drying method called yaki-boshi-hai, which involves baking and drying the seaweed in ash. This process helps preserve the seaweed’s bright color, flavor, and aroma.
Wakame was found to have a significant acute effect on postprandial blood glucose and insulin levels. When consumed with rice, wakame reduced both blood glucose and insulin levels within 30 min compared to consuming rice alone [57]. Wakame is rich in minerals like calcium, iron, phosphate, fiber, and vitamin K. These nutrients make it a beneficial food for bone health and digestion. To maximize mineral absorption, it is recommended to season wakame with vinegar. Due to its low-calorie content, wakame is a popular choice for those on diets. Wakame is helpful in the prevention of metabolic syndrome [58].
Nori
Nori, a popular seaweed in Japan, is primarily sold in two main types: susabinori and asakusanori. It is available in three forms: fresh (namanori), dried (kannori), and grilled (yakinori). Most nori sold in stores is grilled. To produce dried and grilled nori, fresh sheets of seaweed are spread out like washi paper and allowed to dry. Nori has numerous culinary uses, serving as a wrap for sushi, onigiri, and isobemochi (grilled rice cakes). It is also an ingredient in furikake seasoning and a topping for zaru soba (chilled noodles) and ramen. Flavored nori, made from grilled nori coated with sauce, is a popular accompaniment to rice.
Nori was used to prepare sauce by fermentation procedure and compared with soy and fish sauces, indicating a rich source of potassium and nitrogen [59].
Approximately 40% of nori demand is for home consumption and gifting, while 60% is for processing. The majority of processed nori is used in onigiri sold at convenience stores. Fresh nori finds its way into dishes like tsukdani (a preserved food) and miso soup and can be added to pasta. Often referred to as a “vegetable of the sea”, nori is rich in vitamin C, protein, and dietary fiber. Consuming nori (dried laver) was associated with lower diastolic blood pressure levels in boys, suggesting that seaweed intake may help prevent hypertension in childhood [60]. Nori, a type of red seaweed commonly cultivated in Asia, has been found to enhance the immune response in mouse macrophages. Extracts from nori, scientifically known as Porphyra tenera, were shown to regulate the NF-κB IκB kinase (IKK) signaling pathway, a key pathway involved in immune function [61].
3.2. Preclinical and Clinical Evaluation on the Efficacy and Safety of Seaweed for Obesity Treatment
The U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) follow a similar four-phase drug development process. Each phase involves multiple stages, ensuring the safety and efficacy of new medications as follows: (1) Discovery and Development: identification of potential drug candidates through research and screening; characterization of the molecular targets and mechanisms of action. (2) Preclinical Research: laboratory testing (in vitro) to assess the drug’s properties and interactions with biological molecules; animal studies (in vivo) to evaluate safety, efficacy, and pharmacokinetics. (3) Clinical Trials: Phase I: small-scale studies in healthy volunteers to assess safety and tolerability. Phase II: larger studies in patients with the target disease to evaluate efficacy and identify optimal dosage. Phase III: large-scale clinical trials to confirm efficacy, safety, and optimal dosage in a diverse patient population. (4) Regulatory Review and Approval: submission of comprehensive data to regulatory agencies for review; evaluation of safety, efficacy, and quality standards; approval for commercialization if the drug meets regulatory requirements. (5) Post-Market Surveillance: ongoing monitoring of the drug’s safety and efficacy after it enters the market; identification and reporting of adverse events; potential for updates to the drug’s label or restrictions on use.
To establish seaweed compounds as drugs, rigorous preclinical and clinical research is indispensable. These studies aim to characterize the compound’s chemical structure; assess its biochemical properties, including stability and reactivity; and evaluate its pharmacological activities, such as potency, toxicity, and selectivity. If these studies yield promising results, the compound can proceed to pharmacodynamic and pharmacokinetic studies. These assess how the drug interacts with the body and how it is absorbed, distributed, metabolized, and excreted.
Numerous preclinical and clinical studies have explored the potential of seaweed compounds for treating obesity. Key findings include limited clinical trials: many seaweed compounds remain in preclinical stages, with relatively few progressing to clinical trials; varied methodology: the wide range of methodologies used in preclinical studies has hindered the advancement of some compounds; and regulatory challenges: several seaweed compounds have not been approved by drug regulatory agencies, limiting their clinical development. While seaweed compounds offer potential therapeutic benefits, significant research and regulatory hurdles remain to be overcome before they can be established as effective drugs. Continued efforts are needed to advance the development of seaweed-derived therapeutics for diabetes and other diseases.
3.1. Japanese Famous Edible Seaweed
In Japan, seaweeds like Mozuku, Hijiki, Wakame, and Nori are highly prized for their nutritional value and health benefits. These seaweeds are popular choices due to their potential to lower blood pressure, reduce the risk of cardiovascular diseases, and aid in weight management and metabolic health. The Japanese seaweeds are illustrated in Figure 4.
Mozuku
Mozuku, a type of seaweed, is primarily farmed in Okinawa, where it is known for its chewy texture and slightly slimy consistency. Residents of Okinawa Prefecture exhibited significantly higher consumption of Mozuku compared to those residing outside the prefecture [52]. The average Japanese adult consumes approximately 14.3 g of seaweed per day [53].
In contrast, Itomozuku, found in the Hokuriku region, is significantly slimmer with finer strands. Mozuku contains fucoidan, a soluble dietary fiber that can be easily absorbed when added to miso or other soups. Consuming Mozuku with vinegar and soy sauce can enhance calcium absorption due to the citric acid in the vinegar. This nutritious seaweed offers a refreshing and smooth texture, making it a popular side dish.
Hijiki
Hijiki, a type of seaweed, is typically sold in two forms: soft, bud-like mehijiki and chewy, long-stemmed nagahjiki. While both fresh and dried hijiki are available, the majority of hijiki sold in Japan is imported. Dried hijiki is often rehydrated and cooked as nimono, a simmered dish that often includes carrots, aburaage (deep-fried tofu), soybeans, and shiitake mushrooms. Hijiki can be incorporated into various Japanese dishes, such as rice dishes, salads, tamagoyaki (rolled omelet), tempura, shiraae (mashed tofu salad), sunomono, and miso soup. Renowned for its high calcium content, hijiki is a valuable food for those seeking to address calcium deficiencies. Hijiki algae extract showed the weakest antimicrobial activity [54]. Hijiki seaweed contained the highest amount of calcium, with a concentration of 8853.82 milligrams per kilogram [55]. The study also indicated that cobalt is present in hijiki and kombu. Hijiki fusiformis demonstrated the strongest antioxidant activity, reaching 65% [56].
Wakame
Wakame, a type of seaweed, is a popular ingredient in Japanese cuisine. It is prized for its nutritional value, low-calorie content, and ability to aid digestion. Wakame is often found in dried form but it can also be purchased fresh, cooked, salted, or charcoal-dried. While most wakame available in Japan is farmed, the town of Fukaura in Aomori specializes in natural wakame harvested from the ocean. They use a traditional drying method called yaki-boshi-hai, which involves baking and drying the seaweed in ash. This process helps preserve the seaweed’s bright color, flavor, and aroma.
Wakame was found to have a significant acute effect on postprandial blood glucose and insulin levels. When consumed with rice, wakame reduced both blood glucose and insulin levels within 30 min compared to consuming rice alone [57]. Wakame is rich in minerals like calcium, iron, phosphate, fiber, and vitamin K. These nutrients make it a beneficial food for bone health and digestion. To maximize mineral absorption, it is recommended to season wakame with vinegar. Due to its low-calorie content, wakame is a popular choice for those on diets. Wakame is helpful in the prevention of metabolic syndrome [58].
Nori
Nori, a popular seaweed in Japan, is primarily sold in two main types: susabinori and asakusanori. It is available in three forms: fresh (namanori), dried (kannori), and grilled (yakinori). Most nori sold in stores is grilled. To produce dried and grilled nori, fresh sheets of seaweed are spread out like washi paper and allowed to dry. Nori has numerous culinary uses, serving as a wrap for sushi, onigiri, and isobemochi (grilled rice cakes). It is also an ingredient in furikake seasoning and a topping for zaru soba (chilled noodles) and ramen. Flavored nori, made from grilled nori coated with sauce, is a popular accompaniment to rice.
Nori was used to prepare sauce by fermentation procedure and compared with soy and fish sauces, indicating a rich source of potassium and nitrogen [59].
Approximately 40% of nori demand is for home consumption and gifting, while 60% is for processing. The majority of processed nori is used in onigiri sold at convenience stores. Fresh nori finds its way into dishes like tsukdani (a preserved food) and miso soup and can be added to pasta. Often referred to as a “vegetable of the sea”, nori is rich in vitamin C, protein, and dietary fiber. Consuming nori (dried laver) was associated with lower diastolic blood pressure levels in boys, suggesting that seaweed intake may help prevent hypertension in childhood [60]. Nori, a type of red seaweed commonly cultivated in Asia, has been found to enhance the immune response in mouse macrophages. Extracts from nori, scientifically known as Porphyra tenera, were shown to regulate the NF-κB IκB kinase (IKK) signaling pathway, a key pathway involved in immune function [61].
3.2. Preclinical and Clinical Evaluation on the Efficacy and Safety of Seaweed for Obesity Treatment
The U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) follow a similar four-phase drug development process. Each phase involves multiple stages, ensuring the safety and efficacy of new medications as follows: (1) Discovery and Development: identification of potential drug candidates through research and screening; characterization of the molecular targets and mechanisms of action. (2) Preclinical Research: laboratory testing (in vitro) to assess the drug’s properties and interactions with biological molecules; animal studies (in vivo) to evaluate safety, efficacy, and pharmacokinetics. (3) Clinical Trials: Phase I: small-scale studies in healthy volunteers to assess safety and tolerability. Phase II: larger studies in patients with the target disease to evaluate efficacy and identify optimal dosage. Phase III: large-scale clinical trials to confirm efficacy, safety, and optimal dosage in a diverse patient population. (4) Regulatory Review and Approval: submission of comprehensive data to regulatory agencies for review; evaluation of safety, efficacy, and quality standards; approval for commercialization if the drug meets regulatory requirements. (5) Post-Market Surveillance: ongoing monitoring of the drug’s safety and efficacy after it enters the market; identification and reporting of adverse events; potential for updates to the drug’s label or restrictions on use.
To establish seaweed compounds as drugs, rigorous preclinical and clinical research is indispensable. These studies aim to characterize the compound’s chemical structure; assess its biochemical properties, including stability and reactivity; and evaluate its pharmacological activities, such as potency, toxicity, and selectivity. If these studies yield promising results, the compound can proceed to pharmacodynamic and pharmacokinetic studies. These assess how the drug interacts with the body and how it is absorbed, distributed, metabolized, and excreted.
Numerous preclinical and clinical studies have explored the potential of seaweed compounds for treating obesity. Key findings include limited clinical trials: many seaweed compounds remain in preclinical stages, with relatively few progressing to clinical trials; varied methodology: the wide range of methodologies used in preclinical studies has hindered the advancement of some compounds; and regulatory challenges: several seaweed compounds have not been approved by drug regulatory agencies, limiting their clinical development. While seaweed compounds offer potential therapeutic benefits, significant research and regulatory hurdles remain to be overcome before they can be established as effective drugs. Continued efforts are needed to advance the development of seaweed-derived therapeutics for diabetes and other diseases.
4. Conventional Treatment of Obesity
The treatment of obesity by medicine has attracted a lot of attention from clinicians and patients, though the quantity of additional weight loss owing to it is modest (<5 kg at 1 year) [62]. This amount of weight loss has been shown to enhance glycemic management, dyslipidemia, hypoglycemic agent sensitivity, and high blood pressure in overweight patients. Several factors enhance obesity. According to the latest study published in Lancet 2023, Metreleptin and Setmelanotide are currently used for rare obesity syndromes, while five other medications (orlistat, phentermine/topiramate, naltrexone/bupropion, liraglutide, and semaglutide) are approved for non-syndromic obesity by the FDA [63].
Metreleptin
Metreleptin (AstraZeneca plc, Cambridge, UK) is a synthetic form of leptin approved by the FDA in 2014 for use as a replacement therapy in patients with congenital or acquired lipodystrophy who have a deficiency in leptin production. Metreleptin is administered as a daily subcutaneous injection. The starting dose ranges from 0.06 mg/kg/day to 0.13 mg/kg/day, depending on the severity of obesity. Metreleptin shows its potential effect around 6 months later; the study published in the Lancet indicated that it reduces cholesterol, triglyceride, and urinary glucose at six months [63]. It also decreases lean body mass and body weight at six months.
Setmelanotide
Setmelanotide,(Rhythm Pharmaceuticals, Inc., Boston, MA, USA) a melanocortin-4 (MC4) receptor agonist, was approved by the FDA in 2020 for chronic weight management. It is administered as a subcutaneous injection and is indicated for patients aged 6 and older with obesity caused by genetic deficiencies in proopiomelanocortin or the leptin receptor (LEPR). The failure of activation of the MC4 pathway enhances hunger and causes childhood-onset obesity. Furthermore, setmelanotide enhances MC4 receptor signaling, reduces hunger, and enhances body weight loss by increasing energy expenditure.
Phentermine/Topiramate
Phentermine/Topiramate (Qysmia, Vivus pharmaceutical Ltd., Campbell, CA, USA) was approved by the FDA in 2012 for the chronic management of obesity. However, due to severe neuropsychological side effects, it was not approved by the European Medical Agency (EMA). The memory-related issue was also noticed as a side effect in obese patients [64].
Naltrexone/Bupropion
Naltrexone/Bupropion, (Orexigen Therapeutics, Inc., La Jolla, CA, USA) (2014/15 FDA approved) acts as a dopamine (DA) and NE reuptake inhibitor along with opioid receptor antagonist, used in bulimia or anorexia nervosa and alcohol dependence. Obesity is associated with the alteration of proopiomelanocortin (POMC) neurons and the reward system of the hypothalamus. NB activates (POMC) neurons in the hypothalamus and increases the production of POMC peptide hormone, which ultimately reduces food intake and weight loss [65].
Semaglutide
Semaglutide (Wegovy, Novo Nordisk, Patel Nagar, New Delhi, India), a GLP-1 analog approved by the FDA in 2021, is used to treat obesity. However, it is not recommended for individuals with a personal or family history of medullary thyroid carcinoma, those with multiple endocrine neoplasia syndrome type 2, or pregnant women.
Tirzepatide
Tirzepatide,(Eli Lilly and Company, Indianapolis, Indiana) received FDA approval, 8 November 2024) for obesity treatment.The novel medicine used for the treatment of obesity, is not used in type I diabetes and severe gastrointestinal disease. Although, it is recommended for type II diabetes [63]. Tirzepatide stimulates insulin secretion from the pancreas, helping to lower blood sugar levels. Additionally, it increases adiponectin levels. Tirzepatide is a 39 amino acid peptide that reduces hunger and decreases hyperglycemia. It has a half-life of 5 days and reaches a maximum level in serum 2 in 3 days [66]. Based on their mechanism of action, the drug treatment of obesity is classified into three categories: (a) medicine that interferes with fat absorption, (b) medicine that increases energy expenditure and thermogenesis, and (c) medicine that cuts back food intake.
4.1. Medicine That Interferes with Fat Absorption
Sibutramine and orlistat are copious important drugs that are included in this category.
4.1.1. Orlistat
Gastric and pancreatic lipases are key enzymes of fat digestion in humans and Orlistat is a selective inhibitor of gastric and pancreatic lipases. It acts on serine residue, which is part of the active site present in these enzymes [67,68]. It prevents the hydrolysis of triglyceride into monomer-unit monosaccharide, which is absorbed in the intestine. Orlistat is a derivative of lipstatin isolated from actinomycete toxytricini [69]. It is an artificial drug designed to act on a target site and block the absorption of dietary fat [70]. Orlistat may be an artificially modified by-product of lipstatin that can be used in the regulation of body weight control [71]. Orlistat is a medication that works by blocking the absorption of dietary fat. Common side effects include flatulence, bloating, abdominal pain, and indigestion, which are often caused by undigested fat passing through the digestive system [62]. Patients taking orlistat should be monitored for nutrient deficiencies, as the medication can reduce fat absorption, leading to a potential shortage of fat-soluble vitamins (A, D, E, and K). A study on obesity found that orlistat treatment was associated with significant reductions in waist circumference, total cholesterol, LDL cholesterol, and blood pressure and improvements in glucose levels and insulin sensitivity [72].
4.1.2. Lipstatin
Lipstatin, a compound initially isolated from the actinobacterium Streptomyces toxytricini, contains a β-lactone ring responsible for its irreversible enzyme inhibition [73]. The potent inhibitory action of orlistat has raised concerns about its potential to interfere with the absorption of certain vitamins. Extensive research is underway to identify extracts from diverse natural sources, including plants, fungi, algae, and bacteria, that exhibit inhibitory activity against pancreatic lipase [74].
4.1.3. Sibutramine
Sibutramine is a dual monoamine reuptake inhibitor, affecting both norepinephrine and serotonin. It is believed to promote modest weight loss by reducing appetite and increasing energy expenditure. Compared with alternative agents’ adverse effects are restricted; however, flatulence, bloating, abdominal pain, and indigestion [75] side effects from anti-obesity medicine are a significant concern for their therapeutic usage. Therefore, sibutramine is the first drug opposing obesity that has been withdrawn from the market because of the aspect of vessel events and strokes [76].
4.2. Medicine That Increases Energy Expenditure and Thermogenesis
Ephedrine and caffeine can be included in this category. One future placebo-controlled test with caffeine and ephedrine or their combination shows that the mixture of caffeine and ephedrine greatly affected weight loss compared to individual use. These substances are contained in some health supplements. β3-AR agonist in mice increases UPC1 protein expression in brown adipose tissue (BAT), induces thermogenesis, and ultimately decreases body weight [77]. In humans, Mirabegron increases ATGL and UCP1 protein in the mitochondria of BAT and also increases insulin sensitivity [78]. β3-adrenergic receptor agonists recently emerged as novel pharmacological therapeutics to counteract obesity and other metabolic diseases [79].
4.3. Medicine Suppressing Hunger and Satiety
Anti-obesity medications include noradrenergic, serotonergic, and serotonergic–adrenergic drugs. Some of these medications target the gamma-aminobutyric acid (GABA) or cannabinoid receptors, while others are peptides that reduce appetite or promote feelings of fullness [80]. Many weight-management medications target neurotransmitters in the central nervous system to reduce appetite. Noradrenergic drugs, which either release or block the reuptake of the neurotransmitter norepinephrine, can affect food intake. These medications work by interacting with beta-adrenergic receptors in the peripheral hypothalamus, ultimately reducing appetite. Examples of noradrenergic medications include phentermine and diethylpropion [81].
4.3.1. Phentermine and Diethylpropion
Diethylpropion, also known as generic Tenuate, is a deep-rooted medication that has been used to treat obesity for almost 60 years. The Federal Drug Administration (FDA) approved it as an anti-obesity medicine in 1959 as it decreases the sensation of hunger. Many appetite-suppressant medications target noradrenergic and potentially dopaminergic receptors in the brain to promote feelings of fullness. However, weight loss by amphetamine is sustained till 36 months. Comparative studies have shown that phentermine may lead to greater weight loss than diethylpropion over 12 weeks [82,83]. Both medications can cause side effects such as headache, insomnia, irritability, palpitations, and nervousness. However, there is a need for more research to fully understand the effectiveness and safety of phentermine and diethylpropion.
4.3.2. Dexfenfluramine and Fenfluramine
These are serotonin-enhancing medications that work by increasing serotonin levels in the brain. This can lead to a feeling of fullness and reduced appetite, particularly for fatty foods. However, these drugs have been associated with serious cardiovascular side effects, including pulmonary hypertension and heart valve damage [84]. Fluoxetine, a medication commonly used to treat depression, is a selective serotonin reuptake inhibitor (SSRI). It has a wide range of clinical applications for treating depression and has not been linked to pulmonary hypertension or heart valve damage.
4.3.3. Glucagon-like Peptide-1 (GLP-1)
This is an incretin hormone released in response to glucose in the gut. It slows down stomach emptying and stimulates the release of insulin [85]. Though, it causes nausea, reduces appetite, and facilitates weight loss. Incretins are hormones secreted from the epithelium of the small intestine in response to glucose or fat molecules belonging to the glucagon superfamily. In humans, gastric inhibitory peptide (GIP) and glucagon-like peptide-1 (GLP-1) are two major incretins. When blood sugar levels rise, these incretins, secreted from the small intestine, travel to pancreatic beta cells and stimulate the release of insulin, helping to regulate blood glucose levels [86]. Liptins Increase the Levels of Incretins by Blocking Dipeptidyl Peptidase-4 (DPP-4), an enzyme that breaks down incretins and other peptides, and exenatide, an approved Anti-Diabetic Medication, Mimics the Effects of GLP-1 and Has Been Shown to Promote Weight Loss [54,87]. Liraglutide, Another GLP-1 Analog, Has Been Found to Improve blood sugar control and lead to weight loss in patients with type 2 diabetes [88].
Natural bioactive compounds derived from plants and plant products, such as herbs, fruits, and vegetables, are increasingly explored as potential ingredients in anti-obesity products. Due to increasing health awareness among people, these natural plant-based supplements are expected to play a significant role in the development of nature-sourced weight loss solutions.
These allopathic medications alter weight by increasing energy expenditure, suppressing appetite, or inhibiting the secretion of enzymes from exocrine glands to reduce the absorption of macronutrients in the intestines [89]. Weight loss medicine could seem to be an answer to obesity. Medicine for obesity ought to be meant just for patients with BMI > 30 or BMI > 27 with comorbidity [90]. Some conventional obesity medications with mechanisms of action have been illustrated in the Table 1.
4.3.4. Comparison of Seaweed and Conventional Medication on Obesity
Obesity is a major health risk, linked to metabolic syndrome, which includes type 2 diabetes, high cholesterol, high blood pressure, and heart disease. The mechanism of action of conventional drugs is based on signaling pathways related to adipogenesis. Adipogenesis involves multiple signaling pathways, including those activated by insulin and IGF-1 (PI3K/AKT and MAPK/ERK), Wnt/β-catenin, AMPK, Hedgehog (Hh), and bone morphogenic protein (BMP). PPARγ regulates gene expression involved in adipogenesis, glucose and lipid metabolism, inflammation, and other biological processes. The PPARγ2 isoform is predominantly found in adipose tissue and is crucial for fat cell development and maintenance. Activating PPARγ can increase the number of small, insulin-sensitive fat cells and boost adiponectin production, leading to improved insulin sensitivity in the liver and muscles.
Natural products can work in various ways, such as blocking nutrient absorption, reducing fat cell formation, increasing energy expenditure, suppressing appetite, and altering gut bacteria composition. Although a lot of research is required for the comparison of seaweed and conventional medication, nevertheless, the possible comparisons are illustrated in Table 2.
Apart from seaweed, a lot of other natural substances’ roles in obesity have been evaluated earlier, for example, flavanols such as quercetin’s role in obesity has also been evaluated. They promote the programmed cell death process in adipocyte cells to reduce adipogenesis [97]. Green tea also shows an anti-obesity effect indirectly due to the presence of antioxidant properties of polyphenols [98]. Caffeine, another bioactive compound found in tea leaves, stimulates the somatic nervous system and works with catechins to increase energy expenditure and fat oxidation.
Nowadays, the combination of turmeric and mulberry leaf is used by medical practitioners to treat obese patients [27]. Polyphenols have demonstrated beneficial effects in combating obesity. Dietary polyphenols may help prevent fat accumulation by influencing adipocyte metabolism [99]. Flavonoids, a class of polyphenols widely found in nature, have shown promising anti-obesity properties. Plant stanols and sterols are natural compounds that block the absorption of enteral carboxylic acid and bad cholesterol like LDL and reduce weight gain in animal tests. Vegetable oils like corn, soybean, and vegetable oil are the main sources of plant stanols (Table 2).
medsci-12-00055-t002_Table 2
Table 2
Seaweed bioactives vs. conventional therapies: a comparative analysis. This table outlines the bioactive compounds extracted from various seaweed species, their mechanisms of action, and the corresponding conventional treatments for similar conditions.
Type of Seaweed
Bioactive Compounds
Mechanism of Action as Anti-Obesity
Similarity and Comparison with Conventional Drugs (FDA Approved)
Green seaweed (Caulerpa lentillifera)
Siphonoxanthin
Stimulates energy expenditure, prevents lipid accumulation and adipogenesis,decreases fat storage in white adipose tissue,altering gene expression, which can inhibit lipid synthesis in white adipose tissue (WAT) [20].
Similar to the green algae bioactive compound (Siphonoxanthin), ephedrine also enhances energy expenditure by acting adrenergic receptors [100].Statin enhances lipolysis and decreases lipid accumulation in mature adipocytes [101].
Brown seaweed
Fucoxanthin
Reduce the absorption of triglyceride.Modulates the transcription factors/regulators of adipocyte differentiation and development [28].Upregulates the uncoupling protein1 (UCP1) in brown adipose tissue, increases fat oxidation in mitochondria in adipose tissue, and enhances thermogenesis.
Metformin (FDA-approved drug-2022) improves the acetyl-CoA carboxylase phosphorylation and decreases in triacylglycerol levels (triglycerides).It regulates lipogenic gene expression and decreases triglycerides [102].Fibrates act as synthetic ligands for PPARα increasing fatty acids hepatic B-oxidation.
Brown seaweed
Neoxanthin
Inhibits lipid accumulation and decreases gene expression of C/EBPα and PPARγ [22].
Statin prevents adipocyte hypertrophy by increasing the number of small adipocytes and downregulating C/EBPα, PPARγ, SREBP1, leptin, FABP4, and adiponectin [101].
Brown seaweed
Phlorotannin
Inhibit the pancreatic lipase and reduce lipid absorption [24].
Orlistat inhibits pancreatic lipase and reduces lipid absorption like phlorotannin.
Brown seaweed
Alginate
Dietary fiber reduces body weight, gives the feeling of satiety, and reduces appetite by reducing lipid absorption and inhibiting pancreatic lipase [36,37].
Similar to alginate, liraglutide also reduces appetite by downregulating AKT and PI3K pathways, upregulating AMPK, and decreasing lipogenesis in white adipose tissue [103].
Brown seaweeds
Fucoidans
May reduce obesity by either preventing the deposition of fat or promoting lipolysis [38].
Similar to fucoidans, liraglutide shows pro-lipolytic effects in human mature adipocytes [104].
Red Seaweed
Whole red seaweed
It reduces body weight, decreases insulin resistance, and decreases adiponectin [41].
Zepbound new FDA-approved medication (2023) acts on intestine (glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP)) to reduce appetite and food intake.
Green microalga (Haematococcus pluvialis)
Astaxanthin
Inhibits cholesterol synthesis and reduces the size of adipocytes and body weight [42]Acts as a potent antioxidant that neutralizes reactive oxygen species (ROS) and reactive nitrogen species (RNS), reduces low-grade inflammation in adipose tissue, and reduces obesity [46].
Statins lower cholesterol by inhibiting the conversion of HMG-CoA to mevalonic acid [105].
4.1. Medicine That Interferes with Fat Absorption
Sibutramine and orlistat are copious important drugs that are included in this category.
4.1.1. Orlistat
Gastric and pancreatic lipases are key enzymes of fat digestion in humans and Orlistat is a selective inhibitor of gastric and pancreatic lipases. It acts on serine residue, which is part of the active site present in these enzymes [67,68]. It prevents the hydrolysis of triglyceride into monomer-unit monosaccharide, which is absorbed in the intestine. Orlistat is a derivative of lipstatin isolated from actinomycete toxytricini [69]. It is an artificial drug designed to act on a target site and block the absorption of dietary fat [70]. Orlistat may be an artificially modified by-product of lipstatin that can be used in the regulation of body weight control [71]. Orlistat is a medication that works by blocking the absorption of dietary fat. Common side effects include flatulence, bloating, abdominal pain, and indigestion, which are often caused by undigested fat passing through the digestive system [62]. Patients taking orlistat should be monitored for nutrient deficiencies, as the medication can reduce fat absorption, leading to a potential shortage of fat-soluble vitamins (A, D, E, and K). A study on obesity found that orlistat treatment was associated with significant reductions in waist circumference, total cholesterol, LDL cholesterol, and blood pressure and improvements in glucose levels and insulin sensitivity [72].
4.1.2. Lipstatin
Lipstatin, a compound initially isolated from the actinobacterium Streptomyces toxytricini, contains a β-lactone ring responsible for its irreversible enzyme inhibition [73]. The potent inhibitory action of orlistat has raised concerns about its potential to interfere with the absorption of certain vitamins. Extensive research is underway to identify extracts from diverse natural sources, including plants, fungi, algae, and bacteria, that exhibit inhibitory activity against pancreatic lipase [74].
4.1.3. Sibutramine
Sibutramine is a dual monoamine reuptake inhibitor, affecting both norepinephrine and serotonin. It is believed to promote modest weight loss by reducing appetite and increasing energy expenditure. Compared with alternative agents’ adverse effects are restricted; however, flatulence, bloating, abdominal pain, and indigestion [75] side effects from anti-obesity medicine are a significant concern for their therapeutic usage. Therefore, sibutramine is the first drug opposing obesity that has been withdrawn from the market because of the aspect of vessel events and strokes [76].
4.1.1. Orlistat
Gastric and pancreatic lipases are key enzymes of fat digestion in humans and Orlistat is a selective inhibitor of gastric and pancreatic lipases. It acts on serine residue, which is part of the active site present in these enzymes [67,68]. It prevents the hydrolysis of triglyceride into monomer-unit monosaccharide, which is absorbed in the intestine. Orlistat is a derivative of lipstatin isolated from actinomycete toxytricini [69]. It is an artificial drug designed to act on a target site and block the absorption of dietary fat [70]. Orlistat may be an artificially modified by-product of lipstatin that can be used in the regulation of body weight control [71]. Orlistat is a medication that works by blocking the absorption of dietary fat. Common side effects include flatulence, bloating, abdominal pain, and indigestion, which are often caused by undigested fat passing through the digestive system [62]. Patients taking orlistat should be monitored for nutrient deficiencies, as the medication can reduce fat absorption, leading to a potential shortage of fat-soluble vitamins (A, D, E, and K). A study on obesity found that orlistat treatment was associated with significant reductions in waist circumference, total cholesterol, LDL cholesterol, and blood pressure and improvements in glucose levels and insulin sensitivity [72].
4.1.2. Lipstatin
Lipstatin, a compound initially isolated from the actinobacterium Streptomyces toxytricini, contains a β-lactone ring responsible for its irreversible enzyme inhibition [73]. The potent inhibitory action of orlistat has raised concerns about its potential to interfere with the absorption of certain vitamins. Extensive research is underway to identify extracts from diverse natural sources, including plants, fungi, algae, and bacteria, that exhibit inhibitory activity against pancreatic lipase [74].
4.1.3. Sibutramine
Sibutramine is a dual monoamine reuptake inhibitor, affecting both norepinephrine and serotonin. It is believed to promote modest weight loss by reducing appetite and increasing energy expenditure. Compared with alternative agents’ adverse effects are restricted; however, flatulence, bloating, abdominal pain, and indigestion [75] side effects from anti-obesity medicine are a significant concern for their therapeutic usage. Therefore, sibutramine is the first drug opposing obesity that has been withdrawn from the market because of the aspect of vessel events and strokes [76].
4.2. Medicine That Increases Energy Expenditure and Thermogenesis
Ephedrine and caffeine can be included in this category. One future placebo-controlled test with caffeine and ephedrine or their combination shows that the mixture of caffeine and ephedrine greatly affected weight loss compared to individual use. These substances are contained in some health supplements. β3-AR agonist in mice increases UPC1 protein expression in brown adipose tissue (BAT), induces thermogenesis, and ultimately decreases body weight [77]. In humans, Mirabegron increases ATGL and UCP1 protein in the mitochondria of BAT and also increases insulin sensitivity [78]. β3-adrenergic receptor agonists recently emerged as novel pharmacological therapeutics to counteract obesity and other metabolic diseases [79].
4.3. Medicine Suppressing Hunger and Satiety
Anti-obesity medications include noradrenergic, serotonergic, and serotonergic–adrenergic drugs. Some of these medications target the gamma-aminobutyric acid (GABA) or cannabinoid receptors, while others are peptides that reduce appetite or promote feelings of fullness [80]. Many weight-management medications target neurotransmitters in the central nervous system to reduce appetite. Noradrenergic drugs, which either release or block the reuptake of the neurotransmitter norepinephrine, can affect food intake. These medications work by interacting with beta-adrenergic receptors in the peripheral hypothalamus, ultimately reducing appetite. Examples of noradrenergic medications include phentermine and diethylpropion [81].
4.3.1. Phentermine and Diethylpropion
Diethylpropion, also known as generic Tenuate, is a deep-rooted medication that has been used to treat obesity for almost 60 years. The Federal Drug Administration (FDA) approved it as an anti-obesity medicine in 1959 as it decreases the sensation of hunger. Many appetite-suppressant medications target noradrenergic and potentially dopaminergic receptors in the brain to promote feelings of fullness. However, weight loss by amphetamine is sustained till 36 months. Comparative studies have shown that phentermine may lead to greater weight loss than diethylpropion over 12 weeks [82,83]. Both medications can cause side effects such as headache, insomnia, irritability, palpitations, and nervousness. However, there is a need for more research to fully understand the effectiveness and safety of phentermine and diethylpropion.
4.3.2. Dexfenfluramine and Fenfluramine
These are serotonin-enhancing medications that work by increasing serotonin levels in the brain. This can lead to a feeling of fullness and reduced appetite, particularly for fatty foods. However, these drugs have been associated with serious cardiovascular side effects, including pulmonary hypertension and heart valve damage [84]. Fluoxetine, a medication commonly used to treat depression, is a selective serotonin reuptake inhibitor (SSRI). It has a wide range of clinical applications for treating depression and has not been linked to pulmonary hypertension or heart valve damage.
4.3.3. Glucagon-like Peptide-1 (GLP-1)
This is an incretin hormone released in response to glucose in the gut. It slows down stomach emptying and stimulates the release of insulin [85]. Though, it causes nausea, reduces appetite, and facilitates weight loss. Incretins are hormones secreted from the epithelium of the small intestine in response to glucose or fat molecules belonging to the glucagon superfamily. In humans, gastric inhibitory peptide (GIP) and glucagon-like peptide-1 (GLP-1) are two major incretins. When blood sugar levels rise, these incretins, secreted from the small intestine, travel to pancreatic beta cells and stimulate the release of insulin, helping to regulate blood glucose levels [86]. Liptins Increase the Levels of Incretins by Blocking Dipeptidyl Peptidase-4 (DPP-4), an enzyme that breaks down incretins and other peptides, and exenatide, an approved Anti-Diabetic Medication, Mimics the Effects of GLP-1 and Has Been Shown to Promote Weight Loss [54,87]. Liraglutide, Another GLP-1 Analog, Has Been Found to Improve blood sugar control and lead to weight loss in patients with type 2 diabetes [88].
Natural bioactive compounds derived from plants and plant products, such as herbs, fruits, and vegetables, are increasingly explored as potential ingredients in anti-obesity products. Due to increasing health awareness among people, these natural plant-based supplements are expected to play a significant role in the development of nature-sourced weight loss solutions.
These allopathic medications alter weight by increasing energy expenditure, suppressing appetite, or inhibiting the secretion of enzymes from exocrine glands to reduce the absorption of macronutrients in the intestines [89]. Weight loss medicine could seem to be an answer to obesity. Medicine for obesity ought to be meant just for patients with BMI > 30 or BMI > 27 with comorbidity [90]. Some conventional obesity medications with mechanisms of action have been illustrated in the Table 1.
4.3.4. Comparison of Seaweed and Conventional Medication on Obesity
Obesity is a major health risk, linked to metabolic syndrome, which includes type 2 diabetes, high cholesterol, high blood pressure, and heart disease. The mechanism of action of conventional drugs is based on signaling pathways related to adipogenesis. Adipogenesis involves multiple signaling pathways, including those activated by insulin and IGF-1 (PI3K/AKT and MAPK/ERK), Wnt/β-catenin, AMPK, Hedgehog (Hh), and bone morphogenic protein (BMP). PPARγ regulates gene expression involved in adipogenesis, glucose and lipid metabolism, inflammation, and other biological processes. The PPARγ2 isoform is predominantly found in adipose tissue and is crucial for fat cell development and maintenance. Activating PPARγ can increase the number of small, insulin-sensitive fat cells and boost adiponectin production, leading to improved insulin sensitivity in the liver and muscles.
Natural products can work in various ways, such as blocking nutrient absorption, reducing fat cell formation, increasing energy expenditure, suppressing appetite, and altering gut bacteria composition. Although a lot of research is required for the comparison of seaweed and conventional medication, nevertheless, the possible comparisons are illustrated in Table 2.
Apart from seaweed, a lot of other natural substances’ roles in obesity have been evaluated earlier, for example, flavanols such as quercetin’s role in obesity has also been evaluated. They promote the programmed cell death process in adipocyte cells to reduce adipogenesis [97]. Green tea also shows an anti-obesity effect indirectly due to the presence of antioxidant properties of polyphenols [98]. Caffeine, another bioactive compound found in tea leaves, stimulates the somatic nervous system and works with catechins to increase energy expenditure and fat oxidation.
Nowadays, the combination of turmeric and mulberry leaf is used by medical practitioners to treat obese patients [27]. Polyphenols have demonstrated beneficial effects in combating obesity. Dietary polyphenols may help prevent fat accumulation by influencing adipocyte metabolism [99]. Flavonoids, a class of polyphenols widely found in nature, have shown promising anti-obesity properties. Plant stanols and sterols are natural compounds that block the absorption of enteral carboxylic acid and bad cholesterol like LDL and reduce weight gain in animal tests. Vegetable oils like corn, soybean, and vegetable oil are the main sources of plant stanols (Table 2).
medsci-12-00055-t002_Table 2
Table 2
Seaweed bioactives vs. conventional therapies: a comparative analysis. This table outlines the bioactive compounds extracted from various seaweed species, their mechanisms of action, and the corresponding conventional treatments for similar conditions.
Type of Seaweed
Bioactive Compounds
Mechanism of Action as Anti-Obesity
Similarity and Comparison with Conventional Drugs (FDA Approved)
Green seaweed (Caulerpa lentillifera)
Siphonoxanthin
Stimulates energy expenditure, prevents lipid accumulation and adipogenesis,decreases fat storage in white adipose tissue,altering gene expression, which can inhibit lipid synthesis in white adipose tissue (WAT) [20].
Similar to the green algae bioactive compound (Siphonoxanthin), ephedrine also enhances energy expenditure by acting adrenergic receptors [100].Statin enhances lipolysis and decreases lipid accumulation in mature adipocytes [101].
Brown seaweed
Fucoxanthin
Reduce the absorption of triglyceride.Modulates the transcription factors/regulators of adipocyte differentiation and development [28].Upregulates the uncoupling protein1 (UCP1) in brown adipose tissue, increases fat oxidation in mitochondria in adipose tissue, and enhances thermogenesis.
Metformin (FDA-approved drug-2022) improves the acetyl-CoA carboxylase phosphorylation and decreases in triacylglycerol levels (triglycerides).It regulates lipogenic gene expression and decreases triglycerides [102].Fibrates act as synthetic ligands for PPARα increasing fatty acids hepatic B-oxidation.
Brown seaweed
Neoxanthin
Inhibits lipid accumulation and decreases gene expression of C/EBPα and PPARγ [22].
Statin prevents adipocyte hypertrophy by increasing the number of small adipocytes and downregulating C/EBPα, PPARγ, SREBP1, leptin, FABP4, and adiponectin [101].
Brown seaweed
Phlorotannin
Inhibit the pancreatic lipase and reduce lipid absorption [24].
Orlistat inhibits pancreatic lipase and reduces lipid absorption like phlorotannin.
Brown seaweed
Alginate
Dietary fiber reduces body weight, gives the feeling of satiety, and reduces appetite by reducing lipid absorption and inhibiting pancreatic lipase [36,37].
Similar to alginate, liraglutide also reduces appetite by downregulating AKT and PI3K pathways, upregulating AMPK, and decreasing lipogenesis in white adipose tissue [103].
Brown seaweeds
Fucoidans
May reduce obesity by either preventing the deposition of fat or promoting lipolysis [38].
Similar to fucoidans, liraglutide shows pro-lipolytic effects in human mature adipocytes [104].
Red Seaweed
Whole red seaweed
It reduces body weight, decreases insulin resistance, and decreases adiponectin [41].
Zepbound new FDA-approved medication (2023) acts on intestine (glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP)) to reduce appetite and food intake.
Green microalga (Haematococcus pluvialis)
Astaxanthin
Inhibits cholesterol synthesis and reduces the size of adipocytes and body weight [42]Acts as a potent antioxidant that neutralizes reactive oxygen species (ROS) and reactive nitrogen species (RNS), reduces low-grade inflammation in adipose tissue, and reduces obesity [46].
Statins lower cholesterol by inhibiting the conversion of HMG-CoA to mevalonic acid [105].
4.3.1. Phentermine and Diethylpropion
Diethylpropion, also known as generic Tenuate, is a deep-rooted medication that has been used to treat obesity for almost 60 years. The Federal Drug Administration (FDA) approved it as an anti-obesity medicine in 1959 as it decreases the sensation of hunger. Many appetite-suppressant medications target noradrenergic and potentially dopaminergic receptors in the brain to promote feelings of fullness. However, weight loss by amphetamine is sustained till 36 months. Comparative studies have shown that phentermine may lead to greater weight loss than diethylpropion over 12 weeks [82,83]. Both medications can cause side effects such as headache, insomnia, irritability, palpitations, and nervousness. However, there is a need for more research to fully understand the effectiveness and safety of phentermine and diethylpropion.
4.3.2. Dexfenfluramine and Fenfluramine
These are serotonin-enhancing medications that work by increasing serotonin levels in the brain. This can lead to a feeling of fullness and reduced appetite, particularly for fatty foods. However, these drugs have been associated with serious cardiovascular side effects, including pulmonary hypertension and heart valve damage [84]. Fluoxetine, a medication commonly used to treat depression, is a selective serotonin reuptake inhibitor (SSRI). It has a wide range of clinical applications for treating depression and has not been linked to pulmonary hypertension or heart valve damage.
4.3.3. Glucagon-like Peptide-1 (GLP-1)
This is an incretin hormone released in response to glucose in the gut. It slows down stomach emptying and stimulates the release of insulin [85]. Though, it causes nausea, reduces appetite, and facilitates weight loss. Incretins are hormones secreted from the epithelium of the small intestine in response to glucose or fat molecules belonging to the glucagon superfamily. In humans, gastric inhibitory peptide (GIP) and glucagon-like peptide-1 (GLP-1) are two major incretins. When blood sugar levels rise, these incretins, secreted from the small intestine, travel to pancreatic beta cells and stimulate the release of insulin, helping to regulate blood glucose levels [86]. Liptins Increase the Levels of Incretins by Blocking Dipeptidyl Peptidase-4 (DPP-4), an enzyme that breaks down incretins and other peptides, and exenatide, an approved Anti-Diabetic Medication, Mimics the Effects of GLP-1 and Has Been Shown to Promote Weight Loss [54,87]. Liraglutide, Another GLP-1 Analog, Has Been Found to Improve blood sugar control and lead to weight loss in patients with type 2 diabetes [88].
Natural bioactive compounds derived from plants and plant products, such as herbs, fruits, and vegetables, are increasingly explored as potential ingredients in anti-obesity products. Due to increasing health awareness among people, these natural plant-based supplements are expected to play a significant role in the development of nature-sourced weight loss solutions.
These allopathic medications alter weight by increasing energy expenditure, suppressing appetite, or inhibiting the secretion of enzymes from exocrine glands to reduce the absorption of macronutrients in the intestines [89]. Weight loss medicine could seem to be an answer to obesity. Medicine for obesity ought to be meant just for patients with BMI > 30 or BMI > 27 with comorbidity [90]. Some conventional obesity medications with mechanisms of action have been illustrated in the Table 1.
4.3.4. Comparison of Seaweed and Conventional Medication on Obesity
Obesity is a major health risk, linked to metabolic syndrome, which includes type 2 diabetes, high cholesterol, high blood pressure, and heart disease. The mechanism of action of conventional drugs is based on signaling pathways related to adipogenesis. Adipogenesis involves multiple signaling pathways, including those activated by insulin and IGF-1 (PI3K/AKT and MAPK/ERK), Wnt/β-catenin, AMPK, Hedgehog (Hh), and bone morphogenic protein (BMP). PPARγ regulates gene expression involved in adipogenesis, glucose and lipid metabolism, inflammation, and other biological processes. The PPARγ2 isoform is predominantly found in adipose tissue and is crucial for fat cell development and maintenance. Activating PPARγ can increase the number of small, insulin-sensitive fat cells and boost adiponectin production, leading to improved insulin sensitivity in the liver and muscles.
Natural products can work in various ways, such as blocking nutrient absorption, reducing fat cell formation, increasing energy expenditure, suppressing appetite, and altering gut bacteria composition. Although a lot of research is required for the comparison of seaweed and conventional medication, nevertheless, the possible comparisons are illustrated in Table 2.
Apart from seaweed, a lot of other natural substances’ roles in obesity have been evaluated earlier, for example, flavanols such as quercetin’s role in obesity has also been evaluated. They promote the programmed cell death process in adipocyte cells to reduce adipogenesis [97]. Green tea also shows an anti-obesity effect indirectly due to the presence of antioxidant properties of polyphenols [98]. Caffeine, another bioactive compound found in tea leaves, stimulates the somatic nervous system and works with catechins to increase energy expenditure and fat oxidation.
Nowadays, the combination of turmeric and mulberry leaf is used by medical practitioners to treat obese patients [27]. Polyphenols have demonstrated beneficial effects in combating obesity. Dietary polyphenols may help prevent fat accumulation by influencing adipocyte metabolism [99]. Flavonoids, a class of polyphenols widely found in nature, have shown promising anti-obesity properties. Plant stanols and sterols are natural compounds that block the absorption of enteral carboxylic acid and bad cholesterol like LDL and reduce weight gain in animal tests. Vegetable oils like corn, soybean, and vegetable oil are the main sources of plant stanols (Table 2).
medsci-12-00055-t002_Table 2
Table 2
Seaweed bioactives vs. conventional therapies: a comparative analysis. This table outlines the bioactive compounds extracted from various seaweed species, their mechanisms of action, and the corresponding conventional treatments for similar conditions.
Type of Seaweed
Bioactive Compounds
Mechanism of Action as Anti-Obesity
Similarity and Comparison with Conventional Drugs (FDA Approved)
Green seaweed (Caulerpa lentillifera)
Siphonoxanthin
Stimulates energy expenditure, prevents lipid accumulation and adipogenesis,decreases fat storage in white adipose tissue,altering gene expression, which can inhibit lipid synthesis in white adipose tissue (WAT) [20].
Similar to the green algae bioactive compound (Siphonoxanthin), ephedrine also enhances energy expenditure by acting adrenergic receptors [100].Statin enhances lipolysis and decreases lipid accumulation in mature adipocytes [101].
Brown seaweed
Fucoxanthin
Reduce the absorption of triglyceride.Modulates the transcription factors/regulators of adipocyte differentiation and development [28].Upregulates the uncoupling protein1 (UCP1) in brown adipose tissue, increases fat oxidation in mitochondria in adipose tissue, and enhances thermogenesis.
Metformin (FDA-approved drug-2022) improves the acetyl-CoA carboxylase phosphorylation and decreases in triacylglycerol levels (triglycerides).It regulates lipogenic gene expression and decreases triglycerides [102].Fibrates act as synthetic ligands for PPARα increasing fatty acids hepatic B-oxidation.
Brown seaweed
Neoxanthin
Inhibits lipid accumulation and decreases gene expression of C/EBPα and PPARγ [22].
Statin prevents adipocyte hypertrophy by increasing the number of small adipocytes and downregulating C/EBPα, PPARγ, SREBP1, leptin, FABP4, and adiponectin [101].
Brown seaweed
Phlorotannin
Inhibit the pancreatic lipase and reduce lipid absorption [24].
Orlistat inhibits pancreatic lipase and reduces lipid absorption like phlorotannin.
Brown seaweed
Alginate
Dietary fiber reduces body weight, gives the feeling of satiety, and reduces appetite by reducing lipid absorption and inhibiting pancreatic lipase [36,37].
Similar to alginate, liraglutide also reduces appetite by downregulating AKT and PI3K pathways, upregulating AMPK, and decreasing lipogenesis in white adipose tissue [103].
Brown seaweeds
Fucoidans
May reduce obesity by either preventing the deposition of fat or promoting lipolysis [38].
Similar to fucoidans, liraglutide shows pro-lipolytic effects in human mature adipocytes [104].
Red Seaweed
Whole red seaweed
It reduces body weight, decreases insulin resistance, and decreases adiponectin [41].
Zepbound new FDA-approved medication (2023) acts on intestine (glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP)) to reduce appetite and food intake.
Green microalga (Haematococcus pluvialis)
Astaxanthin
Inhibits cholesterol synthesis and reduces the size of adipocytes and body weight [42]Acts as a potent antioxidant that neutralizes reactive oxygen species (ROS) and reactive nitrogen species (RNS), reduces low-grade inflammation in adipose tissue, and reduces obesity [46].
Statins lower cholesterol by inhibiting the conversion of HMG-CoA to mevalonic acid [105].
5. Critical Analysis of Seaweed
Clinical studies indicate that seaweed compounds are generally safe and well tolerated when consumed in small or moderate quantities. However, individuals taking blood-thinning medications should be cautious, as certain seaweed compounds, such as fucoidan, possess anticoagulant properties. Moreover, certain seaweed species can contain high levels of iodine, potentially causing thyroid gland dysfunction in those with iodine sensitivity or deficiency. A European study revealed that Palmaria palmata consumption can elevate iodine levels in adults. Participants who consumed 5 g of Palmaria palmata daily for 28 days showed a notable increase in thyroid-stimulating hormone (TSH) within the normal range [106]. Laminaria digitata contains a significant amount of iodine, with 3.3 g providing over 4000% of the daily recommended intake. Excessive consumption of seaweed with an iodine content exceeding 45 mg/kg of dry weight can potentially impair thyroid function. Considering the widespread availability and use of Laminariaspp. in food products, it is essential to thoroughly analyze the iodine content of these products [107]. In Korean studies, the arsenic level in urine was found to be higher in those who were consuming seaweed. Laminaria digitata contains a significant amount of arsenic, with concentrations ranging from 36 to 131 milligrams per gram of dry weight [108,109]. In light of climate change, monitoring the heavy metal content of seaweed is crucial. Additionally, the presence of rare earth elements, recently discovered in Mediterranean seaweeds, should be closely observed. Excessive exposure to heavy metals can harm fetal development and lead to neurological, developmental, and endocrine disorders [110,111]. While preclinical and clinical studies show promising results, current research has limitations. Many studies are relatively small and short-term, and there is a lack of standardization in seaweed compound preparation and dosing. To confirm the efficacy and safety of these compounds, larger, longer-term studies are necessary, along with the establishment of optimal dosing and preparation methods.
Very limited studies have focused on the bioactive compounds of red seaweed, as most studies have recognized red seaweed as a good source of protein and the current knowledge of the health benefits of red seaweed is largely based on in vitro and animal studies. Currently, regulations regarding the disclosure of mineral, heavy metal, and iodine content in seaweed products, as well as safe portion sizes, are insufficient. To ensure the safety and sustainability of seaweed as a food source, the industry should implement a comprehensive monitoring program for heavy metals and iodine or explore innovative processing methods to reduce harmful contaminants like arsenic.
Seaweed fibers, such as xylan, laminarin, and ulvan, have not received official EFSA approval and, thus, more research is needed to ascertain whether these carbohydrates are safe for human consumption. EFSA concluded that sodium alginate failed to reduce postprandial glycemic responses without a disproportionate increase in postprandial insulinemic responses and, thus, a health claim was rejected. Despite growing in vitro and in vivo evidence supporting fucoidan’s potential anti-obesity benefits, human research on this seaweed-derived compound remains limited. A single randomized double-blind, parallel, placebo-controlled trial found that overweight/obese individuals who consumed fucoidan for 3 months experienced a reduction in diastolic blood pressure and LDL cholesterol but no significant changes in weight, body composition, or other metabolic markers. The dose of fucoidan chosen for this study was 500 mg/d [112]. Animal studies suggest that low-molecular-weight fucoidan reduces LDL and cholesterol in blood but no studies have been conducted in humans to support its LDL-lowering effect. Despite existing regulatory guidelines, clearer regulations and guidance regarding permissible arsenic levels in food products are needed. The UK Food Standards Agency has warned against consuming Sargassum fusiform due to its high inorganic arsenic content. Regular environmental assessments and analysis of arsenic species in seaweed-containing foods are necessary to minimize health risks. Indirect arsenic exposure through livestock feed or fertilizer use should also be considered. While most edible seaweeds contain safe levels of heavy metals, ongoing monitoring and regulation are essential [113,114].
Harmful Effect of Gold Algae
It is difficult to say that all seaweeds are salubrious for health, as golden algae such as Prymnesium parvum are not recommended for consumption, as they are found to be harmful to health in many research studies as they release toxins called Prymnesins. They have potent hemolytic, neurotoxic, and cytotoxic properties [115]. Large-scale fish mortalities have been observed globally [116]. Prymnesium parvum is often called golden algae due to its fucoxanthin pigments, which are found in the chloroplast of cells [117]. Prymnesium parvum produces a variety of toxins, including lipopolysaccharide-like compounds, proteolipids, galactoglycerolipids, and prymnesins. Prymnesins, first isolated and characterized by Igarashi et al. in 1995, exist in two forms: prymnesin-1 and prymnesin-2. Mass mortality in aquaculture ponds was noticed in gill-breathing animals in Poland and Germany in 2022 in the Odra River. Prymnesin causes cell swelling and cell lysis within a few minutes or it may take hours depending on the temperature or pH of the environment
Prymnesins are believed to interact directly with membrane components, particularly sterols, forming micelles that disrupt the plasma membrane. These detergent-like micelles create negatively charged pores, allowing cations to pass through exposed cells. Prymnesins can cause significant harm to fish by damaging their heart, liver, kidneys, nervous system, reproductive system, and endocrine system. Ultimately, these toxins can lead to death. While the direct effects of these toxins on humans are less pronounced, consuming fish contaminated with prymnesins can still be detrimental to human health.
While seaweed consumption in moderate amounts may offer potential benefits for weight management and overall health, further research is needed to establish its efficacy and safety. Long-term human clinical trials are essential for evaluating the potential risks and benefits of seaweed as a dietary supplement.
Harmful Effect of Gold Algae
It is difficult to say that all seaweeds are salubrious for health, as golden algae such as Prymnesium parvum are not recommended for consumption, as they are found to be harmful to health in many research studies as they release toxins called Prymnesins. They have potent hemolytic, neurotoxic, and cytotoxic properties [115]. Large-scale fish mortalities have been observed globally [116]. Prymnesium parvum is often called golden algae due to its fucoxanthin pigments, which are found in the chloroplast of cells [117]. Prymnesium parvum produces a variety of toxins, including lipopolysaccharide-like compounds, proteolipids, galactoglycerolipids, and prymnesins. Prymnesins, first isolated and characterized by Igarashi et al. in 1995, exist in two forms: prymnesin-1 and prymnesin-2. Mass mortality in aquaculture ponds was noticed in gill-breathing animals in Poland and Germany in 2022 in the Odra River. Prymnesin causes cell swelling and cell lysis within a few minutes or it may take hours depending on the temperature or pH of the environment
Prymnesins are believed to interact directly with membrane components, particularly sterols, forming micelles that disrupt the plasma membrane. These detergent-like micelles create negatively charged pores, allowing cations to pass through exposed cells. Prymnesins can cause significant harm to fish by damaging their heart, liver, kidneys, nervous system, reproductive system, and endocrine system. Ultimately, these toxins can lead to death. While the direct effects of these toxins on humans are less pronounced, consuming fish contaminated with prymnesins can still be detrimental to human health.
While seaweed consumption in moderate amounts may offer potential benefits for weight management and overall health, further research is needed to establish its efficacy and safety. Long-term human clinical trials are essential for evaluating the potential risks and benefits of seaweed as a dietary supplement.
6. Conclusions
In many countries, seaweeds are being used as food additives and flavoring agents in the food industry. Various commercial products of seaweed are available in the Asian market, especially in China, Japan, Korea, and India (Figure 5). Japanese people have been using more than 88 seaweed species in meals and edible healthcare products. Among all seaweeds, three common seaweed products in Japan are nori, wakame, and kombu. Various studies have demonstrated that seaweeds are rich in minerals, essential fatty acids, protein, and fibers. Xanthigen, fucoxanthin, and punicic acid bioactive components of various seaweeds have the potential to suppress the differentiation of adipocyte cells and prevent the accumulation of triglyceride in adipocyte cells. Xanthigen downregulates the transcription factors and necessary proteins, which are useful for adipocyte differentiation.
The lipid content is lower in seaweeds relative to total dry weight. The total lipid percentage varies in different seasons in the same seaweed as, in Saccharina latissimi, the polyunsaturated fatty acid and total lipid are highest in March and November while lowest in January. The lipids obtained from seaweeds are highly digestible [114,118]. Humans cannot synthesize linoleic (18:2n-6) and α-linolenic acid (18:3n-3) and cannot convert α-linolenic acid into long-chain essential fatty acid but marine seaweeds have this limited ability to convert α-linolenic acid into eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Omega-3 and omega-6 are essential fatty acids that prevent the risk of cardiovascular disease, cancer, and mental illness and are responsible for the development of the nervous system [119,120,121].
This study was performed in red and brown seaweeds to see the potential source of fatty acid. The fatty acids were extracted from these seaweeds and analyzed by gas chromatography–mass spectrometry. The study reveals that jubata has the highest content of unsaturated fatty acids, including omega-6 fatty acids, while C. jubata and U. pinnatifida may be the seaweeds with the highest nutraceutical potential and have promising health benefits [12].
The animal experimental study showed that consumption of 1% Undaria pinnatifida lipid for 6 weeks decreased body weight significantly, while the consumption of Undaria pinnatifida lipid and n-3 PUFA-rich scallop phospholipids reduce the white adipose tissue content in a mice model [122]. Seaweeds have different concentrations of lipid content, with a maximum of 8.8% lipid present in Porphyra spp., while 74% total fatty acid is present in Plocamium Brasiliense [114,123]. Other seaweeds such as North Bornean seaweeds have also cardiovascular protective and neuroprotective effects [124] and may reduce fat content.
The seaweed effect for anti-obesity was also evaluated in human subjects. The red seaweed Gelidium elegans’ effect on obesity was observed in 78 subjects. Subjects were divided into groups: a placebo group (37 subjects) and 40 in an experiment group. People of 19–50 years old were included in the experiment. The BMI for inclusion criteria was 23.0 and 30.0 kg/m2. A 12-week regimen of daily Gelidium elegans extract (1000 mg) resulted in a reduction in both body weight and BMI. Although, LDL cholesterol and total cholesterol did not change significantly [125].
Existing research indicates a strong correlation between seaweed consumption and reduced obesity. In vitro studies employing 3T3-L1 cells exposed to seaweed extracts ranging from 6.25 to 100 µg/mL for preadipocytes and 1 to 10 µg/mL for mature adipocytes consistently demonstrated decreased triglyceride accumulation. Notably, a dose-dependent effect was observed in the majority of these experiments. Preclinical studies involving rats and mice have further supported these findings. Algae extract administered at dosages of 0.5% to 15% (w/w) in diet or 100 to 500 mg/kg body weight/day for periods of 4 to 16 weeks significantly reduced body fat accumulation in both visceral and subcutaneous adipose tissue in mice. Importantly, these effects were primarily observed in animals fed obesogenic diets. Seaweed extracts have demonstrated the potential to mitigate not only obesity but also associated metabolic disorders such as dyslipidemia, insulin resistance, and fatty liver disease. While the data are compelling for models of diet-induced obesity, the impact of seaweeds on weight management in individuals with established obesity remains less clear. Only a limited number of studies have investigated the effects of algae extracts in animals fed a standard diet. Furthermore, the efficacy of seaweed in counteracting genetic predispositions to obesity has been explored in only a few studies, such as those involving the red seaweed Gelidium amansii in mice.
Additional research is necessary to evaluate the therapeutic potential of seaweed extracts in treating established obesity.
The bioactive compounds of seaweeds play a crucial role in the management of obesity and weight management. However, the majority of investigations address the short-term effect of seaweeds or their ingredients on obesity in animal models. It shows a positive impact in obesity and weight reduction by increasing satiety and reducing energy intake. It also diminished nutrient absorption and delayed gastric clearance and the stimulation of stretch receptors in the stomach. However, chronic studies in humans are required to finalize the conclusion as an anti-obesity effect of seaweeds. Apart from this, the bioavailability of bioactive compounds of marine algae needs to be investigated in humans before considering it as a more potent anti-obesity therapeutic than pharmacological agents. The standard drugs for obesity and weight management have various side effects that can be reduced by utilizing natural seaweed and its bioactive ingredients. However, future investigation of therapeutic efficacy and mechanism of action of seaweeds is required.
Several potential mechanisms underlying the anti-obesity effects of seaweeds in preclinical studies have been proposed. Few studies suggest a reduction in food intake, while several studies support a reduction in nutrients in gut absorption. However, the most promising mechanisms involve alterations to metabolic pathways within target tissues and organs. Seaweeds have been shown to inhibit de novo lipogenesis, thereby reducing fatty acid availability for triglyceride synthesis in white adipose tissue. It is important to note that this metabolic pathway may be less prominent in human adipose tissue compared to rodent models. Ultimately, human clinical trials are essential to determine if the promising anti-obesity effects observed in animal studies can be replicated in people. This will help establish the potential of seaweeds as a preventative or therapeutic strategy for obesity management. |
Title: 7615 Genomic Consequences of GRHL2 Overexpression in ER+ Breast Cancer Cells | Body: |
Title: Steviol rebaudiosides bind to four different sites of the human sweet taste receptor (T1R2/T1R3) complex explaining confusing experiments | Body:
Introduction
Humans can perceive at least five taste qualities, including sweet, umami, bitter, sour, and salty1. Among these tastes, sweet taste plays a vital role in determining food choices. Sweet-tasting foods signify the presence of high-calorie carbohydrates, which are essential for supplying energy to the body through the digestive process. Consequently, individuals tend to gravitate towards consuming sweet foods and beverages2. Excessive calorie intake from carbohydrates can lead to obesity, increasing the risk of type 2 diabetes, cardiovascular diseases, and even certain cancers3. In response to these health concerns, there has been a development of non-caloric sweeteners, such as saccharin4. Other non-caloric sweeteners, despite their higher sweetness compared to sucrose, fail to replicate an authentic sugar taste, diminishing people’s inclination to consume food containing these substitutes. In the pursuit of developing non-caloric sweeteners that mimic the sensory experience of sucrose, understanding the activation of sweet taste receptors becomes crucial, starting from the binding and activation processes of sweet ligands.
The human sweet taste receptor functions as a heterodimer comprised of T1R2 and T1R3 subunits. The T1R subunits belongs to the class C family of G protein-coupled receptors (GPCRs), which also includes metabotropic glutamate receptors (mGluRs)5, the calcium-sensing receptor (CaSR)6, and the γ-aminobutyric acid receptor (GABAR)7. The T1R subunits exhibit structural characteristics typical of a GPCR, as depicted in Fig. 1. It is characterized by a large extracellular Venus Flytrap Domain (VFD) coupled to a short cysteine-rich domain (CRD) that links the VFD to a seven-helix transmembrane domain (TMD)8.Fig. 1Schematic of human heterodimeric sweet taste receptor.The T1R2/T1R3 receptor features multiple binding sites for sweet ligands. Aspartame, neotame, Acesulfame K (Ace-K), Rebaudioside M (RebM), Rebaudioside D (RebD), Rebaudioside C (RebC), and Rebaudioside B (RebB) preferentially bind to the VFD2 domain. Sucrose and Rubusoside (Rubu) bind to both the VFD2 and VFD3 domains. The TMDs engage with various allosteric modulators and sweet compounds. Sweeteners like perillartine and RebB, along with the allosteric modulator S819 [1-((1H-pyrrol-2-yl)methyl)-3-(4-isopropoxyphenyl)thiourea] and amiloride, bind to TMD2. Additionally, RebB, RebC, Neohesperidin Dihydrochalcone (NHDC), cyclamate and lactisole bind to TMD3.
Typically, artificial sweet ligands bind to VFD2 in the T1R2/T1R3 heterodimer sweet taste receptor9–16. The agonist induces a conformational change in VFD2-VFD3 subdomains, transitioning from an open-open to a closed-open conformation. This closed-open structure is believed to contribute to the active state of the T1R2/T1R3 heterodimer sweet receptor13. The activation of the T1R2/T1R3 sweet receptor leads to the activation of a heterotrimeric G protein (GP) complex, consisting of a Gα subunit and a Gβγ dimer. This triggers the exchange of guanosine diphosphate (GDP) for guanosine triphosphate (GTP) on the Gα subunit, causing the dissociation of the Gα subunit. For other class C cases it is not clear whether agonist binding or GP coupling is first, but we believe that GP coupling is first for the GABA receptor17. For Class B (GLP118) and Class F (SMO19) more studies are needed to decide which is first. The GP activation stimulates downstream signaling in the sweet taste pathway, leading to the activation of various intracellular second messengers such as cyclic adenosine monophosphate (cAMP), inositol trisphosphate (IP3), and calcium ions20 that ultimately generate neural impulses to central sensory centers.
The T1R2/T1R3 sweet taste receptors present multiple binding sites for sweet ligands, enabling activation by agonists with diverse structures and potencies. A prominent site is located at the VFD2 domain, accommodating natural sugars like fructose21 and sucrose22, as well as artificial non-caloric sweeteners such as aspartame12, neotame15, and Acesulfame K (Ace-K)23,24. VFD2 also accepts natural sweet proteins like monellin25 and brazzin11. While VFD3 in the T1R3 receptor has fewer interactions, the natural sugar sucrose binds both on the VFD2 and VFD313,22. The TMDs interact with allosteric modulators and sweet compounds. For instance, artificial sweeteners (P-4000, perillartine) and positive allosteric modulator (PAM) S81922,26 bind to TMD2. Negative allosteric modulators (NAMs) inhibiting sweet taste like amiloride interact with TMD227,28, whereas lactisole and Gymnemic acid (GA) bind to TMD315,29,30. The artificial sweeteners, NHDC and cyclamate, bind to TMD3, enhancing the sweet taste sensation29. Sweet proteins, including thaumatin31 and brazzein11, interact with the CRD in the T1R3 subunit. When considering non-protein sweet ligands, potential binding sites include VFD2, VFD3, and the extracellular regions of TMD2 and TMD3. The extracellular VFD structure, with two lobes, provides multiple binding pockets within VFD2 and VFD3, facilitating interactions with sweet ligands. The schematic model of the T1R2/T1R3 heterodimer sweet receptor in Fig. 1 illustrates these binding sites29,32.
Sucrose, sucralose9 and monellin25,33 are reported to bind to both VFD2 and VFD3, demonstrating that some sweet ligands have multiple binding sites on the T1R2/T1R3 sweet receptor. This characteristic likely contributes to the distinct sweetness of sucrose and presents a challenge in creating non-caloric sweeteners that closely mimic its taste. Researchers are attempting to use sweetener mixtures to replicate the taste of sucrose. Blends of sucralose or erythritol with sucrose have also been found suitable for mimicking the sensory profile of sucrose34. Cyclamate/saccharin mixtures, for example, exhibit intense sweetness, being 348 times sweeter sucrose35. However, these mixtures still differ from sucrose in their sensory profiles. Therefore, exploring the multiple binding properties of sweeteners is crucial for selecting non-caloric sweeteners or using sweeteners mixture that closely mimic the taste of sucrose.
Current research on the binding targets of the T1R2/T1R3 heterodimer, has characterized taste receptor agonists predominantly using heterologous cell-based assays that involve overexpressed GPCRs and promiscuous G proteins. These assays typically employ HEK 293 cells26,36,37 or Flp-in 29328–30,38 cells overexpressing the T1R2/T1R3 heterodimer and the Gα protein subunit. The concentration of calcium ions, a second messenger in the sweet taste downstream signaling pathway, is measured to assess the activation of the sweet taste receptor. To explore the specific functions of individual T1R subunits in the heterodimer sweet receptor, researchers can create cell lines expressing only one of the sweet receptor subunits. For example, researchers have created HEK 293 cells expressing only human T1R223 and a human gastric cancer cell line HGT-1, where T1R3 is expressed via mRNA39. In contrast to heterologous clone expression in cell, using endogenous expression in natural cellular environments allows for a more physiologically relevant investigation into sweet taste receptor activation. In cultured mouse and human taste bud cells, agonists induce an increase in calcium levels by interacting with the lipid receptor GPR12040,41. Unlike the previous heterologous clone method, where cells are transduced with a virus encoding the promiscuous G protein, this approach employs dynamic mass redistribution (DMR) to measure stable clones overexpressing T1R2/T1R3 sweet taste receptors42, allowing for effective coupling of the receptors to endogenous G proteins.
Sweetness perception, as a function of concentration, has long been related to the equilibrium binding affinity and binding kinetics43,44. Sweetener-receptor dissociation equilibrium constants (Kd) are the ratio of sweetener dissociation rate constant (koff) and the association rate constant (kon). The inverse of Kd, the Ka or association equilibrium constant, is called the affinity. Various models/ equations exist to account for multiple orthosteric binding sites, allosteric binding behavior, constitutive activation, transducer/ efficacy models (Rmax), agonist rebinding, blend synergies, and other receptor-ligand phenomenon45–49.
Generally, in simpler models based on laws of mass action, the Kd has been one main marker for sweetener intensity since it is a measure of receptor occupancy which is needed for downstream signaling and signal amplification. Sweeteners usually show low affinities. So, generally, low affinity sweeteners need to have high concentrations to maintain high receptor activation. High concentrations could be limited by oral diffusion rates. Natural/nutritive sweeteners have especially low affinities, making traditional binding studies difficult. High Intensity Sweeteners (HIS) sweeteners have higher affinities, but their maximum sweetness levels are suspected to result from the ability to bind at only a subset of receptor locations available for nutritive sweeteners50. Ace-K and perillartine exhibit equilibrium constant values (Kd) values of 164 µM and 373 µM, respectively, in their interactions with human T1R2, as determined by measurements of intrinsic fluorescence23. Furthermore, circular dichroism (CD) measurements revealed a Kd of 100 µM for Ace-K when binding to the extracellular amino-terminal domain (ATD) of human T1R251. A cell-based assay targeting sweet taste receptors evaluates responses indicative of sweet taste sensory signals, encompassing changes in calcium ion concentration and shifts in cellular mass distribution resulting from ligand binding. These sensory responses are quantified by determining the corresponding potency, expressed as the EC50. RebA exhibits EC50 values of 57 µM in the DMR assay and 29 µM in the Fluorometric Imaging Plate Reader (FLIPR) assay42, showcasing consistency between the two techniques. In contrast, S-819 leads to a tenfold difference in potency, with an EC50 of 0.7 µM in the DMR assay and 0.07 µM in the FLIPR assay42. This observed variation might be influenced by the impact of the complex cellular environment on binding responses. Factors such as the Gα protein subunit, cAMP, and IP3 could exert an influence on downstream signaling in sweet taste, thereby affecting calcium release. For HEK293 cells stably expressing the human T1R2/T1R3 sweet taste receptor, reported EC50 values were 1.45 µM for Perillartine, 10.2 µM for NHDC, 14.4 µM for RebA, and 125 µM for Ace-K24, with RebM at 29.54 µM52, determined via FLIPR for calcium mobilization. Belloir et al. derived EC50 values of 2.54 µM for Perillartine and 213 µM for Ace-K using human T1R2/T1R3 heterodimers transiently transfected with a fluorescent calcium biosensor23. While the EC50 values for Ace-K align with its reported Kd of 100 µM51, Perillartine exhibited a substantial decrease compared to its reported Kd of 373 µM when T1R2 was expressed alone23. This inconsistency could be attributed to T1R2 potentially acting as a homodimer, diminishing binding affinity, or it could arise from variations in the cellular environment. Hence, we opted for a receptor-based assay to directly measure the binding response of the T1R2/T1R3 heterodimer, providing a more efficient representation of receptor binding responses and facilitating the investigation of agonist binding on the sweet taste receptor.
Isothermal Titration Calorimetry (ITC)53 and Nuclear Magnetic Resonance (NMR)11 can investigate the agonist binding properties of sweet ligands on the T1R2/T1R3 receptors. However, radioligand binding assays offer the advantage of determining equilibrium dissociation constants for both radioligands and nonradioactive ligands, providing insights into association and dissociation rates54,55, albeit with limitations related to radioligand depletion56. In our research, we utilized radioligand depletion assays with sweet taste receptors to study Steviol Rebaudiosides binding. To further study the binding sites of the sweet taste receptor, we employed Frequency Locking Optical Whispering Evanescent Resonator (FLOWER) to directly observe the response of sweet ligands binding to the receptor. FLOWER is a whispering gallery mode (WGM) resonator based sensor that is highly sensitive to surrounding environmental disturbances57–62. A critical parameter in our study was determining the binding constant Kd for Steviol Rebaudiosides. Throughout our experiments, we controlled the presence or absence of G protein to assess the impact of G protein coupling to the TMD on the binding affinity of the T1R2/T1R3 heterodimer receptor. To complement our experimental findings, we employed computational docking methods to predict the preferred binding sites. This computational approach serves as a valuable reference for our experimental results, particularly for predicting the binding affinity of different sites within the T1R2/T1R3 heterodimer. This integrated approach enhances our understanding of the binding characteristics of human sweet taste receptors.
Results
In this section, we provide a comprehensive analysis of sweet ligand binding to the T1R2/T1R3 receptors, organized into three main parts. First, we present results from radioligand competitive binding assays, which offer insights into the binding affinities and dissociation constants of Steviol Rebaudiosides. Next, we discuss the findings from the FLOWER experiments, which enabled us to directly observe the binding responses of sweet ligands to stabilized heterodimer receptors, including the impact of G protein coupling. Finally, we demonstrate the outcomes of our silico analyses, which involved predicting ligand binding energies and identifying potential binding sites through detailed docking simulations. By combining these experimental and computational approaches, we provide a comprehensive insight into the binding dynamics and interactions of sweet taste receptors with Steviol Rebaudiosides.
Radioligand binding studies: taste cell membrane assays
Kinetics of [3H]-Rebaudioside B binding in the presence or absence of Ligands
Results of kinetic binding experiments using [3H]-Rebaudioside B as radioligand presumably bound at VFD2 are shown in Fig. S3. Association was followed by dissociation which was initiated by adding excess (1000-fold) of cold Rebaudioside B at 60 min (time 0) after the start of incubation. [3H]-Rebaudioside showed a kon = 91.82 M−1 min−1, koff = 0.5321 min−1 and Kd = 0.005795 M. Addition of both GDP and GTP resulted in inhibition of Rebaudioside B binding. GDP had an IC50 = 2.152 mM (Fig. S4) while GTP had an IC50 = 9.222 mM (Fig. S5).
Competition binding experiments with various ligands with [3H]-Rebaudioside B
RebM showed ambiguous binding inhibition with RebB while RebC showed low competitive inhibition (Table S1). This was not expected since all the rebaudiosides are presumed to bind in the same VFD2 site. This could indicate that the RebC inhibition is generated at a binding site outside of the VFD2 site. The Ace-K and S819 results were expected. Ace-K was inhibited by RebB, presumably at the VFD2 site. S819 did not show strong inhibition with RebB and it has not been reported as binding in VFD2.
The positive inhibition results for NHDC and amiloride indicate that RebB may have the ability to bind in both TMD regions.
Kinetics of [3H]-Lactisole binding in the presence or absence of ligands
Association was followed by dissociation which was initiated by adding excess (1000-fold) of cold lactisole at 60 min (time 0) after the start of incubation. Lactisole was presumably bound on TMD3. [3H]-Lactisole binding studies showed kon = 217.7 M−1 min−1, koff = 0.01456 min−1 and Kd = 0.0000669 M (Fig. S6). No inhibition was seen with GDP (Fig. S7) or GTP (Fig. S8).
Competition binding experiments using [3H]-lactisole as radioligand
Expected results were seen for NHDC (inhibition), Ace-K and S819. NHDC is known to bind TMD3 like lactisole while Ace-K and S819 do not. The positive inhibition seen with RebM and RebC was unexpected. This could indicate that either lactisole binds at other sites, such as the VFD2 or that RebC and RebM also bind at the TMD3 and are inhibited by lactisole. Since the lactisole site is known to be a negative allosteric binding site, the binding of these Rebs at this location could explain the low potency of these sweeteners where they bind at the TMD3 at high concentrations for a negative allosteric effect after the activation of the receptor by orthosteric binding at the VFD2 at lower concentrations. Relative binding energies are needed to determine if this is possible.
Kinetics of [3H]-Perrilartine binding in the presence or absence of non-labeled test ligands
Association was followed by dissociation which was initiated by adding excess of cold Perillartine at 60 min (time 0) after the start of incubation. Perillartine presumably binds on TMD2. [3H]-Perrilartine had a kon = 184288 M-1 min-1, koff = 0.5337 min-1 and a Kd = 0.000002896 M (2.896 µM) (Fig. S9). Both GDP and GTP had an inhibitory effect on the binding of perillaratine (GDP IC50 = 12.65 mM, Fig. S10; GTP IC50 = 13.167 mM, Fig. S11).
Competition binding experiments using [3H]-Perillartine as radioligand
RebM and RebC showed on competitive inhibition with perillartine as expected since there are no reports of these binding in TMD2. However, amiloride and S819 are known to bind at TMD2 but did not show inhibition with perillartine. This was unexpected. Also unexpected were the high levels of inhibition seen with Ace-K and NHDC which are not known to bind at TMD2 and thus should not show such inhibition. The inhibition of NHDC could indicate that either NHDC can bind at TMD2 or that perillartine can bind at the TMD3 site where NHDC also binds.
Kinetics of [14C]-sucrose binding in the presence or absence of ligands
Radio-labeled sucrose binding was performed as mentioned above. This was to observe the target sweetener. Association was followed by dissociation which was initiated by adding excess of cold sucrose at 60 min after the start of incubation. [14C]-sucrose showed kon = 15.06 M−1 min−1, koff = 0.1863 min−1 and Kd = 0.0123 M (Fig. S12). This was expectedly higher than the previously mentioned high intensity sweeteners.
Competition binding experiments using [14C]-sucrose as radioligand against NHDC
As a method check, the binding competition between sucrose, a VFD2 and 3 binder and NHDC, a TMD3 binder, was performed. Inhibition was observed (Ki = 0.01362 M, Fig. S13).
During this study, very high Kd values have been observed that were in the millimolar range. These values were calculated from the kinetic binding experiments from the ratio of koff /kon. One earlier report has investigated the binding of sweet molecules to bovine taste bud cells63. Kd values reported were of the order of millimolar, which corroborates our findings. For instance, the Kd value for sucrose was 1.1 mM and 3.4 mM for glucose. Such high Kd values may drastically affect the stability of radioligand-receptor complex during washings to separate bound from free ligand. While these dissociation constants and those observed during our study are significantly higher than the usually observed, they might represent a system with different physiological requirements. In taste system, loose binding may be of advantage as a tight binding will result in persistent taste sensation due to the stability of taste receptor-ligand complex and cause taste confusions. A weak taste receptor-tastant complex will subsequently allow the processing of new incoming taste information.
It is known that the presence of nucleotides decrease the binding of agonists but not antagonists with receptors64. We observed during the current study that binding of radioligands, i.e., [3H]-Perillartine and [3H]-RebB, decreased with increasing concentrations of GTP and GDP but not for [3H]-Lactisole. This observation is consistent with the known agonist and antagonist activity of these molecules and indicates that the binding of these molecules depends on the availability of receptors that are coupled to a G protein65. Future studies should use labeled [35S]-GTPγS could give a better understanding of the efficacy and effect on the binding of ligands and provide information about the possible allosteric modulation by other ligands65.
FLOWER binding studies
Binding with and without C-20
Upon binding to the T1R2/T1R3 receptor in FLOWER experiment, sweet ligands induce a red shift in the microtoroid’s resonance. This shift arises from the interaction between receptor-ligand binding and the microtoroid’s evanescent electrical field. Figure 2 shows the binding response of Rubu and RebM. The binding response curve consistently exhibits a peak at approximately 150 s after sweet ligand injection, followed by a decrease and stabilization within the subsequent 400 s to a sustained response, representing around two-thirds to zero of the peak response magnitude. With increasing concentration, both the final stabilized binding response and peak value also increase. Previous work on DMR assays for the human sweet taste receptor has revealed that measuring the peak DMR response offers a more robust assay window compared to measuring the final and sustained DMR response42.Hence, we choose the peak response in the FLOWER resonance shift signal as the binding response in our human sweet taste receptor assays.Fig. 2FLOWER binding response to increasing concentrations of sweet ligand binding to the human sweet receptor T1R2/T1R3.a Binding curves for Rubu with TMD3 open and no C20. b Binding curves for RebM with TMD2 open and no C20.
Binding curves were constructed by plotting the extracted resonance shift peak value as a function of ligand concentration. In some binding response curves, after reaching saturation, the binding response continues to increase linearly with the sweet ligand concentration. This continued increase after the saturation point is considered nonspecific binding occurring at high ligand concentrations. To determine the binding affinity, we employed a one-site specific binding model66 that takes into account the background response and non-specific binding. The model is fit to the experimental data to assess the binding affinity and the Hill slope. The binding constant Kd and Hill slope nH re determined through the curve fitting process according to the following equation:1\documentclass[12pt]{minimal}
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\begin{document}$$\triangle \lambda =\frac{{B}_{\max }{* [L]}^{{n}_{H}}}{{{K}_{d}}^{{n}_{H}}+{[L]}^{{n}_{H}}}+{{\rm{NS}}}* \left[{{\rm{L}}}\right]+{B}_{{background}}$$\end{document}△λ=Bmax*[L]nHKdnH+[L]nH+NS*L+Bbackgroundwhere Bmax is the maximum specific binding, ∆λ is the resonance shift at response peak, NS is the non-specific binding parameter, [L] represents the concentration of the sweet ligand solution, and Bbackground is the background response. To create a normalized binding curve, we eliminated the background signal and non-specific binding response from the experimental data. Following this, the data points were normalized by dividing each point by the maximum observed resonance shift in the dataset,2\documentclass[12pt]{minimal}
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\begin{document}$${\triangle \lambda }_{{norm}}=\frac{\triangle \lambda -{B}_{{background}}}{{B}_{\max }}-\frac{{{\rm{NS}}}* \left[{{\rm{L}}}\right]}{{B}_{\max }}=\frac{{[L]}^{{n}_{H}}}{{{K}_{d}}^{{n}_{H}}+{[L]}^{{n}_{H}}}$$\end{document}△λnorm=△λ−BbackgroundBmax−NS*LBmax=[L]nHKdnH+[L]nH
This normalization process enhances the clarity of the specific binding response curve, facilitating a more precise evaluation of the binding constant Kd and Hill slope nH. The normalized binding curve and experiment data points are shown in Fig. S2. The binding constant Kd is listed in Table 1 and the Hill slope nH is listed in Table 2. The R2 of the binding response curves are shown in Table S6. When the Hill slope, nH is less than 1, suggesting negative cooperativity, the binding site number (N) satisfies the condition \documentclass[12pt]{minimal}
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\begin{document}$$N\ge \frac{1}{{n}_{H}}$$\end{document}N≥1nH67. Table 2 might contain errors in the Hill slope due to the errors inherent in experimental measurements and the curve fitting process. When the Hill slope nH is significantly less than 1, it is considered indicative of multiple binding sites. In Table 2, all four sweet ligands, RebC, Rubu, RebD, and RebM, exhibit instances where the Hill slope is significantly less than 1. Following the experimental principle \documentclass[12pt]{minimal}
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\begin{document}$$N\ge \frac{1}{{n}_{H}}$$\end{document}N≥1nH, this suggests that these four sweet ligands possess multiple binding sites on the human T1R2/T1R3 sweet receptor.Table 1Summary of binding constant Kd for T1R2/T1R3 with various sweet ligandsT1R2/T1R3RebCRubusosideRebMRebDTMD2 open (No C20)340 µM2.4 µM190 nMTMD3 open (No C20)84 µM3.0 µM22 nM2.8 nMTMD2 open (+C20)0.59 µM0.28 nMTMD3 open (+C20)1.1 µM26 nM2.6 nM46 pMTable 2Summary of Hill slope nH for binding of various ligands to the human sweet receptor T1R2/T1R3T1R2/T1R3RebCRubusosideRebMRebDTMD2 open (No C20)0.280.360.30TMD3 open (No C20)0.520.200.550.22TMD2 open (+C20)1.00.75TMD3 open (+C20)0.120.930.250.84
Considering the sweetness levels of the ligands in Table 1, RebC is categorized as a low-sweet ligand, while RebM and RebD are categorized as high-sweet ligands, with RebM slightly surpassing RebD in sweetness. Rubu falls in the intermediate sweetness category, positioned between RebD and the low-sweet RebC. In Table 1, across all scenarios involving human sweet T1R2/T1R3 receptors, the binding constant Kd consistently shows an inverse correlation with sweetness, except for RebD. Despite having similar sweetness to RebM, RebD deviates from this pattern, suggesting lower efficiency in transmitting the sweetness biological signal. The stronger binding affinity of RebD results in a lower sweetness signal compared to RebM. In all cases where G protein C20 is introduced to the microtoroid functionalized with sweet T1R2/T1R3 receptors before sweet ligand injection, a significant reduction in the binding constant kd is observed. This implies that the coupling of G protein C20 to the TMD of the T1R2/T1R3 heterodimer enhances the heterodimer sweet receptor’s binding affinity upon its first activation.
The interaction with antibodies elicits distinct effects on the T1R2/T1R3 sweet receptor. Attaching antibodies to the Helix 8 C-terminus of the GPCR places them at a significant distance from the extracellular TMD binding site, indicating minimal impact on sweet ligand binding at the TMD binding site. When the antibody is attached to the TMD Helix 8 site, potentially involved in G protein coupling to TMD, it may impede C20 binding to TMD. In the absence of C20, RebM and RebC exhibit a lower binding constant Kd in the TMD2 open case than the TMD3 open case. According to the Hill slope nH for the multiple binding of negative cooperativity binding in the supplementary information, the enhanced binding affinity at the orthosteric binding site leads to a decrease in the binding constant Kd with an increase in Hill slope nH. The elevated Hill slope in the TMD2 open case suggests increased binding affinity at the orthosteric binding site (VFD2), as indicated by docking simulations for RebM and RebC. The anti-flag antibody coupled to TMD2 may reorient TMD2 to stabilize the VFD2 conformation through CRD2, enhancing VFD2 binding affinity for RebM and RebC. Notably, this antibody’s enhancement effect is relatively weaker compared to that of G protein C20 coupling to TMD. However, for Rubu, whose orthosteric binding site is VDF3 as indicated by docking simulations, the binding constant Kd remains largely unchanged between the TMD2 open case and TMD3 open case in the absence of C20. The TMD3 open case’s Hill slope even shows a slight decrease, suggesting that the anti-rho antibody coupled to TMD3 may not enhance the VFD3 binding affinity for Rubu. It appears that the anti-rho antibody coupled to TMD3 does not induce reorientation of TMD3 or stabilize the VFD3 conformation but don’t enhance the VFD3 binding affinity for Rubu.
In the presence of G protein C20, RebM and RebC exhibit a lower binding constant Kd transitioning from the TMD3 open case to the TMD2 open case, especially for the high-sweet RebM. Simultaneously, the Hill slope of the TMD2 open case is higher than the TMD3 open case, indicating enhanced binding affinity at the orthosteric binding site (VFD2). For RebM and RebC in the TMD3 open case, the presence of C20 results in a lower binding constant Kd and Hill slope nH compared to the absence of C20. As per the Hill slope nH for the multiple binding case for the negative cooperativity binding in the supplementary information, the orthostatic binding site’s binding affinity enhancement, causes the binding constant Kd to decrease with a decrease in Hill slope nH. Therefore, G protein coupling to TMD3 enhances the orthostatic binding site of RebM and RebC, which could be VFD3, TMD2, or TMD3. For Rubu, whose orthosteric binding site is VFD3, in the TMD3 open case, the binding constant Kd decreases with C20 coupling to TMD3. Simultaneously, the presence of G protein C20 increases the Hill slope nH close to 1, signifying enhancement at the orthosteric binding site VFD3. The experimental results suggest that G protein might bind to both TMD3 and TMD2 intracellular regions and enhance the binding of the corresponding binding sites VFD2 and VFD3 separately for the Steviol Rebaudiosides.
In silico binding energies
Docking results of various Rebs at multiple binding sites, VFD2, VFD3, TMD2, and TMD3
All docking results of various steviol glycosides with or without C20 at VFD2, VFD3, TMD2, and TMD3 are summarized in Table 3. Based on UCavE, most of steviol glycosides without C20 prefers to bind at the orthosteric binding site, cVFD2, except RebB and Rubu.Table 3Sweetness data at 300 ppm20 and calculated binding energies (kcals/mol) of various steviol glycosides at the closed venus fly trap domain 2 (cVFD2), transmembrane domain 2 (TMD2), TMD3, and open VFD3LigandSweetness at 300 ppmUcav E (kal/mol)BE ordercVFD2TMD2TMD3oVFD3Reb1-57.40–156.96–102.72–26.20–38.83VFD2>TMD2>VFD3>TMD3RebM6.55–135.74–89.80–34.23–7.09VFD2>TMD2>TMD3>VFD3RebD6.15–123.85–70.85–47.90–86.27VFD2>VFD3>TMD2>TMD3isoRebM6.01–119.92–79.40–40.36–48.98VFD2>TMD2>VFD3>TMD3RebA5.90–104.15–70.92–85.19–82.31VFD2>TMD3>VFD3>TMD2RebB5.20–72.84–96.11–98.53–24.48TMD3>TMD2>VFD2>VFD3Rubu3.87–64.66–52.21–61.30–82.41VFD3>VFD2>TMD3>TMD2Reb1-23.13–178.39–102.69–57.41–38.29VFD2>TMD2>TMD3>VFD3RebC2.64–101.22–97.15–77.10–52.18VFD2>TMD2>TMD3>VFD3hydRebM1.14–125.02–91.55–42.71–44.69VFD2>TMD2>VFD3>TMD3LigandC20 at TMD3Ucav E (kal/mol)BE orderVFD2TMD2TMD3oVFD3RebM–125.44–86.23–17.96–102.80VFD2>VFD3>TMD2>TMD3RebD–98.10–80.84–74.23–70.02VFD2>TMD2>TMD3>VFD3Rubu–84.77–61.66–39.08–42.10VFD2>TMD2>VFD3>TMD3RebC–94.57–79.47–75.47–40.81VFD2>TMD2>TMD3>VFD3LigandC20 at TMD2Ucav E (kal/mol)BE orderVFD2TMD2TMD3oVFD3RebM–98.37–52.10–64.81–95.54VFD2>VFD3>TMD3>TMD2RebD–85.13–75.83–60.62–82.11VFD2>VFD3>TMD2>TMD3Rubu–43.88–69.10–50.80–75.95VFD3>TMD2>TMD3>VFD2RebC–79.54–43.27–83.24–71.82TMD3>VFD2>VFD3>TMD2The binding energy (BE) was ordered unified cavity energy (UCav E), which is the sum of the non-bonding energy in the unified binding site within 5 Å of ligands.
Compared to the orthosteric binding site (UCav E: –64.66 kcal/mol), Rubu which is the smallest steviol glycoside shows more stable interaction at VFD3 (UCav E: -82.41 kcal/mol), where natural sugars, sucrose or fructose, can also bind.
RebB is the only ligand with a charged group among the steviol glycosides, which was used as a radiolabeled ligand for this binding study. Because of the presence of the charged group, RebB showed a different binding preference compared to other steviol glycosides. It has much favorable interactions at TMDs (UCav E: –98.53 kcal/mol for TMD3, -96.11 kcal/mol for TMD2) rather than VFD2 (UCav E: –72.84 kcal/mol). As shown in Fig. 3, the carboxylate group at the R1 site of RebB can form a salt bridge with R725 (5.37) at TMD2 and with the protonated H734 (5.44) at TMD3. RebB also has several H-bonds at the backbone of L719 (EC2) /S726 (EC2) and the side chains of R790 (7.34) at TMD3. The best binding site for RebB is the allosteric binding site, TMD3. Thus, this preferred binding of RebB at TMD3 and not VFD2 results in ambiguous data for RebM at VFD2 from the radio-labeled ligand binding study in Table 3.Fig. 3The binding poses of RebB at T1R2 TMD (left) and T1R3 TMD (right).The salt bridge is displayed in the dotted circle. The carboxylate group at the R1 site of RebB can form the salt bridge with R725 (5.37) at TMD2 and the protonated H734 (5.44) at TMD3, respectively. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
When C20 is present at TMD3, all four cases of RebM, RebD, RebC, and Rubu prefer to bind to the orthosteric binding site, VFD2. However, when C20 is present at TMD2, RebM and RebD with high sweetness do not affect the binding preference, while Rubu and RebC with low sweetness differs in binding preference (Table 3). The order of UCav binding energy differs in the low sweet cases. Rubu first binds at oVFD3, while RebC first binds at TMD3.
Docking result of various ligands
T1R2/T1R3 TMD3 binders
Cyclamate allosteric agonist, Neohesperidin dihydrochalcone (NHDC) artificial sweetener, and lactisole negative allosteric modulator are well-known TMD3 binders to the T1R2/T1R3 heterodimer. When we used lactosole as a radiolabeled ligand, the VFD2 binder Ace-K (artificial sweetener) and the TMD2 binder S819 (positive allosteric modulator) do not compete with lactisole (Table 4). However, TMD3 binder NHDC shows the highest inhibition with Ki of 333.7 µM among the 6 ligands (RebM, RebC, Ace-K, S819, NHDC, and amiloride). Our docking study also found that NHDC has the lowest UCav binding E (the second lowest BE), consistent with the highest inhibition (Ki) among six ligands (Table 4). From the experimental observation, the NHDC binding site at the human sweet taste receptor overlaps with those for the sweetener cyclamate and the sweet taste inhibitor lactisole68. The observed binding site of cyclamate and/or lactisole are Q636 (3.28), Q637 (3.29), S640 (3.32), H641 (3.33), H721 (EC2), R723 (5.36), S729 (5.42), F730 (5.43), A733 (5.46), F778 (6.51), V779 (6.52), L782 (6.55), R790 (7.34), and L798 (7.36) of hT1R3. Additional important amino acids for NHDC binding are Y699 (4.60), W775 (6.48), and C801 (7.39) from point-mutation experiments. From our docking study, NHDC forms multiple H-bonds at the backbone of F624 (2.60) and V720 (EC2) and with the side chains of Q636 (3.28), Q637 (3.29), S640 (3.32), C722 (EC2), S726 (EC2), R790 (7.34), and Q794 (7.38), as shown in Fig. 4. Steviol glycosides, RebC and RebM, also show mM level binding affinities, 6.98 and 11.75, respectively. RebC has better scoring energies than RebM does, which also agrees with experimental observations. Ligands that bind to other sites, Ace-K, S-819, and amiloride, reveal higher binding energy at TMD3, which also have fewer H-bonds. As shown in Fig. 4, S819 has H-bonds at H641 (3.33) and N737 (5.47). Amiloride forms H-bonds at S640 (3.32), C722 (EC2), R723 (EC2), R790 (7.34), and Q794 (7.38). Ace-K has H-bonds at Q637 (3.29) and R790 (7.34).Table 4The binding affinity (Ki, M) for TMD3 binding using the radiolabeled ligand, lactisole and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–98.96–134.05–22.17NHDC0.33–89.28–124.72–86.05RebAN.A.–84.67–141.65–58.73RebC6.98–76.44–126.12–60.65RubuN.A.–60.22–104.68–52.59S819N.C.–50.89–59.95–40.16Amiloride––34.26–58.34–39.84RebM11.75–33.13–121.89–35.84AceKN.C.–29.68–38.23–31.38N.A. not available, N.C. not competition.Lactisole was used as a radiolabeled ligand. The ligands were ordered by UCav E.Fig. 4Binding sites of various sweeteners at TMD3 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 VFD2 binders
The artificial sweeteners saccharin and aspartame as well as the natural sweeteners sugar and stevioside can bind to the VFD2 of th eT1R2/T1R3 heterodimer. From docking studies, we found RebM, which has high sweetness, shows the lowest binding energy in all scoring energies (UCav, BE, SolvE), although RebM gave an ambiguous result in experiment because of its low solubility (Table 5). The second lowest energy is from RebC, which has lower sweetness than does RebM. Ace-K has the highest Kd value (bad binder to VFD2), resulting in the highest binding energy. However, the best binder to VFD2 was amiloride (TMD2 binder), although the binding energy shows it to be unfavorable. In addition, several ligands known to bind TMD2 or TMD3 were found at a detectable binding affinity. The third lowest one by UCav and BE is the TMD3 binder NHDC, which displays a slightly lower binding affinity. One possibility of these mixed data is the multiple binding sites of RebB used for the radiolabeling. Based on the experiment (Table 4), since RebC can bind to TMD3, RebB without sugars at the R1 position which is smaller than RebC is also predicted to bind to TMD3. As shown in Fig. 5, S819 and Ace-K have two hydrogen bonds at D142 and R383. Amiloride forms multiple hydrogen bonds at Y103, N143, S165, D278, and R383. NHDC has hydrogen bonds at N44, N143, D213, and R383. RebM made hydrogen bonds at S40, D142, D278, D307, R339, and R383. Our docking pose of RebB at the binding site of TMD3 shows the possibility of a salt-bridge at the protonated histidine, H734 (5.44), as shown in Fig. 3 (right). Based on our docking study, RebB can also bind to TMD2. Supporting this, RebB at the binding site of TMD2 formed a salt-bridge at R725 (5.37) as well as H-bonds at N731 (5. 43) with the terminal carboxylate (Fig. 3, left). So, the high binding affinity data of amiloride might arise from TMD2 binding. The other possibility derives from the multiple binding sites of the tested ligands. For example, amiloride (TMD2 binder) could bind to multiple binding sites because of its small size and its ability to form multiple hydrogen bonds. Thus, this discrepancy arises from mixed data of multiple binding sites of tested ligands as well as the radio labeled ligand.Table 5The binding inhibition constants (Ki) for VFD2 binding using the radio labeled ligand, RebB and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebMAmbiguous–132.58–209.99–174.32RebAN.A.–104.33–149.66–120.72RebC18.99–98.94–141.82–105.55NHDC63.37–75.28–122.49–90.06RebBN.A.–69.61–125.98–101.37S819Ambiguous–49.86–76.06–47.80Amiloride1.35–29.34–56.26–51.41AceK1096.00–25.09–35.34–28.07N.A. not available.The ligands are ordered by UCav E. Disagreement with experiments is shown by italics.Fig. 5Binding sites of various sweeteners at VFD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 TMD2 binders
Allosteric agonist perillatine, antagonist amiloride, and positive allosteric modulator S819 are reported as TMD2 binders of the T1R2/T1R3 heterodimer. From experiments for testing the binding affinity at TMD2 (Table 6) using perillartine for radiolabeling, there is much ambiguous data. Even TMD2 binders such as S819 and amiloride result in ambiguities. Unexpectedly, Ace-K (VFD2 binder) and NHDC (TMD3 binder) reveal a detectable level of binding affinity.Table 6The binding inhibition constants (Ki) for TMD2 binding using the radio labeled ligand, perillartine and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–102.55–179.98–27.33RebCAmbiguous–96.95–143.56–52.28NHDC293.7–92.24–131.81–83.08RebMAmbiguous–90.03–180.89–96.04RebAN.A.–70.92–122.32–56.96S819Ambiguous–44.73–64.23–47.13AceK142–31.64–37.94–21.55AmilorideAmbiguous–27.50–57.20–47.35N.A. not available.The ligands are ordered by Ucav E. Disagreement with experiment is displayed in italics.
From docking, we have good agreement between theory and experiment for three cases (S819, NHDC, amiloride). NHDC has a better binding energy than does S-819 or amiloride. However, Ace-K is less favorable than NHDC. Since perillartine (agonist) has a structure very similar to amiloride (antagonist), we suspect this mixed data arises from multiple binding sites of the radiolabeled and the test ligand. As shown in Fig. 6, NHDC has hydrogen bonds at R3.28, S5.51, R5.37, R7.34, and D7.38. RebM made hydrogen bonds at R3.28, K4.53, D5.47, S5.51, and N7.45. S819 has a hydrogen bond at D7.38. Amiloride forms multiple hydrogen bonds at N5.43, T5.44, S6.48 and N7.45. Ace-K has a salt bridge at R5.37 with one hydrogen bond at N5.43.Fig. 6Binding sites of various sweeteners at TMD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
Docking result of radio-labeled ligands, Perillatine and Lactisole
From the docking study, perillatine, an agonist of the human sweet taste receptor, interacts with N799 (7.37) at TMD2 of T1R2/T1R3 heterodimer with UCav E of -25.92 kcal/mol and BE of -35.28 kcal/mol, as shown in Fig. 7 (Left). Lactisole as a negative allosteric modulator can bind to TMD3 of T1R2/T1R3 heterodimer. From our docking study, lactisole, an inhibitor of the human sweet taste receptor, interacts with S640 (3.32), the protonated H641 (3.33), H734 (5. 44) and Q794 (7.38) at TMD3 of T1R2/T1R3 heterodimer as shown in Fig. 7 (Right). This docking result of lactisole agrees with the inhibitory activity of (±)-lactisole, which was not determined by H641A and Q794N mutation at T1R3 TMD30.Fig. 7The binding poses of perillatine at TMD2 (left) and lactosole at TMD3 (right) of the T1R2/T1R3 heterodimer.The ligand is represented by sticks with different colors, while the side chains forming hydrogen bonds are labeled.
In silico binding site discovery
We discovered nine sites using the SiteMap method. The sweet heterodimer appears to have many potential binding sites that make interpretation of the experimental binding data difficult. Two sites discovered correspond to the canonical VFD2 binding site (in silico site 5, Fig. S14) and VFD3 binding site (in silico site 1, Figure S14).
Two additional sites were found near the interaction of the VFD. Site 4 was near the top intersection of the VFD. Site 6 was near the bottom intersection of the VFD. The VFD2 and VFD3 upper distances, near site 4 have been correlated with binding and the active state of the receptor of some ligands69. Binding in this site could potentially influence binding and activation. Binding in site 6 could influence the transmission of allosteric movements between the heterodimer section.
Site 9 was found at the intersection of the VFD2 and CRD2. Binding at site 9 could influence the canonical binding site of VFD2.
Two sites (8 and 10) were more associated with TMD, 10 was located at the intersection of TMD2 and CRD2. This was near the sweet protein binding site. Presumably binding at this site could activate the receptor. Finally, site 8 is located on the intracellular side of TMD3. Binding at this site could potentially impact the G protein if it is bound on TMD3.
The δG of binding from MM/GBSA (Table S4) are shown below for high sweet (RebB) medium sweet (Rubu) and low sweet (RebC) ligands. Site 5 in Fig. S14 was selected as the canonical VFD2 binding site and MM/GBSA values are shown as the delta from those of site 5 to enable a comparison. Sites 2, 6 and 10 show better relative binding than site 5 (VFD2) from RebB. RebC showed the same but with the addition of site 8. Rubu showed less favorable relative binding at all sites probably due to the smaller size and less sugar attachments.
The SiteMap analysis indicates the potential for several non-canonical binding sites that could influence the interpretation of experimental binding results.
AlphaFold structure comparison
We compared our MD structures of RebM bound TAS1R2/1R3 with C20 at T1R2TMD or T1R3TMD with AlphaFold 3 structures, generated using an AI model developed by Google DeepMind and Isomorphic Labs available at https://golgi.sandbox.google.com/. Three FASTA sequences were derived from our MD structures, while AlphaFold 3 predicted a total of five structures. As depicted in Figure S16, all five AlphaFold structures exhibited significant similarity. For the comparison of our MD structure of RebM-bound TAS1R2/1R3 with C20 at T1R3TMD, the RMSD values from PyMOL were 14.05, 13.82, 13.82, 13.97, and 14.51 Å for models 0–4, respectively. Similarly, for our MD structure of RebM-bound TAS1R2/1R3 with C20 at T1R2TMD, the RMSD values were 13.29, 12.48, 12.85, 13.08, and 13.47 Å for models 0–4, respectively. Both structures exhibited identical TMD interfaces such as TM6-6, which is known TMD interface in Class C GPCRs upon activation.
We observed a discrepancy in the disulfide bonding patterns within the VFD region (Table S5). The AlphaFold model indicates inter-disulfide bridges between C359 (1R2) and C129 (1R3), whereas our predicted model shows an intra-disulfide bridge between C359 and C363 in 1R2VFD. For 1R3VFD, the AlphaFold model depicts a disulfide bridge between C370 and C373, while our model predicts two disulfide bridges: one between C351 and C370 and another between C373 and C375. This disparity arises from the use of different templates for VFD model generation. Our VFD model was based on the structure of the rat metabotropic glutamate receptor 1 (mGluR1) (PDB ID: 1ewt). Subsequently, the crystal structure of the medaka fish taste receptor T1R2/1R3 complex with L-glutamine (PDB ID: 5x2m) was reported, showing an inter-disulfide bridge between VFDs.
However. the putative C20 binding site in the AlphaFold model was located in EC2 or CRD2, rather than between TM3 and TM6, which is recognized as the G protein coupling site. The top AlphaFold model (model 0) revealed a salt bridge between K345 (K-11) and D688 (EC2 of 1R2), as illustrated in Figure S17. Conversely, in our MD structure of RebM-bound TAS1R2/1R3 with C20 at T1R2TMD, two salt bridges were observed among R664 (IC2), E346 (E-9), and D341 (D-15), whereas in the structure with C20 at T1R3TMD, one salt bridge was noted between R760 (6.33) and the C-terminal end of F354 (F-1), as shown in Figure S17. Thus, AlphaFold 3 was not able to predict the C20 binding site within the known cytoplasmic end of TM3-6 of GPCRs.
Radioligand binding studies: taste cell membrane assays
Kinetics of [3H]-Rebaudioside B binding in the presence or absence of Ligands
Results of kinetic binding experiments using [3H]-Rebaudioside B as radioligand presumably bound at VFD2 are shown in Fig. S3. Association was followed by dissociation which was initiated by adding excess (1000-fold) of cold Rebaudioside B at 60 min (time 0) after the start of incubation. [3H]-Rebaudioside showed a kon = 91.82 M−1 min−1, koff = 0.5321 min−1 and Kd = 0.005795 M. Addition of both GDP and GTP resulted in inhibition of Rebaudioside B binding. GDP had an IC50 = 2.152 mM (Fig. S4) while GTP had an IC50 = 9.222 mM (Fig. S5).
Competition binding experiments with various ligands with [3H]-Rebaudioside B
RebM showed ambiguous binding inhibition with RebB while RebC showed low competitive inhibition (Table S1). This was not expected since all the rebaudiosides are presumed to bind in the same VFD2 site. This could indicate that the RebC inhibition is generated at a binding site outside of the VFD2 site. The Ace-K and S819 results were expected. Ace-K was inhibited by RebB, presumably at the VFD2 site. S819 did not show strong inhibition with RebB and it has not been reported as binding in VFD2.
The positive inhibition results for NHDC and amiloride indicate that RebB may have the ability to bind in both TMD regions.
Kinetics of [3H]-Lactisole binding in the presence or absence of ligands
Association was followed by dissociation which was initiated by adding excess (1000-fold) of cold lactisole at 60 min (time 0) after the start of incubation. Lactisole was presumably bound on TMD3. [3H]-Lactisole binding studies showed kon = 217.7 M−1 min−1, koff = 0.01456 min−1 and Kd = 0.0000669 M (Fig. S6). No inhibition was seen with GDP (Fig. S7) or GTP (Fig. S8).
Competition binding experiments using [3H]-lactisole as radioligand
Expected results were seen for NHDC (inhibition), Ace-K and S819. NHDC is known to bind TMD3 like lactisole while Ace-K and S819 do not. The positive inhibition seen with RebM and RebC was unexpected. This could indicate that either lactisole binds at other sites, such as the VFD2 or that RebC and RebM also bind at the TMD3 and are inhibited by lactisole. Since the lactisole site is known to be a negative allosteric binding site, the binding of these Rebs at this location could explain the low potency of these sweeteners where they bind at the TMD3 at high concentrations for a negative allosteric effect after the activation of the receptor by orthosteric binding at the VFD2 at lower concentrations. Relative binding energies are needed to determine if this is possible.
Kinetics of [3H]-Perrilartine binding in the presence or absence of non-labeled test ligands
Association was followed by dissociation which was initiated by adding excess of cold Perillartine at 60 min (time 0) after the start of incubation. Perillartine presumably binds on TMD2. [3H]-Perrilartine had a kon = 184288 M-1 min-1, koff = 0.5337 min-1 and a Kd = 0.000002896 M (2.896 µM) (Fig. S9). Both GDP and GTP had an inhibitory effect on the binding of perillaratine (GDP IC50 = 12.65 mM, Fig. S10; GTP IC50 = 13.167 mM, Fig. S11).
Competition binding experiments using [3H]-Perillartine as radioligand
RebM and RebC showed on competitive inhibition with perillartine as expected since there are no reports of these binding in TMD2. However, amiloride and S819 are known to bind at TMD2 but did not show inhibition with perillartine. This was unexpected. Also unexpected were the high levels of inhibition seen with Ace-K and NHDC which are not known to bind at TMD2 and thus should not show such inhibition. The inhibition of NHDC could indicate that either NHDC can bind at TMD2 or that perillartine can bind at the TMD3 site where NHDC also binds.
Kinetics of [14C]-sucrose binding in the presence or absence of ligands
Radio-labeled sucrose binding was performed as mentioned above. This was to observe the target sweetener. Association was followed by dissociation which was initiated by adding excess of cold sucrose at 60 min after the start of incubation. [14C]-sucrose showed kon = 15.06 M−1 min−1, koff = 0.1863 min−1 and Kd = 0.0123 M (Fig. S12). This was expectedly higher than the previously mentioned high intensity sweeteners.
Competition binding experiments using [14C]-sucrose as radioligand against NHDC
As a method check, the binding competition between sucrose, a VFD2 and 3 binder and NHDC, a TMD3 binder, was performed. Inhibition was observed (Ki = 0.01362 M, Fig. S13).
During this study, very high Kd values have been observed that were in the millimolar range. These values were calculated from the kinetic binding experiments from the ratio of koff /kon. One earlier report has investigated the binding of sweet molecules to bovine taste bud cells63. Kd values reported were of the order of millimolar, which corroborates our findings. For instance, the Kd value for sucrose was 1.1 mM and 3.4 mM for glucose. Such high Kd values may drastically affect the stability of radioligand-receptor complex during washings to separate bound from free ligand. While these dissociation constants and those observed during our study are significantly higher than the usually observed, they might represent a system with different physiological requirements. In taste system, loose binding may be of advantage as a tight binding will result in persistent taste sensation due to the stability of taste receptor-ligand complex and cause taste confusions. A weak taste receptor-tastant complex will subsequently allow the processing of new incoming taste information.
It is known that the presence of nucleotides decrease the binding of agonists but not antagonists with receptors64. We observed during the current study that binding of radioligands, i.e., [3H]-Perillartine and [3H]-RebB, decreased with increasing concentrations of GTP and GDP but not for [3H]-Lactisole. This observation is consistent with the known agonist and antagonist activity of these molecules and indicates that the binding of these molecules depends on the availability of receptors that are coupled to a G protein65. Future studies should use labeled [35S]-GTPγS could give a better understanding of the efficacy and effect on the binding of ligands and provide information about the possible allosteric modulation by other ligands65.
Kinetics of [3H]-Rebaudioside B binding in the presence or absence of Ligands
Results of kinetic binding experiments using [3H]-Rebaudioside B as radioligand presumably bound at VFD2 are shown in Fig. S3. Association was followed by dissociation which was initiated by adding excess (1000-fold) of cold Rebaudioside B at 60 min (time 0) after the start of incubation. [3H]-Rebaudioside showed a kon = 91.82 M−1 min−1, koff = 0.5321 min−1 and Kd = 0.005795 M. Addition of both GDP and GTP resulted in inhibition of Rebaudioside B binding. GDP had an IC50 = 2.152 mM (Fig. S4) while GTP had an IC50 = 9.222 mM (Fig. S5).
Competition binding experiments with various ligands with [3H]-Rebaudioside B
RebM showed ambiguous binding inhibition with RebB while RebC showed low competitive inhibition (Table S1). This was not expected since all the rebaudiosides are presumed to bind in the same VFD2 site. This could indicate that the RebC inhibition is generated at a binding site outside of the VFD2 site. The Ace-K and S819 results were expected. Ace-K was inhibited by RebB, presumably at the VFD2 site. S819 did not show strong inhibition with RebB and it has not been reported as binding in VFD2.
The positive inhibition results for NHDC and amiloride indicate that RebB may have the ability to bind in both TMD regions.
Kinetics of [3H]-Lactisole binding in the presence or absence of ligands
Association was followed by dissociation which was initiated by adding excess (1000-fold) of cold lactisole at 60 min (time 0) after the start of incubation. Lactisole was presumably bound on TMD3. [3H]-Lactisole binding studies showed kon = 217.7 M−1 min−1, koff = 0.01456 min−1 and Kd = 0.0000669 M (Fig. S6). No inhibition was seen with GDP (Fig. S7) or GTP (Fig. S8).
Competition binding experiments using [3H]-lactisole as radioligand
Expected results were seen for NHDC (inhibition), Ace-K and S819. NHDC is known to bind TMD3 like lactisole while Ace-K and S819 do not. The positive inhibition seen with RebM and RebC was unexpected. This could indicate that either lactisole binds at other sites, such as the VFD2 or that RebC and RebM also bind at the TMD3 and are inhibited by lactisole. Since the lactisole site is known to be a negative allosteric binding site, the binding of these Rebs at this location could explain the low potency of these sweeteners where they bind at the TMD3 at high concentrations for a negative allosteric effect after the activation of the receptor by orthosteric binding at the VFD2 at lower concentrations. Relative binding energies are needed to determine if this is possible.
Kinetics of [3H]-Perrilartine binding in the presence or absence of non-labeled test ligands
Association was followed by dissociation which was initiated by adding excess of cold Perillartine at 60 min (time 0) after the start of incubation. Perillartine presumably binds on TMD2. [3H]-Perrilartine had a kon = 184288 M-1 min-1, koff = 0.5337 min-1 and a Kd = 0.000002896 M (2.896 µM) (Fig. S9). Both GDP and GTP had an inhibitory effect on the binding of perillaratine (GDP IC50 = 12.65 mM, Fig. S10; GTP IC50 = 13.167 mM, Fig. S11).
Competition binding experiments using [3H]-Perillartine as radioligand
RebM and RebC showed on competitive inhibition with perillartine as expected since there are no reports of these binding in TMD2. However, amiloride and S819 are known to bind at TMD2 but did not show inhibition with perillartine. This was unexpected. Also unexpected were the high levels of inhibition seen with Ace-K and NHDC which are not known to bind at TMD2 and thus should not show such inhibition. The inhibition of NHDC could indicate that either NHDC can bind at TMD2 or that perillartine can bind at the TMD3 site where NHDC also binds.
Kinetics of [14C]-sucrose binding in the presence or absence of ligands
Radio-labeled sucrose binding was performed as mentioned above. This was to observe the target sweetener. Association was followed by dissociation which was initiated by adding excess of cold sucrose at 60 min after the start of incubation. [14C]-sucrose showed kon = 15.06 M−1 min−1, koff = 0.1863 min−1 and Kd = 0.0123 M (Fig. S12). This was expectedly higher than the previously mentioned high intensity sweeteners.
Competition binding experiments using [14C]-sucrose as radioligand against NHDC
As a method check, the binding competition between sucrose, a VFD2 and 3 binder and NHDC, a TMD3 binder, was performed. Inhibition was observed (Ki = 0.01362 M, Fig. S13).
During this study, very high Kd values have been observed that were in the millimolar range. These values were calculated from the kinetic binding experiments from the ratio of koff /kon. One earlier report has investigated the binding of sweet molecules to bovine taste bud cells63. Kd values reported were of the order of millimolar, which corroborates our findings. For instance, the Kd value for sucrose was 1.1 mM and 3.4 mM for glucose. Such high Kd values may drastically affect the stability of radioligand-receptor complex during washings to separate bound from free ligand. While these dissociation constants and those observed during our study are significantly higher than the usually observed, they might represent a system with different physiological requirements. In taste system, loose binding may be of advantage as a tight binding will result in persistent taste sensation due to the stability of taste receptor-ligand complex and cause taste confusions. A weak taste receptor-tastant complex will subsequently allow the processing of new incoming taste information.
It is known that the presence of nucleotides decrease the binding of agonists but not antagonists with receptors64. We observed during the current study that binding of radioligands, i.e., [3H]-Perillartine and [3H]-RebB, decreased with increasing concentrations of GTP and GDP but not for [3H]-Lactisole. This observation is consistent with the known agonist and antagonist activity of these molecules and indicates that the binding of these molecules depends on the availability of receptors that are coupled to a G protein65. Future studies should use labeled [35S]-GTPγS could give a better understanding of the efficacy and effect on the binding of ligands and provide information about the possible allosteric modulation by other ligands65.
FLOWER binding studies
Binding with and without C-20
Upon binding to the T1R2/T1R3 receptor in FLOWER experiment, sweet ligands induce a red shift in the microtoroid’s resonance. This shift arises from the interaction between receptor-ligand binding and the microtoroid’s evanescent electrical field. Figure 2 shows the binding response of Rubu and RebM. The binding response curve consistently exhibits a peak at approximately 150 s after sweet ligand injection, followed by a decrease and stabilization within the subsequent 400 s to a sustained response, representing around two-thirds to zero of the peak response magnitude. With increasing concentration, both the final stabilized binding response and peak value also increase. Previous work on DMR assays for the human sweet taste receptor has revealed that measuring the peak DMR response offers a more robust assay window compared to measuring the final and sustained DMR response42.Hence, we choose the peak response in the FLOWER resonance shift signal as the binding response in our human sweet taste receptor assays.Fig. 2FLOWER binding response to increasing concentrations of sweet ligand binding to the human sweet receptor T1R2/T1R3.a Binding curves for Rubu with TMD3 open and no C20. b Binding curves for RebM with TMD2 open and no C20.
Binding curves were constructed by plotting the extracted resonance shift peak value as a function of ligand concentration. In some binding response curves, after reaching saturation, the binding response continues to increase linearly with the sweet ligand concentration. This continued increase after the saturation point is considered nonspecific binding occurring at high ligand concentrations. To determine the binding affinity, we employed a one-site specific binding model66 that takes into account the background response and non-specific binding. The model is fit to the experimental data to assess the binding affinity and the Hill slope. The binding constant Kd and Hill slope nH re determined through the curve fitting process according to the following equation:1\documentclass[12pt]{minimal}
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\begin{document}$$\triangle \lambda =\frac{{B}_{\max }{* [L]}^{{n}_{H}}}{{{K}_{d}}^{{n}_{H}}+{[L]}^{{n}_{H}}}+{{\rm{NS}}}* \left[{{\rm{L}}}\right]+{B}_{{background}}$$\end{document}△λ=Bmax*[L]nHKdnH+[L]nH+NS*L+Bbackgroundwhere Bmax is the maximum specific binding, ∆λ is the resonance shift at response peak, NS is the non-specific binding parameter, [L] represents the concentration of the sweet ligand solution, and Bbackground is the background response. To create a normalized binding curve, we eliminated the background signal and non-specific binding response from the experimental data. Following this, the data points were normalized by dividing each point by the maximum observed resonance shift in the dataset,2\documentclass[12pt]{minimal}
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\begin{document}$${\triangle \lambda }_{{norm}}=\frac{\triangle \lambda -{B}_{{background}}}{{B}_{\max }}-\frac{{{\rm{NS}}}* \left[{{\rm{L}}}\right]}{{B}_{\max }}=\frac{{[L]}^{{n}_{H}}}{{{K}_{d}}^{{n}_{H}}+{[L]}^{{n}_{H}}}$$\end{document}△λnorm=△λ−BbackgroundBmax−NS*LBmax=[L]nHKdnH+[L]nH
This normalization process enhances the clarity of the specific binding response curve, facilitating a more precise evaluation of the binding constant Kd and Hill slope nH. The normalized binding curve and experiment data points are shown in Fig. S2. The binding constant Kd is listed in Table 1 and the Hill slope nH is listed in Table 2. The R2 of the binding response curves are shown in Table S6. When the Hill slope, nH is less than 1, suggesting negative cooperativity, the binding site number (N) satisfies the condition \documentclass[12pt]{minimal}
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\begin{document}$$N\ge \frac{1}{{n}_{H}}$$\end{document}N≥1nH67. Table 2 might contain errors in the Hill slope due to the errors inherent in experimental measurements and the curve fitting process. When the Hill slope nH is significantly less than 1, it is considered indicative of multiple binding sites. In Table 2, all four sweet ligands, RebC, Rubu, RebD, and RebM, exhibit instances where the Hill slope is significantly less than 1. Following the experimental principle \documentclass[12pt]{minimal}
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\begin{document}$$N\ge \frac{1}{{n}_{H}}$$\end{document}N≥1nH, this suggests that these four sweet ligands possess multiple binding sites on the human T1R2/T1R3 sweet receptor.Table 1Summary of binding constant Kd for T1R2/T1R3 with various sweet ligandsT1R2/T1R3RebCRubusosideRebMRebDTMD2 open (No C20)340 µM2.4 µM190 nMTMD3 open (No C20)84 µM3.0 µM22 nM2.8 nMTMD2 open (+C20)0.59 µM0.28 nMTMD3 open (+C20)1.1 µM26 nM2.6 nM46 pMTable 2Summary of Hill slope nH for binding of various ligands to the human sweet receptor T1R2/T1R3T1R2/T1R3RebCRubusosideRebMRebDTMD2 open (No C20)0.280.360.30TMD3 open (No C20)0.520.200.550.22TMD2 open (+C20)1.00.75TMD3 open (+C20)0.120.930.250.84
Considering the sweetness levels of the ligands in Table 1, RebC is categorized as a low-sweet ligand, while RebM and RebD are categorized as high-sweet ligands, with RebM slightly surpassing RebD in sweetness. Rubu falls in the intermediate sweetness category, positioned between RebD and the low-sweet RebC. In Table 1, across all scenarios involving human sweet T1R2/T1R3 receptors, the binding constant Kd consistently shows an inverse correlation with sweetness, except for RebD. Despite having similar sweetness to RebM, RebD deviates from this pattern, suggesting lower efficiency in transmitting the sweetness biological signal. The stronger binding affinity of RebD results in a lower sweetness signal compared to RebM. In all cases where G protein C20 is introduced to the microtoroid functionalized with sweet T1R2/T1R3 receptors before sweet ligand injection, a significant reduction in the binding constant kd is observed. This implies that the coupling of G protein C20 to the TMD of the T1R2/T1R3 heterodimer enhances the heterodimer sweet receptor’s binding affinity upon its first activation.
The interaction with antibodies elicits distinct effects on the T1R2/T1R3 sweet receptor. Attaching antibodies to the Helix 8 C-terminus of the GPCR places them at a significant distance from the extracellular TMD binding site, indicating minimal impact on sweet ligand binding at the TMD binding site. When the antibody is attached to the TMD Helix 8 site, potentially involved in G protein coupling to TMD, it may impede C20 binding to TMD. In the absence of C20, RebM and RebC exhibit a lower binding constant Kd in the TMD2 open case than the TMD3 open case. According to the Hill slope nH for the multiple binding of negative cooperativity binding in the supplementary information, the enhanced binding affinity at the orthosteric binding site leads to a decrease in the binding constant Kd with an increase in Hill slope nH. The elevated Hill slope in the TMD2 open case suggests increased binding affinity at the orthosteric binding site (VFD2), as indicated by docking simulations for RebM and RebC. The anti-flag antibody coupled to TMD2 may reorient TMD2 to stabilize the VFD2 conformation through CRD2, enhancing VFD2 binding affinity for RebM and RebC. Notably, this antibody’s enhancement effect is relatively weaker compared to that of G protein C20 coupling to TMD. However, for Rubu, whose orthosteric binding site is VDF3 as indicated by docking simulations, the binding constant Kd remains largely unchanged between the TMD2 open case and TMD3 open case in the absence of C20. The TMD3 open case’s Hill slope even shows a slight decrease, suggesting that the anti-rho antibody coupled to TMD3 may not enhance the VFD3 binding affinity for Rubu. It appears that the anti-rho antibody coupled to TMD3 does not induce reorientation of TMD3 or stabilize the VFD3 conformation but don’t enhance the VFD3 binding affinity for Rubu.
In the presence of G protein C20, RebM and RebC exhibit a lower binding constant Kd transitioning from the TMD3 open case to the TMD2 open case, especially for the high-sweet RebM. Simultaneously, the Hill slope of the TMD2 open case is higher than the TMD3 open case, indicating enhanced binding affinity at the orthosteric binding site (VFD2). For RebM and RebC in the TMD3 open case, the presence of C20 results in a lower binding constant Kd and Hill slope nH compared to the absence of C20. As per the Hill slope nH for the multiple binding case for the negative cooperativity binding in the supplementary information, the orthostatic binding site’s binding affinity enhancement, causes the binding constant Kd to decrease with a decrease in Hill slope nH. Therefore, G protein coupling to TMD3 enhances the orthostatic binding site of RebM and RebC, which could be VFD3, TMD2, or TMD3. For Rubu, whose orthosteric binding site is VFD3, in the TMD3 open case, the binding constant Kd decreases with C20 coupling to TMD3. Simultaneously, the presence of G protein C20 increases the Hill slope nH close to 1, signifying enhancement at the orthosteric binding site VFD3. The experimental results suggest that G protein might bind to both TMD3 and TMD2 intracellular regions and enhance the binding of the corresponding binding sites VFD2 and VFD3 separately for the Steviol Rebaudiosides.
Binding with and without C-20
Upon binding to the T1R2/T1R3 receptor in FLOWER experiment, sweet ligands induce a red shift in the microtoroid’s resonance. This shift arises from the interaction between receptor-ligand binding and the microtoroid’s evanescent electrical field. Figure 2 shows the binding response of Rubu and RebM. The binding response curve consistently exhibits a peak at approximately 150 s after sweet ligand injection, followed by a decrease and stabilization within the subsequent 400 s to a sustained response, representing around two-thirds to zero of the peak response magnitude. With increasing concentration, both the final stabilized binding response and peak value also increase. Previous work on DMR assays for the human sweet taste receptor has revealed that measuring the peak DMR response offers a more robust assay window compared to measuring the final and sustained DMR response42.Hence, we choose the peak response in the FLOWER resonance shift signal as the binding response in our human sweet taste receptor assays.Fig. 2FLOWER binding response to increasing concentrations of sweet ligand binding to the human sweet receptor T1R2/T1R3.a Binding curves for Rubu with TMD3 open and no C20. b Binding curves for RebM with TMD2 open and no C20.
Binding curves were constructed by plotting the extracted resonance shift peak value as a function of ligand concentration. In some binding response curves, after reaching saturation, the binding response continues to increase linearly with the sweet ligand concentration. This continued increase after the saturation point is considered nonspecific binding occurring at high ligand concentrations. To determine the binding affinity, we employed a one-site specific binding model66 that takes into account the background response and non-specific binding. The model is fit to the experimental data to assess the binding affinity and the Hill slope. The binding constant Kd and Hill slope nH re determined through the curve fitting process according to the following equation:1\documentclass[12pt]{minimal}
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\begin{document}$$\triangle \lambda =\frac{{B}_{\max }{* [L]}^{{n}_{H}}}{{{K}_{d}}^{{n}_{H}}+{[L]}^{{n}_{H}}}+{{\rm{NS}}}* \left[{{\rm{L}}}\right]+{B}_{{background}}$$\end{document}△λ=Bmax*[L]nHKdnH+[L]nH+NS*L+Bbackgroundwhere Bmax is the maximum specific binding, ∆λ is the resonance shift at response peak, NS is the non-specific binding parameter, [L] represents the concentration of the sweet ligand solution, and Bbackground is the background response. To create a normalized binding curve, we eliminated the background signal and non-specific binding response from the experimental data. Following this, the data points were normalized by dividing each point by the maximum observed resonance shift in the dataset,2\documentclass[12pt]{minimal}
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\begin{document}$${\triangle \lambda }_{{norm}}=\frac{\triangle \lambda -{B}_{{background}}}{{B}_{\max }}-\frac{{{\rm{NS}}}* \left[{{\rm{L}}}\right]}{{B}_{\max }}=\frac{{[L]}^{{n}_{H}}}{{{K}_{d}}^{{n}_{H}}+{[L]}^{{n}_{H}}}$$\end{document}△λnorm=△λ−BbackgroundBmax−NS*LBmax=[L]nHKdnH+[L]nH
This normalization process enhances the clarity of the specific binding response curve, facilitating a more precise evaluation of the binding constant Kd and Hill slope nH. The normalized binding curve and experiment data points are shown in Fig. S2. The binding constant Kd is listed in Table 1 and the Hill slope nH is listed in Table 2. The R2 of the binding response curves are shown in Table S6. When the Hill slope, nH is less than 1, suggesting negative cooperativity, the binding site number (N) satisfies the condition \documentclass[12pt]{minimal}
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\begin{document}$$N\ge \frac{1}{{n}_{H}}$$\end{document}N≥1nH67. Table 2 might contain errors in the Hill slope due to the errors inherent in experimental measurements and the curve fitting process. When the Hill slope nH is significantly less than 1, it is considered indicative of multiple binding sites. In Table 2, all four sweet ligands, RebC, Rubu, RebD, and RebM, exhibit instances where the Hill slope is significantly less than 1. Following the experimental principle \documentclass[12pt]{minimal}
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\begin{document}$$N\ge \frac{1}{{n}_{H}}$$\end{document}N≥1nH, this suggests that these four sweet ligands possess multiple binding sites on the human T1R2/T1R3 sweet receptor.Table 1Summary of binding constant Kd for T1R2/T1R3 with various sweet ligandsT1R2/T1R3RebCRubusosideRebMRebDTMD2 open (No C20)340 µM2.4 µM190 nMTMD3 open (No C20)84 µM3.0 µM22 nM2.8 nMTMD2 open (+C20)0.59 µM0.28 nMTMD3 open (+C20)1.1 µM26 nM2.6 nM46 pMTable 2Summary of Hill slope nH for binding of various ligands to the human sweet receptor T1R2/T1R3T1R2/T1R3RebCRubusosideRebMRebDTMD2 open (No C20)0.280.360.30TMD3 open (No C20)0.520.200.550.22TMD2 open (+C20)1.00.75TMD3 open (+C20)0.120.930.250.84
Considering the sweetness levels of the ligands in Table 1, RebC is categorized as a low-sweet ligand, while RebM and RebD are categorized as high-sweet ligands, with RebM slightly surpassing RebD in sweetness. Rubu falls in the intermediate sweetness category, positioned between RebD and the low-sweet RebC. In Table 1, across all scenarios involving human sweet T1R2/T1R3 receptors, the binding constant Kd consistently shows an inverse correlation with sweetness, except for RebD. Despite having similar sweetness to RebM, RebD deviates from this pattern, suggesting lower efficiency in transmitting the sweetness biological signal. The stronger binding affinity of RebD results in a lower sweetness signal compared to RebM. In all cases where G protein C20 is introduced to the microtoroid functionalized with sweet T1R2/T1R3 receptors before sweet ligand injection, a significant reduction in the binding constant kd is observed. This implies that the coupling of G protein C20 to the TMD of the T1R2/T1R3 heterodimer enhances the heterodimer sweet receptor’s binding affinity upon its first activation.
The interaction with antibodies elicits distinct effects on the T1R2/T1R3 sweet receptor. Attaching antibodies to the Helix 8 C-terminus of the GPCR places them at a significant distance from the extracellular TMD binding site, indicating minimal impact on sweet ligand binding at the TMD binding site. When the antibody is attached to the TMD Helix 8 site, potentially involved in G protein coupling to TMD, it may impede C20 binding to TMD. In the absence of C20, RebM and RebC exhibit a lower binding constant Kd in the TMD2 open case than the TMD3 open case. According to the Hill slope nH for the multiple binding of negative cooperativity binding in the supplementary information, the enhanced binding affinity at the orthosteric binding site leads to a decrease in the binding constant Kd with an increase in Hill slope nH. The elevated Hill slope in the TMD2 open case suggests increased binding affinity at the orthosteric binding site (VFD2), as indicated by docking simulations for RebM and RebC. The anti-flag antibody coupled to TMD2 may reorient TMD2 to stabilize the VFD2 conformation through CRD2, enhancing VFD2 binding affinity for RebM and RebC. Notably, this antibody’s enhancement effect is relatively weaker compared to that of G protein C20 coupling to TMD. However, for Rubu, whose orthosteric binding site is VDF3 as indicated by docking simulations, the binding constant Kd remains largely unchanged between the TMD2 open case and TMD3 open case in the absence of C20. The TMD3 open case’s Hill slope even shows a slight decrease, suggesting that the anti-rho antibody coupled to TMD3 may not enhance the VFD3 binding affinity for Rubu. It appears that the anti-rho antibody coupled to TMD3 does not induce reorientation of TMD3 or stabilize the VFD3 conformation but don’t enhance the VFD3 binding affinity for Rubu.
In the presence of G protein C20, RebM and RebC exhibit a lower binding constant Kd transitioning from the TMD3 open case to the TMD2 open case, especially for the high-sweet RebM. Simultaneously, the Hill slope of the TMD2 open case is higher than the TMD3 open case, indicating enhanced binding affinity at the orthosteric binding site (VFD2). For RebM and RebC in the TMD3 open case, the presence of C20 results in a lower binding constant Kd and Hill slope nH compared to the absence of C20. As per the Hill slope nH for the multiple binding case for the negative cooperativity binding in the supplementary information, the orthostatic binding site’s binding affinity enhancement, causes the binding constant Kd to decrease with a decrease in Hill slope nH. Therefore, G protein coupling to TMD3 enhances the orthostatic binding site of RebM and RebC, which could be VFD3, TMD2, or TMD3. For Rubu, whose orthosteric binding site is VFD3, in the TMD3 open case, the binding constant Kd decreases with C20 coupling to TMD3. Simultaneously, the presence of G protein C20 increases the Hill slope nH close to 1, signifying enhancement at the orthosteric binding site VFD3. The experimental results suggest that G protein might bind to both TMD3 and TMD2 intracellular regions and enhance the binding of the corresponding binding sites VFD2 and VFD3 separately for the Steviol Rebaudiosides.
In silico binding energies
Docking results of various Rebs at multiple binding sites, VFD2, VFD3, TMD2, and TMD3
All docking results of various steviol glycosides with or without C20 at VFD2, VFD3, TMD2, and TMD3 are summarized in Table 3. Based on UCavE, most of steviol glycosides without C20 prefers to bind at the orthosteric binding site, cVFD2, except RebB and Rubu.Table 3Sweetness data at 300 ppm20 and calculated binding energies (kcals/mol) of various steviol glycosides at the closed venus fly trap domain 2 (cVFD2), transmembrane domain 2 (TMD2), TMD3, and open VFD3LigandSweetness at 300 ppmUcav E (kal/mol)BE ordercVFD2TMD2TMD3oVFD3Reb1-57.40–156.96–102.72–26.20–38.83VFD2>TMD2>VFD3>TMD3RebM6.55–135.74–89.80–34.23–7.09VFD2>TMD2>TMD3>VFD3RebD6.15–123.85–70.85–47.90–86.27VFD2>VFD3>TMD2>TMD3isoRebM6.01–119.92–79.40–40.36–48.98VFD2>TMD2>VFD3>TMD3RebA5.90–104.15–70.92–85.19–82.31VFD2>TMD3>VFD3>TMD2RebB5.20–72.84–96.11–98.53–24.48TMD3>TMD2>VFD2>VFD3Rubu3.87–64.66–52.21–61.30–82.41VFD3>VFD2>TMD3>TMD2Reb1-23.13–178.39–102.69–57.41–38.29VFD2>TMD2>TMD3>VFD3RebC2.64–101.22–97.15–77.10–52.18VFD2>TMD2>TMD3>VFD3hydRebM1.14–125.02–91.55–42.71–44.69VFD2>TMD2>VFD3>TMD3LigandC20 at TMD3Ucav E (kal/mol)BE orderVFD2TMD2TMD3oVFD3RebM–125.44–86.23–17.96–102.80VFD2>VFD3>TMD2>TMD3RebD–98.10–80.84–74.23–70.02VFD2>TMD2>TMD3>VFD3Rubu–84.77–61.66–39.08–42.10VFD2>TMD2>VFD3>TMD3RebC–94.57–79.47–75.47–40.81VFD2>TMD2>TMD3>VFD3LigandC20 at TMD2Ucav E (kal/mol)BE orderVFD2TMD2TMD3oVFD3RebM–98.37–52.10–64.81–95.54VFD2>VFD3>TMD3>TMD2RebD–85.13–75.83–60.62–82.11VFD2>VFD3>TMD2>TMD3Rubu–43.88–69.10–50.80–75.95VFD3>TMD2>TMD3>VFD2RebC–79.54–43.27–83.24–71.82TMD3>VFD2>VFD3>TMD2The binding energy (BE) was ordered unified cavity energy (UCav E), which is the sum of the non-bonding energy in the unified binding site within 5 Å of ligands.
Compared to the orthosteric binding site (UCav E: –64.66 kcal/mol), Rubu which is the smallest steviol glycoside shows more stable interaction at VFD3 (UCav E: -82.41 kcal/mol), where natural sugars, sucrose or fructose, can also bind.
RebB is the only ligand with a charged group among the steviol glycosides, which was used as a radiolabeled ligand for this binding study. Because of the presence of the charged group, RebB showed a different binding preference compared to other steviol glycosides. It has much favorable interactions at TMDs (UCav E: –98.53 kcal/mol for TMD3, -96.11 kcal/mol for TMD2) rather than VFD2 (UCav E: –72.84 kcal/mol). As shown in Fig. 3, the carboxylate group at the R1 site of RebB can form a salt bridge with R725 (5.37) at TMD2 and with the protonated H734 (5.44) at TMD3. RebB also has several H-bonds at the backbone of L719 (EC2) /S726 (EC2) and the side chains of R790 (7.34) at TMD3. The best binding site for RebB is the allosteric binding site, TMD3. Thus, this preferred binding of RebB at TMD3 and not VFD2 results in ambiguous data for RebM at VFD2 from the radio-labeled ligand binding study in Table 3.Fig. 3The binding poses of RebB at T1R2 TMD (left) and T1R3 TMD (right).The salt bridge is displayed in the dotted circle. The carboxylate group at the R1 site of RebB can form the salt bridge with R725 (5.37) at TMD2 and the protonated H734 (5.44) at TMD3, respectively. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
When C20 is present at TMD3, all four cases of RebM, RebD, RebC, and Rubu prefer to bind to the orthosteric binding site, VFD2. However, when C20 is present at TMD2, RebM and RebD with high sweetness do not affect the binding preference, while Rubu and RebC with low sweetness differs in binding preference (Table 3). The order of UCav binding energy differs in the low sweet cases. Rubu first binds at oVFD3, while RebC first binds at TMD3.
Docking result of various ligands
T1R2/T1R3 TMD3 binders
Cyclamate allosteric agonist, Neohesperidin dihydrochalcone (NHDC) artificial sweetener, and lactisole negative allosteric modulator are well-known TMD3 binders to the T1R2/T1R3 heterodimer. When we used lactosole as a radiolabeled ligand, the VFD2 binder Ace-K (artificial sweetener) and the TMD2 binder S819 (positive allosteric modulator) do not compete with lactisole (Table 4). However, TMD3 binder NHDC shows the highest inhibition with Ki of 333.7 µM among the 6 ligands (RebM, RebC, Ace-K, S819, NHDC, and amiloride). Our docking study also found that NHDC has the lowest UCav binding E (the second lowest BE), consistent with the highest inhibition (Ki) among six ligands (Table 4). From the experimental observation, the NHDC binding site at the human sweet taste receptor overlaps with those for the sweetener cyclamate and the sweet taste inhibitor lactisole68. The observed binding site of cyclamate and/or lactisole are Q636 (3.28), Q637 (3.29), S640 (3.32), H641 (3.33), H721 (EC2), R723 (5.36), S729 (5.42), F730 (5.43), A733 (5.46), F778 (6.51), V779 (6.52), L782 (6.55), R790 (7.34), and L798 (7.36) of hT1R3. Additional important amino acids for NHDC binding are Y699 (4.60), W775 (6.48), and C801 (7.39) from point-mutation experiments. From our docking study, NHDC forms multiple H-bonds at the backbone of F624 (2.60) and V720 (EC2) and with the side chains of Q636 (3.28), Q637 (3.29), S640 (3.32), C722 (EC2), S726 (EC2), R790 (7.34), and Q794 (7.38), as shown in Fig. 4. Steviol glycosides, RebC and RebM, also show mM level binding affinities, 6.98 and 11.75, respectively. RebC has better scoring energies than RebM does, which also agrees with experimental observations. Ligands that bind to other sites, Ace-K, S-819, and amiloride, reveal higher binding energy at TMD3, which also have fewer H-bonds. As shown in Fig. 4, S819 has H-bonds at H641 (3.33) and N737 (5.47). Amiloride forms H-bonds at S640 (3.32), C722 (EC2), R723 (EC2), R790 (7.34), and Q794 (7.38). Ace-K has H-bonds at Q637 (3.29) and R790 (7.34).Table 4The binding affinity (Ki, M) for TMD3 binding using the radiolabeled ligand, lactisole and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–98.96–134.05–22.17NHDC0.33–89.28–124.72–86.05RebAN.A.–84.67–141.65–58.73RebC6.98–76.44–126.12–60.65RubuN.A.–60.22–104.68–52.59S819N.C.–50.89–59.95–40.16Amiloride––34.26–58.34–39.84RebM11.75–33.13–121.89–35.84AceKN.C.–29.68–38.23–31.38N.A. not available, N.C. not competition.Lactisole was used as a radiolabeled ligand. The ligands were ordered by UCav E.Fig. 4Binding sites of various sweeteners at TMD3 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 VFD2 binders
The artificial sweeteners saccharin and aspartame as well as the natural sweeteners sugar and stevioside can bind to the VFD2 of th eT1R2/T1R3 heterodimer. From docking studies, we found RebM, which has high sweetness, shows the lowest binding energy in all scoring energies (UCav, BE, SolvE), although RebM gave an ambiguous result in experiment because of its low solubility (Table 5). The second lowest energy is from RebC, which has lower sweetness than does RebM. Ace-K has the highest Kd value (bad binder to VFD2), resulting in the highest binding energy. However, the best binder to VFD2 was amiloride (TMD2 binder), although the binding energy shows it to be unfavorable. In addition, several ligands known to bind TMD2 or TMD3 were found at a detectable binding affinity. The third lowest one by UCav and BE is the TMD3 binder NHDC, which displays a slightly lower binding affinity. One possibility of these mixed data is the multiple binding sites of RebB used for the radiolabeling. Based on the experiment (Table 4), since RebC can bind to TMD3, RebB without sugars at the R1 position which is smaller than RebC is also predicted to bind to TMD3. As shown in Fig. 5, S819 and Ace-K have two hydrogen bonds at D142 and R383. Amiloride forms multiple hydrogen bonds at Y103, N143, S165, D278, and R383. NHDC has hydrogen bonds at N44, N143, D213, and R383. RebM made hydrogen bonds at S40, D142, D278, D307, R339, and R383. Our docking pose of RebB at the binding site of TMD3 shows the possibility of a salt-bridge at the protonated histidine, H734 (5.44), as shown in Fig. 3 (right). Based on our docking study, RebB can also bind to TMD2. Supporting this, RebB at the binding site of TMD2 formed a salt-bridge at R725 (5.37) as well as H-bonds at N731 (5. 43) with the terminal carboxylate (Fig. 3, left). So, the high binding affinity data of amiloride might arise from TMD2 binding. The other possibility derives from the multiple binding sites of the tested ligands. For example, amiloride (TMD2 binder) could bind to multiple binding sites because of its small size and its ability to form multiple hydrogen bonds. Thus, this discrepancy arises from mixed data of multiple binding sites of tested ligands as well as the radio labeled ligand.Table 5The binding inhibition constants (Ki) for VFD2 binding using the radio labeled ligand, RebB and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebMAmbiguous–132.58–209.99–174.32RebAN.A.–104.33–149.66–120.72RebC18.99–98.94–141.82–105.55NHDC63.37–75.28–122.49–90.06RebBN.A.–69.61–125.98–101.37S819Ambiguous–49.86–76.06–47.80Amiloride1.35–29.34–56.26–51.41AceK1096.00–25.09–35.34–28.07N.A. not available.The ligands are ordered by UCav E. Disagreement with experiments is shown by italics.Fig. 5Binding sites of various sweeteners at VFD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 TMD2 binders
Allosteric agonist perillatine, antagonist amiloride, and positive allosteric modulator S819 are reported as TMD2 binders of the T1R2/T1R3 heterodimer. From experiments for testing the binding affinity at TMD2 (Table 6) using perillartine for radiolabeling, there is much ambiguous data. Even TMD2 binders such as S819 and amiloride result in ambiguities. Unexpectedly, Ace-K (VFD2 binder) and NHDC (TMD3 binder) reveal a detectable level of binding affinity.Table 6The binding inhibition constants (Ki) for TMD2 binding using the radio labeled ligand, perillartine and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–102.55–179.98–27.33RebCAmbiguous–96.95–143.56–52.28NHDC293.7–92.24–131.81–83.08RebMAmbiguous–90.03–180.89–96.04RebAN.A.–70.92–122.32–56.96S819Ambiguous–44.73–64.23–47.13AceK142–31.64–37.94–21.55AmilorideAmbiguous–27.50–57.20–47.35N.A. not available.The ligands are ordered by Ucav E. Disagreement with experiment is displayed in italics.
From docking, we have good agreement between theory and experiment for three cases (S819, NHDC, amiloride). NHDC has a better binding energy than does S-819 or amiloride. However, Ace-K is less favorable than NHDC. Since perillartine (agonist) has a structure very similar to amiloride (antagonist), we suspect this mixed data arises from multiple binding sites of the radiolabeled and the test ligand. As shown in Fig. 6, NHDC has hydrogen bonds at R3.28, S5.51, R5.37, R7.34, and D7.38. RebM made hydrogen bonds at R3.28, K4.53, D5.47, S5.51, and N7.45. S819 has a hydrogen bond at D7.38. Amiloride forms multiple hydrogen bonds at N5.43, T5.44, S6.48 and N7.45. Ace-K has a salt bridge at R5.37 with one hydrogen bond at N5.43.Fig. 6Binding sites of various sweeteners at TMD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
Docking result of radio-labeled ligands, Perillatine and Lactisole
From the docking study, perillatine, an agonist of the human sweet taste receptor, interacts with N799 (7.37) at TMD2 of T1R2/T1R3 heterodimer with UCav E of -25.92 kcal/mol and BE of -35.28 kcal/mol, as shown in Fig. 7 (Left). Lactisole as a negative allosteric modulator can bind to TMD3 of T1R2/T1R3 heterodimer. From our docking study, lactisole, an inhibitor of the human sweet taste receptor, interacts with S640 (3.32), the protonated H641 (3.33), H734 (5. 44) and Q794 (7.38) at TMD3 of T1R2/T1R3 heterodimer as shown in Fig. 7 (Right). This docking result of lactisole agrees with the inhibitory activity of (±)-lactisole, which was not determined by H641A and Q794N mutation at T1R3 TMD30.Fig. 7The binding poses of perillatine at TMD2 (left) and lactosole at TMD3 (right) of the T1R2/T1R3 heterodimer.The ligand is represented by sticks with different colors, while the side chains forming hydrogen bonds are labeled.
Docking results of various Rebs at multiple binding sites, VFD2, VFD3, TMD2, and TMD3
All docking results of various steviol glycosides with or without C20 at VFD2, VFD3, TMD2, and TMD3 are summarized in Table 3. Based on UCavE, most of steviol glycosides without C20 prefers to bind at the orthosteric binding site, cVFD2, except RebB and Rubu.Table 3Sweetness data at 300 ppm20 and calculated binding energies (kcals/mol) of various steviol glycosides at the closed venus fly trap domain 2 (cVFD2), transmembrane domain 2 (TMD2), TMD3, and open VFD3LigandSweetness at 300 ppmUcav E (kal/mol)BE ordercVFD2TMD2TMD3oVFD3Reb1-57.40–156.96–102.72–26.20–38.83VFD2>TMD2>VFD3>TMD3RebM6.55–135.74–89.80–34.23–7.09VFD2>TMD2>TMD3>VFD3RebD6.15–123.85–70.85–47.90–86.27VFD2>VFD3>TMD2>TMD3isoRebM6.01–119.92–79.40–40.36–48.98VFD2>TMD2>VFD3>TMD3RebA5.90–104.15–70.92–85.19–82.31VFD2>TMD3>VFD3>TMD2RebB5.20–72.84–96.11–98.53–24.48TMD3>TMD2>VFD2>VFD3Rubu3.87–64.66–52.21–61.30–82.41VFD3>VFD2>TMD3>TMD2Reb1-23.13–178.39–102.69–57.41–38.29VFD2>TMD2>TMD3>VFD3RebC2.64–101.22–97.15–77.10–52.18VFD2>TMD2>TMD3>VFD3hydRebM1.14–125.02–91.55–42.71–44.69VFD2>TMD2>VFD3>TMD3LigandC20 at TMD3Ucav E (kal/mol)BE orderVFD2TMD2TMD3oVFD3RebM–125.44–86.23–17.96–102.80VFD2>VFD3>TMD2>TMD3RebD–98.10–80.84–74.23–70.02VFD2>TMD2>TMD3>VFD3Rubu–84.77–61.66–39.08–42.10VFD2>TMD2>VFD3>TMD3RebC–94.57–79.47–75.47–40.81VFD2>TMD2>TMD3>VFD3LigandC20 at TMD2Ucav E (kal/mol)BE orderVFD2TMD2TMD3oVFD3RebM–98.37–52.10–64.81–95.54VFD2>VFD3>TMD3>TMD2RebD–85.13–75.83–60.62–82.11VFD2>VFD3>TMD2>TMD3Rubu–43.88–69.10–50.80–75.95VFD3>TMD2>TMD3>VFD2RebC–79.54–43.27–83.24–71.82TMD3>VFD2>VFD3>TMD2The binding energy (BE) was ordered unified cavity energy (UCav E), which is the sum of the non-bonding energy in the unified binding site within 5 Å of ligands.
Compared to the orthosteric binding site (UCav E: –64.66 kcal/mol), Rubu which is the smallest steviol glycoside shows more stable interaction at VFD3 (UCav E: -82.41 kcal/mol), where natural sugars, sucrose or fructose, can also bind.
RebB is the only ligand with a charged group among the steviol glycosides, which was used as a radiolabeled ligand for this binding study. Because of the presence of the charged group, RebB showed a different binding preference compared to other steviol glycosides. It has much favorable interactions at TMDs (UCav E: –98.53 kcal/mol for TMD3, -96.11 kcal/mol for TMD2) rather than VFD2 (UCav E: –72.84 kcal/mol). As shown in Fig. 3, the carboxylate group at the R1 site of RebB can form a salt bridge with R725 (5.37) at TMD2 and with the protonated H734 (5.44) at TMD3. RebB also has several H-bonds at the backbone of L719 (EC2) /S726 (EC2) and the side chains of R790 (7.34) at TMD3. The best binding site for RebB is the allosteric binding site, TMD3. Thus, this preferred binding of RebB at TMD3 and not VFD2 results in ambiguous data for RebM at VFD2 from the radio-labeled ligand binding study in Table 3.Fig. 3The binding poses of RebB at T1R2 TMD (left) and T1R3 TMD (right).The salt bridge is displayed in the dotted circle. The carboxylate group at the R1 site of RebB can form the salt bridge with R725 (5.37) at TMD2 and the protonated H734 (5.44) at TMD3, respectively. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
When C20 is present at TMD3, all four cases of RebM, RebD, RebC, and Rubu prefer to bind to the orthosteric binding site, VFD2. However, when C20 is present at TMD2, RebM and RebD with high sweetness do not affect the binding preference, while Rubu and RebC with low sweetness differs in binding preference (Table 3). The order of UCav binding energy differs in the low sweet cases. Rubu first binds at oVFD3, while RebC first binds at TMD3.
Docking result of various ligands
T1R2/T1R3 TMD3 binders
Cyclamate allosteric agonist, Neohesperidin dihydrochalcone (NHDC) artificial sweetener, and lactisole negative allosteric modulator are well-known TMD3 binders to the T1R2/T1R3 heterodimer. When we used lactosole as a radiolabeled ligand, the VFD2 binder Ace-K (artificial sweetener) and the TMD2 binder S819 (positive allosteric modulator) do not compete with lactisole (Table 4). However, TMD3 binder NHDC shows the highest inhibition with Ki of 333.7 µM among the 6 ligands (RebM, RebC, Ace-K, S819, NHDC, and amiloride). Our docking study also found that NHDC has the lowest UCav binding E (the second lowest BE), consistent with the highest inhibition (Ki) among six ligands (Table 4). From the experimental observation, the NHDC binding site at the human sweet taste receptor overlaps with those for the sweetener cyclamate and the sweet taste inhibitor lactisole68. The observed binding site of cyclamate and/or lactisole are Q636 (3.28), Q637 (3.29), S640 (3.32), H641 (3.33), H721 (EC2), R723 (5.36), S729 (5.42), F730 (5.43), A733 (5.46), F778 (6.51), V779 (6.52), L782 (6.55), R790 (7.34), and L798 (7.36) of hT1R3. Additional important amino acids for NHDC binding are Y699 (4.60), W775 (6.48), and C801 (7.39) from point-mutation experiments. From our docking study, NHDC forms multiple H-bonds at the backbone of F624 (2.60) and V720 (EC2) and with the side chains of Q636 (3.28), Q637 (3.29), S640 (3.32), C722 (EC2), S726 (EC2), R790 (7.34), and Q794 (7.38), as shown in Fig. 4. Steviol glycosides, RebC and RebM, also show mM level binding affinities, 6.98 and 11.75, respectively. RebC has better scoring energies than RebM does, which also agrees with experimental observations. Ligands that bind to other sites, Ace-K, S-819, and amiloride, reveal higher binding energy at TMD3, which also have fewer H-bonds. As shown in Fig. 4, S819 has H-bonds at H641 (3.33) and N737 (5.47). Amiloride forms H-bonds at S640 (3.32), C722 (EC2), R723 (EC2), R790 (7.34), and Q794 (7.38). Ace-K has H-bonds at Q637 (3.29) and R790 (7.34).Table 4The binding affinity (Ki, M) for TMD3 binding using the radiolabeled ligand, lactisole and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–98.96–134.05–22.17NHDC0.33–89.28–124.72–86.05RebAN.A.–84.67–141.65–58.73RebC6.98–76.44–126.12–60.65RubuN.A.–60.22–104.68–52.59S819N.C.–50.89–59.95–40.16Amiloride––34.26–58.34–39.84RebM11.75–33.13–121.89–35.84AceKN.C.–29.68–38.23–31.38N.A. not available, N.C. not competition.Lactisole was used as a radiolabeled ligand. The ligands were ordered by UCav E.Fig. 4Binding sites of various sweeteners at TMD3 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 VFD2 binders
The artificial sweeteners saccharin and aspartame as well as the natural sweeteners sugar and stevioside can bind to the VFD2 of th eT1R2/T1R3 heterodimer. From docking studies, we found RebM, which has high sweetness, shows the lowest binding energy in all scoring energies (UCav, BE, SolvE), although RebM gave an ambiguous result in experiment because of its low solubility (Table 5). The second lowest energy is from RebC, which has lower sweetness than does RebM. Ace-K has the highest Kd value (bad binder to VFD2), resulting in the highest binding energy. However, the best binder to VFD2 was amiloride (TMD2 binder), although the binding energy shows it to be unfavorable. In addition, several ligands known to bind TMD2 or TMD3 were found at a detectable binding affinity. The third lowest one by UCav and BE is the TMD3 binder NHDC, which displays a slightly lower binding affinity. One possibility of these mixed data is the multiple binding sites of RebB used for the radiolabeling. Based on the experiment (Table 4), since RebC can bind to TMD3, RebB without sugars at the R1 position which is smaller than RebC is also predicted to bind to TMD3. As shown in Fig. 5, S819 and Ace-K have two hydrogen bonds at D142 and R383. Amiloride forms multiple hydrogen bonds at Y103, N143, S165, D278, and R383. NHDC has hydrogen bonds at N44, N143, D213, and R383. RebM made hydrogen bonds at S40, D142, D278, D307, R339, and R383. Our docking pose of RebB at the binding site of TMD3 shows the possibility of a salt-bridge at the protonated histidine, H734 (5.44), as shown in Fig. 3 (right). Based on our docking study, RebB can also bind to TMD2. Supporting this, RebB at the binding site of TMD2 formed a salt-bridge at R725 (5.37) as well as H-bonds at N731 (5. 43) with the terminal carboxylate (Fig. 3, left). So, the high binding affinity data of amiloride might arise from TMD2 binding. The other possibility derives from the multiple binding sites of the tested ligands. For example, amiloride (TMD2 binder) could bind to multiple binding sites because of its small size and its ability to form multiple hydrogen bonds. Thus, this discrepancy arises from mixed data of multiple binding sites of tested ligands as well as the radio labeled ligand.Table 5The binding inhibition constants (Ki) for VFD2 binding using the radio labeled ligand, RebB and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebMAmbiguous–132.58–209.99–174.32RebAN.A.–104.33–149.66–120.72RebC18.99–98.94–141.82–105.55NHDC63.37–75.28–122.49–90.06RebBN.A.–69.61–125.98–101.37S819Ambiguous–49.86–76.06–47.80Amiloride1.35–29.34–56.26–51.41AceK1096.00–25.09–35.34–28.07N.A. not available.The ligands are ordered by UCav E. Disagreement with experiments is shown by italics.Fig. 5Binding sites of various sweeteners at VFD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 TMD2 binders
Allosteric agonist perillatine, antagonist amiloride, and positive allosteric modulator S819 are reported as TMD2 binders of the T1R2/T1R3 heterodimer. From experiments for testing the binding affinity at TMD2 (Table 6) using perillartine for radiolabeling, there is much ambiguous data. Even TMD2 binders such as S819 and amiloride result in ambiguities. Unexpectedly, Ace-K (VFD2 binder) and NHDC (TMD3 binder) reveal a detectable level of binding affinity.Table 6The binding inhibition constants (Ki) for TMD2 binding using the radio labeled ligand, perillartine and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–102.55–179.98–27.33RebCAmbiguous–96.95–143.56–52.28NHDC293.7–92.24–131.81–83.08RebMAmbiguous–90.03–180.89–96.04RebAN.A.–70.92–122.32–56.96S819Ambiguous–44.73–64.23–47.13AceK142–31.64–37.94–21.55AmilorideAmbiguous–27.50–57.20–47.35N.A. not available.The ligands are ordered by Ucav E. Disagreement with experiment is displayed in italics.
From docking, we have good agreement between theory and experiment for three cases (S819, NHDC, amiloride). NHDC has a better binding energy than does S-819 or amiloride. However, Ace-K is less favorable than NHDC. Since perillartine (agonist) has a structure very similar to amiloride (antagonist), we suspect this mixed data arises from multiple binding sites of the radiolabeled and the test ligand. As shown in Fig. 6, NHDC has hydrogen bonds at R3.28, S5.51, R5.37, R7.34, and D7.38. RebM made hydrogen bonds at R3.28, K4.53, D5.47, S5.51, and N7.45. S819 has a hydrogen bond at D7.38. Amiloride forms multiple hydrogen bonds at N5.43, T5.44, S6.48 and N7.45. Ace-K has a salt bridge at R5.37 with one hydrogen bond at N5.43.Fig. 6Binding sites of various sweeteners at TMD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
Docking result of radio-labeled ligands, Perillatine and Lactisole
From the docking study, perillatine, an agonist of the human sweet taste receptor, interacts with N799 (7.37) at TMD2 of T1R2/T1R3 heterodimer with UCav E of -25.92 kcal/mol and BE of -35.28 kcal/mol, as shown in Fig. 7 (Left). Lactisole as a negative allosteric modulator can bind to TMD3 of T1R2/T1R3 heterodimer. From our docking study, lactisole, an inhibitor of the human sweet taste receptor, interacts with S640 (3.32), the protonated H641 (3.33), H734 (5. 44) and Q794 (7.38) at TMD3 of T1R2/T1R3 heterodimer as shown in Fig. 7 (Right). This docking result of lactisole agrees with the inhibitory activity of (±)-lactisole, which was not determined by H641A and Q794N mutation at T1R3 TMD30.Fig. 7The binding poses of perillatine at TMD2 (left) and lactosole at TMD3 (right) of the T1R2/T1R3 heterodimer.The ligand is represented by sticks with different colors, while the side chains forming hydrogen bonds are labeled.
T1R2/T1R3 TMD3 binders
Cyclamate allosteric agonist, Neohesperidin dihydrochalcone (NHDC) artificial sweetener, and lactisole negative allosteric modulator are well-known TMD3 binders to the T1R2/T1R3 heterodimer. When we used lactosole as a radiolabeled ligand, the VFD2 binder Ace-K (artificial sweetener) and the TMD2 binder S819 (positive allosteric modulator) do not compete with lactisole (Table 4). However, TMD3 binder NHDC shows the highest inhibition with Ki of 333.7 µM among the 6 ligands (RebM, RebC, Ace-K, S819, NHDC, and amiloride). Our docking study also found that NHDC has the lowest UCav binding E (the second lowest BE), consistent with the highest inhibition (Ki) among six ligands (Table 4). From the experimental observation, the NHDC binding site at the human sweet taste receptor overlaps with those for the sweetener cyclamate and the sweet taste inhibitor lactisole68. The observed binding site of cyclamate and/or lactisole are Q636 (3.28), Q637 (3.29), S640 (3.32), H641 (3.33), H721 (EC2), R723 (5.36), S729 (5.42), F730 (5.43), A733 (5.46), F778 (6.51), V779 (6.52), L782 (6.55), R790 (7.34), and L798 (7.36) of hT1R3. Additional important amino acids for NHDC binding are Y699 (4.60), W775 (6.48), and C801 (7.39) from point-mutation experiments. From our docking study, NHDC forms multiple H-bonds at the backbone of F624 (2.60) and V720 (EC2) and with the side chains of Q636 (3.28), Q637 (3.29), S640 (3.32), C722 (EC2), S726 (EC2), R790 (7.34), and Q794 (7.38), as shown in Fig. 4. Steviol glycosides, RebC and RebM, also show mM level binding affinities, 6.98 and 11.75, respectively. RebC has better scoring energies than RebM does, which also agrees with experimental observations. Ligands that bind to other sites, Ace-K, S-819, and amiloride, reveal higher binding energy at TMD3, which also have fewer H-bonds. As shown in Fig. 4, S819 has H-bonds at H641 (3.33) and N737 (5.47). Amiloride forms H-bonds at S640 (3.32), C722 (EC2), R723 (EC2), R790 (7.34), and Q794 (7.38). Ace-K has H-bonds at Q637 (3.29) and R790 (7.34).Table 4The binding affinity (Ki, M) for TMD3 binding using the radiolabeled ligand, lactisole and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–98.96–134.05–22.17NHDC0.33–89.28–124.72–86.05RebAN.A.–84.67–141.65–58.73RebC6.98–76.44–126.12–60.65RubuN.A.–60.22–104.68–52.59S819N.C.–50.89–59.95–40.16Amiloride––34.26–58.34–39.84RebM11.75–33.13–121.89–35.84AceKN.C.–29.68–38.23–31.38N.A. not available, N.C. not competition.Lactisole was used as a radiolabeled ligand. The ligands were ordered by UCav E.Fig. 4Binding sites of various sweeteners at TMD3 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 VFD2 binders
The artificial sweeteners saccharin and aspartame as well as the natural sweeteners sugar and stevioside can bind to the VFD2 of th eT1R2/T1R3 heterodimer. From docking studies, we found RebM, which has high sweetness, shows the lowest binding energy in all scoring energies (UCav, BE, SolvE), although RebM gave an ambiguous result in experiment because of its low solubility (Table 5). The second lowest energy is from RebC, which has lower sweetness than does RebM. Ace-K has the highest Kd value (bad binder to VFD2), resulting in the highest binding energy. However, the best binder to VFD2 was amiloride (TMD2 binder), although the binding energy shows it to be unfavorable. In addition, several ligands known to bind TMD2 or TMD3 were found at a detectable binding affinity. The third lowest one by UCav and BE is the TMD3 binder NHDC, which displays a slightly lower binding affinity. One possibility of these mixed data is the multiple binding sites of RebB used for the radiolabeling. Based on the experiment (Table 4), since RebC can bind to TMD3, RebB without sugars at the R1 position which is smaller than RebC is also predicted to bind to TMD3. As shown in Fig. 5, S819 and Ace-K have two hydrogen bonds at D142 and R383. Amiloride forms multiple hydrogen bonds at Y103, N143, S165, D278, and R383. NHDC has hydrogen bonds at N44, N143, D213, and R383. RebM made hydrogen bonds at S40, D142, D278, D307, R339, and R383. Our docking pose of RebB at the binding site of TMD3 shows the possibility of a salt-bridge at the protonated histidine, H734 (5.44), as shown in Fig. 3 (right). Based on our docking study, RebB can also bind to TMD2. Supporting this, RebB at the binding site of TMD2 formed a salt-bridge at R725 (5.37) as well as H-bonds at N731 (5. 43) with the terminal carboxylate (Fig. 3, left). So, the high binding affinity data of amiloride might arise from TMD2 binding. The other possibility derives from the multiple binding sites of the tested ligands. For example, amiloride (TMD2 binder) could bind to multiple binding sites because of its small size and its ability to form multiple hydrogen bonds. Thus, this discrepancy arises from mixed data of multiple binding sites of tested ligands as well as the radio labeled ligand.Table 5The binding inhibition constants (Ki) for VFD2 binding using the radio labeled ligand, RebB and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebMAmbiguous–132.58–209.99–174.32RebAN.A.–104.33–149.66–120.72RebC18.99–98.94–141.82–105.55NHDC63.37–75.28–122.49–90.06RebBN.A.–69.61–125.98–101.37S819Ambiguous–49.86–76.06–47.80Amiloride1.35–29.34–56.26–51.41AceK1096.00–25.09–35.34–28.07N.A. not available.The ligands are ordered by UCav E. Disagreement with experiments is shown by italics.Fig. 5Binding sites of various sweeteners at VFD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
T1R2/T1R3 TMD2 binders
Allosteric agonist perillatine, antagonist amiloride, and positive allosteric modulator S819 are reported as TMD2 binders of the T1R2/T1R3 heterodimer. From experiments for testing the binding affinity at TMD2 (Table 6) using perillartine for radiolabeling, there is much ambiguous data. Even TMD2 binders such as S819 and amiloride result in ambiguities. Unexpectedly, Ace-K (VFD2 binder) and NHDC (TMD3 binder) reveal a detectable level of binding affinity.Table 6The binding inhibition constants (Ki) for TMD2 binding using the radio labeled ligand, perillartine and the scoring energy (UCav, BE, SolvE) from the docking studyLigandKi (mM)UCavBESolvERebBN.A.–102.55–179.98–27.33RebCAmbiguous–96.95–143.56–52.28NHDC293.7–92.24–131.81–83.08RebMAmbiguous–90.03–180.89–96.04RebAN.A.–70.92–122.32–56.96S819Ambiguous–44.73–64.23–47.13AceK142–31.64–37.94–21.55AmilorideAmbiguous–27.50–57.20–47.35N.A. not available.The ligands are ordered by Ucav E. Disagreement with experiment is displayed in italics.
From docking, we have good agreement between theory and experiment for three cases (S819, NHDC, amiloride). NHDC has a better binding energy than does S-819 or amiloride. However, Ace-K is less favorable than NHDC. Since perillartine (agonist) has a structure very similar to amiloride (antagonist), we suspect this mixed data arises from multiple binding sites of the radiolabeled and the test ligand. As shown in Fig. 6, NHDC has hydrogen bonds at R3.28, S5.51, R5.37, R7.34, and D7.38. RebM made hydrogen bonds at R3.28, K4.53, D5.47, S5.51, and N7.45. S819 has a hydrogen bond at D7.38. Amiloride forms multiple hydrogen bonds at N5.43, T5.44, S6.48 and N7.45. Ace-K has a salt bridge at R5.37 with one hydrogen bond at N5.43.Fig. 6Binding sites of various sweeteners at TMD2 of the human heterodimeric sweet taste receptor.The binding poses of (A) NHDC, (B) RebC, (C) RebM, (D) S819, (E) Amiloride, and (F) Ace-K. The ligand is represented by green sticks, while the side chains forming hydrogen bonds are shown in cyan sticks with labels. The ligand interaction diagram can be found in Fig. S16.
Docking result of radio-labeled ligands, Perillatine and Lactisole
From the docking study, perillatine, an agonist of the human sweet taste receptor, interacts with N799 (7.37) at TMD2 of T1R2/T1R3 heterodimer with UCav E of -25.92 kcal/mol and BE of -35.28 kcal/mol, as shown in Fig. 7 (Left). Lactisole as a negative allosteric modulator can bind to TMD3 of T1R2/T1R3 heterodimer. From our docking study, lactisole, an inhibitor of the human sweet taste receptor, interacts with S640 (3.32), the protonated H641 (3.33), H734 (5. 44) and Q794 (7.38) at TMD3 of T1R2/T1R3 heterodimer as shown in Fig. 7 (Right). This docking result of lactisole agrees with the inhibitory activity of (±)-lactisole, which was not determined by H641A and Q794N mutation at T1R3 TMD30.Fig. 7The binding poses of perillatine at TMD2 (left) and lactosole at TMD3 (right) of the T1R2/T1R3 heterodimer.The ligand is represented by sticks with different colors, while the side chains forming hydrogen bonds are labeled.
In silico binding site discovery
We discovered nine sites using the SiteMap method. The sweet heterodimer appears to have many potential binding sites that make interpretation of the experimental binding data difficult. Two sites discovered correspond to the canonical VFD2 binding site (in silico site 5, Fig. S14) and VFD3 binding site (in silico site 1, Figure S14).
Two additional sites were found near the interaction of the VFD. Site 4 was near the top intersection of the VFD. Site 6 was near the bottom intersection of the VFD. The VFD2 and VFD3 upper distances, near site 4 have been correlated with binding and the active state of the receptor of some ligands69. Binding in this site could potentially influence binding and activation. Binding in site 6 could influence the transmission of allosteric movements between the heterodimer section.
Site 9 was found at the intersection of the VFD2 and CRD2. Binding at site 9 could influence the canonical binding site of VFD2.
Two sites (8 and 10) were more associated with TMD, 10 was located at the intersection of TMD2 and CRD2. This was near the sweet protein binding site. Presumably binding at this site could activate the receptor. Finally, site 8 is located on the intracellular side of TMD3. Binding at this site could potentially impact the G protein if it is bound on TMD3.
The δG of binding from MM/GBSA (Table S4) are shown below for high sweet (RebB) medium sweet (Rubu) and low sweet (RebC) ligands. Site 5 in Fig. S14 was selected as the canonical VFD2 binding site and MM/GBSA values are shown as the delta from those of site 5 to enable a comparison. Sites 2, 6 and 10 show better relative binding than site 5 (VFD2) from RebB. RebC showed the same but with the addition of site 8. Rubu showed less favorable relative binding at all sites probably due to the smaller size and less sugar attachments.
The SiteMap analysis indicates the potential for several non-canonical binding sites that could influence the interpretation of experimental binding results.
AlphaFold structure comparison
We compared our MD structures of RebM bound TAS1R2/1R3 with C20 at T1R2TMD or T1R3TMD with AlphaFold 3 structures, generated using an AI model developed by Google DeepMind and Isomorphic Labs available at https://golgi.sandbox.google.com/. Three FASTA sequences were derived from our MD structures, while AlphaFold 3 predicted a total of five structures. As depicted in Figure S16, all five AlphaFold structures exhibited significant similarity. For the comparison of our MD structure of RebM-bound TAS1R2/1R3 with C20 at T1R3TMD, the RMSD values from PyMOL were 14.05, 13.82, 13.82, 13.97, and 14.51 Å for models 0–4, respectively. Similarly, for our MD structure of RebM-bound TAS1R2/1R3 with C20 at T1R2TMD, the RMSD values were 13.29, 12.48, 12.85, 13.08, and 13.47 Å for models 0–4, respectively. Both structures exhibited identical TMD interfaces such as TM6-6, which is known TMD interface in Class C GPCRs upon activation.
We observed a discrepancy in the disulfide bonding patterns within the VFD region (Table S5). The AlphaFold model indicates inter-disulfide bridges between C359 (1R2) and C129 (1R3), whereas our predicted model shows an intra-disulfide bridge between C359 and C363 in 1R2VFD. For 1R3VFD, the AlphaFold model depicts a disulfide bridge between C370 and C373, while our model predicts two disulfide bridges: one between C351 and C370 and another between C373 and C375. This disparity arises from the use of different templates for VFD model generation. Our VFD model was based on the structure of the rat metabotropic glutamate receptor 1 (mGluR1) (PDB ID: 1ewt). Subsequently, the crystal structure of the medaka fish taste receptor T1R2/1R3 complex with L-glutamine (PDB ID: 5x2m) was reported, showing an inter-disulfide bridge between VFDs.
However. the putative C20 binding site in the AlphaFold model was located in EC2 or CRD2, rather than between TM3 and TM6, which is recognized as the G protein coupling site. The top AlphaFold model (model 0) revealed a salt bridge between K345 (K-11) and D688 (EC2 of 1R2), as illustrated in Figure S17. Conversely, in our MD structure of RebM-bound TAS1R2/1R3 with C20 at T1R2TMD, two salt bridges were observed among R664 (IC2), E346 (E-9), and D341 (D-15), whereas in the structure with C20 at T1R3TMD, one salt bridge was noted between R760 (6.33) and the C-terminal end of F354 (F-1), as shown in Figure S17. Thus, AlphaFold 3 was not able to predict the C20 binding site within the known cytoplasmic end of TM3-6 of GPCRs.
Discussions
Our study offers significant insights into the complex mechanisms underlying sweet taste perception, particularly focusing on the multiple binding sites of sweetners on the T1R2/T1R3 sweet taste receptor. Current research has predominantly assumed a single preferred binding sites for sweet ligands. However, our findings show that sweet ligands can interact with multiple binding sites on the T1R2/T1R3 receptor complex, especially in cell-free environments lacking G protein coupling.
We employed a radioligand binding competition experiment to investigate binding site interactions for various ligands. Notably, the VFD2 binders RebB and RebM did not exhibit competitive binding, suggesting that these ligands might occupy different pockets within the same binding site. Similarly, the TMD2 binders S819 and amiloride showed ambiguous competitive binding with perillartine (preferred binding on TMD2), further supporting our hypothesis of multiple binding pockets. Our results also demonstrated unexpected competitive binding between ligands presumed to bind to different domains. For instance, RebB (a VFD2 binder) showed competitive binding with NHDC and amiloride (both TMD3 binders). Lactisole (TMD3) also competed with RebM (VFD2) and RebC (VFD2), reinforcing the concept of multiple binding sites. These findings align with our molecular dynamics (MD) simulations and docking studies, which indicate that each sweetener can modulate the free energy landscape of the T1R2/T1R3 heterodimer by binding to multiple sites.
Our findings corroborate previous research showing that cell-based assays, which detect ligand binding through downstream signaling, might not capture the full complexity of ligand-receptor interactions. These assays often miss weaker or non-signaling binding events that our radio-ligand competition and MD simulations have revealed. SiteMap simulations further confirmed the presence of multiple binding sites, identifying nine distinct binding pockets on the human sweet taste receptor. Our concept of multiple binding sites could explain the potency limits of high intensity sweeteners where activation begins with primary binding at a high affinity orthosteric site but is limited by a binding of the same ligand at a secondary low affinity negative allosteric site for “auto-antagonism”. This effect could be confounded in most experimental binding studies.
To investigate the sensory mechanism of the sweet taste receptor, we focused on the activation of the T1R2/T1R3 heterodimer. Based on our 3D predicted structure of the sweet receptor22, we employ MD simulations on direct and allosteric agonists to understand their impact on receptor conformation. Agonists bound to VFD2 induce significant conformational changes that progress from VFD2 through VFD3 to CRD3 and finally to TMD3, transforming the TMD dimer interface from the TM5/6 with TM5/6 interface to the TM6 with TM6 interface22, which leads to G protein activation. While our MD simulations provide support for G protein coupling to TMD3, the specific subunit to which the G protein couples remains ambiguous experimentally. Despite the understanding that the G protein must couple to the intracellular face of TMD in the heterodimer sweet receptor, the precise position—whether G protein couples to TMD2, TMD3, or both—remains undetermined experimentally. G protein coupling to each TMD region is a possibility. The TMD containing human T1R2 activates Gαi/o subunits70. Overexpression of human T1R2 in a HEK293S cell line has been shown to activate G proteins through perillartine binding to TMD223. Concurrently, in HGT-1 cells expressing only T1R3 subunit receptors, sweet taste downstream signaling is induced by various sweet ligands39.
Computationally based predictions of the 3D structures of GPCRs (GEnSeMBLE)71,72 and of the binding sites for ligands (DarwinDock)73 have played a major role in understanding the structures and functions of GPCRs. These methods have been validated for predictions of bitter taste receptors TAS2R74–76 and for many class A GPCRs77–85. In particular, for Class A GPCRs, we showed that the mechanism involves first precoupling of the inactive GPCR with the inactive G-protein (GP) to form the “precoupled structure”, with the GDP still bound closely between the RAS and helical subdomains of Gα and not activated. Upon binding of agonist, the GP opens to release GDP with subsequent signaling86. In contrast our earlier study of the T1R2/T1R3 sweet receptor (without the GP) concluded that binding of the agonist to VFD2 opens up the intracellular region of TMD3 to bind and activate the GP. There is no experimental structure for T1R2/T1R3, but our computations find that the carboxy terminus of the Gα protein is in direct contact with the GPCR. Here the last 20 amino acid residues of the GP (GNAT3), play an important function as the G protein is coupled to the T1R2/T1R3 heterodimer sweet receptor and then activated42. In this paper we consider binding of the ligands with and without the C20 terminal helix of Gα that plays the important role in activation.
We used Molecular docking techniques to successfully predict the preferred binding sites for RebC, Rubu, RebM, and RebD, providing fundamental information on the preferred binding of Steviol Rebaudiosides. In the FLOWER experiments, we observed that G protein coupling may occur at both TMD2 and TMD3. This coupling event has distinct effects on ligand binding affinity, with G protein coupling to TMD3 enhancing Rubu binding affinity at the VFD3 binding site. For RebC and RebM, G protein coupling to TMD2 enhanced VFD2 binding, while G protein coupling to TMD3 increased the binding affinity for VFD3 and TMD3. These observations support our hypothesis that the activation process of the sweet heterodimer is modulated by G protein coupling. Without the G protein, the activation of sweet ligand-binding may fail to sufficiently stabilize the heterodimer conformation, potentially leading to the opening of numerous binding pockets for Steviol Rebaudiosides ligands.
Conclusions
In conclusion, we carried out a comprehensive investigation into multiple binding sites for Steviol Rebaudiosides to the human sweet taste heterodimer, integrating experimental results with computational simulations. The cell-free stable heterodimer used in FLOWER enabled a direct measurement of the Steviol Rebaudiosides ligand binding response, leading to determination of the binding constant (Kd).
Our docking results support multiple binding sites of the tested ligands as well as the radio labeled ligand, which can explain the mixed radio-ligand binding experiment data. We found different preferred binding sites depending on the ligand modification of steviol glycosides. Molecular docking, successfully predicted the preferred binding sites for RebC, Rubu, RebM, and RebD, serving as fundamental information for the preferred binding of Steviol Rebaudiosides. Analysis of the experimental results from FLOWER revealed significant insights into the interaction between the sweet taste receptor T1R2/T1R3 and Steviol Rebaudiosides. C20 G-protein binding can alter GPCR affinity to the high-affinity state for steviol glycosides. This observation lends support to the hypothesis that the activation process of the sweet heterodimer is intricately modulated by G protein coupling. Without the G protein, the activation of the sweet ligand-binding may fail to sufficiently stabilize the heterodimer conformation, potentially leading to the opening of numerous binding pockets for Steviol Rebaudiosides ligands.
The identification of multiple binding sites on the T1R2/T1R3 heterodimer for Steviol Rebaudiosides ligands emphasizes the inherent complexity in ligand-receptor interactions. This discovery establishes a foundation for continued exploration within the realms of sensory perception and molecular studies. These studies increase understanding of the structure of this heterodimeric sweet taste receptor and can act as a guide for rational structure-based design of non-caloric sweeteners, which can be used in therapeutic applications for treatment of obesity and related metabolic dysfunctions such as diabetes as well as sweetness enhancers that can enable lower sugar usage levels while retaining sweetness.
Methods
Two cell-free stable heterodimer methods were used: radioligand depletion with cloned sweet taste receptors and attached G proteins in a stable membrane preparation from taste bud cells, and FLOWER with attached stabilized heterodimer receptors cloned and expressed from heterologous cells and tested with a G protein 20-mer C-terminus peptide.
Receptor radioligand binding study: taste bud cell membrane assays
Various ligands were used based on their reported binding behavior: RebM (CAS 1220616-44-3) presumed binding at VFD2, Ace-K (CAS 55589-62-3) reported binding at VFD211,13,15,16, RebC (CAS 63550-99-2) presumed binding at VFD2, positive allosteric modifier S819 (CAS 902130-77-2, Thiourea, N-[4-(1-methylethoxy)phenyl]-N’-(1H-pyrrol-2-ylmethyl)-) reported binding at TMD2, amiloride HCL (CAS 2016-88-8) reported binding at TMD227, perillaratine (CAS 30950-27-7) reported binding at TMD227,37,87 and Neohesperidin dihydrochalcone (NHDC, CAS 20702-77-6) reported binding at TMD368.
The radio-labeled ligands in Table 7 were selected for their binding at the different orthosteric and allosteric binding sites. Sweeteners trigger a sweet taste by binding to lingual heterodimeric receptor (T1R2/T1R3). Most of the natural sugars like sucrose, glucose, and sucralose, bind to the extracellular domain, called VFD of T1R2 and T1R3, whereas dipeptide sweeteners, like aspartame and neotame, bind only to the T1R2 VFD. In the current study, we employed radioactive agents: [3H]-Rebaudioside B, [3H]- Perillartine and [3H]-lactisole and assessed its binding to human taste bud cells. We also employed non-labeled ligands as competitors to calculate the binding kinetics.Table 7Radioligand selected for the binding studyRadioligandSpecific activityBinding site[3H]-Lactisole28.2 Ci/mmol(1.04 TBq/mmol)TMD383[3H]-Perillartine17.1 Ci/mmol(633 GBq/mmol)TMD280[3H]-Rebaudioside B3.2 Ci/mmol(85 GBq/mmol)presumed VFD2[14C]-Sucrose400–700 mCi/mmol(14.8–25.9 GBq/mmol)VFD2 and VFD39,86
Cell handling and isolation of cell membrane
Human taste bud cell lines were routinely grown and maintained in T75 flasks in IMDM medium supplemented with MCDB153 medium and serum. On the day of experimentation, cells were washed with PBS and scrapped off, followed by centrifugation at 1200 RPM for 5 min. The cell pellet was resuspended in ice-cold homogenization buffer (Tris HCl (pH 7.4) 20 mM, glycerol 10%, protease inhibitor cocktail) and homogenized by using Ultra Turrax homogenizer (24, 000 rpm). This was followed by centrifugation at 1500 × g for 15 min at 4 °C to remove the cell debris and the resulting supernatant was centrifuged at 100,000 × g for 60 min at 4 °C to obtain cell membrane pellet which was re-suspended in homogenization buffer without protease inhibitors and kept at –80 °C until use.
Saturation binding experiments
Saturation binding experiments were conducted in a total volume of 250 μL of homogenizing buffer per well (membrane 150 μL, cold ligand or buffer 50 μL, radioligand 50 μL). Incubation was done for 60 min at room temperature with gentle agitation and stopped by washing with filter harvester. The filter paper was cut and placed in scintillation counting tubes followed by the addition of scintillation liquid and radioactivity counting. Buffers in this study were selected for mutual solubility of the stabilized receptors and stevia glucosides.
Kinetic binding experiments
Association assay (kon)
Incubation was started by adding 50 µL of radioligand. After 1, 2, 5, 10, 15, 20, 30, 45 and 60 min of incubation at room temperature, the 96 well plate was washed with water using filter-harvester, filter paper was appropriately cut and mixed with the scintillation liquid followed by scintillation counting, and analysis by GraphPad Prism software using non-linear regression.
Dissociation assay (koff)
The dissociation assay was started after 1 h of incubation with radioligand, by adding excess of cold ligand in a total assay volume of 250 µL. This time point at which dissociation was started was designated as the time zero. Afterwards, the 96-well plate was washed and scintillation was counted. Data were entered into GraphPad Prism software and koff, kon and kd were determined using non-linear regression.
Competition binding experiments
Competition binding experiments were conducted by using fixed concentration of radioligand, and variable concentrations of unlabeled ligands. Radioligand and the competing agents were incubated for 1 h, followed by washing and scintillation counting. Concentrations were converted to log values and Ki (one site) was calculated by GraphPad Prism software using non-linear regression.
Effect of GTP and GDP on radioligand binding
It has been reported that the presence of GTP decreases agonist, but not antagonist binding in GPCRs64. Hence, radioligand displacement assays in the presence of increasing GTP and GDP concentrations were performed to evaluate the importance of the presence of a bound G protein. For this purpose, fixed concentrations of radioligands were used in the presence of increasing concentrations of GTP or GDP. IC50 values were calculated by using the GraphPad Prism software.
Stabilization of the human heterodimeric taste receptor T1R2/T1R3
A method was developed to generate human full-length heterodimeric taste receptor T1R2/T1R3 consisting of an expression screening, solubilization and stabilization screening and test purification to be used in FLOWER for label free binding measurements.Receptor constructs with different affinity tags are prepared and used for the transient expression in HEK293 cell culture. The literature for the heterodimeric receptor mainly describes experiments in which N-terminal affinity tags were used88 for both, full-length or truncated protein versions89. Therefore, N-terminal tags are also used among others in this approach. Additionally, all expression constructs contain an Avi-tag for biotinylation and the wild-type signal peptide is exchanged to a HA signal sequence. As requested after expression a detergent and co-polymer screening as well as a test purification is performed. In contrast to detergents, DIBMAs and SMAs also isolate lipids from the cell wall contributing to membrane protein stabilization and have the capability to solubilize and form synthetic nanodisc in a single step. No further addition of DIBMA or SMA is needed during downstream processing.
Construct design and vector amplification
For expression and purification of human T1R2/T1R3 heterodimeric protein, both monomers were constructed on separate plasmids for co-expression. For T1R2, a HA signal peptide was added at the N-terminus and a “GSSG” linker was added at the C-terminus followed by an Avi-tag and a FLAG tag. T1R3 was tagged with a N-terminal HA signal peptide and a C-terminal Avi-tag as well as a Rho1D4 tag separated from the protein sequence by “GSSG” linker. The protein sequences were constructed in a pcDNA3.4 vector backbone (Invitrogen). Both plasmids were amplified in E. coli TOP10 cells and purified with the endotoxin-free plasmid DNA purification system from Macherey-Nagel (NucleoBond® PC 10000 EF).
Co-expression of human T1R2 and T1R3 in Expi293F cells
T1R2 and T1R3 plasmids were transiently co-transfected into Expi293F cells (Gibco) using polyethylenimine (PolySciences) at 2.5 × 106 cells/mL in Expi293 expression medium (Gibco) with 10 mM sucrose. The co-transfection was performed with a final DNA concentration of 1.5 µg/mL and 9 µg/mL PEI according to the manufacturer’s instructions. The transfected cultures were incubated at 37 °C with 6% CO2 and shaken at 110 rpm. After 20 h, sodium butyrate and valproic acid were added with a final concentration of 10 mM and 5 mM, respectively. After 72 h post transfection, the cells were collected by centrifugation (2,000x rpm; 15 min; 4 °C) and supplemented with protease inhibitors: 10 µM Leupeptin, 1 mM o-Phenanthroline, 0.1 mM phenylmethylsulfonyl fluoride (PMSF); 10 µM E-64 (N-[N-(L-3-trans-Carboxyoxiran-2-carbonyl)-leucyl]-agmatin, N-(trans-epoxysuccinyl)-L-leucin-4-guanidinbutylamid) and 1 mM Pepstatin A in buffer containing 20 mM Hepes, 100 mM NaCl and 2 mM EDTA. Cell pellets were stored at -80 °C until further use.
Purification and stabilization of human heterodimeric taste receptor T1R2/T1R3 in a synthetic nanodisc using DIBMA Glycerol
Frozen cells were resuspended in protein buffer (20 mM HEPES pH 8.0, 100 mM NaCl, 1 mM EDTA) supplemented with 10 µM Leupeptin, 1 mM o-Phenanthroline, 0.1 mM PMSF, 10 µM E-64 and 1 mM Pepstatin A. After sonication, cell debris was separated from membranes by centrifugation (9000 × g; 30 min; 4 °C). The membranes were further pelleted by centrifugation (100,000 × g; 1 h; 4 °C), and solubilized in protein buffer containing 2.5% (w/v) DIBMA Glycerol (Cube Biotech GmbH, Monheim, Germany) overnight at 4 °C. Solubilized T1R2/T1R3 was separated from un-solubilized material by centrifugation (100,000 × g; 1 h; 4 °C). The supernatant containing the target protein was diluted 1:10 with protein buffer and subsequently loaded onto a pre-equilibrated batch of Rho1D4-MagBeads (Cube Biotech GmbH, Monheim, Germany). To improve the binding, the MagBeads were incubated over night at 4 °C while gently stirring. Washing steps were performed with protein buffer five times under magnetic separation to remove non-specifically bound proteins. T1R2/T1R3 was eluted five time by incubating the beads with protein buffer containing 1 mg/mL Rho1D4 peptide for 30 min at 4 °C. Elution samples were concentrated at 3,000x g (Amicon Ultra-15, Merck Millipore) and further analyzed by size exclusion chromatography on a Superose 6 increase column (GE Healthcare). Fractions corresponding to the heterodimeric size of T1R2/T1R3 were collected and concentrated. The protein concentration was determined with spectroscopic measurement at a wavelength of 280 nm and the quality was validated by SDS-PAGE and Western Blot using FLAG antibodies, separately. Protein samples were flash frozen and stored at −80 °C.
Gα C-terminal 20mer
The unmodified peptide sequence, VFDAVTDIIIKENLKDCGLF, of the GNAT3 C-terminal tail was synthesized for use in binding studies (Thermo Scientific Peptide Synthesis Service) at >98% purity.
Coupling of the sweet heterodimer to FLOWER
Microtoroid fabrication
Microtoroids are fabricated as previously described90. The microtoroid resonators are fabricated on silicon wafers with a 2 μm layer of thermally grown silica. First, circular disc patterns of photoresist with a diameter of 150 µm are patterned via photolithography on the top silica layer of the silicon wafer. These photoresist circular pads act as etch masks during immersion in buffered oxide etchant (BOE) solution (1:6 V/V) at room temperature. After wet etching, any residual photoresist and contaminants are removed using acetone and IPA (isopropyl alcohol). The wafer is then post-baked at 130 °C to remove moisture. The remaining silica disks act as etch masks during exposure to xenon difluoride (XeF2) gas, resulting in uniform undercutting of the silica disks and the formation of silicon pillars that support the silica disks. A thermal reflow process using a CO2 laser is used to shape the silica disk into a microtoroid.
Coupling of the sweet heterodimer to the microtoroid
The sweet heterodimer is coupled to the toroid via EDC/NHS coupling of an antibody amine. The toroid chip is first cleaned with ethanol then dried with nitrogen. The cleaned toroid chip is incubated in 1% v/v APTES in chloroform for 10 min to introduce amino groups onto the chip’s surface. After incubation, the chip was thoroughly rinsed with chloroform to remove any excess APTES and dried with nitrogen. The chip was then incubated overnight in a succinic anhydride solution (100 mg of succinc anhydride in 1 mL of DMSO). The next day, the chip was rinsed sequentially with DMSO and ethanol to remove unreacted succinic anhydride and then blown dry.
For EDC/NHS antibody conjugation, EDC and NHS are equilibrated at room temperature for at least 30 min (1 h recommended) before weighing. NHS and EDC are dissolved in 100 mM MES buffer to form a 100 mM EDC and 100 mM NHS mixed solution. An anti-FLAG (or anti-Rho) antibody solution is added to the mixed NHS/EDC solution to achieve a 30 nM concentration. The microtoroid is incubated in this solution for 15 min to ensure coupling. Following this activation step, the microtoroid chip is thoroughly rinsed with 100 mM MES buffer to remove any unreacted EDC/NHS and create a stable environment for subsequent functionalization.
Prior to microtoroid functionalization with the sweet heterodimer nanodiscs, the toroid chip is incubated in 100 mM ethanolamine for 5 min to block unreacted binding sites. The sweet heterodimer T1R2/T1R3 solution is diluted to 30 nM in HEN buffer. The chip is then incubated in the diluted T1R2/T1R3 heterodimer solution for 1 h to ensure efficient coupling of the sweet heterodimer to the functionalized chip surface. After incubation, the toroid chip is rinsed thoroughly with HEN buffer. The functionalized microtoroid chip is then carefully transferred into an Eppendorf tube containing HEN buffer.
For T1R2/T1R3 sweet heterodimer coupling to the microtoroid, anti-FLAG is coupled to the microtoroid. This coupling ensures that the sweet heterodimer (T1R2/T1R3) connects to the microtoroid through the TMD2 region, leaving the TMD3 region open for binding. In the experiments where it is desired to have the TMD2 region of the sweet heterodimer T1R2/T1R3 open, anti-FLAG can be replaced with anti-Rho.
Binding measurements using FLOWER
We previously developed a system called Frequency Locked Optical Whispering Evanescent Resonator (FLOWER), which enables the rapid detection of single macromolecules in solution58,59,90 as well as proteins91,92, exosomes57, and volatile organic compounds93,94. FLOWER tracks resonance shifts resulting from the binding of molecules or nanoparticles to a WGM optical resonator. WGM resonators are highly sensitive sensors due to their long storage confinement times95–103. They have a larger capture area than traditional plasmonic sensors104, thus enabling rapid detection. Conventional method uses probe laser scanning to track resonance shifts, but the response time is limited by the scan speed. FLOWER uses frequency locking to track resonance shifts, offering a fast response time and greatly improved measurement accuracy. The experimental setup, as illustrated in Fig. S1, involves coupling a probe laser (PL) with a wavelength of 780 nm into a microtoroid resonator through a tapered optical fiber. To optimize the coupling efficiency between the tapered fiber and microtoroid, we control the polarization of the probe laser using a polarization controller (PC). A 2 kHz oscillation dither signal (DS) is input to the frequency modulation port of the probe laser for frequency modulation of the probe laser. The frequency modulated probe laser is split into two arms through a fiber-coupled beam splitter (BS). One arm carries the signal light coupled into the microtoroid through a tapered fiber, while the other serves as the reference light. Both the signal and reference light are then received by an auto-balanced receiver, which can reduce laser intensity noise by 55 – 70 dB. By multiplying the auto-balanced receiver’s electrical output signal by the DS and time-averaging, we obtain an error signal that is directly proportional to the wavelength detuning between the probe laser and the WGM resonance of the microtoroid. The error signal is zero when the probe laser precisely matches the microcavity resonance. Utilizing this error signal in a feedback loop to tune the laser through the frequency modulation port, the proportional-integral-derivative (PID) controller minimizes the error signal, thereby ensuring accurate and stable frequency locking of the probe laser to the microtoroid’s resonance.
We record the resonance shift signal using a high-precision data acquisition card (NI PCI-4461) throughout sweet ligand binding experiments. Our toroidal optical microcavities are first functionalized with human T1R2/T1R3 heterodimer sweet taste receptors. In a pressure-driven prefusion system, we preload both the buffer and sweet ligand sample solution into multiple channels, allowing precise control over solution injection into the microfluidic chamber (Fig. S1) via a LabVIEW program. Initially, the microtoroid chamber undergoes flushing with HEN buffer, resulting in an initial red shift (shift to longer wavelengths) in resonance due to the self-heating of the microtoroid resonator caused by the circulating probe laser light. As we continuously inject HEN buffer, the resonance ceases to exhibit a red shift and stabilizes at a consistent value. This stabilization indicates that the microtoroid has achieved thermal equilibrium. Following this, we record the resonance shift signal curve to establish the sensor’s baseline. We prepare a range of sweet ligand concentrations using the HEN buffer and sequentially inject them into the microfluidic chamber, starting from low concentrations and progressing to higher ones. Between each concentration, we rinse with running buffer, and measure the real-time resonance shift corresponding to each concentration. In the G protein C20 experiments, the HEN buffer is replaced by 10 µM C20 in HEN buffer. During injection, 10 µM C20 in HEN buffer is first introduced into the microfluidic chamber, and all sweet ligand solutions are supplemented with 10 µM C20 to ensure that all ligands are under G protein C20 binding conditions.
In silico ligand binding energies
Predicting ligand binding sites without C20
Our studies used the full T1R2/T1R3 heterodimer structure predicted using GEnSeMBLE71,72 for the TMD helixes. Our original initial structure used homology modeling for the VFD and CRD22. Detailed information is described in our previous paper22. For each ligand (Lactisole, NHDC, Ace-K, S-819, Amiloride, and Perillartine), we considered 10-15 torsional conformations in DarwinDock77, which iteratively generates ~50,000 poses in the putative binding regions for each of the 10-15 ligand conformations and each of the 25 GPCR configurations. The amino acid sidechains in the binding site were optimized for each of the best 100 ligand poses26. For each ligand conformation, the procedure was as follows. Starting from the minimized structure, we performed conformational searches of Mixed torsional/ Low-mode sampling (1000 steps, 1000 steps per rotatable bond, 10 kcal/mol energy window, 0.5 Å RMSD) using the Maestro software105. The low energy conformations were re-minimized using the Dreiding force field22 with some modifications in the hydrogen bond interactions and clustered by 2.0 and 1.0 Å of RMSD in two steps. For docking, low energy conformations for ligands from several clusters were selected. All ligand binding poses for Reb1-5, RebM, RebD, isoRebM, RebA, RebB, Rubu, RebC, and hydroxyRebM at VFD2 were used from a previous docking study22. For the binding poses at VFD3, TMD2 and TMD3, additional sugars were extended from the best binding pose of Rubu from the docking study. To relax the binding site of the additional sugars, 10 cycles of annealing with the geometry constraint on the structure of Rubu were performed. After selecting the best energy conformer, another 1 cycle of annealing and full minimization using the Dreiding force field22 was performed without constraints.
Generation of the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3
The various initial position of GNAT3 C20 were considered using the experimental structures of GPCR- G protein complexes, Class C mGlu2R-Gi (PDB ID: 7MTS106, 7E9G107), mGlu4R-Gi (PDB ID: 7E9H107), GABAB2R-Gi (PDB ID: 7EB2)108, and Class A OPRM-Gi (PDB ID 6DDF)109. We also included our predicted structure of the GNAT3-T1R2/T1R3 heterodimer complex110. After alignment of the GPCR backbone, all residues except the C-terminal 20 amino acids of the Gα protein were deleted followed by minimization of the fixed GPCR after the side chain refinement.
Explicit solvent MD
The C20 bound structure of the T1R2/T1R3 heterodimer at TMD2 or TMD3 were inserted into a continuous infinite POPC lipid bilayer membrane with periodic boundary conditions, while including full solvation with water at physiological salt concentration (0.9% concentration of NaCl, 154 mM).
The disulfide bridges in TAS1R2 were constrained: C59-C102, C359-C363, C405-C410 in the VFD, C495-C514, C499-C517, C520-C535, C538-C551 in CRD, C233-C513 between VFD and CRD, and C6303.25-C720 between TMD and EC2. The disulfide bridges in TAS1R3 were also constrained: C62-C103, C351-C370, C373-C375, C410-C415 in VFD, C499-C518, C503-C521, C524-C538, C541-C554 in CRD, C236-C517 between VFD and CRD, and C6333.25and C722 between TMD and EC2. The POPC lipid available in VMD was used to insert the protein into a lipid-water box, where lipids within 1 Å of the protein and waters within 2 Å of the protein were removed.
For the particle mesh Ewald (PME) in the electrostatics calculation111, the charge of system was balanced through replacing waters with Na+ and Cl− ions. After inserting the 7-helix bundle including loops into the box containing the periodic POPC membrane, water, and ions, we fixed the protein and minimized the lipid, water, ion atoms for 5,000 steps. We then equilibrated with NPT dynamics for 5 ns with 1.0 fs time step while continuing to keep the protein fixed. This allows the lipid and water to readjust to the protein. Then we minimized the full system for 5000 steps and then performed NPT dynamics for 20 ns (10 ns with fixed protein back bone atoms and 10 ns without any constraints) using 1.0 fs time steps. This predicted structure was then equilibrated at 310 K using the NAMD 2.9 (NAnoscale Molecular Dynamics) program112. We used the CHARMM36 force field parameters for the protein, the TIP3 model for water113, and the CHARMM27 force field parameters for the lipids114.
Predicting ligand binding sites at the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3
For the binding site of Rubu at the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3, the last trajectory of the C20 bound structure of T1R2/T1R3 heterodimer was aligned to the protein backbone of the binding site of Rubu at each domain (VFD2, VFD3, TMD2, and TMD3) from the previous docking study22. Then the matched complexes were annealed for 10 cycles between 50 to 600 K. After an additional minimization, the lowest energy conformer was selected. For the binding site of RebM, RebD, and RebC, the additional sugars were extended from the best binding pose of Rubu. To relax the binding site of the additional sugars, we performed 10 cycles of annealing with geometry constraints on the structure of Rubu. After selecting the best energy conformer, another 1 cycle of annealing and full minimization using the Dreiding force field22 were performed without any constraints.
In silico binding site discovery
The discovery of potentially undisclosed binding sites utilized a multi-step workflow. The apo-structure of the full hetero-dimer was used. SiteMap (Schrodinger)115 was used to search for the ten best sites. Glide grids were made for each site then Glide116,117 docking was performed to determine docking scores for the ligands. Molecular mechanics with generalized Born and surface area solvation (MM/GBSA) energies methods were run using Prime118 to determine binding site energies for potential new binding sites.
Receptor radioligand binding study: taste bud cell membrane assays
Various ligands were used based on their reported binding behavior: RebM (CAS 1220616-44-3) presumed binding at VFD2, Ace-K (CAS 55589-62-3) reported binding at VFD211,13,15,16, RebC (CAS 63550-99-2) presumed binding at VFD2, positive allosteric modifier S819 (CAS 902130-77-2, Thiourea, N-[4-(1-methylethoxy)phenyl]-N’-(1H-pyrrol-2-ylmethyl)-) reported binding at TMD2, amiloride HCL (CAS 2016-88-8) reported binding at TMD227, perillaratine (CAS 30950-27-7) reported binding at TMD227,37,87 and Neohesperidin dihydrochalcone (NHDC, CAS 20702-77-6) reported binding at TMD368.
The radio-labeled ligands in Table 7 were selected for their binding at the different orthosteric and allosteric binding sites. Sweeteners trigger a sweet taste by binding to lingual heterodimeric receptor (T1R2/T1R3). Most of the natural sugars like sucrose, glucose, and sucralose, bind to the extracellular domain, called VFD of T1R2 and T1R3, whereas dipeptide sweeteners, like aspartame and neotame, bind only to the T1R2 VFD. In the current study, we employed radioactive agents: [3H]-Rebaudioside B, [3H]- Perillartine and [3H]-lactisole and assessed its binding to human taste bud cells. We also employed non-labeled ligands as competitors to calculate the binding kinetics.Table 7Radioligand selected for the binding studyRadioligandSpecific activityBinding site[3H]-Lactisole28.2 Ci/mmol(1.04 TBq/mmol)TMD383[3H]-Perillartine17.1 Ci/mmol(633 GBq/mmol)TMD280[3H]-Rebaudioside B3.2 Ci/mmol(85 GBq/mmol)presumed VFD2[14C]-Sucrose400–700 mCi/mmol(14.8–25.9 GBq/mmol)VFD2 and VFD39,86
Cell handling and isolation of cell membrane
Human taste bud cell lines were routinely grown and maintained in T75 flasks in IMDM medium supplemented with MCDB153 medium and serum. On the day of experimentation, cells were washed with PBS and scrapped off, followed by centrifugation at 1200 RPM for 5 min. The cell pellet was resuspended in ice-cold homogenization buffer (Tris HCl (pH 7.4) 20 mM, glycerol 10%, protease inhibitor cocktail) and homogenized by using Ultra Turrax homogenizer (24, 000 rpm). This was followed by centrifugation at 1500 × g for 15 min at 4 °C to remove the cell debris and the resulting supernatant was centrifuged at 100,000 × g for 60 min at 4 °C to obtain cell membrane pellet which was re-suspended in homogenization buffer without protease inhibitors and kept at –80 °C until use.
Saturation binding experiments
Saturation binding experiments were conducted in a total volume of 250 μL of homogenizing buffer per well (membrane 150 μL, cold ligand or buffer 50 μL, radioligand 50 μL). Incubation was done for 60 min at room temperature with gentle agitation and stopped by washing with filter harvester. The filter paper was cut and placed in scintillation counting tubes followed by the addition of scintillation liquid and radioactivity counting. Buffers in this study were selected for mutual solubility of the stabilized receptors and stevia glucosides.
Cell handling and isolation of cell membrane
Human taste bud cell lines were routinely grown and maintained in T75 flasks in IMDM medium supplemented with MCDB153 medium and serum. On the day of experimentation, cells were washed with PBS and scrapped off, followed by centrifugation at 1200 RPM for 5 min. The cell pellet was resuspended in ice-cold homogenization buffer (Tris HCl (pH 7.4) 20 mM, glycerol 10%, protease inhibitor cocktail) and homogenized by using Ultra Turrax homogenizer (24, 000 rpm). This was followed by centrifugation at 1500 × g for 15 min at 4 °C to remove the cell debris and the resulting supernatant was centrifuged at 100,000 × g for 60 min at 4 °C to obtain cell membrane pellet which was re-suspended in homogenization buffer without protease inhibitors and kept at –80 °C until use.
Saturation binding experiments
Saturation binding experiments were conducted in a total volume of 250 μL of homogenizing buffer per well (membrane 150 μL, cold ligand or buffer 50 μL, radioligand 50 μL). Incubation was done for 60 min at room temperature with gentle agitation and stopped by washing with filter harvester. The filter paper was cut and placed in scintillation counting tubes followed by the addition of scintillation liquid and radioactivity counting. Buffers in this study were selected for mutual solubility of the stabilized receptors and stevia glucosides.
Kinetic binding experiments
Association assay (kon)
Incubation was started by adding 50 µL of radioligand. After 1, 2, 5, 10, 15, 20, 30, 45 and 60 min of incubation at room temperature, the 96 well plate was washed with water using filter-harvester, filter paper was appropriately cut and mixed with the scintillation liquid followed by scintillation counting, and analysis by GraphPad Prism software using non-linear regression.
Dissociation assay (koff)
The dissociation assay was started after 1 h of incubation with radioligand, by adding excess of cold ligand in a total assay volume of 250 µL. This time point at which dissociation was started was designated as the time zero. Afterwards, the 96-well plate was washed and scintillation was counted. Data were entered into GraphPad Prism software and koff, kon and kd were determined using non-linear regression.
Competition binding experiments
Competition binding experiments were conducted by using fixed concentration of radioligand, and variable concentrations of unlabeled ligands. Radioligand and the competing agents were incubated for 1 h, followed by washing and scintillation counting. Concentrations were converted to log values and Ki (one site) was calculated by GraphPad Prism software using non-linear regression.
Effect of GTP and GDP on radioligand binding
It has been reported that the presence of GTP decreases agonist, but not antagonist binding in GPCRs64. Hence, radioligand displacement assays in the presence of increasing GTP and GDP concentrations were performed to evaluate the importance of the presence of a bound G protein. For this purpose, fixed concentrations of radioligands were used in the presence of increasing concentrations of GTP or GDP. IC50 values were calculated by using the GraphPad Prism software.
Stabilization of the human heterodimeric taste receptor T1R2/T1R3
A method was developed to generate human full-length heterodimeric taste receptor T1R2/T1R3 consisting of an expression screening, solubilization and stabilization screening and test purification to be used in FLOWER for label free binding measurements.Receptor constructs with different affinity tags are prepared and used for the transient expression in HEK293 cell culture. The literature for the heterodimeric receptor mainly describes experiments in which N-terminal affinity tags were used88 for both, full-length or truncated protein versions89. Therefore, N-terminal tags are also used among others in this approach. Additionally, all expression constructs contain an Avi-tag for biotinylation and the wild-type signal peptide is exchanged to a HA signal sequence. As requested after expression a detergent and co-polymer screening as well as a test purification is performed. In contrast to detergents, DIBMAs and SMAs also isolate lipids from the cell wall contributing to membrane protein stabilization and have the capability to solubilize and form synthetic nanodisc in a single step. No further addition of DIBMA or SMA is needed during downstream processing.
Construct design and vector amplification
For expression and purification of human T1R2/T1R3 heterodimeric protein, both monomers were constructed on separate plasmids for co-expression. For T1R2, a HA signal peptide was added at the N-terminus and a “GSSG” linker was added at the C-terminus followed by an Avi-tag and a FLAG tag. T1R3 was tagged with a N-terminal HA signal peptide and a C-terminal Avi-tag as well as a Rho1D4 tag separated from the protein sequence by “GSSG” linker. The protein sequences were constructed in a pcDNA3.4 vector backbone (Invitrogen). Both plasmids were amplified in E. coli TOP10 cells and purified with the endotoxin-free plasmid DNA purification system from Macherey-Nagel (NucleoBond® PC 10000 EF).
Co-expression of human T1R2 and T1R3 in Expi293F cells
T1R2 and T1R3 plasmids were transiently co-transfected into Expi293F cells (Gibco) using polyethylenimine (PolySciences) at 2.5 × 106 cells/mL in Expi293 expression medium (Gibco) with 10 mM sucrose. The co-transfection was performed with a final DNA concentration of 1.5 µg/mL and 9 µg/mL PEI according to the manufacturer’s instructions. The transfected cultures were incubated at 37 °C with 6% CO2 and shaken at 110 rpm. After 20 h, sodium butyrate and valproic acid were added with a final concentration of 10 mM and 5 mM, respectively. After 72 h post transfection, the cells were collected by centrifugation (2,000x rpm; 15 min; 4 °C) and supplemented with protease inhibitors: 10 µM Leupeptin, 1 mM o-Phenanthroline, 0.1 mM phenylmethylsulfonyl fluoride (PMSF); 10 µM E-64 (N-[N-(L-3-trans-Carboxyoxiran-2-carbonyl)-leucyl]-agmatin, N-(trans-epoxysuccinyl)-L-leucin-4-guanidinbutylamid) and 1 mM Pepstatin A in buffer containing 20 mM Hepes, 100 mM NaCl and 2 mM EDTA. Cell pellets were stored at -80 °C until further use.
Purification and stabilization of human heterodimeric taste receptor T1R2/T1R3 in a synthetic nanodisc using DIBMA Glycerol
Frozen cells were resuspended in protein buffer (20 mM HEPES pH 8.0, 100 mM NaCl, 1 mM EDTA) supplemented with 10 µM Leupeptin, 1 mM o-Phenanthroline, 0.1 mM PMSF, 10 µM E-64 and 1 mM Pepstatin A. After sonication, cell debris was separated from membranes by centrifugation (9000 × g; 30 min; 4 °C). The membranes were further pelleted by centrifugation (100,000 × g; 1 h; 4 °C), and solubilized in protein buffer containing 2.5% (w/v) DIBMA Glycerol (Cube Biotech GmbH, Monheim, Germany) overnight at 4 °C. Solubilized T1R2/T1R3 was separated from un-solubilized material by centrifugation (100,000 × g; 1 h; 4 °C). The supernatant containing the target protein was diluted 1:10 with protein buffer and subsequently loaded onto a pre-equilibrated batch of Rho1D4-MagBeads (Cube Biotech GmbH, Monheim, Germany). To improve the binding, the MagBeads were incubated over night at 4 °C while gently stirring. Washing steps were performed with protein buffer five times under magnetic separation to remove non-specifically bound proteins. T1R2/T1R3 was eluted five time by incubating the beads with protein buffer containing 1 mg/mL Rho1D4 peptide for 30 min at 4 °C. Elution samples were concentrated at 3,000x g (Amicon Ultra-15, Merck Millipore) and further analyzed by size exclusion chromatography on a Superose 6 increase column (GE Healthcare). Fractions corresponding to the heterodimeric size of T1R2/T1R3 were collected and concentrated. The protein concentration was determined with spectroscopic measurement at a wavelength of 280 nm and the quality was validated by SDS-PAGE and Western Blot using FLAG antibodies, separately. Protein samples were flash frozen and stored at −80 °C.
Gα C-terminal 20mer
The unmodified peptide sequence, VFDAVTDIIIKENLKDCGLF, of the GNAT3 C-terminal tail was synthesized for use in binding studies (Thermo Scientific Peptide Synthesis Service) at >98% purity.
Association assay (kon)
Incubation was started by adding 50 µL of radioligand. After 1, 2, 5, 10, 15, 20, 30, 45 and 60 min of incubation at room temperature, the 96 well plate was washed with water using filter-harvester, filter paper was appropriately cut and mixed with the scintillation liquid followed by scintillation counting, and analysis by GraphPad Prism software using non-linear regression.
Dissociation assay (koff)
The dissociation assay was started after 1 h of incubation with radioligand, by adding excess of cold ligand in a total assay volume of 250 µL. This time point at which dissociation was started was designated as the time zero. Afterwards, the 96-well plate was washed and scintillation was counted. Data were entered into GraphPad Prism software and koff, kon and kd were determined using non-linear regression.
Competition binding experiments
Competition binding experiments were conducted by using fixed concentration of radioligand, and variable concentrations of unlabeled ligands. Radioligand and the competing agents were incubated for 1 h, followed by washing and scintillation counting. Concentrations were converted to log values and Ki (one site) was calculated by GraphPad Prism software using non-linear regression.
Effect of GTP and GDP on radioligand binding
It has been reported that the presence of GTP decreases agonist, but not antagonist binding in GPCRs64. Hence, radioligand displacement assays in the presence of increasing GTP and GDP concentrations were performed to evaluate the importance of the presence of a bound G protein. For this purpose, fixed concentrations of radioligands were used in the presence of increasing concentrations of GTP or GDP. IC50 values were calculated by using the GraphPad Prism software.
Stabilization of the human heterodimeric taste receptor T1R2/T1R3
A method was developed to generate human full-length heterodimeric taste receptor T1R2/T1R3 consisting of an expression screening, solubilization and stabilization screening and test purification to be used in FLOWER for label free binding measurements.Receptor constructs with different affinity tags are prepared and used for the transient expression in HEK293 cell culture. The literature for the heterodimeric receptor mainly describes experiments in which N-terminal affinity tags were used88 for both, full-length or truncated protein versions89. Therefore, N-terminal tags are also used among others in this approach. Additionally, all expression constructs contain an Avi-tag for biotinylation and the wild-type signal peptide is exchanged to a HA signal sequence. As requested after expression a detergent and co-polymer screening as well as a test purification is performed. In contrast to detergents, DIBMAs and SMAs also isolate lipids from the cell wall contributing to membrane protein stabilization and have the capability to solubilize and form synthetic nanodisc in a single step. No further addition of DIBMA or SMA is needed during downstream processing.
Construct design and vector amplification
For expression and purification of human T1R2/T1R3 heterodimeric protein, both monomers were constructed on separate plasmids for co-expression. For T1R2, a HA signal peptide was added at the N-terminus and a “GSSG” linker was added at the C-terminus followed by an Avi-tag and a FLAG tag. T1R3 was tagged with a N-terminal HA signal peptide and a C-terminal Avi-tag as well as a Rho1D4 tag separated from the protein sequence by “GSSG” linker. The protein sequences were constructed in a pcDNA3.4 vector backbone (Invitrogen). Both plasmids were amplified in E. coli TOP10 cells and purified with the endotoxin-free plasmid DNA purification system from Macherey-Nagel (NucleoBond® PC 10000 EF).
Co-expression of human T1R2 and T1R3 in Expi293F cells
T1R2 and T1R3 plasmids were transiently co-transfected into Expi293F cells (Gibco) using polyethylenimine (PolySciences) at 2.5 × 106 cells/mL in Expi293 expression medium (Gibco) with 10 mM sucrose. The co-transfection was performed with a final DNA concentration of 1.5 µg/mL and 9 µg/mL PEI according to the manufacturer’s instructions. The transfected cultures were incubated at 37 °C with 6% CO2 and shaken at 110 rpm. After 20 h, sodium butyrate and valproic acid were added with a final concentration of 10 mM and 5 mM, respectively. After 72 h post transfection, the cells were collected by centrifugation (2,000x rpm; 15 min; 4 °C) and supplemented with protease inhibitors: 10 µM Leupeptin, 1 mM o-Phenanthroline, 0.1 mM phenylmethylsulfonyl fluoride (PMSF); 10 µM E-64 (N-[N-(L-3-trans-Carboxyoxiran-2-carbonyl)-leucyl]-agmatin, N-(trans-epoxysuccinyl)-L-leucin-4-guanidinbutylamid) and 1 mM Pepstatin A in buffer containing 20 mM Hepes, 100 mM NaCl and 2 mM EDTA. Cell pellets were stored at -80 °C until further use.
Purification and stabilization of human heterodimeric taste receptor T1R2/T1R3 in a synthetic nanodisc using DIBMA Glycerol
Frozen cells were resuspended in protein buffer (20 mM HEPES pH 8.0, 100 mM NaCl, 1 mM EDTA) supplemented with 10 µM Leupeptin, 1 mM o-Phenanthroline, 0.1 mM PMSF, 10 µM E-64 and 1 mM Pepstatin A. After sonication, cell debris was separated from membranes by centrifugation (9000 × g; 30 min; 4 °C). The membranes were further pelleted by centrifugation (100,000 × g; 1 h; 4 °C), and solubilized in protein buffer containing 2.5% (w/v) DIBMA Glycerol (Cube Biotech GmbH, Monheim, Germany) overnight at 4 °C. Solubilized T1R2/T1R3 was separated from un-solubilized material by centrifugation (100,000 × g; 1 h; 4 °C). The supernatant containing the target protein was diluted 1:10 with protein buffer and subsequently loaded onto a pre-equilibrated batch of Rho1D4-MagBeads (Cube Biotech GmbH, Monheim, Germany). To improve the binding, the MagBeads were incubated over night at 4 °C while gently stirring. Washing steps were performed with protein buffer five times under magnetic separation to remove non-specifically bound proteins. T1R2/T1R3 was eluted five time by incubating the beads with protein buffer containing 1 mg/mL Rho1D4 peptide for 30 min at 4 °C. Elution samples were concentrated at 3,000x g (Amicon Ultra-15, Merck Millipore) and further analyzed by size exclusion chromatography on a Superose 6 increase column (GE Healthcare). Fractions corresponding to the heterodimeric size of T1R2/T1R3 were collected and concentrated. The protein concentration was determined with spectroscopic measurement at a wavelength of 280 nm and the quality was validated by SDS-PAGE and Western Blot using FLAG antibodies, separately. Protein samples were flash frozen and stored at −80 °C.
Gα C-terminal 20mer
The unmodified peptide sequence, VFDAVTDIIIKENLKDCGLF, of the GNAT3 C-terminal tail was synthesized for use in binding studies (Thermo Scientific Peptide Synthesis Service) at >98% purity.
Coupling of the sweet heterodimer to FLOWER
Microtoroid fabrication
Microtoroids are fabricated as previously described90. The microtoroid resonators are fabricated on silicon wafers with a 2 μm layer of thermally grown silica. First, circular disc patterns of photoresist with a diameter of 150 µm are patterned via photolithography on the top silica layer of the silicon wafer. These photoresist circular pads act as etch masks during immersion in buffered oxide etchant (BOE) solution (1:6 V/V) at room temperature. After wet etching, any residual photoresist and contaminants are removed using acetone and IPA (isopropyl alcohol). The wafer is then post-baked at 130 °C to remove moisture. The remaining silica disks act as etch masks during exposure to xenon difluoride (XeF2) gas, resulting in uniform undercutting of the silica disks and the formation of silicon pillars that support the silica disks. A thermal reflow process using a CO2 laser is used to shape the silica disk into a microtoroid.
Coupling of the sweet heterodimer to the microtoroid
The sweet heterodimer is coupled to the toroid via EDC/NHS coupling of an antibody amine. The toroid chip is first cleaned with ethanol then dried with nitrogen. The cleaned toroid chip is incubated in 1% v/v APTES in chloroform for 10 min to introduce amino groups onto the chip’s surface. After incubation, the chip was thoroughly rinsed with chloroform to remove any excess APTES and dried with nitrogen. The chip was then incubated overnight in a succinic anhydride solution (100 mg of succinc anhydride in 1 mL of DMSO). The next day, the chip was rinsed sequentially with DMSO and ethanol to remove unreacted succinic anhydride and then blown dry.
For EDC/NHS antibody conjugation, EDC and NHS are equilibrated at room temperature for at least 30 min (1 h recommended) before weighing. NHS and EDC are dissolved in 100 mM MES buffer to form a 100 mM EDC and 100 mM NHS mixed solution. An anti-FLAG (or anti-Rho) antibody solution is added to the mixed NHS/EDC solution to achieve a 30 nM concentration. The microtoroid is incubated in this solution for 15 min to ensure coupling. Following this activation step, the microtoroid chip is thoroughly rinsed with 100 mM MES buffer to remove any unreacted EDC/NHS and create a stable environment for subsequent functionalization.
Prior to microtoroid functionalization with the sweet heterodimer nanodiscs, the toroid chip is incubated in 100 mM ethanolamine for 5 min to block unreacted binding sites. The sweet heterodimer T1R2/T1R3 solution is diluted to 30 nM in HEN buffer. The chip is then incubated in the diluted T1R2/T1R3 heterodimer solution for 1 h to ensure efficient coupling of the sweet heterodimer to the functionalized chip surface. After incubation, the toroid chip is rinsed thoroughly with HEN buffer. The functionalized microtoroid chip is then carefully transferred into an Eppendorf tube containing HEN buffer.
For T1R2/T1R3 sweet heterodimer coupling to the microtoroid, anti-FLAG is coupled to the microtoroid. This coupling ensures that the sweet heterodimer (T1R2/T1R3) connects to the microtoroid through the TMD2 region, leaving the TMD3 region open for binding. In the experiments where it is desired to have the TMD2 region of the sweet heterodimer T1R2/T1R3 open, anti-FLAG can be replaced with anti-Rho.
Microtoroid fabrication
Microtoroids are fabricated as previously described90. The microtoroid resonators are fabricated on silicon wafers with a 2 μm layer of thermally grown silica. First, circular disc patterns of photoresist with a diameter of 150 µm are patterned via photolithography on the top silica layer of the silicon wafer. These photoresist circular pads act as etch masks during immersion in buffered oxide etchant (BOE) solution (1:6 V/V) at room temperature. After wet etching, any residual photoresist and contaminants are removed using acetone and IPA (isopropyl alcohol). The wafer is then post-baked at 130 °C to remove moisture. The remaining silica disks act as etch masks during exposure to xenon difluoride (XeF2) gas, resulting in uniform undercutting of the silica disks and the formation of silicon pillars that support the silica disks. A thermal reflow process using a CO2 laser is used to shape the silica disk into a microtoroid.
Coupling of the sweet heterodimer to the microtoroid
The sweet heterodimer is coupled to the toroid via EDC/NHS coupling of an antibody amine. The toroid chip is first cleaned with ethanol then dried with nitrogen. The cleaned toroid chip is incubated in 1% v/v APTES in chloroform for 10 min to introduce amino groups onto the chip’s surface. After incubation, the chip was thoroughly rinsed with chloroform to remove any excess APTES and dried with nitrogen. The chip was then incubated overnight in a succinic anhydride solution (100 mg of succinc anhydride in 1 mL of DMSO). The next day, the chip was rinsed sequentially with DMSO and ethanol to remove unreacted succinic anhydride and then blown dry.
For EDC/NHS antibody conjugation, EDC and NHS are equilibrated at room temperature for at least 30 min (1 h recommended) before weighing. NHS and EDC are dissolved in 100 mM MES buffer to form a 100 mM EDC and 100 mM NHS mixed solution. An anti-FLAG (or anti-Rho) antibody solution is added to the mixed NHS/EDC solution to achieve a 30 nM concentration. The microtoroid is incubated in this solution for 15 min to ensure coupling. Following this activation step, the microtoroid chip is thoroughly rinsed with 100 mM MES buffer to remove any unreacted EDC/NHS and create a stable environment for subsequent functionalization.
Prior to microtoroid functionalization with the sweet heterodimer nanodiscs, the toroid chip is incubated in 100 mM ethanolamine for 5 min to block unreacted binding sites. The sweet heterodimer T1R2/T1R3 solution is diluted to 30 nM in HEN buffer. The chip is then incubated in the diluted T1R2/T1R3 heterodimer solution for 1 h to ensure efficient coupling of the sweet heterodimer to the functionalized chip surface. After incubation, the toroid chip is rinsed thoroughly with HEN buffer. The functionalized microtoroid chip is then carefully transferred into an Eppendorf tube containing HEN buffer.
For T1R2/T1R3 sweet heterodimer coupling to the microtoroid, anti-FLAG is coupled to the microtoroid. This coupling ensures that the sweet heterodimer (T1R2/T1R3) connects to the microtoroid through the TMD2 region, leaving the TMD3 region open for binding. In the experiments where it is desired to have the TMD2 region of the sweet heterodimer T1R2/T1R3 open, anti-FLAG can be replaced with anti-Rho.
Binding measurements using FLOWER
We previously developed a system called Frequency Locked Optical Whispering Evanescent Resonator (FLOWER), which enables the rapid detection of single macromolecules in solution58,59,90 as well as proteins91,92, exosomes57, and volatile organic compounds93,94. FLOWER tracks resonance shifts resulting from the binding of molecules or nanoparticles to a WGM optical resonator. WGM resonators are highly sensitive sensors due to their long storage confinement times95–103. They have a larger capture area than traditional plasmonic sensors104, thus enabling rapid detection. Conventional method uses probe laser scanning to track resonance shifts, but the response time is limited by the scan speed. FLOWER uses frequency locking to track resonance shifts, offering a fast response time and greatly improved measurement accuracy. The experimental setup, as illustrated in Fig. S1, involves coupling a probe laser (PL) with a wavelength of 780 nm into a microtoroid resonator through a tapered optical fiber. To optimize the coupling efficiency between the tapered fiber and microtoroid, we control the polarization of the probe laser using a polarization controller (PC). A 2 kHz oscillation dither signal (DS) is input to the frequency modulation port of the probe laser for frequency modulation of the probe laser. The frequency modulated probe laser is split into two arms through a fiber-coupled beam splitter (BS). One arm carries the signal light coupled into the microtoroid through a tapered fiber, while the other serves as the reference light. Both the signal and reference light are then received by an auto-balanced receiver, which can reduce laser intensity noise by 55 – 70 dB. By multiplying the auto-balanced receiver’s electrical output signal by the DS and time-averaging, we obtain an error signal that is directly proportional to the wavelength detuning between the probe laser and the WGM resonance of the microtoroid. The error signal is zero when the probe laser precisely matches the microcavity resonance. Utilizing this error signal in a feedback loop to tune the laser through the frequency modulation port, the proportional-integral-derivative (PID) controller minimizes the error signal, thereby ensuring accurate and stable frequency locking of the probe laser to the microtoroid’s resonance.
We record the resonance shift signal using a high-precision data acquisition card (NI PCI-4461) throughout sweet ligand binding experiments. Our toroidal optical microcavities are first functionalized with human T1R2/T1R3 heterodimer sweet taste receptors. In a pressure-driven prefusion system, we preload both the buffer and sweet ligand sample solution into multiple channels, allowing precise control over solution injection into the microfluidic chamber (Fig. S1) via a LabVIEW program. Initially, the microtoroid chamber undergoes flushing with HEN buffer, resulting in an initial red shift (shift to longer wavelengths) in resonance due to the self-heating of the microtoroid resonator caused by the circulating probe laser light. As we continuously inject HEN buffer, the resonance ceases to exhibit a red shift and stabilizes at a consistent value. This stabilization indicates that the microtoroid has achieved thermal equilibrium. Following this, we record the resonance shift signal curve to establish the sensor’s baseline. We prepare a range of sweet ligand concentrations using the HEN buffer and sequentially inject them into the microfluidic chamber, starting from low concentrations and progressing to higher ones. Between each concentration, we rinse with running buffer, and measure the real-time resonance shift corresponding to each concentration. In the G protein C20 experiments, the HEN buffer is replaced by 10 µM C20 in HEN buffer. During injection, 10 µM C20 in HEN buffer is first introduced into the microfluidic chamber, and all sweet ligand solutions are supplemented with 10 µM C20 to ensure that all ligands are under G protein C20 binding conditions.
In silico ligand binding energies
Predicting ligand binding sites without C20
Our studies used the full T1R2/T1R3 heterodimer structure predicted using GEnSeMBLE71,72 for the TMD helixes. Our original initial structure used homology modeling for the VFD and CRD22. Detailed information is described in our previous paper22. For each ligand (Lactisole, NHDC, Ace-K, S-819, Amiloride, and Perillartine), we considered 10-15 torsional conformations in DarwinDock77, which iteratively generates ~50,000 poses in the putative binding regions for each of the 10-15 ligand conformations and each of the 25 GPCR configurations. The amino acid sidechains in the binding site were optimized for each of the best 100 ligand poses26. For each ligand conformation, the procedure was as follows. Starting from the minimized structure, we performed conformational searches of Mixed torsional/ Low-mode sampling (1000 steps, 1000 steps per rotatable bond, 10 kcal/mol energy window, 0.5 Å RMSD) using the Maestro software105. The low energy conformations were re-minimized using the Dreiding force field22 with some modifications in the hydrogen bond interactions and clustered by 2.0 and 1.0 Å of RMSD in two steps. For docking, low energy conformations for ligands from several clusters were selected. All ligand binding poses for Reb1-5, RebM, RebD, isoRebM, RebA, RebB, Rubu, RebC, and hydroxyRebM at VFD2 were used from a previous docking study22. For the binding poses at VFD3, TMD2 and TMD3, additional sugars were extended from the best binding pose of Rubu from the docking study. To relax the binding site of the additional sugars, 10 cycles of annealing with the geometry constraint on the structure of Rubu were performed. After selecting the best energy conformer, another 1 cycle of annealing and full minimization using the Dreiding force field22 was performed without constraints.
Generation of the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3
The various initial position of GNAT3 C20 were considered using the experimental structures of GPCR- G protein complexes, Class C mGlu2R-Gi (PDB ID: 7MTS106, 7E9G107), mGlu4R-Gi (PDB ID: 7E9H107), GABAB2R-Gi (PDB ID: 7EB2)108, and Class A OPRM-Gi (PDB ID 6DDF)109. We also included our predicted structure of the GNAT3-T1R2/T1R3 heterodimer complex110. After alignment of the GPCR backbone, all residues except the C-terminal 20 amino acids of the Gα protein were deleted followed by minimization of the fixed GPCR after the side chain refinement.
Explicit solvent MD
The C20 bound structure of the T1R2/T1R3 heterodimer at TMD2 or TMD3 were inserted into a continuous infinite POPC lipid bilayer membrane with periodic boundary conditions, while including full solvation with water at physiological salt concentration (0.9% concentration of NaCl, 154 mM).
The disulfide bridges in TAS1R2 were constrained: C59-C102, C359-C363, C405-C410 in the VFD, C495-C514, C499-C517, C520-C535, C538-C551 in CRD, C233-C513 between VFD and CRD, and C6303.25-C720 between TMD and EC2. The disulfide bridges in TAS1R3 were also constrained: C62-C103, C351-C370, C373-C375, C410-C415 in VFD, C499-C518, C503-C521, C524-C538, C541-C554 in CRD, C236-C517 between VFD and CRD, and C6333.25and C722 between TMD and EC2. The POPC lipid available in VMD was used to insert the protein into a lipid-water box, where lipids within 1 Å of the protein and waters within 2 Å of the protein were removed.
For the particle mesh Ewald (PME) in the electrostatics calculation111, the charge of system was balanced through replacing waters with Na+ and Cl− ions. After inserting the 7-helix bundle including loops into the box containing the periodic POPC membrane, water, and ions, we fixed the protein and minimized the lipid, water, ion atoms for 5,000 steps. We then equilibrated with NPT dynamics for 5 ns with 1.0 fs time step while continuing to keep the protein fixed. This allows the lipid and water to readjust to the protein. Then we minimized the full system for 5000 steps and then performed NPT dynamics for 20 ns (10 ns with fixed protein back bone atoms and 10 ns without any constraints) using 1.0 fs time steps. This predicted structure was then equilibrated at 310 K using the NAMD 2.9 (NAnoscale Molecular Dynamics) program112. We used the CHARMM36 force field parameters for the protein, the TIP3 model for water113, and the CHARMM27 force field parameters for the lipids114.
Predicting ligand binding sites at the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3
For the binding site of Rubu at the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3, the last trajectory of the C20 bound structure of T1R2/T1R3 heterodimer was aligned to the protein backbone of the binding site of Rubu at each domain (VFD2, VFD3, TMD2, and TMD3) from the previous docking study22. Then the matched complexes were annealed for 10 cycles between 50 to 600 K. After an additional minimization, the lowest energy conformer was selected. For the binding site of RebM, RebD, and RebC, the additional sugars were extended from the best binding pose of Rubu. To relax the binding site of the additional sugars, we performed 10 cycles of annealing with geometry constraints on the structure of Rubu. After selecting the best energy conformer, another 1 cycle of annealing and full minimization using the Dreiding force field22 were performed without any constraints.
Predicting ligand binding sites without C20
Our studies used the full T1R2/T1R3 heterodimer structure predicted using GEnSeMBLE71,72 for the TMD helixes. Our original initial structure used homology modeling for the VFD and CRD22. Detailed information is described in our previous paper22. For each ligand (Lactisole, NHDC, Ace-K, S-819, Amiloride, and Perillartine), we considered 10-15 torsional conformations in DarwinDock77, which iteratively generates ~50,000 poses in the putative binding regions for each of the 10-15 ligand conformations and each of the 25 GPCR configurations. The amino acid sidechains in the binding site were optimized for each of the best 100 ligand poses26. For each ligand conformation, the procedure was as follows. Starting from the minimized structure, we performed conformational searches of Mixed torsional/ Low-mode sampling (1000 steps, 1000 steps per rotatable bond, 10 kcal/mol energy window, 0.5 Å RMSD) using the Maestro software105. The low energy conformations were re-minimized using the Dreiding force field22 with some modifications in the hydrogen bond interactions and clustered by 2.0 and 1.0 Å of RMSD in two steps. For docking, low energy conformations for ligands from several clusters were selected. All ligand binding poses for Reb1-5, RebM, RebD, isoRebM, RebA, RebB, Rubu, RebC, and hydroxyRebM at VFD2 were used from a previous docking study22. For the binding poses at VFD3, TMD2 and TMD3, additional sugars were extended from the best binding pose of Rubu from the docking study. To relax the binding site of the additional sugars, 10 cycles of annealing with the geometry constraint on the structure of Rubu were performed. After selecting the best energy conformer, another 1 cycle of annealing and full minimization using the Dreiding force field22 was performed without constraints.
Generation of the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3
The various initial position of GNAT3 C20 were considered using the experimental structures of GPCR- G protein complexes, Class C mGlu2R-Gi (PDB ID: 7MTS106, 7E9G107), mGlu4R-Gi (PDB ID: 7E9H107), GABAB2R-Gi (PDB ID: 7EB2)108, and Class A OPRM-Gi (PDB ID 6DDF)109. We also included our predicted structure of the GNAT3-T1R2/T1R3 heterodimer complex110. After alignment of the GPCR backbone, all residues except the C-terminal 20 amino acids of the Gα protein were deleted followed by minimization of the fixed GPCR after the side chain refinement.
Explicit solvent MD
The C20 bound structure of the T1R2/T1R3 heterodimer at TMD2 or TMD3 were inserted into a continuous infinite POPC lipid bilayer membrane with periodic boundary conditions, while including full solvation with water at physiological salt concentration (0.9% concentration of NaCl, 154 mM).
The disulfide bridges in TAS1R2 were constrained: C59-C102, C359-C363, C405-C410 in the VFD, C495-C514, C499-C517, C520-C535, C538-C551 in CRD, C233-C513 between VFD and CRD, and C6303.25-C720 between TMD and EC2. The disulfide bridges in TAS1R3 were also constrained: C62-C103, C351-C370, C373-C375, C410-C415 in VFD, C499-C518, C503-C521, C524-C538, C541-C554 in CRD, C236-C517 between VFD and CRD, and C6333.25and C722 between TMD and EC2. The POPC lipid available in VMD was used to insert the protein into a lipid-water box, where lipids within 1 Å of the protein and waters within 2 Å of the protein were removed.
For the particle mesh Ewald (PME) in the electrostatics calculation111, the charge of system was balanced through replacing waters with Na+ and Cl− ions. After inserting the 7-helix bundle including loops into the box containing the periodic POPC membrane, water, and ions, we fixed the protein and minimized the lipid, water, ion atoms for 5,000 steps. We then equilibrated with NPT dynamics for 5 ns with 1.0 fs time step while continuing to keep the protein fixed. This allows the lipid and water to readjust to the protein. Then we minimized the full system for 5000 steps and then performed NPT dynamics for 20 ns (10 ns with fixed protein back bone atoms and 10 ns without any constraints) using 1.0 fs time steps. This predicted structure was then equilibrated at 310 K using the NAMD 2.9 (NAnoscale Molecular Dynamics) program112. We used the CHARMM36 force field parameters for the protein, the TIP3 model for water113, and the CHARMM27 force field parameters for the lipids114.
Predicting ligand binding sites at the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3
For the binding site of Rubu at the C20 bound structure of T1R2/T1R3 heterodimer at TMD2 or TMD3, the last trajectory of the C20 bound structure of T1R2/T1R3 heterodimer was aligned to the protein backbone of the binding site of Rubu at each domain (VFD2, VFD3, TMD2, and TMD3) from the previous docking study22. Then the matched complexes were annealed for 10 cycles between 50 to 600 K. After an additional minimization, the lowest energy conformer was selected. For the binding site of RebM, RebD, and RebC, the additional sugars were extended from the best binding pose of Rubu. To relax the binding site of the additional sugars, we performed 10 cycles of annealing with geometry constraints on the structure of Rubu. After selecting the best energy conformer, another 1 cycle of annealing and full minimization using the Dreiding force field22 were performed without any constraints.
In silico binding site discovery
The discovery of potentially undisclosed binding sites utilized a multi-step workflow. The apo-structure of the full hetero-dimer was used. SiteMap (Schrodinger)115 was used to search for the ten best sites. Glide grids were made for each site then Glide116,117 docking was performed to determine docking scores for the ligands. Molecular mechanics with generalized Born and surface area solvation (MM/GBSA) energies methods were run using Prime118 to determine binding site energies for potential new binding sites.
Supplementary information
Supplementary Information |
Title: Malignant Transformation of Meningioma With | Body:
INTRODUCTION
Meningiomas are the most common primary intracranial tumor in adults, often remaining symptomless and slow-growing [1]. Even when they are symptomatic, approximately 90% are classified as World Health Organization (WHO) grade 1, exhibiting slow growth [2]. High-grade meningiomas, including WHO grades 2 (atypical) and 3 (anaplastic), constitute less than 10% of all meningiomas.
Meningiomas can recur even after a total resection [3], and approximately 29% of high-grade meningiomas arise from malignant transformation (i.e., dedifferentiation) of lower-grade meningiomas. Because of its rarity, the incidence and clinical features of the malignant transformation of benign meningiomas are poorly understood. Recent research on genetic alterations in meningiomas has identified potential therapeutic targets, such as inhibitors of mammalian target of rapamycin, focal adhesion kinase, cyclin-dependent kinase, phosphoinositide-3 kinase, sonic hedgehog signaling, and histone deacetylases. Nevertheless, the mechanisms behind this malignant transformation remain unclear.
Here, we report a 54-year-old male patient who underwent a stepwise malignant transformation of meningioma from grade 1 to grade 3 during 10 years of treatment, including five surgeries and multiple radiation therapies (RTs). There have been numerous case reports on stepwise malignant transformation of meningiomas so far [45]; however, this case report is unique in that we retrospectively examined the telomerase reverse transcriptase promoter (TERTp) mutation—one of the genetic mutations representing grade 3 meningioma—which revealed a c.-124C>T TERTp mutation from the initial grade 1 meningioma.
CASE REPORT
A 54-year-old male patient presented to the neuro-oncology clinic for further evaluation of a right frontal convexity mass with moderate peritumoral edema on outside brain CT (Fig. 1A). He suffered from gradual-onset headaches, intermittent dizziness, and vomiting that started 2 weeks before, though the neurological exam revealed no abnormalities. His brain MRI showed a lobulated, contoured, extra-axial, homogenously enhancing mass at the right frontal lobe and a dural tail suggestive of meningioma (Fig. 1B). Given the size of the mass and the symptoms presented, a surgical resection of the tumor was planned and conducted on January 16, 2013. A Simpson grade I resection was performed, and histopathology revealed a WHO grade 1 meningothelial-type meningioma (Fig. 1C). No unusual events occurred postoperatively. The patient was discharged and regularly followed with MRI at the outpatient clinic.
At the 2-year follow-up, MRI revealed a lobulated, contoured, enhancing, dural-based mass at the right middle frontal cerebral convexity, distant from the initial lesion, without any focal neurologic symptoms. Fractionated stereotactic radiotherapy (FSRT) of 3,000 cGy/3 fractions was performed in May 2015 for the recurrent lesion (Fig. 2A). MRI 1.5 years later showed a newly appeared right peri-Sylvian dural-based mass suggestive of recurrent meningioma, and stereotactic radiosurgery (SRS) with a 1,800-cGy marginal dose was delivered in March 2017 (Fig. 2B). Numerous intermittent seizure-like events were reported following SRS, and anti-epileptic drugs with dexamethasone were administered for the mildly aggravated post-RT perilesional edema. The right temporal lesion shrunk in size with progressive central necrosis, whereas the right frontal lesion seemed stationary.
Two years after the last SRS, follow-up MRI revealed a newly formed high frontal midline lesion 8 mm in diameter (Fig. 3A) and a right temporal convexity meningioma 16 mm thick with peritumoral edema (Fig. 3D), along with the previously irradiated middle frontal and peri-Sylvian lesions (Fig. 3B and C, respectively). A second operation was conducted on June 27, 2019, and five lesions—including the above four and a falx lesion—were resected. Pathology revealed WHO grade 2 recurrent atypical meningioma with treatment-related changes in the irradiated middle frontal and peri-Sylvian lesions (Fig. 3E-H). The patient remained absent of neurologic deficits. At that time, a neurofibromatosis-2 (NF2) gene study was performed considering multiple distant recurrences but demonstrated no pathogenic or likely pathogenic variants. One year later, multiple recurrences were found at the frontal parasagittal area and right temporal pole, with a maximal diameter of 23 mm. A third operation was performed on December 3, 2020, for frontal parasagittal lesion Simpson grade II and temporal pole lesion Simpson grade I resection. The pathology results revealed WHO grade 2 atypical meningioma.
The 3-month follow-up MRI showed a newly appeared small (9 mm in diameter) residual, lobulated, dural-based, enhancing lesion at the surgical margin of the right frontotemporal area suggestive of a small residual meningioma. As SRS was not applicable for the right temporal lesion due to previous RT, the operation and radiotherapy were independently planned. On March 18, 2021, a fourth operation was performed on the right temporal lesion for gross total resection, and pathology revealed a WHO grade 2 atypical meningioma. Then, between March 2021 and May 2022, SRS (2,000 cGy) was performed on the right medial frontal base (olfactory groove) lesion, FSRT (4,500 cGy/10 fractions) was performed on the right high frontal dural thickening mass, and SRT (3,000 cGy/3 fractions) was performed on the right temporal lesion. Post-RT follow-up brain MRI showed that both the right high frontal mass and the temporal mass underwent central necrosis and reductions in size.
Over 8 months of serial follow-up, the right frontal parasagittal lesion reduced in size, a right frontal convexity mass 5 mm in diameter newly appeared (Fig. 4A), and the right temporal lesion grew from 8 to 14 mm and developed central necrosis (Fig. 4B). Moreover, a new right temporal base lesion appeared with partial cystic changes and a maximal diameter of 3.2 cm (Fig. 4C), necessitating a differential diagnosis of recurrent tumor and post-RT changes. A fifth operation for resection was performed on August 1, 2023, and histopathology revealed a WHO grade 3 anaplastic meningioma for the first time (Fig. 4D-F).
Next-generation sequencing (NGS) analysis was retrospectively conducted in January 2024 from formalin-fixed, paraffin-embedded (FFPE) specimens of the first, second, and fifth operations. The panel included 525 of the most common cancer-related genes, such as EGFR, BRCA, and PTEN, from comprehensive genomic database. The extracted DNA was fragmented using sonication before constructing the NGS library. Library preparation was performed using SureSelectXT Low Input Reagent Kits coupled with target enrichment (Agilent Technologies, Santa Clara, CA, USA). Customized probes were designed to capture the exonic regions of the 480 genes selected for the panel (including fusion genes) to detect the clinically actionable genetic variations in cancer. The total size of the targeted regions was 3.79 Mb. The constructed library was quantified using the KAPA Library Quantification Kit on the LightCycler 480 Instrument II (Roche, Basel, Switzerland). Paired-end sequencing was run on the Illumina NextSeq 550Dx sequencer using the NextSeq 500/550 High-Output v2 Kit (Illumina, San Diego, CA, USA). A TERTp somatic mutation (c.-124C>T) was identified in all specimens, while all other tumor-related genes were not detected including CDKN2A/B (Table 1). Thus, according to the 5th edition of the WHO central nervous system (CNS) tumor classification (WHO CNS 5), the patient’s meningiomas could be classified as grade 3 from the initial operation. The timeline table for the clinical course of the patient is illustrated in Table 2. After consultation with an oncologist, hydroxyurea was administered from 1 month after surgery. However, the 3-month follow-up brain MRI revealed a new lesion 9 mm in diameter at the lateral temporal base. The neuro-oncology board recommended SRS for that asymptomatic lesion, and 2,700 cGy/3 fractions was delivered. The patient was followed with caution for recurrence or new lesions.
DISCUSSION
Introduction of molecular markers in the diagnosis of CNS tumors and meningiomas
The WHO grading system for CNS tumors was initially introduced in 1979 and has undergone several major revisions in the decades since. Although the concept of atypical meningiomas was not introduced until 1993, many studies identified this aggressive subtype following the research published by Jellinger and Slowik [6] and Jääskeläinen et al. [7]. The WHO CNS 5—by far the most recent—was published in 2021 and further advanced the role of molecular diagnostics in CNS tumor classification while remaining wedded to other established approaches, such as histology and immunohistochemistry. This version also introduced new tumor types and subtypes, some of which are based on novel diagnostic technologies, such as DNA methylome profiling.
For meningiomas, the WHO CNS 5 retains the previous version’s grading system of 1–3 with 15 stratified subtypes. It emphasizes that the criteria defining atypical (grade 2) or anaplastic (grade 3) meningioma should be applied regardless of the underlying subtype. Previously, the factors differentiating meningiomas of grades 1–3 were solely histomorphologic. Aggressive, high-grade features included increased cellularity or mitotic index, a high nuclear-to-cytoplasmic ratio, prominent nucleoli, patternless or sheet-like growth, and the presence of necrosis or brain invasion [8]. However, despite its widespread use, the old grading system failed to accurately predict the clinical behavior, aggressiveness, and long-term recurrence of particular meningiomas [9]. In the new classification, several molecular biomarkers are associated with the classification and grading of meningiomas, including SMARCE1 (clear cell subtype), BAP1 (rhabdoid and papillary subtypes), and KLF4/TRAF7 (secretory subtype) mutations; TERTp mutation [10] and/or homozygous deletion of CDKN2A/B [11] (WHO grade 3); loss of nuclear H3K27me3 expression [12] (potentially worse prognosis); and methylome profiling [13] (methylation classes associated with increased risk of recurrence). Other key genes/molecular profiles characteristically alerted include NF2, AKT1, SMO, and PIK3CA in subtypes [14]. Importantly, according to the WHO CNS 5, no single, definitive criterion for grading meningiomas exists—appropriately combining the histopathologic, subtype (morphological), and mutational analysis results is essential for accurate diagnosis. Other genomic analyses, such as DNA copy number variations and DNA methylation profiles, have also proven more accurate in estimating the risk of recurrence compared with conventional histopathologic grading systems [115].
Multiple/recurrent meningiomas, malignant transformation of meningiomas, and TERT promoter mutation
Multiple meningiomas (MMs), referring to a subset of meningioma patients bearing two or more spatially separated, synchronous or metachronous tumors, account for 1%–10% of all meningiomas [16]. MMs are allegedly associated with genetic mutations, such as NF2, and are more frequently grade 2 or 3 meningiomas. However, Araújo Pereira et al. [17], in their analysis of MMs, revealed WHO grade 1 predominance similar to single meningiomas, and fewer than half of MM patients (43.89%) needed treatment.
Approximately 20%–40% of all high-grade meningiomas are secondary tumors that originally developed from WHO grade 1 tumors [18]. The literature suggests a step-by-step genetic progression in which the deletion of chromosome 22 is the fundamental genetic alteration and deletions in other chromosomes, such as 1p, 14q, and 10q, occur during malignant transformation [1920]. Consequently, high-grade meningiomas are classified as either de novo or secondary tumors. Some genetic differences between these two types have been reported: specifically, TERTp mutation is sometimes found in the latter but rarely in the former [212223]. Current research efforts aim to elucidate the histopathological changes associated with the malignant transformation of meningiomas, which is widely recognized as a combination of various oncogenic events. Some of the reported risk factors of malignant transformation of benign meningiomas include the histopathologic features of increased mitotic index [24] and non-skull base tumor location, without any apparent association with the patient’s sex or the primary treatment modalities (surgery or SRS) [25]. Moreover, several genetic mutations have also been linked to the malignant transformation of meningiomas, including FOXM1, TOP2A, BIRC5, MYBL2, and even TERTp [2226]. Few of these known risk factors were found in our case, as the tumor occupied a non-skull base location and harbored a TERTp mutation throughout the progression. Furthermore, although the histopathology from the first operation did not show an increased mitotic index, there was a prominent increase following the second operation. The underlying pathophysiological mechanisms behind the malignant transformation of meningiomas remain incompletely understood.
The systematic review and meta-analysis conducted by Nakasu et al. [25] specified an incidence rate of malignant transformation in benign meningiomas of 2.98 per 1,000 person-years. Individual case data revealed that the median time for malignant transformation was 5.0 years, with some cases of malignant change occurring up to 30 years after the initial surgery. Our case showed malignant transformation from grade 1 to grade 2 in 6 years, and from grade 2 to grade 3 in another 4 years.
TERTp mutation has been discovered as a driving factor in the aggressiveness of meningiomas, leading to reduced survival rates for patients [27]. Sahm et al. [10] reported that the inclusion of TERTp mutations in the hotspot regions C228T and C250T in meningioma samples from 252 patients enabled higher prognostic power because TERTp mutations were statistically significantly associated with shorter time to progression. Mirian et al. [28] found that TERTp gene alterations (TERT-alt) affected prognosis independent of the previous WHO grades: the recurrence rate was 4.8 times higher in WHO grade 1 or 2 TERT-alt patients compared with WHO grade 3 TERT wild-type patients, and the mortality rate was 2.7 times higher in WHO grade 1 or 2 TERT-alt patients compared with WHO grade 3 TERT wild-type patients. Therefore, TERTp mutation serves as a crucial biomarker, indicating a significantly higher risk of recurrence and death in meningioma patients. In our case, histologic findings from the first operation did not show any aggressive features except for slightly increased cellularity. From the second operation, although the histology from the right temporal and middle frontal lesions with RT-related changes remained relatively non-aggressive, the newly grown temporal lesion despite SRS (Fig. 3D and H) started to show an increased mitotic index (10/10 high-power fields [HPF]). Subsequently, newer aggressive histologic features, such as increased cellularity, patternless or sheet-like growth, and prominent nucleoli, emerged throughout the third and fourth operations, and the mitotic index peaked (21/10 HPF) in the fifth operation to satisfy classification as grade 3 meningioma. However, because the TERTp mutation was found to be present from the first operation despite CDKN2A/B was not, all the lesions from then on can be classified as grade 3 meningiomas according to the WHO CNS 5.
The treatment strategies for high-grade meningiomas resulting from malignant transformation do not specifically differ from ordinary management of meningiomas, though some potential therapies targeting specific molecular markers are on the rise. As TERTp mutation is a widely known pathologic mutation for tumorigenesis, recurrence, and progression, our patient’s meningiomas since the initial diagnosis in 2013 can be understood to be more susceptible to further recurrence and progression than if the mutation was not present. However, TERTp mutations vary from benign to pathogenic. The culprit mutations reported as “pathogenic/likely pathogenic” according to ClinVar (a public genomic database) include -146G>A (referred to as C250T) and -124G>A (referred to as C228T), which form the basis for 2021 WHO CNS 5 modifications [102223]. Our meningioma case presented with a c.-124C>T (C228T) mutation.
The limitations of our case are as follows. First, the NGS results were of poor quality with limited depth, especially for the 2013 specimen due to DNA degradation of the FFPE slides. Second, if the NGS results of pathogenic variants related to malignant transformation of meningioma were available earlier, more aggressive presumptive measures, such as radical resection with wider surgical margins or higher-dosed RT/SRS, could have been considered to prevent the tumor from recurring.
Based on our experience with this case, we suggest routinely performing early-stage genomic analysis, such as NGS, to look for the above-mentioned mutations related to malignant transformation/prognostication, including TERTp mutations, for all newly diagnosed meningioma patients. If any such mutations are detected, appropriate preemptive measures for treating and monitoring TERT-alt patients must be taken for preventative or surveillance purposes.
Introduction of molecular markers in the diagnosis of CNS tumors and meningiomas
The WHO grading system for CNS tumors was initially introduced in 1979 and has undergone several major revisions in the decades since. Although the concept of atypical meningiomas was not introduced until 1993, many studies identified this aggressive subtype following the research published by Jellinger and Slowik [6] and Jääskeläinen et al. [7]. The WHO CNS 5—by far the most recent—was published in 2021 and further advanced the role of molecular diagnostics in CNS tumor classification while remaining wedded to other established approaches, such as histology and immunohistochemistry. This version also introduced new tumor types and subtypes, some of which are based on novel diagnostic technologies, such as DNA methylome profiling.
For meningiomas, the WHO CNS 5 retains the previous version’s grading system of 1–3 with 15 stratified subtypes. It emphasizes that the criteria defining atypical (grade 2) or anaplastic (grade 3) meningioma should be applied regardless of the underlying subtype. Previously, the factors differentiating meningiomas of grades 1–3 were solely histomorphologic. Aggressive, high-grade features included increased cellularity or mitotic index, a high nuclear-to-cytoplasmic ratio, prominent nucleoli, patternless or sheet-like growth, and the presence of necrosis or brain invasion [8]. However, despite its widespread use, the old grading system failed to accurately predict the clinical behavior, aggressiveness, and long-term recurrence of particular meningiomas [9]. In the new classification, several molecular biomarkers are associated with the classification and grading of meningiomas, including SMARCE1 (clear cell subtype), BAP1 (rhabdoid and papillary subtypes), and KLF4/TRAF7 (secretory subtype) mutations; TERTp mutation [10] and/or homozygous deletion of CDKN2A/B [11] (WHO grade 3); loss of nuclear H3K27me3 expression [12] (potentially worse prognosis); and methylome profiling [13] (methylation classes associated with increased risk of recurrence). Other key genes/molecular profiles characteristically alerted include NF2, AKT1, SMO, and PIK3CA in subtypes [14]. Importantly, according to the WHO CNS 5, no single, definitive criterion for grading meningiomas exists—appropriately combining the histopathologic, subtype (morphological), and mutational analysis results is essential for accurate diagnosis. Other genomic analyses, such as DNA copy number variations and DNA methylation profiles, have also proven more accurate in estimating the risk of recurrence compared with conventional histopathologic grading systems [115].
Multiple/recurrent meningiomas, malignant transformation of meningiomas, and TERT promoter mutation
Multiple meningiomas (MMs), referring to a subset of meningioma patients bearing two or more spatially separated, synchronous or metachronous tumors, account for 1%–10% of all meningiomas [16]. MMs are allegedly associated with genetic mutations, such as NF2, and are more frequently grade 2 or 3 meningiomas. However, Araújo Pereira et al. [17], in their analysis of MMs, revealed WHO grade 1 predominance similar to single meningiomas, and fewer than half of MM patients (43.89%) needed treatment.
Approximately 20%–40% of all high-grade meningiomas are secondary tumors that originally developed from WHO grade 1 tumors [18]. The literature suggests a step-by-step genetic progression in which the deletion of chromosome 22 is the fundamental genetic alteration and deletions in other chromosomes, such as 1p, 14q, and 10q, occur during malignant transformation [1920]. Consequently, high-grade meningiomas are classified as either de novo or secondary tumors. Some genetic differences between these two types have been reported: specifically, TERTp mutation is sometimes found in the latter but rarely in the former [212223]. Current research efforts aim to elucidate the histopathological changes associated with the malignant transformation of meningiomas, which is widely recognized as a combination of various oncogenic events. Some of the reported risk factors of malignant transformation of benign meningiomas include the histopathologic features of increased mitotic index [24] and non-skull base tumor location, without any apparent association with the patient’s sex or the primary treatment modalities (surgery or SRS) [25]. Moreover, several genetic mutations have also been linked to the malignant transformation of meningiomas, including FOXM1, TOP2A, BIRC5, MYBL2, and even TERTp [2226]. Few of these known risk factors were found in our case, as the tumor occupied a non-skull base location and harbored a TERTp mutation throughout the progression. Furthermore, although the histopathology from the first operation did not show an increased mitotic index, there was a prominent increase following the second operation. The underlying pathophysiological mechanisms behind the malignant transformation of meningiomas remain incompletely understood.
The systematic review and meta-analysis conducted by Nakasu et al. [25] specified an incidence rate of malignant transformation in benign meningiomas of 2.98 per 1,000 person-years. Individual case data revealed that the median time for malignant transformation was 5.0 years, with some cases of malignant change occurring up to 30 years after the initial surgery. Our case showed malignant transformation from grade 1 to grade 2 in 6 years, and from grade 2 to grade 3 in another 4 years.
TERTp mutation has been discovered as a driving factor in the aggressiveness of meningiomas, leading to reduced survival rates for patients [27]. Sahm et al. [10] reported that the inclusion of TERTp mutations in the hotspot regions C228T and C250T in meningioma samples from 252 patients enabled higher prognostic power because TERTp mutations were statistically significantly associated with shorter time to progression. Mirian et al. [28] found that TERTp gene alterations (TERT-alt) affected prognosis independent of the previous WHO grades: the recurrence rate was 4.8 times higher in WHO grade 1 or 2 TERT-alt patients compared with WHO grade 3 TERT wild-type patients, and the mortality rate was 2.7 times higher in WHO grade 1 or 2 TERT-alt patients compared with WHO grade 3 TERT wild-type patients. Therefore, TERTp mutation serves as a crucial biomarker, indicating a significantly higher risk of recurrence and death in meningioma patients. In our case, histologic findings from the first operation did not show any aggressive features except for slightly increased cellularity. From the second operation, although the histology from the right temporal and middle frontal lesions with RT-related changes remained relatively non-aggressive, the newly grown temporal lesion despite SRS (Fig. 3D and H) started to show an increased mitotic index (10/10 high-power fields [HPF]). Subsequently, newer aggressive histologic features, such as increased cellularity, patternless or sheet-like growth, and prominent nucleoli, emerged throughout the third and fourth operations, and the mitotic index peaked (21/10 HPF) in the fifth operation to satisfy classification as grade 3 meningioma. However, because the TERTp mutation was found to be present from the first operation despite CDKN2A/B was not, all the lesions from then on can be classified as grade 3 meningiomas according to the WHO CNS 5.
The treatment strategies for high-grade meningiomas resulting from malignant transformation do not specifically differ from ordinary management of meningiomas, though some potential therapies targeting specific molecular markers are on the rise. As TERTp mutation is a widely known pathologic mutation for tumorigenesis, recurrence, and progression, our patient’s meningiomas since the initial diagnosis in 2013 can be understood to be more susceptible to further recurrence and progression than if the mutation was not present. However, TERTp mutations vary from benign to pathogenic. The culprit mutations reported as “pathogenic/likely pathogenic” according to ClinVar (a public genomic database) include -146G>A (referred to as C250T) and -124G>A (referred to as C228T), which form the basis for 2021 WHO CNS 5 modifications [102223]. Our meningioma case presented with a c.-124C>T (C228T) mutation.
The limitations of our case are as follows. First, the NGS results were of poor quality with limited depth, especially for the 2013 specimen due to DNA degradation of the FFPE slides. Second, if the NGS results of pathogenic variants related to malignant transformation of meningioma were available earlier, more aggressive presumptive measures, such as radical resection with wider surgical margins or higher-dosed RT/SRS, could have been considered to prevent the tumor from recurring.
Based on our experience with this case, we suggest routinely performing early-stage genomic analysis, such as NGS, to look for the above-mentioned mutations related to malignant transformation/prognostication, including TERTp mutations, for all newly diagnosed meningioma patients. If any such mutations are detected, appropriate preemptive measures for treating and monitoring TERT-alt patients must be taken for preventative or surveillance purposes. |
Title: Rapid
Photoinduced
Self-Healing, Controllable Drug
Release, Skin Adhesion Ability, and Mechanical Stability of Hydrogels
Incorporating Linker-Modified Gold Nanoparticles and Nanogels | Body:
1
Introduction
Hydrogels are interesting
materials that have found a broad range
of applications and attracted interest in various disciplines, including
engineering, materials science, and life sciences.1−5 Among them, the hydrogels based on poly(N-isopropylacrylamide) (pNIPAm) are extremely valuable for use in
tissue engineering and drug delivery systems due to their thermoresponsive
nature, where exact control over the swelling and shrinking processes
can be used to get precise and controlled responses.6 Other properties, like self-healing, mechanical stability,
and adhesiveness, can be achieved by formulating hybrid hydrogels
based on the NIPAm monomer.7,8
One method used
to enrich the properties of pNIPAm hydrogels is
to introduce nanoparticles and produce nanocomposite hydrogels. There
are many different nanoparticles, such as carbon-based, polymeric,
and metallic nanoparticles, which can be combined with a polymeric
network.9−12 These nanoparticles interact either chemically or physically with
the network and provide hydrogels with specific characteristics. The
first pNIPAm nanocomposite hydrogel was produced by Haraguchi and
Takehisa, and the clay used in that nanocomposite improved significantly
its mechanical strength and other characteristics.13
The self-healing ability of nanocomposite hydrogels
is one of the
important characteristics that has been extensively studied in the
past decade for its potential to enhance gel functionality and durability.14−16 Gold nanoparticles (GNPs) are widely used as metallic nanoparticles
in the development of nanocomposite hydrogels, particularly for enhancing
the hydrogels’ properties, such as their self-healing ability.17−19 Due to their special optical, electrical, and catalytic properties,
GNPs are valuable for improving the performance of hydrogels. Scientists
can precisely tune the mechanical, optical, and biological characteristics
of the resulting hybrid materials by adjusting the size, shape, and
surface chemistry of GNPs.20,21 In addition, GNPs exhibit
an intense absorption band in the near-infrared (NIR) spectrum. This
phenomenon is caused by the collective oscillation of conduction band
electrons induced by their interaction with an electromagnetic field.
This phenomenon is known as localized surface plasmon resonance.22 The integration of a thermoresponsive polymer
like pNIPAm with GNPs has the potential of creating nanocomposite
hydrogels with advantageous synergistic features. These developments
have resulted in the creation of stimulus-responsive hydrogels that
can target therapeutic delivery and undergo quick regulated drug release
from the gels.23,24 However, it is crucial to build
a durable interaction between GNPs and the polymeric network. Marcelo
et al. prepared smart catalyst nanocomposites based on pNIPAm and
GNPs. In that hydrogel, the catechol groups were used to reduce HAuCl4 for functionalizing the hydrogels with GNPs.25 Also, the cross-linking copolymerization of pNIPAm and
the monomers containing either thiol or dithiol groups was reported
as an effective way to build Au-nanocomposite hydrogels.26 Consequently, it became evident that the Au–sulfur
interactions in the hydrogels are able to recover damaged hybrid materials
and provide benefits such as enhanced mechanical strength, biocompatibility,
and adjustable optical characteristics.
Scheme 1
Scheme of Synthesis of p(NIPAm-BISS) Nanogel
Similarly, the integration of laser irradiation
with the self-healing
procedure should enable control over the repair mechanism. Significant
progress in controlling and efficiently utilizing light-responsive
components within the hydrogel matrix has been made in enhancing the
self-healing action. This paved the way for the mechanisms that can
accomplish on-demand repair.27−30 By using a NIR laser, the self-healing time of controlled
hydrogels can be reduced to 1–2 min, resulting in a recovery
rate of approximately 99%.31 Some varieties
of self-healing hydrogels, however, take a longer time to repair the
gel completely; for example, Chen et al.32 who produced a Cu-nanocomposite hydrogel and used an NIR laser to
achieve self-healing after 10 min of radiation exposure. In addition
to self-healing, Chen et al.33 designed
an antibacterial gold nanocomposite hydrogel with real-time infection
monitoring using a NIR laser, aimed at effective wound healing. These
properties are particularly valuable in biomedical applications where
precise and controlled drug release, such as the delivery of the anticancer
drug doxorubicin (DOX), can be achieved.34−36 Wearable technology
(including sensors and actuators), drug delivery systems, tissue engineering,
and other fields could benefit greatly from these advanced features.37−39
Our paper presents research on hydrogels with a particular
emphasis
on the self-healing process initiated by laser technology, drug release
capabilities, mechanical stability, and adhesive properties on a variety
of substrates. We employed free-radical polymerization to prepare
the nanocomposite hydrogel. Au nanoparticles were modified with a
newly synthesized cross-linker containing –S–S–
bonds, N,N′-bis(acryloyl)cystine (BISS). BISS-modified
gold nanoparticles exhibited Au–sulfur interactions in the
hydrogel network. These Au–sulfur interactions, upon NIR-laser
irradiation, can impart self-healing properties and facilitate drug
release within the hydrogel structure. The incorporation of the p(NIPAm-BISS)
nanogel in the hydrogel exhibited greater temperature changes. Despite
the low concentration of GNPs, the synthesized hydrogels exhibited
a fast thermal response, rapid and controllable self-healing, and
high drug release efficiency. In addition to enhancing self-healing
and drug releasing, the hydrogels also exhibited strong adhesion to
the skin. This adhesive property was achieved through the presence
of excess carboxylic groups from BISS and other functional groups
in the hydrogel, which created strong bonds between the hydrogel and
the tissue. The adhesive properties of these hydrogels on various
substrates have generated significant interest in biomedical applications,
including tissue engineering scaffolds and wound dressings.40−42
Additionally, the mechanical strength of the gels was increased
by the addition of GNPs due to the formation of a cross-linked network
inside the gel matrix that could increase the elasticity and structural
integrity of the material. With this advancement, the mechanically
improved gels can be used in such fields as biomedical devices, biosensors,
and actuators.43−45
In summary, by utilizing GNPs and Au–sulfur
interactions,
we successfully developed hydrogels with controllable self-healing
and drug release capabilities along with strong mechanical strength
and adhesion. These properties make the hydrogels promising candidates
for further biomedical applications.
2
Materials and Methods
2.1
Chemicals
and Materials
N-Isopropylacrylamide (NIPAm,
>97.0%), potassium persulfate (KPS,
>99.9%), ammonium persulfate (APS, >98.8%), N,N,N′,N′-tetramethylethylenediamine (TEMED, >99.0%), and tetrachloroauric
(III) acid trihydrate (HAuCl4·3H2O, >99.9%)
were purchased from Sigma-Aldrich. Citric acid monohydrate (pure p.
a., C6H8O7·H2O) and
sodium peroxide (NaOH, >98.8%) were bought from POCH. The N,N′-bis(acryloyl)cystine (BISS) cross-linker used
in this study, was synthesized by us according to our previous publication.46 All chemical compounds were used without alteration
except for the NIPAm monomer, which was recrystallized from a toluene/hexane
mixture (30:70 v/v). For the preparation of the solutions, high-purity
water of a 0.05 μS·cm–1 conductivity
was prepared by using a Milli-Q Plus/Millipore purification system.
2.2
Synthesis of Gold Nanoparticles
Gold
nanoparticles (GNPs) were synthesized following the established procedures
with slight modifications.47 Briefly, either
125 μL or 375 μL or 625 μL of a 0.12 M solution
of HAuCl4 was mixed with 50 mL of distilled water and gently
brought to boiling. Subsequently, either 1 mL or 3 mL or 5 mL of a
38.7 mM solution of citric acid, with pH adjusted to 6–7 using
NaOH, were added incrementally under continuous stirring until the
solution turned deep red, which indicated GNP formation. By maintaining
the ratios of HAuCl4 and citric acid, the solutions of
various GNP concentrations were obtained and entitled GNP1, GNP2,
and GNP3. Finally, the solutions were cooled to room temperature,
purified by dialysis, and stored in light-protected containers.
2.3
Synthesis of Nanogels
P(NIPAm-BISS)
nanogels (NG) were synthesized through a semibatch surfactant-free
precipitation polymerization method [nanohydrogel with N,N’-bis(acryloyl)cystine cross-linker
for high drug loading] (Scheme 1). The polymerization took place in a round-bottom, two-neck,
50 mL flask equipped with a stirrer, operating at approximately 250
rpm, and inlet and outlet ports for inert gas. The primary monomer,
NIPAm (97 mM), was dissolved in a flask with 15 mL of deionized water,
along with TEMED (17 μL) and APS (2.3 mM). After a 30 min deoxygenation
period at 25 °C, the dropwise addition of an aqueous solution
containing the cross-linker BISS (3 mM, in 2 mL of deionized water)
was conducted using a peristaltic pump with a flow rate of 2.6 mL·h–1. The reaction proceeded for 2 h. After that, the
obtained mixture underwent dialysis against deionized water for 1
week using a dialysis tube with a cutoff molecular weight of 10,000
Da (Spectra/Por 7 Dialysis Membrane) to remove any unreacted reagents.
The resultant nanogel solution had a concentration of ca. 5 mg·mL–1 and an average size of approximately 26 ± 6
nm [based on transmission electron microscopy (TEM) results]. The
TEM image, the graph of the size distribution of the nanogel particles
(determined using Nano Measurer 1.2 software based on TEM images),
and the dynamic light scattering (DLS) graph are given in the Supporting
Information (Figures S2 and S3).
2.4
Synthesis of Nanocomposite Hydrogels
For the synthesis
of nanocomposite hydrogels, free-radical polymerization
was employed. Various concentrations of NIPAm (1.00 and 1.40 M), BISS
(0.35, 1.00, and 2.00 mM), GNP (GNP1, GNP2, and GNP3), KPS (11.00
mM), and TEMED (2.65 mM) solutions were used. The polymerization process
comprised three distinct steps. Initially, BISS and 2.5 mL of GNP
solution were mixed and sonicated for 20 min in a sonication bath.
Meanwhile, the NIPAm monomer was dissolved in either 2.5 mL of water
or NG dispersion in an ice bath under a nitrogen atmosphere. Subsequently,
in the second step, the BISS-modified GNP solution was introduced
to the monomer solution, and the resulting mixture was stirred in
an ice bath under an argon (Ar) environment for 20 min. In the final
step, the KPS solution and TEMED were swiftly added. The polymerization
commenced upon the introduction of KPS and proceeded overnight in
a refrigerator.
2.5
UV–Vis Spectroscopy
To confirm
the functionalization of GNPs, changes in optical transmittance were
examined at a wavelength of 550 nm utilizing a Lambda 25, PerkinElmer
UV–vis spectrometer.
2.6
Dynamic Light Scattering
The hydrodynamic
diameters of the GNPs and the mixture of GNPs and BISS were evaluated
utilizing a Malvern Zetasizer apparatus (Nano ZS, UK). This instrument
featured a 4 mW helium–neon laser that emitted light with a
wavelength of 632.8 nm. The measurements were conducted at a scattering
angle of 173°.
2.7
Morphology
TEM
investigations were
performed with a Talos F200X (FEI Company) microscope operated at
200 kV to characterize the NG particles, GNPs, BISS-modified GNPs,
and gel samples. The measurements were performed in both TEM and scanning
TEM (STEM) modes using a high-angle annular dark-field (HAADF) detector
and energy-dispersive X-ray spectroscopy mode of a Bruker BD4 spectrometer
to evaluate the elemental distribution of carbon, sulfur, and gold.
The morphology and elemental composition of the gel sample were
investigated with a Quantax 400 Bruker EDS/energy-dispersive X-ray
(EDX) detector in conjunction with a Merlin, ZEISS field emission
scanning electron microscope (FESEM). Prior to measurement, a Mini
Sputter Coater (Polaron SC7620) was used to deposit a 5 nm layer of
Pd alloy onto the samples under vacuum. The samples were flash-frozen
in liquid nitrogen and then freeze-dried using a specialized lyophilizer,
Labconco FreeZone device at −82 °C and a pressure of 0.03
mbar.
2.8
Mechanical Performance
The tensile
properties of the hydrogels were assessed by utilizing an EZ-SX Shim-Pol
tensile test machine both before and after performing the self-healing.
All experiments were conducted in triplicate. Tensile tests were executed
at a constant strain rate of 50 mm·min–1.
The compression tests were conducted using a 20 N load cell. Cylindrical
hydrogel samples, measuring 10 mm in diameter and 10 mm in height,
underwent compression at a rate of 50 mm·min–1 under ambient conditions.
Rheological assessments were performed
using an MCR302 strain-controlled
rheometer (Anton Paar, Graz, Austria) with plate-and-plate geometry
(diameter, 15 mm). Two distinct modes were employed: (a) an amplitude
sweep test was conducted at a fixed angular frequency of 10 rad·s–1 at 20 °C, where the amplitude of oscillatory
strains ranged from 0.01 to 1000%. Additionally, frequency sweep tests
were conducted with a fixed strain amplitude of 10% across a frequency
range of 1–100 rad·s–1. A PolyScience
circulating bath was used to control the temperature during the rheological
experiments. To minimize evaporation and maintain the temperature
constant, a cap was employed throughout all measurements. The measurements
were carried out in triplicate.
2.9
Self-Healing
Tests and Photothermal Experiments
For self-healing tests,
the hydrogel samples in the form of a cylinder
of diameter ca. 5 mm were cut in half with a sharp blade. The resulting
halves were placed on a glass slide and brought into contact at the
position of the cut. The joint was irradiated from above with laser
light. A Ti:sapphire femtosecond laser (Spectra Physics MaiTai) tuned
to a central wavelength of 800 nm was employed. Its output beam was
shaped with the help of a spherical and a cylindrical lens to form
an elliptical spot at the position of the sample (see Figure 1). The minor and major axes
of the ellipse had lengths of approximately 3 and 8 mm (measured at
half-maximum intensity), respectively. The longer axis was parallel
to the cut. The power of the laser beam was reduced to 0.75 W, which
resulted in light intensity at the sample position of ca. 4 W·cm–2. The experiment was conducted at room temperature.
The self-healing efficiency was studied by doing the tensile tests.
Figure 1
Scheme
of polymerization of the nanocomposite hydrogel incorporating
BISS-modified GNPs and NG and sample irradiation by the NIR laser
(800 nm) to regenerate Au–sulfur bonds and facilitate the healing
process of the fractured part of the hydrogel.
In the studies of the hydrogel photothermal response,
the same
cylindrical gel geometry was employed. The thermal images of the samples
were obtained with an infrared thermal imaging camera (Fluke Ti400).
2.10
Adhesiveness Test
2.10.1
Lap-Shear
Test
Lap-shear testing
was carried out by using the tensile machine (EZ-SX Shim-pol). The
adhesive strength values were calculated by dividing the maximum shear
force by the standardized contact area of 200 mm2, achieved
by overlapping tissue samples. Each hydrogel specimen underwent testing
with three replicates. Fresh commercially available vacuum-sealed
porcine tissue was stored temporarily at 4 °C until use. Rectangular
sections measuring 10 mm × 100 mm were precisely cut from the
fresh porcine using a razor blade. Excess fat was removed to ensure
uniformity. The thickness of the tissue sections was 2 mm.48
To preserve tissue hydration, the tissue
was immersed in a buffer solution prior to testing. Preloading of
the tissue–gel–tissue piece was applied at 1.5 N in
the normal direction for a duration of 10 s. The overlapping area
was 10 × 20 mm. A 20 N load cell, integrated with the tensile
machine, measured the lap shear of the tissue–gel–tissue
stickiness. The samples were clamped between two fixtures and subjected
to parallel pulling at a velocity of 50 mm·min–1.
2.10.2
T-Peel Test
Using the aforementioned
tensile machine, T-peel tests were performed. This entailed bonding
two porcine samples, each 10 mm wide, 100 mm long, and 2 mm thick,
using the hydrogel. The overlapping region of the tissue samples was
sized 10 mm × 20 mm. Following this, the free ends of both tissue
samples were individually secured to the tensile machine grips.
Subsequently, the upper tissue was subjected to upward pulling at
a peeling angle of 180° and a constant velocity of 50 mm·min–1, while the peeling force (Fp) was simultaneously recorded. The measurement of the work
of adhesion, necessary for the separation of the hydrogel-bonded tissues,
was subsequently calculated as 2 × Fp·w–1, where w is the width of the sample.49
2.11
Determination of Load and Release of Doxorubicin
(DOX)
2.11.1
Loading of DOX into the Hydrogels
DOX, a widely used anticancer drug, was incorporated into the hydrogels
by using the incubation method. Initially, 10 mg of the hydrogels
was combined with 0.5 mg of DOX and dispersed in 10 mL of a phosphate
buffer solution (pH = 7.4; 0.15 M). The mixture underwent heating
process at 45 °C for 10 min, followed by cooling to 25 °C.
This heating and cooling cycle was repeated several times to ensure
thorough incorporation. Subsequently, the supernatant containing any
unbound DOX was replaced with fresh phosphate buffer solution, and
this process was iterated until excess DOX was effectively removed.
The amount of DOX loaded into the gels was determined by subtracting
the mass of free DOX present in the supernatant from the total mass
of DOX utilized in the loading process (eq 1). The concentration of DOX in the solution
was quantified by measuring its absorbance at 480 nm. A calibration
plot was employed for accurate determination.50
Drug loading capacity (DLC) was evaluated using eq 11where mtotalDOX is
the total mass of DOX
used in the loading process, mfreeDOX is the mass of DOX that
was not bound to the gel, and m(gel + DOX) is the mass of the gel with DOX.
2.11.2
DOX
Release from the Hydrogels
The release of DOX from the hydrogels
was assessed after laser irradiation
by using UV/vis absorption spectroscopy. The experimental setup mirrored
that of the self-healing tests with the exception of removing the
cylindrical lens. Consequently, the laser spot on the sample became
circular and measured ca. 8 mm in diameter (at half-maximum). The
laser beam operated at 0.75 W, yielding a light intensity of approximately
4 W·cm–2. A solution consisting of 10 mL of
phosphate buffer containing the DOX-loaded gel was prepared. Then
the absorbance of DOX released into the supernatant was measured.51
2.1
Chemicals
and Materials
N-Isopropylacrylamide (NIPAm,
>97.0%), potassium persulfate (KPS,
>99.9%), ammonium persulfate (APS, >98.8%), N,N,N′,N′-tetramethylethylenediamine (TEMED, >99.0%), and tetrachloroauric
(III) acid trihydrate (HAuCl4·3H2O, >99.9%)
were purchased from Sigma-Aldrich. Citric acid monohydrate (pure p.
a., C6H8O7·H2O) and
sodium peroxide (NaOH, >98.8%) were bought from POCH. The N,N′-bis(acryloyl)cystine (BISS) cross-linker used
in this study, was synthesized by us according to our previous publication.46 All chemical compounds were used without alteration
except for the NIPAm monomer, which was recrystallized from a toluene/hexane
mixture (30:70 v/v). For the preparation of the solutions, high-purity
water of a 0.05 μS·cm–1 conductivity
was prepared by using a Milli-Q Plus/Millipore purification system.
2.2
Synthesis of Gold Nanoparticles
Gold
nanoparticles (GNPs) were synthesized following the established procedures
with slight modifications.47 Briefly, either
125 μL or 375 μL or 625 μL of a 0.12 M solution
of HAuCl4 was mixed with 50 mL of distilled water and gently
brought to boiling. Subsequently, either 1 mL or 3 mL or 5 mL of a
38.7 mM solution of citric acid, with pH adjusted to 6–7 using
NaOH, were added incrementally under continuous stirring until the
solution turned deep red, which indicated GNP formation. By maintaining
the ratios of HAuCl4 and citric acid, the solutions of
various GNP concentrations were obtained and entitled GNP1, GNP2,
and GNP3. Finally, the solutions were cooled to room temperature,
purified by dialysis, and stored in light-protected containers.
2.3
Synthesis of Nanogels
P(NIPAm-BISS)
nanogels (NG) were synthesized through a semibatch surfactant-free
precipitation polymerization method [nanohydrogel with N,N’-bis(acryloyl)cystine cross-linker
for high drug loading] (Scheme 1). The polymerization took place in a round-bottom, two-neck,
50 mL flask equipped with a stirrer, operating at approximately 250
rpm, and inlet and outlet ports for inert gas. The primary monomer,
NIPAm (97 mM), was dissolved in a flask with 15 mL of deionized water,
along with TEMED (17 μL) and APS (2.3 mM). After a 30 min deoxygenation
period at 25 °C, the dropwise addition of an aqueous solution
containing the cross-linker BISS (3 mM, in 2 mL of deionized water)
was conducted using a peristaltic pump with a flow rate of 2.6 mL·h–1. The reaction proceeded for 2 h. After that, the
obtained mixture underwent dialysis against deionized water for 1
week using a dialysis tube with a cutoff molecular weight of 10,000
Da (Spectra/Por 7 Dialysis Membrane) to remove any unreacted reagents.
The resultant nanogel solution had a concentration of ca. 5 mg·mL–1 and an average size of approximately 26 ± 6
nm [based on transmission electron microscopy (TEM) results]. The
TEM image, the graph of the size distribution of the nanogel particles
(determined using Nano Measurer 1.2 software based on TEM images),
and the dynamic light scattering (DLS) graph are given in the Supporting
Information (Figures S2 and S3).
2.4
Synthesis of Nanocomposite Hydrogels
For the synthesis
of nanocomposite hydrogels, free-radical polymerization
was employed. Various concentrations of NIPAm (1.00 and 1.40 M), BISS
(0.35, 1.00, and 2.00 mM), GNP (GNP1, GNP2, and GNP3), KPS (11.00
mM), and TEMED (2.65 mM) solutions were used. The polymerization process
comprised three distinct steps. Initially, BISS and 2.5 mL of GNP
solution were mixed and sonicated for 20 min in a sonication bath.
Meanwhile, the NIPAm monomer was dissolved in either 2.5 mL of water
or NG dispersion in an ice bath under a nitrogen atmosphere. Subsequently,
in the second step, the BISS-modified GNP solution was introduced
to the monomer solution, and the resulting mixture was stirred in
an ice bath under an argon (Ar) environment for 20 min. In the final
step, the KPS solution and TEMED were swiftly added. The polymerization
commenced upon the introduction of KPS and proceeded overnight in
a refrigerator.
2.5
UV–Vis Spectroscopy
To confirm
the functionalization of GNPs, changes in optical transmittance were
examined at a wavelength of 550 nm utilizing a Lambda 25, PerkinElmer
UV–vis spectrometer.
2.6
Dynamic Light Scattering
The hydrodynamic
diameters of the GNPs and the mixture of GNPs and BISS were evaluated
utilizing a Malvern Zetasizer apparatus (Nano ZS, UK). This instrument
featured a 4 mW helium–neon laser that emitted light with a
wavelength of 632.8 nm. The measurements were conducted at a scattering
angle of 173°.
2.7
Morphology
TEM
investigations were
performed with a Talos F200X (FEI Company) microscope operated at
200 kV to characterize the NG particles, GNPs, BISS-modified GNPs,
and gel samples. The measurements were performed in both TEM and scanning
TEM (STEM) modes using a high-angle annular dark-field (HAADF) detector
and energy-dispersive X-ray spectroscopy mode of a Bruker BD4 spectrometer
to evaluate the elemental distribution of carbon, sulfur, and gold.
The morphology and elemental composition of the gel sample were
investigated with a Quantax 400 Bruker EDS/energy-dispersive X-ray
(EDX) detector in conjunction with a Merlin, ZEISS field emission
scanning electron microscope (FESEM). Prior to measurement, a Mini
Sputter Coater (Polaron SC7620) was used to deposit a 5 nm layer of
Pd alloy onto the samples under vacuum. The samples were flash-frozen
in liquid nitrogen and then freeze-dried using a specialized lyophilizer,
Labconco FreeZone device at −82 °C and a pressure of 0.03
mbar.
2.8
Mechanical Performance
The tensile
properties of the hydrogels were assessed by utilizing an EZ-SX Shim-Pol
tensile test machine both before and after performing the self-healing.
All experiments were conducted in triplicate. Tensile tests were executed
at a constant strain rate of 50 mm·min–1.
The compression tests were conducted using a 20 N load cell. Cylindrical
hydrogel samples, measuring 10 mm in diameter and 10 mm in height,
underwent compression at a rate of 50 mm·min–1 under ambient conditions.
Rheological assessments were performed
using an MCR302 strain-controlled
rheometer (Anton Paar, Graz, Austria) with plate-and-plate geometry
(diameter, 15 mm). Two distinct modes were employed: (a) an amplitude
sweep test was conducted at a fixed angular frequency of 10 rad·s–1 at 20 °C, where the amplitude of oscillatory
strains ranged from 0.01 to 1000%. Additionally, frequency sweep tests
were conducted with a fixed strain amplitude of 10% across a frequency
range of 1–100 rad·s–1. A PolyScience
circulating bath was used to control the temperature during the rheological
experiments. To minimize evaporation and maintain the temperature
constant, a cap was employed throughout all measurements. The measurements
were carried out in triplicate.
2.9
Self-Healing
Tests and Photothermal Experiments
For self-healing tests,
the hydrogel samples in the form of a cylinder
of diameter ca. 5 mm were cut in half with a sharp blade. The resulting
halves were placed on a glass slide and brought into contact at the
position of the cut. The joint was irradiated from above with laser
light. A Ti:sapphire femtosecond laser (Spectra Physics MaiTai) tuned
to a central wavelength of 800 nm was employed. Its output beam was
shaped with the help of a spherical and a cylindrical lens to form
an elliptical spot at the position of the sample (see Figure 1). The minor and major axes
of the ellipse had lengths of approximately 3 and 8 mm (measured at
half-maximum intensity), respectively. The longer axis was parallel
to the cut. The power of the laser beam was reduced to 0.75 W, which
resulted in light intensity at the sample position of ca. 4 W·cm–2. The experiment was conducted at room temperature.
The self-healing efficiency was studied by doing the tensile tests.
Figure 1
Scheme
of polymerization of the nanocomposite hydrogel incorporating
BISS-modified GNPs and NG and sample irradiation by the NIR laser
(800 nm) to regenerate Au–sulfur bonds and facilitate the healing
process of the fractured part of the hydrogel.
In the studies of the hydrogel photothermal response,
the same
cylindrical gel geometry was employed. The thermal images of the samples
were obtained with an infrared thermal imaging camera (Fluke Ti400).
2.10
Adhesiveness Test
2.10.1
Lap-Shear
Test
Lap-shear testing
was carried out by using the tensile machine (EZ-SX Shim-pol). The
adhesive strength values were calculated by dividing the maximum shear
force by the standardized contact area of 200 mm2, achieved
by overlapping tissue samples. Each hydrogel specimen underwent testing
with three replicates. Fresh commercially available vacuum-sealed
porcine tissue was stored temporarily at 4 °C until use. Rectangular
sections measuring 10 mm × 100 mm were precisely cut from the
fresh porcine using a razor blade. Excess fat was removed to ensure
uniformity. The thickness of the tissue sections was 2 mm.48
To preserve tissue hydration, the tissue
was immersed in a buffer solution prior to testing. Preloading of
the tissue–gel–tissue piece was applied at 1.5 N in
the normal direction for a duration of 10 s. The overlapping area
was 10 × 20 mm. A 20 N load cell, integrated with the tensile
machine, measured the lap shear of the tissue–gel–tissue
stickiness. The samples were clamped between two fixtures and subjected
to parallel pulling at a velocity of 50 mm·min–1.
2.10.2
T-Peel Test
Using the aforementioned
tensile machine, T-peel tests were performed. This entailed bonding
two porcine samples, each 10 mm wide, 100 mm long, and 2 mm thick,
using the hydrogel. The overlapping region of the tissue samples was
sized 10 mm × 20 mm. Following this, the free ends of both tissue
samples were individually secured to the tensile machine grips.
Subsequently, the upper tissue was subjected to upward pulling at
a peeling angle of 180° and a constant velocity of 50 mm·min–1, while the peeling force (Fp) was simultaneously recorded. The measurement of the work
of adhesion, necessary for the separation of the hydrogel-bonded tissues,
was subsequently calculated as 2 × Fp·w–1, where w is the width of the sample.49
2.10.1
Lap-Shear
Test
Lap-shear testing
was carried out by using the tensile machine (EZ-SX Shim-pol). The
adhesive strength values were calculated by dividing the maximum shear
force by the standardized contact area of 200 mm2, achieved
by overlapping tissue samples. Each hydrogel specimen underwent testing
with three replicates. Fresh commercially available vacuum-sealed
porcine tissue was stored temporarily at 4 °C until use. Rectangular
sections measuring 10 mm × 100 mm were precisely cut from the
fresh porcine using a razor blade. Excess fat was removed to ensure
uniformity. The thickness of the tissue sections was 2 mm.48
To preserve tissue hydration, the tissue
was immersed in a buffer solution prior to testing. Preloading of
the tissue–gel–tissue piece was applied at 1.5 N in
the normal direction for a duration of 10 s. The overlapping area
was 10 × 20 mm. A 20 N load cell, integrated with the tensile
machine, measured the lap shear of the tissue–gel–tissue
stickiness. The samples were clamped between two fixtures and subjected
to parallel pulling at a velocity of 50 mm·min–1.
2.10.2
T-Peel Test
Using the aforementioned
tensile machine, T-peel tests were performed. This entailed bonding
two porcine samples, each 10 mm wide, 100 mm long, and 2 mm thick,
using the hydrogel. The overlapping region of the tissue samples was
sized 10 mm × 20 mm. Following this, the free ends of both tissue
samples were individually secured to the tensile machine grips.
Subsequently, the upper tissue was subjected to upward pulling at
a peeling angle of 180° and a constant velocity of 50 mm·min–1, while the peeling force (Fp) was simultaneously recorded. The measurement of the work
of adhesion, necessary for the separation of the hydrogel-bonded tissues,
was subsequently calculated as 2 × Fp·w–1, where w is the width of the sample.49
2.11
Determination of Load and Release of Doxorubicin
(DOX)
2.11.1
Loading of DOX into the Hydrogels
DOX, a widely used anticancer drug, was incorporated into the hydrogels
by using the incubation method. Initially, 10 mg of the hydrogels
was combined with 0.5 mg of DOX and dispersed in 10 mL of a phosphate
buffer solution (pH = 7.4; 0.15 M). The mixture underwent heating
process at 45 °C for 10 min, followed by cooling to 25 °C.
This heating and cooling cycle was repeated several times to ensure
thorough incorporation. Subsequently, the supernatant containing any
unbound DOX was replaced with fresh phosphate buffer solution, and
this process was iterated until excess DOX was effectively removed.
The amount of DOX loaded into the gels was determined by subtracting
the mass of free DOX present in the supernatant from the total mass
of DOX utilized in the loading process (eq 1). The concentration of DOX in the solution
was quantified by measuring its absorbance at 480 nm. A calibration
plot was employed for accurate determination.50
Drug loading capacity (DLC) was evaluated using eq 11where mtotalDOX is
the total mass of DOX
used in the loading process, mfreeDOX is the mass of DOX that
was not bound to the gel, and m(gel + DOX) is the mass of the gel with DOX.
2.11.2
DOX
Release from the Hydrogels
The release of DOX from the hydrogels
was assessed after laser irradiation
by using UV/vis absorption spectroscopy. The experimental setup mirrored
that of the self-healing tests with the exception of removing the
cylindrical lens. Consequently, the laser spot on the sample became
circular and measured ca. 8 mm in diameter (at half-maximum). The
laser beam operated at 0.75 W, yielding a light intensity of approximately
4 W·cm–2. A solution consisting of 10 mL of
phosphate buffer containing the DOX-loaded gel was prepared. Then
the absorbance of DOX released into the supernatant was measured.51
2.11.1
Loading of DOX into the Hydrogels
DOX, a widely used anticancer drug, was incorporated into the hydrogels
by using the incubation method. Initially, 10 mg of the hydrogels
was combined with 0.5 mg of DOX and dispersed in 10 mL of a phosphate
buffer solution (pH = 7.4; 0.15 M). The mixture underwent heating
process at 45 °C for 10 min, followed by cooling to 25 °C.
This heating and cooling cycle was repeated several times to ensure
thorough incorporation. Subsequently, the supernatant containing any
unbound DOX was replaced with fresh phosphate buffer solution, and
this process was iterated until excess DOX was effectively removed.
The amount of DOX loaded into the gels was determined by subtracting
the mass of free DOX present in the supernatant from the total mass
of DOX utilized in the loading process (eq 1). The concentration of DOX in the solution
was quantified by measuring its absorbance at 480 nm. A calibration
plot was employed for accurate determination.50
Drug loading capacity (DLC) was evaluated using eq 11where mtotalDOX is
the total mass of DOX
used in the loading process, mfreeDOX is the mass of DOX that
was not bound to the gel, and m(gel + DOX) is the mass of the gel with DOX.
2.11.2
DOX
Release from the Hydrogels
The release of DOX from the hydrogels
was assessed after laser irradiation
by using UV/vis absorption spectroscopy. The experimental setup mirrored
that of the self-healing tests with the exception of removing the
cylindrical lens. Consequently, the laser spot on the sample became
circular and measured ca. 8 mm in diameter (at half-maximum). The
laser beam operated at 0.75 W, yielding a light intensity of approximately
4 W·cm–2. A solution consisting of 10 mL of
phosphate buffer containing the DOX-loaded gel was prepared. Then
the absorbance of DOX released into the supernatant was measured.51
3
Results
and Discussion
3.1
Preparation of the Hydrogel
This
study aimed at developing a hydrogel with several properties, including
self-healing, self-adhesiveness, drug release capabilities, and mechanical
stability by incorporating Au–sulfur interactions into a pNIPAm
hydrogel network. To achieve this, gold nanoparticles (GNPs) and an N,N′-bis(acryloyl)cystine (BISS) cross-linker were
synthesized and integrated with the pNIPAm network through the Au–sulfur
bonding. The hydrogels were synthesized by using the free-radical
polymerization method. Within the hydrogel structure, the synergistic
interactions between GNPs and the sulfur-containing polymer chains
contributed to the self-healing property and mechanical stability.
The optothermal characteristics of GNPs enabled reversible switching
of Au–S interactions using an NIR laser. These reversible interactions
resulted in the reconstruction of polymer networks in the damaged
areas, see Figure 1. In parallel, BISS and pNIPAm were engaged in the formation of covalent
cross-linking, which enhanced the mechanical properties. Various hydrogel
formulations of different concentrations of the NIPAm monomer, BISS,
and GNPs were synthesized. Extra formulations incorporating a p(NIPAm-BISS)
nanogel (NG) were prepared as well. Although we synthesized and examined
the self-healing properties of all formulations with different concentrations,
only the hydrogels listed in Table 1 demonstrated self-healing properties following NIR
laser irradiation, and these formulations were selected for additional
assessment. The remaining formulations were not mentioned because
they lacked the necessary self-healing properties.
Table 1
Concentrations of Substrates for Hydrogel
Synthesis
hydrogel
NIPAm/M
BISS/mM
AuNPa
NG suspension/mL
water/mL
(pNGB1)
1.0
2.00
GNP2
2.5
(pNGB2)
1.4
0.35
GNP3
2.5
(pNGB3)
1.0
1.00
GNP3
2.5
(pNGB4)
1.4
1.00
GNP3
2.5
(pNGB_NG1)
1.0
1.00
GNP3
2.5
(pNGB_NG2)
1.4
0.35
GNP3
2.5
(pNGB_NG3)
1.4
1.00
GNP3
2.5
a
In case of AuNPs,
go to the Synthesis
of Gold Nanoparticles section.
3.2
Dynamic Light Scattering and UV–Vis
Spectroscopy
Dynamic light scattering (DLS) analysis was
carried out to examine the size distribution of GNPs (Figure 2A). The results indicated the
presence of GNPs with an average size of 22 ± 8 nm. The average
hydrodynamic diameter, derived from the DLS data, served as a vital
parameter for characterizing GNPs within the hydrogel matrix. Moreover,
the relatively narrow distribution suggested consistent material properties
and performance. The DLS analysis confirmed the successful synthesis
of GNPs.
Figure 2
Size distribution of synthesized GNPs (A), size distribution of
BISS-modified GNPs (B), UV–visible spectra of GNPs, BISS, and
BISS-modified GNPs (C), SEM image illustrating the morphology of GNPs
(D), SEM images depicting a cross-section of the hydrogel (E), and
distributions of Au and sulfur within the hydrogel captured by EDX-SEM
(F).
To produce the hydrogels, we initially
synthesized
GNPs dispersed
in water. Following this, we introduced specific quantities of BISS,
which contains –S–S– group within its structure.
The size of the mixture of BISS-modified GNPs was 33 ± 15 nm,
with a wider distribution but still within a good range (see Figure 2B). Also, the BISS
molecules underwent targeted adsorption onto the surface of the GNPs,
forming Au–sulfur bonds. An analysis of the UV–vis absorption
spectra showed a distinctive purple peak shifting from 551 to 570
nm upon the addition of BISS (see Figure 2C). This indicated the successful functionalization
of GNPs.52
3.3
Surface
Microscopy
The structure
and morphology of GNPs and the hydrogel were investigated using scanning
electron microscopy (SEM).
In Figure 2D, an SEM image of GNPs is shown. The image
illustrated the particle size and dispersity of the GNPs after drying.
The measured mean diameter of the dried particles was 25.3 ±
9.1 nm, indicating a uniform shape and dispersion. This finding agreed
with the results obtained from DLS analysis. Typical TEM images of
GNPs with and without BISS, and EDS-HAADF elemental mapping of Au
and sulfur in these solutions, are added to the Supporting Information
(Figure S5).
To visualize the three-dimensional
network of the hydrogels, the
samples were freeze-dried and fractured.53Figure 2E presents
representative images of the cross-sectional surface. The hydrogel
displayed a honeycomb-like structure with a uniform mesh, indicating
a homogeneous structure and morphology. The well-defined network architecture
of the hydrogel serves as a crucial feature that significantly influences
other properties. As depicted in Figure 2F, the EDX analysis demonstrates good dispersion
of sulfur and Au atoms within the hydrogel matrix. TEM images of the
hydrogel and EDS-HAADF elemental mapping of Au, sulfur, and carbon
within the hydrogel have been added to the Supporting Information
(Figure S4). It is clearly visible that
the GNPs are uniformly distributed within the polymer matrix.
3.4
Temperature Responsiveness of the Hydrogels
The thermal
behaviors of the hydrogels of various ratios of NIPAm,
GNPs, BISS, and NG (listed in Table 1) were examined. The hydrogels were exposed to laser
irradiation (800 nm, 0.75 W), and their thermal changes over time
were monitored by using a thermal imaging infrared camera.
The
results shown in Figure 3A indicate that the composition of the hydrogels significantly influenced
the thermal behavior. Hydrogels with higher amounts of GNPs and BISS
exhibited faster and more pronounced thermal changes compared to those
with lower amounts. The volume phase transition of hydrogels occurred
more quickly, and the time necessary for maximum shrinkage was shortened
due to the higher Au content. Additionally, hydrogels containing the
NG dispersion showed enhanced temperature responsiveness. Introduction
of NG reduced the mechanical rigidity of the hydrogel as seen in the
tensile test results. The use of NG could increase the intermolecular
spacing between polymeric chains and reduce the number of cross-linking
spots between the chains. In general, the nanogels can alter the water
content and reduce the mechanical strength. These factors resulted
in enhanced temperature-responsive changes and improved thermal conductivity.
Also, the larger concentration of NIPAm in pNGB-NG1 compared to pNGB3
is expected to result in an increased ability of the hydrogel structure
to absorb and retain heat. This could lead to an increase in temperature
following laser irradiation, particularly if the hydrogel efficiently
absorbed the irradiation energy.54,55
Figure 3
Temperature
mapping (A) and temperature changes of the prepared
hydrogels over time of exposure (B), recovery of tensile strength
in the prepared hydrogels following the self-healing process facilitated
by the NIR laser irradiation (the mean values for three replicate
samples ± standard deviation are shown) (C), and self-healing
process photography before, during, and after laser irradiation (D)
for pNB (D1), pNGB4 (D2), and pNGB_NG3 (D3).
Next, the temperature changes during laser irradiation
were monitored
over time. Figure 3B illustrates the temperature changes observed during 250 s of laser
irradiation. All samples exhibited a rapid increase in temperature,
followed by a plateau. The hydrogels with higher amounts of GNPs and
BISS showed faster temperature increases and reached 42 °C after
120 s, surpassing the phase transition temperature of pNIPAm. Conversely,
the hydrogels with lower amounts of GNPs and BISS did not reach the
phase transition temperature of pNIPAm. After incorporating NG into
the hydrogels, they showed a rapid temperature increase, and for pNGB-NG3,
the temperature reached 60 °C after 120 s.
3.5
Self-Healing Investigation
To investigate
the self-healing behavior induced by irradiation, hydrogel samples
were cut and exposed to the NIR laser beam (800 nm) for 2 min, as
depicted in Figure 3D. The light transmittance at the fracture site decreased after irradiation;
this was accompanied by a color change to opaque white, indicating
that the temperature exceeded the critical phase transition temperature
of pNIPAm. Subsequently, the hydrogel exhibited healing and regained
its original stretchable form. In contrast, the pure pNIPAm hydrogel
lacked self-healing ability, as evidenced in Figure 3D1. Notably, fractures in pure hydrogel samples
remained unrepaired after NIR laser irradiation, highlighting the
role of laser light interaction with the surface plasmon of GNPs and
the restoration of Au–sulfur bonds in the healing process.
As depicted in Figure 3D2,D3, both GNP-cross-linked hydrogels were cut into separate pieces
and subsequently brought into contact. Upon exposure to the NIR laser
beam, the pieces underwent self-healing within 2 min, and the healed
GNP-cross-linked hydrogels regained considerable stretch.
Additionally,
the recovery of tensile strength (in %) for all prepared hydrogels
(see Table 1) after
treatment/irradiation with laser light is depicted in Figure 3C. These tensile experiments
indicate the correlation between healing efficiency and hydrogel composition.
Results indicated that the recovery was enhanced with increasing
concentrations of BISS and GNPs in the hydrogel. Minimal opaque regions
observed at the fractures of the pure pNIPAm hydrogel suggested that
while the temperature could approach the critical phase transition
temperature of pNIPAm, it failed to induce a full phase transition.
Moreover, low concentrations of GNPs or BISS were insufficient to
meet the temperature change required for the self-healing process.
The samples with lower concentrations exhibited limited post-healing
stretchability, emphasizing the importance of the presence of GNPs
for enhancing the self-healing ability.
Also, the elevated concentrations
of NIPAm monomer and BISS increased
the total number of cross-linking points within the gel networks,
further enhancing mechanical integrity. According to the literature,
the thermo-sensitivity of pNIPAm is essential for NIR laser-induced
self-healing, which provides remote control capabilities.56
Consequently, pNGB_NG3 and pNGB4 were
selected as optimal samples
for further investigation because of their greater recovery percentage
(pNGB_NG3 and pNGB4 regained 84% and 76% of their original strength
after self-healing). However, since the inclusion of NG reduces strength,
a comprehensive examination of other gel properties was conducted
and is reported in subsequent sections.
3.6
Mechanical
Properties
3.6.1
Tensile Test
Figure 4 presents the mechanical properties of pNGB4
and pNGB_NG3 hydrogels determined through typical and cyclic tensile
tests with optimal gel formulations.
Figure 4
Tensile curves for pNGB_NG3 and pNGB4
before and after self-healing
(A), cyclic tensile curves of pNGB4 (B) and pNGB_NG3 (C) under different
strains: 100% (black line), 200% (pink line), 300% (blue line), and
400% (green line). Dissipated energy bar chart for pNGB_NG3 and pNGB4
(the mean values for three replicate samples ± standard deviation
are shown) (D).
Figure 4A depicts
typical stress–strain curves of the pNGB4 and pNGB_NG3 hydrogels.
These curves provide insights into the mechanical behaviors of the
hydrogels before and after self-healing. The results indicated that
pNGB4 hydrogels can be stretched to 1200% elongation compared to their
initial length and ruptured at a stress of 17 kPa. The hydrogel showed
self-healing capabilities after laser irradiation, achieving 1400%
elongation and withstanding a stress at break of 11 kPa, which indicates
the formation of new Au–sulfur bonds during the self-healing
process.
Similarly, pNGB_NG3 hydrogels exhibited high stretchability,
reaching
1110% elongation compared to the initial length and rupturing at a
stress of 11 kPa. After self-healing, the hydrogel showed approximately
1180% elongation and broke at a stress of 8 kPa, indicating effective
self-repair mechanisms involving rearrangement and formation of new
Au–sulfur bonds.
The obtained results demonstrated the
significant influence of
Au–sulfur interactions on the mechanical properties and self-healing
capabilities of the prepared hydrogels. Laser irradiation promoted
and facilitated self-healing through the formation of new Au–sulfur
bonds. The stress–strain curves of pure hydrogels (1.4 M NIPAm,
0.9 mM BISS) with and without NG have been added to the Supporting
Information (Figure S1). These results
show that the addition of GNPs can increase the mechanical stability.
Figure 4B,C presents
representative cyclic tensile tests on the pNGB4 and pNGB_NG3 hydrogels,
respectively. Loading–unloading tests were conducted to evaluate
the energy dissipation of the hydrogels (Figure 4D). The applied strain started at 100% and
was progressively increased by 100% after each cycle until reaching
400%. The behavior of the hydrogel composites during cyclic loading
and unloading was interpreted based on the observed hysteresis loops,
which indicated the dissipation of energy during the mechanical testing.
The obtained results highlight the capability of pNGB4 and pNGB_NG3
hydrogels to effectively dissipate mechanical energy during cyclic
loading–unloading tests. The incorporation of the nanogel into
the hydrogel matrix altered its mechanical properties, resulting in
softer behavior and reduced energy dissipation. However, these changes
are minor and negligible. In general, nanogels can alter the water
content and act as defects or stress concentrators under stress, reducing
the mechanical strength. However, the small differences observed in Figure 4 indicate that the
overall cross-linking density remains unaffected due to the good homogeneity
and strong interfacial interaction between the p(NIPAm-BISS) nanogel
and the hydrogel structure.
These findings contribute to the
understanding of the mechanical
behavior and energy dissipation mechanisms of these hydrogel composites,
which are essential for various applications requiring robust and
relatively tough materials.
3.6.2
Compression
Test
To further evaluate
the compression recovery performance, the hydrogels underwent typical
and cyclic compression tests with increasing strain (10–80%),
as depicted in Figure 5.
Figure 5
Normal compression curves for pNGB_NG3 and pNGB4 (A), cyclic compression
curves of pNGB4 (B), and pNGB_NG3 (C) under different strains: 10%
(black), 20% (red), 30% (green), 50% (blue), and 80% (pink). Dissipated
energy graph for the optimal composition of pNGB_NG3 and pNGB4 (the
mean values for three replicate samples ± standard deviation
are shown) (D).
Figure 5A displays
the compression test results for the pNGB4 and pNGB_NG3 hydrogels.
The results indicated that pNGB_NG3 was slightly softer than pNGB4,
which is consistent with the findings from the tensile tests.
The loading–unloading compression measurements were aligned
with the cyclic loading–unloading tensile tests. As depicted
in Figure 5B,C, both
pNGB4 and pNGB_NG3 hydrogels exhibited compression unloading curves
that mirrored the loading curve up to 30% deformation, with small
hysteresis loops, confirming the tensile results. Larger hysteresis
loops were observed at higher compressions, particularly at 80%. Additionally, Figure 5D reveals that pNGB4
exhibited higher dissipated energy than pNGB_NG3.
Based on the
above results, the prepared hydrogels not only exhibited
enhanced compressive strength but also good dissipated energy during
cyclic compressive loading–unloading.
3.6.3
Rheology
Test
Figure 6 presents the storage modulus
(G′) and loss modulus (G″)
of the hydrogels as functions of amplitude and frequency. Figure 6A displays the storage
modulus (G′) and loss modulus (G″) of the pNGB4 and pNGB_NG3 hydrogels as functions of amplitude
(γ) to determine the linear viscoelastic region of the hydrogel.
Both G′ values are higher than G″, which indicates their gel-like properties.
Figure 6
Storage (filled symbols)
and loss modulus (empty symbols) plotted
versus amplitude (A) and angular frequency (B) for pNGB_NG3 and pNGB4.
The tests were done on three replicate samples, and the mean values
are depicted in the graph.
Figure 6B presents
the findings of the frequency sweep tests. A γ value of 10%
was selected for the frequency sweep measurements. The results revealed
that G′ exceeded G″
across the entire angular frequency (ω) range, indicating predominant
viscoelastic behavior. The variation of G′
and G″ with angular frequencies illustrates
typical behavior for gel-like samples, with both moduli exhibiting
little change with frequency. The highest storage modulus was observed
for pNGB4 at G′ ∼ 960 Pa, whereas for
pNGB_NG3, G′ was ca. 510 Pa. These results
indicated that the pNGB4 hydrogel exhibited higher stiffness compared
to pNGB_NG3; it was consistent with other mechanical tests.
3.7
Adhesion Properties
We tested several
materials to assess the adhesion properties of the prepared gel. The
excellent adhesion of the hydrogel to various substrates, including
rubber, copper, glass, polypropylene (PP), porcine tissue, steel,
polytetrafluoroethylene (PTFE), and polyethylene terephthalate (PET),
is illustrated in Figure 7A. The adhesion properties of the prepared hydrogels were
further evaluated using T-peel, lap-shear, and cyclic adhesion tests.
Figure 7
Adhesion
of various surfaces to the prepared hydrogels (A), adhesion
of pNGB_NG3 and pNGB4 in the T-peel test (B) and in lap-shear test
results versus displacement (C). Adhesive strength of all the prepared
samples (D) and cyclic adhesive strength results for pNGB_NG3 and
pNGB4 (the mean values for three replicate samples ± standard
deviation are shown) (E) in the lap-shear test.
During the T-peel test, the peeling force (Fp) was measured while separating the hydrogel
from two porcine
tissues at a 180° angle. As can be seen in Figure 7B, the T-peel tests on porcine tissues demonstrated
strong adhesion of the optimal hydrogel samples, with maximum peeling
adhesion values of approximately 70 N·m–1 for
pNGB4 and 120 N·m–1 for pNGB_NG3. The interfacial
toughness values of pNGB_NG3 and pNGB4 were calculated and were approximately
22.5 and 25.3 J·m–2, respectively. The results
of interfacial toughness are within satisfactory range.57
The lap-shear tests were conducted to
assess the adhesive strength
of the hydrogels. Shear forces were applied to two porcine tissues
bonded with the hydrogel, and adhesive strength values were calculated
based on the maximum shear force and the overlapping area. Figure 7C presents the dependence
of adhesive strength versus displacement for both optimal samples
for comparison, and Figure 7D shows the adhesion strength of all the prepared hydrogels
to porcine tissue in the lap-shear test. The results from both Figure 7C,D indicate that
higher concentrations of the GNPs, BISS, and the NIPAm monomers led
to increased adhesion, while the sample without GNPs showed the lowest
adhesive strength.58 Notably, samples containing
NG exhibited higher adhesion compared to those without NG. In particular,
pNGB_NG3 showed the highest adhesive strength of 2.4 kPa. Figure 7E presents the cyclic
adhesion tests. The results revealed that the adhesion of pNGB4 remained
relatively stable over five cycles, with only a slight decrease in
the adhesion percentage compared to pNGB_NG3.
The adhesion results
indicated that the adhesion of hydrogels to
porcine tissue was influenced by the composition of the hydrogel,
particularly the concentrations of GNPs, BISS, and NIPAm monomers.
According to the literature, the presence of amide, amine, and carboxylic
acid groups facilitated increased electrostatic interactions, hydrogen
bonding, and chemical interactions with carboxyl and other functional
groups on tissue proteins. The disulfide bonds in BISS can form covalent
links with thiol groups present in proteins on the tissue surface.
Additionally, the electrostatic interactions between negatively charged
GNPs and positively charged regions on tissue surfaces, as well as
hydrogen bonds and other noncovalent interactions, can contribute
enhancement of adhesiveness.59 Furthermore,
the presence of NG also enhanced the adhesion properties (through
additional molecules of NIPAm and BISS). The stability of adhesion
over multiple cycles indicated the suitability of these hydrogels
for practical applications requiring repeated adhesion.
3.8
DOX Release
DOX was selected as the
model anticancer drug to evaluate its release from the hydrogel matrix.
Biocompatibility testing is essential for potential clinical applications.
The investigated hydrogel consists of components such as NIPAm-based
hydrogels and gold nanoparticles, whose biocompatibility has already
been confirmed in previous studies.60,61However, to
ensure safety, further testing, such as cell viability assays should
be conducted. NIR irradiation was utilized to induce drug release
from both hydrogel formulations (pNGB4 and pNGB_NG3). The DOX with
48% DLC release as a function of time is illustrated in Figure 8. Initially, without irradiation,
a very limited drug release was observed from both hydrogel formulations.
However, upon laser irradiation, a significant increase in DOX release
was observed over time. With continuous irradiation, the concentration
of DOX in the buffer increased, reaching 0.07 and 0.08 mg·mL–1 for pNGB4 and pNGB_NG3, respectively, after 80 min.
This study demonstrated that the laser irradiation triggered the release
of DOX from the hydrogel matrix, with higher amounts released in the
presence of NG. As mentioned previously, the introduction of NG into
the hydrogel reduced its mechanical rigidity by increasing the intermolecular
spacing between polymer chains and decreasing the number of cross-linking
points between them. This led to higher amounts of DOX being released.
Figure 8
DOX released
from optimal samples over time of irradiation and
over time without irradiation. The test was done on three replicate
samples, and the mean values are depicted in the graph.
3.1
Preparation of the Hydrogel
This
study aimed at developing a hydrogel with several properties, including
self-healing, self-adhesiveness, drug release capabilities, and mechanical
stability by incorporating Au–sulfur interactions into a pNIPAm
hydrogel network. To achieve this, gold nanoparticles (GNPs) and an N,N′-bis(acryloyl)cystine (BISS) cross-linker were
synthesized and integrated with the pNIPAm network through the Au–sulfur
bonding. The hydrogels were synthesized by using the free-radical
polymerization method. Within the hydrogel structure, the synergistic
interactions between GNPs and the sulfur-containing polymer chains
contributed to the self-healing property and mechanical stability.
The optothermal characteristics of GNPs enabled reversible switching
of Au–S interactions using an NIR laser. These reversible interactions
resulted in the reconstruction of polymer networks in the damaged
areas, see Figure 1. In parallel, BISS and pNIPAm were engaged in the formation of covalent
cross-linking, which enhanced the mechanical properties. Various hydrogel
formulations of different concentrations of the NIPAm monomer, BISS,
and GNPs were synthesized. Extra formulations incorporating a p(NIPAm-BISS)
nanogel (NG) were prepared as well. Although we synthesized and examined
the self-healing properties of all formulations with different concentrations,
only the hydrogels listed in Table 1 demonstrated self-healing properties following NIR
laser irradiation, and these formulations were selected for additional
assessment. The remaining formulations were not mentioned because
they lacked the necessary self-healing properties.
Table 1
Concentrations of Substrates for Hydrogel
Synthesis
hydrogel
NIPAm/M
BISS/mM
AuNPa
NG suspension/mL
water/mL
(pNGB1)
1.0
2.00
GNP2
2.5
(pNGB2)
1.4
0.35
GNP3
2.5
(pNGB3)
1.0
1.00
GNP3
2.5
(pNGB4)
1.4
1.00
GNP3
2.5
(pNGB_NG1)
1.0
1.00
GNP3
2.5
(pNGB_NG2)
1.4
0.35
GNP3
2.5
(pNGB_NG3)
1.4
1.00
GNP3
2.5
a
In case of AuNPs,
go to the Synthesis
of Gold Nanoparticles section.
3.2
Dynamic Light Scattering and UV–Vis
Spectroscopy
Dynamic light scattering (DLS) analysis was
carried out to examine the size distribution of GNPs (Figure 2A). The results indicated the
presence of GNPs with an average size of 22 ± 8 nm. The average
hydrodynamic diameter, derived from the DLS data, served as a vital
parameter for characterizing GNPs within the hydrogel matrix. Moreover,
the relatively narrow distribution suggested consistent material properties
and performance. The DLS analysis confirmed the successful synthesis
of GNPs.
Figure 2
Size distribution of synthesized GNPs (A), size distribution of
BISS-modified GNPs (B), UV–visible spectra of GNPs, BISS, and
BISS-modified GNPs (C), SEM image illustrating the morphology of GNPs
(D), SEM images depicting a cross-section of the hydrogel (E), and
distributions of Au and sulfur within the hydrogel captured by EDX-SEM
(F).
To produce the hydrogels, we initially
synthesized
GNPs dispersed
in water. Following this, we introduced specific quantities of BISS,
which contains –S–S– group within its structure.
The size of the mixture of BISS-modified GNPs was 33 ± 15 nm,
with a wider distribution but still within a good range (see Figure 2B). Also, the BISS
molecules underwent targeted adsorption onto the surface of the GNPs,
forming Au–sulfur bonds. An analysis of the UV–vis absorption
spectra showed a distinctive purple peak shifting from 551 to 570
nm upon the addition of BISS (see Figure 2C). This indicated the successful functionalization
of GNPs.52
3.3
Surface
Microscopy
The structure
and morphology of GNPs and the hydrogel were investigated using scanning
electron microscopy (SEM).
In Figure 2D, an SEM image of GNPs is shown. The image
illustrated the particle size and dispersity of the GNPs after drying.
The measured mean diameter of the dried particles was 25.3 ±
9.1 nm, indicating a uniform shape and dispersion. This finding agreed
with the results obtained from DLS analysis. Typical TEM images of
GNPs with and without BISS, and EDS-HAADF elemental mapping of Au
and sulfur in these solutions, are added to the Supporting Information
(Figure S5).
To visualize the three-dimensional
network of the hydrogels, the
samples were freeze-dried and fractured.53Figure 2E presents
representative images of the cross-sectional surface. The hydrogel
displayed a honeycomb-like structure with a uniform mesh, indicating
a homogeneous structure and morphology. The well-defined network architecture
of the hydrogel serves as a crucial feature that significantly influences
other properties. As depicted in Figure 2F, the EDX analysis demonstrates good dispersion
of sulfur and Au atoms within the hydrogel matrix. TEM images of the
hydrogel and EDS-HAADF elemental mapping of Au, sulfur, and carbon
within the hydrogel have been added to the Supporting Information
(Figure S4). It is clearly visible that
the GNPs are uniformly distributed within the polymer matrix.
3.4
Temperature Responsiveness of the Hydrogels
The thermal
behaviors of the hydrogels of various ratios of NIPAm,
GNPs, BISS, and NG (listed in Table 1) were examined. The hydrogels were exposed to laser
irradiation (800 nm, 0.75 W), and their thermal changes over time
were monitored by using a thermal imaging infrared camera.
The
results shown in Figure 3A indicate that the composition of the hydrogels significantly influenced
the thermal behavior. Hydrogels with higher amounts of GNPs and BISS
exhibited faster and more pronounced thermal changes compared to those
with lower amounts. The volume phase transition of hydrogels occurred
more quickly, and the time necessary for maximum shrinkage was shortened
due to the higher Au content. Additionally, hydrogels containing the
NG dispersion showed enhanced temperature responsiveness. Introduction
of NG reduced the mechanical rigidity of the hydrogel as seen in the
tensile test results. The use of NG could increase the intermolecular
spacing between polymeric chains and reduce the number of cross-linking
spots between the chains. In general, the nanogels can alter the water
content and reduce the mechanical strength. These factors resulted
in enhanced temperature-responsive changes and improved thermal conductivity.
Also, the larger concentration of NIPAm in pNGB-NG1 compared to pNGB3
is expected to result in an increased ability of the hydrogel structure
to absorb and retain heat. This could lead to an increase in temperature
following laser irradiation, particularly if the hydrogel efficiently
absorbed the irradiation energy.54,55
Figure 3
Temperature
mapping (A) and temperature changes of the prepared
hydrogels over time of exposure (B), recovery of tensile strength
in the prepared hydrogels following the self-healing process facilitated
by the NIR laser irradiation (the mean values for three replicate
samples ± standard deviation are shown) (C), and self-healing
process photography before, during, and after laser irradiation (D)
for pNB (D1), pNGB4 (D2), and pNGB_NG3 (D3).
Next, the temperature changes during laser irradiation
were monitored
over time. Figure 3B illustrates the temperature changes observed during 250 s of laser
irradiation. All samples exhibited a rapid increase in temperature,
followed by a plateau. The hydrogels with higher amounts of GNPs and
BISS showed faster temperature increases and reached 42 °C after
120 s, surpassing the phase transition temperature of pNIPAm. Conversely,
the hydrogels with lower amounts of GNPs and BISS did not reach the
phase transition temperature of pNIPAm. After incorporating NG into
the hydrogels, they showed a rapid temperature increase, and for pNGB-NG3,
the temperature reached 60 °C after 120 s.
3.5
Self-Healing Investigation
To investigate
the self-healing behavior induced by irradiation, hydrogel samples
were cut and exposed to the NIR laser beam (800 nm) for 2 min, as
depicted in Figure 3D. The light transmittance at the fracture site decreased after irradiation;
this was accompanied by a color change to opaque white, indicating
that the temperature exceeded the critical phase transition temperature
of pNIPAm. Subsequently, the hydrogel exhibited healing and regained
its original stretchable form. In contrast, the pure pNIPAm hydrogel
lacked self-healing ability, as evidenced in Figure 3D1. Notably, fractures in pure hydrogel samples
remained unrepaired after NIR laser irradiation, highlighting the
role of laser light interaction with the surface plasmon of GNPs and
the restoration of Au–sulfur bonds in the healing process.
As depicted in Figure 3D2,D3, both GNP-cross-linked hydrogels were cut into separate pieces
and subsequently brought into contact. Upon exposure to the NIR laser
beam, the pieces underwent self-healing within 2 min, and the healed
GNP-cross-linked hydrogels regained considerable stretch.
Additionally,
the recovery of tensile strength (in %) for all prepared hydrogels
(see Table 1) after
treatment/irradiation with laser light is depicted in Figure 3C. These tensile experiments
indicate the correlation between healing efficiency and hydrogel composition.
Results indicated that the recovery was enhanced with increasing
concentrations of BISS and GNPs in the hydrogel. Minimal opaque regions
observed at the fractures of the pure pNIPAm hydrogel suggested that
while the temperature could approach the critical phase transition
temperature of pNIPAm, it failed to induce a full phase transition.
Moreover, low concentrations of GNPs or BISS were insufficient to
meet the temperature change required for the self-healing process.
The samples with lower concentrations exhibited limited post-healing
stretchability, emphasizing the importance of the presence of GNPs
for enhancing the self-healing ability.
Also, the elevated concentrations
of NIPAm monomer and BISS increased
the total number of cross-linking points within the gel networks,
further enhancing mechanical integrity. According to the literature,
the thermo-sensitivity of pNIPAm is essential for NIR laser-induced
self-healing, which provides remote control capabilities.56
Consequently, pNGB_NG3 and pNGB4 were
selected as optimal samples
for further investigation because of their greater recovery percentage
(pNGB_NG3 and pNGB4 regained 84% and 76% of their original strength
after self-healing). However, since the inclusion of NG reduces strength,
a comprehensive examination of other gel properties was conducted
and is reported in subsequent sections.
3.6
Mechanical
Properties
3.6.1
Tensile Test
Figure 4 presents the mechanical properties of pNGB4
and pNGB_NG3 hydrogels determined through typical and cyclic tensile
tests with optimal gel formulations.
Figure 4
Tensile curves for pNGB_NG3 and pNGB4
before and after self-healing
(A), cyclic tensile curves of pNGB4 (B) and pNGB_NG3 (C) under different
strains: 100% (black line), 200% (pink line), 300% (blue line), and
400% (green line). Dissipated energy bar chart for pNGB_NG3 and pNGB4
(the mean values for three replicate samples ± standard deviation
are shown) (D).
Figure 4A depicts
typical stress–strain curves of the pNGB4 and pNGB_NG3 hydrogels.
These curves provide insights into the mechanical behaviors of the
hydrogels before and after self-healing. The results indicated that
pNGB4 hydrogels can be stretched to 1200% elongation compared to their
initial length and ruptured at a stress of 17 kPa. The hydrogel showed
self-healing capabilities after laser irradiation, achieving 1400%
elongation and withstanding a stress at break of 11 kPa, which indicates
the formation of new Au–sulfur bonds during the self-healing
process.
Similarly, pNGB_NG3 hydrogels exhibited high stretchability,
reaching
1110% elongation compared to the initial length and rupturing at a
stress of 11 kPa. After self-healing, the hydrogel showed approximately
1180% elongation and broke at a stress of 8 kPa, indicating effective
self-repair mechanisms involving rearrangement and formation of new
Au–sulfur bonds.
The obtained results demonstrated the
significant influence of
Au–sulfur interactions on the mechanical properties and self-healing
capabilities of the prepared hydrogels. Laser irradiation promoted
and facilitated self-healing through the formation of new Au–sulfur
bonds. The stress–strain curves of pure hydrogels (1.4 M NIPAm,
0.9 mM BISS) with and without NG have been added to the Supporting
Information (Figure S1). These results
show that the addition of GNPs can increase the mechanical stability.
Figure 4B,C presents
representative cyclic tensile tests on the pNGB4 and pNGB_NG3 hydrogels,
respectively. Loading–unloading tests were conducted to evaluate
the energy dissipation of the hydrogels (Figure 4D). The applied strain started at 100% and
was progressively increased by 100% after each cycle until reaching
400%. The behavior of the hydrogel composites during cyclic loading
and unloading was interpreted based on the observed hysteresis loops,
which indicated the dissipation of energy during the mechanical testing.
The obtained results highlight the capability of pNGB4 and pNGB_NG3
hydrogels to effectively dissipate mechanical energy during cyclic
loading–unloading tests. The incorporation of the nanogel into
the hydrogel matrix altered its mechanical properties, resulting in
softer behavior and reduced energy dissipation. However, these changes
are minor and negligible. In general, nanogels can alter the water
content and act as defects or stress concentrators under stress, reducing
the mechanical strength. However, the small differences observed in Figure 4 indicate that the
overall cross-linking density remains unaffected due to the good homogeneity
and strong interfacial interaction between the p(NIPAm-BISS) nanogel
and the hydrogel structure.
These findings contribute to the
understanding of the mechanical
behavior and energy dissipation mechanisms of these hydrogel composites,
which are essential for various applications requiring robust and
relatively tough materials.
3.6.2
Compression
Test
To further evaluate
the compression recovery performance, the hydrogels underwent typical
and cyclic compression tests with increasing strain (10–80%),
as depicted in Figure 5.
Figure 5
Normal compression curves for pNGB_NG3 and pNGB4 (A), cyclic compression
curves of pNGB4 (B), and pNGB_NG3 (C) under different strains: 10%
(black), 20% (red), 30% (green), 50% (blue), and 80% (pink). Dissipated
energy graph for the optimal composition of pNGB_NG3 and pNGB4 (the
mean values for three replicate samples ± standard deviation
are shown) (D).
Figure 5A displays
the compression test results for the pNGB4 and pNGB_NG3 hydrogels.
The results indicated that pNGB_NG3 was slightly softer than pNGB4,
which is consistent with the findings from the tensile tests.
The loading–unloading compression measurements were aligned
with the cyclic loading–unloading tensile tests. As depicted
in Figure 5B,C, both
pNGB4 and pNGB_NG3 hydrogels exhibited compression unloading curves
that mirrored the loading curve up to 30% deformation, with small
hysteresis loops, confirming the tensile results. Larger hysteresis
loops were observed at higher compressions, particularly at 80%. Additionally, Figure 5D reveals that pNGB4
exhibited higher dissipated energy than pNGB_NG3.
Based on the
above results, the prepared hydrogels not only exhibited
enhanced compressive strength but also good dissipated energy during
cyclic compressive loading–unloading.
3.6.3
Rheology
Test
Figure 6 presents the storage modulus
(G′) and loss modulus (G″)
of the hydrogels as functions of amplitude and frequency. Figure 6A displays the storage
modulus (G′) and loss modulus (G″) of the pNGB4 and pNGB_NG3 hydrogels as functions of amplitude
(γ) to determine the linear viscoelastic region of the hydrogel.
Both G′ values are higher than G″, which indicates their gel-like properties.
Figure 6
Storage (filled symbols)
and loss modulus (empty symbols) plotted
versus amplitude (A) and angular frequency (B) for pNGB_NG3 and pNGB4.
The tests were done on three replicate samples, and the mean values
are depicted in the graph.
Figure 6B presents
the findings of the frequency sweep tests. A γ value of 10%
was selected for the frequency sweep measurements. The results revealed
that G′ exceeded G″
across the entire angular frequency (ω) range, indicating predominant
viscoelastic behavior. The variation of G′
and G″ with angular frequencies illustrates
typical behavior for gel-like samples, with both moduli exhibiting
little change with frequency. The highest storage modulus was observed
for pNGB4 at G′ ∼ 960 Pa, whereas for
pNGB_NG3, G′ was ca. 510 Pa. These results
indicated that the pNGB4 hydrogel exhibited higher stiffness compared
to pNGB_NG3; it was consistent with other mechanical tests.
3.6.1
Tensile Test
Figure 4 presents the mechanical properties of pNGB4
and pNGB_NG3 hydrogels determined through typical and cyclic tensile
tests with optimal gel formulations.
Figure 4
Tensile curves for pNGB_NG3 and pNGB4
before and after self-healing
(A), cyclic tensile curves of pNGB4 (B) and pNGB_NG3 (C) under different
strains: 100% (black line), 200% (pink line), 300% (blue line), and
400% (green line). Dissipated energy bar chart for pNGB_NG3 and pNGB4
(the mean values for three replicate samples ± standard deviation
are shown) (D).
Figure 4A depicts
typical stress–strain curves of the pNGB4 and pNGB_NG3 hydrogels.
These curves provide insights into the mechanical behaviors of the
hydrogels before and after self-healing. The results indicated that
pNGB4 hydrogels can be stretched to 1200% elongation compared to their
initial length and ruptured at a stress of 17 kPa. The hydrogel showed
self-healing capabilities after laser irradiation, achieving 1400%
elongation and withstanding a stress at break of 11 kPa, which indicates
the formation of new Au–sulfur bonds during the self-healing
process.
Similarly, pNGB_NG3 hydrogels exhibited high stretchability,
reaching
1110% elongation compared to the initial length and rupturing at a
stress of 11 kPa. After self-healing, the hydrogel showed approximately
1180% elongation and broke at a stress of 8 kPa, indicating effective
self-repair mechanisms involving rearrangement and formation of new
Au–sulfur bonds.
The obtained results demonstrated the
significant influence of
Au–sulfur interactions on the mechanical properties and self-healing
capabilities of the prepared hydrogels. Laser irradiation promoted
and facilitated self-healing through the formation of new Au–sulfur
bonds. The stress–strain curves of pure hydrogels (1.4 M NIPAm,
0.9 mM BISS) with and without NG have been added to the Supporting
Information (Figure S1). These results
show that the addition of GNPs can increase the mechanical stability.
Figure 4B,C presents
representative cyclic tensile tests on the pNGB4 and pNGB_NG3 hydrogels,
respectively. Loading–unloading tests were conducted to evaluate
the energy dissipation of the hydrogels (Figure 4D). The applied strain started at 100% and
was progressively increased by 100% after each cycle until reaching
400%. The behavior of the hydrogel composites during cyclic loading
and unloading was interpreted based on the observed hysteresis loops,
which indicated the dissipation of energy during the mechanical testing.
The obtained results highlight the capability of pNGB4 and pNGB_NG3
hydrogels to effectively dissipate mechanical energy during cyclic
loading–unloading tests. The incorporation of the nanogel into
the hydrogel matrix altered its mechanical properties, resulting in
softer behavior and reduced energy dissipation. However, these changes
are minor and negligible. In general, nanogels can alter the water
content and act as defects or stress concentrators under stress, reducing
the mechanical strength. However, the small differences observed in Figure 4 indicate that the
overall cross-linking density remains unaffected due to the good homogeneity
and strong interfacial interaction between the p(NIPAm-BISS) nanogel
and the hydrogel structure.
These findings contribute to the
understanding of the mechanical
behavior and energy dissipation mechanisms of these hydrogel composites,
which are essential for various applications requiring robust and
relatively tough materials.
3.6.2
Compression
Test
To further evaluate
the compression recovery performance, the hydrogels underwent typical
and cyclic compression tests with increasing strain (10–80%),
as depicted in Figure 5.
Figure 5
Normal compression curves for pNGB_NG3 and pNGB4 (A), cyclic compression
curves of pNGB4 (B), and pNGB_NG3 (C) under different strains: 10%
(black), 20% (red), 30% (green), 50% (blue), and 80% (pink). Dissipated
energy graph for the optimal composition of pNGB_NG3 and pNGB4 (the
mean values for three replicate samples ± standard deviation
are shown) (D).
Figure 5A displays
the compression test results for the pNGB4 and pNGB_NG3 hydrogels.
The results indicated that pNGB_NG3 was slightly softer than pNGB4,
which is consistent with the findings from the tensile tests.
The loading–unloading compression measurements were aligned
with the cyclic loading–unloading tensile tests. As depicted
in Figure 5B,C, both
pNGB4 and pNGB_NG3 hydrogels exhibited compression unloading curves
that mirrored the loading curve up to 30% deformation, with small
hysteresis loops, confirming the tensile results. Larger hysteresis
loops were observed at higher compressions, particularly at 80%. Additionally, Figure 5D reveals that pNGB4
exhibited higher dissipated energy than pNGB_NG3.
Based on the
above results, the prepared hydrogels not only exhibited
enhanced compressive strength but also good dissipated energy during
cyclic compressive loading–unloading.
3.6.3
Rheology
Test
Figure 6 presents the storage modulus
(G′) and loss modulus (G″)
of the hydrogels as functions of amplitude and frequency. Figure 6A displays the storage
modulus (G′) and loss modulus (G″) of the pNGB4 and pNGB_NG3 hydrogels as functions of amplitude
(γ) to determine the linear viscoelastic region of the hydrogel.
Both G′ values are higher than G″, which indicates their gel-like properties.
Figure 6
Storage (filled symbols)
and loss modulus (empty symbols) plotted
versus amplitude (A) and angular frequency (B) for pNGB_NG3 and pNGB4.
The tests were done on three replicate samples, and the mean values
are depicted in the graph.
Figure 6B presents
the findings of the frequency sweep tests. A γ value of 10%
was selected for the frequency sweep measurements. The results revealed
that G′ exceeded G″
across the entire angular frequency (ω) range, indicating predominant
viscoelastic behavior. The variation of G′
and G″ with angular frequencies illustrates
typical behavior for gel-like samples, with both moduli exhibiting
little change with frequency. The highest storage modulus was observed
for pNGB4 at G′ ∼ 960 Pa, whereas for
pNGB_NG3, G′ was ca. 510 Pa. These results
indicated that the pNGB4 hydrogel exhibited higher stiffness compared
to pNGB_NG3; it was consistent with other mechanical tests.
3.7
Adhesion Properties
We tested several
materials to assess the adhesion properties of the prepared gel. The
excellent adhesion of the hydrogel to various substrates, including
rubber, copper, glass, polypropylene (PP), porcine tissue, steel,
polytetrafluoroethylene (PTFE), and polyethylene terephthalate (PET),
is illustrated in Figure 7A. The adhesion properties of the prepared hydrogels were
further evaluated using T-peel, lap-shear, and cyclic adhesion tests.
Figure 7
Adhesion
of various surfaces to the prepared hydrogels (A), adhesion
of pNGB_NG3 and pNGB4 in the T-peel test (B) and in lap-shear test
results versus displacement (C). Adhesive strength of all the prepared
samples (D) and cyclic adhesive strength results for pNGB_NG3 and
pNGB4 (the mean values for three replicate samples ± standard
deviation are shown) (E) in the lap-shear test.
During the T-peel test, the peeling force (Fp) was measured while separating the hydrogel
from two porcine
tissues at a 180° angle. As can be seen in Figure 7B, the T-peel tests on porcine tissues demonstrated
strong adhesion of the optimal hydrogel samples, with maximum peeling
adhesion values of approximately 70 N·m–1 for
pNGB4 and 120 N·m–1 for pNGB_NG3. The interfacial
toughness values of pNGB_NG3 and pNGB4 were calculated and were approximately
22.5 and 25.3 J·m–2, respectively. The results
of interfacial toughness are within satisfactory range.57
The lap-shear tests were conducted to
assess the adhesive strength
of the hydrogels. Shear forces were applied to two porcine tissues
bonded with the hydrogel, and adhesive strength values were calculated
based on the maximum shear force and the overlapping area. Figure 7C presents the dependence
of adhesive strength versus displacement for both optimal samples
for comparison, and Figure 7D shows the adhesion strength of all the prepared hydrogels
to porcine tissue in the lap-shear test. The results from both Figure 7C,D indicate that
higher concentrations of the GNPs, BISS, and the NIPAm monomers led
to increased adhesion, while the sample without GNPs showed the lowest
adhesive strength.58 Notably, samples containing
NG exhibited higher adhesion compared to those without NG. In particular,
pNGB_NG3 showed the highest adhesive strength of 2.4 kPa. Figure 7E presents the cyclic
adhesion tests. The results revealed that the adhesion of pNGB4 remained
relatively stable over five cycles, with only a slight decrease in
the adhesion percentage compared to pNGB_NG3.
The adhesion results
indicated that the adhesion of hydrogels to
porcine tissue was influenced by the composition of the hydrogel,
particularly the concentrations of GNPs, BISS, and NIPAm monomers.
According to the literature, the presence of amide, amine, and carboxylic
acid groups facilitated increased electrostatic interactions, hydrogen
bonding, and chemical interactions with carboxyl and other functional
groups on tissue proteins. The disulfide bonds in BISS can form covalent
links with thiol groups present in proteins on the tissue surface.
Additionally, the electrostatic interactions between negatively charged
GNPs and positively charged regions on tissue surfaces, as well as
hydrogen bonds and other noncovalent interactions, can contribute
enhancement of adhesiveness.59 Furthermore,
the presence of NG also enhanced the adhesion properties (through
additional molecules of NIPAm and BISS). The stability of adhesion
over multiple cycles indicated the suitability of these hydrogels
for practical applications requiring repeated adhesion.
3.8
DOX Release
DOX was selected as the
model anticancer drug to evaluate its release from the hydrogel matrix.
Biocompatibility testing is essential for potential clinical applications.
The investigated hydrogel consists of components such as NIPAm-based
hydrogels and gold nanoparticles, whose biocompatibility has already
been confirmed in previous studies.60,61However, to
ensure safety, further testing, such as cell viability assays should
be conducted. NIR irradiation was utilized to induce drug release
from both hydrogel formulations (pNGB4 and pNGB_NG3). The DOX with
48% DLC release as a function of time is illustrated in Figure 8. Initially, without irradiation,
a very limited drug release was observed from both hydrogel formulations.
However, upon laser irradiation, a significant increase in DOX release
was observed over time. With continuous irradiation, the concentration
of DOX in the buffer increased, reaching 0.07 and 0.08 mg·mL–1 for pNGB4 and pNGB_NG3, respectively, after 80 min.
This study demonstrated that the laser irradiation triggered the release
of DOX from the hydrogel matrix, with higher amounts released in the
presence of NG. As mentioned previously, the introduction of NG into
the hydrogel reduced its mechanical rigidity by increasing the intermolecular
spacing between polymer chains and decreasing the number of cross-linking
points between them. This led to higher amounts of DOX being released.
Figure 8
DOX released
from optimal samples over time of irradiation and
over time without irradiation. The test was done on three replicate
samples, and the mean values are depicted in the graph.
4
Conclusions
In conclusion, our study
confirmed that the incorporation of N,N′-bis(acryloyl)cystine
(BISS)-modified gold nanoparticles
(GNPs) into pNIPAm hydrogels enhanced various properties, notably
improving mechanical strength, energy dissipation, adhesion, and drug
release. The integration of the photothermal properties of the hydrogels
with laser irradiation facilitated rapid and nearly complete healing
after damage. Furthermore, the introduction of p(NIPAm-BISS) nanogels
(NG), synthesized from NIPAm and BISS, further augmented drug release
capabilities and adhesion properties. These advancements address the
demand from biomedicine for multifunctional materials suitable for
applications in tissue engineering, drug delivery, and wound healing. |
Title: A highly selective cell-based fluorescent biosensor for genistein detection | Body:
1
Introduction
Genistein is a phytochemical prized for its medicinal properties. Clinically, it exhibits antioxidant, anti-inflammatory, anti-osteoclastic, anticancer, and anti-obesity activities [1], [2], [3], [4]. Molecularly, genistein is similar to estradiol, the major endogenous estrogen in humans, and thus has seen substantial interest for use in hormone replacement therapy. Independent of its estrogenic activities, genistein has also been shown to inhibit the activities of topoisomerase II [5,6] and protein tyrosine kinases [7,8], which are master regulators that can either promote or inhibit the activities of many other receptors, enzymes and transcription factors [9,10]. Due to the multi-target nature of its molecular function, genistein has also been shown to have tumorigenic properties in certain tissues [11,12]. Vigorous studies are currently underway on how to best deploy genistein for therapeutic purposes.
Genistein belongs to a class of polyphenolic compounds called flavonoids. All flavonoids share a core polyphenolic ring structure but differ in their degree of saturation and oxidation, ring positioning and functional group substitution [13]. Found ubiquitously throughout the plant kingdom, flavonoids play important roles in different aspects of plant development, and in plant interactions with animals and microbes [14]. In legumes, genistein is primarily involved in promoting symbiotic relationships with rhizobial bacteria and in the defense against fungal pathogens [15], [16], [17], [18]. Genistein is found abundantly in legumes such as soybean [19], but because soybean also produces many other flavonoids, purification of genistein results in co-extraction of a mixture of many different flavonoids. For pharmaceutical purposes, a pure product is often desired. An economical way to produce pure genistein is using synthetic biology and microbial cell factories.
Combined with our understanding of cell metabolism and biosynthetic pathways, we can use synthetic biology to design and construct novel biological parts and recombinant microorganisms to produce heterologous compounds. The key to making synthetic biology a viable manufacturing option is our ability to improve the performance of chosen cell factories. The improvement process usually involves reiterations of the design-build-test-learn process [20], with the testing step being the rate-limiting step in most cases as it usually involves target product extraction and measurement via an often low-throughput analytical method. A way to resolve this bottleneck is the development of metabolite biosensors. A metabolite biosensor detects the amount of a target molecule present and translates that information into a signal that is easily read by current cell sorting technologies. Principally, a biosensor consists of a sensor component possessing a high affinity towards the ligand of interest, and a reporter component, e.g. a fluorescent protein, that enables a simple and fast read-out. When built into a cell factory, a biosensor can dramatically increase the testing capacity, since a mixture of cells can be screened according to how much product is being made. The ability of cell factories to display their own metabolite concentration can greatly speed up the synthetic biology workflow [20,21].
Genistein has previously been synthesized from different precursors by microorganisms such as Escherichia coli and Saccharomyces cerevisiae
[22], [23], [24], [25], [26]. To improve the performance of such cell factories, a biosensor would serve as a useful tool. A genistein-specific biosensor could potentially exploit the estrogenic nature of genistein. In mammals, estrogens such as estradiol exert their function via the estrogen receptors α and β (ERα, ERβ), which are intracellular hormone receptors that activate gene transcription in the presence of estrogens. Besides estrogens, many other estrogenic chemicals [e.g. dichlorodiphenyltrichloroethane (DDT), bisphenol A (BPA)], drugs [selective estrogen receptor modulators (SERMs)], and phytochemicals [e.g. coumestrol, equol, zearalenone, resveratrol, naringenin, kaempferol, daidzein, phloretin, quercetin and genistein] have been shown to bind to the ERs [27], [28], [29]. The human ERα localizes primarily to the nucleus. In the presence of its ligand, ligand binding induces a conformational change in the ligand binding domain (LBD) of the ERα that results in dimerization of ERα and subsequent transcriptional activation of target genes. The N-terminal half of ERα contains a constitutively active domain (AF-1), a DNA binding domain (DBD) and hinge region, with a nuclear localization signal (NLS) straddling the DBD and hinge region. The C-terminal half of ERα contains a ligand binding domain (LBD) [30], [31], [32], [33]. Biologists have successfully used heterologously expressed human ERα in other organisms [34], [35], [36].
In S. cerevisiae, a chimeric protein has been engineered to consist of the human ERα ligand binding domain fused with the yeast Gal4 DNA binding domain (Gal4DBD), and the herpes simplex VP16 transcriptional activating domain (Fig. 1a). This chimeric protein is called GEV (Gal4DBD-ERα-VP16) [37]. Upon binding to estradiol, GEV translocates to the nucleus, where it binds to the yeast GAL1 promoter and activates downstream gene expression (Fig. 1b) [38]. In yeast, GEV is a powerful tool for manipulating target gene expression because it induces a graded response that is proportional to the concentration of estradiol [39]. Since GEV contains the ligand binding domain of the ERα and ERα has also been found to respond to genistein, GEV should, in theory, be responsive to genistein in a graded manner.Fig. 1Transcriptional activator GEV as genistein biosensor. (a) The GEV chimeric protein consists of the Gal4 DNA binding domain (Gal4DBD), Estrogen Receptor α ligand binding domain (ERαLBD) and VP16 transactivating domain (VP16TD). (b) Schematic illustration showing the proposed mechanism of GEV biosensor action based on its subcellular localization in the presence of estradiol [38]. Cytoplasmic GEV is translocated to the nucleus upon ligand binding. Ligand-bound nuclear GEV then binds to the GAL1 promoter and activates downstream reporter transcription. (c, d) Median fluorescence intensity (MFI) values (c) and fold change in MFI upon genistein induction (d) of GEV biosensor strains based on GFP intensity measurement by flow cytometry. Cells were incubated with or without 100 µM genistein for 22 h. Values are shown as mean +/- s.d. from three biological replicates. AU, arbitrary units.Fig 1
In this study, we show that GEV does respond to genistein but not to other flavonoids that have been reported to interact with the ER [27,29], making it a highly selective biosensor applicable in S. cerevisiae. We improved the dynamic range of the biosensor from a 5- to a 20-fold signal increase upon genistein induction by engineering the transactivation domain of the sensor protein, its expression level, and the reporter promoter sequence. Finally, we demonstrate that the biosensor's operational range is in line with current genistein titers produced by yeast and can thus be used for measuring in vivo genistein production.
2
Materials and methods
2.1
Strains and plasmids
S. cerevisiae CEN.PK 113–11C was used as host strain for biosensor engineering and testing. E. coli DH5α was used for plasmid assembly and propagation. Engineered yeast strains, plasmids and primers used in this study are listed in Supplementary Tables S1–S3.
2.2
Media and culture conditions
Yeast Extract-Peptone-Dextrose (YPD) medium consisted of 20 g/L yeast peptone from meat, 10 g/L yeast extract and 20 g/L glucose. YPD with G418 (YPD+G418) was made by adding 200 mg/L geneticin (G418) to YPD; YPD with G418 and nourseothricin (YPD+G418+nourseothricin) was made by adding 200 mg/L G418 and 100 mg/mL nourseothricin to YPD. Synthetic defined uracil dropout medium (SD-URA) consisted of 0.77 g/L complete supplement mix without uracil, 6.9 g/L yeast nitrogen base without amino acids and 20 g/L glucose. SD medium with 5-fluoroorotic acid (5-FOA plates) was made by adding 1 g/L 5-fluoroorotic acid and 50 mg/L uracil to SD-URA. Plates of aforementioned media were made by adding 20 g/L agar. Complete supplement medium (CSM) medium consisted of 0.79 g/L complete supplement mix, 6.9 g/L yeast nitrogen base without amino acids and 20 g/L glucose. Delft medium [40], pH 4.5, consisted of 7.5 g/L (NH4)2SO4, 14.4 g/L KH2PO4, 0.5 g/L Mg2SO4×7H2O, 2 mL/L trace metals solution and 1 mL/L vitamin solution. The trace metal solution consisted of 4.5 g/L CaCl2·2H2O, 4.5 g/L ZnSO4·7H2O, 3 g/L FeSO4·7H2O, 1 g/L H3BO3, 1 g/L MnCl2·4H2O, 0.4 g/L Na2MoO4·2H2O, 0.3 g/L CoCl2·6H2O, 0.3 g/L CuSO4·5H2O, 0.1 g/L KI and 19 g/L Na2EDTA·2H2O and the vitamin solution consisted of 50 mg/L D-biotin, 200 mg/L p-aminobenzoic acid, 1 g/L nicotinic acid, 1 g/LD-pantothenic acid hemicalciumsalt, 1 g/L pyridoxine-HCl, 1 g/L thiamine-HCl and 25 g/L myo-inositol. Unless otherwise stated, yeast cultures were grown at 30 °C with 220 RPM shaking.
For E. coli cultivation, LB medium consisting of 10 g/L peptone from casein, 10 g/L NaCl and 5 g/L yeast extract was used. The pH was set to 7.0 using 5 M NaOH. Ampicillin was added at 100 mg/L for plasmid selection. Agar was added at 16 g/L to prepare solid media. E. coli cultures were grown at 37 °C with 180 RPM shaking.
Yeast nitrogen base, complete supplement mix and complete supplement mix without uracil were purchased from Formedium (Norfolk, UK). All other chemicals were purchased from Sigma-Aldrich/Merck (Darmstadt, Germany).
2.3
Whole-plasmid PCR
A new method for performing simple plasmid manipulation was developed during the course of this study. In this method, a pair of divergent primers harboring 20–30-mer overlapping regions at their 5′ ends were used to amplify the whole plasmid, as demonstrated in supplemental Fig. S1. For simple substitution or insertion, sequences to replace or be added were incorporated into primers at the 5′ overlapping region. For truncation, the 5′ overlapping region on each of the divergent primers contained approximately 10 bases immediately upstream and downstream of the truncation site such that there were around 20 bases of complementarity between the two primers. This method, which we named whole-plasmid PCR, relies on the processivity and fidelity of proofreading DNA polymerases, their lack of 5′ → 3′ exonuclease activity, and the ability of E. coli DH5α to perform simple homologous recombination in vivo. The workflow for whole-plasmid PCR involved 1) PCR amplification, 2) removal of template plasmid through DpnI digestion, and 3) direct E. coli transformation of unpurified reaction mix. For amplification, a 20 µL reaction mix containing 20 ng of template plasmid, 4 nmol of each dNTP, 10 pmol of each primer and 0.4 U Phusion HF DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) in 1X Phusion HF Buffer was prepared. The reaction mix was then cycled through the following steps: initial denaturation at 98 °C for 2 min; 20 cycles of 98 °C for 7 s, annealing temperature for 10 s, 72 °C for 30 s/kb plasmid; followed by final extension at 72 °C for 10 min. After amplification, 0.5 µL FastDigest DpnI was added directly to the PCR reaction mix and incubated at 37 °C for at least 1 h to digest the template plasmid. After DpnI digestion, 5 µL of unpurified reaction mix was added to 50 µL chemically competent E. coli DH5α [41] for transformation using a standard heat-shock protocol. Briefly, the transformation mix was incubated on ice for 30 min, followed by a 30 s incubation at 42 °C, then returned to ice for 2 min. Transformed cells were allowed to recover by adding 500 µL LB followed by shaking at 200 RPM, 37 °C for 1 h. After recovery, 100 µL of the mix was plated onto a pre-warmed LB plate with appropriate selection marker. 2–3 colonies from the transformation plate were picked and sequenced to verify successful plasmid manipulation. Small colonies should be avoided as they may contain concatemers. This method routinely yielded >90% correct plasmids and has been used to successfully manipulate plasmids as large as 10 kb.
Whole-plasmid PCR was successfully used to replace (e.g. exchange of gRNA encoding sequence in existing gRNA plasmids), add (e.g. adding short regulatory sequences), and remove sequences from a plasmid (e.g. NLS removal). One constraint is that the replacement sequence or added sequence needs to be short enough to fit on a custom oligonucleotide primer.
2.4
Yeast strain engineering
Unless otherwise stated, yeast genetic manipulation was performed using standard procedures [42,43]. Oligonucleotide primers were obtained from either Eurofins Genomics Germany GmbH (Ebersberg, Germany) or Integrated DNA Technologies (Coralville, IA, USA) and restriction enzymes were purchased from Thermo Fisher Scientific (Waltham, MA, USA). The GEV/GEVPR/GEVPR2 sequences, all reporter promoter sequences and genistein pathway gene sequences are given in the Supplementary.
A yeast strain harboring deletions in the GAL4 coding sequence and the GAL1–10 promoter was generated by sequential deletion of these loci using CRISPR/Cas9-mediated homologous recombination. For each deletion, an all-in-one plasmid encoding both Cas9 and target-specific gRNA was constructed by replacing the gRNA sequence of pECAS9-gRNA-KlURA3-tHFD1 (generated in [44]) using whole-plasmid PCR with primer pairs LC141/LC140 for targeting the GAL4 ORF, or LC131/LC136 for targeting the GAL1–10 promoter. The S. cerevisiae host strain was transformed with the plasmid together with a 120-mer repair fragment constructed by annealing complementary oligos LCF007/LCF008 for GAL4 ORF deletion, or LCF003/LCF004 for the GAL1–10 promoter. Successful transformants were selected based on growth on SD-URA plates. After transformation, genomic DNA was extracted and deletion was confirmed by PCR using primers LCD003/LCD004 for GAL4 ORF deletion, or LCD001/LCD002 for GAL1–10 promoter deletion. The CRISPR plasmid was removed by 5-FOA counter-selection before the next round of deletion or downstream use of the strain.
Strains expressing the GEV/GEVPR/GEVPR2 sensor protein and/or reporter protein were constructed by sequential gene integration using the EasyClone MarkerFree Toolkit [45]. All GEV/GEVPR/GEVPR2 sensor constructs were integrated into chromosomal locus X-4; all reporter constructs were integrated into chromosomal locus XI-1. Genistein pathway constructs were integrated into chromosomal locus XI-3. Integrative plasmids were assembled following the EasyClone MarkerFree manual with modifications described below.
To assemble the integrative plasmids for TDH3p-GEV-CYC1t and TDH3p-GEVPR-CYC1t integrations (pLC125 and pLC126, respectively), Gibson cloning (New England Biolabs, Ipswich, MA, USA) was performed using SfaAI/SacI-digested pCfB3035 as vector and PCR-amplified promoter and ORF fragments as inserts. TDH3p was amplified from pTS-38 [46] using primer pairs LC185/LC186. GEV was amplified from a custom synthetic DNA template (sequence provided in Supplementary) that was codon-optimized for expression in S. cerevisiae (Doulix, Explora Biotech, Venice, Italy), using primer pair LC191/LC192. GEVPR was amplified in two pieces: a fragment encoding Gal4DBD and ERLBD was amplified from aforementioned synthetic GEV template using LC191/LC204, and a fragment encoding VPR was amplified from dCas9V2 [47] using LC205/LC206. To generate the plasmid for TDH3p-GEVPR2-CYC1t integration, whole-plasmid PCR was performed using LC207/LC208 from pLC126 to remove the NLS. To assemble the integrative plasmids for REV1p-GEV-CYC1t, REV1p-GEVPR-CYC1t, REV1p-GEVPR2-CYC1t integration (pLC135, pLC151, pLC152, respectively), Gibson cloning was performed using SfaAI/SacI digested pCfB3035 as vector and PCR-amplified promoter and ORF fragments as inserts. The REV1 promoter was amplified from CEN.PK 113–11C genomic DNA using LC247/LC248. GEV was amplified from pLC125 using LC249/192. GEVPR was amplified from pLC126 using LC249/206. GEVPR2 was amplified from pLC127 using LC249/LC206.
To assemble the plasmid for promoter A (pLC121) integration, Gibson cloning was performed using SfaAI/PstI-digested pCfB3036 as vector and PCR-amplified GAL1 promoter and yeGFP ORF fragments as inserts. The native GAL1 promoter was amplified from CEN.PK 113–11C genomic DNA using LC199/LC200. yeGFP was amplified from pTS-37 [46] using LC201/LC190.
To generate the plasmid for integrating promoter B (pLC155), whole-plasmid PCR was performed using LC267/LC268 from pLC121 to add the sequence gta upstream of the GAL1 promoter.
To assemble plasmids for promoters C (pLC156) and G (pLC167) integration, Gibson cloning was used to replace the native GAL1 promoter in pLC121 with promoter C or G. The GAL1 promoter with deleted Mig1BS for promoter C was amplified using LC267/LC200. The 13X Gal4BS synthetic promoter G was amplified from pIOX179 using LC271/LC272 [48]. Although Joshi et al. stated that pIOX179 contained 10 Gal4 binding sites in their 2007 study, we found that the promoter actually contains 13 binding sites.
To generate plasmids for integrating promoters D, E and H, a 78-bp fragment containing 3XGal4BS flanked by BcuI sites was generated from primer extension of oligonucleotides LC269/LC270, and subcloned into pLC121, pLC155 and pLC156, respectively, following BcuI digestion. This was suitable because the Gal4BS sequence is palindromic. In addition to BcuI digestion, plasmid vectors were also treated with alkaline phosphatase (Thermo Fisher Scientific (Waltham, MA, USA)) to prevent self-ligation. Promoter F was from the same transformation reaction as promoter H but contained an unintended base change that destroyed the first Gal4BS. Given that it was our best-performing promoter, it was a serendipitous mistake stemming most likely from an error during oligonucleotide synthesis.
The genistein pathway genes were codon-optimized for S. cerevisiae and synthesized by GenScript Biotech (Piscataway Township, NJ, USA). A genistein pathway plasmid (pLC160) was generated with Gibson assembly, using XhoI/SacI-digested p416TEF [49] as vector, and PCR-amplified GmCPR, GmHID, TpIFS, promoters and terminators. The CYC1 terminator used was present on the linearized vector. GmCPR was amplified using primers LC283/LC284. GmHID was amplified using primers LC289/LC290. TpIFS was amplified using primers LC279/LC280. The ENO2 and CCW12 promoters and the TPI1 terminator were amplified from purified yeast genomic DNA using primers LC281/LC282, LC287/LC288 and LC291/LC292, respectively. The TDH3 promoter was amplified from pTS38 [46] using primers LC293/LC186. The ADH1 terminator was amplified from pLC155 using primers LC285/LC286. After bacterial transformation, plasmids were purified and sequenced. Note: Successful transformants produced small colonies.
The plasmid for genomic integration of (TDH3p-TpIFS-CYC1t)+(CCW12p-GmHID-TPI1t)+(TEF1p-GmCPR-ADH1t) (pDL091) was constructed by Gibson assembly using PstI/SacI digested vector pCfB2904 and PCR-amplified GmCPR, TEF1 promoter and GmHID+TpIFS expression cassettes as inserts. GmCPR was amplified using primers DL372/DL373. TEF1p was amplified from genomic DNA using primers DL371/DL266. The GmHID and TpIFS expression cassettes including promoters and terminators were amplified from pLC160 using DL374/DL364.
The plasmids for integration of (TDH3p-GeIFS/GmIFS/MtIFS-CYC1t)+(CCW12p-GmHID-TPI1t)+(TEF1p-GmCPR-ADH1t) (pDL092–094) were constructed by Gibson assembly of two PCR-amplified DNA fragments into the SacI/SfaAI-digested vector pCfB2904. The GmHID and GmCPR expression cassettes were amplified from pDL091 using primers DL002 and DL371. GeIFS was amplified by DL360/DL361, GmIFS was amplified using DL362/363 and MtIFS was amplified by DL360/DL378 from synthesized templates.
Transformation was carried out according to the EasyClone MarkerFree manual with modifications. Briefly, the Cas9-encoding plasmid pCfB2312 was first used to transform gal4∆ GAL1–10p∆ cells, and the resulting strain was maintained as host. Next, an overnight preculture of host cells was diluted with 15 mL YPD+G418 to an OD600 of 0.2. The culture was allowed to grow for 6 h at 30 °C, 200 RPM to an OD600 of 1. Cells were pelleted and washed once with LiAc Mix (100 mM lithium acetate in TE) and resuspended in 180 µL LiAc Mix. For each transformation, 25 µL of resuspended cells were mixed with a 17-µL solution containing 1 µg of XbaI-linearized EasyClone integrative plasmid, 5 µg salmon sperm DNA and 250 ng gRNA helper plasmid. To this mixture, 200 µL PEG Mix (40% PEG 3350, 100 mM lithium acetate in TE) was added and mixed. The transformation mix was allowed to rest at 30 °C for 1 h before applying a heat shock at 42 °C for 20 min. The supernatant was removed after a 10 s spin, and the pellet was resuspended in 500 µL YPD. Cells were allowed to recover at 30 °C, 200 RPM for 1.5 h before plating on YPD+G418+nourseothricin. After transformation, genomic DNA was extracted and integration was confirmed via PCR according to the EasyClone MarkerFree manual. Confirmed transformants were grown in non-selective conditions to promote the removal of the Cas9-coding pCfB2312 and gRNA helper plasmid. Briefly, cells were cultivated in YPD for 2 days before streaking onto YPD plates. After 24-h growth on YPD, cells were replica-plated onto YPD+G418 and YPD+nourseothricin plates. Single colonies showing lack of growth on selective plates were restreaked onto new YPD plates. Removal of plasmids was confirmed by lack of growth on selective media prior to downstream processes.
2.5
Flow cytometry
Cells were grown in a Growth Profiler (Enzyscreen BV, Heemstede, the Netherlands) set at 30 °C and 250 RPM using 96-well Growth Profiler plates according to the manufacturer's instructions. Precultures were prepared by inoculation of 250 µL CSM. After overnight growth, 5 µL of precultures was added to 100 µL Delft medium supplemented with 2% glucose and 20 mg/L each of L-histidine HCl and uracil, then 5 µL of diluted cultures were added to 250 µL of the same medium. For flavonoid-induced cultures, flavonoid solution in DMSO was added to the medium to a final DMSO concentration of 2%, whereas DMSO without flavonoid was added to uninduced cultures. After 22 h growth, 1 µL of each culture was diluted into 200 µL PBS to arrest cell growth before loading onto a Guava® easyCyte™ flow cytometer (Luminex Corporation, s-Hertogenbosch, the Netherlands). GFP signal from 5000 detected cells per sample was measured with an excitation wavelength of 488 nm and a 525/30 BP filter. Raw data collected was analyzed using FlowJo version 10 software (FlowJo LLC, Ashland, OR, USA). Median intensity of the log-scale GFP fluorescence was used as the readout for analysis.
2.6
High-performance liquid chromatography (HPLC)
To measure intracellular metabolites, cell cultures were centrifuged at 4000 RPM for 10 min and the supernatant was removed. For total intra- and extracellular metabolite samples, the whole cell culture was used for sample preparation. Samples were freeze-dried for 48 h and metabolites were extracted with absolute ethanol by vortexing at high speed for 10 min. Samples were analyzed using a Dionex UltiMate 3000 HPLC (ThermoFisher Scientific, Waltham, MA, USA) connected to a UVD 340 U UV/VIS diode array detector (ThermoFisher Scientific, Waltham, MA, USA) and a Discovery™ HS F5 column (15 cm x 4.6 mm, 5 µm particle size) (Sigma-Aldrich, St. Louis, MO, USA). A gradient elution program was used at a flow rate of 1.2 mL/min, using 10 mM ammonium formate, pH 3 (A) and acetonitrile (B). The eluent gradient started with 15% B (0–1.5 min), followed by an increase to 20% B (1.5–3 min), 25% B (3–24 min), 45% B (24–25 min), 50% B (25–27 min) and a final decrease to 15% B (27–28 min). Then, 10 µL of sample was injected into the column for each measurement. The column was set to 30 °C. Genistein and naringenin were detected at a wavelength of 280 nm at retention times of 13.8 min and 14.2 min, respectively. Analytical standards of naringenin were obtained from Sigma-Aldrich (St. Louis, MO, USA) and genistein was obtained from HWI group (Rülzheim, Germany).
2.1
Strains and plasmids
S. cerevisiae CEN.PK 113–11C was used as host strain for biosensor engineering and testing. E. coli DH5α was used for plasmid assembly and propagation. Engineered yeast strains, plasmids and primers used in this study are listed in Supplementary Tables S1–S3.
2.2
Media and culture conditions
Yeast Extract-Peptone-Dextrose (YPD) medium consisted of 20 g/L yeast peptone from meat, 10 g/L yeast extract and 20 g/L glucose. YPD with G418 (YPD+G418) was made by adding 200 mg/L geneticin (G418) to YPD; YPD with G418 and nourseothricin (YPD+G418+nourseothricin) was made by adding 200 mg/L G418 and 100 mg/mL nourseothricin to YPD. Synthetic defined uracil dropout medium (SD-URA) consisted of 0.77 g/L complete supplement mix without uracil, 6.9 g/L yeast nitrogen base without amino acids and 20 g/L glucose. SD medium with 5-fluoroorotic acid (5-FOA plates) was made by adding 1 g/L 5-fluoroorotic acid and 50 mg/L uracil to SD-URA. Plates of aforementioned media were made by adding 20 g/L agar. Complete supplement medium (CSM) medium consisted of 0.79 g/L complete supplement mix, 6.9 g/L yeast nitrogen base without amino acids and 20 g/L glucose. Delft medium [40], pH 4.5, consisted of 7.5 g/L (NH4)2SO4, 14.4 g/L KH2PO4, 0.5 g/L Mg2SO4×7H2O, 2 mL/L trace metals solution and 1 mL/L vitamin solution. The trace metal solution consisted of 4.5 g/L CaCl2·2H2O, 4.5 g/L ZnSO4·7H2O, 3 g/L FeSO4·7H2O, 1 g/L H3BO3, 1 g/L MnCl2·4H2O, 0.4 g/L Na2MoO4·2H2O, 0.3 g/L CoCl2·6H2O, 0.3 g/L CuSO4·5H2O, 0.1 g/L KI and 19 g/L Na2EDTA·2H2O and the vitamin solution consisted of 50 mg/L D-biotin, 200 mg/L p-aminobenzoic acid, 1 g/L nicotinic acid, 1 g/LD-pantothenic acid hemicalciumsalt, 1 g/L pyridoxine-HCl, 1 g/L thiamine-HCl and 25 g/L myo-inositol. Unless otherwise stated, yeast cultures were grown at 30 °C with 220 RPM shaking.
For E. coli cultivation, LB medium consisting of 10 g/L peptone from casein, 10 g/L NaCl and 5 g/L yeast extract was used. The pH was set to 7.0 using 5 M NaOH. Ampicillin was added at 100 mg/L for plasmid selection. Agar was added at 16 g/L to prepare solid media. E. coli cultures were grown at 37 °C with 180 RPM shaking.
Yeast nitrogen base, complete supplement mix and complete supplement mix without uracil were purchased from Formedium (Norfolk, UK). All other chemicals were purchased from Sigma-Aldrich/Merck (Darmstadt, Germany).
2.3
Whole-plasmid PCR
A new method for performing simple plasmid manipulation was developed during the course of this study. In this method, a pair of divergent primers harboring 20–30-mer overlapping regions at their 5′ ends were used to amplify the whole plasmid, as demonstrated in supplemental Fig. S1. For simple substitution or insertion, sequences to replace or be added were incorporated into primers at the 5′ overlapping region. For truncation, the 5′ overlapping region on each of the divergent primers contained approximately 10 bases immediately upstream and downstream of the truncation site such that there were around 20 bases of complementarity between the two primers. This method, which we named whole-plasmid PCR, relies on the processivity and fidelity of proofreading DNA polymerases, their lack of 5′ → 3′ exonuclease activity, and the ability of E. coli DH5α to perform simple homologous recombination in vivo. The workflow for whole-plasmid PCR involved 1) PCR amplification, 2) removal of template plasmid through DpnI digestion, and 3) direct E. coli transformation of unpurified reaction mix. For amplification, a 20 µL reaction mix containing 20 ng of template plasmid, 4 nmol of each dNTP, 10 pmol of each primer and 0.4 U Phusion HF DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) in 1X Phusion HF Buffer was prepared. The reaction mix was then cycled through the following steps: initial denaturation at 98 °C for 2 min; 20 cycles of 98 °C for 7 s, annealing temperature for 10 s, 72 °C for 30 s/kb plasmid; followed by final extension at 72 °C for 10 min. After amplification, 0.5 µL FastDigest DpnI was added directly to the PCR reaction mix and incubated at 37 °C for at least 1 h to digest the template plasmid. After DpnI digestion, 5 µL of unpurified reaction mix was added to 50 µL chemically competent E. coli DH5α [41] for transformation using a standard heat-shock protocol. Briefly, the transformation mix was incubated on ice for 30 min, followed by a 30 s incubation at 42 °C, then returned to ice for 2 min. Transformed cells were allowed to recover by adding 500 µL LB followed by shaking at 200 RPM, 37 °C for 1 h. After recovery, 100 µL of the mix was plated onto a pre-warmed LB plate with appropriate selection marker. 2–3 colonies from the transformation plate were picked and sequenced to verify successful plasmid manipulation. Small colonies should be avoided as they may contain concatemers. This method routinely yielded >90% correct plasmids and has been used to successfully manipulate plasmids as large as 10 kb.
Whole-plasmid PCR was successfully used to replace (e.g. exchange of gRNA encoding sequence in existing gRNA plasmids), add (e.g. adding short regulatory sequences), and remove sequences from a plasmid (e.g. NLS removal). One constraint is that the replacement sequence or added sequence needs to be short enough to fit on a custom oligonucleotide primer.
2.4
Yeast strain engineering
Unless otherwise stated, yeast genetic manipulation was performed using standard procedures [42,43]. Oligonucleotide primers were obtained from either Eurofins Genomics Germany GmbH (Ebersberg, Germany) or Integrated DNA Technologies (Coralville, IA, USA) and restriction enzymes were purchased from Thermo Fisher Scientific (Waltham, MA, USA). The GEV/GEVPR/GEVPR2 sequences, all reporter promoter sequences and genistein pathway gene sequences are given in the Supplementary.
A yeast strain harboring deletions in the GAL4 coding sequence and the GAL1–10 promoter was generated by sequential deletion of these loci using CRISPR/Cas9-mediated homologous recombination. For each deletion, an all-in-one plasmid encoding both Cas9 and target-specific gRNA was constructed by replacing the gRNA sequence of pECAS9-gRNA-KlURA3-tHFD1 (generated in [44]) using whole-plasmid PCR with primer pairs LC141/LC140 for targeting the GAL4 ORF, or LC131/LC136 for targeting the GAL1–10 promoter. The S. cerevisiae host strain was transformed with the plasmid together with a 120-mer repair fragment constructed by annealing complementary oligos LCF007/LCF008 for GAL4 ORF deletion, or LCF003/LCF004 for the GAL1–10 promoter. Successful transformants were selected based on growth on SD-URA plates. After transformation, genomic DNA was extracted and deletion was confirmed by PCR using primers LCD003/LCD004 for GAL4 ORF deletion, or LCD001/LCD002 for GAL1–10 promoter deletion. The CRISPR plasmid was removed by 5-FOA counter-selection before the next round of deletion or downstream use of the strain.
Strains expressing the GEV/GEVPR/GEVPR2 sensor protein and/or reporter protein were constructed by sequential gene integration using the EasyClone MarkerFree Toolkit [45]. All GEV/GEVPR/GEVPR2 sensor constructs were integrated into chromosomal locus X-4; all reporter constructs were integrated into chromosomal locus XI-1. Genistein pathway constructs were integrated into chromosomal locus XI-3. Integrative plasmids were assembled following the EasyClone MarkerFree manual with modifications described below.
To assemble the integrative plasmids for TDH3p-GEV-CYC1t and TDH3p-GEVPR-CYC1t integrations (pLC125 and pLC126, respectively), Gibson cloning (New England Biolabs, Ipswich, MA, USA) was performed using SfaAI/SacI-digested pCfB3035 as vector and PCR-amplified promoter and ORF fragments as inserts. TDH3p was amplified from pTS-38 [46] using primer pairs LC185/LC186. GEV was amplified from a custom synthetic DNA template (sequence provided in Supplementary) that was codon-optimized for expression in S. cerevisiae (Doulix, Explora Biotech, Venice, Italy), using primer pair LC191/LC192. GEVPR was amplified in two pieces: a fragment encoding Gal4DBD and ERLBD was amplified from aforementioned synthetic GEV template using LC191/LC204, and a fragment encoding VPR was amplified from dCas9V2 [47] using LC205/LC206. To generate the plasmid for TDH3p-GEVPR2-CYC1t integration, whole-plasmid PCR was performed using LC207/LC208 from pLC126 to remove the NLS. To assemble the integrative plasmids for REV1p-GEV-CYC1t, REV1p-GEVPR-CYC1t, REV1p-GEVPR2-CYC1t integration (pLC135, pLC151, pLC152, respectively), Gibson cloning was performed using SfaAI/SacI digested pCfB3035 as vector and PCR-amplified promoter and ORF fragments as inserts. The REV1 promoter was amplified from CEN.PK 113–11C genomic DNA using LC247/LC248. GEV was amplified from pLC125 using LC249/192. GEVPR was amplified from pLC126 using LC249/206. GEVPR2 was amplified from pLC127 using LC249/LC206.
To assemble the plasmid for promoter A (pLC121) integration, Gibson cloning was performed using SfaAI/PstI-digested pCfB3036 as vector and PCR-amplified GAL1 promoter and yeGFP ORF fragments as inserts. The native GAL1 promoter was amplified from CEN.PK 113–11C genomic DNA using LC199/LC200. yeGFP was amplified from pTS-37 [46] using LC201/LC190.
To generate the plasmid for integrating promoter B (pLC155), whole-plasmid PCR was performed using LC267/LC268 from pLC121 to add the sequence gta upstream of the GAL1 promoter.
To assemble plasmids for promoters C (pLC156) and G (pLC167) integration, Gibson cloning was used to replace the native GAL1 promoter in pLC121 with promoter C or G. The GAL1 promoter with deleted Mig1BS for promoter C was amplified using LC267/LC200. The 13X Gal4BS synthetic promoter G was amplified from pIOX179 using LC271/LC272 [48]. Although Joshi et al. stated that pIOX179 contained 10 Gal4 binding sites in their 2007 study, we found that the promoter actually contains 13 binding sites.
To generate plasmids for integrating promoters D, E and H, a 78-bp fragment containing 3XGal4BS flanked by BcuI sites was generated from primer extension of oligonucleotides LC269/LC270, and subcloned into pLC121, pLC155 and pLC156, respectively, following BcuI digestion. This was suitable because the Gal4BS sequence is palindromic. In addition to BcuI digestion, plasmid vectors were also treated with alkaline phosphatase (Thermo Fisher Scientific (Waltham, MA, USA)) to prevent self-ligation. Promoter F was from the same transformation reaction as promoter H but contained an unintended base change that destroyed the first Gal4BS. Given that it was our best-performing promoter, it was a serendipitous mistake stemming most likely from an error during oligonucleotide synthesis.
The genistein pathway genes were codon-optimized for S. cerevisiae and synthesized by GenScript Biotech (Piscataway Township, NJ, USA). A genistein pathway plasmid (pLC160) was generated with Gibson assembly, using XhoI/SacI-digested p416TEF [49] as vector, and PCR-amplified GmCPR, GmHID, TpIFS, promoters and terminators. The CYC1 terminator used was present on the linearized vector. GmCPR was amplified using primers LC283/LC284. GmHID was amplified using primers LC289/LC290. TpIFS was amplified using primers LC279/LC280. The ENO2 and CCW12 promoters and the TPI1 terminator were amplified from purified yeast genomic DNA using primers LC281/LC282, LC287/LC288 and LC291/LC292, respectively. The TDH3 promoter was amplified from pTS38 [46] using primers LC293/LC186. The ADH1 terminator was amplified from pLC155 using primers LC285/LC286. After bacterial transformation, plasmids were purified and sequenced. Note: Successful transformants produced small colonies.
The plasmid for genomic integration of (TDH3p-TpIFS-CYC1t)+(CCW12p-GmHID-TPI1t)+(TEF1p-GmCPR-ADH1t) (pDL091) was constructed by Gibson assembly using PstI/SacI digested vector pCfB2904 and PCR-amplified GmCPR, TEF1 promoter and GmHID+TpIFS expression cassettes as inserts. GmCPR was amplified using primers DL372/DL373. TEF1p was amplified from genomic DNA using primers DL371/DL266. The GmHID and TpIFS expression cassettes including promoters and terminators were amplified from pLC160 using DL374/DL364.
The plasmids for integration of (TDH3p-GeIFS/GmIFS/MtIFS-CYC1t)+(CCW12p-GmHID-TPI1t)+(TEF1p-GmCPR-ADH1t) (pDL092–094) were constructed by Gibson assembly of two PCR-amplified DNA fragments into the SacI/SfaAI-digested vector pCfB2904. The GmHID and GmCPR expression cassettes were amplified from pDL091 using primers DL002 and DL371. GeIFS was amplified by DL360/DL361, GmIFS was amplified using DL362/363 and MtIFS was amplified by DL360/DL378 from synthesized templates.
Transformation was carried out according to the EasyClone MarkerFree manual with modifications. Briefly, the Cas9-encoding plasmid pCfB2312 was first used to transform gal4∆ GAL1–10p∆ cells, and the resulting strain was maintained as host. Next, an overnight preculture of host cells was diluted with 15 mL YPD+G418 to an OD600 of 0.2. The culture was allowed to grow for 6 h at 30 °C, 200 RPM to an OD600 of 1. Cells were pelleted and washed once with LiAc Mix (100 mM lithium acetate in TE) and resuspended in 180 µL LiAc Mix. For each transformation, 25 µL of resuspended cells were mixed with a 17-µL solution containing 1 µg of XbaI-linearized EasyClone integrative plasmid, 5 µg salmon sperm DNA and 250 ng gRNA helper plasmid. To this mixture, 200 µL PEG Mix (40% PEG 3350, 100 mM lithium acetate in TE) was added and mixed. The transformation mix was allowed to rest at 30 °C for 1 h before applying a heat shock at 42 °C for 20 min. The supernatant was removed after a 10 s spin, and the pellet was resuspended in 500 µL YPD. Cells were allowed to recover at 30 °C, 200 RPM for 1.5 h before plating on YPD+G418+nourseothricin. After transformation, genomic DNA was extracted and integration was confirmed via PCR according to the EasyClone MarkerFree manual. Confirmed transformants were grown in non-selective conditions to promote the removal of the Cas9-coding pCfB2312 and gRNA helper plasmid. Briefly, cells were cultivated in YPD for 2 days before streaking onto YPD plates. After 24-h growth on YPD, cells were replica-plated onto YPD+G418 and YPD+nourseothricin plates. Single colonies showing lack of growth on selective plates were restreaked onto new YPD plates. Removal of plasmids was confirmed by lack of growth on selective media prior to downstream processes.
2.5
Flow cytometry
Cells were grown in a Growth Profiler (Enzyscreen BV, Heemstede, the Netherlands) set at 30 °C and 250 RPM using 96-well Growth Profiler plates according to the manufacturer's instructions. Precultures were prepared by inoculation of 250 µL CSM. After overnight growth, 5 µL of precultures was added to 100 µL Delft medium supplemented with 2% glucose and 20 mg/L each of L-histidine HCl and uracil, then 5 µL of diluted cultures were added to 250 µL of the same medium. For flavonoid-induced cultures, flavonoid solution in DMSO was added to the medium to a final DMSO concentration of 2%, whereas DMSO without flavonoid was added to uninduced cultures. After 22 h growth, 1 µL of each culture was diluted into 200 µL PBS to arrest cell growth before loading onto a Guava® easyCyte™ flow cytometer (Luminex Corporation, s-Hertogenbosch, the Netherlands). GFP signal from 5000 detected cells per sample was measured with an excitation wavelength of 488 nm and a 525/30 BP filter. Raw data collected was analyzed using FlowJo version 10 software (FlowJo LLC, Ashland, OR, USA). Median intensity of the log-scale GFP fluorescence was used as the readout for analysis.
2.6
High-performance liquid chromatography (HPLC)
To measure intracellular metabolites, cell cultures were centrifuged at 4000 RPM for 10 min and the supernatant was removed. For total intra- and extracellular metabolite samples, the whole cell culture was used for sample preparation. Samples were freeze-dried for 48 h and metabolites were extracted with absolute ethanol by vortexing at high speed for 10 min. Samples were analyzed using a Dionex UltiMate 3000 HPLC (ThermoFisher Scientific, Waltham, MA, USA) connected to a UVD 340 U UV/VIS diode array detector (ThermoFisher Scientific, Waltham, MA, USA) and a Discovery™ HS F5 column (15 cm x 4.6 mm, 5 µm particle size) (Sigma-Aldrich, St. Louis, MO, USA). A gradient elution program was used at a flow rate of 1.2 mL/min, using 10 mM ammonium formate, pH 3 (A) and acetonitrile (B). The eluent gradient started with 15% B (0–1.5 min), followed by an increase to 20% B (1.5–3 min), 25% B (3–24 min), 45% B (24–25 min), 50% B (25–27 min) and a final decrease to 15% B (27–28 min). Then, 10 µL of sample was injected into the column for each measurement. The column was set to 30 °C. Genistein and naringenin were detected at a wavelength of 280 nm at retention times of 13.8 min and 14.2 min, respectively. Analytical standards of naringenin were obtained from Sigma-Aldrich (St. Louis, MO, USA) and genistein was obtained from HWI group (Rülzheim, Germany).
3
Results and discussion
3.1
Transcriptional activator GEV as genistein biosensor
To test whether GEV could function as a genistein biosensor in yeast, its gene driven by the TDH3 promoter (TDH3p-GEV) was integrated into the yeast genome along with a GFP reporter under the transcriptional control of the GAL1 promoter (GAL1p-GFP). After 22 h of cultivation in media containing either no genistein or 100 µM genistein, cells were collected, and the median fluorescence intensity (MFI) was measured by flow cytometry. The fluorescence intensity of cells containing only the TDH3p-GEV sensor protein expression cassette or the GAL1p-GFP reporter expression cassette and wild-type cells were collected as control. Compared with the control cells, cells expressing both TDH3p-GEV and GAL1p-GFP had the highest background fluorescence without genistein induction. However, a significant increase in fluorescence signal was observed upon genistein addition only in cells containing both TDH3p-GEV and GAL1p-GFP (Fig. 1c). When the magnitude of increase was calculated by dividing the fluorescence intensity with genistein induction (100 µM) over fluorescence intensity without genistein induction (0 µM), there was a 5-fold increase observed only in cells containing the complete circuit (Fig. 1d). Taken together, GEV can function as a biosensor for genistein in yeast.
3.2
Genistein biosensor optimization through GEV protein engineering
We sought to improve the performance of our genistein biosensor by first engineering the GEV sensor protein. Since it was observed that the biosensor produced a high fluorescence signal in the absence of genistein, the first step towards optimization was to reduce the background fluorescence by decreasing the amount of GEV sensor protein in the cell. This was accomplished by replacing the strong constitutive TDH3 promoter with a weaker REV1 promoter [50]. Furthermore, to increase the sensitivity of the sensor protein, its ability to activate transcription was enhanced by replacing the VP16 transactivating domain of the GEV with a stronger transcriptional activator, VP64-p65-Rta (VPR) [51]. VPR contains VP64, four repeats of VP16 [52], p65, the transactivating domain of human RelA [53], and the transactivating domain of Rta, a transcriptional activator encoded by the Epstein-Barr virus [54]. Lastly, the NLS sequence that was embedded between VP64 and p65 in the original version of VPR was removed based on the importance of differential GEV nuclear translocation to its function. The Gal4-ER-VPR chimeric protein was given the name GEVPR. The version of GEVPR that had the NLS removed from VPR was given the name GEVPR2 (Fig. 2a). To test whether GEVPR and GEVPR2 were indeed improvements over GEV, all three versions of the sensor protein, driven by either the TDH3 or the REV1 promoter, were introduced into cells expressing the reporter GAL1p-GFP. Cells expressing different versions of the sensor protein without the GAL1p-GFP reporter were used as controls. After 22 h of cultivation in media containing either no genistein or 100 µM genistein, cells were collected, and the MFI was measured by flow cytometry (Fig. 2b). As expected, replacing the TDH3 promoter with the REV1 promoter reduced both background fluorescence and genistein-induced signal in GEV, GEVPR and GEVPR2; meanwhile, replacing VP16 with VPR increased both background and signal fluorescence when sensor protein expression was driven by either the TDH3 or the REV1 promoter. Surprisingly, removing the NLS sequence in VPR caused a decrease in both background and signal fluorescence only when sensor protein expression was driven by the REV1 promoter. This could suggest that the NLS in VPR only plays a minor role in determining the localization of the chimeric transcription factor. The ERα LBD used in this study consists of amino acids 282–576 of the intact ERα and contains one of the three known ERα NLSs [55]. Although previous studies have shown that a truncated ERα containing only this NLS does not localize to the nucleus in either the presence or absence of a ligand, it is possible that this NLS can still facilitate nuclear localization in our chimeric protein [56,57]. These results might also indicate that mechanisms other than differential localization, for instance TF dimerization induced by ligand binding, could play a more important role in TF activation. To better understand how each version of the sensor protein affects the performance of the biosensor system, fold increases in fluorescence were calculated by dividing the MFI after genistein induction by the MFI without genistein induction. Overall, REV1p-GEVPR and REV1p-GEVPR2 outperformed other versions of the biosensor (Fig. 2c).Fig. 2Genistein biosensor optimization through GEV protein engineering. (a) Diagram comparing the domain composition of GEV, GEVPR and GEVPR2. (b, c) MFI values (b) and fold change in MFI upon genistein induction (c) of various genistein sensor strains based on GFP intensity measurement by flow cytometry. Genes encoding sensor proteins were driven by either a strong (TDH3p) or weak (REV1p) promoter. Cells were incubated with or without 100 µM genistein for 22 h. Values are shown as mean +/- s.d. from three biological replicates. AU, arbitrary units.Fig 2
3.3
Genistein biosensor optimization through reporter promoter engineering
Next, we sought to further improve our biosensor by engineering the GFP reporter, focusing our efforts on the reporter promoter. For the reporter to respond to the Gal4DBD-containing GEV sensor protein, its expression was driven by the yeast native GAL1 promoter (Fig. 3a, reporter A), which contains at its 5’ end a cluster of three canonical Gal4 binding sites, a non-canonical Gal4 binding site and two binding sites for Mig1, a transcriptional regulator that inhibits the expression of certain GAL genes in the presence of glucose [58]. In addition to the core GAL1 promoter, a previous study discovered that the bases GTA immediately upstream of the three canonical Gal4 binding sites are required for gene transcription by endogenous Gal4 [59]. Based on all this available knowledge on the GAL1 promoter, several modifications to the core native promoter were tested: 1) extending the core promoter 25 bases upstream of the native GAL1 promoter that included GTA bases; 2) removing Mig1 binding sites; and 3) adding multiple Gal4 binding sites 13 bases upstream of the native binding sites, with the additional Gal4 binding sites added 1–2 bases apart, mimicking the relative positions of Gal4 binding sites in the native GAL1 promoter. These modifications and their different combinations (Fig. 3a, reporters B-F), along with a synthetic promoter containing 13 Gal4 binding sites (Fig. 3a, reporter G) [48] were tested in conjunction with the core native GAL1 promoter to compare their effects on GFP signal intensity. For biosensor testing, all promoter-GFP variations were introduced into yeast strains containing either REV1p-GEVPR or REV1p-GEVPR2. Cells expressing the different versions of the promoter-GFP constructs without either REV1p-GEVPR or REV1p-GEVPR2 were used as controls. After 22 h of cultivation in media containing either no genistein or 100 µM genistein, cells were collected, and the MFI was measured by flow cytometry. Contrary to previous observations made with endogenous Gal4 transcription factor and GAL1 promoter, the inclusion of bases GTA upstream of the GAL1 promoter did not increase fluorescence intensity (Fig. 3b reporters B, C, E, F) [59]. A possible explanation for this discrepancy is that the reporter used in our study was integrated in a different site from the ho locus that was used in the previous study. As expected, removing Mig1 inhibitor binding sites increased both background and signal fluorescence (Fig. 3b reporters C, F). Interestingly, while having six Gal4 binding sites did not result in an increase in background and signal fluorescence, having five Gal4 binding sites did (Supplementary Fig. S2). Fortuitously, the promoter with five Gal4 binding sites came about because one of the cloning primers used harbored an error that abolished the first of the three upstream additional Gal4 binding sites. Finally, no fluorescence increase upon genistein induction was observed for the synthetic promoter containing 13 Gal4 binding sites. The synthetic promoter also produced the highest fluorescence in the absence of the sensor protein component. Besides the numerous Gal4 binding sites, this synthetic promoter used the HIS3 promoter as core promoter instead of the GAL1 promoter, leaving the possibility that factors other than GEVPR or GEVPR2 can activate transcription from the synthetic promoter (Fig. 3b reporter G). To tease out the best-performing reporter and sensor protein combination, the fold increase in fluorescence was calculated by dividing MFI after genistein induction by MFI without genistein induction. Overall, the biosensor using REV1p-GEVPR2 in combination with reporter F gave the highest fold change in reporter intensity upon genistein induction (Fig. 3c).Fig. 3Genistein biosensor optimization through reporter promoter engineering. (a) Schematic illustrations showing known motifs on the native GAL1 promoter (A) and engineered variants (B-G). (b, c) MFI values (b) and fold change in MFI upon genistein induction (c) of various genistein biosensor strains based on GFP intensity measurement by flow cytometry. Genes encoding sensor proteins were driven by the weak REV1 promoter. Cells were incubated with or without 100 µM genistein for 22 h. Values are shown as mean +/- s.d. from three biological replicates. AU, arbitrary units.Fig 3
3.4
Determination of biosensor selectivity
Similarity in structure among flavonoids can potentially lead to specificity issues regarding the development of biosensors. To assess whether the sensor protein is specific to genistein, we tested our improved biosensor against several other flavonoids. These flavonoids were chosen because they are either precursors or possible by-products in the genistein synthesis pathway (naringenin chalcone, naringenin, phloretin) most structurally similar to genistein (daidzein), or known to trigger the ER (quercetin, kaempferol, daidzein, resveratrol) [27,29]. Including genistein, a total of 8 flavonoids were tested at the respective maximum concentration at which each flavonoid remained soluble in the media. As controls, cells expressing either the reporter or sensor protein alone were treated in parallel (Supplementary Fig. S3). After 22 h of cultivation in media containing the different flavonoids, cells were collected and the MFI was measured through flow cytometry. Out of the 8 flavonoids tested, genistein was the only flavonoid that elicited detectable signals from our biosensor (Fig. 4). Control cells containing either the reporter or sensor protein expression cassette alone did not show any significant fold increase in fluorescence upon treatment with each flavonoid. To our surprise, the biosensor did not respond to quercetin, kaempferol, daidzein or resveratrol, even though these flavonoids have been shown to trigger ERα [27,29]. One possibility is that genistein is structurally more estrogenic compared to other flavonoids. Indeed, Kuiper et al. performed solid-phase binding and solubilized receptor-ligand binding assays, which showed higher binding affinities of genistein (relative to 17β-estradiol) to both ERα and ERβ compared to daidzein, apigenin, kaempferol, quercetin, naringenin and phloretin [27]. Ligand-binding experiments by fluorescence polarization were performed by Mueller et al., showing that genistein also exhibits higher relative binding affinities to ERα/β than resveratrol [29]. Through luciferase activity assays in ERα or ERβ expressing human cell lines, genistein was further found to have stronger transactivation activities than other flavonoids tested [27,29]. Moreover, genistein can compete with estradiol for ER binding whereas quercetin for instance cannot [60]. Although these experiments were conducted under different conditions, they may explain why our GEV-based biosensor is highly specific to genistein. Along the same lines, the absence of induction signal from other flavonoids could also be due to the flavonoids being present in insufficient concentrations. Each flavonoid was tested at its maximal soluble concentration in our media. It is conceivable that these concentrations were not high enough for flavonoids to bind to ERα. Another possibility is that other flavonoids exert different effects on estrogen receptor functions compared to genistein. For example, the interaction of quercetin with ERα requires ER domains other than the ligand binding domain [60]. Perhaps the mechanism of interaction between ligand and receptor differs among flavonoids. Overall, our improved biosensor is sufficiently selective for most applications.Fig. 4Determination of biosensor specificity. Fold change in MFI after induction with various flavonoids. Flavonoids were tested at the highest concentrations at which they were soluble in growth media as determined empirically: 44 µM naringenin chalcone, 184 µM naringenin, 74 µM genistein, 18 µM kaempferol, 47 µM daidzein, 272 µM resveratrol, 365 µM phloretin, 66 µM quercetin. Cells were incubated in media containing flavonoids for 22 h. Values were calculated based on GFP intensity measurement by flow cytometry and are shown as mean +/- s.d. from three biological replicates.Fig 4
3.5
Operational range characterization for the improved genistein biosensor
For most applications, the biosensor needs to be able to not just detect the presence of the target molecule, but also report on its concentration. As an example, for an application such as high-throughput cell screening, a larger signal separation between concentrations means easier downstream sorting. To determine how well our improved biosensor can distinguish between different amounts of genistein, cells were grown in media containing increasing genistein concentrations. The same treatment was performed on cells containing the original GEV biosensor for comparison. After 22 h of cultivation in the genistein-containing media, cells were collected and the MFI was measured by flow cytometry. Both biosensor variants responded to concentrations as low as 2 µM. Although more genistein resulted in higher GFP signals in both strains, greater signal separation was observed with the improved variant. For example, the original biosensor measured an average MFI of 134 AU at 6 µM genistein and 654 AU at 100 µM genistein, whereas the improved biosensor measured an average MFI of 22 AU at 6 µM genistein and 217 AU at 100 µM genistein (Supplementary Fig. S4). This resulted in a 7-fold and a 16-fold increase in MFI for the original and improved biosensor, respectively (Fig. 5). In conclusion, the improved biosensor functions better at distinguishing between genistein concentrations. Concentrations above 100 µM could not be tested due to the limited solubility of genistein in media.Fig. 5Operational range characterization for the improved genistein biosensor. Fold changes in MFI upon induction with increasing genistein concentrations based on GFP intensity measurement by flow cytometry. Comparison is made between the best-performing biosensor strain (solid dark gray bar) and the original biosensor (solid light gray bar). Reporter only controls for each strain (striped bars). Cells were incubated with or without indicated concentration of genistein for 22 h. Values are shown as mean +/- s.d. from three biological replicates.Fig 5
3.6
Biosensor application for in vivo genistein production
The goal for developing this biosensor was to be able to distinguish yeast strains producing different amounts of genistein. In plants, genistein is synthesized from the flavonoid naringenin via a two-step process: hydroxylation and concurrent aryl ring migration, followed by dehydration and double bond formation. The hydroxylation and ring migration step is catalyzed by isoflavone synthase (IFS), a cytochrome P450 monooxygenase that requires a cytochrome P450 reductase (CPR) for electron supply. The dehydration and double bond formation step can occur spontaneously but is facilitated by 2-hydroxyisoflavanone dehydratase (HID) in plants (Fig. 6a) [22]. When plant IFS genes from Glycine max (GmIFS), Glycyrrhiza echinata (GeIFS) or Medicago truncatula (MtIFS), and CPR and HID from G. max (GmCPR, GmHID) were introduced into our improved biosensor strain, the amount of genistein produced corresponded with the naringenin concentration added to the media (Fig. 6b). While the GmIFS expressing strain produced the highest amount of genistein (Fig. S5), Trifolium pratense IFS, which was reported to have the highest activity in S. cerevisiae, did not produce any genistein in our strain (data not shown). By measuring both fluorescence intensity and genistein amount, one could test how well the biosensor signal correlated with the level of genistein production. After cells were incubated for 22 h in media containing different concentrations of naringenin, the median fluorescence intensity was measured by flow cytometry, and the total intra- and extracellular genistein titer was measured by HPLC. A biosensor strain without expression of IFS, CPR and HID was included as control to confirm that naringenin itself does not induce GFP expression (data not shown). In general, a higher genistein titer resulted in a higher GFP signal within each replicate, but the correlation was not consistent across replicates (Fig. 6c). We argue that what the sensor protein actually detects, is the intracellular genistein level. When compared to intracellular genistein level, the GFP signal was found to be in good correlation at R2= 0.9498 (Fig. 6d). We conclude that our improved biosensor responds in a consistent, linear fashion to intracellular genistein levels.Fig. 6Biosensor application for in vivo genistein production. (a) Schematic representation of heterologous genistein synthesis from naringenin [22]. Enzymes used are IFS, CPR and HID originating from G. max. (b) After 22 h cultivation of yGEN06 in media containing stated naringenin concentration, the genistein titer was measured using HPLC. (c, d) After 22 h cultivation of yGEN06 in media containing different naringenin concentrations, fluorescence intensity was detected by flow cytometry and total genistein titer (c) and intracellular genistein amount (d) were detected by HPLC. The intracellular genistein amount was normalized to culture OD.Fig 6
3.1
Transcriptional activator GEV as genistein biosensor
To test whether GEV could function as a genistein biosensor in yeast, its gene driven by the TDH3 promoter (TDH3p-GEV) was integrated into the yeast genome along with a GFP reporter under the transcriptional control of the GAL1 promoter (GAL1p-GFP). After 22 h of cultivation in media containing either no genistein or 100 µM genistein, cells were collected, and the median fluorescence intensity (MFI) was measured by flow cytometry. The fluorescence intensity of cells containing only the TDH3p-GEV sensor protein expression cassette or the GAL1p-GFP reporter expression cassette and wild-type cells were collected as control. Compared with the control cells, cells expressing both TDH3p-GEV and GAL1p-GFP had the highest background fluorescence without genistein induction. However, a significant increase in fluorescence signal was observed upon genistein addition only in cells containing both TDH3p-GEV and GAL1p-GFP (Fig. 1c). When the magnitude of increase was calculated by dividing the fluorescence intensity with genistein induction (100 µM) over fluorescence intensity without genistein induction (0 µM), there was a 5-fold increase observed only in cells containing the complete circuit (Fig. 1d). Taken together, GEV can function as a biosensor for genistein in yeast.
3.2
Genistein biosensor optimization through GEV protein engineering
We sought to improve the performance of our genistein biosensor by first engineering the GEV sensor protein. Since it was observed that the biosensor produced a high fluorescence signal in the absence of genistein, the first step towards optimization was to reduce the background fluorescence by decreasing the amount of GEV sensor protein in the cell. This was accomplished by replacing the strong constitutive TDH3 promoter with a weaker REV1 promoter [50]. Furthermore, to increase the sensitivity of the sensor protein, its ability to activate transcription was enhanced by replacing the VP16 transactivating domain of the GEV with a stronger transcriptional activator, VP64-p65-Rta (VPR) [51]. VPR contains VP64, four repeats of VP16 [52], p65, the transactivating domain of human RelA [53], and the transactivating domain of Rta, a transcriptional activator encoded by the Epstein-Barr virus [54]. Lastly, the NLS sequence that was embedded between VP64 and p65 in the original version of VPR was removed based on the importance of differential GEV nuclear translocation to its function. The Gal4-ER-VPR chimeric protein was given the name GEVPR. The version of GEVPR that had the NLS removed from VPR was given the name GEVPR2 (Fig. 2a). To test whether GEVPR and GEVPR2 were indeed improvements over GEV, all three versions of the sensor protein, driven by either the TDH3 or the REV1 promoter, were introduced into cells expressing the reporter GAL1p-GFP. Cells expressing different versions of the sensor protein without the GAL1p-GFP reporter were used as controls. After 22 h of cultivation in media containing either no genistein or 100 µM genistein, cells were collected, and the MFI was measured by flow cytometry (Fig. 2b). As expected, replacing the TDH3 promoter with the REV1 promoter reduced both background fluorescence and genistein-induced signal in GEV, GEVPR and GEVPR2; meanwhile, replacing VP16 with VPR increased both background and signal fluorescence when sensor protein expression was driven by either the TDH3 or the REV1 promoter. Surprisingly, removing the NLS sequence in VPR caused a decrease in both background and signal fluorescence only when sensor protein expression was driven by the REV1 promoter. This could suggest that the NLS in VPR only plays a minor role in determining the localization of the chimeric transcription factor. The ERα LBD used in this study consists of amino acids 282–576 of the intact ERα and contains one of the three known ERα NLSs [55]. Although previous studies have shown that a truncated ERα containing only this NLS does not localize to the nucleus in either the presence or absence of a ligand, it is possible that this NLS can still facilitate nuclear localization in our chimeric protein [56,57]. These results might also indicate that mechanisms other than differential localization, for instance TF dimerization induced by ligand binding, could play a more important role in TF activation. To better understand how each version of the sensor protein affects the performance of the biosensor system, fold increases in fluorescence were calculated by dividing the MFI after genistein induction by the MFI without genistein induction. Overall, REV1p-GEVPR and REV1p-GEVPR2 outperformed other versions of the biosensor (Fig. 2c).Fig. 2Genistein biosensor optimization through GEV protein engineering. (a) Diagram comparing the domain composition of GEV, GEVPR and GEVPR2. (b, c) MFI values (b) and fold change in MFI upon genistein induction (c) of various genistein sensor strains based on GFP intensity measurement by flow cytometry. Genes encoding sensor proteins were driven by either a strong (TDH3p) or weak (REV1p) promoter. Cells were incubated with or without 100 µM genistein for 22 h. Values are shown as mean +/- s.d. from three biological replicates. AU, arbitrary units.Fig 2
3.3
Genistein biosensor optimization through reporter promoter engineering
Next, we sought to further improve our biosensor by engineering the GFP reporter, focusing our efforts on the reporter promoter. For the reporter to respond to the Gal4DBD-containing GEV sensor protein, its expression was driven by the yeast native GAL1 promoter (Fig. 3a, reporter A), which contains at its 5’ end a cluster of three canonical Gal4 binding sites, a non-canonical Gal4 binding site and two binding sites for Mig1, a transcriptional regulator that inhibits the expression of certain GAL genes in the presence of glucose [58]. In addition to the core GAL1 promoter, a previous study discovered that the bases GTA immediately upstream of the three canonical Gal4 binding sites are required for gene transcription by endogenous Gal4 [59]. Based on all this available knowledge on the GAL1 promoter, several modifications to the core native promoter were tested: 1) extending the core promoter 25 bases upstream of the native GAL1 promoter that included GTA bases; 2) removing Mig1 binding sites; and 3) adding multiple Gal4 binding sites 13 bases upstream of the native binding sites, with the additional Gal4 binding sites added 1–2 bases apart, mimicking the relative positions of Gal4 binding sites in the native GAL1 promoter. These modifications and their different combinations (Fig. 3a, reporters B-F), along with a synthetic promoter containing 13 Gal4 binding sites (Fig. 3a, reporter G) [48] were tested in conjunction with the core native GAL1 promoter to compare their effects on GFP signal intensity. For biosensor testing, all promoter-GFP variations were introduced into yeast strains containing either REV1p-GEVPR or REV1p-GEVPR2. Cells expressing the different versions of the promoter-GFP constructs without either REV1p-GEVPR or REV1p-GEVPR2 were used as controls. After 22 h of cultivation in media containing either no genistein or 100 µM genistein, cells were collected, and the MFI was measured by flow cytometry. Contrary to previous observations made with endogenous Gal4 transcription factor and GAL1 promoter, the inclusion of bases GTA upstream of the GAL1 promoter did not increase fluorescence intensity (Fig. 3b reporters B, C, E, F) [59]. A possible explanation for this discrepancy is that the reporter used in our study was integrated in a different site from the ho locus that was used in the previous study. As expected, removing Mig1 inhibitor binding sites increased both background and signal fluorescence (Fig. 3b reporters C, F). Interestingly, while having six Gal4 binding sites did not result in an increase in background and signal fluorescence, having five Gal4 binding sites did (Supplementary Fig. S2). Fortuitously, the promoter with five Gal4 binding sites came about because one of the cloning primers used harbored an error that abolished the first of the three upstream additional Gal4 binding sites. Finally, no fluorescence increase upon genistein induction was observed for the synthetic promoter containing 13 Gal4 binding sites. The synthetic promoter also produced the highest fluorescence in the absence of the sensor protein component. Besides the numerous Gal4 binding sites, this synthetic promoter used the HIS3 promoter as core promoter instead of the GAL1 promoter, leaving the possibility that factors other than GEVPR or GEVPR2 can activate transcription from the synthetic promoter (Fig. 3b reporter G). To tease out the best-performing reporter and sensor protein combination, the fold increase in fluorescence was calculated by dividing MFI after genistein induction by MFI without genistein induction. Overall, the biosensor using REV1p-GEVPR2 in combination with reporter F gave the highest fold change in reporter intensity upon genistein induction (Fig. 3c).Fig. 3Genistein biosensor optimization through reporter promoter engineering. (a) Schematic illustrations showing known motifs on the native GAL1 promoter (A) and engineered variants (B-G). (b, c) MFI values (b) and fold change in MFI upon genistein induction (c) of various genistein biosensor strains based on GFP intensity measurement by flow cytometry. Genes encoding sensor proteins were driven by the weak REV1 promoter. Cells were incubated with or without 100 µM genistein for 22 h. Values are shown as mean +/- s.d. from three biological replicates. AU, arbitrary units.Fig 3
3.4
Determination of biosensor selectivity
Similarity in structure among flavonoids can potentially lead to specificity issues regarding the development of biosensors. To assess whether the sensor protein is specific to genistein, we tested our improved biosensor against several other flavonoids. These flavonoids were chosen because they are either precursors or possible by-products in the genistein synthesis pathway (naringenin chalcone, naringenin, phloretin) most structurally similar to genistein (daidzein), or known to trigger the ER (quercetin, kaempferol, daidzein, resveratrol) [27,29]. Including genistein, a total of 8 flavonoids were tested at the respective maximum concentration at which each flavonoid remained soluble in the media. As controls, cells expressing either the reporter or sensor protein alone were treated in parallel (Supplementary Fig. S3). After 22 h of cultivation in media containing the different flavonoids, cells were collected and the MFI was measured through flow cytometry. Out of the 8 flavonoids tested, genistein was the only flavonoid that elicited detectable signals from our biosensor (Fig. 4). Control cells containing either the reporter or sensor protein expression cassette alone did not show any significant fold increase in fluorescence upon treatment with each flavonoid. To our surprise, the biosensor did not respond to quercetin, kaempferol, daidzein or resveratrol, even though these flavonoids have been shown to trigger ERα [27,29]. One possibility is that genistein is structurally more estrogenic compared to other flavonoids. Indeed, Kuiper et al. performed solid-phase binding and solubilized receptor-ligand binding assays, which showed higher binding affinities of genistein (relative to 17β-estradiol) to both ERα and ERβ compared to daidzein, apigenin, kaempferol, quercetin, naringenin and phloretin [27]. Ligand-binding experiments by fluorescence polarization were performed by Mueller et al., showing that genistein also exhibits higher relative binding affinities to ERα/β than resveratrol [29]. Through luciferase activity assays in ERα or ERβ expressing human cell lines, genistein was further found to have stronger transactivation activities than other flavonoids tested [27,29]. Moreover, genistein can compete with estradiol for ER binding whereas quercetin for instance cannot [60]. Although these experiments were conducted under different conditions, they may explain why our GEV-based biosensor is highly specific to genistein. Along the same lines, the absence of induction signal from other flavonoids could also be due to the flavonoids being present in insufficient concentrations. Each flavonoid was tested at its maximal soluble concentration in our media. It is conceivable that these concentrations were not high enough for flavonoids to bind to ERα. Another possibility is that other flavonoids exert different effects on estrogen receptor functions compared to genistein. For example, the interaction of quercetin with ERα requires ER domains other than the ligand binding domain [60]. Perhaps the mechanism of interaction between ligand and receptor differs among flavonoids. Overall, our improved biosensor is sufficiently selective for most applications.Fig. 4Determination of biosensor specificity. Fold change in MFI after induction with various flavonoids. Flavonoids were tested at the highest concentrations at which they were soluble in growth media as determined empirically: 44 µM naringenin chalcone, 184 µM naringenin, 74 µM genistein, 18 µM kaempferol, 47 µM daidzein, 272 µM resveratrol, 365 µM phloretin, 66 µM quercetin. Cells were incubated in media containing flavonoids for 22 h. Values were calculated based on GFP intensity measurement by flow cytometry and are shown as mean +/- s.d. from three biological replicates.Fig 4
3.5
Operational range characterization for the improved genistein biosensor
For most applications, the biosensor needs to be able to not just detect the presence of the target molecule, but also report on its concentration. As an example, for an application such as high-throughput cell screening, a larger signal separation between concentrations means easier downstream sorting. To determine how well our improved biosensor can distinguish between different amounts of genistein, cells were grown in media containing increasing genistein concentrations. The same treatment was performed on cells containing the original GEV biosensor for comparison. After 22 h of cultivation in the genistein-containing media, cells were collected and the MFI was measured by flow cytometry. Both biosensor variants responded to concentrations as low as 2 µM. Although more genistein resulted in higher GFP signals in both strains, greater signal separation was observed with the improved variant. For example, the original biosensor measured an average MFI of 134 AU at 6 µM genistein and 654 AU at 100 µM genistein, whereas the improved biosensor measured an average MFI of 22 AU at 6 µM genistein and 217 AU at 100 µM genistein (Supplementary Fig. S4). This resulted in a 7-fold and a 16-fold increase in MFI for the original and improved biosensor, respectively (Fig. 5). In conclusion, the improved biosensor functions better at distinguishing between genistein concentrations. Concentrations above 100 µM could not be tested due to the limited solubility of genistein in media.Fig. 5Operational range characterization for the improved genistein biosensor. Fold changes in MFI upon induction with increasing genistein concentrations based on GFP intensity measurement by flow cytometry. Comparison is made between the best-performing biosensor strain (solid dark gray bar) and the original biosensor (solid light gray bar). Reporter only controls for each strain (striped bars). Cells were incubated with or without indicated concentration of genistein for 22 h. Values are shown as mean +/- s.d. from three biological replicates.Fig 5
3.6
Biosensor application for in vivo genistein production
The goal for developing this biosensor was to be able to distinguish yeast strains producing different amounts of genistein. In plants, genistein is synthesized from the flavonoid naringenin via a two-step process: hydroxylation and concurrent aryl ring migration, followed by dehydration and double bond formation. The hydroxylation and ring migration step is catalyzed by isoflavone synthase (IFS), a cytochrome P450 monooxygenase that requires a cytochrome P450 reductase (CPR) for electron supply. The dehydration and double bond formation step can occur spontaneously but is facilitated by 2-hydroxyisoflavanone dehydratase (HID) in plants (Fig. 6a) [22]. When plant IFS genes from Glycine max (GmIFS), Glycyrrhiza echinata (GeIFS) or Medicago truncatula (MtIFS), and CPR and HID from G. max (GmCPR, GmHID) were introduced into our improved biosensor strain, the amount of genistein produced corresponded with the naringenin concentration added to the media (Fig. 6b). While the GmIFS expressing strain produced the highest amount of genistein (Fig. S5), Trifolium pratense IFS, which was reported to have the highest activity in S. cerevisiae, did not produce any genistein in our strain (data not shown). By measuring both fluorescence intensity and genistein amount, one could test how well the biosensor signal correlated with the level of genistein production. After cells were incubated for 22 h in media containing different concentrations of naringenin, the median fluorescence intensity was measured by flow cytometry, and the total intra- and extracellular genistein titer was measured by HPLC. A biosensor strain without expression of IFS, CPR and HID was included as control to confirm that naringenin itself does not induce GFP expression (data not shown). In general, a higher genistein titer resulted in a higher GFP signal within each replicate, but the correlation was not consistent across replicates (Fig. 6c). We argue that what the sensor protein actually detects, is the intracellular genistein level. When compared to intracellular genistein level, the GFP signal was found to be in good correlation at R2= 0.9498 (Fig. 6d). We conclude that our improved biosensor responds in a consistent, linear fashion to intracellular genistein levels.Fig. 6Biosensor application for in vivo genistein production. (a) Schematic representation of heterologous genistein synthesis from naringenin [22]. Enzymes used are IFS, CPR and HID originating from G. max. (b) After 22 h cultivation of yGEN06 in media containing stated naringenin concentration, the genistein titer was measured using HPLC. (c, d) After 22 h cultivation of yGEN06 in media containing different naringenin concentrations, fluorescence intensity was detected by flow cytometry and total genistein titer (c) and intracellular genistein amount (d) were detected by HPLC. The intracellular genistein amount was normalized to culture OD.Fig 6
4
Conclusion
In this work, we developed a highly specific genistein biosensor in S. cerevisiae based on the previously developed GEV transcriptional activator. The maximum dynamic range of the biosensor was expanded from 5- to 20-fold by rational engineering of different aspects of the sensor protein/reporter design. We further determined an operational range of 2–100 µM (with higher concentrations being limited by genistein solubility in media) and confirmed the applicability of the biosensor for intracellular genistein detection. For future optimization one might consider exploring other DNA and ligand binding domains for the sensor protein that may provide even higher affinities or improved orthogonality. For instance, exchanging the Gal4DBD for a heterologous one like the bacterial LexA DBD [61,62] could avoid off-target activation of endogenous Gal4-regulated genes and permit biosensor application in galactose-containing medium. In addition to ERα, genistein has also been reported to trigger ERβ, the other estrogen receptor in humans. ERβ has a higher affinity for genistein (0.86%) compared to ERα (0.032%) relative to estradiol [63]. Therefore, replacing the LBD of GEVPR2 with the LBD of ERβ could in theory produce a biosensor that is more sensitive to low levels of genistein.
Biosensors have the potential to expedite the test phase of the design-build-test-learn cycle by facilitating microbial cell factory screening and thus shortening the overall time required for the development of a novel production process. Taken together, we think that our sensor can aid in the development of a genistein-producing S. cerevisiae strain.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: Polyamine oxidation enzymes regulate sexual mating/filamentation and pathogenicity in | Body:
1
INTRODUCTION
The sugarcane smut disease that seriously affects sugarcane yield and results in economic losses is caused by the dimorphic fungus Sporisorium scitamineum, which has three lifestyles: haploid yeast‐type sporidia (unable to infect the host) of two opposite mating types (MAT‐1 and MAT‐2), dikaryotic hyphae, and diploid teliospores (Piepenbring et al., 2002; Yan et al., 2016). Sexual mating between haploid sporidia of different mating types gives rise to dikaryotic hyphae capable of infecting host canes (Yan et al., 2016). The dikaryotic hyphae grow in the apoplastic space between the plant cells of young apical tissues for weeks to months, before formation of teliospores wrapped by plant tissues in a ‘whip’ shape, the typical symptom of sugarcane smut disease (Lu et al., 2021). Therefore, sexual mating/filamentation and teliospore formation play a key role in S. scitamineum pathogenicity.
Polyamines are small organic polycations with primary and secondary amino groups. They are widely found in all living organisms and participate in a variety of biological processes (Rocha & Wilson, 2019). The most common polyamines are diamine putrescine (PUT), triamine spermidine (SPD), and tetramine spermine (SPM), although a large number of algae, fungi, and bacterial species do not contain SPM (Hamana & Matsuzaki, 1982; Tavladoraki et al., 2012; Valdes‐Santiago et al., 2010). Polyamine biosynthesis, conjugation, catabolism, acetylation, and transport are strictly regulated to maintain the homeostasis of the polyamine pool and intracellular environment (Tavladoraki et al., 2012). Insufficient or excessive levels of polyamines are detrimental to cell growth and development and may trigger a series of reactions leading to cell death (Gerlin et al., 2021; Kusano et al., 2008). Polyamine metabolism pathways in plants and animals have been extensively studied (Casero & Marton, 2007; Efrose et al., 2008; Kusano et al., 2008). Our previous work elucidated the role of the polyamine biosynthesis pathway in S. scitamineum (Yin et al., 2024). Besides de novo biosynthesis of polyamines, intracellular polyamine homeostasis can be maintained by oxidization and/or acetylation. Amine oxidases catalyse the oxidative de‐amination of polyamines at their primary or secondary amino‐groups, which can be classified according to their cofactors as flavine‐containing polyamine oxidases or copper‐containing amine oxidases (CuAOs) (Cona et al., 2006; Planas‐Portell et al., 2013). Flavine‐containing amine oxidases include acetylated polyamine oxidase (APAO), polyamine oxidase (PAO), monoamine oxidase (MAO), and SPM oxidase (SMO) (McGrath et al., 2011; Miller‐Fleming et al., 2015). Two types of quinone cofactors exist in CuAOs, namely 2,4,5‐trihydroxyphenylalanine quinone (TPQ) and lysine tyrosyl quinone (LTQ) (McGrath et al., 2011).
Polyamine oxidases from plants and bacteria can oxidize both SPM and SPD, resulting in products including 4‐aminobutyraldehyde, 1,3‐diaminopropane, and hydrogen peroxide (H2O2), a type of reactive oxygen species (ROS) causing oxidative stress and damage to cellular components. However, the production of ROS during polyamine oxidation can also activate the antioxidative response in cells (Murray Stewart et al., 2018). The first characterized PAO, maize PAO (ZmPAO), is an apoplastic enzyme (Tavladoraki et al., 1998). AtPAO1, AtPAO2, AtPAO3, and AtPAO4 of Arabidopsis thaliana preferentially oxidize the free form of SPD, SPM, T‐SPM (thermospermine), or Nor‐SPM (norspermine), to produce 1,3‐aminopropanal, H2O2, PUT or SPD (Fincato et al., 2011; Kamada‐Nobusada et al., 2008; Moschou et al., 2008; Ono et al., 2012; Tavladoraki et al., 2006, 2016). Besides the aforementioned substrates, AtPAO5 oxidizes also N
1‐acetyl‐SPM (Ahou et al., 2014; Liu et al., 2014), and regulates A. thaliana growth (Ahou et al., 2014; Kim et al., 2014; Tavladoraki et al., 2016). Catabolism of polyamines in mammals is sequentially catalysed by SMO, which specifically oxidizes SPM to produce SPD, H2O2, and 1,3‐aminopropanal, and then by inducible SPM/SPD N1‐acetyltransferase (SSAT, which transfers an acetyl group to SPM or SPD) or the constitutively expressed N1‐acetylpolyamine oxidase (APAO) (oxidase acetyl‐SPM and acetyl‐SPD) (Cervelli et al., 2013, 2016; Polticelli et al., 2012). SSAT‐mediated acetylation can reduce the positive charge of polyamines, thereby preventing their interaction with other molecules (Seiler, 1987). The acetylated SPD or SPM can be excreted or oxidized by APAO, to become SPD or SPM again (Wallace et al., 2003). These cyclic reactions allow the cells to rapidly regulate the intracellular concentration of SPD or SPM (Seiler, 1987). Saccharomyces cerevisiae PAO (named Fms1) is able to oxidize SPM and acetyl‐SPM, producing SPD and 1,3‐aminopropanal (Huang et al., 2005; Landry & Sternglanz, 2003; Tavladoraki et al., 1998). CuAOs from microbes, animals, and plants catalyse the oxidative deamination of primary amines in polyamines, but no activity has been found against secondary or tertiary amines of PUT and SPD, as opposed to PAOs (Largeron et al., 2010; Shepard & Dooley, 2015). CuAO‐catalysed oxidation forms the corresponding aldehydes, with concomitant reduction of molecular oxygen to H2O2 (Parsons et al., 1995). Amino oxidases including PAOs and CuAOs contribute to polyamines and cellular redox homeostasis in plant cells during growth, development, and response to abiotic or biotic stresses (Angelini et al., 2010; Cona et al., 2006; Rea et al., 2004; Walters, 2003; Wu et al., 2003). However, biological function and regulatory mechanism of oxidation of polyamines has not been reported in S. scitamineum.
In this study, we identified SsPAO and SsCuAO1 in S. scitamineum and characterized their biological functions by reverse genetics. Deletion of SsPAO or SsCuAO1 led to reduced sporidia growth, reduced sexual mating/filamentation, and weak or loss of pathogenicity in S. scitamineum. Our results further demonstrate that modulation of intracellular polyamines by SsPAO or SsCuAO1 could regulate the cAMP‐PKA pathway, which may affect autophagy in S. scitamineum. Overall, our work reveals that SsPAO and SsCuAO1 regulate sexual mating/filamentation and pathogenicity in S. scitamineum, probably by affecting turnover of intracellular polyamines and the consequent redox homeostasis.
2
RESULTS
2.1
SsPAO and SsCuAO1 modulate polyamine levels
To identify enzymes catalysing oxidation of polyamines in S. scitamineum, we used the FMS1‐polyamine oxidase PAO (XP_011392213.1) (Valdes‐Santiago et al., 2010) and the annotated copper amine oxidase 1 CuAO1 (XP_011390076.1) in Ustilago maydis, as the queries to perform a BLASTP search on the NCBI website (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome). CDU22952.1 (encoded by SPSC_01582) was identified as an orthologue of U. maydis PAO, and CDR87209.1 (encoded by SPSC_00335) was orthologous to U. maydis CuAO1. We named these two proteins as SsPAO and SsCuAO1, respectively. Phylogenetic analysis using SsPAO and SsCuAO1 and their orthologues from smut fungi or ascomycetes (Table S1) showed that these two proteins were closest to the orthologues in Sporisorium reilianum, forming a clade within smut fungi and separating from ascomycetes (Figure S1a,b). Based on prediction by the SmartBLAST tool (https://blast.ncbi.nlm.nih.gov/smartblast/), SsPAO contains a conserved amino_oxidase domain (PF01593) (Figure S1a), and SsCuAO1 contains a copper amine oxidase N2‐terminal domain (PF02727) and a copper amine oxidase catalytic domain (PF01179) (Figure S1b).
By homologous recombination, we generated deletion mutants of the SsPAO or SsCuAO1 genes in both MAT‐1 and MAT‐2 backgrounds, as verified by Southern blotting (Figure S2a,b). We named the single‐deletion mutants as ss1paoΔ (in MAT‐1 background) and ss2paoΔ (in MAT‐2 background), ss1cuao1Δ (in MAT‐1 background), and ss2cuao1Δ (in MAT‐2 background). Meanwhile, the genetic complementation strains were generated, namely Ss1PAO‐COM, Ss2PAO‐COM, Ss1CuAO1‐COM, and Ss2CuAO1‐COM, and were verified by PCR (Figure S2c,d). Fluorescence microscopy analysis revealed that the fusion proteins GFP‐SsPAO and SsCuAO1‐RFP used for genetic complementation were localized in the cytoplasm (Figure S2e,f). For all the deletion mutants and complementation strains, at least two independent strains were obtained and used for the following assessments.
To assess the possible function of SsPAO and SsCuAO1 in polyamine metabolism, we measured the intracellular and extracellular levels of PUT, SPD, and SPM in the wild‐type (WT) (MAT‐1), ss1paoΔ, and ss1cuao1Δ mutant by liquid chromatography‐mass spectrometry (LC–MS) analysis, following an established protocol (Yin et al., 2024). The intracellular level of PUT was significantly higher in ss1paoΔ (p < 0.05) and ss1cuao1Δ (p < 0.001) compared to MAT‐1, but there was no significant difference in intracellular SPD or SPM level (Figure 1a). The extracellular level of SPM was significantly higher in ss1cuao1Δ (p < 0.01) compared to MAT‐1 (Figure 1a). We further evaluated the effect of polyamines on transcription of SsPAO and SsCuAO1 genes. Liquid minimal medium (MM)‐cultured MAT‐1 sporidia were treated with PUT (1 mM), SPD (1 mM), or SPM (1 mM), for 90 min, before total RNA extraction for reverse transcription‐quantitative PCR (RT‐qPCR) analysis. The result showed that PUT treatment caused down‐regulation of SsCuAO1 and polyamine biosynthetic genes (SsAGA, SsAGT, SsODC, SsSAMDC, and SsSPDS/SPMS) (Figure 1b). In contrast, SPD treatment up‐regulated transcription of SsPAO, SsCuAO1, and SsAGA, but down‐regulated SsSAMDC and SsSPDS/SPMS transcription (Figure 1b). SPM treatment up‐regulated SsCuAO1 but down‐regulated SsPAO and the biosynthetic genes (Figure 1b). Our previous study showed that SsCuAO1 was up‐regulated in the ss1agaΔ mutant (Yin et al., 2024), probably as a response to elevated PUT or SPM levels. Consistent with the results obtained here, this suggests that SsPAO may be involved in SPD oxidation to produce PUT, and SsCuAO1 may oxidize SPM and/or SPD to produce SPD and PUT, respectively.
FIGURE 1
SsPAO and SsCuAO1 contribute to maintaining polyamine levels in Sporisorium scitamineum. (a) Measurement of intracellular and extracellular polyamines of MAT‐1, ss1paoΔ or ss1cuao1Δ. Fresh haploid sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Polyamine contents were measured from crude extracts of fungal cells or from the supernatant of culture medium, by LC–MS. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, versus MAT‐1. (b) Transcriptional profiling of SsPAO, SsCuAO1 and polyamine biosynthesis genes in MAT‐1 after exogenous supply of 1 mM putrescine (PUT), spermidine (SPD) or spermine (SPM). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus control (untreated cell). (c) Transcriptional profiling of polyamine biosynthesis genes and ‘a locus’ genes (SsMFA1 and SsPRA1) in ss1paoΔ and ss1cuao1Δ, compared to MAT‐1. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus MAT‐1. (d) Transcriptional profiling of SsPAO or SsCuAO1 genes during early stage of sugarcane infection. At 0, 3 or 7 days post‐inoculation (dpi), approximately 3 cm stems of the inoculated site were sampled for RNA extraction. ***p < 0.001, ****p < 0.0001, versus SsPAO (0 dpi). For (b–d), the relative gene expression levels were calculated according to the −ΔΔC
t method (Livak & Schmittgen, 2001), with Actin as an internal control. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3).
During sexual mating/filamentation, relative expression levels of polyamine biosynthesis genes and the ‘a locus’ genes (pheromone MFA1 and pheromone receptor PRA1) were examined in these two mutants compared to WT MAT‐1 × MAT‐2. Results showed that SsODC, SsSAMDC, SsSPDS/SPMS, SsMFA1, and SsPRA1 were down‐regulated in the ss1paoΔ mutant. On the other hand, SsSAMDC and SsSPDS/SPMS were down‐regulated while SsMFA1 and SsPRA1 were up‐regulated in the ss1cuao1Δ mutant (Figure 1c). Subsequently, we measured the relative transcription levels of SsPAO and SsCuAO1 during the early stage of host infection, when changes in intracellular and/or extracellular levels of polyamines in sugarcane–S. scitamineum interface were expected. The results showed that SsPAO and SsCuAO1 were down‐regulated at 3 and 7 days post‐inoculation (dpi) (Figure 1d). We infer that the pathogen needs to maintain a high level of polyamines, especially for SPD as reported (Yin et al., 2024), during host infection, thus down‐regulating SsPAO and SsCuAO1 at this stage.
Overall, our results showed that SsPAO and SsCuAO1 modulate polyamine levels in S. scitamineum during sexual mating/filamentation and host infection stages.
2.2
SsPAO
and
SsCuAO1
are required for sporidia growth, sexual mating, and filamentation
Next, we assessed the yeast‐like growth and development of sporidia of ss1paoΔ and ss1cuao1Δ mutants in comparison to MAT‐1. Under the microscope, we noticed that ss1paoΔ sporidia were significantly (p < 0.0001) longer than MAT‐1, but there was no significant difference in ss1cuao1Δ compared with MAT‐1 (Figure 2a,b). Growth curve analysis showed that the logarithmic growth phase of ss1paoΔ and ss1cuao1Δ significantly lagged behind that of MAT‐1 (Figure S3a,b). Addition of PUT (1 mM), SPD (1 mM) or SPM (0.1 mM) did not affect the growth of MAT‐1 or ss1paoΔ (Figure S3a,b), whereas SPM significantly suppressed the growth of ss1cuao1Δ sporidia (Figure S3b). There was no significant difference in colony morphology of ss1paoΔ and MAT‐1 when spotted on potato dextrose agar (PDA) or minimal medium (MM) in the presence of PUT (1 mM), SPD (1 mM), or SPM (0.1 mM), whereas SPM significantly suppressed growth of ss1cuao1Δ sporidia on solid MM (Figure S3c). Overall, loss of SsPAO or SsCuAO1 led to a lag in sporidia growth of S. scitamineum, and ss1cuao1Δ seemed more sensitive to exogenous SPM treatment, further supporting that SsCuAO1, but not SsPAO, may be involved in SPM oxidation.
FIGURE 2
SsPAO and SsCuAO1 are required for sporidia growth and sexual mating/filamentation. (a) Microscopic observation of sporidia morphology of MAT‐1, ss1paoΔ and ss1cuao1Δ. The fresh haploid sporidia were cultured in liquid YePS medium for 16 h and subject to microscopic observation and imaging. Scale bar = 10 μm. (b) Quantitative analysis of the length of MAT‐1, ss1paoΔ and ss1cuao1Δ. n = 20. ****p < 0.0001, versus MAT‐1. (c) Mating assay of wild‐type (WT) (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). Fresh haploid sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Equal volume of the compatible WT strain or mutants of opposite mating types were mixed and spotted onto potato dextrose agar (PDA), containing or without (control) putrescine (PUT, 0.1 mM), spermidine (SPD, 1 mM) or spermine (SPM, 0.1 mM). The mating cultures were incubated at 28°C for 2–3 days, before observation and imaging. Scale bar = 1 mm. (d) Mating assay of WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ), SsPAO‐COM (Ss1PAO‐COM × Ss2PAO‐COM), sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ), and SsCuAO1‐COM (Ss1CuAO1‐COM × Ss2CuAO1‐COM). Fresh haploid sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Equal volume of the compatible WT strain or mutants of opposite mating types were mixed and spotted onto potato dextrose agar. The mating cultures were incubated at 28°C for 2–3 days, before observation and imaging. Scale bar = 1 mm. (e) Transcriptional profiling of sexual mating genes in ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ mutants, in comparison to MAT‐1 × MAT‐2. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ****p < 0.0001, versus MAT‐1 × MAT‐2.
Dikaryotic hyphal growth after sexual mating was significantly reduced in ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ compared to MAT‐1 × MAT‐2 (Figure 2c). Genetic complementation of SsPAO or SsCuAO1 fully restored dikaryotic hyphae in the respective mutants (Figure 2d), confirming that these two genes were indeed required for S. scitamineum sexual mating/filamentation. Exogenous addition of a low concentration of PUT (0.1 mM) sightly promoted filamentation of MAT‐1 × MAT‐2 and ss1cuao1Δ × ss2cuao1Δ, whereas SPD (1 mM) or SPM (0.1 mM) markedly restored sexual mating/filamentation of ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ (Figure 2c). RT‐qPCR analysis showed that the relative expression levels of the genes involved in sexual mating/filamentation, including SsKPP2, SsbE, SsbW, SsPRF1, and SsROP1, were all significantly down‐regulated in ss1paoΔ × ss2paoΔ, while SsGPA3, SsUAC1, SsADR1, SsFUZ7, SsKPP4, SsKPP2, SsbE, SsbW, SsPRF1, SsROP1, and SsCRK1 were all down‐regulated in ss1cuao1Δ × ss2cuao1Δ (Figure 2e). We infer that increased intracellular PUT content in ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ could be responsible for the defective sexual mating/filamentation.
Overall, we conclude that SsPAO and SsCuAO1 are required for the sporidia growth, sexual mating and filamentation of S. scitamineum.
2.3
SsPAO
and
SsCuAO1
are required for S. scitamineum pathogenicity
We further assessed the role of SsPAO and SsCuAO1 in S. scitamineum pathogenicity. Sugarcane seedlings were inoculated with MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ, or ss1cuao1Δ × ss2cuao1Δ sporidia and disease symptoms were documented at 3–6 months post‐inoculation. The disease incidence (appearance of whip structures) of wild‐type infected plants was 40.8 ± 8.2% (n = 20), and 9.2 ± 5.9% (n = 20) for those infected by ss1paoΔ × ss2paoΔ. No black whip symptom was found in the seedlings infected by ss1cuao1Δ × ss2cuao1Δ (n = 20) (Figure 3a,b). We measured the average plant height as an indicator of smut fungus infection. The mutant‐infected plants or noninfected (inoculated with double‐distilled water as a blank control) plants were significantly taller than those infected by MAT‐1 × MAT‐2 (Figure 3c). Teliospores of MAT‐1 × MAT‐2 or ss1paoΔ × ss2paoΔ collected from the diseased plants, as verified by PCR amplification (Figure S4a), were assessed for germination. After 30 days of incubation under a cyclic light/dark condition, the promycelia formed from wild‐type teliospores displayed obvious and dense light and dark bands (width of 2.44 ± 0.45 mm), while the sspaoΔ promycelial colony appeared looser (width of 3.90 ± 0.29 mm; Figure 3d,e).
FIGURE 3
SsPAO and SsCuAO1 are required for Sporisorium scitamineum pathogenicity. (a) Pathogenicity assay. One millilitre of mixed sporidia of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ was injected into the seedlings of sugarcane cultivar ROC22, with double‐distilled water serving as a blank control. Each treatment contained at least 20 seedlings (n = 20). Red boxed region is shown enlarged, showing the ‘black whip’ symptoms appearing on top of seedlings infected by the wild type (WT) (MAT‐1 × MAT‐2). (b) Incidence rate of disease symptom in the seedlings infected by WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) or sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). *p < 0.05, **p < 0.01, versus WT (MAT‐1 × MAT‐2). (c) Quantitative analysis of the heights of infected seedlings. n = 34, 21, 20, 23, respectively. ***p < 0.001, ****p < 0.0001, versus WT (MAT‐1 × MAT‐2). (d) WT or sspaoΔ teliospores were allowed to germinate under a cyclic light/dark (24 h/24 h) condition for 30 days. The resultant promycelial colonies were photographed. Scale bar = 9 mm. (e) Quantitative analysis of the band width of promycelial colonies from WT or sspaoΔ teliospores. Bar chart depicts the mean ± SD derived from three independent biological repeats, each containing five replications (n = 5). ***p < 0.001, versus WT. (f) Analysis of fungal biomass by quantitative PCR. Mixed sporidia of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ were injected into the sugarcane seedlings, and the infected stems (about 3 cm) were sampled for DNA extraction at 3 and 7 days post‐inoculation (dpi). Fungal Actin gene and sugarcane GAPDH gene served as internal controls. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). ***p < 0.001, versus WT (MAT‐1 × MAT‐2).
Next, we measured the fungal biomass in the infected seedlings at 3 and 7 dpi. The biomass of ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ mutants was comparable to MAT‐1 × MAT‐2 at 3 dpi (Figure 3f), further supporting that the oxidation of polyamines may not be needed at the early stage of host infection. At 7 dpi, biomass of the WT strain became almost 2‐fold of that at 3 dpi, while ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ did not seem obviously increased in the infected plants (Figure 3f). This indicates that the mutants were less capable of adapting to the host environment and/or quickly proliferating in planta.
Overall, we conclude that SsPAO‐ and SsCuAO1‐mediated polyamine homeostasis plays an important role in host colonization and pathogenicity of S. scitamineum.
2.4
Polyamine oxidation affects cAMP‐PKA pathway
It has been established that cAMP signalling pathway regulates sexual mating and filamentation in smut fungi including S. scitamineum (Chang et al., 2019; Kaffarnik et al., 2003). We therefore assessed the effect of cAMP on sexual mating/filamentation of WT, sspaoΔ or sscuao1Δ. A low concentration cAMP (0.1 mM) fully restored filamentation of sspaoΔ or sscuao1Δ (Figure 4a). In contrast, a high concentration cAMP (1 mM) inhibited filamentation of WT but was less inhibitory to filamentation of sscuao1Δ (Figure 4a). cAMP, of either low or high concentration, did not affect sporidia growth of MAT‐1, ss1paoΔ or ss1cuao1Δ (Figure S4b). However, the intracellular contents of cAMP were comparable in ss1paoΔ or ss1cuao1Δ to MAT‐1 sporidia, or in ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ to MAT‐1 × MAT‐2 (Figure 4b). By RT‐qPCR, we assessed transcriptional levels of genes encoding enzymes for cAMP synthesis (adenylate cyclase, UAC1) or hydrolysis (3′,5′‐cyclic‐AMP phosphodiesterase, PDE1) in WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) or sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). The results showed that SsUAC1 was significantly down‐regulated in ss1cuao1Δ × ss2cuao1Δ and up‐regulated in ss1paoΔ × ss2paoΔ compared to MAT‐1 × MAT‐2, whereas SsPDE1 was significantly down‐regulated in ss1cuao1Δ × ss2cuao1Δ (Figure 4c).
FIGURE 4
Assessment of cAMP‐PKA pathway in sspaoΔ and sscuao1Δ mutants. (a) cAMP differentially regulates sexual mating/filamentation in sspaoΔ and sscuao1Δ mutants. The mating cultures were allowed to grow on potato dextrose agar (PDA) containing (0.1 or 1 mM) or without (control) cAMP, at 28°C for 2–3 days, before observation and imaging. Scale bar = 1 mm. (b) Measurement of intracellular cAMP level of MAT‐1, ss1paoΔ, ss1cuao1Δ, wild‐type (WT) (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). Sporidia cultured in liquid YePS medium for 24 h before measurement of the intracellular cAMP level. For mating cultures, sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Equal volumes of the compatible WT strain or mutants of opposite mating types were mixed and spotted onto solid YePS medium. The mating cultures were incubated at 28°C for 2–3 days, before measurement of the intracellular cAMP level. The cAMP level was measured from crude extracts, by a Complete cAMP ELISA kit. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). (c) Transcriptional profile of SsUAC1 and SsPDE1 in WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, ****p < 0.0001, versus WT (MAT‐1 × MAT‐2). (d) Mating assay of WT (MAT‐1 × MAT‐2), ss1paoΔ × MAT‐2, GAPD::SsPRF1 (ss1paoΔ) × MAT‐2, ss1cuao1Δ × MAT‐2 and GAPD::SsPRF1 (ss1cuao1Δ) × MAT‐2. Scale bar = 1 mm.
We further constructed a reintegrated homologous fragment for overexpression of PRF1 (pheromone response factor 1) gene, which is known as a central transcriptional regulator in sexual mating/filamentation in S. scitamineum (Zhu et al., 2019). By homologous recombination we inserted the SsPRF1 overexpression fragments in the genome of the ss1paoΔ and ss1cuao1Δ mutants, resulting in GAPD::SsPRF1 (ss1paoΔ) and GAPD::SsPRF1 (ss1cuao1Δ) strains as verified by RT‐qPCR (Figure S4c). Filamentation was slightly promoted in GAPD::SsPRF1 (ss1cuao1Δ) × MAT‐2, but not in GAPD::SsPRF1 (ss1paoΔ) × MAT‐2 (Figure 4d).
Taken together, we conclude that deletion of SsCuAO1 leads to the imbalance of intracellular polyamines, which affects cAMP‐PKA signalling, but not cAMP level that caused defective sexual mating/filamentation.
2.5
SsPAO
and
SsCuAO1
regulate autophagy
It has been reported that SPD induces autophagy in yeast, and PUT stimulates autophagy in the phytopathogenic fungus Colletotrichum higginsianum (Madeo et al., 2010; Yan et al., 2022). We wondered whether cAMP and/or polyamines regulate autophagy in S. scitamineum. Therefore, we used GFP‐SsAtg8 as a marker to monitor non‐selective autophagy in MAT‐1. Both the full‐length GFP‐SsAtg8 fusion protein (42 kDa) and the band corresponding to free GFP peptide (27 kDa) could be detected by anti‐GFP antibody. When the fungal cells were cultured in rich medium (YePS), usually only the full‐length GFP‐SsAtg8 band was detected, whereas the free GFP peptide was detected and accumulated in the fungal cells cultured under nitrogen starvation (on MM−N) conditions (Figure 5a,b). This verifies that the strain GFP‐SsATG8:MAT‐1 could be used for monitoring autophagy activity in S. scitamineum. The ratio of GFP:(GFP + GFP‐ATG8) was used as an indicator of autophagy level. When treated with PUT, SPD, SPM, or cAMP, the GFP:(GFP + GFP‐ATG8) ratio was increased under both YePS and MM−N culture conditions (Figure 5a,b), indicating that polyamines and cAMP were capable of enhancing autophagy activity, especially under rich medium conditions.
FIGURE 5
SsPAO and SsCuAO1 regulate autophagy. (a) Fungal cells were cultured in liquid YePS medium (rich medium) for 16 h, then transferred to fresh liquid YePS or MM−N (nitrogen starvation), with or without supplement of putrescine (PUT, 0.1 mM or 1 mM), spermidine (SPD, 0.1 mM or 1 mM), spermine (SPM, 0.1 mM or 1 mM), or cAMP (0.1 mM or 1 mM), for further incubation for 4 h. Immunoblotting analysis with anti‐GFP antibody was performed to evaluate autophagy activity. Coomassie brilliant blue (CBB) staining served as loading control. The GFP:(GFP + GFP‐ATG8) ratio was calculated based on pixel intensity of the detected bands. Three biological replicates were performed with similar results. (b) Quantification of the autophagy flux based on area and pixel intensity of the detected protein bands, measured by using ImageJ software (v. 1.53 k). Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). **p < 0.01, ***p < 0.001, ****p < 0.0001, versus control condition (YePS). (c) MAT‐1, ss1paoΔ or ss1cuao1Δ sporidia were grown in YePS or MM−N, with or without exogenous addition of PUT or SPD, before total protein extraction. Full‐length or PE‐conjugated form of SsAtg8 were detected using anti‐ATG8 antibody. The ratio of ATG8‐PE:(ATG8 + ATG8‐PE) was calculated based on pixel intensity of the detected bands. Three biological replicates were performed with similar results. (d) Quantification of the autophagy flux based on pixel intensity. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). ***p < 0.001, versus control condition (YePS).
We further assessed autophagy activity in MAT‐1, ss1paoΔ and ss1cuao1Δ, using an anti‐Atg8 antibody. The full‐length SsAtg8 peptide could be detected as a band of approximately 15 kDa, and the band slightly smaller than full‐length SsAtg8 corresponds to the PE‐conjugated variant, SsAtg8‐PE (Figure 5c), reflecting autophagy induction (Kabeya et al., 2000). When grown in YePS medium, the MAT‐1 strain contained lower amounts of SsAtg8‐PE (Figure 5c,d), suggesting that a rich nutrient environment repressed autophagy induction. When grown in MM−N medium, the MAT‐1 strain exhibited increased amounts of SsAtg8‐PE (Figure 5c,d). However, both YePS‐ or MM‐N‐cultured ss1paoΔ mutant displayed a higher level of SsAtg8‐PE (Figure 5c,d), indicating an enhanced level of autophagy induction. In contrast, lower amounts of SsAtg8‐PE were detected in the ss1cuao1Δ mutant under autophagy‐induction conditions (MM−N; Figure 5c,d). Exogenous addition of PUT or SPD caused a decrease in the ATG8‐PE:(ATG8 + ATG8‐PE) ratio in the ss1paoΔ mutant compared to the untreated condition (Figure 5c,d). In contrast, exogenous PUT or SPD led to an enhanced ATG8‐PE:(ATG8 + ATG8‐PE) ratio in ss1cuao1Δ compared to untreated condition (Figure 5c,d). These results indicate that autophagy activity is regulated by SsPAO and SsCuAO1, not just by their function on polyamine homeostasis but may involve other mechanisms.
We also examined expression levels of autophagy genes SsATG1, SsATG13, SsATG17, and SsATG8, in MAT‐1, ss1paoΔ and ss1cuao1Δ, cultured in YePS or MM−N. All the tested genes were up‐regulated in MAT‐1 in response to nitrogen starvation (MM−N) (Figure S4d). Transcriptional levels of SsATG8 were higher in ss1paoΔ under MM−N compared to YePS conditions. However, transcriptional levels of SsATG8 were not significantly different in ss1cuao1Δ under MM−N compared to YePS conditions (Figure S4d). This result suggests that SsPAO may negatively, and SsCuAO1 positively, regulate autophagy in S. scitamineum. Furthermore, treatment with rapamycin (55 nM), an established autophagy inducer (Yoo et al., 2019), slightly suppressed ss1paoΔ filamentation but slightly promoted ss1cuao1Δ filamentation (Figure S4e). This also indicates that the basal level of autophagy may be higher in the ss1paoΔ mutant and lower in the ss1cuao1Δ mutant.
Overall, our results showed that SsPAO‐ and SsCuAO1‐mediated polyamine metabolism differentially regulates autophagy activity in S. scitamineum.
2.6
SsPAO
and
SsCuAO1
are involved in stress tolerance
Stress tolerance analysis showed that ss1paoΔ and ss1cuao1Δ mutants were more sensitive to Congo Red (CR, 1 mM, cell wall stress), but showed no difference compared with MAT‐1 when grown on media containing NaCl (500 mM, osmotic stress), sorbitol (1 M, osmotic stress), or SDS (0.1 mM, cell wall stress) (Figure S5a). The mating ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ cultures hardly produce dimorphic hyphae under stressful conditions, whereas MAT‐1 × MAT‐2 could also produce short dikaryotic hyphae (Figure 6a).
FIGURE 6
SsPAO and SsCuAO1 are involved in stress tolerance in Sporisorium scitamineum. (a) Mating assay of wild‐type (WT) (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ) under treatment of NaCl (500 mM), sorbitol (1 M), Congo Red (CR, 1 mM) or SDS (0.1 mM), at 28°C for 2–3 days. Scale bar = 1 mm. (b) Mating cultures of WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) or sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ) were under H2O2 treatment (0.2 mM, 0.5 mM or 1 mM). The mating cultures were incubated at 28°C for 2–3 days before observation and imaging. Scale bar = 1 mm. (c) Measurement of intracellular H2O2 level in haploid sporidia of MAT‐1, ss1paoΔ and ss1cuao1Δ grown in liquid YePS medium for 2 days, using Amplex Red Hydrogen Peroxide/Peroxidase Assay kit. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing four replications (n = 4). (d) Transcriptional profile of catalase 1 (SsCAT1), catalase 2 (SsCAT2), peroxiredoxin 1 (SsPRX1), peroxidase (SsPOD), glutathione S‐transferase (SsGST), glutathione S‐transferase 1 (SsGST1), and superoxide dismutase 2 (SsSOD2) genes in MAT‐1, ss1paoΔ and ss1cuao1Δ. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus MAT‐1.
PAO‐ and CuAO‐mediated amine oxidation produce H2O2 as one of the products (Parsons et al., 1995; Walters, 2003); therefore, they may be potentially involved in tolerance to oxidative stress. We therefore evaluated tolerance to oxidative stress in sporidia of MAT‐1, ss1paoΔ or ss1cuao1Δ, grown on PDA containing various concentrations of H2O2. The ss1paoΔ and ss1cuao1Δ mutants showed no difference in sensitivity to H2O2 compared to MAT‐1 (Figure S5b). We also assessed the sexual mating/filamentation of S. scitamineum under oxidative stress. The result showed that a low concentration H2O2 (0.2 mM) slightly promoted filamentation of ss1paoΔ × ss2paoΔ. In contrast, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ hardly produced hyphae under 1 mM H2O2, while MAT‐1 × MAT‐2 could still produce some extremely short hyphae (Figure 6b). We measured contents of H2O2 in MAT‐1, ss1paoΔ and ss1cuao1 and found that the intracellular H2O2 level was not significantly different in ss1paoΔ or ss1cuao1 compared to MAT‐1 (Figure 6c). Exogenous addition of antioxidants, including glutathione (GSH, 1 mM), N‐acetylcysteine (NAC, 1 mM), or vitamin C (VC, 1 mM), did not restore the defect of sexual mating/filamentation in ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ (Figure S5c). By RT‐qPCR, we assessed transcriptional levels of genes involved in redox homeostasis and/or response to oxidative stress, in MAT‐1, ss1paoΔ and ss1cuao1 mutants. The results showed that SsCAT1 and SsGST were significantly down‐regulated in ss1paoΔ, whereas SsCAT1, SsPRX1, SsPOD, and SsSOD2 were down‐regulated in ss1cuao1Δ, compared to MAT‐1 (Figure 6d).
We further assessed the role of SsPAO‐ and SsCuAO1‐mediated polyamine oxidation in modulating ROS accumulation during fungal infection to sugarcane. The results showed that exogenous added PUT promoted, while exogenous SPD inhibited, accumulation of ROS in sugarcane at the infection sites in the early stages (24–96 hpi; Figure 7a–c; Figure S6a). ROS accumulation was weaker in the sugarcane inoculated with sspaoΔ, sscuao1Δ, or ssatg8Δ mutant, at 48 hpi, but stronger than WT‐infected canes at 96 hpi (Figure 7d; Figure S6b). As a response to fungal infection, the RBOH (respiratory burst oxidase homologue) genes encoding ROS‐generating enzymes were up‐regulated in sugarcane inoculated with sspaoΔ or sscuao1Δ mutants compared to the WT‐infected sugarcane, at 96 hpi (Figure 7e), and might be responsible for the elevated ROS levels in the sspaoΔ or sscuao1Δ infected plants at this time point (Figure 7d; Figure S6b). On the other hand, the ROS scavenger encoding gene CAT1 was up‐regulated in sscuao1Δ‐infected sugarcane at 48 hpi but dropped to a comparable level to WT or sspaoΔ infected sugarcanes at 96 hpi (Figure 7e). These results indicate that changes in polyamine levels at the early stage of fungal infection affect redox homeostasis at the fungus–sugarcane interface, possibly by differentially regulating ROS generator genes in the infected plants. Therefore, SsPAO and SsCuAO1 function is required for modulating polyamine homeostasis at this stage.
FIGURE 7
SsPAO and SsCuAO1 affect host reactive oxygen species (ROS) accumulation during early infection stage. (a–d) Quantification of the amount of 3,3′‐diaminobenzidine (DAB) staining of the injected plant tissue based on the pixel intensity. Average optical density (AOD) = IOD (Integrated density)/Area, measured by ImageJ (v. 1.53 k). Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus WT (Control). PUT, putrescine; SPD, spermidine; SPM, spermine. (e) Transcriptional profile of catalase 1 (CAT1, Gene ID: Sspon.01G0050630‐2D), peroxidase (POD, Gene ID: Sspon.02G0012680‐1A) and respiratory burst oxidase homologues (RBOH, Gene ID: Sspon.06G0014970‐3D) genes in sugarcane at 48 and 96 hours post‐inoculation (hpi). The sugarcane glyceraldehyde dehydrogenase (GAPDH) gene served as an internal control. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, versus WT (48 hpi).
Taken together, we conclude that SsPAO and SsCuAO1 are involved in stress tolerance and regulating host ROS accumulation in early infection stages of infection by S. scitamineum.
2.1
SsPAO and SsCuAO1 modulate polyamine levels
To identify enzymes catalysing oxidation of polyamines in S. scitamineum, we used the FMS1‐polyamine oxidase PAO (XP_011392213.1) (Valdes‐Santiago et al., 2010) and the annotated copper amine oxidase 1 CuAO1 (XP_011390076.1) in Ustilago maydis, as the queries to perform a BLASTP search on the NCBI website (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome). CDU22952.1 (encoded by SPSC_01582) was identified as an orthologue of U. maydis PAO, and CDR87209.1 (encoded by SPSC_00335) was orthologous to U. maydis CuAO1. We named these two proteins as SsPAO and SsCuAO1, respectively. Phylogenetic analysis using SsPAO and SsCuAO1 and their orthologues from smut fungi or ascomycetes (Table S1) showed that these two proteins were closest to the orthologues in Sporisorium reilianum, forming a clade within smut fungi and separating from ascomycetes (Figure S1a,b). Based on prediction by the SmartBLAST tool (https://blast.ncbi.nlm.nih.gov/smartblast/), SsPAO contains a conserved amino_oxidase domain (PF01593) (Figure S1a), and SsCuAO1 contains a copper amine oxidase N2‐terminal domain (PF02727) and a copper amine oxidase catalytic domain (PF01179) (Figure S1b).
By homologous recombination, we generated deletion mutants of the SsPAO or SsCuAO1 genes in both MAT‐1 and MAT‐2 backgrounds, as verified by Southern blotting (Figure S2a,b). We named the single‐deletion mutants as ss1paoΔ (in MAT‐1 background) and ss2paoΔ (in MAT‐2 background), ss1cuao1Δ (in MAT‐1 background), and ss2cuao1Δ (in MAT‐2 background). Meanwhile, the genetic complementation strains were generated, namely Ss1PAO‐COM, Ss2PAO‐COM, Ss1CuAO1‐COM, and Ss2CuAO1‐COM, and were verified by PCR (Figure S2c,d). Fluorescence microscopy analysis revealed that the fusion proteins GFP‐SsPAO and SsCuAO1‐RFP used for genetic complementation were localized in the cytoplasm (Figure S2e,f). For all the deletion mutants and complementation strains, at least two independent strains were obtained and used for the following assessments.
To assess the possible function of SsPAO and SsCuAO1 in polyamine metabolism, we measured the intracellular and extracellular levels of PUT, SPD, and SPM in the wild‐type (WT) (MAT‐1), ss1paoΔ, and ss1cuao1Δ mutant by liquid chromatography‐mass spectrometry (LC–MS) analysis, following an established protocol (Yin et al., 2024). The intracellular level of PUT was significantly higher in ss1paoΔ (p < 0.05) and ss1cuao1Δ (p < 0.001) compared to MAT‐1, but there was no significant difference in intracellular SPD or SPM level (Figure 1a). The extracellular level of SPM was significantly higher in ss1cuao1Δ (p < 0.01) compared to MAT‐1 (Figure 1a). We further evaluated the effect of polyamines on transcription of SsPAO and SsCuAO1 genes. Liquid minimal medium (MM)‐cultured MAT‐1 sporidia were treated with PUT (1 mM), SPD (1 mM), or SPM (1 mM), for 90 min, before total RNA extraction for reverse transcription‐quantitative PCR (RT‐qPCR) analysis. The result showed that PUT treatment caused down‐regulation of SsCuAO1 and polyamine biosynthetic genes (SsAGA, SsAGT, SsODC, SsSAMDC, and SsSPDS/SPMS) (Figure 1b). In contrast, SPD treatment up‐regulated transcription of SsPAO, SsCuAO1, and SsAGA, but down‐regulated SsSAMDC and SsSPDS/SPMS transcription (Figure 1b). SPM treatment up‐regulated SsCuAO1 but down‐regulated SsPAO and the biosynthetic genes (Figure 1b). Our previous study showed that SsCuAO1 was up‐regulated in the ss1agaΔ mutant (Yin et al., 2024), probably as a response to elevated PUT or SPM levels. Consistent with the results obtained here, this suggests that SsPAO may be involved in SPD oxidation to produce PUT, and SsCuAO1 may oxidize SPM and/or SPD to produce SPD and PUT, respectively.
FIGURE 1
SsPAO and SsCuAO1 contribute to maintaining polyamine levels in Sporisorium scitamineum. (a) Measurement of intracellular and extracellular polyamines of MAT‐1, ss1paoΔ or ss1cuao1Δ. Fresh haploid sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Polyamine contents were measured from crude extracts of fungal cells or from the supernatant of culture medium, by LC–MS. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, versus MAT‐1. (b) Transcriptional profiling of SsPAO, SsCuAO1 and polyamine biosynthesis genes in MAT‐1 after exogenous supply of 1 mM putrescine (PUT), spermidine (SPD) or spermine (SPM). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus control (untreated cell). (c) Transcriptional profiling of polyamine biosynthesis genes and ‘a locus’ genes (SsMFA1 and SsPRA1) in ss1paoΔ and ss1cuao1Δ, compared to MAT‐1. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus MAT‐1. (d) Transcriptional profiling of SsPAO or SsCuAO1 genes during early stage of sugarcane infection. At 0, 3 or 7 days post‐inoculation (dpi), approximately 3 cm stems of the inoculated site were sampled for RNA extraction. ***p < 0.001, ****p < 0.0001, versus SsPAO (0 dpi). For (b–d), the relative gene expression levels were calculated according to the −ΔΔC
t method (Livak & Schmittgen, 2001), with Actin as an internal control. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3).
During sexual mating/filamentation, relative expression levels of polyamine biosynthesis genes and the ‘a locus’ genes (pheromone MFA1 and pheromone receptor PRA1) were examined in these two mutants compared to WT MAT‐1 × MAT‐2. Results showed that SsODC, SsSAMDC, SsSPDS/SPMS, SsMFA1, and SsPRA1 were down‐regulated in the ss1paoΔ mutant. On the other hand, SsSAMDC and SsSPDS/SPMS were down‐regulated while SsMFA1 and SsPRA1 were up‐regulated in the ss1cuao1Δ mutant (Figure 1c). Subsequently, we measured the relative transcription levels of SsPAO and SsCuAO1 during the early stage of host infection, when changes in intracellular and/or extracellular levels of polyamines in sugarcane–S. scitamineum interface were expected. The results showed that SsPAO and SsCuAO1 were down‐regulated at 3 and 7 days post‐inoculation (dpi) (Figure 1d). We infer that the pathogen needs to maintain a high level of polyamines, especially for SPD as reported (Yin et al., 2024), during host infection, thus down‐regulating SsPAO and SsCuAO1 at this stage.
Overall, our results showed that SsPAO and SsCuAO1 modulate polyamine levels in S. scitamineum during sexual mating/filamentation and host infection stages.
2.2
SsPAO
and
SsCuAO1
are required for sporidia growth, sexual mating, and filamentation
Next, we assessed the yeast‐like growth and development of sporidia of ss1paoΔ and ss1cuao1Δ mutants in comparison to MAT‐1. Under the microscope, we noticed that ss1paoΔ sporidia were significantly (p < 0.0001) longer than MAT‐1, but there was no significant difference in ss1cuao1Δ compared with MAT‐1 (Figure 2a,b). Growth curve analysis showed that the logarithmic growth phase of ss1paoΔ and ss1cuao1Δ significantly lagged behind that of MAT‐1 (Figure S3a,b). Addition of PUT (1 mM), SPD (1 mM) or SPM (0.1 mM) did not affect the growth of MAT‐1 or ss1paoΔ (Figure S3a,b), whereas SPM significantly suppressed the growth of ss1cuao1Δ sporidia (Figure S3b). There was no significant difference in colony morphology of ss1paoΔ and MAT‐1 when spotted on potato dextrose agar (PDA) or minimal medium (MM) in the presence of PUT (1 mM), SPD (1 mM), or SPM (0.1 mM), whereas SPM significantly suppressed growth of ss1cuao1Δ sporidia on solid MM (Figure S3c). Overall, loss of SsPAO or SsCuAO1 led to a lag in sporidia growth of S. scitamineum, and ss1cuao1Δ seemed more sensitive to exogenous SPM treatment, further supporting that SsCuAO1, but not SsPAO, may be involved in SPM oxidation.
FIGURE 2
SsPAO and SsCuAO1 are required for sporidia growth and sexual mating/filamentation. (a) Microscopic observation of sporidia morphology of MAT‐1, ss1paoΔ and ss1cuao1Δ. The fresh haploid sporidia were cultured in liquid YePS medium for 16 h and subject to microscopic observation and imaging. Scale bar = 10 μm. (b) Quantitative analysis of the length of MAT‐1, ss1paoΔ and ss1cuao1Δ. n = 20. ****p < 0.0001, versus MAT‐1. (c) Mating assay of wild‐type (WT) (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). Fresh haploid sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Equal volume of the compatible WT strain or mutants of opposite mating types were mixed and spotted onto potato dextrose agar (PDA), containing or without (control) putrescine (PUT, 0.1 mM), spermidine (SPD, 1 mM) or spermine (SPM, 0.1 mM). The mating cultures were incubated at 28°C for 2–3 days, before observation and imaging. Scale bar = 1 mm. (d) Mating assay of WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ), SsPAO‐COM (Ss1PAO‐COM × Ss2PAO‐COM), sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ), and SsCuAO1‐COM (Ss1CuAO1‐COM × Ss2CuAO1‐COM). Fresh haploid sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Equal volume of the compatible WT strain or mutants of opposite mating types were mixed and spotted onto potato dextrose agar. The mating cultures were incubated at 28°C for 2–3 days, before observation and imaging. Scale bar = 1 mm. (e) Transcriptional profiling of sexual mating genes in ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ mutants, in comparison to MAT‐1 × MAT‐2. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ****p < 0.0001, versus MAT‐1 × MAT‐2.
Dikaryotic hyphal growth after sexual mating was significantly reduced in ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ compared to MAT‐1 × MAT‐2 (Figure 2c). Genetic complementation of SsPAO or SsCuAO1 fully restored dikaryotic hyphae in the respective mutants (Figure 2d), confirming that these two genes were indeed required for S. scitamineum sexual mating/filamentation. Exogenous addition of a low concentration of PUT (0.1 mM) sightly promoted filamentation of MAT‐1 × MAT‐2 and ss1cuao1Δ × ss2cuao1Δ, whereas SPD (1 mM) or SPM (0.1 mM) markedly restored sexual mating/filamentation of ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ (Figure 2c). RT‐qPCR analysis showed that the relative expression levels of the genes involved in sexual mating/filamentation, including SsKPP2, SsbE, SsbW, SsPRF1, and SsROP1, were all significantly down‐regulated in ss1paoΔ × ss2paoΔ, while SsGPA3, SsUAC1, SsADR1, SsFUZ7, SsKPP4, SsKPP2, SsbE, SsbW, SsPRF1, SsROP1, and SsCRK1 were all down‐regulated in ss1cuao1Δ × ss2cuao1Δ (Figure 2e). We infer that increased intracellular PUT content in ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ could be responsible for the defective sexual mating/filamentation.
Overall, we conclude that SsPAO and SsCuAO1 are required for the sporidia growth, sexual mating and filamentation of S. scitamineum.
2.3
SsPAO
and
SsCuAO1
are required for S. scitamineum pathogenicity
We further assessed the role of SsPAO and SsCuAO1 in S. scitamineum pathogenicity. Sugarcane seedlings were inoculated with MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ, or ss1cuao1Δ × ss2cuao1Δ sporidia and disease symptoms were documented at 3–6 months post‐inoculation. The disease incidence (appearance of whip structures) of wild‐type infected plants was 40.8 ± 8.2% (n = 20), and 9.2 ± 5.9% (n = 20) for those infected by ss1paoΔ × ss2paoΔ. No black whip symptom was found in the seedlings infected by ss1cuao1Δ × ss2cuao1Δ (n = 20) (Figure 3a,b). We measured the average plant height as an indicator of smut fungus infection. The mutant‐infected plants or noninfected (inoculated with double‐distilled water as a blank control) plants were significantly taller than those infected by MAT‐1 × MAT‐2 (Figure 3c). Teliospores of MAT‐1 × MAT‐2 or ss1paoΔ × ss2paoΔ collected from the diseased plants, as verified by PCR amplification (Figure S4a), were assessed for germination. After 30 days of incubation under a cyclic light/dark condition, the promycelia formed from wild‐type teliospores displayed obvious and dense light and dark bands (width of 2.44 ± 0.45 mm), while the sspaoΔ promycelial colony appeared looser (width of 3.90 ± 0.29 mm; Figure 3d,e).
FIGURE 3
SsPAO and SsCuAO1 are required for Sporisorium scitamineum pathogenicity. (a) Pathogenicity assay. One millilitre of mixed sporidia of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ was injected into the seedlings of sugarcane cultivar ROC22, with double‐distilled water serving as a blank control. Each treatment contained at least 20 seedlings (n = 20). Red boxed region is shown enlarged, showing the ‘black whip’ symptoms appearing on top of seedlings infected by the wild type (WT) (MAT‐1 × MAT‐2). (b) Incidence rate of disease symptom in the seedlings infected by WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) or sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). *p < 0.05, **p < 0.01, versus WT (MAT‐1 × MAT‐2). (c) Quantitative analysis of the heights of infected seedlings. n = 34, 21, 20, 23, respectively. ***p < 0.001, ****p < 0.0001, versus WT (MAT‐1 × MAT‐2). (d) WT or sspaoΔ teliospores were allowed to germinate under a cyclic light/dark (24 h/24 h) condition for 30 days. The resultant promycelial colonies were photographed. Scale bar = 9 mm. (e) Quantitative analysis of the band width of promycelial colonies from WT or sspaoΔ teliospores. Bar chart depicts the mean ± SD derived from three independent biological repeats, each containing five replications (n = 5). ***p < 0.001, versus WT. (f) Analysis of fungal biomass by quantitative PCR. Mixed sporidia of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ were injected into the sugarcane seedlings, and the infected stems (about 3 cm) were sampled for DNA extraction at 3 and 7 days post‐inoculation (dpi). Fungal Actin gene and sugarcane GAPDH gene served as internal controls. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). ***p < 0.001, versus WT (MAT‐1 × MAT‐2).
Next, we measured the fungal biomass in the infected seedlings at 3 and 7 dpi. The biomass of ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ mutants was comparable to MAT‐1 × MAT‐2 at 3 dpi (Figure 3f), further supporting that the oxidation of polyamines may not be needed at the early stage of host infection. At 7 dpi, biomass of the WT strain became almost 2‐fold of that at 3 dpi, while ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ did not seem obviously increased in the infected plants (Figure 3f). This indicates that the mutants were less capable of adapting to the host environment and/or quickly proliferating in planta.
Overall, we conclude that SsPAO‐ and SsCuAO1‐mediated polyamine homeostasis plays an important role in host colonization and pathogenicity of S. scitamineum.
2.4
Polyamine oxidation affects cAMP‐PKA pathway
It has been established that cAMP signalling pathway regulates sexual mating and filamentation in smut fungi including S. scitamineum (Chang et al., 2019; Kaffarnik et al., 2003). We therefore assessed the effect of cAMP on sexual mating/filamentation of WT, sspaoΔ or sscuao1Δ. A low concentration cAMP (0.1 mM) fully restored filamentation of sspaoΔ or sscuao1Δ (Figure 4a). In contrast, a high concentration cAMP (1 mM) inhibited filamentation of WT but was less inhibitory to filamentation of sscuao1Δ (Figure 4a). cAMP, of either low or high concentration, did not affect sporidia growth of MAT‐1, ss1paoΔ or ss1cuao1Δ (Figure S4b). However, the intracellular contents of cAMP were comparable in ss1paoΔ or ss1cuao1Δ to MAT‐1 sporidia, or in ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ to MAT‐1 × MAT‐2 (Figure 4b). By RT‐qPCR, we assessed transcriptional levels of genes encoding enzymes for cAMP synthesis (adenylate cyclase, UAC1) or hydrolysis (3′,5′‐cyclic‐AMP phosphodiesterase, PDE1) in WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) or sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). The results showed that SsUAC1 was significantly down‐regulated in ss1cuao1Δ × ss2cuao1Δ and up‐regulated in ss1paoΔ × ss2paoΔ compared to MAT‐1 × MAT‐2, whereas SsPDE1 was significantly down‐regulated in ss1cuao1Δ × ss2cuao1Δ (Figure 4c).
FIGURE 4
Assessment of cAMP‐PKA pathway in sspaoΔ and sscuao1Δ mutants. (a) cAMP differentially regulates sexual mating/filamentation in sspaoΔ and sscuao1Δ mutants. The mating cultures were allowed to grow on potato dextrose agar (PDA) containing (0.1 or 1 mM) or without (control) cAMP, at 28°C for 2–3 days, before observation and imaging. Scale bar = 1 mm. (b) Measurement of intracellular cAMP level of MAT‐1, ss1paoΔ, ss1cuao1Δ, wild‐type (WT) (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). Sporidia cultured in liquid YePS medium for 24 h before measurement of the intracellular cAMP level. For mating cultures, sporidia were cultured in liquid YePS medium for 12 h, washed twice with double‐distilled water, and adjusted to OD600 = 1.0. Equal volumes of the compatible WT strain or mutants of opposite mating types were mixed and spotted onto solid YePS medium. The mating cultures were incubated at 28°C for 2–3 days, before measurement of the intracellular cAMP level. The cAMP level was measured from crude extracts, by a Complete cAMP ELISA kit. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). (c) Transcriptional profile of SsUAC1 and SsPDE1 in WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ). Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, ****p < 0.0001, versus WT (MAT‐1 × MAT‐2). (d) Mating assay of WT (MAT‐1 × MAT‐2), ss1paoΔ × MAT‐2, GAPD::SsPRF1 (ss1paoΔ) × MAT‐2, ss1cuao1Δ × MAT‐2 and GAPD::SsPRF1 (ss1cuao1Δ) × MAT‐2. Scale bar = 1 mm.
We further constructed a reintegrated homologous fragment for overexpression of PRF1 (pheromone response factor 1) gene, which is known as a central transcriptional regulator in sexual mating/filamentation in S. scitamineum (Zhu et al., 2019). By homologous recombination we inserted the SsPRF1 overexpression fragments in the genome of the ss1paoΔ and ss1cuao1Δ mutants, resulting in GAPD::SsPRF1 (ss1paoΔ) and GAPD::SsPRF1 (ss1cuao1Δ) strains as verified by RT‐qPCR (Figure S4c). Filamentation was slightly promoted in GAPD::SsPRF1 (ss1cuao1Δ) × MAT‐2, but not in GAPD::SsPRF1 (ss1paoΔ) × MAT‐2 (Figure 4d).
Taken together, we conclude that deletion of SsCuAO1 leads to the imbalance of intracellular polyamines, which affects cAMP‐PKA signalling, but not cAMP level that caused defective sexual mating/filamentation.
2.5
SsPAO
and
SsCuAO1
regulate autophagy
It has been reported that SPD induces autophagy in yeast, and PUT stimulates autophagy in the phytopathogenic fungus Colletotrichum higginsianum (Madeo et al., 2010; Yan et al., 2022). We wondered whether cAMP and/or polyamines regulate autophagy in S. scitamineum. Therefore, we used GFP‐SsAtg8 as a marker to monitor non‐selective autophagy in MAT‐1. Both the full‐length GFP‐SsAtg8 fusion protein (42 kDa) and the band corresponding to free GFP peptide (27 kDa) could be detected by anti‐GFP antibody. When the fungal cells were cultured in rich medium (YePS), usually only the full‐length GFP‐SsAtg8 band was detected, whereas the free GFP peptide was detected and accumulated in the fungal cells cultured under nitrogen starvation (on MM−N) conditions (Figure 5a,b). This verifies that the strain GFP‐SsATG8:MAT‐1 could be used for monitoring autophagy activity in S. scitamineum. The ratio of GFP:(GFP + GFP‐ATG8) was used as an indicator of autophagy level. When treated with PUT, SPD, SPM, or cAMP, the GFP:(GFP + GFP‐ATG8) ratio was increased under both YePS and MM−N culture conditions (Figure 5a,b), indicating that polyamines and cAMP were capable of enhancing autophagy activity, especially under rich medium conditions.
FIGURE 5
SsPAO and SsCuAO1 regulate autophagy. (a) Fungal cells were cultured in liquid YePS medium (rich medium) for 16 h, then transferred to fresh liquid YePS or MM−N (nitrogen starvation), with or without supplement of putrescine (PUT, 0.1 mM or 1 mM), spermidine (SPD, 0.1 mM or 1 mM), spermine (SPM, 0.1 mM or 1 mM), or cAMP (0.1 mM or 1 mM), for further incubation for 4 h. Immunoblotting analysis with anti‐GFP antibody was performed to evaluate autophagy activity. Coomassie brilliant blue (CBB) staining served as loading control. The GFP:(GFP + GFP‐ATG8) ratio was calculated based on pixel intensity of the detected bands. Three biological replicates were performed with similar results. (b) Quantification of the autophagy flux based on area and pixel intensity of the detected protein bands, measured by using ImageJ software (v. 1.53 k). Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). **p < 0.01, ***p < 0.001, ****p < 0.0001, versus control condition (YePS). (c) MAT‐1, ss1paoΔ or ss1cuao1Δ sporidia were grown in YePS or MM−N, with or without exogenous addition of PUT or SPD, before total protein extraction. Full‐length or PE‐conjugated form of SsAtg8 were detected using anti‐ATG8 antibody. The ratio of ATG8‐PE:(ATG8 + ATG8‐PE) was calculated based on pixel intensity of the detected bands. Three biological replicates were performed with similar results. (d) Quantification of the autophagy flux based on pixel intensity. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). ***p < 0.001, versus control condition (YePS).
We further assessed autophagy activity in MAT‐1, ss1paoΔ and ss1cuao1Δ, using an anti‐Atg8 antibody. The full‐length SsAtg8 peptide could be detected as a band of approximately 15 kDa, and the band slightly smaller than full‐length SsAtg8 corresponds to the PE‐conjugated variant, SsAtg8‐PE (Figure 5c), reflecting autophagy induction (Kabeya et al., 2000). When grown in YePS medium, the MAT‐1 strain contained lower amounts of SsAtg8‐PE (Figure 5c,d), suggesting that a rich nutrient environment repressed autophagy induction. When grown in MM−N medium, the MAT‐1 strain exhibited increased amounts of SsAtg8‐PE (Figure 5c,d). However, both YePS‐ or MM‐N‐cultured ss1paoΔ mutant displayed a higher level of SsAtg8‐PE (Figure 5c,d), indicating an enhanced level of autophagy induction. In contrast, lower amounts of SsAtg8‐PE were detected in the ss1cuao1Δ mutant under autophagy‐induction conditions (MM−N; Figure 5c,d). Exogenous addition of PUT or SPD caused a decrease in the ATG8‐PE:(ATG8 + ATG8‐PE) ratio in the ss1paoΔ mutant compared to the untreated condition (Figure 5c,d). In contrast, exogenous PUT or SPD led to an enhanced ATG8‐PE:(ATG8 + ATG8‐PE) ratio in ss1cuao1Δ compared to untreated condition (Figure 5c,d). These results indicate that autophagy activity is regulated by SsPAO and SsCuAO1, not just by their function on polyamine homeostasis but may involve other mechanisms.
We also examined expression levels of autophagy genes SsATG1, SsATG13, SsATG17, and SsATG8, in MAT‐1, ss1paoΔ and ss1cuao1Δ, cultured in YePS or MM−N. All the tested genes were up‐regulated in MAT‐1 in response to nitrogen starvation (MM−N) (Figure S4d). Transcriptional levels of SsATG8 were higher in ss1paoΔ under MM−N compared to YePS conditions. However, transcriptional levels of SsATG8 were not significantly different in ss1cuao1Δ under MM−N compared to YePS conditions (Figure S4d). This result suggests that SsPAO may negatively, and SsCuAO1 positively, regulate autophagy in S. scitamineum. Furthermore, treatment with rapamycin (55 nM), an established autophagy inducer (Yoo et al., 2019), slightly suppressed ss1paoΔ filamentation but slightly promoted ss1cuao1Δ filamentation (Figure S4e). This also indicates that the basal level of autophagy may be higher in the ss1paoΔ mutant and lower in the ss1cuao1Δ mutant.
Overall, our results showed that SsPAO‐ and SsCuAO1‐mediated polyamine metabolism differentially regulates autophagy activity in S. scitamineum.
2.6
SsPAO
and
SsCuAO1
are involved in stress tolerance
Stress tolerance analysis showed that ss1paoΔ and ss1cuao1Δ mutants were more sensitive to Congo Red (CR, 1 mM, cell wall stress), but showed no difference compared with MAT‐1 when grown on media containing NaCl (500 mM, osmotic stress), sorbitol (1 M, osmotic stress), or SDS (0.1 mM, cell wall stress) (Figure S5a). The mating ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ cultures hardly produce dimorphic hyphae under stressful conditions, whereas MAT‐1 × MAT‐2 could also produce short dikaryotic hyphae (Figure 6a).
FIGURE 6
SsPAO and SsCuAO1 are involved in stress tolerance in Sporisorium scitamineum. (a) Mating assay of wild‐type (WT) (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) and sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ) under treatment of NaCl (500 mM), sorbitol (1 M), Congo Red (CR, 1 mM) or SDS (0.1 mM), at 28°C for 2–3 days. Scale bar = 1 mm. (b) Mating cultures of WT (MAT‐1 × MAT‐2), sspaoΔ (ss1paoΔ × ss2paoΔ) or sscuao1Δ (ss1cuao1Δ × ss2cuao1Δ) were under H2O2 treatment (0.2 mM, 0.5 mM or 1 mM). The mating cultures were incubated at 28°C for 2–3 days before observation and imaging. Scale bar = 1 mm. (c) Measurement of intracellular H2O2 level in haploid sporidia of MAT‐1, ss1paoΔ and ss1cuao1Δ grown in liquid YePS medium for 2 days, using Amplex Red Hydrogen Peroxide/Peroxidase Assay kit. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing four replications (n = 4). (d) Transcriptional profile of catalase 1 (SsCAT1), catalase 2 (SsCAT2), peroxiredoxin 1 (SsPRX1), peroxidase (SsPOD), glutathione S‐transferase (SsGST), glutathione S‐transferase 1 (SsGST1), and superoxide dismutase 2 (SsSOD2) genes in MAT‐1, ss1paoΔ and ss1cuao1Δ. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus MAT‐1.
PAO‐ and CuAO‐mediated amine oxidation produce H2O2 as one of the products (Parsons et al., 1995; Walters, 2003); therefore, they may be potentially involved in tolerance to oxidative stress. We therefore evaluated tolerance to oxidative stress in sporidia of MAT‐1, ss1paoΔ or ss1cuao1Δ, grown on PDA containing various concentrations of H2O2. The ss1paoΔ and ss1cuao1Δ mutants showed no difference in sensitivity to H2O2 compared to MAT‐1 (Figure S5b). We also assessed the sexual mating/filamentation of S. scitamineum under oxidative stress. The result showed that a low concentration H2O2 (0.2 mM) slightly promoted filamentation of ss1paoΔ × ss2paoΔ. In contrast, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ hardly produced hyphae under 1 mM H2O2, while MAT‐1 × MAT‐2 could still produce some extremely short hyphae (Figure 6b). We measured contents of H2O2 in MAT‐1, ss1paoΔ and ss1cuao1 and found that the intracellular H2O2 level was not significantly different in ss1paoΔ or ss1cuao1 compared to MAT‐1 (Figure 6c). Exogenous addition of antioxidants, including glutathione (GSH, 1 mM), N‐acetylcysteine (NAC, 1 mM), or vitamin C (VC, 1 mM), did not restore the defect of sexual mating/filamentation in ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ (Figure S5c). By RT‐qPCR, we assessed transcriptional levels of genes involved in redox homeostasis and/or response to oxidative stress, in MAT‐1, ss1paoΔ and ss1cuao1 mutants. The results showed that SsCAT1 and SsGST were significantly down‐regulated in ss1paoΔ, whereas SsCAT1, SsPRX1, SsPOD, and SsSOD2 were down‐regulated in ss1cuao1Δ, compared to MAT‐1 (Figure 6d).
We further assessed the role of SsPAO‐ and SsCuAO1‐mediated polyamine oxidation in modulating ROS accumulation during fungal infection to sugarcane. The results showed that exogenous added PUT promoted, while exogenous SPD inhibited, accumulation of ROS in sugarcane at the infection sites in the early stages (24–96 hpi; Figure 7a–c; Figure S6a). ROS accumulation was weaker in the sugarcane inoculated with sspaoΔ, sscuao1Δ, or ssatg8Δ mutant, at 48 hpi, but stronger than WT‐infected canes at 96 hpi (Figure 7d; Figure S6b). As a response to fungal infection, the RBOH (respiratory burst oxidase homologue) genes encoding ROS‐generating enzymes were up‐regulated in sugarcane inoculated with sspaoΔ or sscuao1Δ mutants compared to the WT‐infected sugarcane, at 96 hpi (Figure 7e), and might be responsible for the elevated ROS levels in the sspaoΔ or sscuao1Δ infected plants at this time point (Figure 7d; Figure S6b). On the other hand, the ROS scavenger encoding gene CAT1 was up‐regulated in sscuao1Δ‐infected sugarcane at 48 hpi but dropped to a comparable level to WT or sspaoΔ infected sugarcanes at 96 hpi (Figure 7e). These results indicate that changes in polyamine levels at the early stage of fungal infection affect redox homeostasis at the fungus–sugarcane interface, possibly by differentially regulating ROS generator genes in the infected plants. Therefore, SsPAO and SsCuAO1 function is required for modulating polyamine homeostasis at this stage.
FIGURE 7
SsPAO and SsCuAO1 affect host reactive oxygen species (ROS) accumulation during early infection stage. (a–d) Quantification of the amount of 3,3′‐diaminobenzidine (DAB) staining of the injected plant tissue based on the pixel intensity. Average optical density (AOD) = IOD (Integrated density)/Area, measured by ImageJ (v. 1.53 k). Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, versus WT (Control). PUT, putrescine; SPD, spermidine; SPM, spermine. (e) Transcriptional profile of catalase 1 (CAT1, Gene ID: Sspon.01G0050630‐2D), peroxidase (POD, Gene ID: Sspon.02G0012680‐1A) and respiratory burst oxidase homologues (RBOH, Gene ID: Sspon.06G0014970‐3D) genes in sugarcane at 48 and 96 hours post‐inoculation (hpi). The sugarcane glyceraldehyde dehydrogenase (GAPDH) gene served as an internal control. Bar chart depicts the mean ± SE derived from three independent biological repeats, each containing three replications (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001, versus WT (48 hpi).
Taken together, we conclude that SsPAO and SsCuAO1 are involved in stress tolerance and regulating host ROS accumulation in early infection stages of infection by S. scitamineum.
3
DISCUSSION
Our previous work proved that polyamines play differential roles in growth and pathogenic development of sugarcane smut fungus, probably by collaboratively regulating intracellular redox homeostasis with the cAMP‐PKA signalling pathway (Yin et al., 2024). In particular, a low level of PUT promoted, while a high level of PUT suppressed, filamentous growth after sexual mating (Yin et al., 2024). On the other hand, SPD plays a positive role in filamentous growth and pathogenicity (Yin et al., 2024). It has been reported that low concentrations of SPD significantly activate teliospore germination and polyamine levels increase in smut teliospores after contact with sugarcane glycoproteins as a plant‐defensive mechanism (Sanchez‐Elordi et al., 2019). In this study, we identified two potential polyamine oxidation enzymes, SsPAO and SsCuAO1, that also contribute to homeostasis of intracellular and extracellular polyamines. SsPAO and SsCuAO1 are both required for sporidia growth, sexual mating/filamentation, and pathogenicity.
Polyamine oxidation contributes to plant or animal cell development and/or death by modulating polyamine homeostasis and/or producing biologically active products, including H2O2 (Kang et al., 2023; Murray Stewart et al., 2018; Tavladoraki et al., 2016). A striking increase in maize polyamine biosynthesis, mainly free and conjugated PUT, occurrs in the tumours induced by the fungus and in the neighbouring plant tissues (Rodriguez‐Kessler et al., 2008). It has been reported that plant CuAOs mainly catalyse PUT and CAD oxidation, and PAOs oxidize SPD and SPM (Tavladoraki et al., 2016). rBjPAO1 and rBjPAO2 from amphioxus Branchiostoma japonicum were shown to catalyse oxidation of SPM and N
1‐acetyl‐SPM, respectively (Wang et al., 2016). The PAO gene was identified in the model fungal pathogen U. maydis and PAO was confirmed to catalyse conversion of SPD to PUT (Valdes‐Santiago et al., 2010). Saccharomyces cerevisiae Fms1 (yeast orthologue of PAO) can oxidize SPM, N
1‐acetyl‐SPM, N
1‐acetyl‐SPD, and N
8‐acetyl‐SPD, but not free SPD (Landry & Sternglanz, 2003). Compared to PAO, very little has been reported on CuAO enzymes in pathogenic fungi. Although CuAOs have been reported in Aspergillus spp. (Frebort et al., 1996, 1999; Sugawara et al., 2014, 2015), their oxidation substrates might not be polyamines. In this study, based on transcription changes of SsPAO or SsCuAO1 genes in response to treatment with different polyamines (Figure 1b), we infer that SsCuAO1 may be induced by SPM and SPD to catalyse oxidation of these two polyamines, while the oxidation product PUT suppresses SsCuAO1 transcription. On the other hand, SPD induced, while SPM suppressed, SsPAO transcription; therefore, we infer that SsPAO preferably catalyses oxidation of SPD (probably to produce PUT). It is unclear whether SsPAO or SsCuAO1 is involved in PUT oxidation, as exogenous PUT did not seem to induce transcription of SsPAO, and down‐regulated SsCuAO1 transcription (Figure 1b). However, loss of SsPAO or SsCuAO1 caused only accumulation of intracellular PUT but no obvious changes in SPD or SPM (Figure 1a). We infer that changes in polyamines may reflect the combined effect of disturbances of polyamine oxidation (direct effect) and polyamine biosynthesis (maybe indirectly). Given that loss of SsPAO or SsCuAO1 led to down‐regulation of SsSAMDC and SsSPDS/SPMS (both for converting PUT to SPD) (Figure 1c), we infer that accumulation of PUT in the ss1pao1Δ or ss1cuao1Δ mutants may be due to disruption of the PUT‐to‐SPD conversion, while the defect of SPD oxidation might be masked (or functionally redundant with other amide oxidases). Besides, exogenous addition of a high concentration (1 mM) of SPM affected yeast‐like growth of ss1cuao1Δ mutant, but not WT or ss1paoΔ mutant (Figure S3a,b), further supporting that SsCuAO1 may be involved in SPM oxidation, but SsPAO is not. Overall, we infer that the most likely substrate of SsPAO may be SPD, and the most likely substrates of SsCuAO1 may be SPD or SPM.
H2O2 is an important product of PAO‐ or CuAO‐mediated polyamine oxidation (Murray Stewart et al., 2018; Tavladoraki et al., 2016); therefore, PAOs and CuAOs are expected to play a role in regulating redox homeostasis under different physiological conditions. Plant PAOs exhibit the greatest diversity among eukaryotes, with origins linked to archaeal PAO‐like proteins and eukaryotic PAOs (Salvi & Tavladoraki, 2020). In the outer tissues of the maize mesocotyl, PAO is up‐regulated by light and down‐regulated by auxin (Cona et al., 2003). The transcriptional level of ZmPAO1 is up‐regulated and thus leads to increased H2O2 levels in maize tumours (Jasso‐Robles et al., 2016). GmPAO positively regulates plant resistance against the oomycetous pathogen Phytophthora sojae (Yang et al., 2022). The plant PAO activities were higher in the incompatible interaction at all the stages during Fusarium oxysporum f. sp. ricini–castor interaction (Mhaske et al., 2013). CuAO in Vicia faba guard cells is an essential enzymatic source for H2O2 production via degradation of PUT (An et al., 2008). CuAO1 of A. thaliana contributes to abscisic acid (ABA) and polyamines‐induced nitric oxide biosynthesis during stress response (Wimalasekera et al., 2011). Besides, osmotic stress could induce oxidation of polyamines to regulate defence responses and the susceptibility of grapevine to Botrytis cinerea (Hatmi et al., 2014). These studies demonstrate that plants modulate polyamine homeostasis by PAOs or CuAOs function, during which process H2O2 is produced and likely to act as a response to stressful conditions including pathogen infection. On the other hand, H2O2 production also contributes to fungal cell growth and development. It has been shown that the cAMP/PKA signalling pathway modulates expression of genes involved in redox homeostasis, thus regulating intracellular H2O2 accumulation that favours filamentous growth of S. scitamineum (Chang et al., 2019). However, this study showed that deletion of SsPAO or SsCuAO1 did not cause obvious changes in intracellular H2O2 levels (Figure 6c). Furthermore, exogenous addition of either H2O2 or antioxidants could not restore the filamentation defect of sspaoΔ or sscuao1Δ mutants (Figure 6b; Figure S5c), suggesting that SsPAO‐ or SsCuAO1‐mediated polyamine oxidation may not be a major contributor for generating intracellular H2O2. ROS‐generating and scavenging enzyme‐encoding genes, CAT1, POD, and RBOH, have been reported to be up‐regulated in sugarcane upon S. scitamineum infection (Wu et al., 2022, 2024). Our results showed that PUT promoted, while SPD inhibited, the accumulation of ROS, as quantified based on DAB staining with the infected sites of sugarcane stem at early infection stages (Figure 7a–c; Figure S6a), and such redox homeostasis during fungus–sugarcane interaction is probably regulated by SsPAO, SsCuAO1, and SsATG8 (Figure 7d; Figure S6b).
It has been reported that SPD induces autophagy in chondrocytes by inducing expression of the acetyltransferase EP300, which promotes autophagy activity, probably by post‐translational modification of autophagy proteins (Sacitharan et al., 2018). Similarly, SPD can induce autophagy in mice and human cells through post‐translational modification of the translation factor EIF5A, which in turn increases the synthesis of the master transcriptional factor TFEB that governs autophagy (Zhang & Simon, 2020). In cucumber, SPD induces expression of autophagy and the RBOH gene, both contributing to autophagy induction (Zhang et al., 2021). SPM‐induced autophagy in plants is accompanied by an increase in the secondary messengers ROS and nitric oxide (NO) (Dmitrieva et al., 2018). From these reports, we can see that polyamines could promote autophagy by directing activating expression of autophagy genes, or in an indirect way by promoting ROS (including H2O2) levels. On the other hand, the cAMP/PKA pathway has been shown to be involved in regulating autophagy activity in yeast and fungal cells (Perez‐Diaz et al., 2023; Stephan et al., 2010; Yu & Rollins, 2022). In this study, we showed that individual addition of cAMP, PUT, SPD, or SPM could induce autophagy in S. scitamineum (Figure 5a,b). We further confirmed that autophagy activity was enhanced in the ss1paoΔ mutant and reduced in ss1cuao1Δ (Figure 5c,d). SsATG8 was up‐regulated in the ss1paoΔ mutant after a 4 h exposure to nitrogen starvation (MM−N), but there was no significant difference in ss1cuao1Δ under MM−N compared to YePS medium (Figure S4d). We infer that polyamine‐based regulation of autophagy is through expression of autophagy genes, which may also dependent on the cAMP/PKA pathway. Furthermore, cAMP/polyamine‐based regulation of autophagy may contribute to S. scitamineum filamentation and/or pathogenicity. Treatment with rapamycin, a chemical targeting TOR kinase to induce autophagy (Wang & Zhang, 2019), slightly enhanced filamentation of the ss1cuao1Δ mutant while slightly suppressing filamentation of the ss1paoΔ mutant (Figure S4e). Our previous study shows that pseudohyphae increase in ssatg8Δ mutant, which may account for loss of pathogenicity in this mutant (Zhang et al., 2019). We infer that dysregulated autophagy in the sspaoΔ or sscuao1Δ mutants may also be responsible for loss or reduced pathogenicity.
In summary, our current and previous (Yin et al., 2024) works reveal that biosynthesis and oxidation of polyamines serve a mechanism for maintaining intracellular homeostasis of polyamines and ROS (H2O2), interconnecting with the cAMP‐PKA signalling pathway and autophagy for contribution to S. scitamineum filamentous growth and pathogenicity. A proposed working model is illustrated in Figure 8.
FIGURE 8
A proposed working model. Polyamine biosynthesis and oxidation together maintain intracellular homeostasis of polyamines, especially putrescine (PUT) and spermidine (SPD), which are capable of regulating intracellular reactive oxygen species (ROS) (H2O2) and autophagy, thereby contributing to the filamentous growth and pathogenicity of Sporisorium scitamineum.
4
EXPERIMENTAL PROCEDURES
4.1
Fungal strains and growth conditions
The wild‐type strains of MAT‐1 (a1 b1) and MAT‐2 (a2 b2), previously isolated, identified, and maintained by our laboratory, were used for the generation of all transformations/mutants in this study (Yan et al., 2016). Details of the fungal strains generated and used in this study are listed in Table S2. The culture media employed include yeast extract‐peptone‐sucrose medium (YePS; yeast extract 1%, peptone 2%, sugar 2%, pH 6.5), minimal medium (MM; yeast nitrogen base 0.17%, glucose 2%, 10 mM ammonium sulphate, pH 6.8), and potato dextrose agar (PDA powder 4%; DingGuo).
For sexual mating assay, fresh haploid sporidia were cultured in liquid YePS medium overnight. Subsequently, the sporidia were collected after washing twice with sterile double‐distilled water and the concentration was adjusted to OD600 = 1.0. The haploid sporidia of opposite mating types were mixed at equal volume (OD600 = 1.0) and spotted on PDA or solid MM, and kept in the dark in a 28°C incubator for 2–3 days before photographing.
For haploid growth analysis, fresh haploid sporidia were cultured in liquid YePS medium overnight, washed twice with sterile double‐distilled water, and adjusted to OD600 = 1.0. Fresh haploid sporidia (OD600 = 1.0, equivalent to cfu = 107/mL) of WT and mutants were diluted in a gradient into 107, 106, 105, 104, and 103 spotted onto PDA or MM, and kept in dark in a 28°C incubator for 2–3 days before photographing.
For growth curve analysis, fresh haploid sporidia were cultured in liquid YePS medium overnight, washed twice with fresh liquid YePS medium, and adjusted to OD600 = 0.01, then added to 120 μL of the culture into a 100‐well honeycomb plate, incubated in the automatic microbial growth curve analyser (Oy Growth Curves Ab Ltd; Bioscreen C) with the setting as 28°C, for 3 days.
4.2
Nucleic acid‐related manipulation
Genomic DNA of S. scitamineum strains was extracted from the sporidia culture grown on solid YePS medium for 2 days at 28°C, using a Fungal DNA Midi Kit (Omega). Southern blot analysis was performed with the digoxigenin (DIG) High Prime DNA labelling and detection starter kit II (Roche). For verification of ss1paoΔ and ss2paoΔ mutants, 5 μg genomic DNA of indicated strains was digested with HindIII/PstI (NEB). For verification of ss1cuao1Δ and ss2cuao1Δ mutants, 5 μg genomic DNA of indicated strains was digested with PvuI/SacI (NEB). Digested products were electrophoresed in a 0.75% agarose gel and then transferred onto Hybond‐N+ membrane (Amersham). The specific probe labelled with DIG‐11‐dUTP using DIG‐High prime was PCR amplified from the 5′ flanking sequence of the SsPAO gene and the 3′ flanking sequence of the SsCuAO1 gene. Hybridization and detection were performed according to the instruction manual provided by Roche Applied Science.
Total RNA extract from fresh haploid sporidia was performed with FastPure Plant Total RNA Isolation Kit (Vazyme) following the kit instructions. The mating culture of WT or mutants was mixed with the compatible sporidia and inoculated on solid YePS medium at 28°C for 2 days, before total RNA extraction. PUT, SPD, or SPM treatment (1 mM) was carried out 90 min before total RNA extraction, no chemicals were added as a control. For cDNA synthesis, total RNA from each strain was reverse transcribed using the HiScript III 1st Strand cDNA Synthesis Kit (+gDNA wiper). qPCR with cDNA as template was run on the QuantStudio 6 Flex (Life Technologies) with ChamQ Universal SYBR qPCR Master Mix (Vazyme). Relative gene expression level was calculated with −ΔΔCt method (Livak & Schmittgen, 2001) with the cytoskeletal protein gene ACTIN as an internal control. The experiment was conducted in triplicate and for two independent biological replicates.
4.3
Plasmid construction and fungal transformation
Double‐joint PCR was performed to construct the fragment for the replacement of resident target gene by the hygromycin (HPT) or zeocin (ZEO) gene following the strategy described previously (Yu et al., 2004). Polyethylene glycol (PEG)‐mediated protoplast transformation was used to delete and complement the target gene by following the established protocols (Chang et al., 2019; Cui et al., 2022). For generation of deletion mutants, the flanking DNA (1 kb 5′ and 3′) of the resident target gene was PCR amplified using genomic DNA of MAT‐1 strain as the template and the HPT gene with plasmid pEX2 as the template using the primer pairs (Table S3). For generation of SsCuAO1 complemented strains, the encoding sequences were PCR amplified using the primer pairs (Table S3). The PCR products were ligated into the vector pEX2‐zeocin‐dsRed to create an in‐frame fusion with the RFP‐encoding sequence at its C‐terminus, driven by the original promoter. The recombinant plasmid was sequenced for verification, before being transferred into the corresponding mutant strains by PEG‐mediated protoplast transformation. For generation of SsPAO complementation strains, a reintegrated homologous fragment, composed of native promoter of SsPAO (LB‐SsPAO), coding sequence of GFP and SsPAO, expression cassette of ZEO
R gene, and the downstream fragment of SsPAO‐RB, was amplified by fusion PCR, and then transformed into ss1pao∆ or ss2pao∆ protoplasts through PEG‐mediated protoplast transformation. The complementation strains were verified by PCR amplification using the primers listed in Table S3. For generation of SsPRF1‐overexpressing strains, a reintegrated homologous fragment, composed of the upstream fragment of LB‐SsPRF1, the strong constitutive promoter P
GAPD
, coding sequence of SsPRF1, expression cassette of ZEO
R
gene, and the downstream fragment of SsPRF1‐RB, was amplified by fusion PCR, and then transformed into ss1pao∆ or ss1cuao1∆ protoplasts through PEG‐mediated protoplast transformation. The overexpressing strains were verified by PCR amplification using the primers listed in Table S3. For generation of GFP‐SsATG8:MAT‐1 strain, a reintegrated homologous fragment, composed of the native promoter of SsATG8 (LB‐SsATG8), coding sequence of GFP and SsATG8, expression cassette of ZEO
R
gene, and the downstream fragment of SsATG8‐RB, was amplified by fusion PCR, and then transformed into MAT‐1 protoplasts through PEG‐mediated protoplast transformation. All hygromycin‐resistant or zeocin‐resistant transformants were firstly screened by PCR and then confirmed by Southern blot analysis or fluorescence detection.
4.4
Chemical treatment
Putrescine (PUT; Sigma‐Aldrich), spermidine trihydrochloride (SPD; Sigma‐Aldrich), spermine (SPM; Sigma‐Aldrich), adenosine 3′,5′‐cyclic monophosphate (3′,5′‐cAMP; Sigma‐Aldrich), H2O2 (DaMao), N‐acetyl‐L‐cysteine (NAC; Aladdin), GSH (glutathione reduced; Aladdin), Vitamin C (VC; Aladdin), and rapamycin (RAPA; Aladdin) were used in this study.
4.5
Relative fungal biomass and pathogenicity assays
Fungal biomass assay of the inoculated sugarcane seedlings was carried out according to our previously described (Yin et al., 2024). The haploid sporidia of S. scitamineum strains were grown on liquid YePS medium in a shaking incubator at 28°C for 1–2 days. The fresh haploid sporidia were collected, resuspended in the sterile double‐distilled water, adjusted to 107 cells/mL, and mixed with haploid sporidia of opposite mating types in equal volume (1:1) and 1 mL of such mixture was injected into sugarcane seedlings, and total DNA of the inoculated sugarcane tissue was extracted at 3 and 7 dpi. The relative fungal biomass was measured using the fungal ACTIN gene as a reference, whereas the sugarcane glyceraldehyde dehydrogenase (GAPDH) gene served as an internal control.
For assessment of pathogenicity, sugarcane stems of susceptible cultivar ROC22 were soaked in 1 L mixture (1:1 vol/vol) of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ sporidia (107 cells/mL for each), at 28°C for 30 min, and then planted in pots (five or six seedlings per pot). Two weeks later, the seedlings were further inoculated with 1 mL mixture (1:1 vol/vol) of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ. Wild‐type mixture MAT‐1 × MAT‐2 served as a positive control and double‐distilled water inoculation as a negative control. Inoculated plants were kept in a greenhouse with the setting as 28°C, 80% humidity, and a 12 h/12 h light/dark cycle, for 3–6 months. Pathogenicity assay for the wild‐type and all mutants was repeated two times and each replicate with at least 20 plants. The symptoms of black whip were documented and photographed at about 6 months post‐inoculation. Meanwhile, the plant height of sugarcane was measured. Percentage (%) of black whip/total seedlings was estimated.
4.6
Microscopic observation and imaging
Fresh haploid sporidia were cultured in liquid YePS medium at 28°C, 200 rpm overnight. The GFP or RFP signals were observed and photographed in S. scitamineum haploid sporidia, using an Axio Observer Z1 microscope (Zeiss) equipped with sCMOS camera (PCO Edge).
4.7
Histochemical localization of H2O2
The histochemical localization of H2O2 following the established methods with some modification (Libik‐Konieczny et al., 2015). Sugarcane leaves were infiltrated with a solution of 2 mg/mL 3,3′‐diaminobenzidine (DAB; Macklin) prepared in water, pH 5.5. Infiltration was carried for 1 h in the dark at room temperature. DAB forms a deep‐brown polymerization product upon reaction with H2O2 in the presence of peroxidases. Samples incubated in DAB were rinsed with distilled water. Chlorophyll was extracted with ethanol:chloroform (80:20; vol/vol) supplemented with 0.15% (vol/vol) trichloroacetic acid. The bleached leaves were submerged in a glycerol:water (1:1 vol/vol) solution and pictures were taken using an Axio Observer Z1 microscope equipped with sCMOS camera.
4.8
Quantification of polyamines, H2O2
, and cAMP
The polyamines were extracted and detected following the established methods with some modifications (Yin et al., 2024; Zeljkovic et al., 2024). Frozen cells were homogenized in 4 mL of 5% (wt/vol) with cold perchloric acid solution, and the homogenate was kept in ice for 60 min. The mixture was then centrifuged at 14,000 g at 4°C for 30 min. Then, 500 μL of supernatant was transferred to a 10 mL centrifuge tube and the supernatant sample derivatized using benzoyl chloride. The sample solution was analysed using the Thermo Scientific UltiMate 3000 Rapid Separation Quaternary System Liquid Mass Spectrometry (LC–MS) using Single Ion Monitoring (SIM) single ion detection scan mode, reversed‐phase column C18 (Supelco Discovery, 25 cm × 4.6 mm, particle size 5 μm), column temperature 40°C, flow rate 0.2 mL/min, injection volume 10 μL aliquots. Benzoylated polyamines were eluted with 60% (vol/vol) methanol. Signal mass spectrometry scans were set to PUT at 297.16 m/z, SPD at 458.24 m/z, and SPM at 619.33 m/z. Data analysis was performed using the Thermo Xcalibur software (Thermo Fisher Scientific).
Intracellular H2O2 was quantified by a Amplex Red Hydrogen Peroxide/Peroxidase Assay Kit (ThermoFisher) and following the manufacturer's instruction. The fresh haploid sporidia were collected after cultured in liquid YePS medium for 2 days at 28°C and fungal samples were then collected by centrifugation. After grinding the fungus with liquid nitrogen, 0.1 g of fungal sample was dissolved in 10 times the volume of 1 × buffer, and the supernatant was obtained by centrifugation for subsequent analysis.
The fresh haploid sporidia were collected after growing on liquid YePS medium for 16 h at 28°C before cAMP extraction and detection following a previously described method (Chang et al., 2019). The dikaryotic hyphae were collected after growing on solid YePS medium for 2 days and then intracellular cAMP was extracted and quantified.
4.9
Immunoblotting
To monitor the autophagic process, the GFP‐SsATG8 fusion construct driven by the original promoter and a full‐length SsATG8 gene coding region was transformed into MAT‐1. The GFP‐SsATG8:MAT‐1, MAT‐1, ss1paoΔ and ss1cuao1Δ strains were first cultured in liquid YePS medium at 28°C for 16 h, and then washed with sterile double‐distilled water and subjected to nitrogen starvation (cultured in MM−N for 4 h) to induce non‐selective autophagy. For immunoblot analysis, the total protein was extracted from the fresh haploid sporidia after inducing non‐selective autophagy. Protein samples were fractioned by SDS‐PAGE gels (Smart‐Lifesciences, 4%–12%, for detection using anti‐GFP antibody, or SurePAGE, Bis‐Tris, 15%, for detection using anti‐ATG8 antibody), then transferred to a polyvinylidene difluoride (PVDF) membrane. Immunoblotting for GFP‐ATG8 cleavage was performed with primary antibody (anti‐GFP antibody; Abcam) at the recommended dilutions. Immunoblotting for ATG8 cleavage was performed with primary antibody (anti‐ATG8 antibody; MBL). Horseradish peroxidase (HRP)‐conjugated goat anti‐mouse IgG (Abbkine) was used as the secondary antibody. The efficient chemiluminescence kit (Genview) was used to detect the chemiluminescent signals. The intensity of the protein bands was determined by the ImageJ software.
4.10
Statistical analysis
Data were presented as mean ± SE. Statistical analysis was performed using GraphPad Prism 7 software, and p < 0.05 were considered statistically significant.
4.1
Fungal strains and growth conditions
The wild‐type strains of MAT‐1 (a1 b1) and MAT‐2 (a2 b2), previously isolated, identified, and maintained by our laboratory, were used for the generation of all transformations/mutants in this study (Yan et al., 2016). Details of the fungal strains generated and used in this study are listed in Table S2. The culture media employed include yeast extract‐peptone‐sucrose medium (YePS; yeast extract 1%, peptone 2%, sugar 2%, pH 6.5), minimal medium (MM; yeast nitrogen base 0.17%, glucose 2%, 10 mM ammonium sulphate, pH 6.8), and potato dextrose agar (PDA powder 4%; DingGuo).
For sexual mating assay, fresh haploid sporidia were cultured in liquid YePS medium overnight. Subsequently, the sporidia were collected after washing twice with sterile double‐distilled water and the concentration was adjusted to OD600 = 1.0. The haploid sporidia of opposite mating types were mixed at equal volume (OD600 = 1.0) and spotted on PDA or solid MM, and kept in the dark in a 28°C incubator for 2–3 days before photographing.
For haploid growth analysis, fresh haploid sporidia were cultured in liquid YePS medium overnight, washed twice with sterile double‐distilled water, and adjusted to OD600 = 1.0. Fresh haploid sporidia (OD600 = 1.0, equivalent to cfu = 107/mL) of WT and mutants were diluted in a gradient into 107, 106, 105, 104, and 103 spotted onto PDA or MM, and kept in dark in a 28°C incubator for 2–3 days before photographing.
For growth curve analysis, fresh haploid sporidia were cultured in liquid YePS medium overnight, washed twice with fresh liquid YePS medium, and adjusted to OD600 = 0.01, then added to 120 μL of the culture into a 100‐well honeycomb plate, incubated in the automatic microbial growth curve analyser (Oy Growth Curves Ab Ltd; Bioscreen C) with the setting as 28°C, for 3 days.
4.2
Nucleic acid‐related manipulation
Genomic DNA of S. scitamineum strains was extracted from the sporidia culture grown on solid YePS medium for 2 days at 28°C, using a Fungal DNA Midi Kit (Omega). Southern blot analysis was performed with the digoxigenin (DIG) High Prime DNA labelling and detection starter kit II (Roche). For verification of ss1paoΔ and ss2paoΔ mutants, 5 μg genomic DNA of indicated strains was digested with HindIII/PstI (NEB). For verification of ss1cuao1Δ and ss2cuao1Δ mutants, 5 μg genomic DNA of indicated strains was digested with PvuI/SacI (NEB). Digested products were electrophoresed in a 0.75% agarose gel and then transferred onto Hybond‐N+ membrane (Amersham). The specific probe labelled with DIG‐11‐dUTP using DIG‐High prime was PCR amplified from the 5′ flanking sequence of the SsPAO gene and the 3′ flanking sequence of the SsCuAO1 gene. Hybridization and detection were performed according to the instruction manual provided by Roche Applied Science.
Total RNA extract from fresh haploid sporidia was performed with FastPure Plant Total RNA Isolation Kit (Vazyme) following the kit instructions. The mating culture of WT or mutants was mixed with the compatible sporidia and inoculated on solid YePS medium at 28°C for 2 days, before total RNA extraction. PUT, SPD, or SPM treatment (1 mM) was carried out 90 min before total RNA extraction, no chemicals were added as a control. For cDNA synthesis, total RNA from each strain was reverse transcribed using the HiScript III 1st Strand cDNA Synthesis Kit (+gDNA wiper). qPCR with cDNA as template was run on the QuantStudio 6 Flex (Life Technologies) with ChamQ Universal SYBR qPCR Master Mix (Vazyme). Relative gene expression level was calculated with −ΔΔCt method (Livak & Schmittgen, 2001) with the cytoskeletal protein gene ACTIN as an internal control. The experiment was conducted in triplicate and for two independent biological replicates.
4.3
Plasmid construction and fungal transformation
Double‐joint PCR was performed to construct the fragment for the replacement of resident target gene by the hygromycin (HPT) or zeocin (ZEO) gene following the strategy described previously (Yu et al., 2004). Polyethylene glycol (PEG)‐mediated protoplast transformation was used to delete and complement the target gene by following the established protocols (Chang et al., 2019; Cui et al., 2022). For generation of deletion mutants, the flanking DNA (1 kb 5′ and 3′) of the resident target gene was PCR amplified using genomic DNA of MAT‐1 strain as the template and the HPT gene with plasmid pEX2 as the template using the primer pairs (Table S3). For generation of SsCuAO1 complemented strains, the encoding sequences were PCR amplified using the primer pairs (Table S3). The PCR products were ligated into the vector pEX2‐zeocin‐dsRed to create an in‐frame fusion with the RFP‐encoding sequence at its C‐terminus, driven by the original promoter. The recombinant plasmid was sequenced for verification, before being transferred into the corresponding mutant strains by PEG‐mediated protoplast transformation. For generation of SsPAO complementation strains, a reintegrated homologous fragment, composed of native promoter of SsPAO (LB‐SsPAO), coding sequence of GFP and SsPAO, expression cassette of ZEO
R gene, and the downstream fragment of SsPAO‐RB, was amplified by fusion PCR, and then transformed into ss1pao∆ or ss2pao∆ protoplasts through PEG‐mediated protoplast transformation. The complementation strains were verified by PCR amplification using the primers listed in Table S3. For generation of SsPRF1‐overexpressing strains, a reintegrated homologous fragment, composed of the upstream fragment of LB‐SsPRF1, the strong constitutive promoter P
GAPD
, coding sequence of SsPRF1, expression cassette of ZEO
R
gene, and the downstream fragment of SsPRF1‐RB, was amplified by fusion PCR, and then transformed into ss1pao∆ or ss1cuao1∆ protoplasts through PEG‐mediated protoplast transformation. The overexpressing strains were verified by PCR amplification using the primers listed in Table S3. For generation of GFP‐SsATG8:MAT‐1 strain, a reintegrated homologous fragment, composed of the native promoter of SsATG8 (LB‐SsATG8), coding sequence of GFP and SsATG8, expression cassette of ZEO
R
gene, and the downstream fragment of SsATG8‐RB, was amplified by fusion PCR, and then transformed into MAT‐1 protoplasts through PEG‐mediated protoplast transformation. All hygromycin‐resistant or zeocin‐resistant transformants were firstly screened by PCR and then confirmed by Southern blot analysis or fluorescence detection.
4.4
Chemical treatment
Putrescine (PUT; Sigma‐Aldrich), spermidine trihydrochloride (SPD; Sigma‐Aldrich), spermine (SPM; Sigma‐Aldrich), adenosine 3′,5′‐cyclic monophosphate (3′,5′‐cAMP; Sigma‐Aldrich), H2O2 (DaMao), N‐acetyl‐L‐cysteine (NAC; Aladdin), GSH (glutathione reduced; Aladdin), Vitamin C (VC; Aladdin), and rapamycin (RAPA; Aladdin) were used in this study.
4.5
Relative fungal biomass and pathogenicity assays
Fungal biomass assay of the inoculated sugarcane seedlings was carried out according to our previously described (Yin et al., 2024). The haploid sporidia of S. scitamineum strains were grown on liquid YePS medium in a shaking incubator at 28°C for 1–2 days. The fresh haploid sporidia were collected, resuspended in the sterile double‐distilled water, adjusted to 107 cells/mL, and mixed with haploid sporidia of opposite mating types in equal volume (1:1) and 1 mL of such mixture was injected into sugarcane seedlings, and total DNA of the inoculated sugarcane tissue was extracted at 3 and 7 dpi. The relative fungal biomass was measured using the fungal ACTIN gene as a reference, whereas the sugarcane glyceraldehyde dehydrogenase (GAPDH) gene served as an internal control.
For assessment of pathogenicity, sugarcane stems of susceptible cultivar ROC22 were soaked in 1 L mixture (1:1 vol/vol) of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ or ss1cuao1Δ × ss2cuao1Δ sporidia (107 cells/mL for each), at 28°C for 30 min, and then planted in pots (five or six seedlings per pot). Two weeks later, the seedlings were further inoculated with 1 mL mixture (1:1 vol/vol) of MAT‐1 × MAT‐2, ss1paoΔ × ss2paoΔ and ss1cuao1Δ × ss2cuao1Δ. Wild‐type mixture MAT‐1 × MAT‐2 served as a positive control and double‐distilled water inoculation as a negative control. Inoculated plants were kept in a greenhouse with the setting as 28°C, 80% humidity, and a 12 h/12 h light/dark cycle, for 3–6 months. Pathogenicity assay for the wild‐type and all mutants was repeated two times and each replicate with at least 20 plants. The symptoms of black whip were documented and photographed at about 6 months post‐inoculation. Meanwhile, the plant height of sugarcane was measured. Percentage (%) of black whip/total seedlings was estimated.
4.6
Microscopic observation and imaging
Fresh haploid sporidia were cultured in liquid YePS medium at 28°C, 200 rpm overnight. The GFP or RFP signals were observed and photographed in S. scitamineum haploid sporidia, using an Axio Observer Z1 microscope (Zeiss) equipped with sCMOS camera (PCO Edge).
4.7
Histochemical localization of H2O2
The histochemical localization of H2O2 following the established methods with some modification (Libik‐Konieczny et al., 2015). Sugarcane leaves were infiltrated with a solution of 2 mg/mL 3,3′‐diaminobenzidine (DAB; Macklin) prepared in water, pH 5.5. Infiltration was carried for 1 h in the dark at room temperature. DAB forms a deep‐brown polymerization product upon reaction with H2O2 in the presence of peroxidases. Samples incubated in DAB were rinsed with distilled water. Chlorophyll was extracted with ethanol:chloroform (80:20; vol/vol) supplemented with 0.15% (vol/vol) trichloroacetic acid. The bleached leaves were submerged in a glycerol:water (1:1 vol/vol) solution and pictures were taken using an Axio Observer Z1 microscope equipped with sCMOS camera.
4.8
Quantification of polyamines, H2O2
, and cAMP
The polyamines were extracted and detected following the established methods with some modifications (Yin et al., 2024; Zeljkovic et al., 2024). Frozen cells were homogenized in 4 mL of 5% (wt/vol) with cold perchloric acid solution, and the homogenate was kept in ice for 60 min. The mixture was then centrifuged at 14,000 g at 4°C for 30 min. Then, 500 μL of supernatant was transferred to a 10 mL centrifuge tube and the supernatant sample derivatized using benzoyl chloride. The sample solution was analysed using the Thermo Scientific UltiMate 3000 Rapid Separation Quaternary System Liquid Mass Spectrometry (LC–MS) using Single Ion Monitoring (SIM) single ion detection scan mode, reversed‐phase column C18 (Supelco Discovery, 25 cm × 4.6 mm, particle size 5 μm), column temperature 40°C, flow rate 0.2 mL/min, injection volume 10 μL aliquots. Benzoylated polyamines were eluted with 60% (vol/vol) methanol. Signal mass spectrometry scans were set to PUT at 297.16 m/z, SPD at 458.24 m/z, and SPM at 619.33 m/z. Data analysis was performed using the Thermo Xcalibur software (Thermo Fisher Scientific).
Intracellular H2O2 was quantified by a Amplex Red Hydrogen Peroxide/Peroxidase Assay Kit (ThermoFisher) and following the manufacturer's instruction. The fresh haploid sporidia were collected after cultured in liquid YePS medium for 2 days at 28°C and fungal samples were then collected by centrifugation. After grinding the fungus with liquid nitrogen, 0.1 g of fungal sample was dissolved in 10 times the volume of 1 × buffer, and the supernatant was obtained by centrifugation for subsequent analysis.
The fresh haploid sporidia were collected after growing on liquid YePS medium for 16 h at 28°C before cAMP extraction and detection following a previously described method (Chang et al., 2019). The dikaryotic hyphae were collected after growing on solid YePS medium for 2 days and then intracellular cAMP was extracted and quantified.
4.9
Immunoblotting
To monitor the autophagic process, the GFP‐SsATG8 fusion construct driven by the original promoter and a full‐length SsATG8 gene coding region was transformed into MAT‐1. The GFP‐SsATG8:MAT‐1, MAT‐1, ss1paoΔ and ss1cuao1Δ strains were first cultured in liquid YePS medium at 28°C for 16 h, and then washed with sterile double‐distilled water and subjected to nitrogen starvation (cultured in MM−N for 4 h) to induce non‐selective autophagy. For immunoblot analysis, the total protein was extracted from the fresh haploid sporidia after inducing non‐selective autophagy. Protein samples were fractioned by SDS‐PAGE gels (Smart‐Lifesciences, 4%–12%, for detection using anti‐GFP antibody, or SurePAGE, Bis‐Tris, 15%, for detection using anti‐ATG8 antibody), then transferred to a polyvinylidene difluoride (PVDF) membrane. Immunoblotting for GFP‐ATG8 cleavage was performed with primary antibody (anti‐GFP antibody; Abcam) at the recommended dilutions. Immunoblotting for ATG8 cleavage was performed with primary antibody (anti‐ATG8 antibody; MBL). Horseradish peroxidase (HRP)‐conjugated goat anti‐mouse IgG (Abbkine) was used as the secondary antibody. The efficient chemiluminescence kit (Genview) was used to detect the chemiluminescent signals. The intensity of the protein bands was determined by the ImageJ software.
4.10
Statistical analysis
Data were presented as mean ± SE. Statistical analysis was performed using GraphPad Prism 7 software, and p < 0.05 were considered statistically significant.
CONFLICT OF INTEREST STATEMENT
No conflict of interest exists during the conduct of this research and in preparation of this manuscript.
Supporting information
Figure S1.
Figure S2.
Figure S3.
Figure S4.
Figure S5.
Figure S6.
Table S1.
Table S2.
Table S3. |
Title: Antioxidant and Skin-Whitening Efficacy of a Novel Decapeptide (DP, KGYSSYICDK) Derived from Fish By-Products | Body:
1. Introduction
The skin is directly exposed to various oxidative stresses due to environmental factors. Prolonged exposure to ultraviolet rays can lead to the excessive generation of reactive oxygen species (ROS), damaging the antioxidant defense system within cells and accelerating the expression of genes related to oxidative stress [1,2], pigmentation disorders, photoaging, skin cancer, cataracts, immune system impairment, and cancer induction [3,4]. To counteract these effects, recent research has focused on extracting useful components from natural sources with low toxicity for developing food, medical materials, and anti-aging or whitening cosmetic ingredients.
Melanin is a stable polymeric pigment found in both plants and animals. It is synthesized in melanocytes through a complex, multi-step process involving the enzyme tyrosinase, which catalyzes the conversion of l-Tyrosine to 3,4-dihydroxyphenylalanine (DOPA), and subsequently to dopaquinone [5]. Melanin production is stimulated by factors like ultraviolet rays and oxidative stress, leading to the release of α-melanocyte stimulating hormone (α-MSH), which triggers melanogenesis by activating signaling pathways in melanocytes [6]. α-MSH is produced in various skin cells such as melanocytes and keratinocytes, as well as in the intermediate lobe of the pituitary gland and the hypothalamus. It binds to the melanocortin receptor (MC1R), which is a membrane receptor specifically expressed in melanocytes. This binding activates adenylyl cyclase, amplifies intracellular cAMP signaling, and induces the activation of protein kinase A (PKA). Through the activation of cAMP response element-binding protein (CREB), it increases the expression of the microphthalmia-associated transcription factor (MITF), which is a basic helix–loop–helix leucine zipper (b-HLH-Zip) transcription factor in melanocytes [5].
Current skin-whitening strategies primarily involve either reducing existing melanin or inhibiting tyrosinase, the enzyme responsible for melanin synthesis [7]. Various chemical agents and plant extracts have been used in skin-whitening products, but issues such as discoloration, odor, and cytotoxicity have limited their application [8]. Therefore, there is a need for effective whitening agents that are free from adverse effects like skin irritation and allergic reactions. Many studies have been conducted to explore various whitening mechanisms [9,10].
Marine fish proteins are composed of small peptides and often exist in an inactive form along with the entire protein sequence. Enzymatic hydrolysis is frequently used to isolate short, bioactive peptides from marine organisms and seafood waste [11]. Peptides serve as important active components in various pharmaceutical and cosmeceutical applications [12,13]. Bioactive peptides typically consist of 3–20 amino acid residues and exhibit a range of biological activities [14,15,16].
In previous research, we identified a decapeptide (DP, KGYSSYICDK) from fish by-products with free radical scavenging and tyrosinase inhibitory activities [17]. This study aims to evaluate the antioxidant and skin-whitening effects of this novel decapeptide (DP) through in vitro and in silico analyses and to establish its potential as a new skin-whitening agent.
2. Results
2.1. Structural Characterization of DP
The process of preparation of C. notate by-product hydrolysate, measurement of ABTS radical scavenging activity of the hydrolysate, amino acid sequencing and synthesis, and isolation of the decapeptide by amino acid sequencing and synthesis is shown in Figure 1. Figure 2A shows the 3D virtual structure of the DP peptide. The wheel and net projections have been proposed to represent in two dimensions the tridimensional helical structures and facilitate the observation of their properties, especially in terms of residue polarity and intramolecular bonding (Figure 2B). Net projections are used for the same structures as wheels but provide a different perspective to the visualization of the helixes. At least two chains must be specified as a homodimer (Figure 2C).
These results suggest that DP peptides are likely to adopt α-helix-dominated conformations upon interactions with cell membranes, and the mechanism of action of the peptides could be anticipated via their amphipathic helical properties.
2.2. Mass Spectra of DP
Figure 3A shows the result of the mass spectrum of DP analyzed by LC-HRMS. Figure 3B. shows the result of the mass spectrum of DP analyzed by LC-MS. And Figure 3C shows the result of the purity of DP analyzed by RP-HPLC.
2.3. Effects of DP in Ferric Reducing Antioxidant Power (FRAP) and 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic Acid) (ABTS) Radical Scavenging Activity
To compare the antioxidant activity of DP, FRAP values were measured against FeSO4 standards (Figure 4A). DP exhibited dose-dependent FRAP activity, with significant activity at 1000 μM, comparable to Vitamin C (Vit. C, p < 0.05). Additionally, DP demonstrated concentration-dependent ABTS radical scavenging activity, with over 90% scavenging at concentrations above 20 μM, significantly outperforming Vit. C (p < 0.05) (Figure 4B).
2.4. Inhibition of Hydrogen Peroxide (H2O2)-Induced Cell Damage and Reactive Oxygen Species (ROS) Reduction by DP in HaCaT Cells
To assess DP’s protective effects against H2O2-induced damage, HaCaT cells were treated with 50, 100, and 200 μM of DP and subjected to 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays. DP enhanced cell viability in a dose-dependent manner (Figure 5A). ROS levels, measured with Dichloro-dihydro-fluorescein diacetate (DCFH-DA), were significantly reduced by DP treatment (Figure 5B), indicating its potential to mitigate oxidative stress.
2.5. Inhibition of Tyrosinase Activity by DP
Tyrosinase, a copper-containing enzyme, plays a crucial role in melanin formation. To effectively inhibit the synthesis of melanin polymers, the tyrosinase inhibitory activity of DP was measured against mushroom-derived tyrosinase (Figure 6). At concentrations of 62.5, 125, 250, 500, and 1000 μM, DP exhibited tyrosinase inhibitory activities of 7.05%, 19.62%, 26.29%, 62.67%, and 84.38%, respectively. When compared to Vit. C, a standard whitening agent, at the same concentration, DP showed similar tyrosinase inhibitory activity, suggesting its potential as a functional material for whitening.
2.6. Effect of DP on Melanin Content and Morphology in α-MSH-Induced B16F10 Melanoma Cells
Before measuring the melanin content in B16F10 melanoma cells, the cytotoxicity of DP was assessed at concentrations of 12.5, 50, and 100 μM. No cytotoxicity was observed at any of these concentrations after 24 h (Figure 7A). For further analysis, B16F10 melanoma cells were treated with DP at concentrations of 0.1, 1, and 10 μM, along with α-MSH (200 nM), and incubated for 24 h. The melanin production was then measured. Morphological observations confirmed that melanin formation was inhibited starting from the 0.1 μM concentration of DP (Figure 7B). The results of the melanin production assay showed that the melanin content increased by 152.36% in the α-MSH treated group compared to the control group. In contrast, melanin production decreased to 120.32%, 105.24%, and 83.21% at 0.1, 1, and 10 μM concentrations of DP, respectively, compared to the α-MSH treated group (Figure 7C).
2.7. Inhibition of MITF, Tyrosinase, and MC1R mRNA Expression by DP
To evaluate whether DP affects the expression of melanin-related genes such as MITF, tyrosinase, and MC1R, Reverse transcription polymerase chain reaction (RT-PCR) was used to investigate gene expression in α-MSH-induced B16F10 cells. Since previous experiments indicated that DP at concentrations of 0.1, 1, and 10 μM significantly affected melanin synthesis, gene expression was also assessed at these concentrations. As shown in Figure 8, treatment with DP at concentrations of 0.1, 1, and 10 μM resulted in a dose-dependent and significant decrease in the mRNA levels of MITF, tyrosinase, and MC1R. These results suggest that DP downregulates the expression of MITF, tyrosinase, and MC1R mRNA, thereby influencing melanin production in α-MSH-stimulated B16F10 cells.
2.8. Molecular Docking Model of DP with Tyrosinase Protein
The 3D structure of mushroom tyrosinase, and the bioactive effects of KVARP (Figure 9A), L-TYROSINE (Figure 9B), L-DOPA (Figure 9C), and KGYSSYICDK (Figure 9D) peptide were concatenated with Schrödinger Suite software. Water molecules in the protein-crystal structure must be removed before the docking procedure. The molecular docking results about mushroom tyrosinase KVARP, L-TYROSINE, L-DOPA, and KGYSSYICDK peptide, respectively, were showed in best interaction poses with −8.64 kcal/mol of docking score and −19.35 kcal/mol of MMGBSA score (Figure 9D). Current commercially used KVARP is −6.66 kcal/mol of docking score and −18.40 kcal/mol of MMGBSA score (Figure 9A), while L-tyrosine and L-DOPA were −9.01 and −8.39 kcal/mol of docking score and −14.60 and −15.08 kcal/mol of MMGBSA score (Figure 9B,C). KGYSSYICDK peptide has numerous interactions with the amino acid residue of human tyrosinase and is similar to that of mushroom tyrosinase (Figure 9D).
The structure of KVARP, L-TYROSINE, and L-DOPA is shown in yellow, with the main amino acid sequence identified as HIS85, VAL150, GLU103, GLU322, ALA323, ALA246, and LYS79; HIS85, GLY281, PHE264, and CU400; PHE264, GLY 281, GLU256, HIS85, and CU400 (Figure 9A–C, left panel). The structure of KGYSSYICDK (Figure 9D, left panel) identified as LYS79, HIS85, ASP348, GLU103, ARG268, and PRO270. The backbones of TYR78 and GLU103 interact with the NH2 moiety of KGYSSYICDK via hydrogen bonds. The backbone of another GLY281, HIS85, and ARG285 interact with the NH2 moiety of KGYSSYICDK via hydrogen bonding. In addition, the side chains of PRO270 also interact with the NH2 moiety of KGYSSYICDK (Figure 9D, left panel). A two-dimensional representation of the interaction between mushroom tyrosinase (PDB: 2Y9X) and the KVARP, L-TYROSINE, L-DOPA, and KGYSSYICDK peptide active site are shown in Figure 9A–D, right panel.
KVARP, L-TYROSINE, L-DOPA, and KGYSSYICDK had 11, 6, 6, 6, and 14 hydrophobic interactions (lime green), and the polar interaction (light blue) was 4, 8, 9, and 10. The GLY281 interaction (white color) was found in L-TYROSINE and KGYSSYICDK. Interestingly, a negative-charged interaction with GLU256 (Orange color) was found in L-TYROSINE and L-DOPA, and a positive-charged interaction (violet color) was found in KVARP and KGYSSYICDK (Figure 9A–D, right panel).
2.1. Structural Characterization of DP
The process of preparation of C. notate by-product hydrolysate, measurement of ABTS radical scavenging activity of the hydrolysate, amino acid sequencing and synthesis, and isolation of the decapeptide by amino acid sequencing and synthesis is shown in Figure 1. Figure 2A shows the 3D virtual structure of the DP peptide. The wheel and net projections have been proposed to represent in two dimensions the tridimensional helical structures and facilitate the observation of their properties, especially in terms of residue polarity and intramolecular bonding (Figure 2B). Net projections are used for the same structures as wheels but provide a different perspective to the visualization of the helixes. At least two chains must be specified as a homodimer (Figure 2C).
These results suggest that DP peptides are likely to adopt α-helix-dominated conformations upon interactions with cell membranes, and the mechanism of action of the peptides could be anticipated via their amphipathic helical properties.
2.2. Mass Spectra of DP
Figure 3A shows the result of the mass spectrum of DP analyzed by LC-HRMS. Figure 3B. shows the result of the mass spectrum of DP analyzed by LC-MS. And Figure 3C shows the result of the purity of DP analyzed by RP-HPLC.
2.3. Effects of DP in Ferric Reducing Antioxidant Power (FRAP) and 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic Acid) (ABTS) Radical Scavenging Activity
To compare the antioxidant activity of DP, FRAP values were measured against FeSO4 standards (Figure 4A). DP exhibited dose-dependent FRAP activity, with significant activity at 1000 μM, comparable to Vitamin C (Vit. C, p < 0.05). Additionally, DP demonstrated concentration-dependent ABTS radical scavenging activity, with over 90% scavenging at concentrations above 20 μM, significantly outperforming Vit. C (p < 0.05) (Figure 4B).
2.4. Inhibition of Hydrogen Peroxide (H2O2)-Induced Cell Damage and Reactive Oxygen Species (ROS) Reduction by DP in HaCaT Cells
To assess DP’s protective effects against H2O2-induced damage, HaCaT cells were treated with 50, 100, and 200 μM of DP and subjected to 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays. DP enhanced cell viability in a dose-dependent manner (Figure 5A). ROS levels, measured with Dichloro-dihydro-fluorescein diacetate (DCFH-DA), were significantly reduced by DP treatment (Figure 5B), indicating its potential to mitigate oxidative stress.
2.5. Inhibition of Tyrosinase Activity by DP
Tyrosinase, a copper-containing enzyme, plays a crucial role in melanin formation. To effectively inhibit the synthesis of melanin polymers, the tyrosinase inhibitory activity of DP was measured against mushroom-derived tyrosinase (Figure 6). At concentrations of 62.5, 125, 250, 500, and 1000 μM, DP exhibited tyrosinase inhibitory activities of 7.05%, 19.62%, 26.29%, 62.67%, and 84.38%, respectively. When compared to Vit. C, a standard whitening agent, at the same concentration, DP showed similar tyrosinase inhibitory activity, suggesting its potential as a functional material for whitening.
2.6. Effect of DP on Melanin Content and Morphology in α-MSH-Induced B16F10 Melanoma Cells
Before measuring the melanin content in B16F10 melanoma cells, the cytotoxicity of DP was assessed at concentrations of 12.5, 50, and 100 μM. No cytotoxicity was observed at any of these concentrations after 24 h (Figure 7A). For further analysis, B16F10 melanoma cells were treated with DP at concentrations of 0.1, 1, and 10 μM, along with α-MSH (200 nM), and incubated for 24 h. The melanin production was then measured. Morphological observations confirmed that melanin formation was inhibited starting from the 0.1 μM concentration of DP (Figure 7B). The results of the melanin production assay showed that the melanin content increased by 152.36% in the α-MSH treated group compared to the control group. In contrast, melanin production decreased to 120.32%, 105.24%, and 83.21% at 0.1, 1, and 10 μM concentrations of DP, respectively, compared to the α-MSH treated group (Figure 7C).
2.7. Inhibition of MITF, Tyrosinase, and MC1R mRNA Expression by DP
To evaluate whether DP affects the expression of melanin-related genes such as MITF, tyrosinase, and MC1R, Reverse transcription polymerase chain reaction (RT-PCR) was used to investigate gene expression in α-MSH-induced B16F10 cells. Since previous experiments indicated that DP at concentrations of 0.1, 1, and 10 μM significantly affected melanin synthesis, gene expression was also assessed at these concentrations. As shown in Figure 8, treatment with DP at concentrations of 0.1, 1, and 10 μM resulted in a dose-dependent and significant decrease in the mRNA levels of MITF, tyrosinase, and MC1R. These results suggest that DP downregulates the expression of MITF, tyrosinase, and MC1R mRNA, thereby influencing melanin production in α-MSH-stimulated B16F10 cells.
2.8. Molecular Docking Model of DP with Tyrosinase Protein
The 3D structure of mushroom tyrosinase, and the bioactive effects of KVARP (Figure 9A), L-TYROSINE (Figure 9B), L-DOPA (Figure 9C), and KGYSSYICDK (Figure 9D) peptide were concatenated with Schrödinger Suite software. Water molecules in the protein-crystal structure must be removed before the docking procedure. The molecular docking results about mushroom tyrosinase KVARP, L-TYROSINE, L-DOPA, and KGYSSYICDK peptide, respectively, were showed in best interaction poses with −8.64 kcal/mol of docking score and −19.35 kcal/mol of MMGBSA score (Figure 9D). Current commercially used KVARP is −6.66 kcal/mol of docking score and −18.40 kcal/mol of MMGBSA score (Figure 9A), while L-tyrosine and L-DOPA were −9.01 and −8.39 kcal/mol of docking score and −14.60 and −15.08 kcal/mol of MMGBSA score (Figure 9B,C). KGYSSYICDK peptide has numerous interactions with the amino acid residue of human tyrosinase and is similar to that of mushroom tyrosinase (Figure 9D).
The structure of KVARP, L-TYROSINE, and L-DOPA is shown in yellow, with the main amino acid sequence identified as HIS85, VAL150, GLU103, GLU322, ALA323, ALA246, and LYS79; HIS85, GLY281, PHE264, and CU400; PHE264, GLY 281, GLU256, HIS85, and CU400 (Figure 9A–C, left panel). The structure of KGYSSYICDK (Figure 9D, left panel) identified as LYS79, HIS85, ASP348, GLU103, ARG268, and PRO270. The backbones of TYR78 and GLU103 interact with the NH2 moiety of KGYSSYICDK via hydrogen bonds. The backbone of another GLY281, HIS85, and ARG285 interact with the NH2 moiety of KGYSSYICDK via hydrogen bonding. In addition, the side chains of PRO270 also interact with the NH2 moiety of KGYSSYICDK (Figure 9D, left panel). A two-dimensional representation of the interaction between mushroom tyrosinase (PDB: 2Y9X) and the KVARP, L-TYROSINE, L-DOPA, and KGYSSYICDK peptide active site are shown in Figure 9A–D, right panel.
KVARP, L-TYROSINE, L-DOPA, and KGYSSYICDK had 11, 6, 6, 6, and 14 hydrophobic interactions (lime green), and the polar interaction (light blue) was 4, 8, 9, and 10. The GLY281 interaction (white color) was found in L-TYROSINE and KGYSSYICDK. Interestingly, a negative-charged interaction with GLU256 (Orange color) was found in L-TYROSINE and L-DOPA, and a positive-charged interaction (violet color) was found in KVARP and KGYSSYICDK (Figure 9A–D, right panel).
3. Discussion
Marine fish are primarily used as a food source for human consumption, leading to the production of fish meat in various fish processing industries. However, these industries generate large amounts of waste, including heads, tails, and bones, which exacerbates environmental pollution. To address this issue, by-products generated in the seafood processing industry are utilized to extract bioactive compounds beneficial to human health. This process helps reduce pollution and also enhances the value of fish processing by-products [18,19,20]. Fish processing waste contains a significant amount of useful protein, which serves as a source for bioactive peptide extraction [21,22,23]. Peptides have been extensively studied as active ingredients in cosmetics due to their high biocompatibility and protein-mimicking activities [24]. In recent studies, fish-derived peptides have shown promising applications in various fields, including cosmetics, due to their antioxidant and anti-inflammatory properties [25,26]. Furthermore, recent research highlights the potential of fish by-products in producing peptides with significant bioactivities, such as antioxidant, anti-inflammatory, and anti-melanogenic effects [27]. In this study, we evaluated the antioxidant activity of a major bioactive peptide, decapeptide (DP, KGYSSYICDK), found in fish by-products from the Chungcheong West Coast region of the Republic of Korea in HaCaT cells and investigated its anti-melanogenic effects in mouse melanoma (B16F10) cells. Additionally, we conducted in silico analysis with tyrosinase protein to explore its potential as a new skin-whitening agent.
Physiologically beneficial compounds cannot be assessed for antioxidant activity using a single method. Therefore, to investigate and understand these possible mechanisms, we performed FRAP activity and ABTS radical scavenging assays to determine the antioxidant activity of decapeptide. Since antioxidant capacity is strongly related to reducing ability, FRAP analysis is used as a method to measure the antioxidant activity of various compounds [28]. Decapeptide demonstrated a concentration-dependent FRAP activity (Figure 4A). The ABTS free radical scavenging is one of the most widely used spectrophotometric methods to confirm the antioxidant capacity of compounds. Decapeptide’s ABTS free radical scavenging activity was concentration-dependent and continuously improved with increasing sample concentration (Figure 4B). This study demonstrates that decapeptide can scavenge free radicals through a single-electron transfer reaction mechanism (ABTS assay) [29].
H2O2 is a commonly used oxidant for establishing oxidative stress models, as it can induce excessive ROS production within cells, leading to an imbalance between oxidative and antioxidative levels [30]. In this study, decapeptide treatment improved the survival rate of HaCaT cells exposed to H2O2 (Figure 5A) and significantly reduced intracellular ROS levels (Figure 5B). This suggests that decapeptide enhances the antioxidant activity of HaCaT cells and can alleviate cell damage induced by H2O2.
Skin pigmentation primarily occurs in melanocytes of the basal layer of the skin, which are stimulated by ultraviolet (UV) radiation. Stimulated keratinocytes secrete α-MSH [31]. Consequently, UV exposure induces melanin production, leading to hyperpigmentation [32]. The development of whitening agents has primarily focused on inhibiting melanin production in melanocytes by regulating melanin-related factors [33,34,35]. Recently, research has also explored other targets for depigmentation, including the movement, transfer, and degradation of melanosomes, with several substances identified [36,37,38]. Since pigmentation involves various mechanisms, combining treatments that target different aspects may be an effective approach.
Tyrosinase plays a crucial role in melanin biosynthesis [39]. Therefore, the mechanism supporting the efficacy of skin-whitening agents involves reducing tyrosinase activity to inhibit melanin production [40]. The expression of the tyrosinase gene is known to be regulated by MITF [41]. The MC1R, which recognizes α-MSH from external stimuli, activates the CREB signaling pathway. The first step in this process is the conversion of ATP to cAMP. During this process, higher molecular components, including PKA and CREB, are phosphorylated and subsequently transcribe the lower molecular component, MITF [42]. In this study, the anti-melanogenic activity of decapeptide was found to be mediated by inhibiting tyrosinase activity and α-MSH-induced melanin synthesis in B16F10 melanoma cells without inducing cytotoxicity (Figure 7). This anti-melanogenic activity of decapeptide appears to be mediated by the downregulation of mRNA expression of tyrosinase, MITF, and MC1R induced by α-MSH (Figure 8).
The main influence factors of the peptide’s biological activity are the sequence and position of the amino acid [43]. Therefore, molecular docking plays a common and important role in the area of structural molecular biology. There are numerous successful examples obtained with the help of this [44]. Experimental identification of anti-tyrosinases is expensive and time-consuming. Therefore, computational methods provided a preliminary solution to this hypothesis, allowing researchers to use molecular docking to investigate the effect of compounds in the extract on tyrosinase binding and predict and report on their anti-tyrosinase ability. Many studies have used machine learning (ML) methods to create and use a variety of peptide prediction tools, including anti-cancer, anti-inflammatory, anti-biofilm, antibacterial, and cell penetration. This study also utilized the following research. These tools were developed by extracting characteristics such as amino acid composition (AAC), dipeptide composition (DPC), and biochemical composition (BCP) from amino acid sequences.
A molecular docking study was also conducted to examine the binding conformations of all the synthesized compounds within the catalytic pocket of the enzyme tyrosinase. The docked ligand–protein complexes were investigated based on docking and MMGBSA score. The docking scores had little fluctuations, and the comparison depicted no significant energy difference among all docked molecules due to the similar basic skeleton of the ligands. Therefore, the majority of the ligands showed efficient docking energy values. From docking results, the most active compound KGYSSYICDK was visualized to determine its interactions in the catalytic site of the protein tyrosinase. The interaction energy of the KGYSSYICDK inhibitors was −8.64 kcal/mol of docking score and −19.35 kcal/mol of MMGBSA score, respectively, which were superior to the positive control −6.66 kcal/mol of docking score and −18.40 kcal/mol of MMGBSA score.
Four strong hydrogen bond contacts were observed. The orthohydroxyl phenolic moiety picks up a hydrogen bond (2.03 Å) interaction with the side chain carbonyl of ARG 268, and this phenolic ring is further stabilized by π–π stacking with side chain HIS85. The keto carbonyl oxygen from the tail moiety of this compound interacts through another hydrogen bond with neighboring PRO270 (1.98 Å), predicting a competitive type of inhibition for KGYSSYICDK. Interestingly, both L-TYROSINE and L-DOPA, PHE264, His 85, His 263, and GLY281 (Cu 401) are conserved residues in the core region. KVARP, L-TYROSINE, L-DOPA, and KGYSSYICDK all bind to HIS85, revealing that this residue is a very important amino acid. It seems to be that tyrosinase inhibition traps histidine amino acid coordinated with Cu ions, which played an important role in the activity.
Our experimental results provided a strong basis for the efficiency of our screening and docking strategies. Our study enriched the function study of human tyrosinase, suggested promising drug candidates for the treatment of hyperpigmentation and malignant melanoma, and provided two potential whitening functional agents. Furthermore, the appropriate structure optimization of KGYSSYICDK and structure–activity relationship study needs to be further studied.
4. Materials and Methods
4.1. Experimental Materials
The decapeptide (DP, KGYSSYICDK) used in this study was synthesized by A&Pep Co. (Osong, Republic of Korea). All reagents for antioxidant and whitening assays were purchased from Sigma-Aldrich (St. Louis, MO, USA). For cell culture, fetal bovine serum (FBS), penicillin, and Dulbecco’s Modified Eagle’s Medium (DMEM) were obtained from Gibco BRL Co. (Grand Island, NY, USA).
4.2. Peptide Isolation from Hydrolysate of Chromis Notate By-Product
In this study, the heads of C. notate by-products were collected from the distribution center of Jeju Island FPC (Fisheries Processing & Marketing Center) in Republic of Korea, and used as hydrolysate preparation samples [17]. The collected C. notate heads were freeze-dried in a freeze-dryer (Freeze drying, Vision, Daejeon, Republic of Korea), powdered, and used as hydrolysate samples. One gram of powdered C. notate head was added to 50 mL of distilled water with pH 7.0, 20 mg (μL) of hydrolyzing enzyme, and 40 mM of Na2SO3, and hydrolyzed in a shaking incubator at 50 °C for 8 h. The enzymatic reaction was stopped by centrifugation at 100 °C. The hydrolysate was filtered using Whatman No. 41 filter paper (Whatman Ltd., Maidstone, Kent, UK), freeze-dried in a lyophilizer (Freeze drying, Vision, Daejeon, Republic of Korea), powdered, and stored in a freezer until use (Figure 1).
The ABTS radical scavenging activity of C. notate head hydrolysate was determined by an adaptation of the ABTS radical cation decolorization assay of Re et al. [45]. A 7 mM ABTS solution and a 2.45 mM potassium persulfate (Sigma Co., Fukushima, Japan) solution were mixed (v/v) to a final concentration and allowed to stand in the dark at room temperature (RT) for 24 h. Afterward, distilled water was added to dilute the solution until the absorbance at 732 nm reached 0.70 (±0.02). Then, 990 μL of this diluted ABTS solution was mixed with 10 μL of the sample solution and incubated at RT for 1 min. The absorbance was measured at 732 nm (Figure 1).
To isolate peptides from C. notate head hydrolysate, we first used a 3500 Da dialysis membrane to separate the sample in and out of the membrane, then lyophilized it and determined its antioxidant activity by measuring ABTS radical scavenging activity. Fractions with high ABTS radical scavenging activity were separated by FPLC equipped with a GPC (Superdex TM 30 Increase 10/300 GL) column, followed by lyophilization of the fractions. The ABTS radical scavenging activity of the fractions separated using FPLC was measured and the fraction with the best activity was selected. The selected fractions were separated using HPLC equipped with a C18 (ZORBAX SB-C18, 4.6 × 250 mm, 5 μm) column and lyophilized to powder. The ABTS radical scavenging activity of the fractions separated using HPLC was measured to identify the superior fractions (Figure 1).
The amino acid sequence of the final isolated fraction of CHAO-3-1 from C. notate by-product hydrolysate was sequenced from the N-terminus using the automated Edman degradation method using a protein sequencer (PPSQ-51A, Shimadzu, Kyoto, Japan) in KAIST (Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea).
4.3. Peptide Synthesis
To identify the molecular weight, analysis was performed by Liquid Chromatography and High-Resolution Mass Spectrometry (LC-HRMS, Waters SYNAPT G2) and it was detected by Ultra Performance Liquid Chromatography (UPLC, Waters Acquity UPLC System, Milford, MA, USA) use for direct injection (injection volume is 5 μL) at the flow rate of 0.25 mL/min, under mobile phase 0.1% formic acid in water/acetonitrile (50:50) direct gradient at isocratic condition. The LC-HRMS analysis conditions included ESI Positive mode, detail conditions are Capillary = +3.1 kv, Sampling cone = 40, Extraction cone = 4.0, Source = 120 °C, Desolvation = 350 °C, Cone gas = 130 L/h, Desolvation gas = 800 L/h and Ms range is 50 to 1200. The analyzed mass-to-charge ratio (m/z) is shown in the Figure 3A. Mass spectrum was expressed in the form of doubly charged ions.
To identify the molecular weight, Liquid Chromatograph–Mass Spectrometry LC-MS (SHIMADZU LCMS-2020, Kyoto, Japan) analysis was performed, and it was detected by LC-MS at the flow rate of 0.20 mL/min under mobile phase under mobile phase 0.1% formic acid in water/acetonitrile (50:50) using a C18 column (particle size 5 µm, 4.6 mm × 250 mm). The LC-MS analysis conditions included ESI Positive mode, Nebulizing gas flow rate = 3 L/min, Curved Desolvation Line (CDL) voltage = −20.0 V, CDL Temp. = 250 °C, Block Heater Temp. 250 °C, Probe Bias = +4.5 kV, and Detector voltage = 1.5 kV. The analyzed mass-to-charge ratio (m/z) is shown in Figure 3B.
The purity of the synthesized final peptide was analyzed by analytical Reversed-Phase High-Performance Liquid Chromatography (RP-HPLC, Waters 2695, Milford, MA, USA) and UV–Vis Detector. Using a C18 column (particle size 5 µm, 4.6 mm × 250 mm) in condition of measurement wavelength of the detector is 216 nm under the flow rate of 1 mL/min. Acetonitrile (ACN) containing 0.1% trifluoroacetic acid (TFA) and water containing 0.1% TFA were used as the mobile phase solvent of HPLC, and the purity analysis of the peptide was observed by changing the concentration of ACN over time. The purity of DP analyzed according to the measurement value was 98.5% (Figure 3C).
4.4. FRAP Measurement
FRAP was measured using the method adapted from Benzie and Strain [46]. The FRAP reagent was prepared by mixing a 300 mM sodium acetate buffer (pH 3.6), a 10 mM 2,4,6-tripyridyl-s-triazine (TPTZ) solution dissolved in 40 mM HCl, and a 20 mM FeCl3 solution in a 10:1:1 ratio just before the experiment. A 10 μL sample of each concentration was mixed with 200 μL of FRAP reagent, incubated for 5 min at 37 °C, and then the absorbance was measured at 593 nm using a Microplate Spectrophotometer (xMarkTM, BIO-RAD, Hercules, CA, USA). The FRAP value of the sample was determined by applying the absorbance values to a standard calibration curve prepared with 0–5 mM FeSO4·H2O, and the results were expressed as FeSO4 equivalent mM/μM.
4.5. Inhibition of H2O2-Induced Cell Damage and ROS Removal in HaCaT Cells
4.5.1. HaCaT Cell Culture
The cell line used in the experiment, the HaCaT cell line, a human keratinocyte, was purchased from the Korea Cell Line Bank (KCLB, Seoul, Republic of Korea). HaCaT cells were cultured in a DMEM medium supplemented with 10% FBS and 1% penicillin–streptomycin. The cells were subcultured every 2 to 3 days in an incubator maintained at 37 °C and 5% CO2.
4.5.2. Cytotoxicity Test
To assess the cytotoxicity of DP and H2O2, cells (1 × 104 in a 96-well plate) were cultured at 37 °C and 5% CO2 for 24 h. The cultured cells were then treated with DP and H2O2. After 24 h of treatment, 10 μL of MTT (5 mg/mL) solution was added, and the cells were incubated for 4 h. Following this, the supernatant was removed, and 100 μL of DMSO was added to each well. The absorbance was measured at 550 nm using an ELISA reader.
4.5.3. Measurement of ROS in H2O2-Induced HaCaT Cells
HaCaT cells were seeded at 1 × 104 cells per well in a 96-well black plate and cultured for 24 h. The cultured cells were treated with DP at concentrations of 50, 100, and 200 μM and then exposed to 500 μM H2O2 for 24 h to induce ROS generation. After H2O2 treatment, the cells were stained with 25 μM dichlorodihydro-fluorescein diacetate (DCFH-DA) for 30 min. The fluorescence intensity corresponding to intracellular ROS production was measured using a fluorescent spectrophotometer at an excitation wavelength of 485 nm and an emission wavelength of 530 nm. The results were expressed as ROS production (%) relative to the fluorescence measurement value of the untreated control, which was set to 100.
4.6. Measurement of Tyrosinase Inhibitory Activity
The inhibitory activity of tyrosinase, which is involved in the key steps of melanin synthesis, was measured using a colorimetric method modified from Masamoto et al. [47] by detecting the formation of DOPA chrome as a result of the enzyme’s action. As a substrate, a mixture containing 0.2 mL of 5 mM DL-DOPA solution, 0.2 mL of 0.1 M phosphate buffer (pH 6.8), and 0.5 mL of sample solution was prepared. To this mixture, 0.1 mL of mushroom tyrosinase (Sigma Co., 250 unit/mL) was added and incubated at 37 °C for 10 min. After incubation, the absorbance was measured at a wavelength of 475 nm to calculate the tyrosinase inhibitory activity.
4.7. Inhibition of Melanin Production in α-MSH Induced B16F10 Melanoma Cells
4.7.1. B16F10 Melanoma Cell Culture
B16F10 melanoma cells were obtained from the KCLB and cultured in DMEM medium supplemented with 10% FBS (Gibco BRL, Grand Island, NY, USA) and 1% penicillin. The cells were incubated in a humidified incubator at 37 °C and 5% CO2.
4.7.2. MTT Assay
To determine the cytotoxicity of DP and establish the concentration range for the experiment, an MTT assay was conducted. Cells were seeded in 96-well plates at a density of 1.0 × 104 cells/well. After 24 h, once the cells were fully attached, the medium was replaced with fresh medium containing 200 nM α-MSH and various concentrations of DP. After another 24 h of incubation, an MTT assay was performed.
4.7.3. Measurement of Melanin Content
To measure melanin production in B16F10 cells, cells were seeded in 6-well plates at a density of 5 × 104 cells/well. After 24 h, the medium was replaced with fresh medium containing 2% FBS, 200 nM α-MSH, and various concentrations of DP. The cells were then incubated for 72 h. After incubation, the cells were washed with PBS, harvested by centrifugation, and the melanin content was visually inspected. The harvested cells were dissolved in 400 μL of 0.2 N NaOH solution and incubated at 60 °C for 1 h. The absorbance was measured at 405 nm using a microplate reader.
4.7.4. RT-PCR
Total RNA was extracted using the High Pure RNA Isolation Kit (Invitrogen Co., Waltham, MA, USA) according to the manufacturer’s instructions and quantified using a NanoDrop (Thermo Scientific, Waltham, MA, USA). For the reverse transcription reaction, 1 μg of isolated RNA and 1 μL of oligo DT (100 pM) were combined with DEPC to a total volume of 11 μL, followed by the reverse transcription reaction using the RT Premix Kit (Bioneer Co., Daejeon, Republic of Korea) as per the manufacturer’s protocol. PCR for MITF, Tyrosinase, and MC1R genes was conducted using 2 μL of the cDNA products obtained from the reverse transcription reaction, with the PCR Premix Kit (Bioneer Co.) according to the manufacturer’s instructions. The cDNA products amplified by reverse transcription and polymerase chain reaction were analyzed by electrophoresis on 2.0% agarose gels containing ethidium bromide. The primer sequences used for PCR are provided in Table 1.
4.8. Molecular Docking Analysis
4.8.1. Receptor Grid Generation
For grid generation, the catalytic pocket is selected from its crystallized ligand and literature [48]. The grid was generated by specifying the crystallized ligand of the active site of the target protein. The receptor grid box was defined as 20 Å box. After grid preparation, Glide dock precision docking experiment was performed with default docking setup parameters reporting the top-ranked poses per ligand [49]. The predicted binding scores (binding energies, Supplement S1) and proper orientation of ligands within the catalytic region of tyrosinase were also performed. Finally, the most favorable binding mode of active compounds within the binding pocket was investigated in terms of docking score, and 3D graphical images of the binding pose of the best-docked score were visualized with Maestro (Schrödinger, New York, NY, USA).
4.8.2. Molecular Docking
Molecular docking of mushroom tyrosinase (PDB ID: 2Y9X) was performed by the Ligand Docking module in Schrödinger Suite (Schrödinger software version 2024-1, LLC, New York, NY, USA). The active site of human tyrosinase was similar to that of mushroom tyrosinase (PDB ID: 2Y9X), which contained two metal ions coordinated with six histidine residues [48].
4.9. Statistical Analysis
All experimental results are presented as mean ± standard deviation. Statistical significance was verified using ANOVA with the SPSS statistics software (ver. 25, IBM Co., Armonk, NY, USA). The statistical significance of the mean values was analyzed using Duncan’s multiple range test at the p < 0.05 level.
4.1. Experimental Materials
The decapeptide (DP, KGYSSYICDK) used in this study was synthesized by A&Pep Co. (Osong, Republic of Korea). All reagents for antioxidant and whitening assays were purchased from Sigma-Aldrich (St. Louis, MO, USA). For cell culture, fetal bovine serum (FBS), penicillin, and Dulbecco’s Modified Eagle’s Medium (DMEM) were obtained from Gibco BRL Co. (Grand Island, NY, USA).
4.2. Peptide Isolation from Hydrolysate of Chromis Notate By-Product
In this study, the heads of C. notate by-products were collected from the distribution center of Jeju Island FPC (Fisheries Processing & Marketing Center) in Republic of Korea, and used as hydrolysate preparation samples [17]. The collected C. notate heads were freeze-dried in a freeze-dryer (Freeze drying, Vision, Daejeon, Republic of Korea), powdered, and used as hydrolysate samples. One gram of powdered C. notate head was added to 50 mL of distilled water with pH 7.0, 20 mg (μL) of hydrolyzing enzyme, and 40 mM of Na2SO3, and hydrolyzed in a shaking incubator at 50 °C for 8 h. The enzymatic reaction was stopped by centrifugation at 100 °C. The hydrolysate was filtered using Whatman No. 41 filter paper (Whatman Ltd., Maidstone, Kent, UK), freeze-dried in a lyophilizer (Freeze drying, Vision, Daejeon, Republic of Korea), powdered, and stored in a freezer until use (Figure 1).
The ABTS radical scavenging activity of C. notate head hydrolysate was determined by an adaptation of the ABTS radical cation decolorization assay of Re et al. [45]. A 7 mM ABTS solution and a 2.45 mM potassium persulfate (Sigma Co., Fukushima, Japan) solution were mixed (v/v) to a final concentration and allowed to stand in the dark at room temperature (RT) for 24 h. Afterward, distilled water was added to dilute the solution until the absorbance at 732 nm reached 0.70 (±0.02). Then, 990 μL of this diluted ABTS solution was mixed with 10 μL of the sample solution and incubated at RT for 1 min. The absorbance was measured at 732 nm (Figure 1).
To isolate peptides from C. notate head hydrolysate, we first used a 3500 Da dialysis membrane to separate the sample in and out of the membrane, then lyophilized it and determined its antioxidant activity by measuring ABTS radical scavenging activity. Fractions with high ABTS radical scavenging activity were separated by FPLC equipped with a GPC (Superdex TM 30 Increase 10/300 GL) column, followed by lyophilization of the fractions. The ABTS radical scavenging activity of the fractions separated using FPLC was measured and the fraction with the best activity was selected. The selected fractions were separated using HPLC equipped with a C18 (ZORBAX SB-C18, 4.6 × 250 mm, 5 μm) column and lyophilized to powder. The ABTS radical scavenging activity of the fractions separated using HPLC was measured to identify the superior fractions (Figure 1).
The amino acid sequence of the final isolated fraction of CHAO-3-1 from C. notate by-product hydrolysate was sequenced from the N-terminus using the automated Edman degradation method using a protein sequencer (PPSQ-51A, Shimadzu, Kyoto, Japan) in KAIST (Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea).
4.3. Peptide Synthesis
To identify the molecular weight, analysis was performed by Liquid Chromatography and High-Resolution Mass Spectrometry (LC-HRMS, Waters SYNAPT G2) and it was detected by Ultra Performance Liquid Chromatography (UPLC, Waters Acquity UPLC System, Milford, MA, USA) use for direct injection (injection volume is 5 μL) at the flow rate of 0.25 mL/min, under mobile phase 0.1% formic acid in water/acetonitrile (50:50) direct gradient at isocratic condition. The LC-HRMS analysis conditions included ESI Positive mode, detail conditions are Capillary = +3.1 kv, Sampling cone = 40, Extraction cone = 4.0, Source = 120 °C, Desolvation = 350 °C, Cone gas = 130 L/h, Desolvation gas = 800 L/h and Ms range is 50 to 1200. The analyzed mass-to-charge ratio (m/z) is shown in the Figure 3A. Mass spectrum was expressed in the form of doubly charged ions.
To identify the molecular weight, Liquid Chromatograph–Mass Spectrometry LC-MS (SHIMADZU LCMS-2020, Kyoto, Japan) analysis was performed, and it was detected by LC-MS at the flow rate of 0.20 mL/min under mobile phase under mobile phase 0.1% formic acid in water/acetonitrile (50:50) using a C18 column (particle size 5 µm, 4.6 mm × 250 mm). The LC-MS analysis conditions included ESI Positive mode, Nebulizing gas flow rate = 3 L/min, Curved Desolvation Line (CDL) voltage = −20.0 V, CDL Temp. = 250 °C, Block Heater Temp. 250 °C, Probe Bias = +4.5 kV, and Detector voltage = 1.5 kV. The analyzed mass-to-charge ratio (m/z) is shown in Figure 3B.
The purity of the synthesized final peptide was analyzed by analytical Reversed-Phase High-Performance Liquid Chromatography (RP-HPLC, Waters 2695, Milford, MA, USA) and UV–Vis Detector. Using a C18 column (particle size 5 µm, 4.6 mm × 250 mm) in condition of measurement wavelength of the detector is 216 nm under the flow rate of 1 mL/min. Acetonitrile (ACN) containing 0.1% trifluoroacetic acid (TFA) and water containing 0.1% TFA were used as the mobile phase solvent of HPLC, and the purity analysis of the peptide was observed by changing the concentration of ACN over time. The purity of DP analyzed according to the measurement value was 98.5% (Figure 3C).
4.4. FRAP Measurement
FRAP was measured using the method adapted from Benzie and Strain [46]. The FRAP reagent was prepared by mixing a 300 mM sodium acetate buffer (pH 3.6), a 10 mM 2,4,6-tripyridyl-s-triazine (TPTZ) solution dissolved in 40 mM HCl, and a 20 mM FeCl3 solution in a 10:1:1 ratio just before the experiment. A 10 μL sample of each concentration was mixed with 200 μL of FRAP reagent, incubated for 5 min at 37 °C, and then the absorbance was measured at 593 nm using a Microplate Spectrophotometer (xMarkTM, BIO-RAD, Hercules, CA, USA). The FRAP value of the sample was determined by applying the absorbance values to a standard calibration curve prepared with 0–5 mM FeSO4·H2O, and the results were expressed as FeSO4 equivalent mM/μM.
4.5. Inhibition of H2O2-Induced Cell Damage and ROS Removal in HaCaT Cells
4.5.1. HaCaT Cell Culture
The cell line used in the experiment, the HaCaT cell line, a human keratinocyte, was purchased from the Korea Cell Line Bank (KCLB, Seoul, Republic of Korea). HaCaT cells were cultured in a DMEM medium supplemented with 10% FBS and 1% penicillin–streptomycin. The cells were subcultured every 2 to 3 days in an incubator maintained at 37 °C and 5% CO2.
4.5.2. Cytotoxicity Test
To assess the cytotoxicity of DP and H2O2, cells (1 × 104 in a 96-well plate) were cultured at 37 °C and 5% CO2 for 24 h. The cultured cells were then treated with DP and H2O2. After 24 h of treatment, 10 μL of MTT (5 mg/mL) solution was added, and the cells were incubated for 4 h. Following this, the supernatant was removed, and 100 μL of DMSO was added to each well. The absorbance was measured at 550 nm using an ELISA reader.
4.5.3. Measurement of ROS in H2O2-Induced HaCaT Cells
HaCaT cells were seeded at 1 × 104 cells per well in a 96-well black plate and cultured for 24 h. The cultured cells were treated with DP at concentrations of 50, 100, and 200 μM and then exposed to 500 μM H2O2 for 24 h to induce ROS generation. After H2O2 treatment, the cells were stained with 25 μM dichlorodihydro-fluorescein diacetate (DCFH-DA) for 30 min. The fluorescence intensity corresponding to intracellular ROS production was measured using a fluorescent spectrophotometer at an excitation wavelength of 485 nm and an emission wavelength of 530 nm. The results were expressed as ROS production (%) relative to the fluorescence measurement value of the untreated control, which was set to 100.
4.5.1. HaCaT Cell Culture
The cell line used in the experiment, the HaCaT cell line, a human keratinocyte, was purchased from the Korea Cell Line Bank (KCLB, Seoul, Republic of Korea). HaCaT cells were cultured in a DMEM medium supplemented with 10% FBS and 1% penicillin–streptomycin. The cells were subcultured every 2 to 3 days in an incubator maintained at 37 °C and 5% CO2.
4.5.2. Cytotoxicity Test
To assess the cytotoxicity of DP and H2O2, cells (1 × 104 in a 96-well plate) were cultured at 37 °C and 5% CO2 for 24 h. The cultured cells were then treated with DP and H2O2. After 24 h of treatment, 10 μL of MTT (5 mg/mL) solution was added, and the cells were incubated for 4 h. Following this, the supernatant was removed, and 100 μL of DMSO was added to each well. The absorbance was measured at 550 nm using an ELISA reader.
4.5.3. Measurement of ROS in H2O2-Induced HaCaT Cells
HaCaT cells were seeded at 1 × 104 cells per well in a 96-well black plate and cultured for 24 h. The cultured cells were treated with DP at concentrations of 50, 100, and 200 μM and then exposed to 500 μM H2O2 for 24 h to induce ROS generation. After H2O2 treatment, the cells were stained with 25 μM dichlorodihydro-fluorescein diacetate (DCFH-DA) for 30 min. The fluorescence intensity corresponding to intracellular ROS production was measured using a fluorescent spectrophotometer at an excitation wavelength of 485 nm and an emission wavelength of 530 nm. The results were expressed as ROS production (%) relative to the fluorescence measurement value of the untreated control, which was set to 100.
4.6. Measurement of Tyrosinase Inhibitory Activity
The inhibitory activity of tyrosinase, which is involved in the key steps of melanin synthesis, was measured using a colorimetric method modified from Masamoto et al. [47] by detecting the formation of DOPA chrome as a result of the enzyme’s action. As a substrate, a mixture containing 0.2 mL of 5 mM DL-DOPA solution, 0.2 mL of 0.1 M phosphate buffer (pH 6.8), and 0.5 mL of sample solution was prepared. To this mixture, 0.1 mL of mushroom tyrosinase (Sigma Co., 250 unit/mL) was added and incubated at 37 °C for 10 min. After incubation, the absorbance was measured at a wavelength of 475 nm to calculate the tyrosinase inhibitory activity.
4.7. Inhibition of Melanin Production in α-MSH Induced B16F10 Melanoma Cells
4.7.1. B16F10 Melanoma Cell Culture
B16F10 melanoma cells were obtained from the KCLB and cultured in DMEM medium supplemented with 10% FBS (Gibco BRL, Grand Island, NY, USA) and 1% penicillin. The cells were incubated in a humidified incubator at 37 °C and 5% CO2.
4.7.2. MTT Assay
To determine the cytotoxicity of DP and establish the concentration range for the experiment, an MTT assay was conducted. Cells were seeded in 96-well plates at a density of 1.0 × 104 cells/well. After 24 h, once the cells were fully attached, the medium was replaced with fresh medium containing 200 nM α-MSH and various concentrations of DP. After another 24 h of incubation, an MTT assay was performed.
4.7.3. Measurement of Melanin Content
To measure melanin production in B16F10 cells, cells were seeded in 6-well plates at a density of 5 × 104 cells/well. After 24 h, the medium was replaced with fresh medium containing 2% FBS, 200 nM α-MSH, and various concentrations of DP. The cells were then incubated for 72 h. After incubation, the cells were washed with PBS, harvested by centrifugation, and the melanin content was visually inspected. The harvested cells were dissolved in 400 μL of 0.2 N NaOH solution and incubated at 60 °C for 1 h. The absorbance was measured at 405 nm using a microplate reader.
4.7.4. RT-PCR
Total RNA was extracted using the High Pure RNA Isolation Kit (Invitrogen Co., Waltham, MA, USA) according to the manufacturer’s instructions and quantified using a NanoDrop (Thermo Scientific, Waltham, MA, USA). For the reverse transcription reaction, 1 μg of isolated RNA and 1 μL of oligo DT (100 pM) were combined with DEPC to a total volume of 11 μL, followed by the reverse transcription reaction using the RT Premix Kit (Bioneer Co., Daejeon, Republic of Korea) as per the manufacturer’s protocol. PCR for MITF, Tyrosinase, and MC1R genes was conducted using 2 μL of the cDNA products obtained from the reverse transcription reaction, with the PCR Premix Kit (Bioneer Co.) according to the manufacturer’s instructions. The cDNA products amplified by reverse transcription and polymerase chain reaction were analyzed by electrophoresis on 2.0% agarose gels containing ethidium bromide. The primer sequences used for PCR are provided in Table 1.
4.7.1. B16F10 Melanoma Cell Culture
B16F10 melanoma cells were obtained from the KCLB and cultured in DMEM medium supplemented with 10% FBS (Gibco BRL, Grand Island, NY, USA) and 1% penicillin. The cells were incubated in a humidified incubator at 37 °C and 5% CO2.
4.7.2. MTT Assay
To determine the cytotoxicity of DP and establish the concentration range for the experiment, an MTT assay was conducted. Cells were seeded in 96-well plates at a density of 1.0 × 104 cells/well. After 24 h, once the cells were fully attached, the medium was replaced with fresh medium containing 200 nM α-MSH and various concentrations of DP. After another 24 h of incubation, an MTT assay was performed.
4.7.3. Measurement of Melanin Content
To measure melanin production in B16F10 cells, cells were seeded in 6-well plates at a density of 5 × 104 cells/well. After 24 h, the medium was replaced with fresh medium containing 2% FBS, 200 nM α-MSH, and various concentrations of DP. The cells were then incubated for 72 h. After incubation, the cells were washed with PBS, harvested by centrifugation, and the melanin content was visually inspected. The harvested cells were dissolved in 400 μL of 0.2 N NaOH solution and incubated at 60 °C for 1 h. The absorbance was measured at 405 nm using a microplate reader.
4.7.4. RT-PCR
Total RNA was extracted using the High Pure RNA Isolation Kit (Invitrogen Co., Waltham, MA, USA) according to the manufacturer’s instructions and quantified using a NanoDrop (Thermo Scientific, Waltham, MA, USA). For the reverse transcription reaction, 1 μg of isolated RNA and 1 μL of oligo DT (100 pM) were combined with DEPC to a total volume of 11 μL, followed by the reverse transcription reaction using the RT Premix Kit (Bioneer Co., Daejeon, Republic of Korea) as per the manufacturer’s protocol. PCR for MITF, Tyrosinase, and MC1R genes was conducted using 2 μL of the cDNA products obtained from the reverse transcription reaction, with the PCR Premix Kit (Bioneer Co.) according to the manufacturer’s instructions. The cDNA products amplified by reverse transcription and polymerase chain reaction were analyzed by electrophoresis on 2.0% agarose gels containing ethidium bromide. The primer sequences used for PCR are provided in Table 1.
4.8. Molecular Docking Analysis
4.8.1. Receptor Grid Generation
For grid generation, the catalytic pocket is selected from its crystallized ligand and literature [48]. The grid was generated by specifying the crystallized ligand of the active site of the target protein. The receptor grid box was defined as 20 Å box. After grid preparation, Glide dock precision docking experiment was performed with default docking setup parameters reporting the top-ranked poses per ligand [49]. The predicted binding scores (binding energies, Supplement S1) and proper orientation of ligands within the catalytic region of tyrosinase were also performed. Finally, the most favorable binding mode of active compounds within the binding pocket was investigated in terms of docking score, and 3D graphical images of the binding pose of the best-docked score were visualized with Maestro (Schrödinger, New York, NY, USA).
4.8.2. Molecular Docking
Molecular docking of mushroom tyrosinase (PDB ID: 2Y9X) was performed by the Ligand Docking module in Schrödinger Suite (Schrödinger software version 2024-1, LLC, New York, NY, USA). The active site of human tyrosinase was similar to that of mushroom tyrosinase (PDB ID: 2Y9X), which contained two metal ions coordinated with six histidine residues [48].
4.8.1. Receptor Grid Generation
For grid generation, the catalytic pocket is selected from its crystallized ligand and literature [48]. The grid was generated by specifying the crystallized ligand of the active site of the target protein. The receptor grid box was defined as 20 Å box. After grid preparation, Glide dock precision docking experiment was performed with default docking setup parameters reporting the top-ranked poses per ligand [49]. The predicted binding scores (binding energies, Supplement S1) and proper orientation of ligands within the catalytic region of tyrosinase were also performed. Finally, the most favorable binding mode of active compounds within the binding pocket was investigated in terms of docking score, and 3D graphical images of the binding pose of the best-docked score were visualized with Maestro (Schrödinger, New York, NY, USA).
4.8.2. Molecular Docking
Molecular docking of mushroom tyrosinase (PDB ID: 2Y9X) was performed by the Ligand Docking module in Schrödinger Suite (Schrödinger software version 2024-1, LLC, New York, NY, USA). The active site of human tyrosinase was similar to that of mushroom tyrosinase (PDB ID: 2Y9X), which contained two metal ions coordinated with six histidine residues [48].
4.9. Statistical Analysis
All experimental results are presented as mean ± standard deviation. Statistical significance was verified using ANOVA with the SPSS statistics software (ver. 25, IBM Co., Armonk, NY, USA). The statistical significance of the mean values was analyzed using Duncan’s multiple range test at the p < 0.05 level.
5. Conclusions
This study evaluates the antioxidant and skin-whitening effects of a novel decapeptide (DP, KGYSSYICDK) derived from marine fish by-products and demonstrates its potential applications. DP exhibits strong antioxidant activity, as evidenced by FRAP and ABTS radical scavenging assays. It effectively enhances cell viability and reduces ROS generation in H2O2-induced HaCaT cells. Additionally, DP significantly inhibits melanin production in α-MSH-induced B16F10 melanoma cells. This effect is mediated by downregulating the expression of key melanin synthesis-related genes, including MITF, tyrosinase, and MC1R. In silico analysis indicates that DP interacts effectively with the tyrosinase protein, showing substantial inhibitory activity. These findings suggest that DP could be a promising cosmetic ingredient with both antioxidant and skin-whitening properties. Future research should focus on optimizing DP’s structure and exploring its structure–activity relationship to enhance its whitening efficacy. |
Title: POCOP-Ni(II) pincer compounds derived from phloroglucinol. Cytotoxic and antioxidant evaluation | Body:
1 Introduction
Cancer, characterized by uncontrolled cell growth and the invasion of surrounding tissues, stands as one of the most formidable challenges in modern medicine (Marchi et al., 2022). The origins of cancer are often attributed to genetic mutations that disrupt normal cellular regulatory mechanisms, leading to aberrant signaling pathways and unchecked cell division (González-Ballesteros et al., 2022). This disease is broadly classified according to tissue origin and pathological characteristics. In this context, carcinomas arise from epithelial tissues, sarcomas from connective tissues, lymphomas from the lymphatic system, and leukemia from blood-forming tissues. Additionally, cancers are categorized by their degree of differentiation, histological features, and molecular signatures, contributing to a nuanced understanding of the disease landscape (Carbone, 2020).
In 2020, the World Health Organization (WHO) reported more than 19 million cases of cancer globally, of which 10 million resulted in death (Sung et al., 2021). This significant number of deaths has raised concerns about the future of cancer treatments. Since its discovery, cisplatin and its derivatives have remained a powerful tool in the treatment of several types of cancer; nonetheless, these compounds display severe side effects on the human body, including nephrotoxicity, ototoxicity (Wheate et al., 2010; Dilruba and Kalayda, 2016; Qi et al., 2019), and the development of resistance over time. These limitations have increased interest in exploring transition metals to develop novel and selective metallodrugs for cancer therapy.
Nickel, located in group 10 of the transition metals alongside platinum, plays a significant role in diverse biological systems, where it exerts its significance through incorporation into essential metalloproteins, such as urease (Maroney and Ciurli, 2014), [NiFe]-hydrogenase, (Ahmed and Dey, 2019), Acetyl-CoA synthase (Can et al., 2014), Ni-SOD (Wodrich and Hu, 2017), [NiFe]-CO dehydrogenase and lactate racemase (Xu et al., 2016). As a result, Ni(II) complexes have attracted considerable attention as potential anticancer agents due to their similarity to Pt (II) coordination geometry and DNA cross-linking properties (Hernández-Romero et al., 2021).
Although a wide variety of Ni(II) coordination and organometallic complexes with biological and anticancer activity have been extensively documented, the assessment of cytotoxic effects related to Ni(II) pincer complexes remains underexplored, with only a few papers addressing their antibacterial, (Soliman et al., 2019; Shukla et al., 2021), antimicrobial, and anticarcinogenic properties (Hosseini-Kharat et al., 2019; Kim et al., 2020; Muñoz-Patiño et al., 2020). On the other hand, pincer complexes have been increasingly synthesized due to their thermal stability and robustness, leading to various applications as catalysts, sensors, and dendrimers (Bedford et al., 2000; Morales-Morales et al., 2000a; 2000b; Morales-Morales, 2008; van Koten and Gossage, 2016). This growing interest in the multifaceted applications of pincer complexes highlights the need for a more comprehensive exploration of their cytotoxic potential. The limited information on the cytotoxic effects of these specific complexes underscores a significant gap in our knowledge, prompting further investigation to elucidate their potential as anticancer agents.
In the context of our previous research, we reported the synthesis and antibacterial activity of an important series of POCOP-Pd(II) pincer complexes (I-III, Figure 1). These complexes exhibited notable antibacterial efficacy, with MIC values approximately 8 μg mL−1 against the S. aureus ATCC 25923 strain (Aragón-Muriel et al., 2022). Furthermore, molecular docking studies revealed significant interactions with KPC-2 and PBP2A enzymes, providing valuable mechanistic insights.
FIGURE 1
POCOP-Pd(II) pincer complexes previously used as antibacterial agents (I-III), and POCOP-Ni(II) pincer complexes evaluated in this work as antitumoral and antioxidant agents (1a-c, 2).
Building on previous investigations, our research team has also explored the synthesis of POCOP-Ni(II) pincer compounds. However, despite these earlier studies, the cytotoxic activity of these Ni(II) counterparts remains unexplored. In this study, we focused on the biological activity of three para-hydroxy POCOP-Ni(II) pincer complexes (1a-c) previously reported (García-Eleno et al., 2015), and a novel compound (2) newly synthesized. All complexes were tested against six cancer cell lines (U251, PC-3, K562, HCT-15, MCF-7, SK-LU-1) and a non-cancerous line (COS-7). The findings from this work enhance our understanding of the potential applications of POCOP pincer complexes in metallodrug development.
2 Results and discussion
The pincers 1a-c (R =
t
Bu,
i
Pr, Ph, respectively) were synthesized according to the methodology previously reported by our investigation group and their spectroscopic characterization correlated perfectly (García-Eleno et al., 2015). In addition, compound 2 (where R =
t
Bu and R’ = Ph) was synthesized from phloroglucinol, chloro (tert-butyl)phenylphosphine, and anhydrous NiCl2 (see Scheme 1 and details in the experimental part). All compounds were characterized by 1H, 13C{1H}, and 31P{1H} NMR, DART+ MS, ATR-FTIR, and elemental analysis, obtaining the expected signals (see Supplementary Figures S1–S5). Regarding 31P{1H} NMR, the spectroscopic characterization showed signals at 189.8 ppm (1a), 187.3 (1b) 147.7 (1c). For complex 2, two 31P NMR signals were obtained at 160.5 and 159.7 ppm, which would correspond to the racemic and meso isomers (See Supplementary Figure S3). As shown in 1H NMR spectrum, these isomers are present in a 1:1 ratio. Interconversion between the meso and racemic isomers have not been observed during analysis and have not been reported for similar POCOP-Ni(II) (Adhikary et al., 2015).
SCHEME 1
Synthesis of 2.
Crystallization of complex 2 from 1:1 CH2Cl2/MeOH resulted in the crystallization of the racemic isomer (2-rac). Its structure was determined by single-crystal X-ray diffraction (XRD) and molecular structure is shown in Figure 2; crystal data and other information are presented in Supplementary Table S1. Complex 2-rac
crystallized in an orthorhombic system (Pbca), and as seen for the analogous structures (García-Eleno et al., 2015) the metal atom is tri-coordinated by the POCOP pincer and the fourth position is occupied by a chlorine atom adopting a square planar geometry, the heteroalquil (RR’P-Ni-PR’R) substituents on the phosphine ligands prefers to adopt the anti-disposition. The crystal arrangement is mainly stabilized by a hydrogen bond along the a-axis between the chloride ligand and the hydrogen of the para-hydroxyl group, with a Cl (1)-O (3) distance of 3.281 Å [-1/2 + x, y, 1/2-z].
FIGURE 2
Molecular structure of 2-rac
. The ellipsoids are shown at 50% of probability level and hydrogen atoms have been omitted for clarity. Selected bonds (Å) distances and angles (°) for 2-rac
: Ni(1)-Cl (1) 2.2236 (9), Ni(1)-C (5) 1.892 (3), Ni(1)-P (1) 2.1824 (9), Ni(1)-P (2) 2.1808 (9), P (2)-Ni(1)-P (1) 162.43 (4), C (2)-Ni(1)-Cl (1) 179.6 (1), P (2)-C (5)-P (1) 108.0 (1).
To obtain further information on the nature of the molecular packing, the Hirshfeld surface analysis was drawn by using CrystalExplorer software using the CIF file from X-ray studies (Spackman et al., 2021). The surface of compound 2-rac
was mapped over the dnorm function, along with the congeneric compounds (García-Eleno et al., 2015) for comparative reasons, which are illustrated in Figure 3. The red regions demonstrate close contacts (shorter than the sum of van der Waals radii) and are located in the para-hydroxyl group in all the complexes, resulting from contact with chlorine atom (OH···Cl) with distances within the ranges of d (D-A) = 3.127–3.281 Å supporting 1D chains at the supramolecular level, the graph set descriptor found for the four structures was C (8) due to they contain the same skeleton (see representation in capped stick in Supplementary Figure S6 in the Supplementary Material). Additionally, 2D Fingerprint (Spackman and McKinnon, 2002) plots were generated and are listed in Table 1. Apparently, the fingerprints are very similar to each other, reflecting the similar types of interactions found in the crystalline arrangement. However, the percentages of contributions vary considerably; the majority of contributions were H···H, O···H/H···O and Cl···H/H···Cl contacts. The last one was identified as two characteristic symmetrical spikes. Individual contributions are summarized in Chart 1.
FIGURE 3
Hirshfeld surfaces calculated for complexes 1a, 1b, 1c, and 2-rac
. Note: except compound 2-rac
, CIF data were taken from García-Eleno et al. (2015) and only one of both molecules of the asymmetric unit of 1b was plotted.
TABLE 1
Representative fingerprints of non-covalent interactions of 1a, 1b, 1c, and 2-rac
. Distances
d
e
(y-axis) and
d
i
(x-axis) are in Å.
Compound
Cl•••H/H•••Cl (%)
O•••H/H•••O (%)
C•••H/H•••C (%)
H•••H (%)
1a
8.6
6.7
5.3
79.3
1b
6.9
8.3
7.3
77.2
1c
7.3
6.9
32.5
48.4
2-rac
3.9
6.6
20.5
67.1
Quantitative and qualitative structure-activity relationships can be established between noncovalent interactions from Hirshfeld surface analysis of biologically active compounds and their biological activity (Małecka and Budzisz, 2014; Kupcewicz et al., 2016; Małecka et al., 2020). In our study, a notorious variation is observed in the percentage contribution of C•••H/H•••C contacts, with a maximum value of 32.5% for 1c and a minimum value of 5.3% for 1a. Interestingly, this trend is also observed with their cytotoxicity and their calculated interactions with DNA (vide infra).
3 Cytotoxic activity of POCOP-Ni(II) pincer complexes
Although complexes 1a, 1b, and 1c have previously been reported by our research group (García-Eleno et al., 2015), no studies related to their cytotoxic activity have been made. Therefore, a preliminary evaluation of the in vitro cytotoxic activity (Table 2) of these complexes, along with complex 2, was carried out. The assays were performed using the sulforhodamine B protocol, with a concentration of 10 μM of the corresponding complex employing DMSO as a vehicle. Six human cancer cell lines were used: U251 (human glioblastoma), PC-3 (human prostate adenocarcinoma), K562 (human chronic myelogenous leukaemia), HCT-15 (human colorectal adenocarcinoma), MCF-7 (human breast adenocarcinoma) (these cell lines were provided by the National Cancer Institute, United States of America), SK-LU-1 (human lung adenocarcinoma) (this cell line was donated by the Cancer Institute of Mexico). A healthy monkey kidney cell line (COS-7) was also included for comparative purposes. Based on the results obtained, complex 1b exhibited the highest cytotoxic activity, showing a 100% inhibition rate against all cell lines, including COS-7, whereas complex 2 presented significant activity, except for K562. In contrast, complexes 1a and 1c were less toxic against COS7, with inhibition percentages of 47.5% and 79.3%, respectively. The difference in the biological activities of these compounds may be attributed to steric factors of the alkyl groups located on the phosphorus atoms, which could facilitate the release of chloride ions from the coordination sphere, creating a vacant space around the metal atom, allowing it to interact with specific biological targets. Additionally, solubility may facilitate the optimal transport of these compounds. However, further experimental studies are needed to validate these hypotheses.
TABLE 2
Growth inhibition (100%) of cancer cell lines by pincer complexes (10 μM).
Compound
U251
PC-3
K562
HCT-15
MCF-7
SK-LU-1
COS7
1a
44.3
61.2
48.3
55.7
63.6
98.1
47.5
1b
100
100
100
100
100
100
100
1c
100
94.1
81.7
81.5
26.8
100
79.3
2
100
92.9
NC
100
98.7
100
100
Moreover, the IC50 values for complexes 1b and 1c were determined across five cancer cell lines (U251, K562, HCT-15, MCF-7, and SK-LU-1), with cisplatin serving as the control drug (Table 3). Notably, complex 1c demonstrated superior activity compared to complex 1b in multiple cancer cell lines, except for the HCT-15 cell line (7.71 ± 0.6 μM), where complex 1b exhibited greater efficacy (6.84 ± 0.9 μM).
TABLE 3
IC50 values for complexes 1b and 1c (μM).
Compound
U251
K562
HCT-15
MCF-7
SK-LU-1
1b
6.85 ± 0.08
5.81 ± 0.2
6.84 ± 0.9
7.85 ± 0.6
4.41 ± 0.04
1c
2.43 ± 0.3
3.85 ± 0.2
7.71 ± 0.6
6.98 ± 0.4
2.59 ± 0.2
Cisplatin
9.5 ± 0.7
1.2 ± 0.08
13.5 ± 0.7
17.9 ± 3.5
3.4 ± 0.5
In contrast, cisplatin exhibited superior activity against K562 (1.2 ± 0.08 μM) compared with complex 1b and 1c, and SK-LU-1 (3.4 ± 0.5 μM) in the case of complex 1b.
4 Antioxidant activity of POCOP-Ni(II) pincer complexes
Antioxidant compounds have played a crucial role in the biological field, since they are believed to help prevent numerous diseases caused by the production of reactive oxygen species (ROS). Among these diseases, cancer is one that can be originated from the formation of free radicals and an overproduction of ROS. For that reason, the antioxidant activity of the pincer complexes was tested using the thiobarbituric acid reactive substances (TBARS) assay, which involves the production of ROS with FeSO4 in the presence of lipids obtained from rat brains. The assays were conducted using three different concentrations of the complexes under evaluation (1, 10, and 100 μM) (Table 4). It was observed that among the three complexes, compound 1a demonstrated superior antioxidant activity, with an inhibition percentage of 94.77% at a concentration of 10 μM, compared to complexes 1b and 1c, which exhibited inhibition percentages at the same concentration of 40.45% and 32.86%, respectively. However, at a concentration of 100 μM, all complexes showed a high inhibition rate (>90%), indicating that the antioxidant activity of the complexes is concentration dependent. Similar to the cytotoxicity assays, a clear difference in biological activity can be observed when varying the alkyl groups on the phosphine moieties, which could be associated to the electronic effects. Thus, the tert-butyl groups would donate higher electron density to the metal and then to the aromatic ring, stabilizing the phenolate and reducing the lipid radical at a lower concentration (Marchi et al., 2022)
TABLE 4
Antioxidant activity of POCOP-Ni(II) pincer complexes through inhibition of lipid peroxidation (rat brain tissues).
Compound
Concentration (μM)
Inhibition (%)
1a
110100
29.1394.7794.92
1b
110100
24.3540.4594.62
1c
110100
4.6632.8690.67
2
110100
20.9496.2496.76
Homogenized in: PBS; vehicle: DMSO; peroxidation: induced with FeSO4 at 10 μM, 1 h of incubation; EDTA: 2 μM.
IC50 values of the complexes were determined by using butylhydroxytoluene (BHT) and α-tocopherol as controls (Table 5; Supplementary Table S2). Complexes 1a and 1c exhibited lower antioxidant activity compared to BHT and α-tocopherol (13.30 ± 0.77 μM and 19.29 ± 3.04 μM). On the other hand, complex 1b displayed the best antioxidant activity among the three complexes, surpassing α-tocopherol and slightly less active than BHT (1.55 ± 0.08 μM). These results clearly show the effect of alkyl groups on the phosphine groups regarding their antioxidant activity.
TABLE 5
IC50 values (μM) for the antioxidant activity of POCOP-Ni(II) pincer complexes.
Compound
IC50 (μM)
1a
1.55 ± 0.08
1b
13.30 ± 0.77
1c
19.29 ± 3.04
2
2.19 ± 0.05
BHT
1.22 ± 0.44
α-tocopherol
2.16
5 Ethidium bromide displacement assay
To understand the interaction of 1a, 1b, 1c and 2 complexes with DNA, competitive ethidium bromide (EB) fluorescence titration assays were performed (Figures 4, 5). EB is known to be a proficient intercalator and exhibits very weak fluorescence on its own. However, when intercalates with DNA the fluorescence increases considerably. If a compound has the ability to intercalate with DNA in the same manner as EB, a competition for the binding sites occurs, resulting in the release of EB from DNA. As a result, there is a modification in the fluorescence intensity of the EB-DNA adduct as the competing compound concentration in the system increases. As shown in Figures 4, 5, as the concentration of the 1a, 1b, 1c and 2 complexes gradually increases from 0 to 6.7 μM, 0–11.0 μM, 0–5.0 μM and 0–6.7 μM, respectively, the fluorescence intensity of the EB-DNA adduct is progressively reduced. This behaviour indicates that the complexes intercalate between the DNA double helix, competing with EB for binding sites on the DNA and displacing EB, resulting in a decrease in fluorescence intensity. The KSV value for the quenching of fluorescence intensity of EB bound to DNA by compounds 1a, 1b, 1c and 2 was calculated from the Stern–Volmer plot, which showed good linearity in all cases, suggesting that the spectroscopic measurements are consistent with an intercalative mode of interaction between the pincer complexes and DNA. Additionally, it can be observed that the trend in the KSV values increases in the order of 1c > 1a > 1b > 2, with compound 1c having the highest intercalative binding, possibly due to the presence of aromatic rings located over the phosphorus atoms.
FIGURE 4
Fluorescence spectrum of EB-DNA in the presence of an increasing concentration of the 1a (A), 1b (B) and 1c (C) compounds. The arrows indicate changes in emission intensity as a function of complex concentration. On the right, Stern–Volmer plots of the fluorescence titration data are shown.
FIGURE 5
Fluorescence spectrum of EB-DNA in the presence of an increasing concentration of the 2. The arrows indicate changes in emission intensity as a function of complex concentration. On the right, Stern–Volmer plots of the fluorescence titration data are shown.
6 Computational results
Based on the experimental results obtained, it was decided to carry out an in silico study to observe if there is a correlation between the experimental and theoretical parts, offering a valuable insight into the elucidation of the mechanism of action at the molecular level. Moreover, in this section, we will comprehensively analyze the binding interactions between the selected compounds and their molecular targets: DNA, topoisomerase I, and topoisomerase II. These targets were chosen because, on one hand, DNA is involved in cell replication, and inhibiting such replication in cancer cells leads to programmed cell death. Additionally, many drugs are targeted towards this objective. Regarding topoisomerases, I and II, inhibiting these enzymes delays the ligation stage of the cell cycle, which affects the cleavage of the DNA double helix. Molecular docking simulations, allow us to examine the binding sites, binding modes, and key molecular interactions, including hydrogen bonds, π-σ interactions, and hydrophobic interactions Therefore, molecular docking simulations of complexes 1a-1c and 2 were performed on these biomacromolecules. For complex 2, electronic structure was optimized from the racemic isomer (2-rac
), whose molecular structure was obtained by XRD.
6.1 Electronic structure and molecular docking protocol validation
To validate the electronic structure method, we utilized crystallized ligands as benchmarks and contrasted them with results obtained from electronic structure optimizations. Our analysis revealed Root Mean Square Deviation (RMSD) variances of less than 0.3 Å, primarily due to the limited flexibility of the complexes, as demonstrated in Supplementary Table S3.
The validation of the molecular docking method utilized crystallized ligands from structures available in the Protein Data Bank (PDB). Across all three programs, we observed RMSD values of less than 1.0 Å from the docking site, indicating the robustness of these programs in identifying docking sites within the three models of interest: DNA, Topoisomerase I, and Topoisomerase II. By employing multiple computational tools, we aim to cross-validate our results and identify the differences in interaction patterns across the selected targets. Additionally, we obtained affinity energies, which will serve as reference points for evaluating crystallized compounds. This outcome underscores the efficacy of our chosen computational approach in accurately modeling interactions within biologically relevant complexes, as depicted in Figure 6.
FIGURE 6
Validation of the molecular docking process. Left: Redocking of Topoisomerase I with Camptothecin as inhibitor. Right: Topoisomerase II with Etoposide as inhibitor.
6.2 Molecular docking simulations
The analysis of the molecular docking simulations involving DNA, reveals that complexes 1b and 1c exhibited higher activity compared to cisplatin, which was used as a reference. These results agree with those obtained from experiments involving cellular lines (Table 6).
TABLE 6
Exponential Consensus Ranking (ERC) between DNA and the nickel complex.
Molecular docking program
1a
1b
1c
2-rac
cisplatin
Vina (kcal/mol)
−5.9
−6.8
−7.6
−7.0
−6.0
Smina (kcal/mol)
−5.6
−6.7
−7.1
−6.3
−5.7
ATD (kcal/mol)
−1.8
−1.9
−3.8
−2.4
−1.7
ERC
2.8
5.7
16.0
6.2
2.9
In the molecular docking process, the primary interactions identified are π-σ interactions with phenolic rings (Figure 7). The heightened interactions observed with compounds 1c and 2-rac
, in contrast to the diminished interactions with compounds 1a and 1b, can be attributed to the latter inducing electrostatic repulsions with the tert-butyl groups in the case of molecule 1a and isopropyl groups in molecule 1b (Figure 8). This underscores a specificity in how these complexes interact with DNA, where geometric and electronic compatibility play crucial roles in determining their binding efficiency and biological activity.
FIGURE 7
Coordination mode of DNA model with complex 1c. Purple π-σ interaction, black π-anion. Distances are in Angstrom.
FIGURE 8
Representation of the most stable poses depicting the interaction between the DNA model and (A) 1a, (B) 1b, (C) 1c, and (D) 2-rac
complexes.
In the case of Topoisomerase I (Figure 9), a similar pattern of molecular docking energy to that observed with DNA is noted, displaying an Exponential Consensus Ranking (ERC) (Palacio-Rodríguez et al., 2019). comparable to that of DNA, as shown in Table 7. However, notable hydrogen bond interactions are observed between the hydroxyl group and DNA, as illustrated in Figure 10, along with hydrophobic stabilization interactions with the peptide chain of topoisomerase. This suggests that the molecular interactions contributing to the binding affinity and specificity involve not only electrostatic and π-σ interactions but also hydrogen bonding and hydrophobic interactions. These multifaceted interactions enhance the binding efficiency and specificity of the compounds towards both DNA and topoisomerase I, potentially influencing their biological activity by interfering with the normal function of these macromolecules.
FIGURE 9
Representation of the most stable poses depicting the interaction between the Topoisomerase I model and (A) 1a, (B) 1b, (C) 1c, and (D) 2 complexes.
TABLE 7
Exponential Consensus Ranking (ERC) between Topoisomerase I and the nickel complex.
Molecular docking program
1a
1b
1c
2-rac
Ref
Vina (kcal/mol)
−7.2
−7.4
−8.0
−8.3
−5.5
Smina (kcal/mol)
−5.8
−6.6
−8.9
−7.0
−6.5
ATD (kcal/mol)
−2.9
−2.6
−3.5
−1.0
−5.0
ERC
6.7
8.4
29.9
7.6
9.6
FIGURE 10
Best coordination mode of DNA model with complex 1c. Purple π-σ interaction, green H-bond, orange π-π stacking. Distances are in Angstrom.
CHART 1
Plot of percentages of contacts observed in complexes 1a, 1b, 1c, and 2-rac
.
In the case of molecule 1b, we observed a similar affinity behavior with the formation of identical hydrogen bonds. However, the stability of the complex is diminished due to the absence of interactions provided by aromatic groups. Conversely, with molecule 2-rac
, there is a reduction in interactions since stabilization primarily relies on nearby aromatic rings. This underscores the critical role of aromatic rings in stabilizing these complexes. Aromatic rings significantly contribute to stabilization through π-π stacking interactions and hydrophobic effects, which are crucial for the molecular architecture of the complexes. The loss or modification of these aromatic interactions can lead to a noticeable alteration in stability and consequently affect the effectiveness of molecular interactions with the target, such as DNA or proteins like topoisomerase I.
Concerning topoisomerase II, no superior affinity was observed for the crystalline structures compared to the crystallized inhibitor obtained from the Protein Data Bank (PDB), as demonstrated in Table 8. However, like the previous targets, a superior affinity for complex 1c was noted. This suggests that while the novel complexes did not exceed the affinity of the crystallized inhibitor for topoisomerase II, complex 1c distinguishes itself for its consistent ability to interact more favorably across various biological targets.
TABLE 8
Exponential Consensus Ranking (ERC) between Topoisomerase II and the nickel complex.
Molecular docking program
1a
1b
1c
2-rac
Ref
Vina (kcal/mol)
−7.4
−7.1
−9.3
−7.2
−9.7
Smina (kcal/mol)
−5.3
−6.2
−9.7
−6.2
−6.5
ATD (kcal/mol)
−2.8
−2.6
−5.3
−4.6
−12.9
ERC
5.9
6.7
11.1
13.2
27.3
Complexes 1b and 1c exhibit superior activity against DNA structures and topoisomerase I compared to cisplatin, a well-established anticancer agent. This heightened affinity suggests that these complexes possess molecular attributes, such as π-σ interactions with phenolic rings and the capability to form hydrogen bonds, which promote stronger and more targeted interactions with these biomolecules. However, concerning topoisomerase II, the complexes did not surpass the specific inhibitor from the Protein Data Bank (PDB). This suggests that how well a compound binds and interacts closely depends on the three-dimensional structure and chemical properties of the target binding site.
The consistent affinity displayed by complex 1c across various biological targets highlights the significance of diverse molecular interactions in docking. In addition, molecular docking studies could be correlated with the experimental cytotoxicity results in various cancer cell lines, where complex 1c exhibited generally better activity compared to complex 1b and cisplatin. This trend is supported by a higher binding affinity of 1c for both DNA and topoisomerase I, with ERC values of 16.0 and 29.9, respectively. These strong interactions correlate with the higher cytotoxic efficacy of 1c in cell lines such as U251 and K562, where it outperformed cisplatin, as well as in SK-LU-1, where it demonstrated better activity than both cisplatin and complex 1b. Moreover, the π-σ interactions and hydrogen bonds of 1c with DNA and topoisomerase I, identified in the molecular docking simulations, align with its higher experimental efficacy in inhibiting cancer cell replication. On the other hand, while complex 1c consistently showed higher binding affinity for both DNA and topoisomerase I, the results with topoisomerase II revealed that it did not outperform the specific inhibitor from the PDB. This suggests that the complexes, particularly 1c, may have a more target-specific action toward DNA and topoisomerase I, rather than being broad-spectrum inhibitors. The reduced affinity of the complexes for topoisomerase II compared to DNA and topoisomerase I highlights the importance of target-specific interactions in determining biological activity, explaining why complex 1c does not universally outperform in all assays but shows selectivity depending on the target.
6.1 Electronic structure and molecular docking protocol validation
To validate the electronic structure method, we utilized crystallized ligands as benchmarks and contrasted them with results obtained from electronic structure optimizations. Our analysis revealed Root Mean Square Deviation (RMSD) variances of less than 0.3 Å, primarily due to the limited flexibility of the complexes, as demonstrated in Supplementary Table S3.
The validation of the molecular docking method utilized crystallized ligands from structures available in the Protein Data Bank (PDB). Across all three programs, we observed RMSD values of less than 1.0 Å from the docking site, indicating the robustness of these programs in identifying docking sites within the three models of interest: DNA, Topoisomerase I, and Topoisomerase II. By employing multiple computational tools, we aim to cross-validate our results and identify the differences in interaction patterns across the selected targets. Additionally, we obtained affinity energies, which will serve as reference points for evaluating crystallized compounds. This outcome underscores the efficacy of our chosen computational approach in accurately modeling interactions within biologically relevant complexes, as depicted in Figure 6.
FIGURE 6
Validation of the molecular docking process. Left: Redocking of Topoisomerase I with Camptothecin as inhibitor. Right: Topoisomerase II with Etoposide as inhibitor.
6.2 Molecular docking simulations
The analysis of the molecular docking simulations involving DNA, reveals that complexes 1b and 1c exhibited higher activity compared to cisplatin, which was used as a reference. These results agree with those obtained from experiments involving cellular lines (Table 6).
TABLE 6
Exponential Consensus Ranking (ERC) between DNA and the nickel complex.
Molecular docking program
1a
1b
1c
2-rac
cisplatin
Vina (kcal/mol)
−5.9
−6.8
−7.6
−7.0
−6.0
Smina (kcal/mol)
−5.6
−6.7
−7.1
−6.3
−5.7
ATD (kcal/mol)
−1.8
−1.9
−3.8
−2.4
−1.7
ERC
2.8
5.7
16.0
6.2
2.9
In the molecular docking process, the primary interactions identified are π-σ interactions with phenolic rings (Figure 7). The heightened interactions observed with compounds 1c and 2-rac
, in contrast to the diminished interactions with compounds 1a and 1b, can be attributed to the latter inducing electrostatic repulsions with the tert-butyl groups in the case of molecule 1a and isopropyl groups in molecule 1b (Figure 8). This underscores a specificity in how these complexes interact with DNA, where geometric and electronic compatibility play crucial roles in determining their binding efficiency and biological activity.
FIGURE 7
Coordination mode of DNA model with complex 1c. Purple π-σ interaction, black π-anion. Distances are in Angstrom.
FIGURE 8
Representation of the most stable poses depicting the interaction between the DNA model and (A) 1a, (B) 1b, (C) 1c, and (D) 2-rac
complexes.
In the case of Topoisomerase I (Figure 9), a similar pattern of molecular docking energy to that observed with DNA is noted, displaying an Exponential Consensus Ranking (ERC) (Palacio-Rodríguez et al., 2019). comparable to that of DNA, as shown in Table 7. However, notable hydrogen bond interactions are observed between the hydroxyl group and DNA, as illustrated in Figure 10, along with hydrophobic stabilization interactions with the peptide chain of topoisomerase. This suggests that the molecular interactions contributing to the binding affinity and specificity involve not only electrostatic and π-σ interactions but also hydrogen bonding and hydrophobic interactions. These multifaceted interactions enhance the binding efficiency and specificity of the compounds towards both DNA and topoisomerase I, potentially influencing their biological activity by interfering with the normal function of these macromolecules.
FIGURE 9
Representation of the most stable poses depicting the interaction between the Topoisomerase I model and (A) 1a, (B) 1b, (C) 1c, and (D) 2 complexes.
TABLE 7
Exponential Consensus Ranking (ERC) between Topoisomerase I and the nickel complex.
Molecular docking program
1a
1b
1c
2-rac
Ref
Vina (kcal/mol)
−7.2
−7.4
−8.0
−8.3
−5.5
Smina (kcal/mol)
−5.8
−6.6
−8.9
−7.0
−6.5
ATD (kcal/mol)
−2.9
−2.6
−3.5
−1.0
−5.0
ERC
6.7
8.4
29.9
7.6
9.6
FIGURE 10
Best coordination mode of DNA model with complex 1c. Purple π-σ interaction, green H-bond, orange π-π stacking. Distances are in Angstrom.
CHART 1
Plot of percentages of contacts observed in complexes 1a, 1b, 1c, and 2-rac
.
In the case of molecule 1b, we observed a similar affinity behavior with the formation of identical hydrogen bonds. However, the stability of the complex is diminished due to the absence of interactions provided by aromatic groups. Conversely, with molecule 2-rac
, there is a reduction in interactions since stabilization primarily relies on nearby aromatic rings. This underscores the critical role of aromatic rings in stabilizing these complexes. Aromatic rings significantly contribute to stabilization through π-π stacking interactions and hydrophobic effects, which are crucial for the molecular architecture of the complexes. The loss or modification of these aromatic interactions can lead to a noticeable alteration in stability and consequently affect the effectiveness of molecular interactions with the target, such as DNA or proteins like topoisomerase I.
Concerning topoisomerase II, no superior affinity was observed for the crystalline structures compared to the crystallized inhibitor obtained from the Protein Data Bank (PDB), as demonstrated in Table 8. However, like the previous targets, a superior affinity for complex 1c was noted. This suggests that while the novel complexes did not exceed the affinity of the crystallized inhibitor for topoisomerase II, complex 1c distinguishes itself for its consistent ability to interact more favorably across various biological targets.
TABLE 8
Exponential Consensus Ranking (ERC) between Topoisomerase II and the nickel complex.
Molecular docking program
1a
1b
1c
2-rac
Ref
Vina (kcal/mol)
−7.4
−7.1
−9.3
−7.2
−9.7
Smina (kcal/mol)
−5.3
−6.2
−9.7
−6.2
−6.5
ATD (kcal/mol)
−2.8
−2.6
−5.3
−4.6
−12.9
ERC
5.9
6.7
11.1
13.2
27.3
Complexes 1b and 1c exhibit superior activity against DNA structures and topoisomerase I compared to cisplatin, a well-established anticancer agent. This heightened affinity suggests that these complexes possess molecular attributes, such as π-σ interactions with phenolic rings and the capability to form hydrogen bonds, which promote stronger and more targeted interactions with these biomolecules. However, concerning topoisomerase II, the complexes did not surpass the specific inhibitor from the Protein Data Bank (PDB). This suggests that how well a compound binds and interacts closely depends on the three-dimensional structure and chemical properties of the target binding site.
The consistent affinity displayed by complex 1c across various biological targets highlights the significance of diverse molecular interactions in docking. In addition, molecular docking studies could be correlated with the experimental cytotoxicity results in various cancer cell lines, where complex 1c exhibited generally better activity compared to complex 1b and cisplatin. This trend is supported by a higher binding affinity of 1c for both DNA and topoisomerase I, with ERC values of 16.0 and 29.9, respectively. These strong interactions correlate with the higher cytotoxic efficacy of 1c in cell lines such as U251 and K562, where it outperformed cisplatin, as well as in SK-LU-1, where it demonstrated better activity than both cisplatin and complex 1b. Moreover, the π-σ interactions and hydrogen bonds of 1c with DNA and topoisomerase I, identified in the molecular docking simulations, align with its higher experimental efficacy in inhibiting cancer cell replication. On the other hand, while complex 1c consistently showed higher binding affinity for both DNA and topoisomerase I, the results with topoisomerase II revealed that it did not outperform the specific inhibitor from the PDB. This suggests that the complexes, particularly 1c, may have a more target-specific action toward DNA and topoisomerase I, rather than being broad-spectrum inhibitors. The reduced affinity of the complexes for topoisomerase II compared to DNA and topoisomerase I highlights the importance of target-specific interactions in determining biological activity, explaining why complex 1c does not universally outperform in all assays but shows selectivity depending on the target.
7 Conclusion
In this study, four POCOP-Ni(II) pincer compounds (1a, 1b, 1c, and 2) were evaluated as potential agents with anticancer and antioxidant properties. A novel structure (Complex 2), synthesized from phloroglucinol and the corresponding phosphine, and its racemic isomer 2-rac
was meticulously characterized using single-crystal X-ray diffraction (DRX), revealing a distinctive distorted square-planar geometry. The supramolecular interactions identified in the crystalline packing underscore the significance of H•••Cl, O•••H interactions, and van der Waals H•••H contacts in the structure. In fact, mainly the OH•••Cl interactions play a critical role in the four structures to stabilize polymeric interactions and favor crystallization.
Cytotoxic evaluation demonstrated that complexes 1b and 1c exhibited significant antiproliferative activity against a spectrum of cancer cell lines (U251, K562, HCT-15, MCF-7, and SK-LU-1), with IC50 values ranging from 2.43 to 7.85 μM. In contrast, complex 1a showed the highest antioxidant activity, with an IC50 value of 1.55 μM. Furthermore, a competitive fluorescent displacement assay revealed that complexes 1a-c could effectively displace EB from the DNA-EB adduct.
Molecular docking studies of complexes 1b and 1c suggests that these compounds could be effective anticancer treatments, demonstrating strong interactions with DNA and topoisomerase I compared to standard treatments such as cisplatin. However, their performance against topoisomerase II, relative to specific inhibitors from the Protein Data Bank, underscores the complexity of drug development and the necessity for a comprehensive understanding of drug-target interactions. Complex 1c consistently exhibited strong affinity across various targets, indicating its potential for diverse therapeutic applications. These findings emphasize the importance of employing a multidisciplinary approach to advance these complexes into effective anticancer therapies, addressing a critical need in cancer treatment.
These findings underscore the multifaceted potential of the POCOP-Ni(II) pincer compounds studied, with distinct complexes demonstrating promising anticancer and antioxidant properties. The elucidation of their structural characteristics and interactions offers valuable insights into their potential applications in therapeutic and biomedical contexts. Continued exploration will be crucial for unraveling the complex mechanisms and applications of these compounds in medical chemistry and therapeutic development.
8 Experimental section
All chemical compounds were commercially obtained and used as received without further purification. The 1H, 13C{1H}, 31P{1H} NMR spectra were obtained on a Bruker Avance III 400 MHz spectrometer and a Bruker 500 Ascend spectrometer. Chemical shifts are reported in ppm down field of TMS employing the residual signals in the solvent (CDCl3) as internal standard. ATR-IR measurements were performed on a FTIR NICOLET IS50, Thermo Fisher Scientific Spectrometer. Elemental analyses were made on a Thermo Scientific Flash 2000 elemental analyzer, using a Mettler Toledo XP6 Automated-S Microbalance and sulfanilamide as standard (Thermo Scientific BN 217826, attained values N = 16.40%, C = 41.91%, H = 4.65%, and S = 18.63%; certified values N = 16.26%, C = 41.81%, H = 4.71%, and S = 18.62%). MS-DART determinations were recorded in a JEOL The AccuTOF JMS-T100LC Mass spectrometer. Complexes 1a, 1b, and 1c were synthesized according to the methodology previously designed in our investigation group.21 They were characterized by 1H, 13C, and 31P NMR spectroscopy, and elemental analysis showing the expected results.
8.1 Synthesis of compounds 1a-c
The pincers compounds 1a-c (R =
t
Bu,
i
Pr, Ph, respectively) were synthesized according to the methodology reported by our investigation group (García-Eleno et al., 2015). And spectroscopic analysis was entirely correlated.
8.2 Synthesis of compound 2
A Schlenk flask was charged with 1 mmol of phloroglucinol, 2 mmol of chloro (tert-butyl)phenylphosphine, 2 mmol NEt3, and dry THF (30 mL); under a nitrogen atmosphere. Then, the solution was refluxed overnight and evaporated under a vacuum. The crude product was purified by chromatographic column using CH2Cl2 as eluent. The compound was obtained as a yellow solid. Yield 97%. M. p. 143°C–144 °C. 1H NMR (500 MHz, CDCl3) δ 8.13–8.06 (m, 4H, CHAr), 7.50–7.43 (m, 6H, CHAr), 6.11 (s, 1H, CHAr), 6.11 (s, 1H, CHAr), 1.37–1.27 (m, 18H, -C(CH
3)3). 13C{1H} NMR (125.7 MHz, CDCl3) δ 167.9 (m, C-O), 167.7 (m, C-O), 157.6 (s, C-O), 137.8 (s, C-H), 131.7 (s, C-H), 131.2 (s, C-H), 131.1 (s, C-H), 130.9 (m, C-P), 130.7 (m, C-P), 128.3 (s, C-H), 128.2 (s, C-H), 94.5 (t, C-H,
3
J
C-P
= 6.5 Hz), 113.7 (m, C-Ni), 94.4 (t, C-H,
3
J
C-P
= 6.5 Hz), 37.5 (t, -C(CH3)3,
1
J
C-P
= 12.2 Hz), 37.0 (t, -C(CH3)3,
1
J
C-P
= 12.7 Hz), 25.5 (s, -CH(CH3)2), 25.4 (s, -CH(CH3)2). 31P{1H} NMR (202.4 MHz, CDCl3): δ 160.56, 159.71. MS (DART): m/z 547 [M + H]+. IR (ATR, cm−1): 3371 (b, -OH), 1130 (s, C-O-C). Elem. Anal. Calcd. for C26H31ClNiO3P2: C, 57.03; H, 5.71. Found: C, 56.26; H, 6.93.
8.3 Cytotoxic evaluation
The compounds were screened in vitro against human cancer cell lines: HCT-15 (human colorectal adenocarcinoma), MCF-7 (human mammary adenocarcinoma), K562 (human chronic myelogenous leukaemia), U251 (human glioblastoma), PC-3 (human prostatic adenocarcinoma), SK-LU-1 (human lung adenocarcinoma), COS-7 (cell line monkey African green kidney) cell lines were supplied by the National Cancer Institute (United States of America) and were donated by the Cancer Institute of Mexico. The cell lines were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 25 μg/mL amphotericin B (Gibco) and 1% non-essential amino acids (Gibco). They were maintained at 37 °C in a humidified atmosphere with 5% CO2.
Cytotoxicity after treatment of the tumors cells and normal cells with the test compounds was determined using the protein-binding dye sulforhodamine B (SRB) in a microculture assay to measure cell growth (Vichai and Kirtikara, 2006). The cultures were exposed for 48 h to the compound at concentrations 25 μM. After the incubation period, cells were fixed to the plastic substratum by addition of 50 μL of cold 50% aqueous trichloroacetic acid. The plates were incubated at 4°C for 1 h, washed with tap H2O, and air-dried. The trichloroacetic-acid-fixed cells were stained by the addition of 0.4% SRB. Free SRB solution was the removed by washing with 1% aqueous acetic acid. The plates were then air-dried, and the bound dye was solubilized by the addition of 10 mM unbuffered tris base (100 μL). The plates were placed on and shaken for 10 min, and the absorption was determined at 515 nm using an ELISA plate reader (Bio-Tex Instruments). The inhibitory concentration 50 (IC50) values were calculated on extrapolated fit curves based on doses/response data analysed for each compound through lineal regression analysis.
8.4 Lipid peroxidation inhibition
8.4.1 Animals
Adult male Wistar rat (200–250 g) was proveed by Instituto de Fisiología Celular, Universidad Nacional Autónoma de México (UNAM). Procedures and care of animals were conducted in conformity with Mexican Official Norm for Animal Care and Handling (NOM-062-ZOO-1999). They were maintained at 23°C ± 2°C on a 12/12 h light-dark cycle with free access to food and water.
8.4.2 Rat brain homogenate preparation
Animal euthanasia was carried out avoiding unnecessary pain with CO2. The cerebral tissue (whole brain) was rapidly dissected and homogenized in phosphate buffered saline (PBS) solution (0.2 g of KCl, 0.2 g of KH2PO4, 8 g of NaCl, and 2.16 g of NaHPO4 ·7 H2O/l, pH adjusted to 7.4) as reported elsewhere (Rossato et al., 2002; Domínguez et al., 2005) to produce a 1/10 (w/v) homogenate. Homogenate was centrifuged for 10 min at 800 rcf (relative centrifugal field). The supernatant protein content was measured using the Folin and Ciocalteu’s phenol reagent (Lowry et al., 1951) and adjusted wih PBS at 2.66 mg of protein/mL.
As an index of lipid peroxidation, TBARS levels were measured using rat brain homogenates according to the method described by Ng et al. (2000), with some modifications. Supernatant (375 µL) was added with 50 µL of 20 µM EDTA and 25 µL of each sample concentration solved in DMSO (25 µL of DMSO for control group) and incubated at 37°C for 30 min. Lipid peroxidation was started adding 50 µL of freshly solution FeSO4 100 µM and incubated at 37°C for 1 h. The TBARS content was determined as described by Ohkawa et al. (1979) with 500 µL of TBA reagent (0.5% 2-thiobarbituric acid in 0.05 N NaOH and 30% trichloroacetic acid, in 1:1 proportion) was added at each tube and cooled on ice for 10 min, centrifugated at 13,400 rcf for 5 min and heated at 80°C in a water bath for 30 min. After cooling at room temperature, the absorbance of 200 µL of supernatant was measured at ⌊ = 540 nm in a Bio-Tek Microplate Reader Synergy HT. Concentration of TBARS was calculated by interpolation in a standard curve of tetra-methoxypropane (TMP) as a precursor of MDA (Esterbauer and Cheeseman, 1990). Results were expressed as nmoles of TBARS per mg of protein. The inhibition ratio (IR [%]) was calculated using the following formula I
R
=(C-E)*100/C, where C is the absorbance of control and E is the absorbance of the test sample. Butylated hydroxytoluene (BHT) and α-tocopherol were used as positive standards.
All data were represented as mean ± standard error (SEM). Data were analyzed by one-way ANOVA followed by Dunnett´s test for comparison against control. Values of p ≤ 0.05 (*) and p ≤ 0.01 (**) were considered statistically significant. The inhibitory concentration 50 (IC50), was estimated by means of a linear regression.
8.5 Competitive displacement assay
A 4 mM working solution of salmon sperm DNA (ss-DNA) (SIGMA) was prepared in 5 mM Tris-HCl and 5 mM NaCl buffer at pH 7.4 (Backman-Blanco et al., 2020) Compounds 1a, 1b and 1c were dissolved in DMSO at concentrations of 10, 6.66 and 3.3 mM respectively. To get insight whether compounds 1a, 1b and 1c may interact with DNA, an ethidium bromide (EB) displacement assay was performed as mentioned in the literature (Banerjee et al., 2013). Briefly, a 3 mL buffer containing 5 mM Tris-HCl, 5 mM NaCl buffer at pH 7.4 and 5.0 × 10−5 M EB was mixed in a 1 cm fluorescence cuvette with 2.5 × 10−4 M of ss-DNA. The cuvette was placed in an Agilent Cary Eclipse spectrofluorometer and titrated with different amounts of the stock solution of the compounds 1a, 1b and 1c, after thorough mixing the fluorescence spectra were recorded at 25 °C in the range of 540 and 700 nm (⌊ex = 520 nm).
8.6 Computational details for compounds 1a, 1b, 1c and 2-rac
Electronic structure calculations were carried out at the B3LYP/6-31+G (d,p) level of theory. Initial ligand geometries were extracted from crystal structures, subsequently optimized, and subjected to frequency calculations to validate their status as minima on the potential energy surface. To account for solvent effects (water), the SMD continuum method was employed. Atomic charges, essential for molecular docking simulations, were obtained using the NPA scheme (Reed et al., 1985). All the electronic structure calculations were carried out using the Gaussian16 suite of programs (Frisch et al., 2016).
To optimize docking outcomes, minimize the influence of force fields, and reduce system dependency, we employed an Exponential Consensus Ranking (ECR) (Palacio-Rodríguez et al., 2019). This method computes a consensus score, P(i), for each molecule by aggregating exponential ranks from various programs. This consensus approach was applied using three molecular docking programs: AutoDock 4, (Morris et al., 2009), AutoDock Vina, (Trott and Olson, 2010), and Smina, (Koes et al., 2013), which incorporates the Vinardo scoring function.
The molecular docking simulation was performed utilizing DNA models, as well as Topoisomerase I and II, which are crucial targets for contemporary cancer treatments. The three-dimensional structures of DNA models and Topoisomerases I and II were retrieved from PDB codes 1AIO, (Takahara et al., 1995), 1T8I, (L. Staker et al., 2005) and 5GWK, (Wang et al., 2017), respectively. These structures have been previously utilized in computational cancer research. (Backman-Blanco et al., 2020; Jamal, 2020; Madeddu et al., 2022). Receptor file preparation was conducted using AutoDock Tools 1.4.5, (Morris et al., 2009), involving the removal of water molecules, the addition of all hydrogen atoms with nonpolar hydrogens merged into carbon atoms, and incorporation of Gasteiger charges into the receptor models, (Gasteiger and Marsili, 1980), resulting in pdbqt files. Ligand charges were determined from NPA population analysis based on density functional theory calculations. Docking experiments utilized a grid box measuring 60 × 60 × 60 ų along the X, Y, and Z-axes with a spacing of 0.375 Å. Analysis of ligand-receptor complexes was performed using Chimera, (Pettersen et al., 2004), PyMol, (Schrödinger, 2015), and Maestro Schrodinger (Schrödinger Release, 2023-2, 2023) programs.
8.7 Data collection and refinement for compound 2-rac
All crystals were grown by slow evaporation of CH2Cl2, then placed on a Bruker Smart Apex II diffractometer with a Mo-target X-Ray source (λ = 0.71073 Å). The detector was placed at 5.0 cm from the crystals and frames were collected with a scan width of 0.5 cm in ω and an exposure time of 10 s/frame. Frames were integrated with the Bruker SAINT software package using a narrow-frame integration algorithm. Non-systematic absences and intensity statistics were used for space group determination of orthorhombic unit cell for 2-rac
. The structures were solved using Patterson methods using the SHELXS-2014/7 program (Bruker AXS Inc, 2018). The remaining atoms were located via a few cycles of least squares refinements and difference Fourier maps. Hydrogen atoms were input at calculated positions and allowed to ride on the atoms to which they were attached. Thermal parameters were refined for hydrogen atoms on the phenyl groups using a Ueq = 1.2 Å to precedent atom. The final cycles of refinement were carried out on all non-zero data using SHELXL-2014/7 (Sheldrick, 2015). Absorption corrections were applied using the SADABS program (Krause et al., 2015).
8.1 Synthesis of compounds 1a-c
The pincers compounds 1a-c (R =
t
Bu,
i
Pr, Ph, respectively) were synthesized according to the methodology reported by our investigation group (García-Eleno et al., 2015). And spectroscopic analysis was entirely correlated.
8.2 Synthesis of compound 2
A Schlenk flask was charged with 1 mmol of phloroglucinol, 2 mmol of chloro (tert-butyl)phenylphosphine, 2 mmol NEt3, and dry THF (30 mL); under a nitrogen atmosphere. Then, the solution was refluxed overnight and evaporated under a vacuum. The crude product was purified by chromatographic column using CH2Cl2 as eluent. The compound was obtained as a yellow solid. Yield 97%. M. p. 143°C–144 °C. 1H NMR (500 MHz, CDCl3) δ 8.13–8.06 (m, 4H, CHAr), 7.50–7.43 (m, 6H, CHAr), 6.11 (s, 1H, CHAr), 6.11 (s, 1H, CHAr), 1.37–1.27 (m, 18H, -C(CH
3)3). 13C{1H} NMR (125.7 MHz, CDCl3) δ 167.9 (m, C-O), 167.7 (m, C-O), 157.6 (s, C-O), 137.8 (s, C-H), 131.7 (s, C-H), 131.2 (s, C-H), 131.1 (s, C-H), 130.9 (m, C-P), 130.7 (m, C-P), 128.3 (s, C-H), 128.2 (s, C-H), 94.5 (t, C-H,
3
J
C-P
= 6.5 Hz), 113.7 (m, C-Ni), 94.4 (t, C-H,
3
J
C-P
= 6.5 Hz), 37.5 (t, -C(CH3)3,
1
J
C-P
= 12.2 Hz), 37.0 (t, -C(CH3)3,
1
J
C-P
= 12.7 Hz), 25.5 (s, -CH(CH3)2), 25.4 (s, -CH(CH3)2). 31P{1H} NMR (202.4 MHz, CDCl3): δ 160.56, 159.71. MS (DART): m/z 547 [M + H]+. IR (ATR, cm−1): 3371 (b, -OH), 1130 (s, C-O-C). Elem. Anal. Calcd. for C26H31ClNiO3P2: C, 57.03; H, 5.71. Found: C, 56.26; H, 6.93.
8.3 Cytotoxic evaluation
The compounds were screened in vitro against human cancer cell lines: HCT-15 (human colorectal adenocarcinoma), MCF-7 (human mammary adenocarcinoma), K562 (human chronic myelogenous leukaemia), U251 (human glioblastoma), PC-3 (human prostatic adenocarcinoma), SK-LU-1 (human lung adenocarcinoma), COS-7 (cell line monkey African green kidney) cell lines were supplied by the National Cancer Institute (United States of America) and were donated by the Cancer Institute of Mexico. The cell lines were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 25 μg/mL amphotericin B (Gibco) and 1% non-essential amino acids (Gibco). They were maintained at 37 °C in a humidified atmosphere with 5% CO2.
Cytotoxicity after treatment of the tumors cells and normal cells with the test compounds was determined using the protein-binding dye sulforhodamine B (SRB) in a microculture assay to measure cell growth (Vichai and Kirtikara, 2006). The cultures were exposed for 48 h to the compound at concentrations 25 μM. After the incubation period, cells were fixed to the plastic substratum by addition of 50 μL of cold 50% aqueous trichloroacetic acid. The plates were incubated at 4°C for 1 h, washed with tap H2O, and air-dried. The trichloroacetic-acid-fixed cells were stained by the addition of 0.4% SRB. Free SRB solution was the removed by washing with 1% aqueous acetic acid. The plates were then air-dried, and the bound dye was solubilized by the addition of 10 mM unbuffered tris base (100 μL). The plates were placed on and shaken for 10 min, and the absorption was determined at 515 nm using an ELISA plate reader (Bio-Tex Instruments). The inhibitory concentration 50 (IC50) values were calculated on extrapolated fit curves based on doses/response data analysed for each compound through lineal regression analysis.
8.4 Lipid peroxidation inhibition
8.4.1 Animals
Adult male Wistar rat (200–250 g) was proveed by Instituto de Fisiología Celular, Universidad Nacional Autónoma de México (UNAM). Procedures and care of animals were conducted in conformity with Mexican Official Norm for Animal Care and Handling (NOM-062-ZOO-1999). They were maintained at 23°C ± 2°C on a 12/12 h light-dark cycle with free access to food and water.
8.4.2 Rat brain homogenate preparation
Animal euthanasia was carried out avoiding unnecessary pain with CO2. The cerebral tissue (whole brain) was rapidly dissected and homogenized in phosphate buffered saline (PBS) solution (0.2 g of KCl, 0.2 g of KH2PO4, 8 g of NaCl, and 2.16 g of NaHPO4 ·7 H2O/l, pH adjusted to 7.4) as reported elsewhere (Rossato et al., 2002; Domínguez et al., 2005) to produce a 1/10 (w/v) homogenate. Homogenate was centrifuged for 10 min at 800 rcf (relative centrifugal field). The supernatant protein content was measured using the Folin and Ciocalteu’s phenol reagent (Lowry et al., 1951) and adjusted wih PBS at 2.66 mg of protein/mL.
As an index of lipid peroxidation, TBARS levels were measured using rat brain homogenates according to the method described by Ng et al. (2000), with some modifications. Supernatant (375 µL) was added with 50 µL of 20 µM EDTA and 25 µL of each sample concentration solved in DMSO (25 µL of DMSO for control group) and incubated at 37°C for 30 min. Lipid peroxidation was started adding 50 µL of freshly solution FeSO4 100 µM and incubated at 37°C for 1 h. The TBARS content was determined as described by Ohkawa et al. (1979) with 500 µL of TBA reagent (0.5% 2-thiobarbituric acid in 0.05 N NaOH and 30% trichloroacetic acid, in 1:1 proportion) was added at each tube and cooled on ice for 10 min, centrifugated at 13,400 rcf for 5 min and heated at 80°C in a water bath for 30 min. After cooling at room temperature, the absorbance of 200 µL of supernatant was measured at ⌊ = 540 nm in a Bio-Tek Microplate Reader Synergy HT. Concentration of TBARS was calculated by interpolation in a standard curve of tetra-methoxypropane (TMP) as a precursor of MDA (Esterbauer and Cheeseman, 1990). Results were expressed as nmoles of TBARS per mg of protein. The inhibition ratio (IR [%]) was calculated using the following formula I
R
=(C-E)*100/C, where C is the absorbance of control and E is the absorbance of the test sample. Butylated hydroxytoluene (BHT) and α-tocopherol were used as positive standards.
All data were represented as mean ± standard error (SEM). Data were analyzed by one-way ANOVA followed by Dunnett´s test for comparison against control. Values of p ≤ 0.05 (*) and p ≤ 0.01 (**) were considered statistically significant. The inhibitory concentration 50 (IC50), was estimated by means of a linear regression.
8.4.1 Animals
Adult male Wistar rat (200–250 g) was proveed by Instituto de Fisiología Celular, Universidad Nacional Autónoma de México (UNAM). Procedures and care of animals were conducted in conformity with Mexican Official Norm for Animal Care and Handling (NOM-062-ZOO-1999). They were maintained at 23°C ± 2°C on a 12/12 h light-dark cycle with free access to food and water.
8.4.2 Rat brain homogenate preparation
Animal euthanasia was carried out avoiding unnecessary pain with CO2. The cerebral tissue (whole brain) was rapidly dissected and homogenized in phosphate buffered saline (PBS) solution (0.2 g of KCl, 0.2 g of KH2PO4, 8 g of NaCl, and 2.16 g of NaHPO4 ·7 H2O/l, pH adjusted to 7.4) as reported elsewhere (Rossato et al., 2002; Domínguez et al., 2005) to produce a 1/10 (w/v) homogenate. Homogenate was centrifuged for 10 min at 800 rcf (relative centrifugal field). The supernatant protein content was measured using the Folin and Ciocalteu’s phenol reagent (Lowry et al., 1951) and adjusted wih PBS at 2.66 mg of protein/mL.
As an index of lipid peroxidation, TBARS levels were measured using rat brain homogenates according to the method described by Ng et al. (2000), with some modifications. Supernatant (375 µL) was added with 50 µL of 20 µM EDTA and 25 µL of each sample concentration solved in DMSO (25 µL of DMSO for control group) and incubated at 37°C for 30 min. Lipid peroxidation was started adding 50 µL of freshly solution FeSO4 100 µM and incubated at 37°C for 1 h. The TBARS content was determined as described by Ohkawa et al. (1979) with 500 µL of TBA reagent (0.5% 2-thiobarbituric acid in 0.05 N NaOH and 30% trichloroacetic acid, in 1:1 proportion) was added at each tube and cooled on ice for 10 min, centrifugated at 13,400 rcf for 5 min and heated at 80°C in a water bath for 30 min. After cooling at room temperature, the absorbance of 200 µL of supernatant was measured at ⌊ = 540 nm in a Bio-Tek Microplate Reader Synergy HT. Concentration of TBARS was calculated by interpolation in a standard curve of tetra-methoxypropane (TMP) as a precursor of MDA (Esterbauer and Cheeseman, 1990). Results were expressed as nmoles of TBARS per mg of protein. The inhibition ratio (IR [%]) was calculated using the following formula I
R
=(C-E)*100/C, where C is the absorbance of control and E is the absorbance of the test sample. Butylated hydroxytoluene (BHT) and α-tocopherol were used as positive standards.
All data were represented as mean ± standard error (SEM). Data were analyzed by one-way ANOVA followed by Dunnett´s test for comparison against control. Values of p ≤ 0.05 (*) and p ≤ 0.01 (**) were considered statistically significant. The inhibitory concentration 50 (IC50), was estimated by means of a linear regression.
8.5 Competitive displacement assay
A 4 mM working solution of salmon sperm DNA (ss-DNA) (SIGMA) was prepared in 5 mM Tris-HCl and 5 mM NaCl buffer at pH 7.4 (Backman-Blanco et al., 2020) Compounds 1a, 1b and 1c were dissolved in DMSO at concentrations of 10, 6.66 and 3.3 mM respectively. To get insight whether compounds 1a, 1b and 1c may interact with DNA, an ethidium bromide (EB) displacement assay was performed as mentioned in the literature (Banerjee et al., 2013). Briefly, a 3 mL buffer containing 5 mM Tris-HCl, 5 mM NaCl buffer at pH 7.4 and 5.0 × 10−5 M EB was mixed in a 1 cm fluorescence cuvette with 2.5 × 10−4 M of ss-DNA. The cuvette was placed in an Agilent Cary Eclipse spectrofluorometer and titrated with different amounts of the stock solution of the compounds 1a, 1b and 1c, after thorough mixing the fluorescence spectra were recorded at 25 °C in the range of 540 and 700 nm (⌊ex = 520 nm).
8.6 Computational details for compounds 1a, 1b, 1c and 2-rac
Electronic structure calculations were carried out at the B3LYP/6-31+G (d,p) level of theory. Initial ligand geometries were extracted from crystal structures, subsequently optimized, and subjected to frequency calculations to validate their status as minima on the potential energy surface. To account for solvent effects (water), the SMD continuum method was employed. Atomic charges, essential for molecular docking simulations, were obtained using the NPA scheme (Reed et al., 1985). All the electronic structure calculations were carried out using the Gaussian16 suite of programs (Frisch et al., 2016).
To optimize docking outcomes, minimize the influence of force fields, and reduce system dependency, we employed an Exponential Consensus Ranking (ECR) (Palacio-Rodríguez et al., 2019). This method computes a consensus score, P(i), for each molecule by aggregating exponential ranks from various programs. This consensus approach was applied using three molecular docking programs: AutoDock 4, (Morris et al., 2009), AutoDock Vina, (Trott and Olson, 2010), and Smina, (Koes et al., 2013), which incorporates the Vinardo scoring function.
The molecular docking simulation was performed utilizing DNA models, as well as Topoisomerase I and II, which are crucial targets for contemporary cancer treatments. The three-dimensional structures of DNA models and Topoisomerases I and II were retrieved from PDB codes 1AIO, (Takahara et al., 1995), 1T8I, (L. Staker et al., 2005) and 5GWK, (Wang et al., 2017), respectively. These structures have been previously utilized in computational cancer research. (Backman-Blanco et al., 2020; Jamal, 2020; Madeddu et al., 2022). Receptor file preparation was conducted using AutoDock Tools 1.4.5, (Morris et al., 2009), involving the removal of water molecules, the addition of all hydrogen atoms with nonpolar hydrogens merged into carbon atoms, and incorporation of Gasteiger charges into the receptor models, (Gasteiger and Marsili, 1980), resulting in pdbqt files. Ligand charges were determined from NPA population analysis based on density functional theory calculations. Docking experiments utilized a grid box measuring 60 × 60 × 60 ų along the X, Y, and Z-axes with a spacing of 0.375 Å. Analysis of ligand-receptor complexes was performed using Chimera, (Pettersen et al., 2004), PyMol, (Schrödinger, 2015), and Maestro Schrodinger (Schrödinger Release, 2023-2, 2023) programs.
8.7 Data collection and refinement for compound 2-rac
All crystals were grown by slow evaporation of CH2Cl2, then placed on a Bruker Smart Apex II diffractometer with a Mo-target X-Ray source (λ = 0.71073 Å). The detector was placed at 5.0 cm from the crystals and frames were collected with a scan width of 0.5 cm in ω and an exposure time of 10 s/frame. Frames were integrated with the Bruker SAINT software package using a narrow-frame integration algorithm. Non-systematic absences and intensity statistics were used for space group determination of orthorhombic unit cell for 2-rac
. The structures were solved using Patterson methods using the SHELXS-2014/7 program (Bruker AXS Inc, 2018). The remaining atoms were located via a few cycles of least squares refinements and difference Fourier maps. Hydrogen atoms were input at calculated positions and allowed to ride on the atoms to which they were attached. Thermal parameters were refined for hydrogen atoms on the phenyl groups using a Ueq = 1.2 Å to precedent atom. The final cycles of refinement were carried out on all non-zero data using SHELXL-2014/7 (Sheldrick, 2015). Absorption corrections were applied using the SADABS program (Krause et al., 2015). |
Title: Delivery of extracellular vesicles loaded with immune checkpoint inhibitors for immunotherapeutic management of glioma | Body:
1
Introduction
Glioma is an aggressive primary malignant brain tumor characterized by the rapid growth of tumor cells and aggressive infiltration into other parts of the brain [1]. Glioblastoma is the most common glioma in adults, with a median survival time of less than 15 months and a 5-year survival rate lower than 3 % [2,3]. With the high recurrent rate of glioma, few drugs are available to achieve optimal therapeutic effects, and temozolomide is the only USFDA-approved drug for managing newly diagnosed glioblastoma. A new therapeutic strategy for managing glioma is of urgent need [2,[4], [5], [6]], and immunotherapy with immune checkpoint inhibitors (ICI) is a potential option.
USFDA-approved ICIs are monoclonal antibodies targeting CTLA-4, PD-1, or PD-L1 for managing melanoma and several solid tumors [4,7,8]. Preclinical studies of ICIs in glioma management have found benefits in prolonging survival in glioma-bearing mice [9,10], but current clinical trials have shown disappointing therapeutic outcomes of ICIs in glioma management [[11], [12], [13]]. With the unique barrier known as the blood-brain barrier (BBB) existing in the brain, the inefficient delivery of ICIs into the brain may be one reason for the treatment failure. A delivery system is necessary to overcome this obstacle for the efficient delivery of ICIs into the brain.
Extracellular vesicles (EVs) are cell-originated membrane vesicles that exist in various body fluids and culture media. They are composed of lipid bilayer, nucleic acids, and proteins derived from their parent cells. The EVs subtypes, including exosomes and microvesicles, are commonly investigated for therapeutic and drug delivery applications. They are recognized as safe drug delivery systems due to their cell-originated properties, which may reduce immunogenicity and long-term safety concerns. Their small size and lipophilic characteristic facilitate their trafficking through extracellular matrix and physical barriers, providing more efficient drug delivery to the target. EVs also possess characteristics similar to their parent cells; therefore, they can exert some disease management effect [[14], [15], [16], [17]]. EVs derived from macrophages exhibit pro- or anti-inflammatory abilities, which are determined by the conditions of macrophages upon EVs secretion, and therefore they can facilitate tissue repair [18,19] or cancer management [[20], [21], [22]]. However, the low recovery of EVs is one of the limitations that hinder their application in both disease management and drug delivery. It is known that the generation of EVs correlates with the intracellular calcium content [[23], [24], [25]]. Therefore, enhancing the intracellular calcium level is a strategy to facilitate EVs secretion.
Calcium phosphate particles (CaP) are biomaterials commonly used as bone substitutes and drug or gene delivery systems [26]. They are biocompatible due to their similar composition to bone, and their biodegradability is based on different calcium-to-phosphate ratios [27]. Previous studies have indicated that CaP can stimulate EVs secretion from vascular smooth muscle cells [28,29], RAW264.7 and THP-1 cells [30], therefore CaP supplementation can be an optimal method to facilitate EVs secretion from cells.
Successful cases have been achieved with ICIs treatment in managing several cancers, but not in gliomas. Studies have found that EVs have potential in brain delivery of large molecular weight therapeutic agents in the management of brain inflammatory diseases and glioblastoma [[31], [32], [33]]. The application of EVs for ICIs delivery is therefore appropriate for accumulating ICIs in the brain to exert optimal therapeutic effect. The EVs also showed benefits in modulating the tumor microenvironment. For instance, EVs derived from M1 macrophages showed TAM repolarization ability that potentiates immunotherapeutic efficacy [20,34]. CaP is applicated as an EVs secretion stimulant in this study, and it has potential in influencing macrophage polarization. The purpose of this study is to prepare macrophage-derived EVs with CaP supplementation to obtain high amounts of EVs for the preparation of ICI-loaded EVs (ICI/EVs). The ICI/EVs are delivered into the brain for efficient glioma management. The effect of CaP on macrophage polarization was assessed along with the EVs’ ability to induce TAM polarization. The ICI/EVs preparation method was established and optimized to achieve a high ICIs loading efficiency. An optimal delivery strategy was established for efficiently accumulating ICI/EVs in glioma lesions, and the therapeutic effect was evaluated in glioma-bearing animal models (Fig. 1).Fig. 1Schematic illustration of EVs preparation in this study, which includes CaP supplementation and loading of ICIs onto the EVs for efficient intranasal delivery to glioma to exert an immunotherapeutic effect. The figure was created from Biorender.com.Fig. 1
2
Experimental methods
2.1
Materials
The polymer-precipitation reagent ExoQuick-TC and the acetylcholinesterase (AChE) assay EXOCET kit were purchased from System Biosciences (Palo Alto, CA, USA). Anti-mouse PD-L1 (catalog number BE0101, clone 10F.9G2) and anti-mouse CTLA-4 (catalog number BE0164, clone 9D9) were purchased from Bio X Cell (Lebanon, NH, USA). The FITC-preadsorbed rat monoclonal anti-mouse IgG1 (ab133859), mouse iNOS ELISA kit, and antibodies for immunoblotting, immunocytochemistry (ICC), and immunohistochemistry (IHC), were purchased from Abcam (Cambridge, UK). The rat and mouse IgG ELISA kits were obtained from Elabscience (Houston, TX, USA). Streptavidin peroxidase was obtained from Sigma-Aldrich (St. Louis, MO, USA). Mouse TNF-α, IL-1β, and IL-6 ELISA kits were purchased from BioLegend (San Diego, CA, USA). The recombinant mouse PD-L1/B7-H1 chimera protein (catalog number 1019-B7-100) and mouse IL-18 ELISA kit were purchased from R&D Systems (Minneapolis, MN, USA). Biotin-conjugated goat anti-rat IgG (H + L), mouse TGF-β ELISA kit, and antibodies for flow cytometry analysis were purchased from Thermo Fischer Scientific (Waltham, MA, USA). 3,3′,5,5′-tetramethylbenzidine (TMB) was purchased from Millipore (Burlington, MA, USA). D-luciferin, potassium salt was purchased from Gold Biotechnology (St. Louis, MO, USA). The RAW264.7 murine macrophage cell line was purchased from the Bioresource Collection and Research Center (BCRC, Hsinchu, Taiwan). The ALTS1c1 murine astrocytoma cell line was kindly provided by Professor Chi-Shiun Chiang in National Tsing-Hua University, Hsinchu, Taiwan. The GL261-luc murine glioblastoma cell line was purchased from Creative Biolabs (Shirley, NY, USA).
2.2
Preparation and characterization of CaP
The preparation of CaP followed the method previously published with slight modification [30]. CaCl2 solution (11 mM, pH 9) was added to Na2HPO4 solution (66 mM, pH 11 adjusted with NaOH) at a rate of 1.0 ml/min. The pH value was adjusted during this process to reach pH 11 in the final mixture. The mixture was then centrifuged at 1500 × g for 30 min, and the precipitated CaP was washed once with 0.1 mM NaOH, followed by three washes with acetone, and finally washed with deionized water. The suspension was lyophilized to obtain CaP powder. This powder was mixed with 1 part of 1 mm stainless steel beads and 2 parts of deionized water in a volume ratio, and then milled using a tissue blender (Bullet Blender, Next Advance, USA) for 5 min. The recovery of CaP was estimated by comparing the weight before and after blending, and the resulting CaP suspension was autoclaved prior to use.
Particle characteristics, including size and zeta potential, were evaluated using dynamic light scattering (DLS, NanoPlus zeta/nano particle analyzer, Particulate Systems, USA). The morphology was examined under scanning electron microscope (SEM, HITACHI SEM SU8010, Hitachi, Germany). The solubility of CaP in pH 5 disodium citrate buffer was estimated by dissolving CaP in the buffer with vigorous shaking at room temperature until no particles were visible to the naked eye. FTIR (Nicolet 6700, Thermo Scientific, USA) and XRD (Ultima IV-9407F701, Rigaku, Japn) were applied for characterization.
2.3
Optimization of EVs preparation method
The RAW264.7 cells were seeded at a confluency density of 8.8 × 106 cells in 10 cm petri dish and allowed to grow for 24 h prior to conditioning. After conditioning, the conditioned medium was pipetted out and centrifuged (2000 × g, 4 °C, 30 min) to remove cell debris. This conditioned medium was then mixed with a polymer-precipitation reagent according to the manufacturer's instructions and incubated at 4 °C overnight. The mixture was subsequently centrifuged (10,000 × g, 4 °C, 60 min) to pellet the EVs, which were then resuspended in phosphate-buffered saline (PBS). The effects of CaP conditioning time, medium composition, and CaP concentration were evaluated to achieve a high EVs yield. EVs quantities were assessed using AChE assay.
2.4
Characterization of extracellular vesicles
Characterization of EVs followed the published MISEV guideline [35]. The particle size of EVs was characterized with nanoparticle tracking analysis (NTA, NanoSight LM10HS, Malvern Panalytical, UK). Morphology was observed with transmission electron microscope (TEM, JEM-2100F electron microscope, JEOL, Japan). Samples were negatively stained with 2 % uranyl acetated prior to TEM observation. Colocalization of IgG with EVs was determined using CLSM (Fluoview FV3000, Olympus, Japan). The FITC-conjugated rat IgG was sonicated with EVs and stained with PKH26 following the manufacturer's instructions to obtain IgG/EV. One drop of IgG/EV was added on to a glass slide and covered with a cover slip prior to examination.
Immunoblotting of EVs in comparison with RAW264.7 cell lysate was conducted to examine the protein constituent. EVs and RAW264.7 were lysed in RIPA lysis buffer and centrifuged to obtain the supernatant. Protein content was estimated with Bradford reagent, and 5 μg of total protein was loaded onto a polyacrylamide gel for electrophoresis separation. The protein was transferred onto 0.2 μm PVDF membrane and blocked with 5 % skim milk in PBST, followed by incubating with primary antibodies, including ALIX (ab186429, 1:1000), LAMP1 (ab208943, 1:1000), CD63 (ab217345, 1:1000), and β-actin (GTX109369, 1:1000) overnight at 4 °C. Goat anti-rabbit IgG H&L (ab205718,1:10,000) was used for blotting for 1 h at room temperature and developed with ECL for imaging (FluorChem R, ProteinSimple, CA, USA).
2.5
ICIs loading method and aPD-L1 binding affinity evaluation
Methods for optimizing the loading of ICIs are described in the Supporting Information. The established method for preparing ICI/EVs involved mixing 4 μg of aPD-L1 or aCTLA-4 with 0.7 ml of conditioned medium, respectively, and subjecting them to sonication on ice using a probe-type sonicator (MISONIX sonicator 3000, Misonix, USA). The condition was set at 3W output voltage for 3 cycles of 2 s pulse and 2 s pause. The samples were then left on ice for over 30 min prior to being mixed with polymer-precipitation reagent for ICI/EVs isolation following the manufacturer's instructions. The resulting pellets were resuspended in PBS, and the amounts of EVs were quantified using AChE assay and Bradford reagent. To determine the loading efficiency (LE) of ICIs in EVs, the ICI/EVs were lysed with RIPA lysis buffer, and the ICIs in the lysates were quantified using their respective ELISA that detect the IgGs of their host species. The LE of ICIs in EVs were calculated using the following formula:Loadingefficiency(LE%)=ICIamountinlysateSpikedICIamount×100%
The binding affinity of aPD-L1 was analyzed using a published ELISA method with slight modifications [36]. A volume of 100 μl of 2 μg/ml recombinant mouse PD-L1 chimera protein, dissolved in PBS, was coated onto a 96-well plate overnight at 4 °C. The wells were blocked for 1 h with PBST containing 0.1 % bovine serum albumin (BSA) at 37 °C with continuous shaking at 30 rpm, followed by incubation with either aPD-L1/EV samples or aPD-L1 standards in PBS for 2 h. Secondary biotin-conjugated goat anti-rat IgG (1:100,000) and streptavidin-peroxidase (1:50,000) were incubated for 1 h each. The wells were washed two to three times with PBST (PBS containing 0.05 % Tween20) prior to the next incubation step and were washed five times after the streptavidin-peroxidase incubation. The wells were brought to color with TMB, and the reaction was stopped with 2N sulfuric acid. The plates were then read using an ELISA reader (SpectraMax ID5, Molecular Devices, USA) to obtain the optical density at 450 nm.
2.6
Macrophage polarization test
For RAW264.7 cell polarization upon CaP or EV treatment, the cells were seeded at confluency density in 10 cm petri dishes or at a density of 1.2 × 105 cells in 6-well plates for 24 h. The cells were washed thrice with PBS prior to changing the medium. The medium contained 100 μg/ml of CaP or one hundred million EVs, while medium without CaP or EVs was used as the control. After 24 h of conditioning, the medium was pipetted out, centrifuged to remove cell debris, and refrigerated at −20 °C prior to analysis. The remaining cells were lysed with RIPA lysis buffer and analyzed with Bradford reagent. Cytokine concentrations in the medium and iNOS amount in cell lysates were evaluated using TNF-α, IL-1β, IL-6, IL-18, TGF-β, and iNOS ELISA kits following the manufacturer's instructions.
For ICC evaluation of iNOS expression, the cells were seeded in 35 mm μ-dishes (ibidi GmbH, Landkreis München, Germany), followed by the same experimental procedures described in the paragraph above. At designated time points, the medium was discarded, and the cells were washed once with PBS and fixed with 4 % paraformaldehyde for 30 min. After appropriate washing with PBS containing 0.3 % Triton X, the cells were blocked with 3 % BSA in PBS for 1 h iNOS primary antibody (ab3523, 1:100) was added and incubated overnight at 4 °C, followed by incubation with the secondary goat anti-rabbit antibody conjugated with Alexa 647 (ab150079, 1:1000) in the dark for 1 h in room temperature. The nuclei were stained with DAPI for 5 min and examined under fluorescent microscope (Automated fluorescence microscope BX61, Olympus Co., Japan). The cells were washed with PBST after each incubation steps.
2.7
ICI/EV permeation efficiency assessment
A trans-well system was established to evaluate the permeation enhancement effect of EVs. C2BBe1 cells were seeded at a density of 6 × 104 cells in cell culture inserts (Corning Inc., Corning, NY, USA) with a pore size of 0.4 μm and a permeation area of 0.9 cm2. The culture inserts were placed in a 12-well culture plate, and the apical (A) and basolateral (B) sides were filled with 1 and 1.5 ml of culture medium, respectively. The medium on both sides was changed every other day, and trans-epithelial electric resistance (TEER) was measured for 20 days. The TEER was calculated with the following formula, in which RCell indicated the electric resistance measured in the inserts with cells cultured and Rblank indicated blank inserts without cells:TEER(Ω×cm2)=(RCell‐Rblank)×permeationarea
The culture inserts were washed thrice with PBS prior to performing the permeation experiment. The B sides were filled with 2 ml of PBS, and the A sides were filled with 1 ml of 4 μg/ml free aPD-L1 or aPD-L1/EV in PBS. Samples were collected at 0, 1, 2, and 3 h by withdrawing 150 μl of solution from the B sides and replacing them with an equivalent volume of PBS. The aPD-L1 concentrations were evaluated with a rat IgG ELISA, and the cumulative permeated amounts were calculated.
2.8
Establishment of orthotopic brain tumor model
Male C57/BL-6 mice were purchased from the National Laboratory Animal Center. Mice aged 6–9 weeks were applied for the experiments under protocols approved by the Institutional Animal Care and Use Committee (IACUC) of National Cheng Kung University (approval number: 111,084). They were housed in an environment with 40 % humidity, temperature of 22 ± 2 °C, a 12-h light-dark cycle, and free access to food and water. Mice were anesthetized with a mixture of Zoletile and Rompun prior to applying hair remover to the forehead. An incision was made with a scalpel blade, and a hole was drilled into the right side of the skull, 2 mm away from the midline and 1.5 mm posterior to the bregma. The needle was inserted into the brain to a depth of 2 mm, and 105 GL261 or ALTS1c1 cells were pumped into the brain at a flow rate of 1 μl/min. The syringe was slowly removed, and the hole was sealed with bone wax prior to closing the wound with sutures.
2.9
In vivo ICI/EVs distribution in brain and tumor
The permeation enhancement effect of ICI/EVs was evaluated in vivo in both healthy and GL261-bearing mice. The mice were anesthetized with a mixture of Zoletile and Rompun prior to intranasal (IN) or retro-orbital (RO) administration of 10 μg FITC-conjugated IgG/EV to healthy mice and 60 μg of aPD-L1/EV to GL261-bearing mice, in comparison with free IgG or aPD-L1. Mice administered with PBS were used as controls. In RO injection, the drug was drawn into an insulin syringe and injected into the base of the eye while under anesthesia. In IN administration, 2 μl of liquid was administered into one nostril of the mice and left for 1 min prior to administering to the other nostril. This cycle was repeated until full dose was administered. To examine the distribution of IgG and aPD-L1, mice were sacrificed post drug administration. Whole brains were taken and cryo-sectioned into slices of 10 or 5 μm. For the examination of IgG distribution in healthy brains, slices were directly imaged to detect green fluorescence signals within the brain (FluorChem R, ProteinSimple, CA, USA). The fluorescence intensities of IgG were analyzed with Image J, and the cumulative intensity results were obtained by summing up the results of four consecutive slices from one mouse, representing the amount of IgG in one mouse brain. To examine the aPD-L1 distribution in GL261-bearing brains, the slices were stained with Alexa 488-conjugated goat-anti rat IgG (ab150165, 1:1000) to capture aPD-L1 and nuclei were stained with DAPI. The distribution of aPD-L1 within the brain was examined under CLSM (Fluoview FV3000, Olympus, Japan).
2.10
Therapeutic effect evaluation
Mice were initially implanted with orthotopic GL261 or ALTS1c1 tumors, and dosing began 3 days post tumor establishment, which was designated as day 0 of the experiment. The mice were randomly divided into five groups and treated with aPD-L1, aPD-L1/EV, aPD-L1 + aCTLA-4, or aPD-L1/EV + aCTLA-4/EV. Mice treated with PBS or receiving no treatment (designated as the control group) served as negative controls. The ICIs were IN administered on a daily basis for 3 doses, resulting in a total of 60 μg of respective ICIs in each treatment regimen. Tumor volumes were assessed with bioluminescence for GL261 tumors and tumor weight for ALTS1c1 tumors. Tumor bioluminescence was measured at designated time points by intraperitoneal injection of D-luciferin at dose of 150 mg/kg body weight. The bioluminescence was immediately examined with IVIS Spectrum In Vivo Imaging System (Perkin Elmer, MA, USA). Baseline bioluminescence was determined on day 0 prior to dosing. The total bioluminescence flux from each mouse's tumors at designated times was evaluated, and tumor growth was expressed as fold changes from the total flux measured at day 0. Survival duration was assessed by daily recording of body weight for 35 days. Mice were humanely euthanized when body weight loss exceeded 20 % or when apparent weakness or disability was observed.
2.11
Immunohistochemistry staining
Mice were euthanized on day 30 by cervical dislocation. The tumors were isolated and cryo-sectioned into 5 μm slices. The slices were fixed with cold acetone, followed by permeabilization with 0.2 % Triton-X in PBS for 15 min. The slices were then blocked with 3 % BSA for 30 min, and subsequently incubated overnight at 4 °C with anti-mouse F4/80 (ab111101, 1:100) or CD8 (ab217344, 1:500) primary antibodies, followed by incubation with Alexa 488-conjugated secondary antibodies (ab150077, 1:1000) for 1 h. For F4/80-stained slices, an additional step involved blocking and incubating with anti-mouse iNOS (ab3523, 1:50), followed by Alexa 647-conjugated secondary antibodies (ab150079, 1:200) under the same incubation conditions. The nuclei were counterstained with DAPI and examined under a fluorescence microscope. To examine tumor proliferation, the slices were permeabilized and blocked, followed by incubation with anti-mouse Ki67 (ab15580, 5 μg/ml) primary antibodies. The slices were then treated with 0.3 % H2O2 to block endogenous peroxidase activity for 15 min, followed by incubation with HRP-conjugated secondary antibodies (ab6721, 1:1000). The slices were brought to color with DAB substrate (ab64238), and nuclei were counterstained with hematoxylin (Leica biosystems, IL, USA), followed by observation with a microscope.
2.12
Flow cytometry analysis of tumor-associated immune cells
Mice that received full treatment were sacrificed at day 19 and 26 in ALTS1c1-and GL261-bearing mice, respectively, to obtain tumor. The tumors were immersed and minced in RPMI 1640. After removing the supernatant by centrifugation at 5000 rpm for 3 min, the tissue was treated with a digesting buffer containing 1 kU/ml of collagenase type 4 and 2 U/ml deoxyribonuclease I from bovine pancreas in RPMI 1640, and then incubated at 37 °C for 10 min. The digested tissue was passed through a 70 μm cell strainer and washed with PBS, followed by centrifugation at 5000 rpm for 3 min to obtain a cell pellet. RBC lysis buffer was added and incubated at room temperature for 1 min, followed by centrifugation to pellet the cells. The cells were washed once with PBS and the cell count was adjusted to prepare 108 cells in 100 μl PBS. The cells were first stained with fixable viability dye for 30 min at 4 °C, and then washed once with 1 ml flow cytometry staining buffer (FACS buffer). The cells were then blocked with CD16/CD32 monoclonal antibody for 10 min at 4 °C, followed by incubating with surface antibodies for 30 min at 4 °C. The cells were fixed and permeabilized for 30 min and 15 min, respectively, at room temperature. The cells were then stained with intracellular antibodies in permeabilization buffer for 30 min at 4 °C. After appropriate washing with permeabilization buffer, the cells were resuspended in 100 μl FACS buffer and stored at 4 °C prior to analysis (Attune NXT Flow Cytometer, Invitrogen, USA).
2.13
Statistical analysis
Results were expressed as mean ± standard deviation (SD) in in vitro studies and mean ± standard error of the mean (SEM) for in vivo studies. One-way ANOVA with Bonferroni post hoc test was applied for comparison of means between groups. General linear model (GLM) was applied for comparisons of EVs secretion stimulation trend with CaP supplementation, and tumor volume change between groups. Survival between groups were analyzed with Kaplan-Meier method with log rank test. Differences were considered significant at p < 0.05 and all computations were performed with SPSS 22.0.
2.1
Materials
The polymer-precipitation reagent ExoQuick-TC and the acetylcholinesterase (AChE) assay EXOCET kit were purchased from System Biosciences (Palo Alto, CA, USA). Anti-mouse PD-L1 (catalog number BE0101, clone 10F.9G2) and anti-mouse CTLA-4 (catalog number BE0164, clone 9D9) were purchased from Bio X Cell (Lebanon, NH, USA). The FITC-preadsorbed rat monoclonal anti-mouse IgG1 (ab133859), mouse iNOS ELISA kit, and antibodies for immunoblotting, immunocytochemistry (ICC), and immunohistochemistry (IHC), were purchased from Abcam (Cambridge, UK). The rat and mouse IgG ELISA kits were obtained from Elabscience (Houston, TX, USA). Streptavidin peroxidase was obtained from Sigma-Aldrich (St. Louis, MO, USA). Mouse TNF-α, IL-1β, and IL-6 ELISA kits were purchased from BioLegend (San Diego, CA, USA). The recombinant mouse PD-L1/B7-H1 chimera protein (catalog number 1019-B7-100) and mouse IL-18 ELISA kit were purchased from R&D Systems (Minneapolis, MN, USA). Biotin-conjugated goat anti-rat IgG (H + L), mouse TGF-β ELISA kit, and antibodies for flow cytometry analysis were purchased from Thermo Fischer Scientific (Waltham, MA, USA). 3,3′,5,5′-tetramethylbenzidine (TMB) was purchased from Millipore (Burlington, MA, USA). D-luciferin, potassium salt was purchased from Gold Biotechnology (St. Louis, MO, USA). The RAW264.7 murine macrophage cell line was purchased from the Bioresource Collection and Research Center (BCRC, Hsinchu, Taiwan). The ALTS1c1 murine astrocytoma cell line was kindly provided by Professor Chi-Shiun Chiang in National Tsing-Hua University, Hsinchu, Taiwan. The GL261-luc murine glioblastoma cell line was purchased from Creative Biolabs (Shirley, NY, USA).
2.2
Preparation and characterization of CaP
The preparation of CaP followed the method previously published with slight modification [30]. CaCl2 solution (11 mM, pH 9) was added to Na2HPO4 solution (66 mM, pH 11 adjusted with NaOH) at a rate of 1.0 ml/min. The pH value was adjusted during this process to reach pH 11 in the final mixture. The mixture was then centrifuged at 1500 × g for 30 min, and the precipitated CaP was washed once with 0.1 mM NaOH, followed by three washes with acetone, and finally washed with deionized water. The suspension was lyophilized to obtain CaP powder. This powder was mixed with 1 part of 1 mm stainless steel beads and 2 parts of deionized water in a volume ratio, and then milled using a tissue blender (Bullet Blender, Next Advance, USA) for 5 min. The recovery of CaP was estimated by comparing the weight before and after blending, and the resulting CaP suspension was autoclaved prior to use.
Particle characteristics, including size and zeta potential, were evaluated using dynamic light scattering (DLS, NanoPlus zeta/nano particle analyzer, Particulate Systems, USA). The morphology was examined under scanning electron microscope (SEM, HITACHI SEM SU8010, Hitachi, Germany). The solubility of CaP in pH 5 disodium citrate buffer was estimated by dissolving CaP in the buffer with vigorous shaking at room temperature until no particles were visible to the naked eye. FTIR (Nicolet 6700, Thermo Scientific, USA) and XRD (Ultima IV-9407F701, Rigaku, Japn) were applied for characterization.
2.3
Optimization of EVs preparation method
The RAW264.7 cells were seeded at a confluency density of 8.8 × 106 cells in 10 cm petri dish and allowed to grow for 24 h prior to conditioning. After conditioning, the conditioned medium was pipetted out and centrifuged (2000 × g, 4 °C, 30 min) to remove cell debris. This conditioned medium was then mixed with a polymer-precipitation reagent according to the manufacturer's instructions and incubated at 4 °C overnight. The mixture was subsequently centrifuged (10,000 × g, 4 °C, 60 min) to pellet the EVs, which were then resuspended in phosphate-buffered saline (PBS). The effects of CaP conditioning time, medium composition, and CaP concentration were evaluated to achieve a high EVs yield. EVs quantities were assessed using AChE assay.
2.4
Characterization of extracellular vesicles
Characterization of EVs followed the published MISEV guideline [35]. The particle size of EVs was characterized with nanoparticle tracking analysis (NTA, NanoSight LM10HS, Malvern Panalytical, UK). Morphology was observed with transmission electron microscope (TEM, JEM-2100F electron microscope, JEOL, Japan). Samples were negatively stained with 2 % uranyl acetated prior to TEM observation. Colocalization of IgG with EVs was determined using CLSM (Fluoview FV3000, Olympus, Japan). The FITC-conjugated rat IgG was sonicated with EVs and stained with PKH26 following the manufacturer's instructions to obtain IgG/EV. One drop of IgG/EV was added on to a glass slide and covered with a cover slip prior to examination.
Immunoblotting of EVs in comparison with RAW264.7 cell lysate was conducted to examine the protein constituent. EVs and RAW264.7 were lysed in RIPA lysis buffer and centrifuged to obtain the supernatant. Protein content was estimated with Bradford reagent, and 5 μg of total protein was loaded onto a polyacrylamide gel for electrophoresis separation. The protein was transferred onto 0.2 μm PVDF membrane and blocked with 5 % skim milk in PBST, followed by incubating with primary antibodies, including ALIX (ab186429, 1:1000), LAMP1 (ab208943, 1:1000), CD63 (ab217345, 1:1000), and β-actin (GTX109369, 1:1000) overnight at 4 °C. Goat anti-rabbit IgG H&L (ab205718,1:10,000) was used for blotting for 1 h at room temperature and developed with ECL for imaging (FluorChem R, ProteinSimple, CA, USA).
2.5
ICIs loading method and aPD-L1 binding affinity evaluation
Methods for optimizing the loading of ICIs are described in the Supporting Information. The established method for preparing ICI/EVs involved mixing 4 μg of aPD-L1 or aCTLA-4 with 0.7 ml of conditioned medium, respectively, and subjecting them to sonication on ice using a probe-type sonicator (MISONIX sonicator 3000, Misonix, USA). The condition was set at 3W output voltage for 3 cycles of 2 s pulse and 2 s pause. The samples were then left on ice for over 30 min prior to being mixed with polymer-precipitation reagent for ICI/EVs isolation following the manufacturer's instructions. The resulting pellets were resuspended in PBS, and the amounts of EVs were quantified using AChE assay and Bradford reagent. To determine the loading efficiency (LE) of ICIs in EVs, the ICI/EVs were lysed with RIPA lysis buffer, and the ICIs in the lysates were quantified using their respective ELISA that detect the IgGs of their host species. The LE of ICIs in EVs were calculated using the following formula:Loadingefficiency(LE%)=ICIamountinlysateSpikedICIamount×100%
The binding affinity of aPD-L1 was analyzed using a published ELISA method with slight modifications [36]. A volume of 100 μl of 2 μg/ml recombinant mouse PD-L1 chimera protein, dissolved in PBS, was coated onto a 96-well plate overnight at 4 °C. The wells were blocked for 1 h with PBST containing 0.1 % bovine serum albumin (BSA) at 37 °C with continuous shaking at 30 rpm, followed by incubation with either aPD-L1/EV samples or aPD-L1 standards in PBS for 2 h. Secondary biotin-conjugated goat anti-rat IgG (1:100,000) and streptavidin-peroxidase (1:50,000) were incubated for 1 h each. The wells were washed two to three times with PBST (PBS containing 0.05 % Tween20) prior to the next incubation step and were washed five times after the streptavidin-peroxidase incubation. The wells were brought to color with TMB, and the reaction was stopped with 2N sulfuric acid. The plates were then read using an ELISA reader (SpectraMax ID5, Molecular Devices, USA) to obtain the optical density at 450 nm.
2.6
Macrophage polarization test
For RAW264.7 cell polarization upon CaP or EV treatment, the cells were seeded at confluency density in 10 cm petri dishes or at a density of 1.2 × 105 cells in 6-well plates for 24 h. The cells were washed thrice with PBS prior to changing the medium. The medium contained 100 μg/ml of CaP or one hundred million EVs, while medium without CaP or EVs was used as the control. After 24 h of conditioning, the medium was pipetted out, centrifuged to remove cell debris, and refrigerated at −20 °C prior to analysis. The remaining cells were lysed with RIPA lysis buffer and analyzed with Bradford reagent. Cytokine concentrations in the medium and iNOS amount in cell lysates were evaluated using TNF-α, IL-1β, IL-6, IL-18, TGF-β, and iNOS ELISA kits following the manufacturer's instructions.
For ICC evaluation of iNOS expression, the cells were seeded in 35 mm μ-dishes (ibidi GmbH, Landkreis München, Germany), followed by the same experimental procedures described in the paragraph above. At designated time points, the medium was discarded, and the cells were washed once with PBS and fixed with 4 % paraformaldehyde for 30 min. After appropriate washing with PBS containing 0.3 % Triton X, the cells were blocked with 3 % BSA in PBS for 1 h iNOS primary antibody (ab3523, 1:100) was added and incubated overnight at 4 °C, followed by incubation with the secondary goat anti-rabbit antibody conjugated with Alexa 647 (ab150079, 1:1000) in the dark for 1 h in room temperature. The nuclei were stained with DAPI for 5 min and examined under fluorescent microscope (Automated fluorescence microscope BX61, Olympus Co., Japan). The cells were washed with PBST after each incubation steps.
2.7
ICI/EV permeation efficiency assessment
A trans-well system was established to evaluate the permeation enhancement effect of EVs. C2BBe1 cells were seeded at a density of 6 × 104 cells in cell culture inserts (Corning Inc., Corning, NY, USA) with a pore size of 0.4 μm and a permeation area of 0.9 cm2. The culture inserts were placed in a 12-well culture plate, and the apical (A) and basolateral (B) sides were filled with 1 and 1.5 ml of culture medium, respectively. The medium on both sides was changed every other day, and trans-epithelial electric resistance (TEER) was measured for 20 days. The TEER was calculated with the following formula, in which RCell indicated the electric resistance measured in the inserts with cells cultured and Rblank indicated blank inserts without cells:TEER(Ω×cm2)=(RCell‐Rblank)×permeationarea
The culture inserts were washed thrice with PBS prior to performing the permeation experiment. The B sides were filled with 2 ml of PBS, and the A sides were filled with 1 ml of 4 μg/ml free aPD-L1 or aPD-L1/EV in PBS. Samples were collected at 0, 1, 2, and 3 h by withdrawing 150 μl of solution from the B sides and replacing them with an equivalent volume of PBS. The aPD-L1 concentrations were evaluated with a rat IgG ELISA, and the cumulative permeated amounts were calculated.
2.8
Establishment of orthotopic brain tumor model
Male C57/BL-6 mice were purchased from the National Laboratory Animal Center. Mice aged 6–9 weeks were applied for the experiments under protocols approved by the Institutional Animal Care and Use Committee (IACUC) of National Cheng Kung University (approval number: 111,084). They were housed in an environment with 40 % humidity, temperature of 22 ± 2 °C, a 12-h light-dark cycle, and free access to food and water. Mice were anesthetized with a mixture of Zoletile and Rompun prior to applying hair remover to the forehead. An incision was made with a scalpel blade, and a hole was drilled into the right side of the skull, 2 mm away from the midline and 1.5 mm posterior to the bregma. The needle was inserted into the brain to a depth of 2 mm, and 105 GL261 or ALTS1c1 cells were pumped into the brain at a flow rate of 1 μl/min. The syringe was slowly removed, and the hole was sealed with bone wax prior to closing the wound with sutures.
2.9
In vivo ICI/EVs distribution in brain and tumor
The permeation enhancement effect of ICI/EVs was evaluated in vivo in both healthy and GL261-bearing mice. The mice were anesthetized with a mixture of Zoletile and Rompun prior to intranasal (IN) or retro-orbital (RO) administration of 10 μg FITC-conjugated IgG/EV to healthy mice and 60 μg of aPD-L1/EV to GL261-bearing mice, in comparison with free IgG or aPD-L1. Mice administered with PBS were used as controls. In RO injection, the drug was drawn into an insulin syringe and injected into the base of the eye while under anesthesia. In IN administration, 2 μl of liquid was administered into one nostril of the mice and left for 1 min prior to administering to the other nostril. This cycle was repeated until full dose was administered. To examine the distribution of IgG and aPD-L1, mice were sacrificed post drug administration. Whole brains were taken and cryo-sectioned into slices of 10 or 5 μm. For the examination of IgG distribution in healthy brains, slices were directly imaged to detect green fluorescence signals within the brain (FluorChem R, ProteinSimple, CA, USA). The fluorescence intensities of IgG were analyzed with Image J, and the cumulative intensity results were obtained by summing up the results of four consecutive slices from one mouse, representing the amount of IgG in one mouse brain. To examine the aPD-L1 distribution in GL261-bearing brains, the slices were stained with Alexa 488-conjugated goat-anti rat IgG (ab150165, 1:1000) to capture aPD-L1 and nuclei were stained with DAPI. The distribution of aPD-L1 within the brain was examined under CLSM (Fluoview FV3000, Olympus, Japan).
2.10
Therapeutic effect evaluation
Mice were initially implanted with orthotopic GL261 or ALTS1c1 tumors, and dosing began 3 days post tumor establishment, which was designated as day 0 of the experiment. The mice were randomly divided into five groups and treated with aPD-L1, aPD-L1/EV, aPD-L1 + aCTLA-4, or aPD-L1/EV + aCTLA-4/EV. Mice treated with PBS or receiving no treatment (designated as the control group) served as negative controls. The ICIs were IN administered on a daily basis for 3 doses, resulting in a total of 60 μg of respective ICIs in each treatment regimen. Tumor volumes were assessed with bioluminescence for GL261 tumors and tumor weight for ALTS1c1 tumors. Tumor bioluminescence was measured at designated time points by intraperitoneal injection of D-luciferin at dose of 150 mg/kg body weight. The bioluminescence was immediately examined with IVIS Spectrum In Vivo Imaging System (Perkin Elmer, MA, USA). Baseline bioluminescence was determined on day 0 prior to dosing. The total bioluminescence flux from each mouse's tumors at designated times was evaluated, and tumor growth was expressed as fold changes from the total flux measured at day 0. Survival duration was assessed by daily recording of body weight for 35 days. Mice were humanely euthanized when body weight loss exceeded 20 % or when apparent weakness or disability was observed.
2.11
Immunohistochemistry staining
Mice were euthanized on day 30 by cervical dislocation. The tumors were isolated and cryo-sectioned into 5 μm slices. The slices were fixed with cold acetone, followed by permeabilization with 0.2 % Triton-X in PBS for 15 min. The slices were then blocked with 3 % BSA for 30 min, and subsequently incubated overnight at 4 °C with anti-mouse F4/80 (ab111101, 1:100) or CD8 (ab217344, 1:500) primary antibodies, followed by incubation with Alexa 488-conjugated secondary antibodies (ab150077, 1:1000) for 1 h. For F4/80-stained slices, an additional step involved blocking and incubating with anti-mouse iNOS (ab3523, 1:50), followed by Alexa 647-conjugated secondary antibodies (ab150079, 1:200) under the same incubation conditions. The nuclei were counterstained with DAPI and examined under a fluorescence microscope. To examine tumor proliferation, the slices were permeabilized and blocked, followed by incubation with anti-mouse Ki67 (ab15580, 5 μg/ml) primary antibodies. The slices were then treated with 0.3 % H2O2 to block endogenous peroxidase activity for 15 min, followed by incubation with HRP-conjugated secondary antibodies (ab6721, 1:1000). The slices were brought to color with DAB substrate (ab64238), and nuclei were counterstained with hematoxylin (Leica biosystems, IL, USA), followed by observation with a microscope.
2.12
Flow cytometry analysis of tumor-associated immune cells
Mice that received full treatment were sacrificed at day 19 and 26 in ALTS1c1-and GL261-bearing mice, respectively, to obtain tumor. The tumors were immersed and minced in RPMI 1640. After removing the supernatant by centrifugation at 5000 rpm for 3 min, the tissue was treated with a digesting buffer containing 1 kU/ml of collagenase type 4 and 2 U/ml deoxyribonuclease I from bovine pancreas in RPMI 1640, and then incubated at 37 °C for 10 min. The digested tissue was passed through a 70 μm cell strainer and washed with PBS, followed by centrifugation at 5000 rpm for 3 min to obtain a cell pellet. RBC lysis buffer was added and incubated at room temperature for 1 min, followed by centrifugation to pellet the cells. The cells were washed once with PBS and the cell count was adjusted to prepare 108 cells in 100 μl PBS. The cells were first stained with fixable viability dye for 30 min at 4 °C, and then washed once with 1 ml flow cytometry staining buffer (FACS buffer). The cells were then blocked with CD16/CD32 monoclonal antibody for 10 min at 4 °C, followed by incubating with surface antibodies for 30 min at 4 °C. The cells were fixed and permeabilized for 30 min and 15 min, respectively, at room temperature. The cells were then stained with intracellular antibodies in permeabilization buffer for 30 min at 4 °C. After appropriate washing with permeabilization buffer, the cells were resuspended in 100 μl FACS buffer and stored at 4 °C prior to analysis (Attune NXT Flow Cytometer, Invitrogen, USA).
2.13
Statistical analysis
Results were expressed as mean ± standard deviation (SD) in in vitro studies and mean ± standard error of the mean (SEM) for in vivo studies. One-way ANOVA with Bonferroni post hoc test was applied for comparison of means between groups. General linear model (GLM) was applied for comparisons of EVs secretion stimulation trend with CaP supplementation, and tumor volume change between groups. Survival between groups were analyzed with Kaplan-Meier method with log rank test. Differences were considered significant at p < 0.05 and all computations were performed with SPSS 22.0.
3
Results and discussion
3.1
CaP induced EVs secretion and macrophage polarization
The CaP prepared in this study exhibited particle characteristics with a size of around 1.5 μm and a negative surface charge. A PDI around 0.4 indicated a broad particle size distribution for the CaP suspension (Fig. 2(A)). The CaP displayed particulate characteristics with an irregular morphology under SEM (Fig. 2(B)). The solubility of CaP in pH 5 buffer was estimated to be 3.8 ± 0.2 mg/g. The FTIR and XRD spectra were similar to those in previous research [37,38], indicating the successful preparation of CaP (Fig. S1).Fig. 2Characteristics of CaP and its influence on EVs secretion and macrophage polarization. (A) DLS analysis results of particle size and ζ-potential of CaP. (B) SEM image of CaP. (C) Comparison of EVs secretion from RAW264.7 cells with and without 500 μg/ml CaP supplementation. (D) EVs secretion comparison from cells conditioned with varying CaP concentrations. (E) Cytokines concentrations in the conditioned medium and iNOS expression in RAW264.7 cells were analyzed with their respective ELISA and concentrations were adjusted based on the total protein amounts of cells, which were quantified with Bradford reagent. (F) ICC results of iNOS expression in RAW264.7 cells post 4 h conditioning. The red color indicates iNOS expression, and blue denotes nuclei stained with DAPI. Scale bar: 20 μm. (G) A scheme illustrating influence of CaP on macrophages (created from Biorender.com). Statistics for (C) were obtained from GLM with Bonferroni post hoc test, and the asterisk indicate a significant difference compared to the Blank group. Statistics for (D, E) were obtained from one-way ANOVA with Bonferroni post hoc test. Asterisks represent significant differences compared to the indicated groups. All data are expressed as mean ± SD with n = 3, and significance was defined as p < 0.05.Fig. 2
The feasibility of using CaP as EVs secretion stimulant was assessed. A higher amount of EVs secretion was observed in CaP-treated RAW264.7 cells throughout the 48-h experiment compared to cells without CaP treatment, the latter being designated as the blank group (Fig. 2(C)). EVs were secreted from the cells and reached a steady state 12 h post CaP treatment, peaking at 48 h. The effects of the medium and the amount of CaP supplementation on EVs secretion were assessed. PBS supplemented with CaP showed a higher EVs secretion compared to DMEM (Fig. S2), and Fig. 2(D) indicated that conditioning cells with 100 μg/ml CaP in PBS resulted in a higher amount of EVs secretion. Increasing the amount of CaP did not further facilitate EVs secretion, potentially due to cell necrosis from excessive CaP treatment [39].
Cytokine concentrations in the conditioned medium and iNOS expression, as compared between conditioning methods, are analyzed and shown in Fig. 2(E). The DMEM, which is the normal culture condition for RAW264.7, was utilized as the control for the other groups. Cells treated with CaP were compared with those without CaP treatment as well. While TNF-α concentrations were comparable between groups, PBS- and PBS + CaP-conditioning led to significantly higher IL-1β, IL-6, IL-18, and lower TGF-β secretion compared to DMEM-conditioning. CaP-supplemented PBS showed a significantly higher IL-1β and IL-18 secretion compared to PBS group. Additionally, PBS + CaP group displayed higher iNOS expression than PBS and DMEM groups, although the difference was not significant. ICC images of iNOS expression in macrophages 4 h post-conditioning were compared, revealing that macrophages conditioned with CaP showed more iNOS expression than those conditioned with DMEM and PBS (Fig. 2(F)). Based on these findings, it can be deduced that macrophages can polarize into proinflammatory phenotype when conditioned with CaP-containing PBS. To further evaluate the ability of CaP in inducing macrophages polarization, a CaP-containing DMEM group was compared with DMEM and showed in Fig. S3. While not as significant compared to PBS-conditioning, the CaP-containing DMEM group showed significantly more IL-18 and less TGF-β secretion, along with more iNOS expression, compared to DMEM group, indicating the proinflammatory ability of CaP in different medium. The expression tendency was not consistent between cytokines in different groups, it can be due to the different mechanisms that triggered the differences in expression and secretion of cytokines [40].
Simultaneous enhancement of EVs secretion amount and polarization of macrophages into proinflammatory phenotype was achieved by conditioning cells with CaP. This could be attributed to the elevation of intracellular calcium levels, which triggered various cellular responses, including the secretion of cellular materials like cytokines [41] and EVs [23]. Macrophage phagocytosis is particle size-dependent, with diameters of 2∼3 μm are more readily taken up [42,43]. The size of CaP in this research was optimal for macrophage phagocytosis, and the engulfed CaP can be transferred into phagosomes and fused with lysosomes to become phagolysosomes. The low pH environment within phagolysosomes can contribute to CaP degradation, leading to increased osmotic pressure from the release of calcium and phosphate ions. This results in the destruction of the phagolysosome [39,42] and an enhanced intracellular calcium level (Fig. 2(G)). This phenomenon is unlikely exerted by enhanced extracellular calcium concentration since the calcium levels are tightly regulated within cells, which makes adjusting intracellular calcium levels inefficient [32,44]. Other than CaP administration, cellular stress like nutrient deprivation with PBS conditioning, can also result in enhanced intracellular calcium level that facilitate cytokines secretion [45,46], and glucose starvation was suggested to result in cellular secretion of IL-6 and IL-8 [47]. While cells respond differently toward stress, the starvation of cells under PBS culture can be an explanation of the differences in cytokines secretion compared to DMEM.
In this study, a change in macrophage characteristics towards CaP conditioning was observed. CaP supplementation not only enhanced the secretion of EVs but also induced the macrophages polarization, and PBS conditioning potentiated this effect. Agents like lipopolysaccharide (LPS) and IFN-γ are commonly used to polarize macrophages into the proinflammatory phenotype. LPS is a pyrogen undesirable in pharmaceutical preparations, and IFN-γ can induce a strong autoimmune response. Both agents can remain in the EVs products when used for M1 macrophage polarization. The established CaP conditioning method in this study offers a potential alternative for simultaneously polarizing macrophages and enhancing EVs secretion without the residue of the aforementioned agents. While CaP administration resulted in increased secretion of some proinflammatory cytokines from RAW264.7 cells, it is still a different mechanism from infection or autoimmune pathogenesis pathways that result in increased expression of iNOS and secretion of TNF-α, IL-1β, IL-6, and IL-18 along with decreased secretion of anti-inflammatory TGF-β [48]. This can be supported by a previous study showing the efficient induction of TNF-α and IL-6 mRNA expression with LPS treatment on RAW264.7 cells [34]. The insignificant alteration of TNF-α, the main cytokine released immediately in response to pathogen infection [48], indicated the different reaction of RAW264.7 when encountered with CaP rather than LPS. Despite the unique pattern of proinflammatory cytokines secretion, the PBS + CaP treatment of RAW264.7 cells showed proinflammatory phenotype polarization tendency, which was supported by the higher iNOS expression and lower anti-inflammatory TGF-β secretion observed in this study, and the CaP supplementation potentiated this effect as compared to the PBS group (Fig. 2(E)–Fig. S3). Summing up the results of EVs secretion rate as well as cytokines and iNOS expression, the EVs preparation method in this study was established by conditioning RAW264.7 cells with PBS containing 100 μg/ml CaP for 24 h to achieve high EVs secretion amount and to ensure the origin of EVs from cells of proinflammatory phenotype. These will be beneficial in achieving sufficient drug loading and retain the proinflammatory characteristics of EVs for therapeutic application.
3.2
EVs exhibited exosome-like characteristics and capable for loading of ICIs
The EVs prepared in this study, derived from CaP-conditioned macrophages (EV w/CaP), were characterized by their size, morphology, and protein composition as suggested in the MISEV2018 guideline [35]. The average size of EV w/CaP analyzed with NTA was 161.7 ± 22 nm, and its spherical vesicular morphology similar to EVs prepared with polymer precipitation method [49], was identified under TEM (Fig. 3(A)). The slightly decreased particle size observed under TEM may be due to the differences in sample preparation for observation, and similar phenomenon was observed in lipid nanoparticles analyzed with DLS and TEM as well [50]. Immunoblotting of EV w/CaP showed the expression of LAMP1, ALIX and CD63, indicating their endolysosomal and phagolysosomal biogenesis pathways (Fig. 3(B), Fig. S4). The particle size and the expressed proteins indicated the possible exosome entity of the prepared EVs, demonstrating that the conditioning method established in this study stimulated the secretion of exosomes from cells via the known endolysosomal pathway and the proposed CaP-associated phagolysosomal pathway.Fig. 3EVs characteristics and ICI/EVs preparation method establishment. (A) Particle size of EVs was analyzed with NTA, and morphology was observed with TEM. (B) Protein markers in both RAW264.7 cell lysate and EVs were examined through immunoblotting. (C) Cytokines concentrations in the culture medium and iNOS expression post EVs treatment were analyzed with respective ELISA. (D) Immunocytochemistry evaluation of iNOS expression in RAW264.7 cells post EVs treatment in comparison to DMEM-cultured cells. Scale bar: 50 μm. (E) ICIs spiking amount in 0.7 ml of conditioned medium was evaluated by the LE of aPD-L1 and aCTLA-4. (F) Scheme of ELISA for aPD-L1/EV binding affinity evaluation (created from Biorender.com). (G) The binding affinity of aPD-L1/EV toward PD-L1 was analyzed with ELISA in comparison with free aPD-L1 to ensure the preservation of ICI function post preparation. Statistics in (C) were obtained from one way ANOVA with Bonferroni post hoc test. Asterisks represented significant differences between indicated groups, and significance was defined as p < 0.05. All data were expressed as mean ± SD with n = 3 in (C, E) and n = 5 in (G). LE: loading efficiency.Fig. 3
EVs are known for their intercellular communication and microenvironment modulation ability [51]. Based on the findings that CaP induced macrophage polarization and EVs secretion (Fig. 2), the potential macrophage polarizing ability was compared between EV w/CaP and EVs derived from cells without CaP treatment (EV w/o CaP). Fig. 3(C) showed that RAW264.7 cells treated with EV w/CaP tended to polarize to proinflammatory phenotype compared to DMEM and EV w/o CaP groups, as significantly greater amounts of proinflammatory IL-1β and IL-6, as well as higher secretion amount of TNF-α were observed. Consistent with the increased proinflammatory cytokine secretion, significantly less anti-inflammatory TGF-β secretion and more iNOS expression were also observed in cells treated with EV w/CaP, and ICC images also displayed more intracellular iNOS expression in EV w/CaP-treated cells (Fig. 3(D)), confirming the proinflammatory polarization of RAW264.7 cells treated with EV w/CaP. Compared to the DMEM group, the EVs-treated cells showed a different extent of proinflammatory phenotype polarizing tendency, with EV w/CaP treatment exhibited the most significant effect. The proinflammatory characteristics of EV w/CaP may originated from their cargos derived from parent cells, including the cytokines, that contributed to the polarization of macrophage into the proinflammatory phenotype similar to the cells secreting EVs [19,28,34]. While TNF-α, IL-1β, iNOS, and TGF-β showed similar secretion or expression trends compared to the parent RAW264.7 cells treated with CaP, differences in IL-6 and IL-18 secretion trends were observed in EV w/o and w/CaP that are not comparable with the CaP treatment results (Fig. 2, Fig. 3). The differences in IL-6 levels between EV w/o CaP and EV w/CaP, as well as between PBS and PBS + CaP groups, may be overlooked due to their insignificant differences. Another possible reason can be the complex intracellular cargo transportation and sorting while secreting EVs that led to the different reactions of cells toward EVs and CaP treatment. While the ability of EVs in remodeling diseased environment were documented, more research should be conducted to elucidate the underlying mechanism that precipitate to the observed results.
The ICI/EVs preparation method was established by first comparing the LE of aPD-L1 in EV w/CaP with three different loading methods. Table S1 showed that the incubation method led to a relatively lower LE of aPD-L1 in EVs, whereas sonication resulted in a higher LE compared to the freeze/thaw method. To further confirm the suitability of sonication in ICIs loading, FITC-conjugated rat IgG was loaded into PKH26-stained EVs with sonication method, and the respective fluorescence was well-localized (Fig. S5). The sonication method was chosen for the loading of ICIs due to its high aPD-L1 LE, and the sonication condition was further optimized based on the number of sonication cycles and the amount of ICIs spiked. Under the same output voltage, increased sonication cycles had a small influence on EVs concentrations and aPD-L1 LE, but they might lead to a lower total protein concentration, indicating the loss of proteins and their functions during preparation (Fig. S6). The maximum LE of ICIs into EV could be achieved by spiking approximately 4 μg of aPD-L1 or aCTLA-4 into 0.7 ml conditioned medium, as the LE of ICIs was saturated at 4–5 μg (Fig. 3(E)). Based on these results, the ICIs loading method was established by sonicating 4 μg ICIs with 0.7 ml of conditioned medium for 3 cycles. Under this condition, the loading amount of ICIs in EVs was 1.03 ± 0.15 and 1.37 ± 0.04 μg/107 EVs for aPD-L1 and aCTLA-4, respectively. The particle size and TEM image of aPD-L1/EV were analyzed and shown in Fig. S7.
To confirm the preservation of ICIs’ function post loading, the binding affinity of aPD-L1/EV toward PD-L1 was evaluated with ELISA in comparison with free aPD-L1 (Fig. 3(F)). The result in Fig. 3(G) showed a slightly enhanced binding affinity of aPD-L1/EV toward PD-L1 compared to free aPD-L1 but without a significant difference, indicating the preserved target binding ability of ICIs post preparation. The enhanced binding affinity detected in aPD-L1/EV may be due to the high LE of aPD-L1 on EVs that attracted more secondary antibodies. Similar results were also obtained with the ICC results of in vitro aPD-L1/EV distribution toward GL261 cells, where aPD-L1 was more readily distributed to GL261 cells with the aid of EVs compared to free aPD-L1 (Fig. S8). Based on these results, the ICI/EVs preparation method was established with high ICIs loading efficiency and preserved ICIs target binding ability.
3.3
EVs facilitated ICIs delivery with enhanced barrier permeation rate and distribution into orthotopic glioma
Enhanced barrier permeability and efficient delivery represent desirable characteristics of EVs for drug delivery applications [51]. In vitro and in vivo assessments were conducted to evaluate the feasibility of using EVs for delivering ICIs to brain tumors. C2BBe1, a CaCo-2 clone commonly employed for intranasal and barrier permeation studies [52], was utilized in this study to assess the barrier permeability of ICI/EVs (Fig. 4(A)). TEER was recorded to ensure barrier integrity and to monitor monolayer formation, with a TEER value of 455.4 ± 4.8 Ω × cm2 recorded on day 20 (Fig. S9). The permeation results in Fig. 4(B) demonstrated that aPD-L1/EV could readily permeate through the C2BBe1 barrier, exhibiting around a 60 % enhancement in permeated aPD-L1 compared to the free aPD-L1-administered group. This outcome indicated the permeation-enhancing effect of EVs, which could be advantageous for ICIs delivery across intact barriers.Fig. 4Barrier permeation enhancement effect of EVs and efficient delivery of aPD-L1/EV into brain. (A) Scheme of the in vitro barrier permeation test with C2BBe1 trans-well system (created from Biorender.com). (B) Amounts of permeated aPD-L1 with free aPD-L1 or aPD-L1/EV administration through the C2BBe1 barrier. (C) Comparison of drug delivery efficiency with RO or IN administration of FITC-conjugated IgG or IgG/EV. The upper column displays the fluorescence of IgG within brain sections, and the lower column shows the cumulative fluorescence intensity of brain sections. (D) Distribution of aPD-L1 in GL261 tumor-bearing mice. Green fluorescence indicates aPD-L1, and nuclei were stained with DAPI. Statistics in (B, C) were conducted by one-way ANOVA with Bonferroni post hoc test. Asterisks represent a significant difference between indicated groups, with significance defined as p < 0.05. Data were presented as mean ± SD with n = 3.Fig. 4
In vivo studies were performed to assess the delivery efficiency of ICIs with EVs and to determine the optimal delivery method achieving the highest brain accumulation of ICIs. The RO and IN methods were compared with FITC-conjugated IgG as a model drug. In order to increase the accessibility of the ICIs to enter the brain systemically, the RO route was chosen over the tail vein injection due to its close proximity to the brain and the avoidance of first-pass metabolism [53]. IN administration is recognized for its efficient delivery and accumulation of protein drugs into the brain compared to systemic administration due to the higher permeability of nasal epithelium compared to the dense BBB [54]. Fig. 4(C) illustrated that IN administration of IgG/EV resulted in a significantly higher cumulative fluorescence intensity of IgG, with a 67–130 % enhancement compared to the other groups. RO administration of IgG/EV and free IgG exhibited limited brain accumulation effects, potentially due to the rapid systemic distribution of the administered substances rather than proximal entry into the brain. However, no difference was observed between RO and IN administration of free IgG, and higher IgG signals were observed in IgG/EV-administered groups compared to free IgG groups, irrespective of the delivery route, indicating the barrier permeation enhancement effect of EVs. The IN administration method was selected as the preferred drug delivery route for further in vivo studies due to its effective distribution and delivery of IgG/EV within the brain.
The distribution of aPD-L1 within orthotopic glioma brain tissue after IN administration was also evaluated. Fig. 4(D) showed more significant aPD-L1 distribution within the tumor compared to the administration of free aPD-L1, which was primarily located at the tumor margin. This result indicated that EVs facilitated the distribution of ICIs within the brain and tumor, while preserving the targeting ability of aPD-L1 upon loading into EVs. Efficiently delivering ICIs to the brain could also reduce safety concerns. Plasma concentration of IL-6, an indicator of ICIs-related adverse events (irAEs) [55,56], was significantly higher after intravenous (IV) injection of aPD-L1 + aCTLA-4 compared to that administered intranasally (Fig. S10). Notably, two out of the six mice in the IV aPD-L1 + aCTLA-4 group died shortly after dosing, indicating the potential risk of irAEs upon IV administration of ICIs. In contrast, IN administration of ICIs showed a slightly but insignificantly increase in IL-6 plasma concentration compared to the control group. It can be concluded that IN administration led to a constrained distribution and reduced systemic exposure of ICIs, thereby establishing a safe and efficient delivery strategy for ICI/EVs administration. In summary, EVs serve as a suitable delivery system for ICIs, preserving their targeting ability and efficiently permeating through biological barriers to deliver ICIs to the brain and tumor with reduced safety concern.
3.4
ICI/EVs facilitated tumor volume reduction and prolonged survival in glioma-bearing mice
The therapeutic effects of ICI/EVs on tumor-bearing mice were evaluated using two orthotopic glioma models (Fig. 5, Fig. 6). The experimental scheme was illustrated in Fig. 5(A). GL261 tumor volumes were analyzed, and Fig. 5(B and C) shows a continuous enlargement of tumor volumes, characterized by rapid growth from day 0 to day 11, followed by a slight decrease on day 25 in the PBS, aPD-L1, aPD-L1/EV, and aPD-L1 + aCTLA-4 groups. Mice treated with aPD-L1/EV + aCTLA-4/EV showed minimal tumor volume changes during the 25-day observation. This trend was significantly different from the PBS and aPD-L1-treated mice and displayed a 5.5- to 9.5-fold reduction in tumor volume on day 25 compared to the other treatment groups. A slower initial tumor volume enlargement rate was observed in the ICI/EV groups (aPD-L1/EV and aPD-L1/EV + aCTLA-4/EV) than in their respective free ICI groups (aPD-L1 and aPD-L1 + aCTLA-4). Hematoxylin and eosin staining of tumor-bearing brains was also assessed. The tumor volume correlated well with the findings from IVIS, confirming that the tumor reduction effect was due to the therapeutic impact of ICI/EVs and not from luciferase loss (Fig. S11). The survival of GL261-bearing mice post-treatment showed that both PBS and EVs-treated mice had shorter survival durations, with median survival times of 19 and 17 days, respectively. All mice in these groups died within the 35-days study period. Comparable survival rates were observed between the aPD-L1 and aPD-L1/EV treatments in the first 15 days. However, the aPD-L1/EV treatment led to a longer median survival time (30.5 days) than the aPD-L1 treatment (20 days). This highlights the benefits of aPD-L1/EV in extending survival compared to free aPD-L1. Combination treatments with ICIs showed longer survival durations than the single aPD-L1 treatment, with undefined median survival times in both groups. Early death was observed with the aPD-L1 + aCTLA-4 treatment, where the mice experienced rapid body weight loss and met the humane sacrifice criteria. On the contrary, only one mouse in the aPD-L1/EV + aCTLA-4/EV group died on day 35. The statistical results revealed that the aPD-L1/EV + aCTLA-4/EV treatment significantly extended survival compared to all other treatment groups, and the survival durations of aPD-L1/EV and aPD-L1 + aCTLA-4 were significantly longer than that of PBS (Fig. 5(D)). The Ki67 staining results showed minimal positive staining for proliferating cells in glioma tissue treated with aPD-L1/EV + aCTLA-4/EV, in contrast to other treatment groups and the PBS control (Fig. 5(E)). The lowest proliferation index was also found in mice underwent aPD-L1/EV + aCTLA-4/EV treatment (Fig. S12(A). This finding correlates with the tumor volume and survival analysis, suggesting that the combination of ICI/EVs treatment inhibits tumor proliferation, leading to optimal therapeutic outcomes in the GL261 model.Fig. 5Therapeutic effect of ICI/EVs in managing orthotopic GL261 tumors. (A) Schematic of experimental procedures for GL261 tumor management (created from Biorender.com). (B, C) Tumor volumes were evaluated with IVIS, and representative images are shown in (B). Changes in tumor volume were calculated based on the total flux observed on day 0 (C). (D) The survival of orthotopic GL261 tumor-bearing mice post-treatment was analyzed. The aPD-L1/EV + aCTLA-4/EV group demonstrated an extended survival time compared to other treatment groups. (E) Ki67 staining of GL261 tumors. Scale bar: 50 μm. Statistics for (C) were obtained from GLM with Bonferroni post hoc test, and for (D) from the Kaplan-Meier survival analysis with log rank test. The asterisk in (C) indicates a significant difference from the PBS and aPD-L1 treatments. In (D), the asterisk and hash signs represent significant difference from all treatment groups and from the PBS group, respectively. Significance was defined as p < 0.05. Data are presented as mean ± SEM with n = 4 in (C) and in (D) with n = 8∼18.Fig. 5Fig. 6Therapeutic effect of ICI/EVs in managing orthotopic ALTS1c1 tumors. (A) Tumor weights were examined on day 19, revealing reduced tumor weights with aPD-L1/EV + aCTLA-4/EV treatment. (B) Survival of orthotopic ALTS1c1 tumor-bearing mice post treatments, showing prolonged survival duration with aPD-L1/EV + aCTLA-4/EV treatment. (C) Ki67 staining of ALTS1c1 tumors showed no proliferating cells with aPD-L1/EV + aCTLA-4/EV treatment. Scale bar: 50 μm. Statistics for (A) were obtained from one-way ANOVA with Bonferroni post hoc test, and for (B) from the Kaplan-Meier survival analysis with the log rank test. The asterisks in (A, B) indicate a significant difference from the other groups, with significance defined as p < 0.05. Data are presented as mean ± SEM with n = 5 in (A), n = 7 in aPD-L1 + aCTLA-4, aPD-L1/EV + aCTLA-4/EV, and n = 10 in control group in (B).Fig. 6
Based on the therapeutic results observed in GL261-bearing mice, ALTS1c1 was applied to evaluate the therapeutic effect and further confirm the efficacy of ICI/EVs. Only combinatory ICIs treatments were examined, based on the finding that this treatment resulted in better therapeutic outcomes as observed in GL261 (Fig. 6). In ALTS1c1-bearing mice, the aPD-L1/EV + aCTLA-4/EV group exhibited significantly smaller tumor weights, with a 2.5-fold reduction at day 19, compared to the aPD-L1 + aCTLA-4 and control groups, both of which had comparable tumor weights (Fig. 6(A)). A significantly prolonged survival duration was observed in the aPD-L1/EV + aCTLA4/EV group. The median survival time for the control, aPD-L1 + aCTLA-4, and aPD-L1/EV + aCTLA-4/EV groups were 25, 25, and 28 days, respectively (Fig. 6(B)). Fewer Ki67-positive cells were observed in tumors treated with aPD-L1/EV + aCTLA-4/EV, along with the lowest proliferation index compared to the control and aPD-L1 + aCTLA-4 groups (Fig. 6(C)–Fig. S12(B)). In summary, the aPD-L1/EV + aCTLA-4/EV treatment led to tumor reduction, extended survival, and inhibited tumor cell proliferation in both glioma mouse models. The efficient delivery of ICIs into the brain, facilitated by EVs, can be the reason for the observed optimal therapeutic effects.
3.5
Efficient delivery of ICI/EVs facilitated immune modulation in tumor microenvironment
Immune cells, including cytotoxic T cells and M1 macrophages, were analyzed with IHC and flow cytometry in both GL261 and ALTS1c1 tumors to further confirm the immunotherapeutic effects exerted by ICI/EVs (Fig. 7, Fig. 8). CD8+ cytotoxic T cells were observed in IHC results with ICIs and ICI/EVs treatment compared to PBS or control groups in both glioma models. aPD-L1/EV + aCTLA-4/EV treatment resulted in greater infiltration of cytotoxic T cells compared to the other treatment groups (Fig. 7, Fig. 8). The tumor-associated macrophages (TAM) in both glioma models showed a tendency towards M1 phenotype polarization with EVs-administered groups, specifically the aPD-L1/EV and aPD-L1/EV + aCTLA-4/EV treated mice (Fig. 7, Fig. 8). Merely administrating ICIs did not lead to TAM polarization, as indicated by low iNOS expression in IHC results. Flow cytometry analysis was performed to quantify the immune cells within tumors. The results for GL261 (Fig. 7(C–E)) showed slightly higher proportions of T cells (CD3+ cells), cytotoxic T cells (CD3+CD8+ cells), and M1 macrophages (F4/80+iNOS+ cells) in aPD-L1/EV + aCTLA-4/EV-treated mice compared to the other treatment groups. On the other hand, ALTS1c1 tumors showed significantly more infiltrated T cells and polarized M1 macrophages with aPD-L1/EV + aCTLA-4/EV treatment compared to control and aPD-L1 + aCTLA-4 groups, both of which demonstrated similar proportions of immune cells within tumors (Fig. 8(C–E)). The gating strategy and antibody panel are listed in Fig. S13 and Table S2.Fig. 7Immune cells in GL261 tumors post treatment. (A) IHC images of cytotoxic T cells within the tumor tissue. Green fluorescence indicates CD8 expression on T cells, while nuclei are counterstained with DAPI. Scale bar: 50 μm. (B) IHC images of M1 macrophages within tumor tissue. Green fluorescence indicates F4/80-stained macrophages, and red fluorescence indicates iNOS expression. Nuclei are counterstained with DAPI. Scale bar: 20 μm. (C-E) Flow cytometry analysis results for total T cells (C), cytotoxic T cells (D), and M1 macrophages (E). Statistics for (C, D, E) were obtained from one-way ANOVA with Bonferroni post hoc test, with significance defined as p < 0.05. Data are expressed as mean ± SEM with n = 5.Fig. 7Fig. 8Immune cells in ALTS1c1 tumors post treatment. (A) IHC images of cytotoxic T cells in tumor tissue. Green fluorescence indicated CD8 on T cells, and nuclei were counterstained with DAPI. Scale bar: 50 μm. (B) IHC images of M1 macrophages in tumor tissue. Green fluorescence indicated F4/80-stained macrophages and red fluorescence indicated iNOS expression. Nuclei were counterstained with DAPI. Scale bar: 20 μm. (C-E) Flow cytometry analysis results of total T cells (C), cytotoxic T cells (D), and M1 macrophages (E). Asterisks indicated significant difference compared to the other groups with statistics obtained from one-way ANOVA and Bonferroni post hoc test. Data were expressed as mean ± SEM with n = 5.Fig. 8
Several factors, including the cellular communications and cytokines interactions, have been suggested to play a role in establishing the tumor microenvironment (TME). The TME of different tumors consist distinct cellular components, leading to their different extent of reaction toward immunotherapy. Based on the lymphocytes infiltration density into the TME, the tumors can be classified as hot or cold tumors, and hot tumors were generally deemed to be more immune-active and more susceptible to immunotherapy [57]. The challenge in managing glioma with immunotherapeutic strategy lies in the fact that its TME remains immunosuppressive, and TAM, accounting for around 30 % of the tumor mass, contribute to the exhaustion of immune cells and lead to the poor response rate of gliomas toward immunotherapy [58,59]. To overcome the immunosuppressive TME, several strategies can be applied to transform the cold tumors into the hot ones, thus leading to the enhanced infiltration of cytotoxic lymphocytes to fight against tumor cells. Studies have suggested that polarizing TAM into the M1 phenotype overcomes the immunosuppressive condition and enhances immunotherapeutic efficacy [20,34,60]. A combination of immunotherapeutic strategies, including the blockade of multiple immune checkpoints with different mechanisms, can also be beneficial in activating immune responses within the TME [61]. In this study, EVs demonstrated an optimal TAM-polarizing effect toward the M1 phenotype (Fig. 3, Fig. 7(B, E), 8(B, E)). The increased infiltration of T cells and cytotoxic T cells could be attributed to the M1 phenotype polarization of TAM that reversed the immunosuppressive TME and the efficient delivery of ICIs into the tumors with the aid of EVs, both of which realized the combination immunotherapy within the tumor. As observed in Fig. 5, Fig. 6, Fig. 7, Fig. 8, the combination of EVs and ICIs treatment activated immune responses in the TME, leading to the downstream enhancement of immune effector cell infiltration and thus resulting in the optimal therapeutic effect [22,62]. Based on the GL261 tumor volume results in Fig. 5 (C), it can be postulated that the combined immunotherapeutic effect can be achieved within ten days post treatment, resulting in the decreased tumor volume along with the prolonged survival duration.
The treatment outcomes of ICI/EVs were compared in two different orthotopic glioma mouse models due to the heterogenicity of the brain tumors [63]. The distinct cellular nature of GL261 (glioblastoma) and ALTS1c1 (astrocytoma) created different TME, and thus they reacted differently to ICI/EVs treatment (Fig. S14) [64,65]. The significant infiltration of cytotoxic T cells and TAM polarization in the IHC images of GL261 tumors upon ICI/EVs treatment could be the primary reason for the reduced tumor volume and prolonged survival duration (Fig. 5(B–D)). The small tumor volume post aPD-L1/EV + aCTLA-4/EV treatment made it challenging to isolate tumor tissue for flow cytometry analysis. Nevertheless, the quantification results still demonstrated an increased presence of immune cells within GL261 tumors upon aPD-L1/EV + aCTLA-4/EV treatment. On the other hand, although a significant difference in T cell and M1 macrophage quantities was observed in ALTS1c1 tumors upon aPD-L1/EV + aCTLA-4/EV treatment (Fig. 8), the rapid growth of tumor cells [65] might be the reason for the slight prolongation of survival duration compared to the aPD-L1 + aCTLA-4 and control groups (Fig. 6). Given that EVs efficiently delivered ICIs into tumors, dose adjustment may be required to achieve an optimal therapeutic effect in ALTS1c1 tumors [66].
3.1
CaP induced EVs secretion and macrophage polarization
The CaP prepared in this study exhibited particle characteristics with a size of around 1.5 μm and a negative surface charge. A PDI around 0.4 indicated a broad particle size distribution for the CaP suspension (Fig. 2(A)). The CaP displayed particulate characteristics with an irregular morphology under SEM (Fig. 2(B)). The solubility of CaP in pH 5 buffer was estimated to be 3.8 ± 0.2 mg/g. The FTIR and XRD spectra were similar to those in previous research [37,38], indicating the successful preparation of CaP (Fig. S1).Fig. 2Characteristics of CaP and its influence on EVs secretion and macrophage polarization. (A) DLS analysis results of particle size and ζ-potential of CaP. (B) SEM image of CaP. (C) Comparison of EVs secretion from RAW264.7 cells with and without 500 μg/ml CaP supplementation. (D) EVs secretion comparison from cells conditioned with varying CaP concentrations. (E) Cytokines concentrations in the conditioned medium and iNOS expression in RAW264.7 cells were analyzed with their respective ELISA and concentrations were adjusted based on the total protein amounts of cells, which were quantified with Bradford reagent. (F) ICC results of iNOS expression in RAW264.7 cells post 4 h conditioning. The red color indicates iNOS expression, and blue denotes nuclei stained with DAPI. Scale bar: 20 μm. (G) A scheme illustrating influence of CaP on macrophages (created from Biorender.com). Statistics for (C) were obtained from GLM with Bonferroni post hoc test, and the asterisk indicate a significant difference compared to the Blank group. Statistics for (D, E) were obtained from one-way ANOVA with Bonferroni post hoc test. Asterisks represent significant differences compared to the indicated groups. All data are expressed as mean ± SD with n = 3, and significance was defined as p < 0.05.Fig. 2
The feasibility of using CaP as EVs secretion stimulant was assessed. A higher amount of EVs secretion was observed in CaP-treated RAW264.7 cells throughout the 48-h experiment compared to cells without CaP treatment, the latter being designated as the blank group (Fig. 2(C)). EVs were secreted from the cells and reached a steady state 12 h post CaP treatment, peaking at 48 h. The effects of the medium and the amount of CaP supplementation on EVs secretion were assessed. PBS supplemented with CaP showed a higher EVs secretion compared to DMEM (Fig. S2), and Fig. 2(D) indicated that conditioning cells with 100 μg/ml CaP in PBS resulted in a higher amount of EVs secretion. Increasing the amount of CaP did not further facilitate EVs secretion, potentially due to cell necrosis from excessive CaP treatment [39].
Cytokine concentrations in the conditioned medium and iNOS expression, as compared between conditioning methods, are analyzed and shown in Fig. 2(E). The DMEM, which is the normal culture condition for RAW264.7, was utilized as the control for the other groups. Cells treated with CaP were compared with those without CaP treatment as well. While TNF-α concentrations were comparable between groups, PBS- and PBS + CaP-conditioning led to significantly higher IL-1β, IL-6, IL-18, and lower TGF-β secretion compared to DMEM-conditioning. CaP-supplemented PBS showed a significantly higher IL-1β and IL-18 secretion compared to PBS group. Additionally, PBS + CaP group displayed higher iNOS expression than PBS and DMEM groups, although the difference was not significant. ICC images of iNOS expression in macrophages 4 h post-conditioning were compared, revealing that macrophages conditioned with CaP showed more iNOS expression than those conditioned with DMEM and PBS (Fig. 2(F)). Based on these findings, it can be deduced that macrophages can polarize into proinflammatory phenotype when conditioned with CaP-containing PBS. To further evaluate the ability of CaP in inducing macrophages polarization, a CaP-containing DMEM group was compared with DMEM and showed in Fig. S3. While not as significant compared to PBS-conditioning, the CaP-containing DMEM group showed significantly more IL-18 and less TGF-β secretion, along with more iNOS expression, compared to DMEM group, indicating the proinflammatory ability of CaP in different medium. The expression tendency was not consistent between cytokines in different groups, it can be due to the different mechanisms that triggered the differences in expression and secretion of cytokines [40].
Simultaneous enhancement of EVs secretion amount and polarization of macrophages into proinflammatory phenotype was achieved by conditioning cells with CaP. This could be attributed to the elevation of intracellular calcium levels, which triggered various cellular responses, including the secretion of cellular materials like cytokines [41] and EVs [23]. Macrophage phagocytosis is particle size-dependent, with diameters of 2∼3 μm are more readily taken up [42,43]. The size of CaP in this research was optimal for macrophage phagocytosis, and the engulfed CaP can be transferred into phagosomes and fused with lysosomes to become phagolysosomes. The low pH environment within phagolysosomes can contribute to CaP degradation, leading to increased osmotic pressure from the release of calcium and phosphate ions. This results in the destruction of the phagolysosome [39,42] and an enhanced intracellular calcium level (Fig. 2(G)). This phenomenon is unlikely exerted by enhanced extracellular calcium concentration since the calcium levels are tightly regulated within cells, which makes adjusting intracellular calcium levels inefficient [32,44]. Other than CaP administration, cellular stress like nutrient deprivation with PBS conditioning, can also result in enhanced intracellular calcium level that facilitate cytokines secretion [45,46], and glucose starvation was suggested to result in cellular secretion of IL-6 and IL-8 [47]. While cells respond differently toward stress, the starvation of cells under PBS culture can be an explanation of the differences in cytokines secretion compared to DMEM.
In this study, a change in macrophage characteristics towards CaP conditioning was observed. CaP supplementation not only enhanced the secretion of EVs but also induced the macrophages polarization, and PBS conditioning potentiated this effect. Agents like lipopolysaccharide (LPS) and IFN-γ are commonly used to polarize macrophages into the proinflammatory phenotype. LPS is a pyrogen undesirable in pharmaceutical preparations, and IFN-γ can induce a strong autoimmune response. Both agents can remain in the EVs products when used for M1 macrophage polarization. The established CaP conditioning method in this study offers a potential alternative for simultaneously polarizing macrophages and enhancing EVs secretion without the residue of the aforementioned agents. While CaP administration resulted in increased secretion of some proinflammatory cytokines from RAW264.7 cells, it is still a different mechanism from infection or autoimmune pathogenesis pathways that result in increased expression of iNOS and secretion of TNF-α, IL-1β, IL-6, and IL-18 along with decreased secretion of anti-inflammatory TGF-β [48]. This can be supported by a previous study showing the efficient induction of TNF-α and IL-6 mRNA expression with LPS treatment on RAW264.7 cells [34]. The insignificant alteration of TNF-α, the main cytokine released immediately in response to pathogen infection [48], indicated the different reaction of RAW264.7 when encountered with CaP rather than LPS. Despite the unique pattern of proinflammatory cytokines secretion, the PBS + CaP treatment of RAW264.7 cells showed proinflammatory phenotype polarization tendency, which was supported by the higher iNOS expression and lower anti-inflammatory TGF-β secretion observed in this study, and the CaP supplementation potentiated this effect as compared to the PBS group (Fig. 2(E)–Fig. S3). Summing up the results of EVs secretion rate as well as cytokines and iNOS expression, the EVs preparation method in this study was established by conditioning RAW264.7 cells with PBS containing 100 μg/ml CaP for 24 h to achieve high EVs secretion amount and to ensure the origin of EVs from cells of proinflammatory phenotype. These will be beneficial in achieving sufficient drug loading and retain the proinflammatory characteristics of EVs for therapeutic application.
3.2
EVs exhibited exosome-like characteristics and capable for loading of ICIs
The EVs prepared in this study, derived from CaP-conditioned macrophages (EV w/CaP), were characterized by their size, morphology, and protein composition as suggested in the MISEV2018 guideline [35]. The average size of EV w/CaP analyzed with NTA was 161.7 ± 22 nm, and its spherical vesicular morphology similar to EVs prepared with polymer precipitation method [49], was identified under TEM (Fig. 3(A)). The slightly decreased particle size observed under TEM may be due to the differences in sample preparation for observation, and similar phenomenon was observed in lipid nanoparticles analyzed with DLS and TEM as well [50]. Immunoblotting of EV w/CaP showed the expression of LAMP1, ALIX and CD63, indicating their endolysosomal and phagolysosomal biogenesis pathways (Fig. 3(B), Fig. S4). The particle size and the expressed proteins indicated the possible exosome entity of the prepared EVs, demonstrating that the conditioning method established in this study stimulated the secretion of exosomes from cells via the known endolysosomal pathway and the proposed CaP-associated phagolysosomal pathway.Fig. 3EVs characteristics and ICI/EVs preparation method establishment. (A) Particle size of EVs was analyzed with NTA, and morphology was observed with TEM. (B) Protein markers in both RAW264.7 cell lysate and EVs were examined through immunoblotting. (C) Cytokines concentrations in the culture medium and iNOS expression post EVs treatment were analyzed with respective ELISA. (D) Immunocytochemistry evaluation of iNOS expression in RAW264.7 cells post EVs treatment in comparison to DMEM-cultured cells. Scale bar: 50 μm. (E) ICIs spiking amount in 0.7 ml of conditioned medium was evaluated by the LE of aPD-L1 and aCTLA-4. (F) Scheme of ELISA for aPD-L1/EV binding affinity evaluation (created from Biorender.com). (G) The binding affinity of aPD-L1/EV toward PD-L1 was analyzed with ELISA in comparison with free aPD-L1 to ensure the preservation of ICI function post preparation. Statistics in (C) were obtained from one way ANOVA with Bonferroni post hoc test. Asterisks represented significant differences between indicated groups, and significance was defined as p < 0.05. All data were expressed as mean ± SD with n = 3 in (C, E) and n = 5 in (G). LE: loading efficiency.Fig. 3
EVs are known for their intercellular communication and microenvironment modulation ability [51]. Based on the findings that CaP induced macrophage polarization and EVs secretion (Fig. 2), the potential macrophage polarizing ability was compared between EV w/CaP and EVs derived from cells without CaP treatment (EV w/o CaP). Fig. 3(C) showed that RAW264.7 cells treated with EV w/CaP tended to polarize to proinflammatory phenotype compared to DMEM and EV w/o CaP groups, as significantly greater amounts of proinflammatory IL-1β and IL-6, as well as higher secretion amount of TNF-α were observed. Consistent with the increased proinflammatory cytokine secretion, significantly less anti-inflammatory TGF-β secretion and more iNOS expression were also observed in cells treated with EV w/CaP, and ICC images also displayed more intracellular iNOS expression in EV w/CaP-treated cells (Fig. 3(D)), confirming the proinflammatory polarization of RAW264.7 cells treated with EV w/CaP. Compared to the DMEM group, the EVs-treated cells showed a different extent of proinflammatory phenotype polarizing tendency, with EV w/CaP treatment exhibited the most significant effect. The proinflammatory characteristics of EV w/CaP may originated from their cargos derived from parent cells, including the cytokines, that contributed to the polarization of macrophage into the proinflammatory phenotype similar to the cells secreting EVs [19,28,34]. While TNF-α, IL-1β, iNOS, and TGF-β showed similar secretion or expression trends compared to the parent RAW264.7 cells treated with CaP, differences in IL-6 and IL-18 secretion trends were observed in EV w/o and w/CaP that are not comparable with the CaP treatment results (Fig. 2, Fig. 3). The differences in IL-6 levels between EV w/o CaP and EV w/CaP, as well as between PBS and PBS + CaP groups, may be overlooked due to their insignificant differences. Another possible reason can be the complex intracellular cargo transportation and sorting while secreting EVs that led to the different reactions of cells toward EVs and CaP treatment. While the ability of EVs in remodeling diseased environment were documented, more research should be conducted to elucidate the underlying mechanism that precipitate to the observed results.
The ICI/EVs preparation method was established by first comparing the LE of aPD-L1 in EV w/CaP with three different loading methods. Table S1 showed that the incubation method led to a relatively lower LE of aPD-L1 in EVs, whereas sonication resulted in a higher LE compared to the freeze/thaw method. To further confirm the suitability of sonication in ICIs loading, FITC-conjugated rat IgG was loaded into PKH26-stained EVs with sonication method, and the respective fluorescence was well-localized (Fig. S5). The sonication method was chosen for the loading of ICIs due to its high aPD-L1 LE, and the sonication condition was further optimized based on the number of sonication cycles and the amount of ICIs spiked. Under the same output voltage, increased sonication cycles had a small influence on EVs concentrations and aPD-L1 LE, but they might lead to a lower total protein concentration, indicating the loss of proteins and their functions during preparation (Fig. S6). The maximum LE of ICIs into EV could be achieved by spiking approximately 4 μg of aPD-L1 or aCTLA-4 into 0.7 ml conditioned medium, as the LE of ICIs was saturated at 4–5 μg (Fig. 3(E)). Based on these results, the ICIs loading method was established by sonicating 4 μg ICIs with 0.7 ml of conditioned medium for 3 cycles. Under this condition, the loading amount of ICIs in EVs was 1.03 ± 0.15 and 1.37 ± 0.04 μg/107 EVs for aPD-L1 and aCTLA-4, respectively. The particle size and TEM image of aPD-L1/EV were analyzed and shown in Fig. S7.
To confirm the preservation of ICIs’ function post loading, the binding affinity of aPD-L1/EV toward PD-L1 was evaluated with ELISA in comparison with free aPD-L1 (Fig. 3(F)). The result in Fig. 3(G) showed a slightly enhanced binding affinity of aPD-L1/EV toward PD-L1 compared to free aPD-L1 but without a significant difference, indicating the preserved target binding ability of ICIs post preparation. The enhanced binding affinity detected in aPD-L1/EV may be due to the high LE of aPD-L1 on EVs that attracted more secondary antibodies. Similar results were also obtained with the ICC results of in vitro aPD-L1/EV distribution toward GL261 cells, where aPD-L1 was more readily distributed to GL261 cells with the aid of EVs compared to free aPD-L1 (Fig. S8). Based on these results, the ICI/EVs preparation method was established with high ICIs loading efficiency and preserved ICIs target binding ability.
3.3
EVs facilitated ICIs delivery with enhanced barrier permeation rate and distribution into orthotopic glioma
Enhanced barrier permeability and efficient delivery represent desirable characteristics of EVs for drug delivery applications [51]. In vitro and in vivo assessments were conducted to evaluate the feasibility of using EVs for delivering ICIs to brain tumors. C2BBe1, a CaCo-2 clone commonly employed for intranasal and barrier permeation studies [52], was utilized in this study to assess the barrier permeability of ICI/EVs (Fig. 4(A)). TEER was recorded to ensure barrier integrity and to monitor monolayer formation, with a TEER value of 455.4 ± 4.8 Ω × cm2 recorded on day 20 (Fig. S9). The permeation results in Fig. 4(B) demonstrated that aPD-L1/EV could readily permeate through the C2BBe1 barrier, exhibiting around a 60 % enhancement in permeated aPD-L1 compared to the free aPD-L1-administered group. This outcome indicated the permeation-enhancing effect of EVs, which could be advantageous for ICIs delivery across intact barriers.Fig. 4Barrier permeation enhancement effect of EVs and efficient delivery of aPD-L1/EV into brain. (A) Scheme of the in vitro barrier permeation test with C2BBe1 trans-well system (created from Biorender.com). (B) Amounts of permeated aPD-L1 with free aPD-L1 or aPD-L1/EV administration through the C2BBe1 barrier. (C) Comparison of drug delivery efficiency with RO or IN administration of FITC-conjugated IgG or IgG/EV. The upper column displays the fluorescence of IgG within brain sections, and the lower column shows the cumulative fluorescence intensity of brain sections. (D) Distribution of aPD-L1 in GL261 tumor-bearing mice. Green fluorescence indicates aPD-L1, and nuclei were stained with DAPI. Statistics in (B, C) were conducted by one-way ANOVA with Bonferroni post hoc test. Asterisks represent a significant difference between indicated groups, with significance defined as p < 0.05. Data were presented as mean ± SD with n = 3.Fig. 4
In vivo studies were performed to assess the delivery efficiency of ICIs with EVs and to determine the optimal delivery method achieving the highest brain accumulation of ICIs. The RO and IN methods were compared with FITC-conjugated IgG as a model drug. In order to increase the accessibility of the ICIs to enter the brain systemically, the RO route was chosen over the tail vein injection due to its close proximity to the brain and the avoidance of first-pass metabolism [53]. IN administration is recognized for its efficient delivery and accumulation of protein drugs into the brain compared to systemic administration due to the higher permeability of nasal epithelium compared to the dense BBB [54]. Fig. 4(C) illustrated that IN administration of IgG/EV resulted in a significantly higher cumulative fluorescence intensity of IgG, with a 67–130 % enhancement compared to the other groups. RO administration of IgG/EV and free IgG exhibited limited brain accumulation effects, potentially due to the rapid systemic distribution of the administered substances rather than proximal entry into the brain. However, no difference was observed between RO and IN administration of free IgG, and higher IgG signals were observed in IgG/EV-administered groups compared to free IgG groups, irrespective of the delivery route, indicating the barrier permeation enhancement effect of EVs. The IN administration method was selected as the preferred drug delivery route for further in vivo studies due to its effective distribution and delivery of IgG/EV within the brain.
The distribution of aPD-L1 within orthotopic glioma brain tissue after IN administration was also evaluated. Fig. 4(D) showed more significant aPD-L1 distribution within the tumor compared to the administration of free aPD-L1, which was primarily located at the tumor margin. This result indicated that EVs facilitated the distribution of ICIs within the brain and tumor, while preserving the targeting ability of aPD-L1 upon loading into EVs. Efficiently delivering ICIs to the brain could also reduce safety concerns. Plasma concentration of IL-6, an indicator of ICIs-related adverse events (irAEs) [55,56], was significantly higher after intravenous (IV) injection of aPD-L1 + aCTLA-4 compared to that administered intranasally (Fig. S10). Notably, two out of the six mice in the IV aPD-L1 + aCTLA-4 group died shortly after dosing, indicating the potential risk of irAEs upon IV administration of ICIs. In contrast, IN administration of ICIs showed a slightly but insignificantly increase in IL-6 plasma concentration compared to the control group. It can be concluded that IN administration led to a constrained distribution and reduced systemic exposure of ICIs, thereby establishing a safe and efficient delivery strategy for ICI/EVs administration. In summary, EVs serve as a suitable delivery system for ICIs, preserving their targeting ability and efficiently permeating through biological barriers to deliver ICIs to the brain and tumor with reduced safety concern.
3.4
ICI/EVs facilitated tumor volume reduction and prolonged survival in glioma-bearing mice
The therapeutic effects of ICI/EVs on tumor-bearing mice were evaluated using two orthotopic glioma models (Fig. 5, Fig. 6). The experimental scheme was illustrated in Fig. 5(A). GL261 tumor volumes were analyzed, and Fig. 5(B and C) shows a continuous enlargement of tumor volumes, characterized by rapid growth from day 0 to day 11, followed by a slight decrease on day 25 in the PBS, aPD-L1, aPD-L1/EV, and aPD-L1 + aCTLA-4 groups. Mice treated with aPD-L1/EV + aCTLA-4/EV showed minimal tumor volume changes during the 25-day observation. This trend was significantly different from the PBS and aPD-L1-treated mice and displayed a 5.5- to 9.5-fold reduction in tumor volume on day 25 compared to the other treatment groups. A slower initial tumor volume enlargement rate was observed in the ICI/EV groups (aPD-L1/EV and aPD-L1/EV + aCTLA-4/EV) than in their respective free ICI groups (aPD-L1 and aPD-L1 + aCTLA-4). Hematoxylin and eosin staining of tumor-bearing brains was also assessed. The tumor volume correlated well with the findings from IVIS, confirming that the tumor reduction effect was due to the therapeutic impact of ICI/EVs and not from luciferase loss (Fig. S11). The survival of GL261-bearing mice post-treatment showed that both PBS and EVs-treated mice had shorter survival durations, with median survival times of 19 and 17 days, respectively. All mice in these groups died within the 35-days study period. Comparable survival rates were observed between the aPD-L1 and aPD-L1/EV treatments in the first 15 days. However, the aPD-L1/EV treatment led to a longer median survival time (30.5 days) than the aPD-L1 treatment (20 days). This highlights the benefits of aPD-L1/EV in extending survival compared to free aPD-L1. Combination treatments with ICIs showed longer survival durations than the single aPD-L1 treatment, with undefined median survival times in both groups. Early death was observed with the aPD-L1 + aCTLA-4 treatment, where the mice experienced rapid body weight loss and met the humane sacrifice criteria. On the contrary, only one mouse in the aPD-L1/EV + aCTLA-4/EV group died on day 35. The statistical results revealed that the aPD-L1/EV + aCTLA-4/EV treatment significantly extended survival compared to all other treatment groups, and the survival durations of aPD-L1/EV and aPD-L1 + aCTLA-4 were significantly longer than that of PBS (Fig. 5(D)). The Ki67 staining results showed minimal positive staining for proliferating cells in glioma tissue treated with aPD-L1/EV + aCTLA-4/EV, in contrast to other treatment groups and the PBS control (Fig. 5(E)). The lowest proliferation index was also found in mice underwent aPD-L1/EV + aCTLA-4/EV treatment (Fig. S12(A). This finding correlates with the tumor volume and survival analysis, suggesting that the combination of ICI/EVs treatment inhibits tumor proliferation, leading to optimal therapeutic outcomes in the GL261 model.Fig. 5Therapeutic effect of ICI/EVs in managing orthotopic GL261 tumors. (A) Schematic of experimental procedures for GL261 tumor management (created from Biorender.com). (B, C) Tumor volumes were evaluated with IVIS, and representative images are shown in (B). Changes in tumor volume were calculated based on the total flux observed on day 0 (C). (D) The survival of orthotopic GL261 tumor-bearing mice post-treatment was analyzed. The aPD-L1/EV + aCTLA-4/EV group demonstrated an extended survival time compared to other treatment groups. (E) Ki67 staining of GL261 tumors. Scale bar: 50 μm. Statistics for (C) were obtained from GLM with Bonferroni post hoc test, and for (D) from the Kaplan-Meier survival analysis with log rank test. The asterisk in (C) indicates a significant difference from the PBS and aPD-L1 treatments. In (D), the asterisk and hash signs represent significant difference from all treatment groups and from the PBS group, respectively. Significance was defined as p < 0.05. Data are presented as mean ± SEM with n = 4 in (C) and in (D) with n = 8∼18.Fig. 5Fig. 6Therapeutic effect of ICI/EVs in managing orthotopic ALTS1c1 tumors. (A) Tumor weights were examined on day 19, revealing reduced tumor weights with aPD-L1/EV + aCTLA-4/EV treatment. (B) Survival of orthotopic ALTS1c1 tumor-bearing mice post treatments, showing prolonged survival duration with aPD-L1/EV + aCTLA-4/EV treatment. (C) Ki67 staining of ALTS1c1 tumors showed no proliferating cells with aPD-L1/EV + aCTLA-4/EV treatment. Scale bar: 50 μm. Statistics for (A) were obtained from one-way ANOVA with Bonferroni post hoc test, and for (B) from the Kaplan-Meier survival analysis with the log rank test. The asterisks in (A, B) indicate a significant difference from the other groups, with significance defined as p < 0.05. Data are presented as mean ± SEM with n = 5 in (A), n = 7 in aPD-L1 + aCTLA-4, aPD-L1/EV + aCTLA-4/EV, and n = 10 in control group in (B).Fig. 6
Based on the therapeutic results observed in GL261-bearing mice, ALTS1c1 was applied to evaluate the therapeutic effect and further confirm the efficacy of ICI/EVs. Only combinatory ICIs treatments were examined, based on the finding that this treatment resulted in better therapeutic outcomes as observed in GL261 (Fig. 6). In ALTS1c1-bearing mice, the aPD-L1/EV + aCTLA-4/EV group exhibited significantly smaller tumor weights, with a 2.5-fold reduction at day 19, compared to the aPD-L1 + aCTLA-4 and control groups, both of which had comparable tumor weights (Fig. 6(A)). A significantly prolonged survival duration was observed in the aPD-L1/EV + aCTLA4/EV group. The median survival time for the control, aPD-L1 + aCTLA-4, and aPD-L1/EV + aCTLA-4/EV groups were 25, 25, and 28 days, respectively (Fig. 6(B)). Fewer Ki67-positive cells were observed in tumors treated with aPD-L1/EV + aCTLA-4/EV, along with the lowest proliferation index compared to the control and aPD-L1 + aCTLA-4 groups (Fig. 6(C)–Fig. S12(B)). In summary, the aPD-L1/EV + aCTLA-4/EV treatment led to tumor reduction, extended survival, and inhibited tumor cell proliferation in both glioma mouse models. The efficient delivery of ICIs into the brain, facilitated by EVs, can be the reason for the observed optimal therapeutic effects.
3.5
Efficient delivery of ICI/EVs facilitated immune modulation in tumor microenvironment
Immune cells, including cytotoxic T cells and M1 macrophages, were analyzed with IHC and flow cytometry in both GL261 and ALTS1c1 tumors to further confirm the immunotherapeutic effects exerted by ICI/EVs (Fig. 7, Fig. 8). CD8+ cytotoxic T cells were observed in IHC results with ICIs and ICI/EVs treatment compared to PBS or control groups in both glioma models. aPD-L1/EV + aCTLA-4/EV treatment resulted in greater infiltration of cytotoxic T cells compared to the other treatment groups (Fig. 7, Fig. 8). The tumor-associated macrophages (TAM) in both glioma models showed a tendency towards M1 phenotype polarization with EVs-administered groups, specifically the aPD-L1/EV and aPD-L1/EV + aCTLA-4/EV treated mice (Fig. 7, Fig. 8). Merely administrating ICIs did not lead to TAM polarization, as indicated by low iNOS expression in IHC results. Flow cytometry analysis was performed to quantify the immune cells within tumors. The results for GL261 (Fig. 7(C–E)) showed slightly higher proportions of T cells (CD3+ cells), cytotoxic T cells (CD3+CD8+ cells), and M1 macrophages (F4/80+iNOS+ cells) in aPD-L1/EV + aCTLA-4/EV-treated mice compared to the other treatment groups. On the other hand, ALTS1c1 tumors showed significantly more infiltrated T cells and polarized M1 macrophages with aPD-L1/EV + aCTLA-4/EV treatment compared to control and aPD-L1 + aCTLA-4 groups, both of which demonstrated similar proportions of immune cells within tumors (Fig. 8(C–E)). The gating strategy and antibody panel are listed in Fig. S13 and Table S2.Fig. 7Immune cells in GL261 tumors post treatment. (A) IHC images of cytotoxic T cells within the tumor tissue. Green fluorescence indicates CD8 expression on T cells, while nuclei are counterstained with DAPI. Scale bar: 50 μm. (B) IHC images of M1 macrophages within tumor tissue. Green fluorescence indicates F4/80-stained macrophages, and red fluorescence indicates iNOS expression. Nuclei are counterstained with DAPI. Scale bar: 20 μm. (C-E) Flow cytometry analysis results for total T cells (C), cytotoxic T cells (D), and M1 macrophages (E). Statistics for (C, D, E) were obtained from one-way ANOVA with Bonferroni post hoc test, with significance defined as p < 0.05. Data are expressed as mean ± SEM with n = 5.Fig. 7Fig. 8Immune cells in ALTS1c1 tumors post treatment. (A) IHC images of cytotoxic T cells in tumor tissue. Green fluorescence indicated CD8 on T cells, and nuclei were counterstained with DAPI. Scale bar: 50 μm. (B) IHC images of M1 macrophages in tumor tissue. Green fluorescence indicated F4/80-stained macrophages and red fluorescence indicated iNOS expression. Nuclei were counterstained with DAPI. Scale bar: 20 μm. (C-E) Flow cytometry analysis results of total T cells (C), cytotoxic T cells (D), and M1 macrophages (E). Asterisks indicated significant difference compared to the other groups with statistics obtained from one-way ANOVA and Bonferroni post hoc test. Data were expressed as mean ± SEM with n = 5.Fig. 8
Several factors, including the cellular communications and cytokines interactions, have been suggested to play a role in establishing the tumor microenvironment (TME). The TME of different tumors consist distinct cellular components, leading to their different extent of reaction toward immunotherapy. Based on the lymphocytes infiltration density into the TME, the tumors can be classified as hot or cold tumors, and hot tumors were generally deemed to be more immune-active and more susceptible to immunotherapy [57]. The challenge in managing glioma with immunotherapeutic strategy lies in the fact that its TME remains immunosuppressive, and TAM, accounting for around 30 % of the tumor mass, contribute to the exhaustion of immune cells and lead to the poor response rate of gliomas toward immunotherapy [58,59]. To overcome the immunosuppressive TME, several strategies can be applied to transform the cold tumors into the hot ones, thus leading to the enhanced infiltration of cytotoxic lymphocytes to fight against tumor cells. Studies have suggested that polarizing TAM into the M1 phenotype overcomes the immunosuppressive condition and enhances immunotherapeutic efficacy [20,34,60]. A combination of immunotherapeutic strategies, including the blockade of multiple immune checkpoints with different mechanisms, can also be beneficial in activating immune responses within the TME [61]. In this study, EVs demonstrated an optimal TAM-polarizing effect toward the M1 phenotype (Fig. 3, Fig. 7(B, E), 8(B, E)). The increased infiltration of T cells and cytotoxic T cells could be attributed to the M1 phenotype polarization of TAM that reversed the immunosuppressive TME and the efficient delivery of ICIs into the tumors with the aid of EVs, both of which realized the combination immunotherapy within the tumor. As observed in Fig. 5, Fig. 6, Fig. 7, Fig. 8, the combination of EVs and ICIs treatment activated immune responses in the TME, leading to the downstream enhancement of immune effector cell infiltration and thus resulting in the optimal therapeutic effect [22,62]. Based on the GL261 tumor volume results in Fig. 5 (C), it can be postulated that the combined immunotherapeutic effect can be achieved within ten days post treatment, resulting in the decreased tumor volume along with the prolonged survival duration.
The treatment outcomes of ICI/EVs were compared in two different orthotopic glioma mouse models due to the heterogenicity of the brain tumors [63]. The distinct cellular nature of GL261 (glioblastoma) and ALTS1c1 (astrocytoma) created different TME, and thus they reacted differently to ICI/EVs treatment (Fig. S14) [64,65]. The significant infiltration of cytotoxic T cells and TAM polarization in the IHC images of GL261 tumors upon ICI/EVs treatment could be the primary reason for the reduced tumor volume and prolonged survival duration (Fig. 5(B–D)). The small tumor volume post aPD-L1/EV + aCTLA-4/EV treatment made it challenging to isolate tumor tissue for flow cytometry analysis. Nevertheless, the quantification results still demonstrated an increased presence of immune cells within GL261 tumors upon aPD-L1/EV + aCTLA-4/EV treatment. On the other hand, although a significant difference in T cell and M1 macrophage quantities was observed in ALTS1c1 tumors upon aPD-L1/EV + aCTLA-4/EV treatment (Fig. 8), the rapid growth of tumor cells [65] might be the reason for the slight prolongation of survival duration compared to the aPD-L1 + aCTLA-4 and control groups (Fig. 6). Given that EVs efficiently delivered ICIs into tumors, dose adjustment may be required to achieve an optimal therapeutic effect in ALTS1c1 tumors [66].
4
Conclusion
Numerous studies have focused on developing therapeutic strategies to manage glioma, and immunotherapy offers room for progress in achieving efficient glioma therapy. An ICIs delivery strategy with EVs was established in this study. A method to increase the secretion of proinflammatory EVs was developed, which is beneficial for drug delivery system preparation, and efficiently loaded with ICIs. A delivery strategy was established for the efficient accumulation of ICI/EVs within glioma lesion, activating the immune response and ultimately leading to the efficient management of glioma in mouse models. The results of this study suggest the feasibility of using CaP to obtain EVs for delivering ICIs. The simultaneous efficient delivery of ICIs and EVs modulates TME to exert an optimal therapeutic effect in glioma. In conclusion, the EVs-based drug delivery system established in this study holds promise for broad therapeutic applications in managing various brain diseases.
CRediT authorship contribution statement
Shang-Wen Lin: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization. Cheng-Ping Yu: Validation, Methodology, Investigation, Formal analysis. Jui-Chen Tsai: Writing – review & editing, Supervision, Conceptualization. Yan-Jye Shyong: Writing – review & editing, Project administration, Funding acquisition, Conceptualization.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Yan-Jye Shyong reports financial support was provided by 10.13039/100020595National Science and Technology Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: Validation Study of the Parkinson’s Disease Stigma Questionnaire (PDStigmaQuest) | Body:
INTRODUCTION
Stigma is a determinant factor for Parkinson’s disease (PD) patients’ quality of life (QoL).1–3 For the term “stigma”, numerous definitions have been proposed in the last decades.4–7 In the field of chronic illnesses, stigma is often studied as health-related stigma, a “social process, experienced or anticipated, characterized by exclusion, rejection, blame, or devaluation that results from experience or reasonable anticipation of an adverse social judgment”.8 Scambler and Hopkins initially introduced the distinction between felt stigma, including fear of being stigmatized and feelings of shame associated with the disease, and enacted stigma, referring to actual experiences of discrimination.9,10 Fox et al. established a more complex stigma framework, including anticipated stigma (expectations of stigmatization), experienced stigma (actual stigmatization), and internalized stigma (adopting others’ negative beliefs).11
Stigma plays a significant role in PD: For example, patients report experiences of being mislabeled as drunk, being stared at, feeling like being a burden to others, and feeling ashamed.12–15 Especially in the early disease stages, patients try to hide PD-related symptoms from others.13,16 Importantly, stigma can cause social isolation, obstruct seeking medical care, and is associated with non-motor symptoms (NMS) like depression and anxiety.17–22
However, our current knowledge of stigma in PD is sparse and mainly based on qualitative studies. There is currently no specific tool available for addressing the highly complex construct of stigma in PD comprehensively. To our knowledge, to date, mainly generic stigma measures for chronic illnesses or the PD Questionnaire 39 (PDQ-39) stigma subscale consisting of four items have been applied in PD.23–27 Therefore, our objective was to develop and validate a stigma questionnaire specific to PD patients to help to address and evaluate PD stigma. The development process and data of the pilot study presenting the preliminary version of the patient-reported Parkinson’s Disease Stigma Questionnaire (PDStigmaQuest) have been reported previously.28 Here, we report validation data from the new PDStigmaQuest.
METHODS
Study design and participants
This multi-center (Cologne University Hospital, Germany; Movement Disorders Hospital in Beelitz, Germany) and cross-sectional validation study included patients with a diagnosis of PD according to the MDS criteria29 and non-spousal and non-caregiver healthy controls. In- and outpatients were approached for participation in the study. For recruitment of both patients and controls, posters and flyers were used. Exclusion criteria were: age <18 or >90 years, moderate to severe medical conditions other than PD that could have interfered with the ability to complete the study, impaired hearing or sight interfering with study participation, significant cognitive impairment or insufficient knowledge of the German language based on the judgment of the examining health professional, and inability to consent. Additional exclusion criteria for patients were: PD of non-idiopathic form or other clinically relevant neurological diseases besides PD. Additional exclusion criteria for healthy controls were: PD diagnosis or other neurological or psychiatric disorders.
All participants were included between August 2022 and January 2024.
Ethical aspects
All participants provided written informed consent. The study was performed under the principles of the Declaration of Helsinki. The local ethics committee approved the study protocols (Cologne vote: 21-1385; Beelitz vote: 2023-85-BO). The study was registered at the German Clinical Trials Register: DRKS00025513.
Procedures and materials
Patients were tested under regular medication (MedON). Firstly, participants were asked about sociodemographic data, PD patients additionally about their disease history and current treatment. After that, different tools were assessed in German:
The German-language PDStigmaQuest is a patient-reported questionnaire developed based on literature, clinical experience, focus groups, and PD patients’ and caregivers’ feedback.28 The version resulting from the pilot study consisted of 25 items based on the stigma concept by Fox et al. adapted for PD including two optional items for employed PD patients.11,28 To test for uncomfortableness related to PD symptoms, the first item included sub-items to evaluate specific motor symptoms and NMS separately. Five items were reverse-scored items to control for response bias and avoid negative wording.30 Each item was rated on a five-point Likert scale from “never” (0) to “always” (4) regarding the past four weeks. Only item 1 (uncomfortableness related to PD symptoms) included the option “symptom not applicable” for sub-items. The PDStigmaQuest total score was calculated as the sum of all item scores. Since some items directly referred to the disease (e.g., “I try to hide my Parkinson’s symptoms from others”), these could not be answered by healthy controls without PD. The controls were asked to fill in the following generally formulated items: item 1 (uncomfortableness with symptoms), 4 (feeling worth as much as others), 5 (feeling like a burden to others), 7 (feeling useless), 8 (self-respect), 11 (being seen as mentally impaired), 15 (decisions taken by others), 19 (being interrupted), 21 (being taken seriously), and 23 (others acting as feeling uncomfortable in the presence of the patient/control).
For retest evaluation, all patients were asked to complete only the PDStigmaQuest a second time 7–14 days after initial completion.
Beyond the PDStigmaQuest, the following self-rated scales and questionnaires were administered:•The Stigma Scale for Chronic Illness (SSCI) contains 24 items measuring the stigma of chronic illnesses such as PD, Alzheimer’s dementia, or epilepsy.26 It consists of two subscales: self-stigma and enacted stigma. These are rated on a 5-point scale from “never” (1) to “always” (5), resulting in a maximum total score of 120. Higher values indicate higher stigma levels.•The PDQ-39 is the most frequently used questionnaire for QoL in PD.27,31 It contains 39 items in eight different domains. The items are rated on a 5-point scale from “never” (0) to “always” (4). As the domains contain different numbers of items, the domain scores are standardized on a summary index (SI) score from 0 (no impairment) to 100 (maximum impairment). In this study, only the stigma subdomain was used. Due to the domains’ high internal consistency (stigma: Cronbach’s α= 0.80), they are often used independently of other domains.32,33•The Beck Depression Inventory II (BDI-II) is an instrument measuring depression severity.34 It consists of 21 items assessed on a 4-point scale (0–3), resulting in a maximum total score of 63. Higher scores indicate higher depression levels.•The Hospital Anxiety Depression Rating Scale (HADS) is a scale for anxiety and depressive states.35 It consists of 14 items divided into two subscales for anxiety and depression, each including 7 items. These are rated on a 4-point scale (0–3), resulting in a maximum score of 21 points for each subscale.
The following clinician-rated tools were administered:•The Montreal Cognitive Assessment (MoCA) is a short screening test for mild cognitive impairment with various tasks testing the following cognitive domains: Short-term memory, visual-spatial abilities, executive functions, language, attention, concentration, working memory, and orientation.36 A maximum total score of 30 (maximum performance) can be achieved.•The Movement Disorders Society Unified Parkinson’s Disease Rating Scale III (MDS-UPDRS III) is a clinician-based rating scale for motor function in PD.37 It includes 18 items rated on a scale from “normal” (0) to “severe” (4) for different motor aspects (e.g., rigidity, tremor), most of them rated separately for the left and right side of the body, resulting in a maximum total score of 132 (maximum impairment). The MDS-UPDRS III additionally contains a Hoehn and Yahr (HY) classification for motor staging of PD, ranging from stage 0 (no signs of disease) to 5 (wheelchair bound or bedridden unless aided).
The SSCI, PDQ-39 stigma domain, and MDS-UPDRS III were only applied in patients.
Sample size
For conducting exploratory factor analyses (EFA), a minimum of 200 participants is proposed for questionnaires with up to 40 items.38 A ratio of patients to controls of 2:1 was used based on other validation studies.39,40
Data analysis
Descriptive statistics for demographic and clinical characteristics were calculated. The Shapiro-Wilk test was applied to test for normal distribution of data. Levodopa equivalent daily dose (LEDD) was calculated according to the formula of Tomlinson et al.41 The score of PDStigmaQuest item 1 (uncomfortableness with symptoms) was calculated by summing up the scores of all applicable symptoms and dividing the sum by the number of applicable symptoms. Data quality was explored by the proportion of missing data points.
Only for patients, the following analyses were conducted:(1)Dimensionality: For this analysis, the optional stigma domain for employed patients was left out as many patients were retired (n = 137). Further, based on participant feedback, patients had problems answering the reverse-scored items. We found that 15.4% of patients initially answered at least one reverse-scored item in the direction of the other non-reversed items, which was subsequently crossed out and changed. In 11.9% of patients, at least one answer to the reversed items did not match the other answers. Since we aimed to develop an easy-to-use and reliable tool, we decided to remove these five items prior to analysis, avoiding potentially biased item characteristics. Inter-item correlations between all other stigma items were calculated, and items mostly showing correlations <0.3 or >0.9 with other items were removed due to potential lack of fit with other items or collinearity.42 A principal axis EFA with oblique rotation (promax, kappa = 4) was applied as many PDStigmaQuest items were right-skewed, and principal axis EFA does not make distributional assumptions. Oblique rotation was chosen as we assumed that the questionnaire’s stigma factors would correlate and oblique rotation permits correlation between factors.43 The Kaiser-Meyer-Olkin (KMO) measure was applied for testing sampling adequacy. Values >0.7 are considered middling, values >0.8 meritorious, and values >0.9 marvelous.44 KMO values for individual variables should be >0.5. Bartlett’s test of sphericity was used for testing the adequacy of the correlation matrix.(2)Acceptability was tested through floor and ceiling effects (percentage of extreme values≤15%) and skewness.45 For the latter, limits were –1 and +1.46(3)Internal consistency: Cronbach’s alpha was calculated for PDStigmaQuest as a whole as some domains only consisted of 2 items (standard value≥0.7).30,40,47 The work domain not applying to many patients resulted in systematic data loss for internal consistency analysis. Therefore, only for calculating Cronbach’s alpha, items referring to work left out by unemployed/retired patients were coded as zero.48 This approach was considered acceptable because the work domain not applying to a patient also means that the patient cannot be confronted with stigma at work. Further, for every item, corrected item-total correlation (standard value≥0.3) and inter-item correlations (standard value > 0.20 and < 0.75) in every domain were calculated.30,49(4)Test-retest reliability was investigated through Spearman correlation between the initial assessment and the retest (7–14 days later) PDStigmaQuest total score.(5)Convergent validity was tested through correlations with other stigma measures (SSCI, the stigma domain of the PDQ-39; rs > 0.50).50(6)Known-groups validity: Based on the well-established relationship between stigma and depression, known-groups validity was tested by comparing PDStigmaQuest scores in patients with and without depressive symptoms using a Mann-Whitney U test.21 We hypothesized that stigma scores should be higher in PD patients with than without depressive symptoms. To identify patients with depressive symptoms, the cut-off BDI-II≥14 was used as originally suggested by Beck et al. for detecting mild depression.34,51
The following analyses were conducted with data from patients and controls:(1)Group confirmation: To ensure that relevant PD symptoms were not similarly present in healthy controls, Mann-Whitney U tests were conducted between PD patients and healthy controls for the following scales: MoCA total, BDI-II total, HADS-A, and HADS-D. Tests were corrected for multiple comparisons according to the Benjamini-Hochberg procedure.(2)Comparison of PDStigmaQuest scores: To compare PD patients’ and healthy controls’ stigma scores, a Mann-Whitney U test was conducted with new stigma scores summing up only items answered by both groups. We hypothesized that stigma scores should be higher in PD patients than in healthy controls.
All analyses were conducted using Statistical Package for Social Sciences (SPSS; version 28.0). P-values < 0.05 were considered statistically significant.
Study design and participants
This multi-center (Cologne University Hospital, Germany; Movement Disorders Hospital in Beelitz, Germany) and cross-sectional validation study included patients with a diagnosis of PD according to the MDS criteria29 and non-spousal and non-caregiver healthy controls. In- and outpatients were approached for participation in the study. For recruitment of both patients and controls, posters and flyers were used. Exclusion criteria were: age <18 or >90 years, moderate to severe medical conditions other than PD that could have interfered with the ability to complete the study, impaired hearing or sight interfering with study participation, significant cognitive impairment or insufficient knowledge of the German language based on the judgment of the examining health professional, and inability to consent. Additional exclusion criteria for patients were: PD of non-idiopathic form or other clinically relevant neurological diseases besides PD. Additional exclusion criteria for healthy controls were: PD diagnosis or other neurological or psychiatric disorders.
All participants were included between August 2022 and January 2024.
Ethical aspects
All participants provided written informed consent. The study was performed under the principles of the Declaration of Helsinki. The local ethics committee approved the study protocols (Cologne vote: 21-1385; Beelitz vote: 2023-85-BO). The study was registered at the German Clinical Trials Register: DRKS00025513.
Procedures and materials
Patients were tested under regular medication (MedON). Firstly, participants were asked about sociodemographic data, PD patients additionally about their disease history and current treatment. After that, different tools were assessed in German:
The German-language PDStigmaQuest is a patient-reported questionnaire developed based on literature, clinical experience, focus groups, and PD patients’ and caregivers’ feedback.28 The version resulting from the pilot study consisted of 25 items based on the stigma concept by Fox et al. adapted for PD including two optional items for employed PD patients.11,28 To test for uncomfortableness related to PD symptoms, the first item included sub-items to evaluate specific motor symptoms and NMS separately. Five items were reverse-scored items to control for response bias and avoid negative wording.30 Each item was rated on a five-point Likert scale from “never” (0) to “always” (4) regarding the past four weeks. Only item 1 (uncomfortableness related to PD symptoms) included the option “symptom not applicable” for sub-items. The PDStigmaQuest total score was calculated as the sum of all item scores. Since some items directly referred to the disease (e.g., “I try to hide my Parkinson’s symptoms from others”), these could not be answered by healthy controls without PD. The controls were asked to fill in the following generally formulated items: item 1 (uncomfortableness with symptoms), 4 (feeling worth as much as others), 5 (feeling like a burden to others), 7 (feeling useless), 8 (self-respect), 11 (being seen as mentally impaired), 15 (decisions taken by others), 19 (being interrupted), 21 (being taken seriously), and 23 (others acting as feeling uncomfortable in the presence of the patient/control).
For retest evaluation, all patients were asked to complete only the PDStigmaQuest a second time 7–14 days after initial completion.
Beyond the PDStigmaQuest, the following self-rated scales and questionnaires were administered:•The Stigma Scale for Chronic Illness (SSCI) contains 24 items measuring the stigma of chronic illnesses such as PD, Alzheimer’s dementia, or epilepsy.26 It consists of two subscales: self-stigma and enacted stigma. These are rated on a 5-point scale from “never” (1) to “always” (5), resulting in a maximum total score of 120. Higher values indicate higher stigma levels.•The PDQ-39 is the most frequently used questionnaire for QoL in PD.27,31 It contains 39 items in eight different domains. The items are rated on a 5-point scale from “never” (0) to “always” (4). As the domains contain different numbers of items, the domain scores are standardized on a summary index (SI) score from 0 (no impairment) to 100 (maximum impairment). In this study, only the stigma subdomain was used. Due to the domains’ high internal consistency (stigma: Cronbach’s α= 0.80), they are often used independently of other domains.32,33•The Beck Depression Inventory II (BDI-II) is an instrument measuring depression severity.34 It consists of 21 items assessed on a 4-point scale (0–3), resulting in a maximum total score of 63. Higher scores indicate higher depression levels.•The Hospital Anxiety Depression Rating Scale (HADS) is a scale for anxiety and depressive states.35 It consists of 14 items divided into two subscales for anxiety and depression, each including 7 items. These are rated on a 4-point scale (0–3), resulting in a maximum score of 21 points for each subscale.
The following clinician-rated tools were administered:•The Montreal Cognitive Assessment (MoCA) is a short screening test for mild cognitive impairment with various tasks testing the following cognitive domains: Short-term memory, visual-spatial abilities, executive functions, language, attention, concentration, working memory, and orientation.36 A maximum total score of 30 (maximum performance) can be achieved.•The Movement Disorders Society Unified Parkinson’s Disease Rating Scale III (MDS-UPDRS III) is a clinician-based rating scale for motor function in PD.37 It includes 18 items rated on a scale from “normal” (0) to “severe” (4) for different motor aspects (e.g., rigidity, tremor), most of them rated separately for the left and right side of the body, resulting in a maximum total score of 132 (maximum impairment). The MDS-UPDRS III additionally contains a Hoehn and Yahr (HY) classification for motor staging of PD, ranging from stage 0 (no signs of disease) to 5 (wheelchair bound or bedridden unless aided).
The SSCI, PDQ-39 stigma domain, and MDS-UPDRS III were only applied in patients.
Sample size
For conducting exploratory factor analyses (EFA), a minimum of 200 participants is proposed for questionnaires with up to 40 items.38 A ratio of patients to controls of 2:1 was used based on other validation studies.39,40
Data analysis
Descriptive statistics for demographic and clinical characteristics were calculated. The Shapiro-Wilk test was applied to test for normal distribution of data. Levodopa equivalent daily dose (LEDD) was calculated according to the formula of Tomlinson et al.41 The score of PDStigmaQuest item 1 (uncomfortableness with symptoms) was calculated by summing up the scores of all applicable symptoms and dividing the sum by the number of applicable symptoms. Data quality was explored by the proportion of missing data points.
Only for patients, the following analyses were conducted:(1)Dimensionality: For this analysis, the optional stigma domain for employed patients was left out as many patients were retired (n = 137). Further, based on participant feedback, patients had problems answering the reverse-scored items. We found that 15.4% of patients initially answered at least one reverse-scored item in the direction of the other non-reversed items, which was subsequently crossed out and changed. In 11.9% of patients, at least one answer to the reversed items did not match the other answers. Since we aimed to develop an easy-to-use and reliable tool, we decided to remove these five items prior to analysis, avoiding potentially biased item characteristics. Inter-item correlations between all other stigma items were calculated, and items mostly showing correlations <0.3 or >0.9 with other items were removed due to potential lack of fit with other items or collinearity.42 A principal axis EFA with oblique rotation (promax, kappa = 4) was applied as many PDStigmaQuest items were right-skewed, and principal axis EFA does not make distributional assumptions. Oblique rotation was chosen as we assumed that the questionnaire’s stigma factors would correlate and oblique rotation permits correlation between factors.43 The Kaiser-Meyer-Olkin (KMO) measure was applied for testing sampling adequacy. Values >0.7 are considered middling, values >0.8 meritorious, and values >0.9 marvelous.44 KMO values for individual variables should be >0.5. Bartlett’s test of sphericity was used for testing the adequacy of the correlation matrix.(2)Acceptability was tested through floor and ceiling effects (percentage of extreme values≤15%) and skewness.45 For the latter, limits were –1 and +1.46(3)Internal consistency: Cronbach’s alpha was calculated for PDStigmaQuest as a whole as some domains only consisted of 2 items (standard value≥0.7).30,40,47 The work domain not applying to many patients resulted in systematic data loss for internal consistency analysis. Therefore, only for calculating Cronbach’s alpha, items referring to work left out by unemployed/retired patients were coded as zero.48 This approach was considered acceptable because the work domain not applying to a patient also means that the patient cannot be confronted with stigma at work. Further, for every item, corrected item-total correlation (standard value≥0.3) and inter-item correlations (standard value > 0.20 and < 0.75) in every domain were calculated.30,49(4)Test-retest reliability was investigated through Spearman correlation between the initial assessment and the retest (7–14 days later) PDStigmaQuest total score.(5)Convergent validity was tested through correlations with other stigma measures (SSCI, the stigma domain of the PDQ-39; rs > 0.50).50(6)Known-groups validity: Based on the well-established relationship between stigma and depression, known-groups validity was tested by comparing PDStigmaQuest scores in patients with and without depressive symptoms using a Mann-Whitney U test.21 We hypothesized that stigma scores should be higher in PD patients with than without depressive symptoms. To identify patients with depressive symptoms, the cut-off BDI-II≥14 was used as originally suggested by Beck et al. for detecting mild depression.34,51
The following analyses were conducted with data from patients and controls:(1)Group confirmation: To ensure that relevant PD symptoms were not similarly present in healthy controls, Mann-Whitney U tests were conducted between PD patients and healthy controls for the following scales: MoCA total, BDI-II total, HADS-A, and HADS-D. Tests were corrected for multiple comparisons according to the Benjamini-Hochberg procedure.(2)Comparison of PDStigmaQuest scores: To compare PD patients’ and healthy controls’ stigma scores, a Mann-Whitney U test was conducted with new stigma scores summing up only items answered by both groups. We hypothesized that stigma scores should be higher in PD patients than in healthy controls.
All analyses were conducted using Statistical Package for Social Sciences (SPSS; version 28.0). P-values < 0.05 were considered statistically significant.
RESULTS
Demographic characteristics
In total, 201 PD patients and 101 healthy controls matched by age and sex were included in the final analysis. Demographics and clinical characteristics are presented in Table 1.
Table 1
Demographics and clinical characteristics of patients with Parkinson’s disease and healthy controls
Patients
Healthy controls
p
Sex (female)
34.3%
45.5%
0.058
Age (y)a
64.4±9.7: 32–86
62.5±10.1: 42–87
0.110
Education (y)a
15.3±3.1: 7.5–23
16.2±3.3: 9–27
0.036
Family status
0.908
Married
74.1%
70.3%
Single
9.0%
10.9%
Divorced
10.9%
11.9%
Widowed
6.0%
6.9%
Occupation
<0.001
(Self-)employed
30.3%
58.4%
Retired
67.2%
37.6%
Other
2.5%
4.0%
Disease duration (y)a
7.9±5.0: 0.4–26.8
N/A
N/A
LEDD (mg)b
667.6±464.9
N/A
N/A
MDS-UPDRS III (score)b
26.0±12.3
N/A
N/A
HY (stage)c
2.0 (2.0–3.0)
N/A
N/A
Treated with DBS
22.9%
N/A
N/A
DBS, Deep brain stimulation; HY, Hoehn and Yahr; LEDD, Levodopa equivalent daily dose; MDS-UPDRS III, Movement Disorders Society Unified Parkinson’s Disease Rating Scale III; N/A, not applicable. Note: Significant differences are highlighted in bold. aMean±SD: range bMean±SD
cMedian (Interquartilerange).
Data quality and dimensionality
In patients, there was one missing data point in the PDStigmaQuest (0.0001% missing). In healthy controls, no data were missing.
Correlations of item 6 (feeling responsible for PD) were ≥ 0.3 with only two other items, thus item 6 was removed. KMO was 0.84 for the remaining items, showing that our sampling was adequate. Further, KMO values for individual variables were all > 0.5. Bartlett’s test of sphericity was statistically significant (p < 0.001), indicating that our correlation matrix was appropriate for conducting a factor analysis. Kaiser-Guttman criterion extracting factors with eigenvalues > 1 suggested a four-factor solution explaining 46.0 % of the variance. In the pattern matrix, loadings of item 16 on the factors were all < 0.30, so this item was dropped.42 Subsequently, the EFA was completed again showing a four-factor solution explaining 47.9% of the variance (Table 2): 8 items loaded onto a factor interpreted as “felt stigma”, 3 items loaded onto a factor measuring “hiding”, 3 items loaded onto a factor measuring “enacted stigma: rejection”, and 2 items loaded onto a factor measuring “enacted stigma: patronization”. The factor felt stigma was correlated with the other factors hiding (r = 0.50), enacted stigma: rejection (r = 0.39), and enacted stigma: patronization (r = 0.36). Factors enacted stigma: rejection and enacted stigma: patronization were also moderately correlated (r = 0.42).
Table 2
Pattern matrix of the final exploratory factor analysis
Item
Factor
1
2
3
4
Felt stigma
1
In the presence of others, I feel uncomfortable ... [list of symptoms]
0.629
3
I am unhappy about how my Parkinson’s symptoms affect my appearance.
0.563
5
I see myself as a burden to others.
0.647
7
I feel useless.
0.613
9
I worry about how others may react to my Parkinson’s disease.
0.539
0.364
10
I worry about how others will perceive me when my Parkinson’s disease progresses.
0.648
11
I am afraid that others could consider me mentally impaired.
0.467
20
Because of my Parkinson’s symptoms, others have looked at me.
0.403
Hiding
2
I feel uncomfortable when others address me regarding the treatment of my Parkinson’s disease (e.g., pills, patches, pump, or deep brain stimulation).
0.413
12
I try to hide my Parkinson’s symptoms from others.
0.758
13
I have kept my Parkinson’s disease secret from someone.
0.900
Enacted stigma: rejection
17
Friends or family members have turned away from me because of my Parkinson’s disease.
0.632
22
I have got invited by others less often than prior to my Parkinson’s disease.
0.701
23
Others have behaved as if my presence made them feel uncomfortable.
0.676
Enacted stigma: patronization
15
I have experienced others making decisions for me before I can make them for myself.
0.770
19
I have experienced others not letting me talk.
0.320
0.422
PD, Parkinson’s disease. Note: Loadings < 0.3 omitted.42 Items assigned to the respective factor in bold.
PDStigmaQuest scores and acceptability
Descriptive statistics of PD patients’ PDStigmaQuest scores and acceptability parameters are shown in Table 3. The maximum total score for (self-)employed patients summing up the final 18 items is 72, while unemployed/retired patients can achieve a total score of 64 (without the two items referring to work). In PD patients, floor effects were found for the domains hiding, enacted stigma: rejection, enacted stigma: patronization, and optional domain of work, but not for the domain felt stigma and total score. No ceiling effects were found. A moderate skewness was found for domains hiding and enacted stigma: rejection.
Table 3
Distribution and acceptability of PDStigmaQuest domain scores for patients with Parkinson’s disease
Mean
SD
Minimum
Maximum
Maximum achievable
Floor effect (%)
Ceiling effect (%)
Skewness
Felt stigma
9.2
5.4
0
27.9
32
3.0
0
0.6
Hiding
2.4
2.7
0
12.0
12
35.8
0.5
1.2
Enacted stigma: rejection
0.8
1.5
0
7.0
12
67.2
0
2.2
Enacted stigma: patronization
1.8
1.7
0
6.0
8
31.3
0
0.7
Optional: work domain (n = 64)
1.7
1.5
0
5.0
8
31.3
0
0.4
Total Score
14.7
8.9
0
43.91
72
2.0
0
0.6
PDStigmaQuest, Parkinson’s Disease Stigma Questionnaire.
Internal consistency
Cronbach’s alpha was 0.85 for the whole scale. Inter-item correlations and corrected item-total correlations are presented in Table 4.
Table 4
Internal consistency analysis for patients with Parkinson’s disease
Item
n
Inter-item correlation
Item-total correlation
Domain 1: Felt stigma
1
Uncomfortableness related to PD symptoms
200
0.34 – 0.52
0.63
2
Uncomfortableness related to PD appearance
200
0.29 – 0.52
0.51
3
Feeling like a burden to others
200
0.27 – 0.51
0.51
4
Feeling useless
200
0.27 – 0.51
0.53
5
Worries about reactions to PD
200
0.28 – 0.70
0.62
6
Worries about reactions to disease progression
200
0.27 – 0.70
0.64
7
Fear of being seen as mentally impaired
200
0.29 – 0.48
0.56
8
Being observed
200
0.27 – 0.36
0.45
Domain 2: Hiding
9
Feeling uncomfortable being asked about PD treatment
201
0.40 – 0.43
0.46
10
Hiding of PD symptoms
201
0.43 – 0.66
0.67
11
Concealing PD
201
0.40 – 0.66
0.65
Domain 3: Enacted stigma: rejection
12
Rejection by friends and family members
201
0.42 – 0.45
0.51
13
Being invited less often
201
0.45 – 0.49
0.56
14
Others acting as feeling uncomfortable in the presence of the patient
201
0.42 – 0.49
0.54
Domain 4: Enacted stigma: patronization
15
Decisions taken on behalf of patient by others
201
0.44
–
16
Being interrupted
201
0.44
–
Optional domain: Work
17
Fear of devaluation at work
64
0.18
–
18
Unfair treatment at work
64
0.18
–
PD, Parkinson’s disease. Note: In bold are inter-item correlations > 0.2 and item-total correlations≥0.3, representing preferable item characteristics.30,49 New numeration of items refers to the final questionnaire.
Test-retest reliability
Spearman correlation between initial and retest PDStigmaQuest total score was 0.83 (n = 147, p < 0.001).
Convergent validity
The final PDStigmaQuest correlated moderately with the PDQ-39 stigma domain (rs = 0.56, p < 0.001) and strongly with the SSCI total score (rs = 0.69, p < 0.001).
Known groups validity
Final PDStigmaQuest scores were higher in patients with depressive symptoms (mean = 20.8, SD = 9.6) than in patients without depressive symptoms (mean = 13.1, SD = 8.1, p < 0.001).
Comparison of patients and controls
Descriptive characteristics of MoCA total, BDI-II total, HADS-A, and HADS-D score for PD patients and controls are presented in Table 5. All examined clinical characteristics were significantly higher in PD patients than in controls.
Table 5
Comparison of patients with Parkinson’s disease and healthy controls regarding relevant clinical characteristics
Patients
Healthy controls
p
n
Mean
SD
n
Mean
SD
MoCA total
197
26.1
2.6
98
27.7
1.9
<0.001
BDI-II total
197
8.8
6.2
100
5.0
5.0
<0.001
HADS-A
194
4.5
3.2
101
3.6
2.9
0.016
HADS-D
194
4.1
3.2
101
2.3
2.3
<0.001
BDI-II, Beck Depression Inventory II; HADS, Hospital Anxiety and Depression Scale; MoCA, Montreal Cognitive Assessment. Note: Mann-Whitney U tests between patients with Parkinson’s disease and healthy controls to analyze differences in relevant clinical characteristics. Bold font highlights significant results, p < 0.05; All p-values are corrected for multiple comparisons using Benjamini-Hochberg procedure.
New stigma scores summing up only items in the final PDStigmaQuest answered by PD patients and controls were higher in PD patients (mean = 5.5, SD = 3.9) than in healthy controls (mean = 3.3, SD = 2.5, p < 0.001).
Demographic characteristics
In total, 201 PD patients and 101 healthy controls matched by age and sex were included in the final analysis. Demographics and clinical characteristics are presented in Table 1.
Table 1
Demographics and clinical characteristics of patients with Parkinson’s disease and healthy controls
Patients
Healthy controls
p
Sex (female)
34.3%
45.5%
0.058
Age (y)a
64.4±9.7: 32–86
62.5±10.1: 42–87
0.110
Education (y)a
15.3±3.1: 7.5–23
16.2±3.3: 9–27
0.036
Family status
0.908
Married
74.1%
70.3%
Single
9.0%
10.9%
Divorced
10.9%
11.9%
Widowed
6.0%
6.9%
Occupation
<0.001
(Self-)employed
30.3%
58.4%
Retired
67.2%
37.6%
Other
2.5%
4.0%
Disease duration (y)a
7.9±5.0: 0.4–26.8
N/A
N/A
LEDD (mg)b
667.6±464.9
N/A
N/A
MDS-UPDRS III (score)b
26.0±12.3
N/A
N/A
HY (stage)c
2.0 (2.0–3.0)
N/A
N/A
Treated with DBS
22.9%
N/A
N/A
DBS, Deep brain stimulation; HY, Hoehn and Yahr; LEDD, Levodopa equivalent daily dose; MDS-UPDRS III, Movement Disorders Society Unified Parkinson’s Disease Rating Scale III; N/A, not applicable. Note: Significant differences are highlighted in bold. aMean±SD: range bMean±SD
cMedian (Interquartilerange).
Data quality and dimensionality
In patients, there was one missing data point in the PDStigmaQuest (0.0001% missing). In healthy controls, no data were missing.
Correlations of item 6 (feeling responsible for PD) were ≥ 0.3 with only two other items, thus item 6 was removed. KMO was 0.84 for the remaining items, showing that our sampling was adequate. Further, KMO values for individual variables were all > 0.5. Bartlett’s test of sphericity was statistically significant (p < 0.001), indicating that our correlation matrix was appropriate for conducting a factor analysis. Kaiser-Guttman criterion extracting factors with eigenvalues > 1 suggested a four-factor solution explaining 46.0 % of the variance. In the pattern matrix, loadings of item 16 on the factors were all < 0.30, so this item was dropped.42 Subsequently, the EFA was completed again showing a four-factor solution explaining 47.9% of the variance (Table 2): 8 items loaded onto a factor interpreted as “felt stigma”, 3 items loaded onto a factor measuring “hiding”, 3 items loaded onto a factor measuring “enacted stigma: rejection”, and 2 items loaded onto a factor measuring “enacted stigma: patronization”. The factor felt stigma was correlated with the other factors hiding (r = 0.50), enacted stigma: rejection (r = 0.39), and enacted stigma: patronization (r = 0.36). Factors enacted stigma: rejection and enacted stigma: patronization were also moderately correlated (r = 0.42).
Table 2
Pattern matrix of the final exploratory factor analysis
Item
Factor
1
2
3
4
Felt stigma
1
In the presence of others, I feel uncomfortable ... [list of symptoms]
0.629
3
I am unhappy about how my Parkinson’s symptoms affect my appearance.
0.563
5
I see myself as a burden to others.
0.647
7
I feel useless.
0.613
9
I worry about how others may react to my Parkinson’s disease.
0.539
0.364
10
I worry about how others will perceive me when my Parkinson’s disease progresses.
0.648
11
I am afraid that others could consider me mentally impaired.
0.467
20
Because of my Parkinson’s symptoms, others have looked at me.
0.403
Hiding
2
I feel uncomfortable when others address me regarding the treatment of my Parkinson’s disease (e.g., pills, patches, pump, or deep brain stimulation).
0.413
12
I try to hide my Parkinson’s symptoms from others.
0.758
13
I have kept my Parkinson’s disease secret from someone.
0.900
Enacted stigma: rejection
17
Friends or family members have turned away from me because of my Parkinson’s disease.
0.632
22
I have got invited by others less often than prior to my Parkinson’s disease.
0.701
23
Others have behaved as if my presence made them feel uncomfortable.
0.676
Enacted stigma: patronization
15
I have experienced others making decisions for me before I can make them for myself.
0.770
19
I have experienced others not letting me talk.
0.320
0.422
PD, Parkinson’s disease. Note: Loadings < 0.3 omitted.42 Items assigned to the respective factor in bold.
PDStigmaQuest scores and acceptability
Descriptive statistics of PD patients’ PDStigmaQuest scores and acceptability parameters are shown in Table 3. The maximum total score for (self-)employed patients summing up the final 18 items is 72, while unemployed/retired patients can achieve a total score of 64 (without the two items referring to work). In PD patients, floor effects were found for the domains hiding, enacted stigma: rejection, enacted stigma: patronization, and optional domain of work, but not for the domain felt stigma and total score. No ceiling effects were found. A moderate skewness was found for domains hiding and enacted stigma: rejection.
Table 3
Distribution and acceptability of PDStigmaQuest domain scores for patients with Parkinson’s disease
Mean
SD
Minimum
Maximum
Maximum achievable
Floor effect (%)
Ceiling effect (%)
Skewness
Felt stigma
9.2
5.4
0
27.9
32
3.0
0
0.6
Hiding
2.4
2.7
0
12.0
12
35.8
0.5
1.2
Enacted stigma: rejection
0.8
1.5
0
7.0
12
67.2
0
2.2
Enacted stigma: patronization
1.8
1.7
0
6.0
8
31.3
0
0.7
Optional: work domain (n = 64)
1.7
1.5
0
5.0
8
31.3
0
0.4
Total Score
14.7
8.9
0
43.91
72
2.0
0
0.6
PDStigmaQuest, Parkinson’s Disease Stigma Questionnaire.
Internal consistency
Cronbach’s alpha was 0.85 for the whole scale. Inter-item correlations and corrected item-total correlations are presented in Table 4.
Table 4
Internal consistency analysis for patients with Parkinson’s disease
Item
n
Inter-item correlation
Item-total correlation
Domain 1: Felt stigma
1
Uncomfortableness related to PD symptoms
200
0.34 – 0.52
0.63
2
Uncomfortableness related to PD appearance
200
0.29 – 0.52
0.51
3
Feeling like a burden to others
200
0.27 – 0.51
0.51
4
Feeling useless
200
0.27 – 0.51
0.53
5
Worries about reactions to PD
200
0.28 – 0.70
0.62
6
Worries about reactions to disease progression
200
0.27 – 0.70
0.64
7
Fear of being seen as mentally impaired
200
0.29 – 0.48
0.56
8
Being observed
200
0.27 – 0.36
0.45
Domain 2: Hiding
9
Feeling uncomfortable being asked about PD treatment
201
0.40 – 0.43
0.46
10
Hiding of PD symptoms
201
0.43 – 0.66
0.67
11
Concealing PD
201
0.40 – 0.66
0.65
Domain 3: Enacted stigma: rejection
12
Rejection by friends and family members
201
0.42 – 0.45
0.51
13
Being invited less often
201
0.45 – 0.49
0.56
14
Others acting as feeling uncomfortable in the presence of the patient
201
0.42 – 0.49
0.54
Domain 4: Enacted stigma: patronization
15
Decisions taken on behalf of patient by others
201
0.44
–
16
Being interrupted
201
0.44
–
Optional domain: Work
17
Fear of devaluation at work
64
0.18
–
18
Unfair treatment at work
64
0.18
–
PD, Parkinson’s disease. Note: In bold are inter-item correlations > 0.2 and item-total correlations≥0.3, representing preferable item characteristics.30,49 New numeration of items refers to the final questionnaire.
Test-retest reliability
Spearman correlation between initial and retest PDStigmaQuest total score was 0.83 (n = 147, p < 0.001).
Convergent validity
The final PDStigmaQuest correlated moderately with the PDQ-39 stigma domain (rs = 0.56, p < 0.001) and strongly with the SSCI total score (rs = 0.69, p < 0.001).
Known groups validity
Final PDStigmaQuest scores were higher in patients with depressive symptoms (mean = 20.8, SD = 9.6) than in patients without depressive symptoms (mean = 13.1, SD = 8.1, p < 0.001).
Comparison of patients and controls
Descriptive characteristics of MoCA total, BDI-II total, HADS-A, and HADS-D score for PD patients and controls are presented in Table 5. All examined clinical characteristics were significantly higher in PD patients than in controls.
Table 5
Comparison of patients with Parkinson’s disease and healthy controls regarding relevant clinical characteristics
Patients
Healthy controls
p
n
Mean
SD
n
Mean
SD
MoCA total
197
26.1
2.6
98
27.7
1.9
<0.001
BDI-II total
197
8.8
6.2
100
5.0
5.0
<0.001
HADS-A
194
4.5
3.2
101
3.6
2.9
0.016
HADS-D
194
4.1
3.2
101
2.3
2.3
<0.001
BDI-II, Beck Depression Inventory II; HADS, Hospital Anxiety and Depression Scale; MoCA, Montreal Cognitive Assessment. Note: Mann-Whitney U tests between patients with Parkinson’s disease and healthy controls to analyze differences in relevant clinical characteristics. Bold font highlights significant results, p < 0.05; All p-values are corrected for multiple comparisons using Benjamini-Hochberg procedure.
New stigma scores summing up only items in the final PDStigmaQuest answered by PD patients and controls were higher in PD patients (mean = 5.5, SD = 3.9) than in healthy controls (mean = 3.3, SD = 2.5, p < 0.001).
DISCUSSION
In this study, we report validation data from the PDStigmaQuest, the first questionnaire specifically and comprehensively addressing stigma in PD. Our results illustrate that the new PDStigmaQuest, consisting of 18 items, is a valid and reliable self-reported questionnaire to comprehensively assess and evaluate stigma in a real-life PD population. Face validity can be assumed as experts, PD patients, and caregivers developed and reviewed the scale.28
Data quality and dimensionality
The assessment of the PDStigmaQuest revealed high data quality with only 0.0001 % missing data in patients and no missing data in controls. Results from EFA indicated sufficient construct validity. EFA identified 4 factors: felt stigma, hiding, enacted stigma: rejection, and enacted stigma: patronization. We additionally included an optional work domain for employed patients. The identified factors only partially overlap with our initially assumed domains based on one of the latest stigma conceptualizations: uncomfortableness, internalized stigma, anticipated stigma, hiding, and experienced stigma.11 Instead, the factors identified in the EFA align with the earlier stigma model by Scambler and Hopkins, consisting of felt and enacted stigma, extended by the domain of hiding.9 Our proposed domains of uncomfortableness, internalized stigma, and anticipated stigma were grouped as only one factor: felt stigma. Our initially assumed domain experienced stigma was divided into two aspects: rejection and patronizing by others. These also represent two components of the enacted stigma concept by Scambler and Hopkins.9 Our additional hiding domain specific to PD patients due to their partly concealable condition was preserved, with one additional item previously assigned to uncomfortableness (feeling uncomfortable being asked about PD treatment), which could have resulted from the high correlation between the factors felt stigma and hiding (r = 0.50). Fox et al. based their stigma concept on stigma insights concerning mental illness, which may explain the differences observed to the findings of our study in PD.11 In contrast, Scambler and Hopkins investigated stigma for persons with another neurological disease, epilepsy, in which stigma is conceptually closer to PDstigma.9
Acceptability
Moderate floor effects were found for domains of hiding, enacted stigma: rejection, enacted stigma: patronization, and work. In the context of other health conditions, it is often reported that felt stigma, including fear of being stigmatized, is significantly more prevalent than experiences of enacted stigma, e.g., rejection and patronization.52,53 Especially enacted stigma: rejection items refer to extreme forms of stigma, including breaking off contact to the PD patient.54 Therefore, this domain was expected to show floor effects. We nevertheless included these items since they are essential to portraying stigma in PD. Furthermore, it was shown that hiding efforts are more prevalent in the early stages of the disease, potentially leading to moderate floor effects of the domain hiding in our PD cohort representing the general PD population.13 Floor effects of the domain work could be explained by the fact that some PD patients stated to be self-employed and, therefore, the stigma items might not be fully applicable. Future studies should differentiate between employed and self-employed patients when investigating work-related stigma. To date, little is known about PD patients’ work-related stigma, which has to be investigated more intensively in the future, representing an important stigma aspect in PD.55,56 Notably, there were no floor effects for the total score and ceiling effects were absent. In summary, the results indicated an appropriate acceptability of the final PDStigmaQuest.
Internal consistency and test-retest reliability
For the final PDStigmaQuest, Cronbach’s alpha was 0.85, indicating high internal consistency. Inter-item correlations in the different stigma domains were all satisfactory except for work items. In this domain, one item represents felt stigma and the other enacted stigma experiences according to Scambler and Hopkins.9 The differential prevalence of the two stigma aspects could have led to low inter-item correlations with higher values on the felt stigma than on the enacted stigma item. However, given the importance of employment for PD patients, we consider including both stigma types within an optional domain for employed patients necessary. All calculated item-total correlations for domains met standard criteria. Test-retest correlation of the final PDStigmaQuest was 0.83, indicating high test-retest reliability.
Convergent and known-groups validity
The final PDStigmaQuest showed satisfactory correlations with other stigma measures, suggesting high convergent validity. Furthermore, data provided evidence for adequate known-groups validity due to the difference in PDStigmaQuest scores between patients with and without depressive symptoms. This finding is consistent with previous stigma literature, showing higher stigma levels in patients with higher depression levels.19,21,22,57,58
Comparison of PD patients and healthy controls
Comparing PD patients and healthy controls regarding relevant PD symptoms, we observed higher scores in patients than in healthy controls, providing evidence for a representative control group. New stigma scores summing up only items in the final PDStigmaQuest answered by PD patients and controls were significantly higher in PD patients, suggesting that the stigma experiences in the PDStigmaQuest are not equally made by elderly people without PD and rather represent PD-specific experiences.
Limitations
This validation study also has some limitations. Firstly, the two items referring to work-related stigma (n = 64) could not be explored with regard to dimensionality as for EFA, a minimum of 200 cases is required.38 However, we decided to retain these items due to their high importance for employed PD patients. Furthermore, since these items represent a dimension of stigma that can only be experienced by a subgroup of patients, treating this domain separately and not as a part of other stigma aspects affecting the general PD population was considered appropriate. Secondly, only seven items of the final PDStigmaQuest were applicable also to healthy controls and therefore, could be compared to PD patients. All other items were not completed by healthy controls as they included PD-related wording like “because of my Parkinson’s disease” and already implied that healthy controls cannot have these experiences at all. Thirdly, there was a difference between PD patients and healthy controls in years of education as well as employment status. Although to our knowledge, education has not been associated with stigma in PD, it has been identified as an influencing factor in other conditions such as epilepsy so that it would be reasonable controlling years of education in further studies.59 The difference in employment status is somehow expected as PD patients retire 4–7 years earlier than the general population.60 Lastly, the field’s current understanding of stigma in PD remains limited, highlighting the need for cross-validation of our findings in different PD cohorts, with a special need for investigating stigma in different countries and socio-cultural backgrounds as well as providing longitudinal data.
Conclusions
In conclusion, our results indicate that the patient-reported PDStigmaQuest has strong psychometric properties of validity and reliability and is helpful in assessing and evaluating PD-specific stigma. In future, the PDStigmaQuest can be applied to understand the different aspects of PD stigma and their potential influencing factors, e.g., demographics, and its relationship to clinical characteristics in more detail. This might contribute to improve the management of stigma in clinical practice and, as a consequence, patients’ QoL.
Future studies validating the PDStigmaQuest in different languages and independent multi-cultural PD cohorts are warranted.
Data quality and dimensionality
The assessment of the PDStigmaQuest revealed high data quality with only 0.0001 % missing data in patients and no missing data in controls. Results from EFA indicated sufficient construct validity. EFA identified 4 factors: felt stigma, hiding, enacted stigma: rejection, and enacted stigma: patronization. We additionally included an optional work domain for employed patients. The identified factors only partially overlap with our initially assumed domains based on one of the latest stigma conceptualizations: uncomfortableness, internalized stigma, anticipated stigma, hiding, and experienced stigma.11 Instead, the factors identified in the EFA align with the earlier stigma model by Scambler and Hopkins, consisting of felt and enacted stigma, extended by the domain of hiding.9 Our proposed domains of uncomfortableness, internalized stigma, and anticipated stigma were grouped as only one factor: felt stigma. Our initially assumed domain experienced stigma was divided into two aspects: rejection and patronizing by others. These also represent two components of the enacted stigma concept by Scambler and Hopkins.9 Our additional hiding domain specific to PD patients due to their partly concealable condition was preserved, with one additional item previously assigned to uncomfortableness (feeling uncomfortable being asked about PD treatment), which could have resulted from the high correlation between the factors felt stigma and hiding (r = 0.50). Fox et al. based their stigma concept on stigma insights concerning mental illness, which may explain the differences observed to the findings of our study in PD.11 In contrast, Scambler and Hopkins investigated stigma for persons with another neurological disease, epilepsy, in which stigma is conceptually closer to PDstigma.9
Acceptability
Moderate floor effects were found for domains of hiding, enacted stigma: rejection, enacted stigma: patronization, and work. In the context of other health conditions, it is often reported that felt stigma, including fear of being stigmatized, is significantly more prevalent than experiences of enacted stigma, e.g., rejection and patronization.52,53 Especially enacted stigma: rejection items refer to extreme forms of stigma, including breaking off contact to the PD patient.54 Therefore, this domain was expected to show floor effects. We nevertheless included these items since they are essential to portraying stigma in PD. Furthermore, it was shown that hiding efforts are more prevalent in the early stages of the disease, potentially leading to moderate floor effects of the domain hiding in our PD cohort representing the general PD population.13 Floor effects of the domain work could be explained by the fact that some PD patients stated to be self-employed and, therefore, the stigma items might not be fully applicable. Future studies should differentiate between employed and self-employed patients when investigating work-related stigma. To date, little is known about PD patients’ work-related stigma, which has to be investigated more intensively in the future, representing an important stigma aspect in PD.55,56 Notably, there were no floor effects for the total score and ceiling effects were absent. In summary, the results indicated an appropriate acceptability of the final PDStigmaQuest.
Internal consistency and test-retest reliability
For the final PDStigmaQuest, Cronbach’s alpha was 0.85, indicating high internal consistency. Inter-item correlations in the different stigma domains were all satisfactory except for work items. In this domain, one item represents felt stigma and the other enacted stigma experiences according to Scambler and Hopkins.9 The differential prevalence of the two stigma aspects could have led to low inter-item correlations with higher values on the felt stigma than on the enacted stigma item. However, given the importance of employment for PD patients, we consider including both stigma types within an optional domain for employed patients necessary. All calculated item-total correlations for domains met standard criteria. Test-retest correlation of the final PDStigmaQuest was 0.83, indicating high test-retest reliability.
Convergent and known-groups validity
The final PDStigmaQuest showed satisfactory correlations with other stigma measures, suggesting high convergent validity. Furthermore, data provided evidence for adequate known-groups validity due to the difference in PDStigmaQuest scores between patients with and without depressive symptoms. This finding is consistent with previous stigma literature, showing higher stigma levels in patients with higher depression levels.19,21,22,57,58
Comparison of PD patients and healthy controls
Comparing PD patients and healthy controls regarding relevant PD symptoms, we observed higher scores in patients than in healthy controls, providing evidence for a representative control group. New stigma scores summing up only items in the final PDStigmaQuest answered by PD patients and controls were significantly higher in PD patients, suggesting that the stigma experiences in the PDStigmaQuest are not equally made by elderly people without PD and rather represent PD-specific experiences.
Limitations
This validation study also has some limitations. Firstly, the two items referring to work-related stigma (n = 64) could not be explored with regard to dimensionality as for EFA, a minimum of 200 cases is required.38 However, we decided to retain these items due to their high importance for employed PD patients. Furthermore, since these items represent a dimension of stigma that can only be experienced by a subgroup of patients, treating this domain separately and not as a part of other stigma aspects affecting the general PD population was considered appropriate. Secondly, only seven items of the final PDStigmaQuest were applicable also to healthy controls and therefore, could be compared to PD patients. All other items were not completed by healthy controls as they included PD-related wording like “because of my Parkinson’s disease” and already implied that healthy controls cannot have these experiences at all. Thirdly, there was a difference between PD patients and healthy controls in years of education as well as employment status. Although to our knowledge, education has not been associated with stigma in PD, it has been identified as an influencing factor in other conditions such as epilepsy so that it would be reasonable controlling years of education in further studies.59 The difference in employment status is somehow expected as PD patients retire 4–7 years earlier than the general population.60 Lastly, the field’s current understanding of stigma in PD remains limited, highlighting the need for cross-validation of our findings in different PD cohorts, with a special need for investigating stigma in different countries and socio-cultural backgrounds as well as providing longitudinal data.
Conclusions
In conclusion, our results indicate that the patient-reported PDStigmaQuest has strong psychometric properties of validity and reliability and is helpful in assessing and evaluating PD-specific stigma. In future, the PDStigmaQuest can be applied to understand the different aspects of PD stigma and their potential influencing factors, e.g., demographics, and its relationship to clinical characteristics in more detail. This might contribute to improve the management of stigma in clinical practice and, as a consequence, patients’ QoL.
Future studies validating the PDStigmaQuest in different languages and independent multi-cultural PD cohorts are warranted. |
Title: Extraction of Soluble Dietary Fiber from Sunflower Receptacles ( | Body:
1. Introduction
The sunflower (Helianthus annuus L.) is a widely cultivated economic crop and oil crop in the world. It is an important source of high-quality oil, protein, and dietary fiber [1,2,3]. Sunflower receptacles (SRs) are one of the by-products of the sunflower. For a long time, SR has been directly discarded or burned in situ after sunflower seed removal, resulting in environmental pollution and a waste of resources [4]. In recent years, due to its rich dietary fiber (DF) content, SR has been used in animal feed research and achieved certain results, but it is not suitable for extensive use because of its low protein content [5]. Therefore, this study intends to further develop the function of SR and extract its soluble dietary fiber, which is applicable to new products.
Constipation is a common functional gastrointestinal disease. There are many complex causes of constipation. Insufficient exercise, heredity, diet, intestinal flora, age, and gender can cause constipation [6,7]. The most common symptoms of constipation include reduced and difficult defecation, long duration, abdominal distension or pain, and in severe cases, can even lead to life-threatening irritable bowel syndrome and colon cancer [8]. The treatment of constipation currently includes non-drug intervention and drug therapy. In non-drug intervention, it includes increasing dietary fiber and water intake and lifestyle adjustments [9]. When these methods are difficult to work, drug therapy is needed. Various osmotic laxatives are commonly used in the treatment of constipation but may be ineffective and may be accompanied by adverse reactions [10]. Stimulating laxatives are commonly used to treat patients with chronic constipation, but long-term use leads to drug dependence, gastrointestinal tissue damage, and high recurrence rates [11]. Drugs approved by the Food and Drug Administration (FDA) for the treatment of constipation, such as rubiprostone, linalotide, and plecanatide, also have a range of adverse reactions [12]. In view of the limitations of the current clinical medication, the endeavor is to develop anti-constipation products with less gastrointestinal irritation, fewer adverse reactions, and a pure nature.
Dietary fiber is recognized as a beneficial nutrient for human health. Clinical studies have shown that a higher dietary fiber intake can help reduce the risk of several chronic diseases, such as coronary heart disease, colorectal cancer, and breast cancer [13,14,15,16]. In addition, dietary fiber intake can increase fecal volume, reduce transit time, and regulate defecation, fermentation, and the effects on gut microbiota to promote digestive system health [17]. According to the solubility, soluble fiber (SDF) and insoluble fiber (IDF) can be divided [14]. SDF mainly includes pectin, oligosaccharides, glucan, and galactooligosaccharides, while IDF mainly includes cellulose, hemicellulose, and lignin [18,19]. Studies have shown that SDF and IDF have different physiological functions. IDF will not be fermented in the colon, but it increases the volume of feces and promotes gastrointestinal motility through its water-holding capacity [19]. Different from IDF, SDF is more easily utilized by intestinal flora and can affect the types and richness of intestinal microorganisms in the body. Intake of a large amount of SDF can reduce the risk of many chronic distortions [20].
According to the 2020–2025 Dietary Guidelines for American Residents (DGA), more than 90% of women and 97% of men do not meet the recommended intake of dietary fiber [21,22]. The American Nutrition and Dietetic Association calls for daily dietary fiber intake to reach 25 g for adult women and 38 g for adult men [23]. It is noteworthy that SDF has been used in the treatment of functional gastrointestinal disorders (FGIDs) in recent years. A clinical study has shown that guar gum can be used as a non-drug therapy for pediatric FGIDs and has achieved good results [24]. Therefore, the development of dietary fiber products has broad market prospects. However, studies have found that SDF content is relatively low in most dietary fiber-derived foods [25]. Therefore, it is necessary to increase the SDF content in dietary fiber foods to improve the functional and nutritional properties of modified products.
In view of the limitations of the current clinical treatment of constipation, based on the current situation of SR utilization, this paper uses the citric acid extraction method and the cellulase extraction method to modify the extraction of soluble dietary fiber from sunflower plate on the basis of the hot-water extraction method and uses the response surface method to optimize the extraction parameters and obtain the optimal extraction process. Then, the physicochemical properties and functional properties of the three SDFs were evaluated, and the properties of SDFs obtained by different extraction processes were evaluated. Finally, a mouse constipation model was established, which provided a more theoretical basis for the study of SR functional food in the treatment of constipation, and also provided a new way for the transformation of the agricultural and sideline products of SR into treasures having potential application.
2. Materials and Methods
2.1. Materials
Helianthus annuus L. was harvested from Baicheng City, Jilin Province (122°50′ E, 45° 37′ N). The plant specimens were identified by Professor Shuwen Guan from the College of Life Sciences at Jilin University.
Cellulase, α-amylase, and neutral protease were purchased from Beijing Solarbio Science & Technology Co., Ltd. (Beijing, China). Loperamide hydrochloride capsules were purchased from Xi ‘an Janssen Pharmaceutical Co., Ltd. (Xi’an, China); all reagents are of analytical grade.
2.2. Samples and Sample Preparation
Freshly picked sunflowers were deseeded, and the SRs were naturally air-dried to a constant weight. The SR powder was crushed (34,000 r/min, 3 min) with a high-speed grinder (800Y, Yongkang Platinum Europe Co., Ltd., Yongkang, China). Petroleum ether (1:4, w/v) was added to the SR powder for overnight soaking and degreasing, vacuum filtration, and drying in a blast oven (DHG-9070A, Shanghai Yiheng Scientific Instrument Factory, Shanghai, China) at 45 °C to constant weight.
2.3. SDF Extraction
2.3.1. Citric Acid Extraction
In order to prepare SDF by citric acid extraction, the SR powder was mixed with deionized water in a certain proportion, and a certain mass fraction of citric acid was added. At a certain temperature, the water bath (HH-8, Guohua Electric Co., Ltd., Jintan, China) was used for a certain time to obtain the extract. After cooling, the mixture was centrifuged (1840× g, 10 min) by a centrifuge (PK-165, Hunan Pingke Scientific Instrument Co., Ltd., Changsha, China) to obtain the supernatant, then concentrated to a certain volume at 50 °C using a rotary evaporator (RF-52AA, Shanghai Yarong Biochemical Instrument Co., Ltd., Shanghai, China). The concentrated solution was mixed with 4 times the volume of ethanol (95%, v/v) and rested overnight. The precipitate was collected by vacuum filtration and washed with ethanol, and then, a vacuum-drying oven (DZF-6030B, Shanghai Yiheng Scientific Instrument Factory, Shanghai, China) was used to vacuum-dry to a constant weight at 50 °C. The product was designated as ASDF.
2.3.2. Hot-Water Extraction
In order to prepare the SDF by hot-water extraction, the SR powder was mixed with distilled water in a certain proportion, and the extract was obtained by water bath for a certain time at a certain temperature. After cooling, the pH was adjusted to 6.5, and 1% (w/w) α-amylase and neutral protease were added for enzymatic hydrolysis at 50 °C for 60 min. Subsequently, the enzyme was inactivated by a boiling water bath for 5 min. Then the method of solid–liquid separation and ethanol precipitation was used, which was similar to the method described by the citric acid extraction method (Section 2.3.1), and the obtained dietary fiber was expressed as WSDF.
2.3.3. Enzymatic Extraction
In order to prepare SDF by enzyme extraction, the SR powder was mixed with deionized water in a certain proportion, and a certain mass fraction of cellulase (Cat#C8271, 50,000 U/g, Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) was added to adjust the pH to 6.5. The extract was obtained by water bath at a certain temperature for a certain time. Subsequently, the enzyme was killed by a boiling water bath for 5 min. Then, the method described in Section 2.3.2 was used for further enzymatic treatment, and finally, the dietary fiber representing the ESDF was obtained.
2.4. Optimization of SDF Extraction Process
2.4.1. Mono-Factor Experiments
The effects of solid–liquid ratio, extraction time, temperature, citric acid addition, and cellulase addition on the yield of SDF from SR were tested by a single-factor test. For ASDF, the extraction conditions were temperature (50, 60, 70, 80, and 90 °C), extraction time (30, 60, 90, 120, and 150 min), liquid–solid ratio (15, 20, 25, 30, and 35 mL/g), and citric acid addition (0.5, 1.0, 1.5, 2.0, and 2.5%). For the WSDF, the extraction conditions were temperature (50, 60, 70, 80, and 90 °C), extraction time (30, 60, 90, 120, and 150 min), and liquid–solid ratio (20, 25, 30, 35, and 40 mL/g). For the ESDF, the extraction conditions were temperature (45, 50, 55, 60, and 65 °C), extraction time (30, 60, 90, 120, and 150 min), liquid–solid ratio (15, 20, 25, 30, and 35 mL/g), and cellulase addition (0.5, 1.0, 1.5, 2.0, and 2.5%).
2.4.2. Response Surface Experiments
According to the results of the single-factor experiment, the Box–Behnken experiment (BBD) was designed by Design-Expert 8, and then, different experimental extraction conditions were optimized by the response surface method. For hot-water extraction, the independent variables were the liquid–solid ratio (A, 30, 35, and 40 mL/g), extraction time (B, 90, 120, and 150 min), and temperature (C, 70, 80, and 90 °C), and the response index was the WSDF yield. For the citric acid extraction method, the independent variables were the liquid–solid ratio (C, 15, 20, and 25 mL/g), extraction time (B, 60, 90, and 120 min), temperature (A, 60, 70, and 80 °C), and citric acid addition (D, 0.5, 1.0, and 1.5 %), and the response index was the ASDF yield. For the cellulase extraction method, the independent variables were the liquid–solid ratio (A, 20, 25, and 30 mL/g), time (B, 60, 90, and 120 min), temperature (C, 50, 55, and 60 °C), and cellulase addition (D, 1.0, 1.5, and 2.0 %), and the response index was the ESDF yield. The center value is encoded as 0, and the maximum and minimum values are encoded as 1 and −1, respectively. For the hot-water extraction method, a total of 17 groups were designed for the experiment, and 5 groups were repeated for the central point experiment. A total of 29 groups were designed for the citric acid extraction method and the cellulase method, and 5 groups were repeated for the central point experiment. The experimental factors and level design are shown in Tables S1–S3.
2.4.3. Extraction Yield
The calculation formula for the SDF extraction rate of sunflower receptacles is as follows:Extraction yield(%)=(M1/M0)×100%
where M1 is the dry weight (g) of the SDF powder extracted from sunflower receptacles, and M0 is the dry weight (g) of the sunflower receptacles powder used for extraction.
2.5. Analysis of Physicochemical Property
2.5.1. Measurement of Water-Holding Capacity (WHC)
According to the method [26], slightly modified, 1.00 g of the SDF obtained by the different extraction methods was accurately weighed in a centrifuge tube, and 40 mL deionized water was added, shaken well, and balanced at 25 °C for 12 h. Then, it was centrifuged at 1840× g for 15 min, and the weight of the sample after discarding the supernatant was weighed. The water-holding capacity is calculated according to the following formula:WHCg/g=(M2−M0)/M0×100%
where M2 is the mass of the sample after water absorption (g); M0 represents the initial mass (g) of the sample.
2.5.2. Measurement of Oil-Holding Capacity (OHC)
A slightly modified method [27] was used to determine the oil-holding capacity. Accurately weigh 1.00 g of the SDF obtained by different extraction methods in a centrifuge tube, add 12 mL peanut oil, and mix at 4 °C for 1 h. Then, it was centrifuged at 1840× g for 15 min, and the weight of the sample after discarding the supernatant was weighed. The oil-holding capacity calculation formula is as follows:OHCg/g=(M3−M0)/M0×100%
where M3 is the mass of the sample after oil absorption (g); M0 represents the initial mass (g) of the sample.
2.5.3. Measurement of Swelling Capacity (SC)
A total of 0.25 g of the SDF samples obtained by different extraction methods were accurately weighed in an expansion tube. After recording the scale (V1), 10 mL of distilled water were added. The mixture was fully shaken and stood at room temperature for 12 h to record the corresponding scale (V2) of the SDF after water absorption:SC(mL/g)=(V2−V1)/M0×100%
where V1 is the volume (mL) of SDF before water absorption and expansion; V2 is the volume (mL) of SDF after water absorption and expansion; and M0 is the mass (g) of SDF before swelling.
2.6. Structural Characterization of Sunflower Receptacles’ Soluble Dietary Fiber
2.6.1. Scanning Electron Microscopy (SEM) Analysis
The microstructure of SDF obtained by different extraction methods was observed by scanning electron microscopy (JSM-6701F, JEOL, Tokyo, Japan). The samples were fixed and sprayed with gold, and the parameters were set to 5.0 kV acceleration voltage at 5000 times magnification.
2.6.2. Fourier Transform Infrared (FT-IR) Spectroscopy Analysis
The SDFs were fully mixed with KBr (1:100, w/w), fully ground in a quartz mortar and pressed into a transparent sheet with an appropriate thickness. The FT-IR spectrometer (Nicolet 6700, Thermo Fisher Technologies, Waltham, MA, USA) was used to scan at 400~4000 cm−1 wavenumbers.
2.6.3. X-Ray Diffraction (XRD) Analysis
The SDFs obtained by different extraction methods were compared and analyzed by an X-ray diffractometer (Kratos AXIS SUPRA, Bruker Technology Co., Ltd., Karlsruhe, Germany). The voltage was 40 kV. The current was 20 mA. The X-ray wavelength was λ = 0.156 nm, and the scanning range was 5°–70°.
2.7. Animal Experiment
2.7.1. Animals and Their Experiments
All procedures involving animal experiments have been approved by the Animal Ethics Committee of Jilin University. The experimental animals were raised in the Animal Experimental Center of the College of Life Sciences, Jilin University (SPF grade).
SPF male BALB/c mice (18–22 g, 4–6 weeks) were purchased from Liaoning Changsheng Biotechnology Co., Ltd. The experimental animals were not genetically modified during the experiment. The mice were accommodated in the Jilin University Experimental Animal Platform and were housed in different units at a temperature of 23 ± 1 °C and a relative humidity of 40~60%. Good ventilation and light–dark cycle were ensured. Unrestricted food and water were provided during the experiment. During the experiment, the physiological status and body weight of each group of mice were monitored every day, and the daily food intake and water intake of each cage of mice were calculated.
After successful modeling, the control group and the model group were given normal saline 10 mL/kg, and the low-dose group, the middle-dose group, and the high-dose group were given ASDF 0.25 g/kg, 0.5 g/kg, and 1 g/kg, respectively. The dose was 0.1 mL/10 g, with continuous gavage for 8 days twice a day.
2.7.2. Constipation Model Induced by Loperamide Hydrochloride
After one week of adaptive growth, 35 mice were randomly divided into 5 groups, namely the control group, model group, ASDF low-dose group, ASDF medium-dose group, and ASDF high-dose group (n = 7 per group). The blank group was given normal saline 10 mL/kg by gavage, and the other groups were given 10 mL/kg loperamide hydrochloride solution (loperamide hydrochloride dissolved in normal saline, 1 mg/mL) by gavage. The dosage was 0.1 mL/10 g once a day for 16 consecutive days, and modeling was performed [28].
2.7.3. Evaluation of Defecation Function of Constipated Mice
After the animal experiment modeling and the end of treatment, the first black stool discharge time of the mice was measured. Before the experiment, each group of mice was fasted for 16 h, and the mice were free to drink water during the fasting period. The blank group did not undergo any treatment, and each mouse in the other groups was intragastrically administered with loperamide hydrochloride (10 mL/kg) at a dose of 0.1 mL/10 g. After 30 min, all mice were given 0.2 mL of ink by gavage, and the first black stool discharge time, the number of fresh feces discharged within 4 h, and the wet weight of each mouse were recorded. After that, the feces were placed in a blast-drying oven and dried at 60 °C to a constant weight. The moisture content of the feces was calculated according to the following formula:Fecal moisture content(%)=(M5−M4)/M5×100%
where M4 is the dry weight of the feces (g); m5 is the wet weight of feces (g).
It is worth noting that 5 g Arabic gum powder and 5 g activated carbon powder were added to 100 mL distilled water to prepare ink, and the water bath was heated and boiled, repeated many times, until the Arabic gum was completely dissolved, the bottom of the bottle was not precipitated, stored at room temperature, and oscillated evenly before each use.
2.7.4. Small Intestine Propulsion Test
After the end of drug treatment, the intestinal propulsion rate of mice was measured. Before the experiment, the mice in each group were fasted for 16 h, and the mice were free to drink water during the fasting period. The blank group was not treated, and each mouse in the other groups was given loperamide hydrochloride (10 mL/kg) by gavage at a dose of 0.1 mL/10 g. After 30 min, all mice were given 0.2 mL of ink by gavage. After 30 min, the mice in each group were sacrificed by dislocation, and the entire intestine from the pylorus to the end of the colon was immediately collected. The total length of the small intestine and the length of the ink propulsion were measured. The small intestine propulsion rate was calculated as follows:Intestinal propulsive rate(%)=L2/L1×100%
where L1 is the total length of the small intestine (cm), and L2 is the ink propulsion distance (cm).
2.7.5. Histopathology
Part of the colon tissue was fixed in 4% paraformaldehyde for 24 h. The fixed colon tissue was embedded in paraffin and cut into 5 μm thin sections. The colon tissue sections were first dewaxed in xylene, rehydrated in gradient ethanol, and then observed with hematoxylin–eosin (H&E) staining. Histological analysis was performed, and images of colon tissue were observed under an optical microscope (magnified ×400, Nikon, Minato, Japan).
2.8. Statistical Analysis
SPSS18.0 statistical software was used for statistics, and the data were represented by “X ± SD”. One-way variance (ANOVA) was used for statistical analysis, and Tukey’s comparison test was used to obtain a significant difference (p < 0.05 was considered statistically significant, and highly significant differences were indicated at p < 0.01). Origin 2019b software and Graphpad Prism 8.0 software were used to make and process the charts.
2.1. Materials
Helianthus annuus L. was harvested from Baicheng City, Jilin Province (122°50′ E, 45° 37′ N). The plant specimens were identified by Professor Shuwen Guan from the College of Life Sciences at Jilin University.
Cellulase, α-amylase, and neutral protease were purchased from Beijing Solarbio Science & Technology Co., Ltd. (Beijing, China). Loperamide hydrochloride capsules were purchased from Xi ‘an Janssen Pharmaceutical Co., Ltd. (Xi’an, China); all reagents are of analytical grade.
2.2. Samples and Sample Preparation
Freshly picked sunflowers were deseeded, and the SRs were naturally air-dried to a constant weight. The SR powder was crushed (34,000 r/min, 3 min) with a high-speed grinder (800Y, Yongkang Platinum Europe Co., Ltd., Yongkang, China). Petroleum ether (1:4, w/v) was added to the SR powder for overnight soaking and degreasing, vacuum filtration, and drying in a blast oven (DHG-9070A, Shanghai Yiheng Scientific Instrument Factory, Shanghai, China) at 45 °C to constant weight.
2.3. SDF Extraction
2.3.1. Citric Acid Extraction
In order to prepare SDF by citric acid extraction, the SR powder was mixed with deionized water in a certain proportion, and a certain mass fraction of citric acid was added. At a certain temperature, the water bath (HH-8, Guohua Electric Co., Ltd., Jintan, China) was used for a certain time to obtain the extract. After cooling, the mixture was centrifuged (1840× g, 10 min) by a centrifuge (PK-165, Hunan Pingke Scientific Instrument Co., Ltd., Changsha, China) to obtain the supernatant, then concentrated to a certain volume at 50 °C using a rotary evaporator (RF-52AA, Shanghai Yarong Biochemical Instrument Co., Ltd., Shanghai, China). The concentrated solution was mixed with 4 times the volume of ethanol (95%, v/v) and rested overnight. The precipitate was collected by vacuum filtration and washed with ethanol, and then, a vacuum-drying oven (DZF-6030B, Shanghai Yiheng Scientific Instrument Factory, Shanghai, China) was used to vacuum-dry to a constant weight at 50 °C. The product was designated as ASDF.
2.3.2. Hot-Water Extraction
In order to prepare the SDF by hot-water extraction, the SR powder was mixed with distilled water in a certain proportion, and the extract was obtained by water bath for a certain time at a certain temperature. After cooling, the pH was adjusted to 6.5, and 1% (w/w) α-amylase and neutral protease were added for enzymatic hydrolysis at 50 °C for 60 min. Subsequently, the enzyme was inactivated by a boiling water bath for 5 min. Then the method of solid–liquid separation and ethanol precipitation was used, which was similar to the method described by the citric acid extraction method (Section 2.3.1), and the obtained dietary fiber was expressed as WSDF.
2.3.3. Enzymatic Extraction
In order to prepare SDF by enzyme extraction, the SR powder was mixed with deionized water in a certain proportion, and a certain mass fraction of cellulase (Cat#C8271, 50,000 U/g, Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) was added to adjust the pH to 6.5. The extract was obtained by water bath at a certain temperature for a certain time. Subsequently, the enzyme was killed by a boiling water bath for 5 min. Then, the method described in Section 2.3.2 was used for further enzymatic treatment, and finally, the dietary fiber representing the ESDF was obtained.
2.3.1. Citric Acid Extraction
In order to prepare SDF by citric acid extraction, the SR powder was mixed with deionized water in a certain proportion, and a certain mass fraction of citric acid was added. At a certain temperature, the water bath (HH-8, Guohua Electric Co., Ltd., Jintan, China) was used for a certain time to obtain the extract. After cooling, the mixture was centrifuged (1840× g, 10 min) by a centrifuge (PK-165, Hunan Pingke Scientific Instrument Co., Ltd., Changsha, China) to obtain the supernatant, then concentrated to a certain volume at 50 °C using a rotary evaporator (RF-52AA, Shanghai Yarong Biochemical Instrument Co., Ltd., Shanghai, China). The concentrated solution was mixed with 4 times the volume of ethanol (95%, v/v) and rested overnight. The precipitate was collected by vacuum filtration and washed with ethanol, and then, a vacuum-drying oven (DZF-6030B, Shanghai Yiheng Scientific Instrument Factory, Shanghai, China) was used to vacuum-dry to a constant weight at 50 °C. The product was designated as ASDF.
2.3.2. Hot-Water Extraction
In order to prepare the SDF by hot-water extraction, the SR powder was mixed with distilled water in a certain proportion, and the extract was obtained by water bath for a certain time at a certain temperature. After cooling, the pH was adjusted to 6.5, and 1% (w/w) α-amylase and neutral protease were added for enzymatic hydrolysis at 50 °C for 60 min. Subsequently, the enzyme was inactivated by a boiling water bath for 5 min. Then the method of solid–liquid separation and ethanol precipitation was used, which was similar to the method described by the citric acid extraction method (Section 2.3.1), and the obtained dietary fiber was expressed as WSDF.
2.3.3. Enzymatic Extraction
In order to prepare SDF by enzyme extraction, the SR powder was mixed with deionized water in a certain proportion, and a certain mass fraction of cellulase (Cat#C8271, 50,000 U/g, Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) was added to adjust the pH to 6.5. The extract was obtained by water bath at a certain temperature for a certain time. Subsequently, the enzyme was killed by a boiling water bath for 5 min. Then, the method described in Section 2.3.2 was used for further enzymatic treatment, and finally, the dietary fiber representing the ESDF was obtained.
2.4. Optimization of SDF Extraction Process
2.4.1. Mono-Factor Experiments
The effects of solid–liquid ratio, extraction time, temperature, citric acid addition, and cellulase addition on the yield of SDF from SR were tested by a single-factor test. For ASDF, the extraction conditions were temperature (50, 60, 70, 80, and 90 °C), extraction time (30, 60, 90, 120, and 150 min), liquid–solid ratio (15, 20, 25, 30, and 35 mL/g), and citric acid addition (0.5, 1.0, 1.5, 2.0, and 2.5%). For the WSDF, the extraction conditions were temperature (50, 60, 70, 80, and 90 °C), extraction time (30, 60, 90, 120, and 150 min), and liquid–solid ratio (20, 25, 30, 35, and 40 mL/g). For the ESDF, the extraction conditions were temperature (45, 50, 55, 60, and 65 °C), extraction time (30, 60, 90, 120, and 150 min), liquid–solid ratio (15, 20, 25, 30, and 35 mL/g), and cellulase addition (0.5, 1.0, 1.5, 2.0, and 2.5%).
2.4.2. Response Surface Experiments
According to the results of the single-factor experiment, the Box–Behnken experiment (BBD) was designed by Design-Expert 8, and then, different experimental extraction conditions were optimized by the response surface method. For hot-water extraction, the independent variables were the liquid–solid ratio (A, 30, 35, and 40 mL/g), extraction time (B, 90, 120, and 150 min), and temperature (C, 70, 80, and 90 °C), and the response index was the WSDF yield. For the citric acid extraction method, the independent variables were the liquid–solid ratio (C, 15, 20, and 25 mL/g), extraction time (B, 60, 90, and 120 min), temperature (A, 60, 70, and 80 °C), and citric acid addition (D, 0.5, 1.0, and 1.5 %), and the response index was the ASDF yield. For the cellulase extraction method, the independent variables were the liquid–solid ratio (A, 20, 25, and 30 mL/g), time (B, 60, 90, and 120 min), temperature (C, 50, 55, and 60 °C), and cellulase addition (D, 1.0, 1.5, and 2.0 %), and the response index was the ESDF yield. The center value is encoded as 0, and the maximum and minimum values are encoded as 1 and −1, respectively. For the hot-water extraction method, a total of 17 groups were designed for the experiment, and 5 groups were repeated for the central point experiment. A total of 29 groups were designed for the citric acid extraction method and the cellulase method, and 5 groups were repeated for the central point experiment. The experimental factors and level design are shown in Tables S1–S3.
2.4.3. Extraction Yield
The calculation formula for the SDF extraction rate of sunflower receptacles is as follows:Extraction yield(%)=(M1/M0)×100%
where M1 is the dry weight (g) of the SDF powder extracted from sunflower receptacles, and M0 is the dry weight (g) of the sunflower receptacles powder used for extraction.
2.4.1. Mono-Factor Experiments
The effects of solid–liquid ratio, extraction time, temperature, citric acid addition, and cellulase addition on the yield of SDF from SR were tested by a single-factor test. For ASDF, the extraction conditions were temperature (50, 60, 70, 80, and 90 °C), extraction time (30, 60, 90, 120, and 150 min), liquid–solid ratio (15, 20, 25, 30, and 35 mL/g), and citric acid addition (0.5, 1.0, 1.5, 2.0, and 2.5%). For the WSDF, the extraction conditions were temperature (50, 60, 70, 80, and 90 °C), extraction time (30, 60, 90, 120, and 150 min), and liquid–solid ratio (20, 25, 30, 35, and 40 mL/g). For the ESDF, the extraction conditions were temperature (45, 50, 55, 60, and 65 °C), extraction time (30, 60, 90, 120, and 150 min), liquid–solid ratio (15, 20, 25, 30, and 35 mL/g), and cellulase addition (0.5, 1.0, 1.5, 2.0, and 2.5%).
2.4.2. Response Surface Experiments
According to the results of the single-factor experiment, the Box–Behnken experiment (BBD) was designed by Design-Expert 8, and then, different experimental extraction conditions were optimized by the response surface method. For hot-water extraction, the independent variables were the liquid–solid ratio (A, 30, 35, and 40 mL/g), extraction time (B, 90, 120, and 150 min), and temperature (C, 70, 80, and 90 °C), and the response index was the WSDF yield. For the citric acid extraction method, the independent variables were the liquid–solid ratio (C, 15, 20, and 25 mL/g), extraction time (B, 60, 90, and 120 min), temperature (A, 60, 70, and 80 °C), and citric acid addition (D, 0.5, 1.0, and 1.5 %), and the response index was the ASDF yield. For the cellulase extraction method, the independent variables were the liquid–solid ratio (A, 20, 25, and 30 mL/g), time (B, 60, 90, and 120 min), temperature (C, 50, 55, and 60 °C), and cellulase addition (D, 1.0, 1.5, and 2.0 %), and the response index was the ESDF yield. The center value is encoded as 0, and the maximum and minimum values are encoded as 1 and −1, respectively. For the hot-water extraction method, a total of 17 groups were designed for the experiment, and 5 groups were repeated for the central point experiment. A total of 29 groups were designed for the citric acid extraction method and the cellulase method, and 5 groups were repeated for the central point experiment. The experimental factors and level design are shown in Tables S1–S3.
2.4.3. Extraction Yield
The calculation formula for the SDF extraction rate of sunflower receptacles is as follows:Extraction yield(%)=(M1/M0)×100%
where M1 is the dry weight (g) of the SDF powder extracted from sunflower receptacles, and M0 is the dry weight (g) of the sunflower receptacles powder used for extraction.
2.5. Analysis of Physicochemical Property
2.5.1. Measurement of Water-Holding Capacity (WHC)
According to the method [26], slightly modified, 1.00 g of the SDF obtained by the different extraction methods was accurately weighed in a centrifuge tube, and 40 mL deionized water was added, shaken well, and balanced at 25 °C for 12 h. Then, it was centrifuged at 1840× g for 15 min, and the weight of the sample after discarding the supernatant was weighed. The water-holding capacity is calculated according to the following formula:WHCg/g=(M2−M0)/M0×100%
where M2 is the mass of the sample after water absorption (g); M0 represents the initial mass (g) of the sample.
2.5.2. Measurement of Oil-Holding Capacity (OHC)
A slightly modified method [27] was used to determine the oil-holding capacity. Accurately weigh 1.00 g of the SDF obtained by different extraction methods in a centrifuge tube, add 12 mL peanut oil, and mix at 4 °C for 1 h. Then, it was centrifuged at 1840× g for 15 min, and the weight of the sample after discarding the supernatant was weighed. The oil-holding capacity calculation formula is as follows:OHCg/g=(M3−M0)/M0×100%
where M3 is the mass of the sample after oil absorption (g); M0 represents the initial mass (g) of the sample.
2.5.3. Measurement of Swelling Capacity (SC)
A total of 0.25 g of the SDF samples obtained by different extraction methods were accurately weighed in an expansion tube. After recording the scale (V1), 10 mL of distilled water were added. The mixture was fully shaken and stood at room temperature for 12 h to record the corresponding scale (V2) of the SDF after water absorption:SC(mL/g)=(V2−V1)/M0×100%
where V1 is the volume (mL) of SDF before water absorption and expansion; V2 is the volume (mL) of SDF after water absorption and expansion; and M0 is the mass (g) of SDF before swelling.
2.5.1. Measurement of Water-Holding Capacity (WHC)
According to the method [26], slightly modified, 1.00 g of the SDF obtained by the different extraction methods was accurately weighed in a centrifuge tube, and 40 mL deionized water was added, shaken well, and balanced at 25 °C for 12 h. Then, it was centrifuged at 1840× g for 15 min, and the weight of the sample after discarding the supernatant was weighed. The water-holding capacity is calculated according to the following formula:WHCg/g=(M2−M0)/M0×100%
where M2 is the mass of the sample after water absorption (g); M0 represents the initial mass (g) of the sample.
2.5.2. Measurement of Oil-Holding Capacity (OHC)
A slightly modified method [27] was used to determine the oil-holding capacity. Accurately weigh 1.00 g of the SDF obtained by different extraction methods in a centrifuge tube, add 12 mL peanut oil, and mix at 4 °C for 1 h. Then, it was centrifuged at 1840× g for 15 min, and the weight of the sample after discarding the supernatant was weighed. The oil-holding capacity calculation formula is as follows:OHCg/g=(M3−M0)/M0×100%
where M3 is the mass of the sample after oil absorption (g); M0 represents the initial mass (g) of the sample.
2.5.3. Measurement of Swelling Capacity (SC)
A total of 0.25 g of the SDF samples obtained by different extraction methods were accurately weighed in an expansion tube. After recording the scale (V1), 10 mL of distilled water were added. The mixture was fully shaken and stood at room temperature for 12 h to record the corresponding scale (V2) of the SDF after water absorption:SC(mL/g)=(V2−V1)/M0×100%
where V1 is the volume (mL) of SDF before water absorption and expansion; V2 is the volume (mL) of SDF after water absorption and expansion; and M0 is the mass (g) of SDF before swelling.
2.6. Structural Characterization of Sunflower Receptacles’ Soluble Dietary Fiber
2.6.1. Scanning Electron Microscopy (SEM) Analysis
The microstructure of SDF obtained by different extraction methods was observed by scanning electron microscopy (JSM-6701F, JEOL, Tokyo, Japan). The samples were fixed and sprayed with gold, and the parameters were set to 5.0 kV acceleration voltage at 5000 times magnification.
2.6.2. Fourier Transform Infrared (FT-IR) Spectroscopy Analysis
The SDFs were fully mixed with KBr (1:100, w/w), fully ground in a quartz mortar and pressed into a transparent sheet with an appropriate thickness. The FT-IR spectrometer (Nicolet 6700, Thermo Fisher Technologies, Waltham, MA, USA) was used to scan at 400~4000 cm−1 wavenumbers.
2.6.3. X-Ray Diffraction (XRD) Analysis
The SDFs obtained by different extraction methods were compared and analyzed by an X-ray diffractometer (Kratos AXIS SUPRA, Bruker Technology Co., Ltd., Karlsruhe, Germany). The voltage was 40 kV. The current was 20 mA. The X-ray wavelength was λ = 0.156 nm, and the scanning range was 5°–70°.
2.6.1. Scanning Electron Microscopy (SEM) Analysis
The microstructure of SDF obtained by different extraction methods was observed by scanning electron microscopy (JSM-6701F, JEOL, Tokyo, Japan). The samples were fixed and sprayed with gold, and the parameters were set to 5.0 kV acceleration voltage at 5000 times magnification.
2.6.2. Fourier Transform Infrared (FT-IR) Spectroscopy Analysis
The SDFs were fully mixed with KBr (1:100, w/w), fully ground in a quartz mortar and pressed into a transparent sheet with an appropriate thickness. The FT-IR spectrometer (Nicolet 6700, Thermo Fisher Technologies, Waltham, MA, USA) was used to scan at 400~4000 cm−1 wavenumbers.
2.6.3. X-Ray Diffraction (XRD) Analysis
The SDFs obtained by different extraction methods were compared and analyzed by an X-ray diffractometer (Kratos AXIS SUPRA, Bruker Technology Co., Ltd., Karlsruhe, Germany). The voltage was 40 kV. The current was 20 mA. The X-ray wavelength was λ = 0.156 nm, and the scanning range was 5°–70°.
2.7. Animal Experiment
2.7.1. Animals and Their Experiments
All procedures involving animal experiments have been approved by the Animal Ethics Committee of Jilin University. The experimental animals were raised in the Animal Experimental Center of the College of Life Sciences, Jilin University (SPF grade).
SPF male BALB/c mice (18–22 g, 4–6 weeks) were purchased from Liaoning Changsheng Biotechnology Co., Ltd. The experimental animals were not genetically modified during the experiment. The mice were accommodated in the Jilin University Experimental Animal Platform and were housed in different units at a temperature of 23 ± 1 °C and a relative humidity of 40~60%. Good ventilation and light–dark cycle were ensured. Unrestricted food and water were provided during the experiment. During the experiment, the physiological status and body weight of each group of mice were monitored every day, and the daily food intake and water intake of each cage of mice were calculated.
After successful modeling, the control group and the model group were given normal saline 10 mL/kg, and the low-dose group, the middle-dose group, and the high-dose group were given ASDF 0.25 g/kg, 0.5 g/kg, and 1 g/kg, respectively. The dose was 0.1 mL/10 g, with continuous gavage for 8 days twice a day.
2.7.2. Constipation Model Induced by Loperamide Hydrochloride
After one week of adaptive growth, 35 mice were randomly divided into 5 groups, namely the control group, model group, ASDF low-dose group, ASDF medium-dose group, and ASDF high-dose group (n = 7 per group). The blank group was given normal saline 10 mL/kg by gavage, and the other groups were given 10 mL/kg loperamide hydrochloride solution (loperamide hydrochloride dissolved in normal saline, 1 mg/mL) by gavage. The dosage was 0.1 mL/10 g once a day for 16 consecutive days, and modeling was performed [28].
2.7.3. Evaluation of Defecation Function of Constipated Mice
After the animal experiment modeling and the end of treatment, the first black stool discharge time of the mice was measured. Before the experiment, each group of mice was fasted for 16 h, and the mice were free to drink water during the fasting period. The blank group did not undergo any treatment, and each mouse in the other groups was intragastrically administered with loperamide hydrochloride (10 mL/kg) at a dose of 0.1 mL/10 g. After 30 min, all mice were given 0.2 mL of ink by gavage, and the first black stool discharge time, the number of fresh feces discharged within 4 h, and the wet weight of each mouse were recorded. After that, the feces were placed in a blast-drying oven and dried at 60 °C to a constant weight. The moisture content of the feces was calculated according to the following formula:Fecal moisture content(%)=(M5−M4)/M5×100%
where M4 is the dry weight of the feces (g); m5 is the wet weight of feces (g).
It is worth noting that 5 g Arabic gum powder and 5 g activated carbon powder were added to 100 mL distilled water to prepare ink, and the water bath was heated and boiled, repeated many times, until the Arabic gum was completely dissolved, the bottom of the bottle was not precipitated, stored at room temperature, and oscillated evenly before each use.
2.7.4. Small Intestine Propulsion Test
After the end of drug treatment, the intestinal propulsion rate of mice was measured. Before the experiment, the mice in each group were fasted for 16 h, and the mice were free to drink water during the fasting period. The blank group was not treated, and each mouse in the other groups was given loperamide hydrochloride (10 mL/kg) by gavage at a dose of 0.1 mL/10 g. After 30 min, all mice were given 0.2 mL of ink by gavage. After 30 min, the mice in each group were sacrificed by dislocation, and the entire intestine from the pylorus to the end of the colon was immediately collected. The total length of the small intestine and the length of the ink propulsion were measured. The small intestine propulsion rate was calculated as follows:Intestinal propulsive rate(%)=L2/L1×100%
where L1 is the total length of the small intestine (cm), and L2 is the ink propulsion distance (cm).
2.7.5. Histopathology
Part of the colon tissue was fixed in 4% paraformaldehyde for 24 h. The fixed colon tissue was embedded in paraffin and cut into 5 μm thin sections. The colon tissue sections were first dewaxed in xylene, rehydrated in gradient ethanol, and then observed with hematoxylin–eosin (H&E) staining. Histological analysis was performed, and images of colon tissue were observed under an optical microscope (magnified ×400, Nikon, Minato, Japan).
2.7.1. Animals and Their Experiments
All procedures involving animal experiments have been approved by the Animal Ethics Committee of Jilin University. The experimental animals were raised in the Animal Experimental Center of the College of Life Sciences, Jilin University (SPF grade).
SPF male BALB/c mice (18–22 g, 4–6 weeks) were purchased from Liaoning Changsheng Biotechnology Co., Ltd. The experimental animals were not genetically modified during the experiment. The mice were accommodated in the Jilin University Experimental Animal Platform and were housed in different units at a temperature of 23 ± 1 °C and a relative humidity of 40~60%. Good ventilation and light–dark cycle were ensured. Unrestricted food and water were provided during the experiment. During the experiment, the physiological status and body weight of each group of mice were monitored every day, and the daily food intake and water intake of each cage of mice were calculated.
After successful modeling, the control group and the model group were given normal saline 10 mL/kg, and the low-dose group, the middle-dose group, and the high-dose group were given ASDF 0.25 g/kg, 0.5 g/kg, and 1 g/kg, respectively. The dose was 0.1 mL/10 g, with continuous gavage for 8 days twice a day.
2.7.2. Constipation Model Induced by Loperamide Hydrochloride
After one week of adaptive growth, 35 mice were randomly divided into 5 groups, namely the control group, model group, ASDF low-dose group, ASDF medium-dose group, and ASDF high-dose group (n = 7 per group). The blank group was given normal saline 10 mL/kg by gavage, and the other groups were given 10 mL/kg loperamide hydrochloride solution (loperamide hydrochloride dissolved in normal saline, 1 mg/mL) by gavage. The dosage was 0.1 mL/10 g once a day for 16 consecutive days, and modeling was performed [28].
2.7.3. Evaluation of Defecation Function of Constipated Mice
After the animal experiment modeling and the end of treatment, the first black stool discharge time of the mice was measured. Before the experiment, each group of mice was fasted for 16 h, and the mice were free to drink water during the fasting period. The blank group did not undergo any treatment, and each mouse in the other groups was intragastrically administered with loperamide hydrochloride (10 mL/kg) at a dose of 0.1 mL/10 g. After 30 min, all mice were given 0.2 mL of ink by gavage, and the first black stool discharge time, the number of fresh feces discharged within 4 h, and the wet weight of each mouse were recorded. After that, the feces were placed in a blast-drying oven and dried at 60 °C to a constant weight. The moisture content of the feces was calculated according to the following formula:Fecal moisture content(%)=(M5−M4)/M5×100%
where M4 is the dry weight of the feces (g); m5 is the wet weight of feces (g).
It is worth noting that 5 g Arabic gum powder and 5 g activated carbon powder were added to 100 mL distilled water to prepare ink, and the water bath was heated and boiled, repeated many times, until the Arabic gum was completely dissolved, the bottom of the bottle was not precipitated, stored at room temperature, and oscillated evenly before each use.
2.7.4. Small Intestine Propulsion Test
After the end of drug treatment, the intestinal propulsion rate of mice was measured. Before the experiment, the mice in each group were fasted for 16 h, and the mice were free to drink water during the fasting period. The blank group was not treated, and each mouse in the other groups was given loperamide hydrochloride (10 mL/kg) by gavage at a dose of 0.1 mL/10 g. After 30 min, all mice were given 0.2 mL of ink by gavage. After 30 min, the mice in each group were sacrificed by dislocation, and the entire intestine from the pylorus to the end of the colon was immediately collected. The total length of the small intestine and the length of the ink propulsion were measured. The small intestine propulsion rate was calculated as follows:Intestinal propulsive rate(%)=L2/L1×100%
where L1 is the total length of the small intestine (cm), and L2 is the ink propulsion distance (cm).
2.7.5. Histopathology
Part of the colon tissue was fixed in 4% paraformaldehyde for 24 h. The fixed colon tissue was embedded in paraffin and cut into 5 μm thin sections. The colon tissue sections were first dewaxed in xylene, rehydrated in gradient ethanol, and then observed with hematoxylin–eosin (H&E) staining. Histological analysis was performed, and images of colon tissue were observed under an optical microscope (magnified ×400, Nikon, Minato, Japan).
2.8. Statistical Analysis
SPSS18.0 statistical software was used for statistics, and the data were represented by “X ± SD”. One-way variance (ANOVA) was used for statistical analysis, and Tukey’s comparison test was used to obtain a significant difference (p < 0.05 was considered statistically significant, and highly significant differences were indicated at p < 0.01). Origin 2019b software and Graphpad Prism 8.0 software were used to make and process the charts.
3. Results
3.1. Optimization of SDF Extraction Process from Sunflower Receptacles
The optimal extraction process of three SDFs was obtained by single-factor extraction and response surface optimization (Figures S1–S3). The optimum conditions for the extraction process of the ASDF were as follows. The ratio of material to liquid was 21 mL/g. The extraction time was 96 min. The extraction temperature was 68 °C, and the added amount of citric acid was 1.0%. For the WSDF, the optimum conditions were as follows: a solid–liquid ratio of 34 mL/g, an extraction time of 119 min, and an extraction temperature of 81 °C. For the ESDF, the optimum extraction process was as follows. The solid–liquid ratio was 27 mL/g. The extraction time was 84 min. The extraction temperature was 56 °C, and the cellulase addition was 1.7% (Figures S4–S6).
3.2. Physicochemical Properties of SDF from Sunflower Receptacles
Water-holding capacity, swelling capacity, and oil-holding capacity are important physical and chemical properties that can reflect the quality of dietary fiber [29]. The water-holding capacity, swelling capacity, and oil-holding capacity of WSDF, ASDF, and ESDF are shown in Figure 1. The water-holding capacities of the three SDFs were 2.67 ± 0.11 g/g, 4.40 ± 0.11 g/g, and 3.31 ± 0.35 g/g. The expansion forces were 3.33 ± 0.17 mL/g, 5.57 ± 0.26 mL/g, and 4.23 ± 0.25 mL/g. The oil-holding capacities were 2.05 ± 0.20 g/g, 2.50 ± 0.16 g/g, and 2.38 ± 0.21 g/g, respectively. The above results show that ASDF has better water-holding capacity, swelling capacity, and oil-holding capacity than WSDF and ESDF.
3.3. Structural Analysis
Infrared spectroscopy can effectively identify various characteristic functional groups in substances [30]. Figure 2A shows the Fourier transform infrared spectra of the SDF obtained by different extraction methods. According to the spectral analysis, their infrared absorption spectra are similar, indicating that the three SDFs have similar structural characteristics. There is a strong broad peak at about 3400 cm−1, which is attributed to the O-H stretching vibration of pectin and hemicellulose [31]. The characteristic absorption peak near 2930 cm−1 is caused by the C-H stretching vibration of the methyl and methylene groups in the polysaccharide [32]. The absorption peak between 1735 cm−1 and 1600~1650 cm−1 is the stretching vibration of C=O, which is for the C=O in the semi-cellulose ester group and the ester bond of the COOH group in the pectin, respectively [33,34,35]. The absorption peak at about 1420 cm−1 is C-H stretching vibration, and the absorption peak at 1320~1210 cm−1 is O-H variable-angle vibration. The absorption peak between 1000 and1200 cm−1 is the vibration of the glycosidic bond, indicating that there may be a pyranose ring [36]. The SDFs from three sunflower receptacles have similar functional groups, but the peak intensity is slightly different. The intensity difference at 1600~1650 cm−1 is larger, which may be due to the different uronic acid content of SDF.
XRD can analyze the crystal type and structural characteristics of SDF samples obtained by different extraction methods [37]. It can be observed from Figure 2B that there was almost no absorption peak in the three SDFs, and there was no significant difference in the crystal structure of the SDF extracted by these three methods. The results showed that the main structure of several SDF samples was sn amorphous structure, which was consistent with the typical characteristics of SDF [31].
Scanning electron microscopy was used to observe the surface morphology of the SDF obtained by different extraction methods (Figure 2C). From Figure 2C-a, we found that the WSDF extracted by hot-water extraction showed a coexistence of irregular filaments and flakes. From Figure 2C-b, it can be seen that the ESDF obtained by cellulase extraction presents an irregular fiber column, which may be due to the aggregation of many molecules or molecular groups into different patterns of bundles. Figure 2C-c shows that the ASDF obtained by citric acid extraction is a serrated sheet shape with many small pores on the surface. The results showed that different extraction methods had a great influence on the apparent morphology of SDF, and the surface of ASDF has more small pores than that of ESDF and WSDF, which may be one of the reasons why ASDF has a stronger water-holding capacity and expansion force.
3.4. Growth of Mice
In the whole experiment, all mice were in good condition, and no hair removal, fights, or death occurred. During the experiment, the body weight of the mice in each group gradually increased, but there was no significant difference between the groups. The average water intake and food intake of each group of mice were similar to those before modeling, which indicated that the dose and time of loperamide hydrochloride modeling would not cause serious damage to the health of the mice.
3.5. The Effect of Sunflower Receptacles ASDF on Mice Defecation
After 16 days of intragastric administration of loperamide hydrochloride, the first black-stool defecation time of the model group was significantly higher than that of the blank group (p < 0.001), which verified the effectiveness of loperamide hydrochloride modeling. After a period of treatment with ASDF, compared with the model group, the middle-dose group of ASDF significantly reduced the first black-stool defecation time (p < 0.05), while the high-dose group significantly reduced the first black-stool defecation time (p < 0.01). Although there was no significant difference in the low-dose group (p > 0.05), it still reduced part of the first black-stool discharge time (Figure 3A). The above results show that different doses of ASDF can alleviate constipation in mice to varying degrees, and the high-dose group has the best effect.
From Figure 3B, it can be seen that there was no significant difference in the number of defecation particles in mice at 4 h after administration compared with the model group (p > 0.05), but with the continuous increase of gavage dose, the number of defecation particles in mice increased. However, Figure 3C shows that, compared with the model group, the fecal weight of mice at 4 h after administration was significantly increased in the low, medium, and high ASDF dose groups (p < 0.05). In addition, in terms of fecal moisture content, when compared with the model group, the differences between the medium and high ASDF dose groups were significant (p < 0.05). Although there was no significant difference in the low ASDF dose group (p > 0.05), the moisture content was still higher than in the model group, which was equivalent to the moisture content of the control group (Figure 3D). The above results showed that the frequency of defecation, the weight of defecation, and the moisture content of the feces increased after the constipation mouse model was given different doses of ASDF. The weight of feces in the medium-dose ASDF group was comparable to that in the healthy group, while the weight of feces in the high-dose ASDF group was higher than that in the healthy group. In terms of fecal moisture content, the moisture content of the low, medium, and high ASDF dose groups exceeded that of the healthy group. Compared with the mice in the model group, the constipation symptoms of the treatment group have been alleviated to some extent. The effect on the high-dose group was better.
3.6. The Effect of Sunflower Receptacles ASDF on Intestinal Propulsion of Mice
It can be seen from Figure 4A that, compared with the blank group, there was no significant difference in the total length of the small intestine between the model group and the low, medium, and high ASDF dose groups (p > 0.05), indicating that the constipation model induced by loperamide hydrochloride did not affect the intestinal length of the mice. In terms of the ink propulsion distance and the intestinal propulsion rate in mice, compared with the model group, the ink propulsion distance and propulsion rate in the high-dose ASDF group were significantly increased (p > 0.05), and there was no significant difference in the medium-dose ASDF group (p > 0.05). However, compared with the model group, the ink propulsion distance and propulsion rate increased slightly (Figure 4B,C). After the successful establishment of the constipation model, the mice were intragastrically administered with different doses of sunflower disc ASDF. Compared with the model group, the ink propulsion distance and ink propulsion rate of the two groups of mice in the medium-dose and high-dose ASDF groups increased, and the two indicators in the high-dose ASDF group were significantly higher than those in the model control group (p < 0.05). It shows that the high-dose ASDF group has a better recovery effect on the intestinal propulsion ability of constipation mice.
3.7. Analysis of H&E Staining Results of Colon
The results of H&E staining of the colon are shown in Figure 5. Among them, it can be seen from Figure 5A of the blank group that the colon tissue structure is normal, the mucosal crypts are closely arranged, the goblet cells are abundant, the epithelial cells are not denatured and exfoliated, and there is no inflammatory cell infiltration in the tissue. The epithelial cells are shown as black arrows. Red arrows represent goblet cells. Figure 5B shows that, compared with the blank healthy group, the colon tissue structure of the model group changed abnormally, the number of mucosal crypts in some areas decreased significantly, the mucosal epithelial cells fell off, and the lamina propria were exposed, as shown by the red arrow. The number of crypts and goblet cells decreased, and fibrous tissue hyperplasia was seen, as shown by the yellow arrow. Inflammatory cell infiltration can be seen in the mucosal layer, as shown by black arrows. Compared with the model group, the tissue structure of the colon was basically normal in the low-dose group. The mucosal crypts were closely arranged, and the number was not reduced. There was no obvious degeneration and shedding of epithelial cells, and a small amount of inflammatory cell infiltration was observed, as shown by black arrows (Figure 5C). From Figure 5D, the staining of the middle-dose group was compared with that of the model group. The structure of the colon tissue recovered well. The crypts of the mucosa were arranged neatly, and the number was not reduced. No degeneration and shedding of epithelial cells were observed. A small amount of inflammatory cell infiltration was observed in the submucosa, as shown by black arrows. In Figure 5E, compared with the model group, the colonic tissue structure of the high-dose group basically returned to normal, the mucosal crypts were arranged neatly, the number was not reduced, the epithelial cells were not denatured and exfoliated, and a small amount of inflammatory cell infiltration was observed in the submucosa, as shown by black arrows. Through the HE staining results of the mouse colon, it can be seen that the three doses of ASDF have a certain repair function on the destruction of colon tissue caused by constipation and can play a role in protecting the colon.
3.1. Optimization of SDF Extraction Process from Sunflower Receptacles
The optimal extraction process of three SDFs was obtained by single-factor extraction and response surface optimization (Figures S1–S3). The optimum conditions for the extraction process of the ASDF were as follows. The ratio of material to liquid was 21 mL/g. The extraction time was 96 min. The extraction temperature was 68 °C, and the added amount of citric acid was 1.0%. For the WSDF, the optimum conditions were as follows: a solid–liquid ratio of 34 mL/g, an extraction time of 119 min, and an extraction temperature of 81 °C. For the ESDF, the optimum extraction process was as follows. The solid–liquid ratio was 27 mL/g. The extraction time was 84 min. The extraction temperature was 56 °C, and the cellulase addition was 1.7% (Figures S4–S6).
3.2. Physicochemical Properties of SDF from Sunflower Receptacles
Water-holding capacity, swelling capacity, and oil-holding capacity are important physical and chemical properties that can reflect the quality of dietary fiber [29]. The water-holding capacity, swelling capacity, and oil-holding capacity of WSDF, ASDF, and ESDF are shown in Figure 1. The water-holding capacities of the three SDFs were 2.67 ± 0.11 g/g, 4.40 ± 0.11 g/g, and 3.31 ± 0.35 g/g. The expansion forces were 3.33 ± 0.17 mL/g, 5.57 ± 0.26 mL/g, and 4.23 ± 0.25 mL/g. The oil-holding capacities were 2.05 ± 0.20 g/g, 2.50 ± 0.16 g/g, and 2.38 ± 0.21 g/g, respectively. The above results show that ASDF has better water-holding capacity, swelling capacity, and oil-holding capacity than WSDF and ESDF.
3.3. Structural Analysis
Infrared spectroscopy can effectively identify various characteristic functional groups in substances [30]. Figure 2A shows the Fourier transform infrared spectra of the SDF obtained by different extraction methods. According to the spectral analysis, their infrared absorption spectra are similar, indicating that the three SDFs have similar structural characteristics. There is a strong broad peak at about 3400 cm−1, which is attributed to the O-H stretching vibration of pectin and hemicellulose [31]. The characteristic absorption peak near 2930 cm−1 is caused by the C-H stretching vibration of the methyl and methylene groups in the polysaccharide [32]. The absorption peak between 1735 cm−1 and 1600~1650 cm−1 is the stretching vibration of C=O, which is for the C=O in the semi-cellulose ester group and the ester bond of the COOH group in the pectin, respectively [33,34,35]. The absorption peak at about 1420 cm−1 is C-H stretching vibration, and the absorption peak at 1320~1210 cm−1 is O-H variable-angle vibration. The absorption peak between 1000 and1200 cm−1 is the vibration of the glycosidic bond, indicating that there may be a pyranose ring [36]. The SDFs from three sunflower receptacles have similar functional groups, but the peak intensity is slightly different. The intensity difference at 1600~1650 cm−1 is larger, which may be due to the different uronic acid content of SDF.
XRD can analyze the crystal type and structural characteristics of SDF samples obtained by different extraction methods [37]. It can be observed from Figure 2B that there was almost no absorption peak in the three SDFs, and there was no significant difference in the crystal structure of the SDF extracted by these three methods. The results showed that the main structure of several SDF samples was sn amorphous structure, which was consistent with the typical characteristics of SDF [31].
Scanning electron microscopy was used to observe the surface morphology of the SDF obtained by different extraction methods (Figure 2C). From Figure 2C-a, we found that the WSDF extracted by hot-water extraction showed a coexistence of irregular filaments and flakes. From Figure 2C-b, it can be seen that the ESDF obtained by cellulase extraction presents an irregular fiber column, which may be due to the aggregation of many molecules or molecular groups into different patterns of bundles. Figure 2C-c shows that the ASDF obtained by citric acid extraction is a serrated sheet shape with many small pores on the surface. The results showed that different extraction methods had a great influence on the apparent morphology of SDF, and the surface of ASDF has more small pores than that of ESDF and WSDF, which may be one of the reasons why ASDF has a stronger water-holding capacity and expansion force.
3.4. Growth of Mice
In the whole experiment, all mice were in good condition, and no hair removal, fights, or death occurred. During the experiment, the body weight of the mice in each group gradually increased, but there was no significant difference between the groups. The average water intake and food intake of each group of mice were similar to those before modeling, which indicated that the dose and time of loperamide hydrochloride modeling would not cause serious damage to the health of the mice.
3.5. The Effect of Sunflower Receptacles ASDF on Mice Defecation
After 16 days of intragastric administration of loperamide hydrochloride, the first black-stool defecation time of the model group was significantly higher than that of the blank group (p < 0.001), which verified the effectiveness of loperamide hydrochloride modeling. After a period of treatment with ASDF, compared with the model group, the middle-dose group of ASDF significantly reduced the first black-stool defecation time (p < 0.05), while the high-dose group significantly reduced the first black-stool defecation time (p < 0.01). Although there was no significant difference in the low-dose group (p > 0.05), it still reduced part of the first black-stool discharge time (Figure 3A). The above results show that different doses of ASDF can alleviate constipation in mice to varying degrees, and the high-dose group has the best effect.
From Figure 3B, it can be seen that there was no significant difference in the number of defecation particles in mice at 4 h after administration compared with the model group (p > 0.05), but with the continuous increase of gavage dose, the number of defecation particles in mice increased. However, Figure 3C shows that, compared with the model group, the fecal weight of mice at 4 h after administration was significantly increased in the low, medium, and high ASDF dose groups (p < 0.05). In addition, in terms of fecal moisture content, when compared with the model group, the differences between the medium and high ASDF dose groups were significant (p < 0.05). Although there was no significant difference in the low ASDF dose group (p > 0.05), the moisture content was still higher than in the model group, which was equivalent to the moisture content of the control group (Figure 3D). The above results showed that the frequency of defecation, the weight of defecation, and the moisture content of the feces increased after the constipation mouse model was given different doses of ASDF. The weight of feces in the medium-dose ASDF group was comparable to that in the healthy group, while the weight of feces in the high-dose ASDF group was higher than that in the healthy group. In terms of fecal moisture content, the moisture content of the low, medium, and high ASDF dose groups exceeded that of the healthy group. Compared with the mice in the model group, the constipation symptoms of the treatment group have been alleviated to some extent. The effect on the high-dose group was better.
3.6. The Effect of Sunflower Receptacles ASDF on Intestinal Propulsion of Mice
It can be seen from Figure 4A that, compared with the blank group, there was no significant difference in the total length of the small intestine between the model group and the low, medium, and high ASDF dose groups (p > 0.05), indicating that the constipation model induced by loperamide hydrochloride did not affect the intestinal length of the mice. In terms of the ink propulsion distance and the intestinal propulsion rate in mice, compared with the model group, the ink propulsion distance and propulsion rate in the high-dose ASDF group were significantly increased (p > 0.05), and there was no significant difference in the medium-dose ASDF group (p > 0.05). However, compared with the model group, the ink propulsion distance and propulsion rate increased slightly (Figure 4B,C). After the successful establishment of the constipation model, the mice were intragastrically administered with different doses of sunflower disc ASDF. Compared with the model group, the ink propulsion distance and ink propulsion rate of the two groups of mice in the medium-dose and high-dose ASDF groups increased, and the two indicators in the high-dose ASDF group were significantly higher than those in the model control group (p < 0.05). It shows that the high-dose ASDF group has a better recovery effect on the intestinal propulsion ability of constipation mice.
3.7. Analysis of H&E Staining Results of Colon
The results of H&E staining of the colon are shown in Figure 5. Among them, it can be seen from Figure 5A of the blank group that the colon tissue structure is normal, the mucosal crypts are closely arranged, the goblet cells are abundant, the epithelial cells are not denatured and exfoliated, and there is no inflammatory cell infiltration in the tissue. The epithelial cells are shown as black arrows. Red arrows represent goblet cells. Figure 5B shows that, compared with the blank healthy group, the colon tissue structure of the model group changed abnormally, the number of mucosal crypts in some areas decreased significantly, the mucosal epithelial cells fell off, and the lamina propria were exposed, as shown by the red arrow. The number of crypts and goblet cells decreased, and fibrous tissue hyperplasia was seen, as shown by the yellow arrow. Inflammatory cell infiltration can be seen in the mucosal layer, as shown by black arrows. Compared with the model group, the tissue structure of the colon was basically normal in the low-dose group. The mucosal crypts were closely arranged, and the number was not reduced. There was no obvious degeneration and shedding of epithelial cells, and a small amount of inflammatory cell infiltration was observed, as shown by black arrows (Figure 5C). From Figure 5D, the staining of the middle-dose group was compared with that of the model group. The structure of the colon tissue recovered well. The crypts of the mucosa were arranged neatly, and the number was not reduced. No degeneration and shedding of epithelial cells were observed. A small amount of inflammatory cell infiltration was observed in the submucosa, as shown by black arrows. In Figure 5E, compared with the model group, the colonic tissue structure of the high-dose group basically returned to normal, the mucosal crypts were arranged neatly, the number was not reduced, the epithelial cells were not denatured and exfoliated, and a small amount of inflammatory cell infiltration was observed in the submucosa, as shown by black arrows. Through the HE staining results of the mouse colon, it can be seen that the three doses of ASDF have a certain repair function on the destruction of colon tissue caused by constipation and can play a role in protecting the colon.
4. Discussion
Soluble dietary fiber has been proven to have a series of functions, such as strong oil holding, water holding, expansion, induction of intestinal microorganisms, detoxification, and increase of intestinal peristalsis [38]. It can be used to prevent and treat constipation [39], reduce blood lipid [40], and prevent cancer [41]. Therefore, more and more scholars at home and abroad are studying it. At present, the raw materials for the preparation of SDF are mainly concentrated in wheat bran [42], rice bran [43], bean dregs [44], apple pomace [45], sugar beet pulp [35,46], etc., and the resources of SDF need to be further developed. SR is a by-product of sunflower production and processing. It is often abandoned in the field in sunflower-producing areas, causing serious pollution to the environment [4]. Moreover, the content of SDF in sunflower receptacles is low, which limits its application in improving intestinal function.
At present, SDF extraction methods mainly include chemical, physical, and biological technologies. Among them, chemical and enzymatic methods are more commonly used because of their simple operation, low energy consumption, and easy availability of materials [32]. The chemical method is considered to be an important method for improving the solubility of dietary fiber, and it is easy to increase the SDF yield [47]. Among them, acid extraction can effectively hydrolyze hemicellulose, thereby changing the composition of SDF and IDF [48]. In contrast, enzymatic extraction can destroy cellulose, hemicellulose, and lignin in the cell wall and promote the conversion of IDF into SDF [49]. The results of response surface optimization showed that the yield of WSDF in hot-water extraction was 13.17 ± 0.21 %, while the yield of ASDF obtained by citric acid extraction was 13.64 ± 0.27%, and the highest yield obtained by cellulase extraction was 15.29 ± 0.22%, which was higher than the WSDF. Therefore, compared with the water extraction method, both the citric acid extraction method and the cellulase extraction method could improve the extraction rate of SDF from sunflower receptacles, which was similar to other studies [32,50].
Different extraction methods can not only change the yield of SDF but also change the composition and structural properties of SDF and, then, change its physicochemical properties and functional properties [51]. Although SDF can also be obtained by hot-water extraction and cellulase extraction, our study found that the ASDF obtained by citric acid extraction was significantly superior to that from other extraction methods in terms of water-holding capacity, oil-holding capacity, and swelling capacity. Structural analysis shows that the excellent physical and chemical properties of ASDF can be attributed to its improved microstructure, which enables it to capture more water and oil molecules [52]. In addition, Wang et al. also pointed out that the SDF extracted by citric acid has a high water-holding capacity and glucose-adsorption capacity [34]. Due to the high water-holding capacity and swelling force of SDF, it usually can play a good laxative effect [53]. Given that our previous studies have shown that ASDF has good water-holding capacity and swelling power, it can be used as a material for subsequent research on laxative function.
Clinical studies have found that an important cause of constipation is abnormal digestive system function, increased stool transport time, and excessive intestinal absorption of food moisture, which makes the stool hard and difficult to excrete [54]. Dietary fiber is often used as a fecal water carrier to increase the volume of feces, which can effectively alleviate constipation by increasing the volume of feces [55]. With in vivo experiments, we established a mouse constipation model and treated it with citric acid-extracted ASDF. The results showed that the high-dose SDF group was significantly improved in various constipation indicators compared with the model group. Specifically, the first black-stool discharge time was shortened, the fecal water content was increased, and the small intestine peristalsis was enhanced, indicating that SDF can improve constipation by increasing the water content in the colon and stimulating peristalsis. In addition, compared with other dietary fibers, such as okara fiber, sunflower receptacle ASDF also played a good role in the first black-stool discharge time and fecal moisture content [56]. The results of H&E staining showed that the three dose groups of ASDF could improve the damage of colon tissue in constipation mice to a certain extent. The results indicate that ASDF not only contributed to the mechanical passage of feces but also had a protective effect on the inner wall of the gastrointestinal tract. The above results showed that ASDF could relieve constipation in mice, and the therapeutic effect showed a significant dose–effect relationship. These findings are particularly important given that modern consumers are increasingly pursuing drugs with natural and healthy ingredients [57]. Compared with the limitations of current clinical medication for constipation, the results of this study showed the safety and efficacy of sunflower receptacle soluble dietary fiber in the treatment of constipation. It reveals the great potential of the ASDF extracted from agricultural by-product sunflower receptacles in the treatment of constipation and provides a natural alternative therapy for the treatment of clinical constipation.
5. Conclusions
In summary, this study first used three extraction methods to extract and modify the SDF of sunflower receptacles, obtained the optimal modified fiber extraction process through characterization and physical and chemical analysis, and then established a mouse model to explore its mechanism of moistening intestines and laxatives. Our results showed that ASDF showed the best water-holding capacity and swelling capacity in different extraction processes, which was related to its microstructure. This finding provides evidence for the correlation between the physicochemical properties and functional benefits of dietary fiber. In addition, in vivo experimental results showed that ASDF could exert good laxative properties and improve the damage of colon tissue in mice with constipation to a certain extent. Based on the above research, we found that ASDF can be safely applied for the treatment of constipation, and the research results can provide a theoretical reference for the high-value utilization and development of sunflower receptacles as a functional food. |
Title: Muscle Strength and Phase Angle Are Potential Markers for the Efficacy of Multidisciplinary Weight-Loss Program in Patients with Sarcopenic Obesity | Body:
1. Introduction
In patients with obesity, weight loss produces different metabolic benefits such as a reduction in glycemic and lipid profile, lower blood pressure with a reduction in the worsening of clinical complications, and a reduction in cardiovascular risks. These effects are mainly due to a reduction in fat mass (FM), but are frequently associated with a certain degree of fat-free mass (FFM) loss, which includes skeletal muscle mass (MM), and typically represents 20–40% of the body weight loss [1]. Furthermore, muscle mass (MM) is an independent marker of metabolic health, and any loss of MM, even if unintentional, could impair quality of life and lead to a decline in physical function [1]. Thus, it is crucial to assess body composition in patients with obesity undergoing weight loss. Bioelectrical impedance analysis (BIA) is actually a widely used technique in body composition analysis, particularly for assessing the hydration, FFM, and MM of the body [2]. The base for the body composition analysis by BIA is the phase angle (PhA) derived from the relationship between the resistance (Rz) and reactance (Xc) of the body [3]. The PhA provides insights into the body’s cellular health and integrity and could be considered as a potential indicator of various health conditions when used in clinical settings for prognostic outcomes [4,5].
Due to its potential clinical relevance, the PhA is now considered an underestimated parameter useful in clinical practice. In individuals living with obesity, the PhA is influenced by various factors, including sex, age, body composition [such as fat mass (FM) and fat-free mass (FFM)], hydration status, and nutrition state. In fact, the PhA is a well-known biomarker in assessing metabolic health, providing insights into cellular integrity, nutritional status, and overall body composition. In metabolic health, a higher PhA is often correlated with a better nutritional status, reflecting adequate protein stores, muscle mass, and overall body composition. Lower PhA values have been associated with conditions such as metabolic syndrome, type 2 diabetes, and obesity, indicating a higher risk of complications or poorer outcomes, also making it valuable in monitoring disease progression. Actually, the PhA is increasingly used as a prognostic tool in clinical settings. It can help predict outcomes in patients with chronic illnesses, including cardiovascular diseases, cancer, and liver disease. Changes in the phase angle over time can reflect the effectiveness of interventions aimed at improving metabolic health, such as diet, exercise, or medical treatment. An increasing phase angle suggests improvement in cellular health and global metabolic function [3,4,5].
In addition, the PhA positively correlates with various markers of muscle health and overall well-being, such as muscle area, muscle circumference, muscle echo intensity, serum protein levels, quality of life, and physical performance strength [6,7]. Interestingly, in the context of SO, a condition of the simultaneous presence of reduced muscle mass and increased adiposity, the PhA has been studied as an indicator of MM quality and cellular health [8]. A lower PhA in patients with SO is associated with negative health outcomes, reflecting a decreased muscle function and integrity [9,10]. Individuals with SO and a lower PhA have been found to be at increased risk of mortality, show poorer physical performance, and higher incidence of chronic diseases, such as cardiovascular disease, diabetes, and metabolic syndrome [11,12]. The PhA, when combined with other measures such as inflammation markers, can provide a more comprehensive assessment of the health status of individuals with SO [13].
Weight-loss interventions that prioritize the preservation of MM, such as a combination of resistance training and adequate protein intake, are more likely to result in favorable changes in body composition in older adults with obesity [6,7]. Recent studies have indicated that during weight loss induced by bariatric surgery, factors affecting cellular function, such as oxidative damage, and variables related to physical exercise, such as muscle strength and aerobic fitness, may also impact PhA values [8,9,10,11]. Koehler et al. [10] observed a moderate correlation between the PhA and serum transthyretin (TTR) concentrations during rapid weight loss post-RYGB surgery in women. These findings collectively suggest that reduced PhA values in patients undergoing RYGB and sleeve gastrectomy (SG) may indicate a concurrent decline in visceral protein status, lean body mass, and potentially protein nutritional status [10,12]. Considering that the PhA represents a functional parameter of MM related to the disability index, the potential change in the PhA could be used as a marker of changes in functional aspects, especially in patients with SO [13]. At present, few data are available on the PhA variations during weight loss induced by hypocaloric diet and physical activity (multidisciplinary rehabilitation program) in a selected population of patients with SO. We decided to conduct a study to determine whether weight loss, during a 4-week multidisciplinary weight-loss program, worsened or improved sarcopenia in patients with sarcopenic obesity (SO). Additionally, we aimed to assess a predictive model to estimate the probability of improving SO at hospital discharge.
2. Materials and Methods
2.1. Patients
We conducted the study at the Istituto Auxologico Italiano, IRCCS (Scientific Institute for Research, Hospitalization, and Healthcare), Piancavallo (Verbania, Italy), including consecutively admitted in-patients from April 2018 to December 2021. These patients were admitted for diagnostic evaluation and a 4-week multidisciplinary rehabilitation program aimed at addressing obesity through metabolic, nutritional, and psychological interventions. We included patients of both sexes, aged 18 to 90 years, with a BMI ≥ 30 kg/m2, and suitable for body composition assessment. We excluded patients that were not eligible for body composition analysis (such as patients with pacemaker implants and/or leg/arm amputation) and/or not able to perform the handgrip test. The analysis was focused on patients who completed the program and underwent a repeated evaluation within the three days leading up to the hospital discharge. The summary of the patient cohort is reported in Figure 1. This study was approved by the institutional ethics committee of Istituto Auxologico Italiano (details available at https://www.auxologico.it/ricerca-formazione/comitato-etico, accessed on 3 September 2024) with the approval ID number 2021_05_18_07. All procedures were conducted as part of routine hospital care, and each patient provided written informed consent for the use of their data for research purposes upon admission. The research adhered to the principles outlined in the 1964 Helsinki Declaration, its subsequent amendments, and the ethical guidelines set by the institutional and national research.
2.2. Anthropometric Parameters
Body weight (kg) and body height (meters) were measured with precision to the nearest 0.1 kg and 0.5 cm, respectively. A mechanical column scale (Scale-Tronix, Wheaton, IL, USA) and a stadiometer (Scale-Tronix, Wheaton, IL, USA) were used for these measurements. Body mass index (BMI) was then calculated by dividing the body weight by the square of the height (kg/m2). The waist circumference (cm) was measured with non-elastic tape at the level of the umbilicus.
2.3. Body Composition
Body composition analysis was performed by a single-frequency bioimpedance analyzer (BIA 101, Akern®, Pisa, Italy) as previously described [2]. Prior to the measurement, each subject removed their clothing and any metal jewelry and rested in a supine position for five minutes to allow for fluid equilibration in the body. Before each testing session, the impedance analyzer was calibrated using a reference circuit with a known impedance value (Rz = 380 Ω and Xc = 47 Ω) with a maximum allowable error of 1%. The mean coefficient of variation for within-day measurements in steady-state conditions was 1%, and for intra-individual measurements, it was 3%. The inter-operator variability had a mean coefficient of variation of 2%. These values indicate the level of measurement precision and reliability for the bioimpedance analyzer used in the study. Individuals with hydration levels [calculated as the ratio between total body water (L) and fat-free mass (kg)] were excluded when exceeding 80% to avoid overestimation of FFM.
2.4. Skeletal Muscle Mass Index
We applied the following formula of Janssen et al. [14] to calculate skeletal muscle mass. The skeletal muscle mass index (SMMI) was the percentage of the ratio between the SMM and the total body weight in kg [(SMM/W) × 100].
2.5. Muscle Strength
Muscle strength was measured with the handgrip strength test (HGS), using a dynamometer (JAMAR® (Lafayette Instrument Company, Lafayette, IN, USA) isometric dynamometer on both arms, dominant or not). Three repeated measurements were taken for both left and right hand and the mean values were calculated between the measures, as reported in [15].
2.6. Psychological General Well-Being Index (PGWBI) Questionnaire
The Italian version of the PGWBI questionnaire validated by Grossi et al. [16] was used as previously described [17]. The PGWBI scoring for quality of life (QoL) evaluation was considered as follows: “good” with a score >70; “normal” with a score between 60–70; and “poor” with a score < 60. Questionnaires not fully completed (with missing data in subscales sections and before/after the time of the study) were excluded from the analysis.
2.7. Criteria for Definition of SO
The ESPEN/EASO-SO consensus criteria [18] were applied, respecting in sequence, the following two steps of the diagnostic criteria: (1) muscle strength deficit assessed by HGS (<27 kg in men and <16 kg in women) and (2) SMMI deficit (<37% in men and <27.6% in women).
2.8. Multidisciplinary Weight-Loss Program
The intensive rehabilitation program lasted for a duration of 4 weeks. The multidisciplinary rehabilitation program included individualized nutritional intervention, psychological support, and supervised physical activity throughout the hospital stay. None of the patients received pharmacological prescriptions with the intention of achieving drastic weight reduction during the program. All patients received a balanced, hypocaloric Mediterranean diet consisting of three meals a day with 18–20% protein, 27–30% fat (of which <8% saturated fat), 50–55% carbohydrates (<15% simple sugars), and 30 g of fibers from fresh vegetables. Adherence to the 4-week protocol treatment was monitored by the nutritional service’s trained personnel. Under the supervision of a physiotherapist, two 60 min physiotherapy sessions were performed daily, consisting of personalized progressive aerobic training (e.g., walking, recline cycling, and arm ergometer exercises), postural control exercises, and progressive strengthening exercises. These supervised activities were designed to improve joint mobility and enhance cardiorespiratory fitness. Psychological counseling was an integral part of the program and focused on educating patients about the management of their emotions, eating behavior, and establishing control over their food intake.
2.9. Statistical Analysis
Continuous variables with normal distribution are represented as mean and standard deviation and those with a skewed distribution are represented as median and interquartile range [IQR]. Categorical variables are presented as absolute frequency and proportion. For the comparison between SO and non-SO patients of the continuous variables, the T-test (or Wilcoxon test) was performed, while for categorical, the Chi-square test (or Fisher test). The outcome variable of interest was categorical (two levels) sarcopenia status at discharge (SO or No SO). To identify the determinants of No SO state at discharge, we applied a logistic regression model including as covariates two dichotomous variables related to increments in HGS and the PhA and their interaction. We also included the following covariates suggested by clinicians: age, gender, FFM (%), FM (%), number of comorbidities, and weight change (%). From the model, we estimated the marginal predicted probabilities of ‘No SO state’ at discharge, for different combinations of HGS and PhA improvement adjusting for confounders, using the marginal standardization. This method allows inference of the total population from which data are drawn. Briefly, marginal probabilities reflect a weighted average over the distribution of the confounders and are equivalent to estimates obtained by standardizing to the total population [19].
2.1. Patients
We conducted the study at the Istituto Auxologico Italiano, IRCCS (Scientific Institute for Research, Hospitalization, and Healthcare), Piancavallo (Verbania, Italy), including consecutively admitted in-patients from April 2018 to December 2021. These patients were admitted for diagnostic evaluation and a 4-week multidisciplinary rehabilitation program aimed at addressing obesity through metabolic, nutritional, and psychological interventions. We included patients of both sexes, aged 18 to 90 years, with a BMI ≥ 30 kg/m2, and suitable for body composition assessment. We excluded patients that were not eligible for body composition analysis (such as patients with pacemaker implants and/or leg/arm amputation) and/or not able to perform the handgrip test. The analysis was focused on patients who completed the program and underwent a repeated evaluation within the three days leading up to the hospital discharge. The summary of the patient cohort is reported in Figure 1. This study was approved by the institutional ethics committee of Istituto Auxologico Italiano (details available at https://www.auxologico.it/ricerca-formazione/comitato-etico, accessed on 3 September 2024) with the approval ID number 2021_05_18_07. All procedures were conducted as part of routine hospital care, and each patient provided written informed consent for the use of their data for research purposes upon admission. The research adhered to the principles outlined in the 1964 Helsinki Declaration, its subsequent amendments, and the ethical guidelines set by the institutional and national research.
2.2. Anthropometric Parameters
Body weight (kg) and body height (meters) were measured with precision to the nearest 0.1 kg and 0.5 cm, respectively. A mechanical column scale (Scale-Tronix, Wheaton, IL, USA) and a stadiometer (Scale-Tronix, Wheaton, IL, USA) were used for these measurements. Body mass index (BMI) was then calculated by dividing the body weight by the square of the height (kg/m2). The waist circumference (cm) was measured with non-elastic tape at the level of the umbilicus.
2.3. Body Composition
Body composition analysis was performed by a single-frequency bioimpedance analyzer (BIA 101, Akern®, Pisa, Italy) as previously described [2]. Prior to the measurement, each subject removed their clothing and any metal jewelry and rested in a supine position for five minutes to allow for fluid equilibration in the body. Before each testing session, the impedance analyzer was calibrated using a reference circuit with a known impedance value (Rz = 380 Ω and Xc = 47 Ω) with a maximum allowable error of 1%. The mean coefficient of variation for within-day measurements in steady-state conditions was 1%, and for intra-individual measurements, it was 3%. The inter-operator variability had a mean coefficient of variation of 2%. These values indicate the level of measurement precision and reliability for the bioimpedance analyzer used in the study. Individuals with hydration levels [calculated as the ratio between total body water (L) and fat-free mass (kg)] were excluded when exceeding 80% to avoid overestimation of FFM.
2.4. Skeletal Muscle Mass Index
We applied the following formula of Janssen et al. [14] to calculate skeletal muscle mass. The skeletal muscle mass index (SMMI) was the percentage of the ratio between the SMM and the total body weight in kg [(SMM/W) × 100].
2.5. Muscle Strength
Muscle strength was measured with the handgrip strength test (HGS), using a dynamometer (JAMAR® (Lafayette Instrument Company, Lafayette, IN, USA) isometric dynamometer on both arms, dominant or not). Three repeated measurements were taken for both left and right hand and the mean values were calculated between the measures, as reported in [15].
2.6. Psychological General Well-Being Index (PGWBI) Questionnaire
The Italian version of the PGWBI questionnaire validated by Grossi et al. [16] was used as previously described [17]. The PGWBI scoring for quality of life (QoL) evaluation was considered as follows: “good” with a score >70; “normal” with a score between 60–70; and “poor” with a score < 60. Questionnaires not fully completed (with missing data in subscales sections and before/after the time of the study) were excluded from the analysis.
2.7. Criteria for Definition of SO
The ESPEN/EASO-SO consensus criteria [18] were applied, respecting in sequence, the following two steps of the diagnostic criteria: (1) muscle strength deficit assessed by HGS (<27 kg in men and <16 kg in women) and (2) SMMI deficit (<37% in men and <27.6% in women).
2.8. Multidisciplinary Weight-Loss Program
The intensive rehabilitation program lasted for a duration of 4 weeks. The multidisciplinary rehabilitation program included individualized nutritional intervention, psychological support, and supervised physical activity throughout the hospital stay. None of the patients received pharmacological prescriptions with the intention of achieving drastic weight reduction during the program. All patients received a balanced, hypocaloric Mediterranean diet consisting of three meals a day with 18–20% protein, 27–30% fat (of which <8% saturated fat), 50–55% carbohydrates (<15% simple sugars), and 30 g of fibers from fresh vegetables. Adherence to the 4-week protocol treatment was monitored by the nutritional service’s trained personnel. Under the supervision of a physiotherapist, two 60 min physiotherapy sessions were performed daily, consisting of personalized progressive aerobic training (e.g., walking, recline cycling, and arm ergometer exercises), postural control exercises, and progressive strengthening exercises. These supervised activities were designed to improve joint mobility and enhance cardiorespiratory fitness. Psychological counseling was an integral part of the program and focused on educating patients about the management of their emotions, eating behavior, and establishing control over their food intake.
2.9. Statistical Analysis
Continuous variables with normal distribution are represented as mean and standard deviation and those with a skewed distribution are represented as median and interquartile range [IQR]. Categorical variables are presented as absolute frequency and proportion. For the comparison between SO and non-SO patients of the continuous variables, the T-test (or Wilcoxon test) was performed, while for categorical, the Chi-square test (or Fisher test). The outcome variable of interest was categorical (two levels) sarcopenia status at discharge (SO or No SO). To identify the determinants of No SO state at discharge, we applied a logistic regression model including as covariates two dichotomous variables related to increments in HGS and the PhA and their interaction. We also included the following covariates suggested by clinicians: age, gender, FFM (%), FM (%), number of comorbidities, and weight change (%). From the model, we estimated the marginal predicted probabilities of ‘No SO state’ at discharge, for different combinations of HGS and PhA improvement adjusting for confounders, using the marginal standardization. This method allows inference of the total population from which data are drawn. Briefly, marginal probabilities reflect a weighted average over the distribution of the confounders and are equivalent to estimates obtained by standardizing to the total population [19].
3. Results
A total of 2004 patients were considered for this study and participated in the 4-week hospital rehabilitation program (Figure 1). The cohort was composed of 62% female patients, with a mean age of 56 (±14) years. The mean BMI was 42.9 (±6.5) kg/m2. Class I and II of obesity (i.e., BMI between 30 and 39.9 kg /m2) was present in 43% of the cohort and class III (BMI ≥ 40 kg/m2) in 57%. The mean FM was 48.3% (±6.8%), FFM 51.7% (±6.8%), and mean MM 32.9 kg (±8.7 kg). All patients had a deficit in SMMI while the muscle strength deficit was present in 188 patients and 33% of the sample had at least one comorbidity (out of these, 40% had osteoarthritis).
3.1. SO Diagnosis and Characteristics at Admission
When applying ESPEN/EASO SO diagnostic criteria, the frequency of patients with SO was found to be 9.38% (n = 188) (Figure 1). There was a lower proportion of male patients among those with SO compared to without SO diagnosis at admission (31% vs. 38%, p = 0.0576). Additionally, the SO patients were significantly older (mean age 64 ± 12 years vs. 56 ± 14 years, p < 0.0001). We observed a high prevalence of patients aged > 65 years (56.9%), while there was a mean BMI of 40.7 (±5.8) kg/m2 and 49.5% for the cohort in class I and II of obesity, and 50.5% in class III. The mean FM was 48.2% (±6.5%), the mean FFM was 51.3% (±6.5%), the mean MM 28.8 (±7.1) kg, and the PhA 4.14° (±0.83°). The PGWBI score was “Poor” in 49.7%, “Normal” in 23.5%, and “Good” in 26.8%. Males were older, more frequently in class III obesity, and with a better body composition with respect to females.
The gender-specific clinical characteristic of SO patients at admission are reported in Table 1.
3.2. SO Patients at Discharge
At the end of the multidisciplinary program, 42.6% (n = 80) out of 188 SO patients at admission resulted in not being sarcopenic (No SO discharge), while for 108 patients, the SO condition was unchanged (SO discharge). The clinical characteristics of No SO discharge and SO discharge patients are reported in Table 2. The HGS and FM (kg) mean values were statistically higher in No SO discharge (p-value < 0.0001 and p-value = 0.020, respectively). The PhA was not significantly different (p-value = 0.564).
Table 3 reports, for both groups (No SO discharge and SO discharge), the mean change (calculated as value at discharge minus value at admission) in the considered parameters and the p-values related to their comparisons. The PhA was not significantly different in the two groups (p-value = 0.0937). The No SO discharge patients showed a higher increment for HGS and PGWBI score with respect to the SO discharge patients (p-value < 0.0001 and p-value = 0.0054, respectively).
Figure 2 shows the marginal predicted probability of the No SO state at discharge. Patients without an increment in both the PhA and HGS had a probability of 10%. When only the PhA increased, the probability did not change (p-value = 0.141). The HGS increment alone boosted the probability to 65% while in the presence of the PhA increment, the probability rose to 93% (p-value < 0.0001). When implementing the model by including the dichotomous variable SMMI delta changes instead of the variable HGS, we found that the interaction was not statistically significant (p-value = 0.2563).
3.1. SO Diagnosis and Characteristics at Admission
When applying ESPEN/EASO SO diagnostic criteria, the frequency of patients with SO was found to be 9.38% (n = 188) (Figure 1). There was a lower proportion of male patients among those with SO compared to without SO diagnosis at admission (31% vs. 38%, p = 0.0576). Additionally, the SO patients were significantly older (mean age 64 ± 12 years vs. 56 ± 14 years, p < 0.0001). We observed a high prevalence of patients aged > 65 years (56.9%), while there was a mean BMI of 40.7 (±5.8) kg/m2 and 49.5% for the cohort in class I and II of obesity, and 50.5% in class III. The mean FM was 48.2% (±6.5%), the mean FFM was 51.3% (±6.5%), the mean MM 28.8 (±7.1) kg, and the PhA 4.14° (±0.83°). The PGWBI score was “Poor” in 49.7%, “Normal” in 23.5%, and “Good” in 26.8%. Males were older, more frequently in class III obesity, and with a better body composition with respect to females.
The gender-specific clinical characteristic of SO patients at admission are reported in Table 1.
3.2. SO Patients at Discharge
At the end of the multidisciplinary program, 42.6% (n = 80) out of 188 SO patients at admission resulted in not being sarcopenic (No SO discharge), while for 108 patients, the SO condition was unchanged (SO discharge). The clinical characteristics of No SO discharge and SO discharge patients are reported in Table 2. The HGS and FM (kg) mean values were statistically higher in No SO discharge (p-value < 0.0001 and p-value = 0.020, respectively). The PhA was not significantly different (p-value = 0.564).
Table 3 reports, for both groups (No SO discharge and SO discharge), the mean change (calculated as value at discharge minus value at admission) in the considered parameters and the p-values related to their comparisons. The PhA was not significantly different in the two groups (p-value = 0.0937). The No SO discharge patients showed a higher increment for HGS and PGWBI score with respect to the SO discharge patients (p-value < 0.0001 and p-value = 0.0054, respectively).
Figure 2 shows the marginal predicted probability of the No SO state at discharge. Patients without an increment in both the PhA and HGS had a probability of 10%. When only the PhA increased, the probability did not change (p-value = 0.141). The HGS increment alone boosted the probability to 65% while in the presence of the PhA increment, the probability rose to 93% (p-value < 0.0001). When implementing the model by including the dichotomous variable SMMI delta changes instead of the variable HGS, we found that the interaction was not statistically significant (p-value = 0.2563).
4. Discussion
In this study, we observed that, during a multidisciplinary in-hospital weight-loss program, 42.8% of patients affected by SO improved their clinical condition. This result is mainly due to a recovery and a better HGS, which became more significant when associated with a positive increase in the PhA values. This is an important outcome in patients with SO and the monitoring of the changes in these two parameters can provide insights into the effectiveness of interventions aimed at improving body composition and overall health in this patient population.
The goal of rehabilitating individuals with obesity is not only to achieve weight loss, but also to promote functional muscle recovery and strengthening in those who have been weakened by obesity-related factors. It is known that weight-loss programs can unintentionally lead to a loss of skeletal muscle mass [1,20]. Therefore, it is essential to monitor the onset and progression of SO during weight-loss programs, as we have previously recommended in the serial monitoring of the PhA to preserve muscle or body cell mass (BCM) and restore normal fluid distribution [3]. In SO patients, after the rehabilitation program, we observed an improvement that resulted in 42.6% of the patients recovering from the SO condition. The primary factor responsible for this recovery was the improvement in the HGS, while the PhA did not change significantly after one month of rehabilitation. Interestingly, the SO group at discharge showed unchanged PhA values, whereas the No SO group at discharge exhibited a positive trend. This effect is confirmed by the logistic regression analysis where an increase in the PhA values, associated with an increase in HGs, increased the probability of the No SO state at discharge.
The significance of these observations needs to be investigated, in order to be explained. We can speculate that the efficacy of the rehabilitation program, even in a short time, resides in improving the quality of muscle mass rather (HGS) than its amount, leading first to a functional recovery. In fact, we know that in patients with obesity, the risk of increasing the loss of SMM during a weight management program is present with the probability of worsening SO. A ‘therapeutic’ weight loss [1], obtained with a hypocaloric diet + physical activity, might be considered as the combination of FM loss with a very slow rate of SMM loss, or unchanged SMM amount. On the other side, it was previously demonstrated that adding physical activity preserves or slightly improves muscle quality during weight loss, as measured by muscle attenuation and the intermuscular fat percentage, a measure of intramyocellular lipid content [6]. Since the first improvement was observed in HGS, it is tempting to speculate that a short-term weight loss with a balanced Mediterranean diet (with a mean of 18% of protein intake) could reduce the intra-muscular adipocytes (IMATs), improve the protein breakdown, and consequently the muscular function even in the absence of an SMM increase. These aspects deserve future investigations. HGS is a simple, non-invasive, and reliable measure that can provide valuable information about a patient’s muscle health and its use, alongside other assessments to monitor the effectiveness of interventions and to guide treatment decisions, should be encouraged in clinical settings.
Previous studies showed that a PhA increase could be used as a marker of a better nutritional state [3]. In fact, in a review from Akamatsu Y et al. [21], in a young and old population, the PhA was reported to be lower in women than in men, negatively correlated with age, and positively with BMI, MM, and HGS. In an older population with normal body weight, the PhA decreased in sarcopenic subjects and the prevalence of sarcopenia increased when the PhA was low [22]. On the other hand, it is well known that obesity is associated with lower PhA values, particularly in individuals with a BMI over 35 kg/m2. In our study population, the mean PhA value of 4.14° ± 0.83° was in accordance with previous findings [23].
A recent study in a population aged from 60 to 85 yrs with SO women registered lower PhA values [24] than both men (with and without sarcopenia) and No SO women. These differences were considered the expression of the sexual dimorphism of body composition, such as reduction in muscle mass (of about 25–45%) and lesser amounts of type I fiber in women than in men [25].
The phase angle has been studied as a prognostic marker in several clinical conditions and the possibility to monitor the progression of metabolic or cardiovascular risk factors with PhA changes in patients living with obesity is very promising. Few data are available on PhA changes during weight loss and contradictory results were observed with ketogenic enteral nutrition and VLKCD [5,26], independently of the range of weight loss. The preoperative PhA and HGS values can predict postoperative weight loss (%EWL) in patients following bariatric surgery procedures [27]. These measures reflect an increase in body cell mass (BCM) despite a loss of muscle mass, emphasizing the qualitative nature of HGS and the PhA as related measures. Furthermore, a recent meta-analysis by Campa et al. [7] demonstrated that resistance training promotes increases in the PhA, which result from an increase in extracellular resistance (Xc) concurrent with a reduction in intracellular resistance (Rz) in the older population.
Additionally, the group of patients with SO present a high frequency of a “Poor” score of QoL (about 50%) in both sexes. In a previous study, the QoL, evaluated with the SarQol questionnaire, is compromised in women with sarcopenia with a worsening score in those with sarcopenic obesity [28]. In these latter patients, no data are available regarding possible changes after weight loss. We observed that the whole PGWBI score had an improvement in the NoSO discharge patient group, but it was not linked to the probability of not being sarcopenic at discharge. This should be better investigated by also considering clinical complications and concomitant pharmacological therapies in future studies.
5. Conclusions
Following a 4-week multidisciplinary weight-loss program that combined a hypocaloric diet and physical activity, patients with sarcopenic obesity (SO) showed significant improvement. This improvement is primarily attributed to improvements in handgrip strength (HGS) values, with changes in the phase angle (PhA) further enhancing these effects. The PhA is a good indicator of better quality in skeletal muscle mass (SMM) and protein metabolism efficacy. Further research is needed to elucidate the predictive role of the PhA in SO under various nutritional interventions (e.g., supplementary foods, medical foods, very-low-calorie ketogenic diets, bariatric surgery, and new drug therapies) and physical interventions (e.g., aerobic exercise, anaerobic exercise, or their combinations). Additionally, these findings underscore the importance of tailored interventions in the effective management of SO, emphasizing the potential utility of the PhA as a valuable marker for treatment efficacy. |
Title: Cost-Effectiveness Analysis of Telehealth and In-Person Primary Care Visits for People Living with Alzheimer’s Disease-Related Disorders in the State of Nevada | Body:
1. Introduction
To people living with Alzheimer’s Disease-Related Disorders (ADRD), timely and coordinated communication is essential between their informal caregivers and healthcare providers [1]. Specifically, communication between caregivers and primary care providers determines the quality and efficiency of care for people living with ADRD [2]. The state of Nevada is a provider shortage state, ranking as having the fifth fewest primary care providers per 100,000 capita among the 50 states (192.6 vs. 232.0 U.S. average [3]). As a result, Nevada ranks highest in terms of 30-day hospital readmission rates (25.8%); it is the third highest ranking state in terms of ADRD care expenditure due to ED visits and hospital readmissions for Medicare beneficiaries [4]. The COVID-19 pandemic led to the challenges of transportation and social isolation for people living with ADRD and their caregivers. In response, telehealth utilization has been facilitated with legal and financial support; for example, the CARES Act and American Rescue Plan was initiated. In response, the state of Nevada established a resources and education network—Nevada COVID-19 Aging Network Rapid Response (Nevada CAN)—operated by Nevada’s Aging Disability Services Division [5]. Two academic geriatric institutions (University of Nevada, Reno Sanford Center for Aging and University of Nevada, Las Vegas) and a quality improvement organization (Comagine Health) led geriatric workforce and informal caregiver telehealth training for people living with ADRD in the state of Nevada. As a result, geriatric workforce and informal caregiver telehealth training for people living with ADRD in the state of Nevada resulted in improvements in advance care planning, but the challenge of health disparities among racial minorities residing in urban areas was noted [2,6].
The top 5% of healthcare utilizations for complex medical conditions, for example, ADRD, account for 60% of the healthcare expenditure in the U.S. [7,8]. Because concentrated healthcare expenditures are major public burdens, telehealth as an alternative primary care delivery method might save time and reduce the effort of transportation, as well as prevent the labor productivity loss of caregivers for people living with ADRD in the provider shortage area of the state of Nevada. The aim of this study is to compare the cost-effectiveness of primary care delivery in person vs. telehealth visits for people living with ADRD in the state of Nevada.
2. Materials and Methods
2.1. Overview
The Institutional Review Board (IRB) at the University of Nevada, Las Vegas (UNLV), exempted review of this study because it was determined to be a quality improvement/evaluation. Therefore, this study was not subject to IRB approval and oversight evaluation as human subject research (#1613064-1 and 2014-18). We developed a Markov model that simulated the choice of primary care delivery tools: in-person vs. telehealth visits for non-institutionalized people with ADRD. The structure of the model is shown in Figure 1.
2.2. Study Cohort
The base-case population comprised community-dwelling people living with ADRD in the state of Nevada of the U.S. From the electronic health record (EHR) and claim data of urban and rural health systems, 5872 and 1168 primary care telehealth visits were identified, respectively. Using the International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) ADRD diagnostic codes of either F01, F02, or F03, 473 and 105 ADRD telehealth visits were identified from urban and rural health systems, respectively. Finally, we defined telehealth visit users’ individual profiles, finding 58 urban and 33 rural Nevada residents with available informal caregivers. Informal caregivers were unpaid for their caregiver role and lived in the same geographic area of the individual living with ADRD. They were identified from the EHR administrative data. To match demographic characteristics (age, gender, race) with in-person visit users, the multivariate data matching (MDM) process was applied to data extracted from the EHRs of urban and rural health systems [9]. Sample size was determined by a confidence level of 85% and a margin of error within 10% for urban residents, and a confidence level of 75% and a margin error within 10% for rural residents. Figure 2 presents the process of establishing the study cohort of Nevada urban and rural residents. STATA, version 17 (Stata Corp, College Station, TX, USA), was used for this matching process and sample size determination. A single payment program (Medicare Advantage) administrative and utilization claim data source was used in urban and rural health systems from 1 January 2022 to 31 December 2022.
2.3. Parameters
We used data on transition probabilities and mortality rate from urban and rural health systems. Both urban and rural health systems are safety-net and critical-access organizations in the state of Nevada. The model was developed from a third-party payer perspective. All costs were converted to a 2022 baseline using the healthcare inflation rate published elsewhere [10]. Because of the assumption of the same cost between in-person and telehealth primary care visits for people living with ADRD, we estimated costs of (a) telehealth training personnel labor costs for telehealth visits and (b) traveling and labor productivity loss costs for in-person visits. Using this study’s internal personnel labor cost as of fiscal year 2022, telehealth training personnel labor cost was calculated from trainer’s hourly wages and the conversion probability from in-person visits to telehealth visits was calculating using urban and rural health system data. Traveling cost was estimated from the distance between the given home address and healthcare provider address. Labor productivity loss was estimated by the difference between the roundtrip time required to travel from the home address to an in-person primary care clinic, plus in-person primary care visit time vs. the time required to attend a telehealth visit from home (i.e., time savings = roundtrip drive time + [time for in-person primary care visit − time for telehealth visit]) [11]. In addition, we estimated emergency department (ED) visits and the hospitalization-associated costs of in-person and telehealth visits. ED visit and hospitalization-associated utilization information was collected from the Medicare Advantage payor programs. We also verified this information by reviewing ED/hospital discharge summaries, including principal diagnoses and hospital length-of-stay information from EHRs. The ED-visit-associated cost was estimated from the Nevada State Emergency Department Database (SEDD), and hospitalization-associated cost was estimated from the Nevada State Inpatient Database (SID), which is a publicly available dataset, for the period between 1 January 2021 and 31 December 2021 [12,13]. The Nevada SEDD and SID contain the ED visit and hospital discharge records of all community hospitals in the state of Nevada, and were originally developed for the Healthcare Cost and Utilization Project (HCUP) by Agency Healthcare Research and Quality (AHRQ). The Nevada SEDD and SID cover more than 95% of all Nevada ED visits and hospital discharges [12,13]. The Nevada SEDD and SID include anonymous patient-level information, including demographics, diagnostic/procedure codes, ED visits, and hospital utilizations. We collected the number of ED visits and hospitalizations at an individual level, as well as lengths of stay (days) and hospital charges per principal diagnoses from the Nevada SEDD and SID. Then, ED-visit-associated and hospitalization-associated costs were estimated by combining the hospital lengths of stay (days) and the daily average hospital charges per principal diagnoses [2]. ED-visit-associated and hospitalization-associated cost estimates were weighted by age and gender [2]. We assigned caregiver burden as a substitute for quality-of-life (QoL) weights because the direct QOL assessment of people living with ADRD is often challenging to validate, especially for those with severe/advanced ADRD [14]. Caregivers are decision-makers on actual healthcare utilization, for example, calling 911 to go to the ED on behalf of a person living with ADRD. The short-form 12-item Zarit Burden Interview (ZBI-12) has been validated to quantify the burden of caregivers for people living with ADRD [15]. Possible ZBI-12 scores range from 0 (minimum burden) to 48 (maximum burden). We modified a scoring matrix with a range from 0 (maximum burden) to 100 (minimum burden). This study used the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline [16].
2.4. Cost-Effectiveness Analysis
To estimate the effects of demographic and geographic parameters on the Markov model, race parameters were divided into non-Hispanic White individuals vs. others, and location parameters were divided into urban vs. rural. We divided scenarios into the following three models: (A) urban-residing non-Hispanic White individuals, (B) urban-residing racial minorities, and (C) rural residents. We estimated the Incremental Cost-Effectiveness Ratio (ICER) of telehealth visits. Because of the limited life expectancy of people living with ADRD, we used a 10-year horizon with a yearly cycle length to advance time in the model. We discounted all future costs and ZBI-12 at 3% per year. All Markov models were constructed using TreeAge Pro Healthcare version 2024 R1.1 (TreeAge Software Inc., Williamstown, MA, USA) to assess the ICERs of telehealth visits in all three Markov models.
2.1. Overview
The Institutional Review Board (IRB) at the University of Nevada, Las Vegas (UNLV), exempted review of this study because it was determined to be a quality improvement/evaluation. Therefore, this study was not subject to IRB approval and oversight evaluation as human subject research (#1613064-1 and 2014-18). We developed a Markov model that simulated the choice of primary care delivery tools: in-person vs. telehealth visits for non-institutionalized people with ADRD. The structure of the model is shown in Figure 1.
2.2. Study Cohort
The base-case population comprised community-dwelling people living with ADRD in the state of Nevada of the U.S. From the electronic health record (EHR) and claim data of urban and rural health systems, 5872 and 1168 primary care telehealth visits were identified, respectively. Using the International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) ADRD diagnostic codes of either F01, F02, or F03, 473 and 105 ADRD telehealth visits were identified from urban and rural health systems, respectively. Finally, we defined telehealth visit users’ individual profiles, finding 58 urban and 33 rural Nevada residents with available informal caregivers. Informal caregivers were unpaid for their caregiver role and lived in the same geographic area of the individual living with ADRD. They were identified from the EHR administrative data. To match demographic characteristics (age, gender, race) with in-person visit users, the multivariate data matching (MDM) process was applied to data extracted from the EHRs of urban and rural health systems [9]. Sample size was determined by a confidence level of 85% and a margin of error within 10% for urban residents, and a confidence level of 75% and a margin error within 10% for rural residents. Figure 2 presents the process of establishing the study cohort of Nevada urban and rural residents. STATA, version 17 (Stata Corp, College Station, TX, USA), was used for this matching process and sample size determination. A single payment program (Medicare Advantage) administrative and utilization claim data source was used in urban and rural health systems from 1 January 2022 to 31 December 2022.
2.3. Parameters
We used data on transition probabilities and mortality rate from urban and rural health systems. Both urban and rural health systems are safety-net and critical-access organizations in the state of Nevada. The model was developed from a third-party payer perspective. All costs were converted to a 2022 baseline using the healthcare inflation rate published elsewhere [10]. Because of the assumption of the same cost between in-person and telehealth primary care visits for people living with ADRD, we estimated costs of (a) telehealth training personnel labor costs for telehealth visits and (b) traveling and labor productivity loss costs for in-person visits. Using this study’s internal personnel labor cost as of fiscal year 2022, telehealth training personnel labor cost was calculated from trainer’s hourly wages and the conversion probability from in-person visits to telehealth visits was calculating using urban and rural health system data. Traveling cost was estimated from the distance between the given home address and healthcare provider address. Labor productivity loss was estimated by the difference between the roundtrip time required to travel from the home address to an in-person primary care clinic, plus in-person primary care visit time vs. the time required to attend a telehealth visit from home (i.e., time savings = roundtrip drive time + [time for in-person primary care visit − time for telehealth visit]) [11]. In addition, we estimated emergency department (ED) visits and the hospitalization-associated costs of in-person and telehealth visits. ED visit and hospitalization-associated utilization information was collected from the Medicare Advantage payor programs. We also verified this information by reviewing ED/hospital discharge summaries, including principal diagnoses and hospital length-of-stay information from EHRs. The ED-visit-associated cost was estimated from the Nevada State Emergency Department Database (SEDD), and hospitalization-associated cost was estimated from the Nevada State Inpatient Database (SID), which is a publicly available dataset, for the period between 1 January 2021 and 31 December 2021 [12,13]. The Nevada SEDD and SID contain the ED visit and hospital discharge records of all community hospitals in the state of Nevada, and were originally developed for the Healthcare Cost and Utilization Project (HCUP) by Agency Healthcare Research and Quality (AHRQ). The Nevada SEDD and SID cover more than 95% of all Nevada ED visits and hospital discharges [12,13]. The Nevada SEDD and SID include anonymous patient-level information, including demographics, diagnostic/procedure codes, ED visits, and hospital utilizations. We collected the number of ED visits and hospitalizations at an individual level, as well as lengths of stay (days) and hospital charges per principal diagnoses from the Nevada SEDD and SID. Then, ED-visit-associated and hospitalization-associated costs were estimated by combining the hospital lengths of stay (days) and the daily average hospital charges per principal diagnoses [2]. ED-visit-associated and hospitalization-associated cost estimates were weighted by age and gender [2]. We assigned caregiver burden as a substitute for quality-of-life (QoL) weights because the direct QOL assessment of people living with ADRD is often challenging to validate, especially for those with severe/advanced ADRD [14]. Caregivers are decision-makers on actual healthcare utilization, for example, calling 911 to go to the ED on behalf of a person living with ADRD. The short-form 12-item Zarit Burden Interview (ZBI-12) has been validated to quantify the burden of caregivers for people living with ADRD [15]. Possible ZBI-12 scores range from 0 (minimum burden) to 48 (maximum burden). We modified a scoring matrix with a range from 0 (maximum burden) to 100 (minimum burden). This study used the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline [16].
2.4. Cost-Effectiveness Analysis
To estimate the effects of demographic and geographic parameters on the Markov model, race parameters were divided into non-Hispanic White individuals vs. others, and location parameters were divided into urban vs. rural. We divided scenarios into the following three models: (A) urban-residing non-Hispanic White individuals, (B) urban-residing racial minorities, and (C) rural residents. We estimated the Incremental Cost-Effectiveness Ratio (ICER) of telehealth visits. Because of the limited life expectancy of people living with ADRD, we used a 10-year horizon with a yearly cycle length to advance time in the model. We discounted all future costs and ZBI-12 at 3% per year. All Markov models were constructed using TreeAge Pro Healthcare version 2024 R1.1 (TreeAge Software Inc., Williamstown, MA, USA) to assess the ICERs of telehealth visits in all three Markov models.
3. Results
As presented in Table 1, of the 58 Nevada urban residents, the mean age was 81.3 years, with a standard deviation of 9.7 years and a range of 61–98 years. Approximately one-third (37.9%) was 79 years and younger. In terms of gender, 41.4% were male, 55.3% were female, and 3.6% were other. More than half (51.7%) of telehealth visits were to non-Hispanic White individuals; 15.5% were to non-Hispanic Black individuals; 13.8% were to Hispanic or Latino individuals; 12.1% were to non-Hispanic Asian individuals; and 6.9% were to other or mixed-race individuals. Of the 33 Nevada rural residents, the mean age was 82.6 years, with a standard deviation of 6.7 years and a range of 63–89 years. Approximately half (45.4%) were 79 years and younger. In terms of gender, 39.4% were male and 60.6% were female. More than three-fourths (84.8%) of telehealth visits were to non-Hispanic White individuals; 3.0% were to non-Hispanic Black individuals; 6.1% were to Hispanic or Latino individuals; and 6.1% were to other or mixed-race individuals.
Table 2 presents three Markov models and the analysis results by demographic and geographic parameters.
In Model A, concerning urban-residing non-Hispanic White individuals, the probability per year of transitioning from telehealth visits to in-person visits (18.29%) was lower than that from in-person visits to telehealth visits (31.37%). The mortality rate per year of telehealth visit users (8.10%) was lower than that of in-person visit users (11.47%). The mean numbers of ED visits and hospitalization per year in telehealth visit users (1.52, 0.74) were lower than those of utilizing in-person visits (2.24, 1.86), respectively. The incremental cost of telehealth visits (USD 202) was lower than that of in-person visits (USD 230). The ICER per modified ZBI-12 for telehealth visits was USD 9.44 lower than that of in-person visits. In Model B, concerning urban-residing racial minorities, the probability per year of transitioning from telehealth visits to in-person visits (26.77%) was higher than that from in-person visits to telehealth visits (21.67%). The mortality rate of telehealth visit users per year (11.25%) was lower than that of in-person visit users (16.90%). The mean numbers of ED visits and hospitalization per year in telehealth visit users (1.52, 1.22) were lower than those in-person visit users (2.24, 2.71), respectively. The incremental cost of telehealth visits (USD 220) was lower than that of in-person visits (USD 237). The ICER per modified ZBI-12 for telehealth visits was USD 29.26 higher than that of in-person visits. In Model C, concerning rural residents, the probability per year of transitioning from telehealth visits to in-person visits (30.00%) was higher than that from in-person visits to telehealth visits (26.67%). The mortality rate of telehealth visits users per year (7.52%) was lower than that of in-person visit users (10.29%). The mean numbers of ED visits and hospitalization per year for telehealth visit users (0.93, 0.61) were lower than those of in-person visit users (1.20, 0.96), respectively. The incremental cost of telehealth visits (USD 436) was lower than that of in-person visits (USD 503). The ICER per modified ZBI-12 for telehealth visits was USD 320.93 lower than that of in-person visits.
4. Discussion
The purpose of this study is to estimate the cost-effectiveness of telehealth visits as an alternative to in-person visits for people living with ADRD in the state of Nevada, a provider shortage state. The main findings are distributional differences in the cost-saving effects of telehealth primary care in line with racial and geographic parameters. Among urban-residing non-Hispanic White individuals, the ICER per modified ZBI-12 indicates a USD 9.44 cost-saving with telehealth visits; among rural residents, the ICER per modified ZBI-12 indicates a USD 320.93 cost-saving with telehealth visits. Although this study used healthcare perspectives, our Markov models were divided into three models by racial and geographic parameters based on the health equity considerations of this study [17]. These findings align with the hypothesis that the benefits of telehealth visits may facilitate timely and better communication for people living with ADRD than in-person visits. The cost-saving effect of the telehealth visits was the result of reduced ED visits and hospitalizations across the Markov models. This reduced utilization of healthcare led to the enhancement of care efficiency, as noted in the previous literature on the benefits of telehealth for people living with ADRD [1,2]. In the midst of the payment system transition of Medicare beneficiaries from traditional fee-for-service payments to bundle payments, this study’s findings (i.e., the cost-saving effects of telehealth as a result of reducing ED visits/hospitalizations) give an insight into how to reform healthcare provider payments to achieve greater efficiency and value in providing primary care to people living with ADRD [18]. As the Medicare Advantage program was the single payer of this study, telehealth’s cost-saving effects have implications for advancing primary care efficiency for the population of rural residents in this study.
On the other hand, among urban-residing racial minorities, the ICER per modified ZBI-12 indicates a USD 29.26 cost-saving with in-person visits compared to telehealth visits. Although healthcare utilizations in telehealth visit users were fewer than those in in-person visit users among urban-residing racial minorities, a higher transition probability from telehealth visits to in-person visits (26.77%) was noted compared to that from in-person visits to telehealth visits (21.67%). These findings were the opposite to those found for delivery-type transition probability among urban-residing non-Hispanic White individuals (18.29% from telehealth visits to in-person visits; 31.37% from in-person visits to telehealth visits). These findings are consistent with previous telehealth utilization studies that identified that racial minorities living with ADRD were less likely persist with telehealth visits compared to non-Hispanic White individuals living with ADRD [6,19]. Using the same dataset, the previous study examining the racial disparities of telehealth recipients identified that this disparity may have been exacerbated by discussing mentation during telehealth visits [6]. Similar findings and interpretations were noted in another telehealth disparity study in the northeastern area [20]. To reduce disparities related to cost burdens in telehealth visits for urban-residing racial minorities, workforce training is a realistic priority strategy. The Age-Friendly Health System 4Ms framework (What Matters, Mobility, Medication, Mentation) is a locally and interprofessionally adoptable training toolkit for the workforce caring for people living with ADRD, from entry level to leadership [21,22]. This 4Ms framework is also optimal for training the informal caregivers of people living with ADRD to enhance their capacity to cope with the challenges of caring for ADRD individuals via the authentic care partnership model [23].
Finally, as telehealth has been adopted as a modality of care for people living with ADRD since the COVID-19 pandemic, it is essential to develop and support user-friendly health information technology for care partners [1,2]. This is crucial for timely communication between caregivers and healthcare providers in transitional care coordination, especially within two weeks of discharge from acute hospital or/and skilled nursing facilities [1,2]. In provider shortage areas such as the state of Nevada, telehealth can play a pivotal role in identifying community-dwelling individuals with ADRD and referring them to appropriate resources at an early stage. Evidence-based training for the workforce and for caregivers for people living with ADRD is essential to sustain telehealth as a primary care visit delivery tool, especially among racial minorities. If the State Legislature in the 2025 session approves statewide efforts of this initiative, established partnerships among Nevada ADRD stakeholders (University of Nevada, Reno Sanford Center for Aging, Nevada State Aging Disability Services Division, Alzheimer’s Association, Cleveland Clinic Lou Ruvo Center for Brain Health, and Kirk Kerkorian School of Medicine at UNLV) can augment the benefits of telehealth visits’ cost-saving effects for community-dwelling Nevadans living with ADRD.
Telehealth primary care delivery has environmental output implications in all sectors of the economy, both in the short and long term [24]. The interactions between per capita health expenditures and per capita atmospheric pollution have been empirically examined elsewhere using the well-known Environmental Kuznets Curve [25]. Thus, telehealth is a promising method to achieve a greater efficiency and equity of primary care for people living with ADRD and their caregivers in provider shortage areas.
The findings of our study are limited by a single urban and single rural health system with the distinction of safety-net/critical-access systems and a single year of observation, with a single Medicare Advantage payer program.
Further studies with diverse healthcare system partners with different characteristics may generate different findings from our study results. As we reviewed administrative data templates without personal information identification, we did not review the healthcare providers’ progress notes in the EHRs. Therefore, the severity of ADRD was not captured. Although the reasons associated with choosing telehealth or in-person visits were not included in this study, Medicaid and dual Medicare/Medicaid eligible program enrollees were not included because they might have been restricted to only telehealth service reimbursements with audio-only and asynchronous services [26]. Although the majority of telehealth waivers enacted during the COVID-19 public health emergency are set to expire on 31 December 2024, in the absence of legislative action, the CMS has proposed to leave certain key flexibilities in place, including the allowance for physicians and other practitioners to furnish remote “direct supervision” through their immediate availability via audio–video technology [27]. The current CMS reimbursement policy for telehealth may impact the originating site and service site flexibilities from early 2025 [27]. Selection bias might have occurred in the cohort selection process of identifying individuals living with ADRD and their available informal caregivers due to the reliance on data accuracy and administrative data templates. Lastly, we assigned caregiver burden as a substitute for QoL weights. QoL weights are assigned to a particular health state and represent the utility of that state as perceived by society [28,29]. The challenges in the quality-adjusted life years (QALYs) traditional metric for CEA within value assessments might lead to distributive justice being overlooked, resulting in negative consequences for older, sicker stakeholders in communities like people living with ADRD [28,29]. ADRD have a progressive nature. Even with an intervention, telehealth services might not be able to reverse the course of disease progression of people living with ADRD. For example, a 2005 decision using a QALY-based cost-effective assessment from the U.K.’s National Institute of Health and Care Excellence resulted in the denial of cholinesterase inhibitor drugs for ADRD treatment under the U.K.’s National Health Service. As a result, the consumer advocates argued that NICE’s QALY cost-effectiveness assessment neglected to consider caregivers’ benefits from the ADRD treatment [30]. Telehealth primary care can have productivity benefits, enabling the caregivers of people living with ADRD to stay in the formal workforce, perform productive work in their household, volunteer in their community, or provide community resources of value to social welfare. Since the implementation of the Affordable Care Act in the U.S., QALY has been used with substantial limitations due to the Center for Medicare and Medicaid Services barring the QALY criterion to determine the coverage of public insurance [31]. Looking ahead to advance equity perspectives, caregiver burden is a new tool for assessing caregiver burden and healthcare utilizations, ED visits, and hospitalizations.
5. Conclusions
Among urban-residing non-Hispanic White individuals, the ICER per modified ZBI-12 indicates a USD 9.44 cost-saving with telehealth visits; among urban-residing racial minorities, the ICER per modified ZBI-12 indicates a USD 29.26 cost-saving with in-person visits; among rural residents, the ICER per modified ZBI-12 indicates a USD 320.93 cost-saving with telehealth visits. Distributional differences in the cost-saving effects of telehealth primary care were noted in line with racial and geographic parameters. Workforce and caregiver training is necessary for reducing distributional differences, especially among urban-residing racial monitories living with ADRD in provider shortage areas such as the state of Nevada. |
Title: The value of slow-burning science: an interview with Peter Friedl and Bettina Weigelin | Body: |
Title: Clinical Characteristics of Distinct Subgroups of Patients with Primary Sjögren’s Syndrome Classified by Serological Profiles: A Comparison Study | Body:
1. Introduction
Sjögren’s syndrome (SS) is a systemic chronic autoimmune inflammatory disease that mainly affects the exocrine glands via lymphocytic infiltration, resulting in dryness of the eyes and mouth [1]. SS may occur alone (primary form-pSS) or overlap with another well-defined rheumatic disease (secondary form-sSS), indicating differences in pSS and sSS pathology. SS has a worldwide distribution and its phenotype may vary according to geolocation, race, and ethnicity. The clinical presentation of SS is heterogeneous, ranging from symptoms of sicca to systemic diseases [2]. SS is associated with a diversity of autoantibodies due to aberrant B cell activation, with anti-SSA/anti-Ro and anti-SSB/anti-La, RF, and ANA being the most commonly encountered [3]. Anti-Ro/SS-A antibody is present in 50–75% of SS patients, and in approximately half of them, anti-La/SS-B antibody was also detected [4]. The prevalence of ANA and RF was 50–89% and 38–61%, respectively, in the sera of pSS patients [4]. Autoantibodies might lead to the destruction of epithelial cells, causing gland hypofunction and the development of sicca symptoms, which may also lead to extraglandular manifestations [5]. Many organs other than the exocrine glands may be affected in patients with SS, including the skin and joints; the lungs, heart, and gastrointestinal tract, including the pancreas and liver; the kidneys, bladder, and gynecologic system; and both the peripheral nervous system and central nervous system [6]. Immunological markers provide prognostic information for in the diagnosis of the disease, prediction of the results, and extraglandular manifestations [7]. Previous studies have emphasized the association of anti-Ro/SSA antibodies with the development of extra-glandular manifestations such as cutaneous vasculitis, lung involvement, nephritis, and risk of lymphoma [8,9]. A different study indicated that RF may be associated with serological positivity for anti-Ro and anti-La, as well as with severe systemic disease, pulmonary involvement, renal disease, and corticosteroid use [10].
The relationship between anti-Ro/SS-A and anti-La/SS-B autoantibody positivity and clinical findings in patients with Sjögren’s syndrome was further investigated. However, there are few studies in the literature, especially on RF and ANA antibodies and clinical entities. Unfortunately, there have been no studies specifically on quadruple antibody positivity. The present study focused on identifying biomarkers and potential therapeutic targets of SS subsets that vary in their autoantibody profiles. To this end, we compared the clinical and immunological features of SS patients categorized into three serological subgroups: (1) positive for ANA, RF, anti-SSA/Ro and anti-SSB/La antibodies (quadruple-seropositive), (2) negative for ANA, RF, anti-SSA/Ro, and anti-SSB/La antibodies (quadruple-seronegative), and (3) positive for ANA and anti-SSA/Ro antibodies (double-seropositive).
In this study, we aimed to determine differences in clinical findings by comparing the quadruple antibody positivity group with patients with fewer antibodies and those with no antibodies.
2. Materials and Methods
2.1. Study Design, Setting, and Ethics
This comparative study was conducted in the Department of Rheumatology Eskişehir Cıty Hospital, Turkey, between December 2019 and May 2024. Ethical approval was obtained from the local ethics committee (decision date: 23 May 2024, decision no: 2024/30) and the study was performed in accordance with the ethical standards stated in the Declaration of Helsinki and its amendments. The method and purpose of the study were explained to all participants in detail, and written informed consent was obtained from each participant. Patients were invited for examination every 3 or 6 months, written informed consent was obtained from all participants at the time of application, and information was provided about the study.
2.2. Study Population
The study population included 402 consecutive patients with pSS (378 women and 24 men) who fulfilled the 2016 EULAR/ACR criteria [11]. The clinical characteristics of the patients were retrospectively obtained from their medical records. Laboratory information and pathological reports were obtained from the patient’s files in the hospital’s electronic database. As anti-Ro/SSA-positive patients fulfilled the EULAR criteria, minor salivary gland biopsy (MSGB) was not performed. However, MSGB was also performed in some SSA-positive patients who did not fulfill the EULAR criteria and whose sicca symptoms were not prominent. MSGB was performed in all patients with autoantibody positivity or negativity. The exclusion criteria were as follows: combined head and neck radiation, chronic hepatitis C or human immunodeficiency virus infections, prior lymphoproliferative disease, sarcoidosis, graft-versus-host disease, amyloidosis, IgG4-related disease, fibromyalgia and associated systemic autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, mixed connective tissue disease, myopathies, and systemic sclerosis. In all patients, diseases that may cause sSS were excluded on the basis of serological tests and clinical findings. The levels of complement (C3 and C4), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), immunoglobulins (IgG, IgM, and IgA), anti-nuclear antibody (ANA), anti-dsDNA, rheumatoid factor (RF), anti-CCP antibody, and autoantibody-targeted extractable nuclear antigens (ENAs) were detected using the immune blot method, which identified seven different target autoantigens, including U1RNP/Sm, Sm, SS-A, Ro52, SS-B, Scl-70, and Jo-1 from the case records. Patients without insufficient data for SS diagnosis in the registry were excluded from the study. General procedural data and laboratory and clinical information at the onset of pSS, including the first presentation and systemic involvement, were retrospectively reviewed. Clinical data, such as age at diagnosis, disease duration, oral and ocular dryness, constitutional symptoms, and data on joint, pulmonary, kidney, vasculitis, skin, nervous, gastrointestinal tract, and endocrine involvement, were collected. Schirmer’s test was considered positive when less than 5 mm of paper was humid after 5 min. A minor salivary gland biopsy was performed on the lower lip through an incision. Focal lymphocytic sialadenitis in a minor salivary gland biopsy with one or more foci of lymphocytes per 4 mm2 (focus score ≥ 1) was accepted as the histopathological criterion [12].
The presence and titers of the ANAs were determined using an indirect immunofluorescence method with the Hep2 cell line as an antigen and an antibody titer test; the cutoff value for ANA was 1:80. The values used in our laboratory, anti-Ro/SSA, and anti-La/SSB were detected using enzyme immunoassay, while RF of IgM class (normal range < 30 IU/mL) and other autoantibodies were identified by routine laboratory tests. Patients with cytopenias were evaluated hematologically, and those with malignancies, leukemia, drugs, B12, folate, iron deficiency, or anemia of chronic disease were excluded. Leucopenia was defined as white blood cell count < 4.00 × 103/mm3, neutropenia (neutrophil < 1.5 × 103/mm3), anaemia (haemoglobin concentration < 12 g/dL), thrombocytopenia (platelet count < 150 × 103/mm3).
In patients with petechiae and purpura, skin biopsies were performed after excluding drug reactions and xeroderma. To exclude other etiological causes in all patients with vasculitis, previous or current infections, antibiotics used, malignancy status, vaccinations received in the last six months, possible etiological factors, antineutrophil cytoplasmic antibodies (ANCA), immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV) syphilis serology, streptococcal antibodies, abdominal ultrasound examination, chest radiography, fecal occult blood test (FOBT), and skin biopsy were performed. Direct immunofluorescence (DIF) tests for IgA, IgG, IgM, and C3 were performed using skin biopsy. All patients with vasculitis were evaluated for medium- and large-vessel vasculitis, and systemic involvement. All patients were classified according to the 2012 Revised International Chapel Hill Consensus Conference (CHCC) Nomenclature of Vasculitides [13]. Cryoglobulinemic vasculitis was diagnosed when the serum cryoglobulins were positive with characteristic clinical features. Urticarial vasculitis is diagnosed when long-lasting (more than 24 h) indurated weals, which may be itchy, painful, tender, or accompanied by purpura, occur spontaneously or at sites of minor trauma.
Patients were grouped according to the following serological profiles: group 1, positive for ANA, RF, anti-SSA/Ro, and anti-SSB/La antibodies (quadruple-seropositive); group 2, negative for ANA, RF, anti-SSA/Ro, and anti-SSB/La antibodies (quadruple-seronegative); and group 3, positive for ANA and anti-SSA/Ro antibodies (double-seropositive).
2.3. Statistics
All statistical analysis was performed using Statistical Package for Social Sciences (SPSS) 23.0 software (IBM Corp., Armonk, NY, USA). Descriptive values are expressed as numbers (n) and percentages (%) for categorical values; they are expressed as the mean (standard deviation, SD) if normally distributed and as the median (interquartile range, IQR) if not normally distributed. For continuous variables, comparisons of median values were performed using the Mann–Whitney U-test. Student’s t-test was used as a parametric test. The Chi-square test and Fisher’s exact test were used for categorical variables. The statistical significance level was accepted as a p-value < 0.05 in all comparisons.
2.1. Study Design, Setting, and Ethics
This comparative study was conducted in the Department of Rheumatology Eskişehir Cıty Hospital, Turkey, between December 2019 and May 2024. Ethical approval was obtained from the local ethics committee (decision date: 23 May 2024, decision no: 2024/30) and the study was performed in accordance with the ethical standards stated in the Declaration of Helsinki and its amendments. The method and purpose of the study were explained to all participants in detail, and written informed consent was obtained from each participant. Patients were invited for examination every 3 or 6 months, written informed consent was obtained from all participants at the time of application, and information was provided about the study.
2.2. Study Population
The study population included 402 consecutive patients with pSS (378 women and 24 men) who fulfilled the 2016 EULAR/ACR criteria [11]. The clinical characteristics of the patients were retrospectively obtained from their medical records. Laboratory information and pathological reports were obtained from the patient’s files in the hospital’s electronic database. As anti-Ro/SSA-positive patients fulfilled the EULAR criteria, minor salivary gland biopsy (MSGB) was not performed. However, MSGB was also performed in some SSA-positive patients who did not fulfill the EULAR criteria and whose sicca symptoms were not prominent. MSGB was performed in all patients with autoantibody positivity or negativity. The exclusion criteria were as follows: combined head and neck radiation, chronic hepatitis C or human immunodeficiency virus infections, prior lymphoproliferative disease, sarcoidosis, graft-versus-host disease, amyloidosis, IgG4-related disease, fibromyalgia and associated systemic autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, mixed connective tissue disease, myopathies, and systemic sclerosis. In all patients, diseases that may cause sSS were excluded on the basis of serological tests and clinical findings. The levels of complement (C3 and C4), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), immunoglobulins (IgG, IgM, and IgA), anti-nuclear antibody (ANA), anti-dsDNA, rheumatoid factor (RF), anti-CCP antibody, and autoantibody-targeted extractable nuclear antigens (ENAs) were detected using the immune blot method, which identified seven different target autoantigens, including U1RNP/Sm, Sm, SS-A, Ro52, SS-B, Scl-70, and Jo-1 from the case records. Patients without insufficient data for SS diagnosis in the registry were excluded from the study. General procedural data and laboratory and clinical information at the onset of pSS, including the first presentation and systemic involvement, were retrospectively reviewed. Clinical data, such as age at diagnosis, disease duration, oral and ocular dryness, constitutional symptoms, and data on joint, pulmonary, kidney, vasculitis, skin, nervous, gastrointestinal tract, and endocrine involvement, were collected. Schirmer’s test was considered positive when less than 5 mm of paper was humid after 5 min. A minor salivary gland biopsy was performed on the lower lip through an incision. Focal lymphocytic sialadenitis in a minor salivary gland biopsy with one or more foci of lymphocytes per 4 mm2 (focus score ≥ 1) was accepted as the histopathological criterion [12].
The presence and titers of the ANAs were determined using an indirect immunofluorescence method with the Hep2 cell line as an antigen and an antibody titer test; the cutoff value for ANA was 1:80. The values used in our laboratory, anti-Ro/SSA, and anti-La/SSB were detected using enzyme immunoassay, while RF of IgM class (normal range < 30 IU/mL) and other autoantibodies were identified by routine laboratory tests. Patients with cytopenias were evaluated hematologically, and those with malignancies, leukemia, drugs, B12, folate, iron deficiency, or anemia of chronic disease were excluded. Leucopenia was defined as white blood cell count < 4.00 × 103/mm3, neutropenia (neutrophil < 1.5 × 103/mm3), anaemia (haemoglobin concentration < 12 g/dL), thrombocytopenia (platelet count < 150 × 103/mm3).
In patients with petechiae and purpura, skin biopsies were performed after excluding drug reactions and xeroderma. To exclude other etiological causes in all patients with vasculitis, previous or current infections, antibiotics used, malignancy status, vaccinations received in the last six months, possible etiological factors, antineutrophil cytoplasmic antibodies (ANCA), immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV) syphilis serology, streptococcal antibodies, abdominal ultrasound examination, chest radiography, fecal occult blood test (FOBT), and skin biopsy were performed. Direct immunofluorescence (DIF) tests for IgA, IgG, IgM, and C3 were performed using skin biopsy. All patients with vasculitis were evaluated for medium- and large-vessel vasculitis, and systemic involvement. All patients were classified according to the 2012 Revised International Chapel Hill Consensus Conference (CHCC) Nomenclature of Vasculitides [13]. Cryoglobulinemic vasculitis was diagnosed when the serum cryoglobulins were positive with characteristic clinical features. Urticarial vasculitis is diagnosed when long-lasting (more than 24 h) indurated weals, which may be itchy, painful, tender, or accompanied by purpura, occur spontaneously or at sites of minor trauma.
Patients were grouped according to the following serological profiles: group 1, positive for ANA, RF, anti-SSA/Ro, and anti-SSB/La antibodies (quadruple-seropositive); group 2, negative for ANA, RF, anti-SSA/Ro, and anti-SSB/La antibodies (quadruple-seronegative); and group 3, positive for ANA and anti-SSA/Ro antibodies (double-seropositive).
2.3. Statistics
All statistical analysis was performed using Statistical Package for Social Sciences (SPSS) 23.0 software (IBM Corp., Armonk, NY, USA). Descriptive values are expressed as numbers (n) and percentages (%) for categorical values; they are expressed as the mean (standard deviation, SD) if normally distributed and as the median (interquartile range, IQR) if not normally distributed. For continuous variables, comparisons of median values were performed using the Mann–Whitney U-test. Student’s t-test was used as a parametric test. The Chi-square test and Fisher’s exact test were used for categorical variables. The statistical significance level was accepted as a p-value < 0.05 in all comparisons.
3. Results
3.1. Basic Characteristics of Total pSS Patients Included in the Present Study
The baseline characteristics of patients diagnosed with pSS are shown in Table 1. In total, 402 patients with pSS were retrospectively analyzed with a mean age of 54.3 (18–84) years and the patients were predominantly female 378 (94.0%). Among these patients, 78.8% (317/402) were seropositive (with at least one antibody positivity) and 85 (21.1%) were quadruple seronegative. Anti-SSA was present in 252 (62.6%) and anti-SSB in 75 (18.6%) patients. Other autoantibodies, including ANA and RF, were present in 284 (70.6%) and 96 (23.8%) of patients with pSS, respectively. The combined ANA and SSA positivity was 174 (43.2%). The number of quadruple-seropositive patients was 72 (18.6%). ANA staining pattern, with a granular pattern at 61%, nuclear at 20%, homogenous at 10%, centromere at 6%, and other staining patterns at 3%. MSGB was performed on a total of 232 patients and a focus score ≥ 1 was detected in 220 (94.8%) of them.
3.2. Comparison of Clinical Characteristics According to the Presence of Antibodies
Clinical characteristics and comparisons of seropositive, seronegative, and double-seropositive pSS are shown in Table 2 and Table 3. The age at diagnosis for quadruple-seropositive pSS was 42.4 ± 10.8, which was significantly younger than that of patients with double-seropositive and quadruple-seronegative pSS (p = 0.021, p = 0.112). When quadruple-seropositive pSS was compared with quadruple-seronegative, symptoms related to glandular dysfunction, such as xerostomia (97.2% vs. 94.1%, p = 0.112) and xerophthalmia (94.0% vs. 88.2%, p = 0.142), were more common in quadruple-seropositive pSS (Table 2). Similarly, when quadruple-seropositive pSS was compared with double seropositivity, xerostomia (97.2% vs. 92%, p = 0.242) and xerophthalmia (94.0% vs. 90.8%, p = 0.361) were more common in quadruple-seropositive pSS (Table 3). However, this similarity was not statistically significant between the double-seropositive and quadruple-seronegative groups (p = 0.345 and p = 0.526) (Table 4). In terms of organ involvement, salivary gland enlargement, arthralgias, arthritis, Raynaud’s phenomenon, lymphadenopathy, vasculitis-purpura, interstitial lung disease, neurological involvement, autoimmune thyroiditis, renal interstitial disease, anemia, leukopenia, hypergammaglobulinemia, and hypocomplementemia were more common in quadruple-seropositive than in quadruple-seronegative pSS (p < 0.0001) (Table 2). Except for salivary gland enlargement, arthralgia, and hypocomplementemia, other involvements were more frequent in quadruple-seropositive individuals than in those with double seropositivity (p < 0.0001) (Table 3). Similarly, when double seropositivity was compared with quadruple seronegativity, salivary gland enlargement, arthralgia, arthritis, lymphadenopathy, and interstitial lung disease were more common in double seropositivity (p < 0.0001) (Table 4). Interstitial lung disease was observed in 98.2% (55/56) of seropositive (at least one antibody positive) patients, while it was 1.7% (1/56) in seronegative patients (all antibodies negative) (p < 0.0001). NSIP accounted for 89.2% of the ILD patterns, and almost all of them were observed in seropositive patients. Neurological involvement was observed in 95.0% (19/20) of seropositive patients, while it was 5.0% (1/20) of seronegative patients (p < 0.0001). Autoimmune thyroiditis was present in 86 patients (21.3%); 74.4% of these patients were ANA positive and ANA staining: granular pattern, 84%; homogenous at 10%; and nuclear, 6%. Of the autoimmune thyroiditis patients, 79 (91.8%) had Hashimoto’s disease, five had Graves’ disease, and two had subacute thyroiditis. Moreover, autoimmune thyroiditis was observed in 83.7% (72/86) of seropositive patients, while it was 9.3% (8/86) of seronegative patients (p < 0.0001). Cryoglobulinemia, Raynaud’s phenomenon, vasculitis, and renal interstitial disease were not observed in any of the quadruple-seronegative patients. In our patients who were diagnosed with Sjögren’s syndrome and had symptoms such as cough and dyspnea, chest computed tomography (CT) was performed. Patients without any of these symptoms underwent lung radiography. CT was performed for patients with interstitial lung findings.
The laboratory findings of quadruple-seropositive and quadruple-seronegative pSS patients were also compared (Table 2). ESR and CRP levels were lower in the seronegative pSS group (p = 0.125 vs. p = 0.362). Peripheral blood parameters including platelet count (p = 0.001), hemoglobin (p < 0.0001), leukocyte count (p < 0.0001), and lymphopenia (p < 0.0001) levels were lower in quadruple-seropositive pSS. The level of hypergammaglobulinemia was higher in individuals with quadruple seropositivity than in those with quadruple seronegativity (p < 0.0001). No significant differences were observed between quadruple-seronegative and quadruple-seropositive pSS in other laboratory parameters, such as creatinine level, and variables related to liver, renal, and thyroid function. Twelve patients had malignancies, including six patients with breast cancer, one with thyroid follicular cancer, two with colon cancer, one with ovarian cancer, one with melanoma, and one with diffuse large B-cell lymphoma. Malignancy was also observed in both seropositive and seronegative pSS patients and was not statistically significant. No correlation was observed between the autoantibody positivity and malignancy. However, patients with lymphoma are positive for ANA, anti-SSA, and anti-SSB antibodies. Hydroxychloroquine could not be administered to eight patients due to contraindications and to seven patients due to side effects, but it was administered to all other patients. There were no significant differences in the frequency of corticosteroid, methotrexate, hydroxychloroquine sulfate, or azathioprine treatment among the three groups, although the use of pilocarpine hydrochloride was significantly higher in the seropositive group.
Vasculitis was also observed in 20 patients. Of these, 12 had a previous diagnosis of pSS, and eight were diagnosed with pSS during the investigation of vasculitis etiology. After excluding other causes of vasculitis (such as large, medium, and small vessel vasculitis), it was accepted as Sjögren syndrome-associated small vessel vasculitis (SS-SVV) from the vasculitis associated with the systemic disease group. Of these, 15 had leukocytoclastic vasculitis (LCV), three had cryoglobulinemic vasculitis (CV), and two had urticarial vasculitis. There were 10 quadruple-seropositive pSS patients, five double-seropositive, two isolated RF positive, and three quadruple-seronegative patients with vasculitis. Patients with cutaneous vasculitis had a higher prevalence of ANA positivity (75% vs. 70.4%, p = 0.012), RF (65% vs. 22.5%, p < 0.0001), anti-Ro/SS-A antibodies (70% vs. 62.3%, p = 0.014), anti-La/SS-B antibodies (55% vs. 16%, p < 0.0001) compared with pSS patients without vasculitis. All the patients with LCV received oral corticosteroids and hydroxychloroquine. Two patients received corticosteroid doses > 30 mg/day, whereas the others went into remission with lower doses of treatment. Eight patients received immunosuppressive agents (one cyclophosphamide and seven azathioprine). Two patients with urticarial vasculitis were treated with azathioprine. Oral corticosteroids and hydroxychloroquine were administered to three patients with CV, and cyclophosphamide then azathioprine to one patient, azathioprine to one patient, and methotrexate was administered to one patient with predominant arthritis. The average follow-up period for patients with CV was only 14 months, and the patients were periodically examined for lymphoma. Fourteen patients had a single episode of cutaneous vasculitis, and the remaining six had relapsing vasculitis. All our patients with vasculitis survived.
3.1. Basic Characteristics of Total pSS Patients Included in the Present Study
The baseline characteristics of patients diagnosed with pSS are shown in Table 1. In total, 402 patients with pSS were retrospectively analyzed with a mean age of 54.3 (18–84) years and the patients were predominantly female 378 (94.0%). Among these patients, 78.8% (317/402) were seropositive (with at least one antibody positivity) and 85 (21.1%) were quadruple seronegative. Anti-SSA was present in 252 (62.6%) and anti-SSB in 75 (18.6%) patients. Other autoantibodies, including ANA and RF, were present in 284 (70.6%) and 96 (23.8%) of patients with pSS, respectively. The combined ANA and SSA positivity was 174 (43.2%). The number of quadruple-seropositive patients was 72 (18.6%). ANA staining pattern, with a granular pattern at 61%, nuclear at 20%, homogenous at 10%, centromere at 6%, and other staining patterns at 3%. MSGB was performed on a total of 232 patients and a focus score ≥ 1 was detected in 220 (94.8%) of them.
3.2. Comparison of Clinical Characteristics According to the Presence of Antibodies
Clinical characteristics and comparisons of seropositive, seronegative, and double-seropositive pSS are shown in Table 2 and Table 3. The age at diagnosis for quadruple-seropositive pSS was 42.4 ± 10.8, which was significantly younger than that of patients with double-seropositive and quadruple-seronegative pSS (p = 0.021, p = 0.112). When quadruple-seropositive pSS was compared with quadruple-seronegative, symptoms related to glandular dysfunction, such as xerostomia (97.2% vs. 94.1%, p = 0.112) and xerophthalmia (94.0% vs. 88.2%, p = 0.142), were more common in quadruple-seropositive pSS (Table 2). Similarly, when quadruple-seropositive pSS was compared with double seropositivity, xerostomia (97.2% vs. 92%, p = 0.242) and xerophthalmia (94.0% vs. 90.8%, p = 0.361) were more common in quadruple-seropositive pSS (Table 3). However, this similarity was not statistically significant between the double-seropositive and quadruple-seronegative groups (p = 0.345 and p = 0.526) (Table 4). In terms of organ involvement, salivary gland enlargement, arthralgias, arthritis, Raynaud’s phenomenon, lymphadenopathy, vasculitis-purpura, interstitial lung disease, neurological involvement, autoimmune thyroiditis, renal interstitial disease, anemia, leukopenia, hypergammaglobulinemia, and hypocomplementemia were more common in quadruple-seropositive than in quadruple-seronegative pSS (p < 0.0001) (Table 2). Except for salivary gland enlargement, arthralgia, and hypocomplementemia, other involvements were more frequent in quadruple-seropositive individuals than in those with double seropositivity (p < 0.0001) (Table 3). Similarly, when double seropositivity was compared with quadruple seronegativity, salivary gland enlargement, arthralgia, arthritis, lymphadenopathy, and interstitial lung disease were more common in double seropositivity (p < 0.0001) (Table 4). Interstitial lung disease was observed in 98.2% (55/56) of seropositive (at least one antibody positive) patients, while it was 1.7% (1/56) in seronegative patients (all antibodies negative) (p < 0.0001). NSIP accounted for 89.2% of the ILD patterns, and almost all of them were observed in seropositive patients. Neurological involvement was observed in 95.0% (19/20) of seropositive patients, while it was 5.0% (1/20) of seronegative patients (p < 0.0001). Autoimmune thyroiditis was present in 86 patients (21.3%); 74.4% of these patients were ANA positive and ANA staining: granular pattern, 84%; homogenous at 10%; and nuclear, 6%. Of the autoimmune thyroiditis patients, 79 (91.8%) had Hashimoto’s disease, five had Graves’ disease, and two had subacute thyroiditis. Moreover, autoimmune thyroiditis was observed in 83.7% (72/86) of seropositive patients, while it was 9.3% (8/86) of seronegative patients (p < 0.0001). Cryoglobulinemia, Raynaud’s phenomenon, vasculitis, and renal interstitial disease were not observed in any of the quadruple-seronegative patients. In our patients who were diagnosed with Sjögren’s syndrome and had symptoms such as cough and dyspnea, chest computed tomography (CT) was performed. Patients without any of these symptoms underwent lung radiography. CT was performed for patients with interstitial lung findings.
The laboratory findings of quadruple-seropositive and quadruple-seronegative pSS patients were also compared (Table 2). ESR and CRP levels were lower in the seronegative pSS group (p = 0.125 vs. p = 0.362). Peripheral blood parameters including platelet count (p = 0.001), hemoglobin (p < 0.0001), leukocyte count (p < 0.0001), and lymphopenia (p < 0.0001) levels were lower in quadruple-seropositive pSS. The level of hypergammaglobulinemia was higher in individuals with quadruple seropositivity than in those with quadruple seronegativity (p < 0.0001). No significant differences were observed between quadruple-seronegative and quadruple-seropositive pSS in other laboratory parameters, such as creatinine level, and variables related to liver, renal, and thyroid function. Twelve patients had malignancies, including six patients with breast cancer, one with thyroid follicular cancer, two with colon cancer, one with ovarian cancer, one with melanoma, and one with diffuse large B-cell lymphoma. Malignancy was also observed in both seropositive and seronegative pSS patients and was not statistically significant. No correlation was observed between the autoantibody positivity and malignancy. However, patients with lymphoma are positive for ANA, anti-SSA, and anti-SSB antibodies. Hydroxychloroquine could not be administered to eight patients due to contraindications and to seven patients due to side effects, but it was administered to all other patients. There were no significant differences in the frequency of corticosteroid, methotrexate, hydroxychloroquine sulfate, or azathioprine treatment among the three groups, although the use of pilocarpine hydrochloride was significantly higher in the seropositive group.
Vasculitis was also observed in 20 patients. Of these, 12 had a previous diagnosis of pSS, and eight were diagnosed with pSS during the investigation of vasculitis etiology. After excluding other causes of vasculitis (such as large, medium, and small vessel vasculitis), it was accepted as Sjögren syndrome-associated small vessel vasculitis (SS-SVV) from the vasculitis associated with the systemic disease group. Of these, 15 had leukocytoclastic vasculitis (LCV), three had cryoglobulinemic vasculitis (CV), and two had urticarial vasculitis. There were 10 quadruple-seropositive pSS patients, five double-seropositive, two isolated RF positive, and three quadruple-seronegative patients with vasculitis. Patients with cutaneous vasculitis had a higher prevalence of ANA positivity (75% vs. 70.4%, p = 0.012), RF (65% vs. 22.5%, p < 0.0001), anti-Ro/SS-A antibodies (70% vs. 62.3%, p = 0.014), anti-La/SS-B antibodies (55% vs. 16%, p < 0.0001) compared with pSS patients without vasculitis. All the patients with LCV received oral corticosteroids and hydroxychloroquine. Two patients received corticosteroid doses > 30 mg/day, whereas the others went into remission with lower doses of treatment. Eight patients received immunosuppressive agents (one cyclophosphamide and seven azathioprine). Two patients with urticarial vasculitis were treated with azathioprine. Oral corticosteroids and hydroxychloroquine were administered to three patients with CV, and cyclophosphamide then azathioprine to one patient, azathioprine to one patient, and methotrexate was administered to one patient with predominant arthritis. The average follow-up period for patients with CV was only 14 months, and the patients were periodically examined for lymphoma. Fourteen patients had a single episode of cutaneous vasculitis, and the remaining six had relapsing vasculitis. All our patients with vasculitis survived.
4. Discussion
The clinical presentation of pSS is highly heterogeneous [14]. Patients may present with varying clinical manifestations, ranging from sicca symptoms to systemic diseases and lymphoma [2,15]. Anti-SSA and anti-SSB are the most commonly used immunologic biomarkers for the diagnosis of pSS; however, a proportion of patients with pSS are negative for both autoantibodies, and patients have varying levels of RF and ANA antibodies. These autoantibodies cause epithelial cell damage, leading to the development of symptoms [5]. A link between pathological salivary gland ultrasound (SGU) findings and positive autoimmunity was established, and studies have shown that SGU is a strong predictor of positive SGU findings in patients with sicca symptoms [16]. In a similar study conducted according to the number of autoantibodies, pathological SGU findings were observed more frequently in patients with quadruple-seropositive antibodies. The quadruple-seropositive group had the highest frequency of pathological SGU findings (78.1%). Most patients in the partially seropositive (with at least one of the four autoantibodies) group (60.7%) and all seronegative patients had normal SGU findings [17]. These studies have provided information on antibody-associated organ damage. The present study investigated the clinical heterogeneity of Turkish patients with pSS and found that patients with quadruple; double antibodies were significantly different from seronegative pSS patients in several respects. The age at diagnosis of quadruple-seropositive pSS patients was significantly younger than that of double-seropositive and quadruple-seronegative patients. The interval between the onset of sicca symptoms and diagnosis was the shortest in quadruple-seropositive patients, averaging 23 months, whereas it was the longest in quadruple-seronegative patients, averaging 60 months (p < 0.0001). This shows that the presence of autoantibodies in patients with pSS aids early diagnosis. In addition, those with antibody positivity are diagnosed earlier because of their higher disease activity and higher systemic involvement. We found that patients who tested positive for autoantibodies were referred to specialists for autoimmune diseases and were diagnosed earlier. The first interesting finding of our study was the prevalence of patients with quadruple-seropositive reaching almost 17.9% and quadruple-seronegative 21.4% of the total SS population.
When comparing patients with quadruple-seropositive and double-seropositive pSS, the double-positive group was older. Clinical findings such as sicca symptoms and arthralgia were predominant; however, systemic involvement such as salivary gland enlargement, arthritis, Raynaud’s phenomenon, lymphadenopathy, vasculitis, interstitial lung disease, neurological involvement, autoimmune thyroiditis, primary biliary cholangitis, cytopenia, and hypergammaglobulinemia were less common. The difference between quadruple-seropositive and double-seropositive is RF and anti-SSB positive, extraglandular involvement is more in quadruple-seropositive and we think that this is more related to anti-SSB positivity (Table 2 and Table 3). We believe that high disease activity and systemic organ involvement are associated with anti-SSB/La. Previous studies have shown that anti-SSB/La antibodies trigger immunoreactivity, increase systemic antibody production, and cause tissue damage by inducing inflammatory reactions [18,19,20,21]. Arthritis and arthralgia were more common in the quadruple-seropositive group than in the other groups, and we believe that RF positivity was a contributing factor. Supporting this, a previous study found that the frequency of articular manifestations was higher in RF-positive patients with pSS than in RF-negative patients [4].
In general, the presence of autoantibodies correlates with a younger age of onset, female predominance, increased risk of organ involvement, and the presence of other antibodies [22]. In our study, comparing patients with quadruple-seropositive and quadruple-seronegative pSS, the seronegative group was older, and clinical findings and systemic involvement were significantly less common (Table 2). This finding is important because it shows that seropositive antibodies are more likely to develop severe diseases. Autoantibody status is the main factor driving the phenotypic expression of pSS and may help identify subgroups of patients with poor prognosis [3].
Cutaneous vasculitis is observed in approximately 4–10 % of patients with pSS [23,24,25,26] One possible approach for classifying SS-associated small vessel vasculitis (SVV) is to distinguish between cryoglobulinemic vasculitis, urticarial vasculitis, and non-cryoglobulinemic, non-urticarial leukocytoclastic vasculitis [27]. In the same study, CV was diagnosed in 14 (27%) of the 52 SS patients with cutaneous vasculitis [27]. The prevalence of cryoglobulins in pSS is 9–15%, the most common type being type III mixed cryoglobulinemia [28]. In our study, cryoglobulinemia was positive in three patients (0.7%). Three patients had cutaneous vasculitis with hypocomplementemia and were in the quadruple-seropositive group. In our study, there were fewer cases of cryoglobulinemia compared with other studies. The reason for this is that we only studied patients with vasculitis, neurological involvement, and RF positivity together with hypocomplementemia. We observed that the lesions were more generalized and accompanied by necrosis in CV than in other patients with vasculitis. In the same study, LCV was diagnosed in the remaining 26 (50%) patients and urticarial vasculitis was diagnosed in 11 (21%) of the 52 pSS patients with cutaneous vasculitis [27]. In our study, LCV was observed in 15 (75%) and urticarial vasculitis in two (10%). We can associate these differences with the increase in antibody positivity; because the number of autoantibodies was higher in our study. We observed that the duration of remission in patients with hypocomplementemia was short, and relapses were frequent. Additionally, higher doses of corticosteroids were administered to these patients. Based on these data, more aggressive and effective immunosuppressive treatments may need to be initiated in patients with hypocomplementemia. The development of cutaneous vasculitis may have significant prognostic implications, as such patients are more likely to develop other extraglandular manifestations, including lymphoma, and die from disease-related complications than those without vasculitis [27,29,30]. Raynaud’s phenomenon, vasculitis, renal interstitial disease, and cryoglobulinemia were not observed in the quadruple-seronegative patients. These findings suggest that autoantibody-related and circulating immune complexes play a major role in organ damage and may be more frequent in the seropositive group. However, the clinical expression of the disease should not be interpreted solely based on autoantibodies, and the counter-immunoregulation and genetic features should also be considered; therefore, not all seropositive patients are expected to develop a worse clinical phenotype.
Our study has several limitations. This study was conducted at a single center in Turkey, with a retrospective design and the sample size was relatively small, particularly for pSS-associated small vessel vasculitis. The relationship between disease activity and systemic involvement could not be evaluated because of the retrospective nature of the study. Cryoglobulinemia was studied only in vasculitis patients with neurological involvement and in patients with RF positivity and hypocomplementemia. As it has not been studied in other patients, we encountered it less frequently than in previous studies. We could not make a histological comparison between the three groups because they were evaluated by different pathologists, and most had focus scores of >1 or an aggregate number of >1. The follow-up period was 18 months; because of this short follow-up period, we could not observe any long-term complications. We were unable to perform bone marrow biopsies of patients with cytopenia because they did not provide their consent. ESSPRI or ESSDAI scoring could not be performed because our study was retrospective and some data were missing.
5. Conclusions
Extraglandular manifestations are more prevalent in quadruple-seropositive patients than in double-seropositive and quadruple-seronegative patients with pSS. We believe that antibody positivity is a significant factor, particularly in circulating immune complexes and antibody-related organ damage such as vasculitis, Raynaud’s phenomenon, and cryoglobulinemia. The results of this study confirmed the strong influence of immunological markers on the phenotype of pSS at diagnosis. Immunological patterns play a crucial role in the phenotypic expression of the disease, even during the initial diagnostic phase. Consequently, they can guide physicians in designing personalized treatment plans for patients with pSS. |
Title: Integrin-Specific Stimuli-Responsive Nanomaterials for Cancer Theranostics | Body:
1. Introduction
Cancer is a leading cause of death worldwide and is responsible for one in six deaths annually. An increase of 47% is estimated until 2040, equal to 28.4 million of new cancer cases compared to 19.3 million in 2020 [1]. The early diagnosis and treatment of cancer are prerequisite for health management. Conventional chemotherapy and radiotherapy lead to significant side effects due to a lack of selectivity, and surgery might be invasive for patients. The cardiovascular system, kidneys, liver, and lungs are the organs in which chemotherapy drugs and radiation induced by radiotherapy accumulate the most. This accumulation leads to serious side effects such as cardiotoxicity [2], nephrotoxicity [3], hepatotoxicity, and pulmonary fibrosis [4]. Most drugs have DNA in proliferating cells as a biological target. Consequently, tissues such as bone marrow and gastrointestinal tract, being rich in highly proliferating cells, lead to the formation of side effects such as gastrointestinal toxicity, myelosuppression, and immunosuppression [5]. Chemotherapy drugs and radiation also cause oxidative stress and inflammatory response because they present metabolic instability and can induce the formation of secondary tumors [6]. Hence, remarkable efforts are underway to increase the efficiency while maintaining the side effects as much as possible [7]. Immunotherapy and gene therapy arise a great deal of interest among the scientific community for the treatment of cancer despite their remarkable weaknesses including limited response rates, inability to predict clinical efficacy and potential side effects such as autoimmune reactions, while gene therapy is a high-priced resolution [8,9]. Amongst conventional treatments, metal complexes, particularly ruthenium complexes, are considered promising candidates as chemotherapeutic agents in addition to the platinum complexes (cisplatin, carboplatin, and oxaliplatin) [10,11,12]. The most important ones are Nami-A and KP1019 which were introduced into the clinical trials [13]. The most commonly used diagnostic are X-rays, endoscopy, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission tomography (SPECT). However, the low sensitivity and poor contrast between benign and malignant tissues render these techniques insufficient for early diagnosis. Theranostics is a novel strategy that combines therapy and diagnosis. This approach comprises the four principles of medicine or P4-medicine, which are prediction, prevention, personalization, and participation [14]. Nanomedicine is a defined branch of medicine that seeks to apply nanotechnologies in therapy and diagnosis. Nanomaterials are widely used in nanomedicine because of their small size, effective binding to the target, highly controllable physicochemical properties, and high delivery efficiency of the payload, as well as the potential to be complexed with multiple imaging modalities, and the potential combination of the payload with theranostic agents [15]. The main challenge for researchers is to develop stimuli-responsive nanomaterials capable of exploiting internal and/or external stimuli such as pH, reactive oxygen species (ROS), redox, enzymes present in the tumor environment, and external stimuli including light, proximity infrared (NIR), and magnetic field [16]. Stimuli-responsive nanomaterials have shown passive targeting characteristics in vascularized solid tumors due to the enhanced permeability and retention (EPR) effect [17]. However, recent studies have shown that EPR is not the only way for non-targeted nanomaterials to accumulate in the tumor sites. The complexity of tumor microenvironment and patient-to-patient variations in terms of gender, age, and other diseases [18] have led researchers to seek novel alternative strategies in order to transfer the payloads into their precise site of action. One of these successfully tested approaches is targeting the tumor microenvironment using ligand-mediated strategies [19]. In order to improve the ability of the nanomedicine to recognize the tumor sites, ligands capable of selectively binding cancer cells are preferably used. It has been demonstrated that the integrin ligand is over-expressed in tumor blood vessels and in various invasive tumor cells, while its expression on normal cells is not considerable. Integrin ligand-conjugated nanomaterials are internalized via clathrin or caveolin-1 internalization pathways rapidly. Since efficient cancer treatment might be hampered due to different resistance mechanisms, the receptor-mediated endocytosis leads to the intracellular delivery of payloads resulting in efficient therapeutic effects. In addition, there have been several reports on the application of integrin ligands as targeting moieties to transfer various types of delivery systems to the integrin-overexpressed cells. The effects of integrin receptors in tumor growth, metastasis, and angiogenesis have attracted great attention not only to use them as a target for cancer therapy but transferring various payloads into the cells over-expressing these receptors. The integrin-targeted nanomaterials may have several advantages including (i) the reduction of non-specific interactions with normal cells, tissues, and organs which subsequently leads to lower side-effects, (ii) the accumulation of payloads in the precise site of action, higher concentrations of the drug in the target cell or tissue and subsequently the reduction of administrated primary dose, (iii) the reduction of chemo-resistance due to the opportunity to deliver other therapeutic agents with the capability to overcome multi-drug resistance. Thus, integrin is a promising target for the tumor treatment and diagnosis [20]. In this review, we aim to discuss integrin stimuli-responsive nanomaterials in theranostics and the future of this modality for treatment and diagnosis.
2. Transmembrane Receptors
Transmembrane receptors are a large family of proteins formed by more than 800 receptors and 35 heterotrimeric G proteins subunits [21], which cross the cell membrane seven times and thereby are also called seven trans-membranes (7TM). They are characterized by an α-helical structure with an extracellular amino-terminus and an intracellular carboxy-terminus. The term 7TM receptors refers to G-protein-coupled receptors (GPCRs) because signal activation involves the heterodimeric G-proteins [22]. GPCRs were classified on the basis of their sequence homology and functional similarity into six classes: family A or rhodopsin-like receptors, family B or secretin receptor, family C or metabotropic glutamate receptors, family D or parasitic mating pheromone receptors, family E or cyclic AMP receptors, and family F or frizzled and smoothened receptors [23]. Human GPCRs on the basis of the phylogenetic studies are classified into five classes and called “GRAFS”: Glutamate (G), Rhodopsin (R), Adhesion (A), Frizzled (F), and Secretin (S) [24]. The different classes are summarized in Table 1.
The fundamental role of GPCRs is to transduce the extracellular stimuli to intracellular signals [37]. Activation of GPCRs is accomplished via ligand-binding. GPCRs are coupled to heterotrimeric G proteins that are formed by three subunits: Gα, β, and γ. Gα subunits are themselves a member of Gi/o, Gs, Gq/11, and G12/13. The interaction of each subunit with a specific target leads to the modulation of t cellular activity [38]. Once GPCRs interact with the heterotrimeric G, the subunit Gα dissociates from the β and γ subunits and consequently interacts with the β subunit of the integrin, leading to the activation of signaling pathways inside and outside the cell [39].
3. Integrins
Integrins are heterodimeric transmembrane receptors and are formed by two subunits α and β, their combination results in the generation of 24 different receptors with different properties [40]. The two subunits form dimers via non-covalent bonds. The dimer is formed by an NH2-terminal extracellular domain, transmembrane domain, and cytoplasmic tail, and is located across the cell membrane (Figure 1).
The extracellular domain is situated on the cellular surface and binds and interacts with the ligands of the extracellular matrix, such as fibronectin, vitronectin, collagen, and various adhesion receptors. The extracellular domain is linked to a cytoplasmic tail by the transmembrane domain which crosses the cell membrane. The cytoplasmic tail acts as an adaptor protein for intracellular signaling in the formation of focal adhesions and interactions with the cytoskeleton. Integrins are involved in several processes such as cell-cell adhesion and cell-extracellular matrix adhesion, migration/invasion, proliferation, survival, and apoptosis [41,42]. Furthermore, integrins are responsible for activating inside-out and outside-in signaling pathways which are crucial for the exchange of information between ECM proteins and intracellular molecules [39]. Most of the integrins interact with the Arginine-Glycine-Aspartic Acid (RGD) sequence in the presence of the divalent cations, the so-called metal-ion-dependent adhesion site (MIDAS) motif [43]. The RGD sequence was first defined by Ruoslahti et al. in 1984 as the highest conserved fibronectin conserved sequence [44]. In such sequence, arginine forms a salt bridge with the aspartate within the β-propeller while the carboxylic group of the aspartate forms polar interactions in the βA domain. Glycine acts as a spacer and forms hydrophobic interactions only with αv subunit [43]. Kessler et al. introduced a tripeptide sequence into the cyclic pentapeptide c[RGDfK] to improve the metabolic stability and enhance the bioavailability. Kessler and colleagues observed that the phenylalanine exhibiting D stereochemistry nearby the aspartic acid leads to the correct conformation for interacting with the receptor. Importantly, the role of N-Me-Val was not crucial for the activity of the peptide, allowing its replacement by other amino acids [45]. The replacement of N-Me-Val with Lys in the cyclic structure provides an opportunity to conjugate the pentapeptide sequence to nanoparticles for diagnostic and therapeutic approaches [46,47,48].
4. Stimuli in Cancer Therapy
Despite outstanding progress in cancer treatment, cancer still kills millions of people worldwide [49]. Significant angiogenesis, diverse metabolic pathways, and rapid proliferation, as well as high oxidative stress, high expression of some enzymes, and slight acidic conditions make the tumor microenvironment very different compared to other parts of the tumor. All of these factors lead to a poor response of tumor tissues towards the treatment and progression of metastasis [50]. Due to the unique properties of the tumor microenvironment, such as lymphatic drainage and enhanced permeability and retention effect (EPR), nanocarriers are promising vehicles for cancer-targeted drug delivery systems [51]. Although they have been used to deliver therapeutic or imaging agents to tumor cells, several barriers must be overcome for their wide clinical applications. These barriers are included but not limited to burst release, degradation, and leakage of cargos [52].
Stimuli-responsive nanoparticles (SRNs) have been developed to control the rate, location, and time of drug release. Recently, SRNs have attracted great attention for the cancer theranostics and treatments [49,53]. SRNs are materials with specific ability to be triggered by signals from out- or inside the body for precision activation or release (Figure 2) [54].
These systems are functionalized to deliver their cargos in specific sites through responding to external or internal stimuli [55]. The mechanism of action of SRNs can be explained as follows: they are administered and accumulated in tumor tissues through active or passive targeting. Subsequently, the system is activated due to the specific stimuli and releases the cargo [56]. The trigger or stimulus is a force that initiates a cascade of events leading to the nanoparticle activation for diagnosis and/or treatment. The stimulus can be part of the tumor tissue or its source can come from outside the body. SRNs have recently attracted attention because of their special ability for controlled drug release [57]. These stimuli cause charge conversion, drug activation, ligand exposure, size or structure conformation, or even change the biological activity of nanocarriers and promote controlled drug release. SRNs can overcome multi-drug resistance for the treatment of cancer [52]. In addition, they provide efficient concentration of the therapeutic agent in the tumor microenvironment/tumor cells [58]. Considering the pathological conditions of the tumor microenvironment, tumor tissue, and intracellular compartments, different SRNs can be designed, with the aim of increasing biological activity, efficiency, specificity, and even overcoming multi-drug resistance. SRNs can also include nanoparticles loaded with a pro-drug, which are activated in response to tumor stimuli for precision therapy [52]. Several stimuli-responsive nanocarriers have been reported in clinical trials. Liposomes are considered promising and versatile drug vesicles and used to designed a smart liposomal system able to respond to internal or external stimuli [59]. They are formed by natural phospholipids that mimic the properties of biological membranes. By incorporating a polyethylene glycol (PEG) coating, they have revealed a long circulation. Liposomal formulations are able to improve the pharmacokinetics and pharmacodynamics of the drug, reducing its toxicity [60]. Finally, the small size promotes the permeation and retention (EPR) effect, allowing passage into blood vessels and accumulation in tumor tissues [61]. An interesting approach in clinical trials is liposomal cisplatin formulation (LiPlaCis), a drug delivery system formed by liposomes nanocarriers loaded with cisplatin. The system is developed to be degraded by secretory phospholipase A2 (PLA2), an enzyme presents in high concentrations in different cancer types and is used for triggering drug release into the organs/tissues. LiPLaCis shows greater distribution and reduced side effects when compared to the free drug [62]. Among the developed nanoparticles, magnetic nanoparticles are of great interest. In the presence of an alternative magnetic field, they generate local hyperthermia to trigger drug release and tumor ablation. The interaction between magnetic nanocarriers and magnetic field facilitates magnetically driven accumulation of nanocarriers in tumors [52]. Biological tissues are transparent to magnetic fields, allowing magnetic targeting to be followed remotely and non-evasively. With this system, magnetic targeting is more flexible and does not depend on specific receptor expression [63]. Iron oxide (MION) and maghemite (gFe2O3) nanoparticles exhibit low toxicity and high biocompatibility, and thermal effects under different applied magnetic fields and small size are in clinical trials and show interesting results for the treatment of hepatocellular carcinoma and prostate cancer [52]. Another interesting approach is to use polymeric micelles which exhibit a robust core-shell structure, kinetic stability, and ability to solubilize hydrophobic drugs. Such nanoparticles are able to release the drug in the acidic environment of the endosomal or lysosomal compartments of the target tumor cell, using the different pH present in the normal cells [64]. The use of nanoparticles for cancer treatment is increasing, but nanomaterials have an inherent toxicity that can trigger the accumulation of metals into the tissue, side effects, and drug release at a wrong site [65]. The need to develop innovative strategies to reduce nanotoxicity is the challenge that must be addressed to use nanomaterials safely [66]. Some of these systems are listed in Table 2.
4.1. Endogenus Stimuli
4.1.1. pH-Responsive Nanocarriers
Malignant tumors are characterized by low extracellular and interstitial pH, where metabolites, such as lactic acid and CO2, result in a slightly acidic environment [67,68]. In most of cancer cells, aerobic glycolysis pathway is the main way to generate energy, which can result in the production of large amount of lactic acid, in contrast to the oxidative phosphorylation pathway in normal cells [69,70,71]. The pH value in normal cells is approximately 7.4 higher than in tumor tissues (~6.5). The above pH gradient could be advantageous as an endogenous stimulus for stimuli-responsive nanoparticles [72,73]. Acidic extracellular pH plays an important role in the enhancement of cancer therapy [71]. There are different strategies for the synthesis of pH-responsive nanoparticles. The first strategy involves the use of peculiar molecular structures with specific functional groups in the nanoparticles backbone that can be protonated under acidic conditions. These phenomena cause the destruction of the hydrophilic–hydrophobic equilibrium and the destruction of the nanoparticles, leading to drug release by rearranging, expanding, or disintegrating the chemical structure [74]. The second strategy involves the role of chemical bonds, which are stable at normal pH and sensitive to lower pH values. This is known as cleavable acid-responsive bonds. In this approach, nanoparticles would be able to release the drug due to the cleavage of chemical bonds to form the nanostructure [73,75]. The use of pH-Low Insertion Peptides (pHLIPs) is the last strategy. pHLIPs are 36-aa peptides with a high affinity for the cell membrane at physiological pH. At lower pH values, the protonation of Asp and Glu enhanced the hydrophobicity of the peptide. This can affect peptide folding and the consequent insertion into the cell membrane, improving its cellular uptake [71,76]. In one study, Zhang et al. worked on pH-responsive peptide-modified polymeric micelles, containing Paclitaxel (PTX). They designed a H7K(R2)2 with pH-triggered cell-penetration activity. These peptides were conjugated to PLGA-PEG. H7K(R2)2-modified polymeric micelles containing PTX were prepared and characterized. The in vitro release of PTX was observed at pH 6.8 by flow cytometry and confocal microscopy, leading to the inhibition of the growth of human breast adenocarcinoma cells [77]. In another study, they tried to design pH-responsive system containing Doxorubicin (DOX) for enhancing glioma targeting and anti-tumor activity. In this study, H7K(R2)2-modified pH-sensitive liposomes containing DOX were investigated. Liposomes show high sensitivity to the acidic environments and are destabilized under these conditions, enhancing cellular uptake with rapid intracellular release. In vitro tests were conducted on C6 (rat C6 glioma cells) and U87-MG (human glioblastoma cells), and a higher cytotoxic effect was observed compared to normal cells. These data were also confirmed by in vivo tests conducted on nude mice bearing orthotopic GBM U87-MG or C6 tumor models [78]. In 2019, Motealleh et al. developed a 3D-nanocomposite (NC) hydrogel system for pH-responsive surface-mediated delivery of Doxorubicin. The 3D-NC hydrogel was formed using periodic mesoporous organosilicate (PMO) nanoparticles loaded with DOX, where the outer shell was functionalized with poly-L-lysine (PLL) and subsequently embedded into the alginate hydrogel network. PLL is responsible for the electrostatic interactions with the cell membrane and its cellular uptake. The 3D-NC hydrogel reduced the viability of Colo 818 cancer cells owing to the high drug release in the local acidic environment of the Colo 818 compared to normal fibroblast cells [79]. These examples demonstrate promising results regarding the anticancer activity of pH-responsive nanocarriers.
4.1.2. Enzyme-Responsive Nanocarriers
Another strategy for designing drug delivery systems for cancer therapy is the adoption of enzyme-responsive nanocarriers. In the human body, biological activity and metabolic processes are carried out by enzymes with high specificity for their substrates [80]. In contrast to normal cells, tumor cells overexpress specific enzymes such as proteases (matrix metalloproteinases (MMPs) and cathepsin-B), phospholipases, and glycosidases. According to the said literature, stimuli-responsive nanocarriers contain substrates that can be cleaved by particular enzymes present in tumor cells and consequently release the desired cargo at tumor sites [81,82]. MMPs degrade proteins by their proteolytic activity. Compared with normal cells with inhibited MMPs activity, tumor cells overexpress some MMPs, including MMP2, MMP9, and MMP13 [81,83]. Among these enzymes, MMP2 play a critical role in tumor metastasis and invasion [84]. These overexpressed MMPs have been widely exploited by scientists. For example, Zhu et al. attempted to enhance the anticancer activity of PTX by designing a PEG2000-Peptide-PTX conjugate, which responds to upregulated MMP2. The results of the in vitro and in vivo tests revealed the great potential of cancer cell-selective intracellular delivery of an anticancer drug for enhanced cancer chemotherapy [85]. In another study, Liu et al. constructed a drug delivery system based on mesoporous silica nanoparticles (MSN). They used PLGLAR as a peptide that is sensitive to MMP13, bovine serum albumin as an endcap for sealing MSNs, lactobionic acid as a targeting ligand, and DOX hydrochloride as a model drug. These results demonstrated the ability of the system to efficiently inhibit tumor growth with minimal side effects [86]. In another study, Kalafatovic et al. designed a self-assembled micelle system for loading DOX, which was sensitive to MMP9 [87]. Cathepsins are lysosomal cysteine proteases that are either expressed on the cell surface or are released into the extracellular matrix in pathological conditions [88]. Compared to normal tissues, cathepsin B (Cat-B) has a higher concentration and activity in cancer cells. This enzyme can hydrolyze various sequences including Arg-Arg, Ala-Leu, Phe-Arg, Phe-Lys, Ala-Phe-Lys, and Gly-Phe-Leu-Gly [81]. For instance, the Gly-Phe-Leu-Gly sequence has been linked to hydroxypropyl methacrylate (HPMA) copolymer conjugates to release the drugs into lysosomes by the cleavage of the peptide spacer and Cat-B [89]. Recently, Gly-Phe-Leu-Gly oligopeptides have been adopted as enzyme-responsive cores, and DOX as a model anticancer drug. This nanoparticle is degraded by Cat-B and releases the drug efficiently [90].
Phospholipases hydrolyze phospholipids into lipophilic substances and fatty acids. One of the mostly investigated phospholipase family is phospholipase A2 (PLA2) which is divided into intracellular and secretory PLA2 [91,92]. Lee et al. designed a strategy for imaging of prostate cancer cells by using biocompatible up-conversion nanoparticles (UCNPs)-loaded phosphate micelle that could be cleaved by secretory PLA2 [93]. In another study, Ghavami et al. utilized phospholipase sensitive liposomes (PSLs) to encapsulate antisense octaarginine peptide nucleic acid conjugate, which responds to secretory PLA2 [94].
Glycosidases are another family of enzymes that trigger release of drug to malignant sites. They can hydrolyze carbohydrates in lysosomes and play a key role in N-linked glycosylation in the endoplasmic reticulum (ER) and Golgi apparatus [95]. Clahaut et al. worked for the first time on doxorubicin-folate conjugate, which is sensitive to β-galactosidase to induce cell death in acute myeloid leukemia blasts. These folate positive cells efficiently responded to the enzyme-responsive nanocarrier with low adverse effects [96]. In another study, Rastegari et al. designed two different magnetic nanoparticles with β-cyclodextrin and chitosan, loaded with prodigiosin as a model anticancer drug. The results of the study showed that the drug was released by hydrolytic lysosomal enzymes such as α-glucosidase [97]. Kolodych et al. developed a new class of β-galactosidase-cleavable linkers for antibody-drug conjugates (ADCs) by employing trastuzumab as a model drug and loading it using galactoside linkers, monomethyl auristatin E (MMAE), and cysteine-reactive groups. The in vivo results demonstrated that ADCs with galactoside linker have superior therapeutic efficacy than trastuzumab alone [98].
4.1.3. Redox-Responsive Nanocarriers
Reactive Oxygen Species (ROS) include the hydroxyl radicals (HO•), the superoxide anions (O2−), hydrogen peroxide (H2O2), and singlet oxygen (1O2) which are usually produced by incomplete reduction of oxygen [99]. Compared to normal cells, the ROS concentration is much higher in cancer cells, which can be used for preparing drug delivery systems [100]. For example, Wilson et al. constructed thioketal nanoparticles (TKNs) using poly- (1,4-phenyleneacetone dimethylene thioketal) that can deliver small interfering RNA (siRNA). TKNs can be degraded at high ROS concentration and release the siRNA at the site of action [101]. In addition, Lux et al. designed a system containing boronic esters that can be degraded in the presence of H2O2 and release cargos [102]. Recently, Du et al. prepared thioether phosphatidylcholines (S-PCs) and S-PC-based liposomes (S-LPs) loaded with DOX as a model anticancer drug. The results demonstrated higher efficiency of DOX/S-LPs resulting from the rapid release of drug from the conjugate by excess amount of ROS [103].
Glutathione (GSH) is a tri-peptide that contains glutamate, cysteine, and glycine (L-γ-glutamyl-L-cysteinyl-glycine). Despite its crucial role in detoxification, antioxidant activity, and cell cycle regulation, high GSH concentrations result in different pathological conditions, including cancer and neurodegenerative diseases. GSH levels in cytoplasm are approximately 1–10 mM, which is higher than that in the extracellular environment (2–20 µM) [104]. GSH concentration in tumor tissues is at least four-fold higher than that in normal cells. The differences in GSH levels were exploited to develop novel drug delivery systems [105,106,107,108]. Therefore, chemical groups, which are sensitive to high level of GSH including disulfide and diselenide bonds, as well as manganese dioxide (MnO2) have been used in recent studies [109,110,111]. For instance, Li et al. designed a novel drug carrier modified with disulfide-bound PEG and amide-bound polyethylenimine (PEI). High GSH level results in di-sulfide degradation and drug release. This nanocarrier improves the tumor accumulation capability and decreases the side effects of chemotherapy [112]. Recently, Chai et al. constructed a novel drug delivery system by conjugating ibuprofen (BF) to the hyaluronic acid (HA) backbone through a disulfide bond (HA-ss-BF), which can self-assemble into micelles for DOX delivery. This drug delivery system is efficient because of its ability to target cancer cells by recognition of CD44 receptors, inhibition of overexpressed cyclooxygenase-2 in cancer cells, improvement of cellular uptake, and improved biodistribution [113]. Additionally, selenium has special properties, including high reactivity and sensitivity owing to its low electronegativity and high atomic radius. In addition, the Se-Se bond has a lower bond energy that the S-S bond resulting in faster cleavage and faster drug release [114,115,116]. In this context, Wei et al. designed a nanocarrier based on amphiphilic polyester urethane with multiple Se-Se bonds which were loaded with DOX. According to these results, the antitumor activity of the DOX was enhanced in this system [116]. Recently, MnO2 has attracted increasing attention as a GSH-responsive trigger. For example, Zhang et al. constructed a drug delivery system for chemotherapy that reduced the side effects of Bleomycin (BLM). In this study, PEG-modified mesoporous manganese dioxide (HMnO2@PEG) nanoparticles were used to load the antitumor drug BLM. At the tumor site, degradation of this system and drug release occurred simultaneously because of an excess amount of GSH. Then, BLM-Mn2+ formation led to the therapeutic activity of BML while maintaining the side effects as low as possible [117].
4.1.4. Temperature-Responsive Nanocarriers
High temperatures are another major trigger for the design of stimuli-responsive drug delivery systems. Temperature can be employed as an internal stimulus due to the pathological conditions including the hyperthermic nature of the tumor or inflammation. Additionally, temperature can be used as an external stimulus by utilizing external heat sources [118,119]. Therefore, nanocarrier should be stable at body temperature and release the drug when tumor temperature raises. Thermoresponsive polymers exhibit phase-transition, which defines their solubility and Critical Solution Temperature (CST). If the temperature is greater than the lower critical solubility temperature (LCST) of the system, dehydration of the polymer occurs. Consequently, the hydrophobic polymer collapses and releases the drug [120,121,122,123]. Poly-N-isopropylacrylamide (PNIPPAm) is a polymer that is widely used because of its LCST of approximately 35 °C [124,125]. In one study, Xu et al. synthesized a drug carrier using diselenide-bond-linked poly-N-isopropylacrylamide which was then loaded with PTX. This conjugate can self-assemble into core-shell micelles that are simultaneously temperature- and redox-responsive. These results demonstrated that the conjugates are safe and show promising anticancer efficacy [125]. Besides thermoresponsive polymers, thermoresponsive liposomes have been widely investigated for smart drug delivery applications [126]. For example, Thanou et al. loaded topotecan in thermo-responsive liposomes and improved the drug uptake into cancer cells [126]. In another study, Ta et al. prepared a polymer-modified thermo-sensitive liposome (pTSL) loaded with DOX. This system exhibits dual pH/temperature-dependent phase transition properties owing to the coexistence of temperature-responsive N-isopropylacrylamide and pH-responsive propylacrylic acid. By attaching to liposomes, these copolymers were membrane-disruptive in a pH/temperature-dependent manner. pTSL improves release profile and is stable in serum with minimal drug leakage [127].
4.2. Exogenus Stimuli
4.2.1. Light-Responsive Nanocarriers
Nanocarrier uptake and tumor penetration are obstacles that result from the high interstitial pressure and dense extracellular matrix of the tumors. An invasive strategy giving the solution to these problems, which attract considerable attention, is photo-thermal therapy (PTT). Most photoactive chromophores are sensitive to ultraviolet (UV) radiation. Nevertheless, UV light has two disadvantages including poor penetration depth in human tissues and its high phototoxicity and carcinogenic effect on normal cells and tissues [128]. Compared to UV light, near-infrared (NIR) light is more attractive because of its deeper penetration rate and lower cell toxicity. Hence, PTT is induced by light, preferably NIR, which relies on the absorbance ability and the transformation of NIR energy to heat [129,130,131,132]. Besides PTT, there is another strategy for utilizing lights termed photodynamic therapy (PDT). PDT is a minimally invasive method with low side effects that induces cell death via light irradiation of a photosensitizer and generation of highly reactive singlet oxygen species [133,134]. In one study, Chen et al. utilized indocyanine green (ICG), a widely used NIR dye in photothermal therapy, and DOX as a model anticancer drug loaded on a nanoparticle of arylboronic ester and cholesterol modified hyaluronic acid. This combination demonstrated a photothermal effect and a faster trigger release of DOX with NIR irradiation [135]. An interesting approach is proposed by Diaz and co-workers. They used the laser-induced thermal mechanism to give de-hybridization of DNA or RNA sequences from gold nanoparticles. The nanoparticles are loaded with complex hybridizing molecules: a single-stranded complementary DNA, a DNA duplicator, and a basically modified DNA duplicator. DNA duplicators show a different melting point, and when light hits the nanoparticles, causing an increase in temperature, it allows the release of the labeled DNA duplicator in the cancer cells. The system offers the possibility of greater control of drug release [136]. Chen et al. proposed to use red-light-responsive metallopolymer nanocarriers named PolyRuCHL formed by hydrophilic poly(ethylene glycol) (PEG) block and a hydrophobic ruthenium (Ru)-containing block as red-light-responsive linked with chlorambucil (CHL). PolyRuCHL is loaded with DOX. The advantage of red light is the ability to penetrate deeply into tissue for PTT. Red-light irradiation induced a cleavage of Ru-CHL triggering DOX release from the nanoparticles. The authors observed a synergic effect to inhibit the growth and multidrug resistance in breast cancer cell line MCF-7R [137]. The approach used by Fan and co-workers draws inspiration from the properties of liquid metals (LMs) that exhibit excellent photothermal conversion efficiency, generating heat under NIR laser irradiation. To prepare LMs, they used a gallium–indium eutectic alloys (EgaIn) which shows excellent combination of thermal-conductivity, transformability, and a high biocompatibility. The authors prepared a poly (NIPAm-co-MBA) hydrogel (PNM) contained with LM droplets and encapsulated it with DOX to form the final system PNM/LM/DOX. After NIR irradiation, the temperature of the system rises above the lower critical temperature of the solution, which causes the hydrogel to change shape and size. The hydrogel shrinks, inducing a simultaneous release of the aqueous solution and DOX. This controlled release can reduce the amount of drug into normal tissues and thus the side effects [138].
4.2.2. Magneto-Responsive Nanocarriers
Magneto-responsive nanocarrier design is a novel strategy for the development of drug delivery systems. This method has multiple advantages including the following: (1) Superparamagnetic iron oxide nanoparticles act as a contrast agent in magnetic resonance imaging (MRI), which means that they are suitable for drug delivery and as diagnostic imaging agents simultaneously [139], (2) Compared to UV light, magnetism has not shown any particular side effects in the human body [140], and (3) It has no physical interactions with the human body [122]. Although it has several advantages, its low accuracy can be a barrier to its clinical translation [122]. In a study, Yoon et al. designed a dual targeting nanoparticle, which was sensitive to GSH and magnetism. In this work, methoxy PEG was grafted to water-soluble chitosan, DOX conjugated to the backbone of chitosan via disulfide linkage (which made it GSH sensitive), and finally, the conjugation of iron oxide made it magnetically responsive. The in vivo test demonstrated that this system has promising anticancer drug-targeting properties [141]. In Table 3, we summarized a number of nanomaterials and their components that can respond to different stimuli.
4.1. Endogenus Stimuli
4.1.1. pH-Responsive Nanocarriers
Malignant tumors are characterized by low extracellular and interstitial pH, where metabolites, such as lactic acid and CO2, result in a slightly acidic environment [67,68]. In most of cancer cells, aerobic glycolysis pathway is the main way to generate energy, which can result in the production of large amount of lactic acid, in contrast to the oxidative phosphorylation pathway in normal cells [69,70,71]. The pH value in normal cells is approximately 7.4 higher than in tumor tissues (~6.5). The above pH gradient could be advantageous as an endogenous stimulus for stimuli-responsive nanoparticles [72,73]. Acidic extracellular pH plays an important role in the enhancement of cancer therapy [71]. There are different strategies for the synthesis of pH-responsive nanoparticles. The first strategy involves the use of peculiar molecular structures with specific functional groups in the nanoparticles backbone that can be protonated under acidic conditions. These phenomena cause the destruction of the hydrophilic–hydrophobic equilibrium and the destruction of the nanoparticles, leading to drug release by rearranging, expanding, or disintegrating the chemical structure [74]. The second strategy involves the role of chemical bonds, which are stable at normal pH and sensitive to lower pH values. This is known as cleavable acid-responsive bonds. In this approach, nanoparticles would be able to release the drug due to the cleavage of chemical bonds to form the nanostructure [73,75]. The use of pH-Low Insertion Peptides (pHLIPs) is the last strategy. pHLIPs are 36-aa peptides with a high affinity for the cell membrane at physiological pH. At lower pH values, the protonation of Asp and Glu enhanced the hydrophobicity of the peptide. This can affect peptide folding and the consequent insertion into the cell membrane, improving its cellular uptake [71,76]. In one study, Zhang et al. worked on pH-responsive peptide-modified polymeric micelles, containing Paclitaxel (PTX). They designed a H7K(R2)2 with pH-triggered cell-penetration activity. These peptides were conjugated to PLGA-PEG. H7K(R2)2-modified polymeric micelles containing PTX were prepared and characterized. The in vitro release of PTX was observed at pH 6.8 by flow cytometry and confocal microscopy, leading to the inhibition of the growth of human breast adenocarcinoma cells [77]. In another study, they tried to design pH-responsive system containing Doxorubicin (DOX) for enhancing glioma targeting and anti-tumor activity. In this study, H7K(R2)2-modified pH-sensitive liposomes containing DOX were investigated. Liposomes show high sensitivity to the acidic environments and are destabilized under these conditions, enhancing cellular uptake with rapid intracellular release. In vitro tests were conducted on C6 (rat C6 glioma cells) and U87-MG (human glioblastoma cells), and a higher cytotoxic effect was observed compared to normal cells. These data were also confirmed by in vivo tests conducted on nude mice bearing orthotopic GBM U87-MG or C6 tumor models [78]. In 2019, Motealleh et al. developed a 3D-nanocomposite (NC) hydrogel system for pH-responsive surface-mediated delivery of Doxorubicin. The 3D-NC hydrogel was formed using periodic mesoporous organosilicate (PMO) nanoparticles loaded with DOX, where the outer shell was functionalized with poly-L-lysine (PLL) and subsequently embedded into the alginate hydrogel network. PLL is responsible for the electrostatic interactions with the cell membrane and its cellular uptake. The 3D-NC hydrogel reduced the viability of Colo 818 cancer cells owing to the high drug release in the local acidic environment of the Colo 818 compared to normal fibroblast cells [79]. These examples demonstrate promising results regarding the anticancer activity of pH-responsive nanocarriers.
4.1.2. Enzyme-Responsive Nanocarriers
Another strategy for designing drug delivery systems for cancer therapy is the adoption of enzyme-responsive nanocarriers. In the human body, biological activity and metabolic processes are carried out by enzymes with high specificity for their substrates [80]. In contrast to normal cells, tumor cells overexpress specific enzymes such as proteases (matrix metalloproteinases (MMPs) and cathepsin-B), phospholipases, and glycosidases. According to the said literature, stimuli-responsive nanocarriers contain substrates that can be cleaved by particular enzymes present in tumor cells and consequently release the desired cargo at tumor sites [81,82]. MMPs degrade proteins by their proteolytic activity. Compared with normal cells with inhibited MMPs activity, tumor cells overexpress some MMPs, including MMP2, MMP9, and MMP13 [81,83]. Among these enzymes, MMP2 play a critical role in tumor metastasis and invasion [84]. These overexpressed MMPs have been widely exploited by scientists. For example, Zhu et al. attempted to enhance the anticancer activity of PTX by designing a PEG2000-Peptide-PTX conjugate, which responds to upregulated MMP2. The results of the in vitro and in vivo tests revealed the great potential of cancer cell-selective intracellular delivery of an anticancer drug for enhanced cancer chemotherapy [85]. In another study, Liu et al. constructed a drug delivery system based on mesoporous silica nanoparticles (MSN). They used PLGLAR as a peptide that is sensitive to MMP13, bovine serum albumin as an endcap for sealing MSNs, lactobionic acid as a targeting ligand, and DOX hydrochloride as a model drug. These results demonstrated the ability of the system to efficiently inhibit tumor growth with minimal side effects [86]. In another study, Kalafatovic et al. designed a self-assembled micelle system for loading DOX, which was sensitive to MMP9 [87]. Cathepsins are lysosomal cysteine proteases that are either expressed on the cell surface or are released into the extracellular matrix in pathological conditions [88]. Compared to normal tissues, cathepsin B (Cat-B) has a higher concentration and activity in cancer cells. This enzyme can hydrolyze various sequences including Arg-Arg, Ala-Leu, Phe-Arg, Phe-Lys, Ala-Phe-Lys, and Gly-Phe-Leu-Gly [81]. For instance, the Gly-Phe-Leu-Gly sequence has been linked to hydroxypropyl methacrylate (HPMA) copolymer conjugates to release the drugs into lysosomes by the cleavage of the peptide spacer and Cat-B [89]. Recently, Gly-Phe-Leu-Gly oligopeptides have been adopted as enzyme-responsive cores, and DOX as a model anticancer drug. This nanoparticle is degraded by Cat-B and releases the drug efficiently [90].
Phospholipases hydrolyze phospholipids into lipophilic substances and fatty acids. One of the mostly investigated phospholipase family is phospholipase A2 (PLA2) which is divided into intracellular and secretory PLA2 [91,92]. Lee et al. designed a strategy for imaging of prostate cancer cells by using biocompatible up-conversion nanoparticles (UCNPs)-loaded phosphate micelle that could be cleaved by secretory PLA2 [93]. In another study, Ghavami et al. utilized phospholipase sensitive liposomes (PSLs) to encapsulate antisense octaarginine peptide nucleic acid conjugate, which responds to secretory PLA2 [94].
Glycosidases are another family of enzymes that trigger release of drug to malignant sites. They can hydrolyze carbohydrates in lysosomes and play a key role in N-linked glycosylation in the endoplasmic reticulum (ER) and Golgi apparatus [95]. Clahaut et al. worked for the first time on doxorubicin-folate conjugate, which is sensitive to β-galactosidase to induce cell death in acute myeloid leukemia blasts. These folate positive cells efficiently responded to the enzyme-responsive nanocarrier with low adverse effects [96]. In another study, Rastegari et al. designed two different magnetic nanoparticles with β-cyclodextrin and chitosan, loaded with prodigiosin as a model anticancer drug. The results of the study showed that the drug was released by hydrolytic lysosomal enzymes such as α-glucosidase [97]. Kolodych et al. developed a new class of β-galactosidase-cleavable linkers for antibody-drug conjugates (ADCs) by employing trastuzumab as a model drug and loading it using galactoside linkers, monomethyl auristatin E (MMAE), and cysteine-reactive groups. The in vivo results demonstrated that ADCs with galactoside linker have superior therapeutic efficacy than trastuzumab alone [98].
4.1.3. Redox-Responsive Nanocarriers
Reactive Oxygen Species (ROS) include the hydroxyl radicals (HO•), the superoxide anions (O2−), hydrogen peroxide (H2O2), and singlet oxygen (1O2) which are usually produced by incomplete reduction of oxygen [99]. Compared to normal cells, the ROS concentration is much higher in cancer cells, which can be used for preparing drug delivery systems [100]. For example, Wilson et al. constructed thioketal nanoparticles (TKNs) using poly- (1,4-phenyleneacetone dimethylene thioketal) that can deliver small interfering RNA (siRNA). TKNs can be degraded at high ROS concentration and release the siRNA at the site of action [101]. In addition, Lux et al. designed a system containing boronic esters that can be degraded in the presence of H2O2 and release cargos [102]. Recently, Du et al. prepared thioether phosphatidylcholines (S-PCs) and S-PC-based liposomes (S-LPs) loaded with DOX as a model anticancer drug. The results demonstrated higher efficiency of DOX/S-LPs resulting from the rapid release of drug from the conjugate by excess amount of ROS [103].
Glutathione (GSH) is a tri-peptide that contains glutamate, cysteine, and glycine (L-γ-glutamyl-L-cysteinyl-glycine). Despite its crucial role in detoxification, antioxidant activity, and cell cycle regulation, high GSH concentrations result in different pathological conditions, including cancer and neurodegenerative diseases. GSH levels in cytoplasm are approximately 1–10 mM, which is higher than that in the extracellular environment (2–20 µM) [104]. GSH concentration in tumor tissues is at least four-fold higher than that in normal cells. The differences in GSH levels were exploited to develop novel drug delivery systems [105,106,107,108]. Therefore, chemical groups, which are sensitive to high level of GSH including disulfide and diselenide bonds, as well as manganese dioxide (MnO2) have been used in recent studies [109,110,111]. For instance, Li et al. designed a novel drug carrier modified with disulfide-bound PEG and amide-bound polyethylenimine (PEI). High GSH level results in di-sulfide degradation and drug release. This nanocarrier improves the tumor accumulation capability and decreases the side effects of chemotherapy [112]. Recently, Chai et al. constructed a novel drug delivery system by conjugating ibuprofen (BF) to the hyaluronic acid (HA) backbone through a disulfide bond (HA-ss-BF), which can self-assemble into micelles for DOX delivery. This drug delivery system is efficient because of its ability to target cancer cells by recognition of CD44 receptors, inhibition of overexpressed cyclooxygenase-2 in cancer cells, improvement of cellular uptake, and improved biodistribution [113]. Additionally, selenium has special properties, including high reactivity and sensitivity owing to its low electronegativity and high atomic radius. In addition, the Se-Se bond has a lower bond energy that the S-S bond resulting in faster cleavage and faster drug release [114,115,116]. In this context, Wei et al. designed a nanocarrier based on amphiphilic polyester urethane with multiple Se-Se bonds which were loaded with DOX. According to these results, the antitumor activity of the DOX was enhanced in this system [116]. Recently, MnO2 has attracted increasing attention as a GSH-responsive trigger. For example, Zhang et al. constructed a drug delivery system for chemotherapy that reduced the side effects of Bleomycin (BLM). In this study, PEG-modified mesoporous manganese dioxide (HMnO2@PEG) nanoparticles were used to load the antitumor drug BLM. At the tumor site, degradation of this system and drug release occurred simultaneously because of an excess amount of GSH. Then, BLM-Mn2+ formation led to the therapeutic activity of BML while maintaining the side effects as low as possible [117].
4.1.4. Temperature-Responsive Nanocarriers
High temperatures are another major trigger for the design of stimuli-responsive drug delivery systems. Temperature can be employed as an internal stimulus due to the pathological conditions including the hyperthermic nature of the tumor or inflammation. Additionally, temperature can be used as an external stimulus by utilizing external heat sources [118,119]. Therefore, nanocarrier should be stable at body temperature and release the drug when tumor temperature raises. Thermoresponsive polymers exhibit phase-transition, which defines their solubility and Critical Solution Temperature (CST). If the temperature is greater than the lower critical solubility temperature (LCST) of the system, dehydration of the polymer occurs. Consequently, the hydrophobic polymer collapses and releases the drug [120,121,122,123]. Poly-N-isopropylacrylamide (PNIPPAm) is a polymer that is widely used because of its LCST of approximately 35 °C [124,125]. In one study, Xu et al. synthesized a drug carrier using diselenide-bond-linked poly-N-isopropylacrylamide which was then loaded with PTX. This conjugate can self-assemble into core-shell micelles that are simultaneously temperature- and redox-responsive. These results demonstrated that the conjugates are safe and show promising anticancer efficacy [125]. Besides thermoresponsive polymers, thermoresponsive liposomes have been widely investigated for smart drug delivery applications [126]. For example, Thanou et al. loaded topotecan in thermo-responsive liposomes and improved the drug uptake into cancer cells [126]. In another study, Ta et al. prepared a polymer-modified thermo-sensitive liposome (pTSL) loaded with DOX. This system exhibits dual pH/temperature-dependent phase transition properties owing to the coexistence of temperature-responsive N-isopropylacrylamide and pH-responsive propylacrylic acid. By attaching to liposomes, these copolymers were membrane-disruptive in a pH/temperature-dependent manner. pTSL improves release profile and is stable in serum with minimal drug leakage [127].
4.1.1. pH-Responsive Nanocarriers
Malignant tumors are characterized by low extracellular and interstitial pH, where metabolites, such as lactic acid and CO2, result in a slightly acidic environment [67,68]. In most of cancer cells, aerobic glycolysis pathway is the main way to generate energy, which can result in the production of large amount of lactic acid, in contrast to the oxidative phosphorylation pathway in normal cells [69,70,71]. The pH value in normal cells is approximately 7.4 higher than in tumor tissues (~6.5). The above pH gradient could be advantageous as an endogenous stimulus for stimuli-responsive nanoparticles [72,73]. Acidic extracellular pH plays an important role in the enhancement of cancer therapy [71]. There are different strategies for the synthesis of pH-responsive nanoparticles. The first strategy involves the use of peculiar molecular structures with specific functional groups in the nanoparticles backbone that can be protonated under acidic conditions. These phenomena cause the destruction of the hydrophilic–hydrophobic equilibrium and the destruction of the nanoparticles, leading to drug release by rearranging, expanding, or disintegrating the chemical structure [74]. The second strategy involves the role of chemical bonds, which are stable at normal pH and sensitive to lower pH values. This is known as cleavable acid-responsive bonds. In this approach, nanoparticles would be able to release the drug due to the cleavage of chemical bonds to form the nanostructure [73,75]. The use of pH-Low Insertion Peptides (pHLIPs) is the last strategy. pHLIPs are 36-aa peptides with a high affinity for the cell membrane at physiological pH. At lower pH values, the protonation of Asp and Glu enhanced the hydrophobicity of the peptide. This can affect peptide folding and the consequent insertion into the cell membrane, improving its cellular uptake [71,76]. In one study, Zhang et al. worked on pH-responsive peptide-modified polymeric micelles, containing Paclitaxel (PTX). They designed a H7K(R2)2 with pH-triggered cell-penetration activity. These peptides were conjugated to PLGA-PEG. H7K(R2)2-modified polymeric micelles containing PTX were prepared and characterized. The in vitro release of PTX was observed at pH 6.8 by flow cytometry and confocal microscopy, leading to the inhibition of the growth of human breast adenocarcinoma cells [77]. In another study, they tried to design pH-responsive system containing Doxorubicin (DOX) for enhancing glioma targeting and anti-tumor activity. In this study, H7K(R2)2-modified pH-sensitive liposomes containing DOX were investigated. Liposomes show high sensitivity to the acidic environments and are destabilized under these conditions, enhancing cellular uptake with rapid intracellular release. In vitro tests were conducted on C6 (rat C6 glioma cells) and U87-MG (human glioblastoma cells), and a higher cytotoxic effect was observed compared to normal cells. These data were also confirmed by in vivo tests conducted on nude mice bearing orthotopic GBM U87-MG or C6 tumor models [78]. In 2019, Motealleh et al. developed a 3D-nanocomposite (NC) hydrogel system for pH-responsive surface-mediated delivery of Doxorubicin. The 3D-NC hydrogel was formed using periodic mesoporous organosilicate (PMO) nanoparticles loaded with DOX, where the outer shell was functionalized with poly-L-lysine (PLL) and subsequently embedded into the alginate hydrogel network. PLL is responsible for the electrostatic interactions with the cell membrane and its cellular uptake. The 3D-NC hydrogel reduced the viability of Colo 818 cancer cells owing to the high drug release in the local acidic environment of the Colo 818 compared to normal fibroblast cells [79]. These examples demonstrate promising results regarding the anticancer activity of pH-responsive nanocarriers.
4.1.2. Enzyme-Responsive Nanocarriers
Another strategy for designing drug delivery systems for cancer therapy is the adoption of enzyme-responsive nanocarriers. In the human body, biological activity and metabolic processes are carried out by enzymes with high specificity for their substrates [80]. In contrast to normal cells, tumor cells overexpress specific enzymes such as proteases (matrix metalloproteinases (MMPs) and cathepsin-B), phospholipases, and glycosidases. According to the said literature, stimuli-responsive nanocarriers contain substrates that can be cleaved by particular enzymes present in tumor cells and consequently release the desired cargo at tumor sites [81,82]. MMPs degrade proteins by their proteolytic activity. Compared with normal cells with inhibited MMPs activity, tumor cells overexpress some MMPs, including MMP2, MMP9, and MMP13 [81,83]. Among these enzymes, MMP2 play a critical role in tumor metastasis and invasion [84]. These overexpressed MMPs have been widely exploited by scientists. For example, Zhu et al. attempted to enhance the anticancer activity of PTX by designing a PEG2000-Peptide-PTX conjugate, which responds to upregulated MMP2. The results of the in vitro and in vivo tests revealed the great potential of cancer cell-selective intracellular delivery of an anticancer drug for enhanced cancer chemotherapy [85]. In another study, Liu et al. constructed a drug delivery system based on mesoporous silica nanoparticles (MSN). They used PLGLAR as a peptide that is sensitive to MMP13, bovine serum albumin as an endcap for sealing MSNs, lactobionic acid as a targeting ligand, and DOX hydrochloride as a model drug. These results demonstrated the ability of the system to efficiently inhibit tumor growth with minimal side effects [86]. In another study, Kalafatovic et al. designed a self-assembled micelle system for loading DOX, which was sensitive to MMP9 [87]. Cathepsins are lysosomal cysteine proteases that are either expressed on the cell surface or are released into the extracellular matrix in pathological conditions [88]. Compared to normal tissues, cathepsin B (Cat-B) has a higher concentration and activity in cancer cells. This enzyme can hydrolyze various sequences including Arg-Arg, Ala-Leu, Phe-Arg, Phe-Lys, Ala-Phe-Lys, and Gly-Phe-Leu-Gly [81]. For instance, the Gly-Phe-Leu-Gly sequence has been linked to hydroxypropyl methacrylate (HPMA) copolymer conjugates to release the drugs into lysosomes by the cleavage of the peptide spacer and Cat-B [89]. Recently, Gly-Phe-Leu-Gly oligopeptides have been adopted as enzyme-responsive cores, and DOX as a model anticancer drug. This nanoparticle is degraded by Cat-B and releases the drug efficiently [90].
Phospholipases hydrolyze phospholipids into lipophilic substances and fatty acids. One of the mostly investigated phospholipase family is phospholipase A2 (PLA2) which is divided into intracellular and secretory PLA2 [91,92]. Lee et al. designed a strategy for imaging of prostate cancer cells by using biocompatible up-conversion nanoparticles (UCNPs)-loaded phosphate micelle that could be cleaved by secretory PLA2 [93]. In another study, Ghavami et al. utilized phospholipase sensitive liposomes (PSLs) to encapsulate antisense octaarginine peptide nucleic acid conjugate, which responds to secretory PLA2 [94].
Glycosidases are another family of enzymes that trigger release of drug to malignant sites. They can hydrolyze carbohydrates in lysosomes and play a key role in N-linked glycosylation in the endoplasmic reticulum (ER) and Golgi apparatus [95]. Clahaut et al. worked for the first time on doxorubicin-folate conjugate, which is sensitive to β-galactosidase to induce cell death in acute myeloid leukemia blasts. These folate positive cells efficiently responded to the enzyme-responsive nanocarrier with low adverse effects [96]. In another study, Rastegari et al. designed two different magnetic nanoparticles with β-cyclodextrin and chitosan, loaded with prodigiosin as a model anticancer drug. The results of the study showed that the drug was released by hydrolytic lysosomal enzymes such as α-glucosidase [97]. Kolodych et al. developed a new class of β-galactosidase-cleavable linkers for antibody-drug conjugates (ADCs) by employing trastuzumab as a model drug and loading it using galactoside linkers, monomethyl auristatin E (MMAE), and cysteine-reactive groups. The in vivo results demonstrated that ADCs with galactoside linker have superior therapeutic efficacy than trastuzumab alone [98].
4.1.3. Redox-Responsive Nanocarriers
Reactive Oxygen Species (ROS) include the hydroxyl radicals (HO•), the superoxide anions (O2−), hydrogen peroxide (H2O2), and singlet oxygen (1O2) which are usually produced by incomplete reduction of oxygen [99]. Compared to normal cells, the ROS concentration is much higher in cancer cells, which can be used for preparing drug delivery systems [100]. For example, Wilson et al. constructed thioketal nanoparticles (TKNs) using poly- (1,4-phenyleneacetone dimethylene thioketal) that can deliver small interfering RNA (siRNA). TKNs can be degraded at high ROS concentration and release the siRNA at the site of action [101]. In addition, Lux et al. designed a system containing boronic esters that can be degraded in the presence of H2O2 and release cargos [102]. Recently, Du et al. prepared thioether phosphatidylcholines (S-PCs) and S-PC-based liposomes (S-LPs) loaded with DOX as a model anticancer drug. The results demonstrated higher efficiency of DOX/S-LPs resulting from the rapid release of drug from the conjugate by excess amount of ROS [103].
Glutathione (GSH) is a tri-peptide that contains glutamate, cysteine, and glycine (L-γ-glutamyl-L-cysteinyl-glycine). Despite its crucial role in detoxification, antioxidant activity, and cell cycle regulation, high GSH concentrations result in different pathological conditions, including cancer and neurodegenerative diseases. GSH levels in cytoplasm are approximately 1–10 mM, which is higher than that in the extracellular environment (2–20 µM) [104]. GSH concentration in tumor tissues is at least four-fold higher than that in normal cells. The differences in GSH levels were exploited to develop novel drug delivery systems [105,106,107,108]. Therefore, chemical groups, which are sensitive to high level of GSH including disulfide and diselenide bonds, as well as manganese dioxide (MnO2) have been used in recent studies [109,110,111]. For instance, Li et al. designed a novel drug carrier modified with disulfide-bound PEG and amide-bound polyethylenimine (PEI). High GSH level results in di-sulfide degradation and drug release. This nanocarrier improves the tumor accumulation capability and decreases the side effects of chemotherapy [112]. Recently, Chai et al. constructed a novel drug delivery system by conjugating ibuprofen (BF) to the hyaluronic acid (HA) backbone through a disulfide bond (HA-ss-BF), which can self-assemble into micelles for DOX delivery. This drug delivery system is efficient because of its ability to target cancer cells by recognition of CD44 receptors, inhibition of overexpressed cyclooxygenase-2 in cancer cells, improvement of cellular uptake, and improved biodistribution [113]. Additionally, selenium has special properties, including high reactivity and sensitivity owing to its low electronegativity and high atomic radius. In addition, the Se-Se bond has a lower bond energy that the S-S bond resulting in faster cleavage and faster drug release [114,115,116]. In this context, Wei et al. designed a nanocarrier based on amphiphilic polyester urethane with multiple Se-Se bonds which were loaded with DOX. According to these results, the antitumor activity of the DOX was enhanced in this system [116]. Recently, MnO2 has attracted increasing attention as a GSH-responsive trigger. For example, Zhang et al. constructed a drug delivery system for chemotherapy that reduced the side effects of Bleomycin (BLM). In this study, PEG-modified mesoporous manganese dioxide (HMnO2@PEG) nanoparticles were used to load the antitumor drug BLM. At the tumor site, degradation of this system and drug release occurred simultaneously because of an excess amount of GSH. Then, BLM-Mn2+ formation led to the therapeutic activity of BML while maintaining the side effects as low as possible [117].
4.1.4. Temperature-Responsive Nanocarriers
High temperatures are another major trigger for the design of stimuli-responsive drug delivery systems. Temperature can be employed as an internal stimulus due to the pathological conditions including the hyperthermic nature of the tumor or inflammation. Additionally, temperature can be used as an external stimulus by utilizing external heat sources [118,119]. Therefore, nanocarrier should be stable at body temperature and release the drug when tumor temperature raises. Thermoresponsive polymers exhibit phase-transition, which defines their solubility and Critical Solution Temperature (CST). If the temperature is greater than the lower critical solubility temperature (LCST) of the system, dehydration of the polymer occurs. Consequently, the hydrophobic polymer collapses and releases the drug [120,121,122,123]. Poly-N-isopropylacrylamide (PNIPPAm) is a polymer that is widely used because of its LCST of approximately 35 °C [124,125]. In one study, Xu et al. synthesized a drug carrier using diselenide-bond-linked poly-N-isopropylacrylamide which was then loaded with PTX. This conjugate can self-assemble into core-shell micelles that are simultaneously temperature- and redox-responsive. These results demonstrated that the conjugates are safe and show promising anticancer efficacy [125]. Besides thermoresponsive polymers, thermoresponsive liposomes have been widely investigated for smart drug delivery applications [126]. For example, Thanou et al. loaded topotecan in thermo-responsive liposomes and improved the drug uptake into cancer cells [126]. In another study, Ta et al. prepared a polymer-modified thermo-sensitive liposome (pTSL) loaded with DOX. This system exhibits dual pH/temperature-dependent phase transition properties owing to the coexistence of temperature-responsive N-isopropylacrylamide and pH-responsive propylacrylic acid. By attaching to liposomes, these copolymers were membrane-disruptive in a pH/temperature-dependent manner. pTSL improves release profile and is stable in serum with minimal drug leakage [127].
4.2. Exogenus Stimuli
4.2.1. Light-Responsive Nanocarriers
Nanocarrier uptake and tumor penetration are obstacles that result from the high interstitial pressure and dense extracellular matrix of the tumors. An invasive strategy giving the solution to these problems, which attract considerable attention, is photo-thermal therapy (PTT). Most photoactive chromophores are sensitive to ultraviolet (UV) radiation. Nevertheless, UV light has two disadvantages including poor penetration depth in human tissues and its high phototoxicity and carcinogenic effect on normal cells and tissues [128]. Compared to UV light, near-infrared (NIR) light is more attractive because of its deeper penetration rate and lower cell toxicity. Hence, PTT is induced by light, preferably NIR, which relies on the absorbance ability and the transformation of NIR energy to heat [129,130,131,132]. Besides PTT, there is another strategy for utilizing lights termed photodynamic therapy (PDT). PDT is a minimally invasive method with low side effects that induces cell death via light irradiation of a photosensitizer and generation of highly reactive singlet oxygen species [133,134]. In one study, Chen et al. utilized indocyanine green (ICG), a widely used NIR dye in photothermal therapy, and DOX as a model anticancer drug loaded on a nanoparticle of arylboronic ester and cholesterol modified hyaluronic acid. This combination demonstrated a photothermal effect and a faster trigger release of DOX with NIR irradiation [135]. An interesting approach is proposed by Diaz and co-workers. They used the laser-induced thermal mechanism to give de-hybridization of DNA or RNA sequences from gold nanoparticles. The nanoparticles are loaded with complex hybridizing molecules: a single-stranded complementary DNA, a DNA duplicator, and a basically modified DNA duplicator. DNA duplicators show a different melting point, and when light hits the nanoparticles, causing an increase in temperature, it allows the release of the labeled DNA duplicator in the cancer cells. The system offers the possibility of greater control of drug release [136]. Chen et al. proposed to use red-light-responsive metallopolymer nanocarriers named PolyRuCHL formed by hydrophilic poly(ethylene glycol) (PEG) block and a hydrophobic ruthenium (Ru)-containing block as red-light-responsive linked with chlorambucil (CHL). PolyRuCHL is loaded with DOX. The advantage of red light is the ability to penetrate deeply into tissue for PTT. Red-light irradiation induced a cleavage of Ru-CHL triggering DOX release from the nanoparticles. The authors observed a synergic effect to inhibit the growth and multidrug resistance in breast cancer cell line MCF-7R [137]. The approach used by Fan and co-workers draws inspiration from the properties of liquid metals (LMs) that exhibit excellent photothermal conversion efficiency, generating heat under NIR laser irradiation. To prepare LMs, they used a gallium–indium eutectic alloys (EgaIn) which shows excellent combination of thermal-conductivity, transformability, and a high biocompatibility. The authors prepared a poly (NIPAm-co-MBA) hydrogel (PNM) contained with LM droplets and encapsulated it with DOX to form the final system PNM/LM/DOX. After NIR irradiation, the temperature of the system rises above the lower critical temperature of the solution, which causes the hydrogel to change shape and size. The hydrogel shrinks, inducing a simultaneous release of the aqueous solution and DOX. This controlled release can reduce the amount of drug into normal tissues and thus the side effects [138].
4.2.2. Magneto-Responsive Nanocarriers
Magneto-responsive nanocarrier design is a novel strategy for the development of drug delivery systems. This method has multiple advantages including the following: (1) Superparamagnetic iron oxide nanoparticles act as a contrast agent in magnetic resonance imaging (MRI), which means that they are suitable for drug delivery and as diagnostic imaging agents simultaneously [139], (2) Compared to UV light, magnetism has not shown any particular side effects in the human body [140], and (3) It has no physical interactions with the human body [122]. Although it has several advantages, its low accuracy can be a barrier to its clinical translation [122]. In a study, Yoon et al. designed a dual targeting nanoparticle, which was sensitive to GSH and magnetism. In this work, methoxy PEG was grafted to water-soluble chitosan, DOX conjugated to the backbone of chitosan via disulfide linkage (which made it GSH sensitive), and finally, the conjugation of iron oxide made it magnetically responsive. The in vivo test demonstrated that this system has promising anticancer drug-targeting properties [141]. In Table 3, we summarized a number of nanomaterials and their components that can respond to different stimuli.
4.2.1. Light-Responsive Nanocarriers
Nanocarrier uptake and tumor penetration are obstacles that result from the high interstitial pressure and dense extracellular matrix of the tumors. An invasive strategy giving the solution to these problems, which attract considerable attention, is photo-thermal therapy (PTT). Most photoactive chromophores are sensitive to ultraviolet (UV) radiation. Nevertheless, UV light has two disadvantages including poor penetration depth in human tissues and its high phototoxicity and carcinogenic effect on normal cells and tissues [128]. Compared to UV light, near-infrared (NIR) light is more attractive because of its deeper penetration rate and lower cell toxicity. Hence, PTT is induced by light, preferably NIR, which relies on the absorbance ability and the transformation of NIR energy to heat [129,130,131,132]. Besides PTT, there is another strategy for utilizing lights termed photodynamic therapy (PDT). PDT is a minimally invasive method with low side effects that induces cell death via light irradiation of a photosensitizer and generation of highly reactive singlet oxygen species [133,134]. In one study, Chen et al. utilized indocyanine green (ICG), a widely used NIR dye in photothermal therapy, and DOX as a model anticancer drug loaded on a nanoparticle of arylboronic ester and cholesterol modified hyaluronic acid. This combination demonstrated a photothermal effect and a faster trigger release of DOX with NIR irradiation [135]. An interesting approach is proposed by Diaz and co-workers. They used the laser-induced thermal mechanism to give de-hybridization of DNA or RNA sequences from gold nanoparticles. The nanoparticles are loaded with complex hybridizing molecules: a single-stranded complementary DNA, a DNA duplicator, and a basically modified DNA duplicator. DNA duplicators show a different melting point, and when light hits the nanoparticles, causing an increase in temperature, it allows the release of the labeled DNA duplicator in the cancer cells. The system offers the possibility of greater control of drug release [136]. Chen et al. proposed to use red-light-responsive metallopolymer nanocarriers named PolyRuCHL formed by hydrophilic poly(ethylene glycol) (PEG) block and a hydrophobic ruthenium (Ru)-containing block as red-light-responsive linked with chlorambucil (CHL). PolyRuCHL is loaded with DOX. The advantage of red light is the ability to penetrate deeply into tissue for PTT. Red-light irradiation induced a cleavage of Ru-CHL triggering DOX release from the nanoparticles. The authors observed a synergic effect to inhibit the growth and multidrug resistance in breast cancer cell line MCF-7R [137]. The approach used by Fan and co-workers draws inspiration from the properties of liquid metals (LMs) that exhibit excellent photothermal conversion efficiency, generating heat under NIR laser irradiation. To prepare LMs, they used a gallium–indium eutectic alloys (EgaIn) which shows excellent combination of thermal-conductivity, transformability, and a high biocompatibility. The authors prepared a poly (NIPAm-co-MBA) hydrogel (PNM) contained with LM droplets and encapsulated it with DOX to form the final system PNM/LM/DOX. After NIR irradiation, the temperature of the system rises above the lower critical temperature of the solution, which causes the hydrogel to change shape and size. The hydrogel shrinks, inducing a simultaneous release of the aqueous solution and DOX. This controlled release can reduce the amount of drug into normal tissues and thus the side effects [138].
4.2.2. Magneto-Responsive Nanocarriers
Magneto-responsive nanocarrier design is a novel strategy for the development of drug delivery systems. This method has multiple advantages including the following: (1) Superparamagnetic iron oxide nanoparticles act as a contrast agent in magnetic resonance imaging (MRI), which means that they are suitable for drug delivery and as diagnostic imaging agents simultaneously [139], (2) Compared to UV light, magnetism has not shown any particular side effects in the human body [140], and (3) It has no physical interactions with the human body [122]. Although it has several advantages, its low accuracy can be a barrier to its clinical translation [122]. In a study, Yoon et al. designed a dual targeting nanoparticle, which was sensitive to GSH and magnetism. In this work, methoxy PEG was grafted to water-soluble chitosan, DOX conjugated to the backbone of chitosan via disulfide linkage (which made it GSH sensitive), and finally, the conjugation of iron oxide made it magnetically responsive. The in vivo test demonstrated that this system has promising anticancer drug-targeting properties [141]. In Table 3, we summarized a number of nanomaterials and their components that can respond to different stimuli.
5. Stimuli-Responsive Nanomaterials Targeting Integrins
To achieve therapeutic and diagnostic goals, stimuli-responsive nanomaterials can also be designed for theranostics (Figure 3) [179,180].
This modality provides great opportunity for personalized cancer treatment [181]. Theranostic systems provide early specific personalized diagnosis and treatment, longer blood circulation and high resolution for imaging reagents, accumulation of loaded cargo in the tumor tissue, and controlled drug release, which together improve the efficiency and selectivity of treatment and the accuracy of tumor diagnosis with minimum side effects [49,181]. A theranostic system must have optimized diagnostic and therapeutic properties, cost-benefit of efficacy versus toxicity, improved pharmacokinetic and biocompatibility properties, and tumor-targeted ability [182,183]. In Table 4, some examples of stimuli-responsive nanocarriers targeting integrin receptors in cancer therapy are summarized.
5.1. Light-Responsive Theranostic Nanomaterials
Sheng et al. designed biomimetic HDL-like theranostic nanomaterials for personalized cancer therapy as part of a successful investigation of light-responsive theranostic nanomaterials for integrin receptors. For this purpose, the plasma was extracted and purified to extract Apo A. iRGD was conjugated on the Apo A surface by the Sulfosuccinimidyl 4-(nmaleimidomethyl) cyclohexane-1-carboxylate (sulfo-SMCC) as a cross-linker and indocyanine green was loaded as a diagnostic. The hydrodynamic size of nanoparticles was 86.7 ± 1.4 nm, which is desirable for passive targeting to tumor site via the EPR effect. The stability of the nanoparticles was evaluated in PBS for 4 weeks and in fetal bovine serum for 12 h, revealing that the formulation was stable, without precipitation or aggregation. The prepared nanoparticles could accumulate in tumor sites due to recognition by SR-BI receptors of tumor cells and the EPR effect, while they are able to bind to tumor cells and vessels specifically through iRGD interaction with αvβ3 receptors. The targeting was evaluated in 4T1 cells overexpressing of αvβ3. Confocal Laser Scanning Microscopy (CLSM) showed the strongest signal from the treated cells with nanoparticles compared to nanoparticles without iRGD or indocyanine green alone. Therefore, the conjugation of iRGD provided higher targeting capability and tumor homing ability of the nanoparticles. The prepared nanoparticles showed controlled drug release in the blood, and burst release upon NIR irradiation. Therefore, the iRGD conjugated HDL-like nanoparticles were capable to improve phototherapeutic and photodynamic effects [194].
5.2. Enzyme-Responsive Theranostic Nanomaterials
A promising investigation conducted by Cheng et al. developed an integrin-targeted delivery system displaying aggregation-induced emission (AIE) as a theranostic system. The prepared nanoparticles were conjugated by DGR for integrin-targeting, KRRRR peptide for nucleus localization, RRRR as a cell-penetrating peptide and endosomal escape, and AIE as a fluorescent probe for cell imaging, which were assembled with an antisense single-stranded DNA oligonucleotide. The self-assembly ability of peptides and DNA is based on electrostatic interactions, leading to a reduction in the non-specific adsorption and cationic toxicity. The prepared nanoparticles successfully delivered the antisense into the nucleus and showed a remarkable tumor-suppressive effect. In vivo experiments in tumor-bearing mice showed that the prepared nanoparticles accumulated in the tumor, kidney, and liver after 12 h. In addition, the injected nanoparticles diffused quickly into the tumor tissue within an hour. The decreased tumor volume in MDA-MB-231 tumor-bearing mice receiving theranostic nanoparticles, compared to the increased tumor volume in the control group, confirmed the efficiency of the prepared nanoparticles [195]. Another enzyme-responsive theranostic nanoparticle was developed by Hu et al. [196]. They designed mesoporous silica functionalized with MMP-2-responsive fluorescence imaging peptides and cRGD. The delivery system was loaded with camptothecin. The mesoporous silica nanoparticles showed a hexagonal and uniform mesostructure in X-ray diffraction with an average diameter of 150 nm. The targeting effect of cRGD was evaluated in overexpressing cRGD cell line (SCC-7) and low-expressed cRGD cell line (COS7). The results revealed that mesoporous silica nanoparticles had a higher concentration in SCC-7 cells than in mesoporous silica nanoparticles without the cRGD ligand. High amounts of MMP-2 in tumor tissues cleaved fluorescence imaging peptides on the surface of nanoparticles, leading to both tumor imaging and the controlled release of camptothecin from the pores of mesoporous silica nanoparticles. Drug release determination showed that more than 87% of camptothecin was released in 36 h, whereas this amount was 35% in the absence of MMP-2. The results showed a promising approach for integrin-mediated enzyme-responsive theranostics.
5.3. pH-Responsive Theranostic Nanomaterials
To demonstrate the ability of pH-responsive theranostics for integrin receptors, the following investigations are examples of success in this field. In a study, perfluorocarbon nanoparticles encapsulated with a prodrug to target the transcription factor c-MYC (MI3-PD) were developed by Weilbaecher et al. Their findings were based on testing the above-mentioned nanoparticles in a murine breast cancer cell line (PyMT-Bo1, MFI 17), human melanoma cell line (MDA.MB.435, MFI 27), and human endothelial cell line (HUVEC, MFI 42) in vitro. The results showed that the number of M2 macrophages was reduced in 4T1 breast cancer cells treated with αvβ3-MI3-PD-NP. It was observed that the uptake of αvβ3-NP labeled with rhodamine was dependent on surface integrin expression. CD11b-expressing myeloid cells in the bone marrow were used for in vivo testing. Down-regulation of CD206 and arginase 1 in M2 macrophages suggests that nanoparticle-mediated drug delivery of MI3 MYC inhibitors could reduce M2 macrophage polarization and function in vivo [197]. Sun et al. developed iron oxide nanoparticles in the range of 2.5–5 nm. They proposed a new method for MRI detection of tumors by using Fe3O4 NP in vivo, introducing ultrasmall Fe3O4 NPs (<10 nm in hydrodynamic diameter) that have specific targeting ability for tumors. Small hydrodynamic sizes are known to be capable of specific uptake and extravascular ability. To prepare these NPs, they used the thermal decomposition of Fe(CO)5 and air oxidation combined with a different strategy (using the 4-methylcatechol (4a-MC)). They showed that Fe3O4 NPs were mainly accumulated in integrin-overexpressing tumor vasculature and tumor cells. Moreover, they showed that these NPs did not accumulate in the macrophages [198]. In another study, Liao et al. developed flaky black phosphorus (BP) nanosheets with a lateral size of approximately 200 nm and a thickness of 5.46 ± 1.48 nm. In these nanosheets, BP causes pH sensitivity. The measurement of Nrf2/HO-1 is one of the most important key factors for oxidative stress mechanism in cells. The researchers prepared BP-Dox and observed an increased Dox release under acidic conditions in vitro. BPs showed photothermal effects, and after laser irradiation at 808 nm, Dox release was accelerated. However, BPs induce a mild inflammation in healthy mice and oxidative stress in the liver and lung, leading to cell apoptosis. To understand the biological effects of BPs, researchers prepared red blood cell (RBC) membrane-coated BP-conjugated RGD peptides (RGD-RBC@BP) via lipid insertion and co-ultrasound methods for effective photothermal therapy (PTT) of cancer through a targeted strategy. In vitro tests on HeLa cells showed increased cellular uptake, biocompatibility, and photothermal properties. Animal studies showed a prolonged circulation time and accumulation in tumor cells over expressing αvβ3 integrin [199].
5.4. Temperature-Responsive Theranostic Nanomaterials
Another successful strategy for the integrin-mediated delivery of theranostics is the application of temperature-stimuli systems. For instance, Lei et al. developed a temperature-responsive theranostic mesoporous silica nanoparticles loaded with indocyanine green (ICG) and doxorubicin. The system was decorated with RGD to target tumor tissues, as well as PEG, to protect mesoporous silica nanoparticles in the blood circulation, and an MMP-sensitive substrate (PLGVR) to remove PEG in tumor tissues through a host-guest interaction [196]. TEM images confirmed the successful synthesis of mesoporous silica nanoparticles with an average diameter of 60 nm. Both doxorubicin and ICG were loaded by a thermally cleavable gatekeeper that was cleaved due to hyperthermia induced by ICG under NIR illumination. For the in vivo evaluation of tumor imaging and targeting, mesoporous silica nanoparticles were injected intravenously into 4T1 tumor-bearing mice. The results showed that the near-infrared fluorescence (NIRF) of ICG in mice treated with mesoporous silica nanoparticles was detected at 8 h in tumor tissues with high potency compared to other tissues. Significant NIRF was detected at 24 and 48 h indicating the tumor-targeting ability and long-term tumor retention of the nanoparticles. Fluorescence imaging of frozen tumor sections showed weak fluorescence of doxorubicin in mice treated with ICG and doxorubicin, indicating poor retention ability and tumor targeting of free doxorubicin. The intensity was also low in tumor tissues of mice treated with mesoporous silica nanoparticles without NIR irradiation. The red fluorescence intensity of doxorubicin in mice treated with mesoporous silica nanoparticles and NIR was much stronger confirming the targeted drug delivery of doxorubicin with efficient drug release. In other words, released ICG made a light-to-heat conversion, and provided both imaging and burst drug release in the cytoplasm. In another study, RGD and ICG-decorated albumin nanoparticles loaded with artemisinin were synthesized. TEM images showed uniform spherical shapes with an average diameter of 141 nm and a zeta potential of –20 mV. Albumin nanoparticles have shown promising biocompatibility, stability, and temperature responsiveness. The particle size of the nanoparticles increased when exposed to high temperatures generated by ICG under NIR irradiation, leading to the drug release. The drug release study revealed that 61.5% of artemisinin was released after six cycles of NIR irradiation, which proved that the drug had a burst release upon NIR irradiation. In vivo fluorescence images showed that albumin nanoparticles accumulated in tumor tissues at a maximum amount at 24 h after intravenous administration. The generated hyperthermia upon NIR irradiation triggered drug release and caused tumor death [200].
5.1. Light-Responsive Theranostic Nanomaterials
Sheng et al. designed biomimetic HDL-like theranostic nanomaterials for personalized cancer therapy as part of a successful investigation of light-responsive theranostic nanomaterials for integrin receptors. For this purpose, the plasma was extracted and purified to extract Apo A. iRGD was conjugated on the Apo A surface by the Sulfosuccinimidyl 4-(nmaleimidomethyl) cyclohexane-1-carboxylate (sulfo-SMCC) as a cross-linker and indocyanine green was loaded as a diagnostic. The hydrodynamic size of nanoparticles was 86.7 ± 1.4 nm, which is desirable for passive targeting to tumor site via the EPR effect. The stability of the nanoparticles was evaluated in PBS for 4 weeks and in fetal bovine serum for 12 h, revealing that the formulation was stable, without precipitation or aggregation. The prepared nanoparticles could accumulate in tumor sites due to recognition by SR-BI receptors of tumor cells and the EPR effect, while they are able to bind to tumor cells and vessels specifically through iRGD interaction with αvβ3 receptors. The targeting was evaluated in 4T1 cells overexpressing of αvβ3. Confocal Laser Scanning Microscopy (CLSM) showed the strongest signal from the treated cells with nanoparticles compared to nanoparticles without iRGD or indocyanine green alone. Therefore, the conjugation of iRGD provided higher targeting capability and tumor homing ability of the nanoparticles. The prepared nanoparticles showed controlled drug release in the blood, and burst release upon NIR irradiation. Therefore, the iRGD conjugated HDL-like nanoparticles were capable to improve phototherapeutic and photodynamic effects [194].
5.2. Enzyme-Responsive Theranostic Nanomaterials
A promising investigation conducted by Cheng et al. developed an integrin-targeted delivery system displaying aggregation-induced emission (AIE) as a theranostic system. The prepared nanoparticles were conjugated by DGR for integrin-targeting, KRRRR peptide for nucleus localization, RRRR as a cell-penetrating peptide and endosomal escape, and AIE as a fluorescent probe for cell imaging, which were assembled with an antisense single-stranded DNA oligonucleotide. The self-assembly ability of peptides and DNA is based on electrostatic interactions, leading to a reduction in the non-specific adsorption and cationic toxicity. The prepared nanoparticles successfully delivered the antisense into the nucleus and showed a remarkable tumor-suppressive effect. In vivo experiments in tumor-bearing mice showed that the prepared nanoparticles accumulated in the tumor, kidney, and liver after 12 h. In addition, the injected nanoparticles diffused quickly into the tumor tissue within an hour. The decreased tumor volume in MDA-MB-231 tumor-bearing mice receiving theranostic nanoparticles, compared to the increased tumor volume in the control group, confirmed the efficiency of the prepared nanoparticles [195]. Another enzyme-responsive theranostic nanoparticle was developed by Hu et al. [196]. They designed mesoporous silica functionalized with MMP-2-responsive fluorescence imaging peptides and cRGD. The delivery system was loaded with camptothecin. The mesoporous silica nanoparticles showed a hexagonal and uniform mesostructure in X-ray diffraction with an average diameter of 150 nm. The targeting effect of cRGD was evaluated in overexpressing cRGD cell line (SCC-7) and low-expressed cRGD cell line (COS7). The results revealed that mesoporous silica nanoparticles had a higher concentration in SCC-7 cells than in mesoporous silica nanoparticles without the cRGD ligand. High amounts of MMP-2 in tumor tissues cleaved fluorescence imaging peptides on the surface of nanoparticles, leading to both tumor imaging and the controlled release of camptothecin from the pores of mesoporous silica nanoparticles. Drug release determination showed that more than 87% of camptothecin was released in 36 h, whereas this amount was 35% in the absence of MMP-2. The results showed a promising approach for integrin-mediated enzyme-responsive theranostics.
5.3. pH-Responsive Theranostic Nanomaterials
To demonstrate the ability of pH-responsive theranostics for integrin receptors, the following investigations are examples of success in this field. In a study, perfluorocarbon nanoparticles encapsulated with a prodrug to target the transcription factor c-MYC (MI3-PD) were developed by Weilbaecher et al. Their findings were based on testing the above-mentioned nanoparticles in a murine breast cancer cell line (PyMT-Bo1, MFI 17), human melanoma cell line (MDA.MB.435, MFI 27), and human endothelial cell line (HUVEC, MFI 42) in vitro. The results showed that the number of M2 macrophages was reduced in 4T1 breast cancer cells treated with αvβ3-MI3-PD-NP. It was observed that the uptake of αvβ3-NP labeled with rhodamine was dependent on surface integrin expression. CD11b-expressing myeloid cells in the bone marrow were used for in vivo testing. Down-regulation of CD206 and arginase 1 in M2 macrophages suggests that nanoparticle-mediated drug delivery of MI3 MYC inhibitors could reduce M2 macrophage polarization and function in vivo [197]. Sun et al. developed iron oxide nanoparticles in the range of 2.5–5 nm. They proposed a new method for MRI detection of tumors by using Fe3O4 NP in vivo, introducing ultrasmall Fe3O4 NPs (<10 nm in hydrodynamic diameter) that have specific targeting ability for tumors. Small hydrodynamic sizes are known to be capable of specific uptake and extravascular ability. To prepare these NPs, they used the thermal decomposition of Fe(CO)5 and air oxidation combined with a different strategy (using the 4-methylcatechol (4a-MC)). They showed that Fe3O4 NPs were mainly accumulated in integrin-overexpressing tumor vasculature and tumor cells. Moreover, they showed that these NPs did not accumulate in the macrophages [198]. In another study, Liao et al. developed flaky black phosphorus (BP) nanosheets with a lateral size of approximately 200 nm and a thickness of 5.46 ± 1.48 nm. In these nanosheets, BP causes pH sensitivity. The measurement of Nrf2/HO-1 is one of the most important key factors for oxidative stress mechanism in cells. The researchers prepared BP-Dox and observed an increased Dox release under acidic conditions in vitro. BPs showed photothermal effects, and after laser irradiation at 808 nm, Dox release was accelerated. However, BPs induce a mild inflammation in healthy mice and oxidative stress in the liver and lung, leading to cell apoptosis. To understand the biological effects of BPs, researchers prepared red blood cell (RBC) membrane-coated BP-conjugated RGD peptides (RGD-RBC@BP) via lipid insertion and co-ultrasound methods for effective photothermal therapy (PTT) of cancer through a targeted strategy. In vitro tests on HeLa cells showed increased cellular uptake, biocompatibility, and photothermal properties. Animal studies showed a prolonged circulation time and accumulation in tumor cells over expressing αvβ3 integrin [199].
5.4. Temperature-Responsive Theranostic Nanomaterials
Another successful strategy for the integrin-mediated delivery of theranostics is the application of temperature-stimuli systems. For instance, Lei et al. developed a temperature-responsive theranostic mesoporous silica nanoparticles loaded with indocyanine green (ICG) and doxorubicin. The system was decorated with RGD to target tumor tissues, as well as PEG, to protect mesoporous silica nanoparticles in the blood circulation, and an MMP-sensitive substrate (PLGVR) to remove PEG in tumor tissues through a host-guest interaction [196]. TEM images confirmed the successful synthesis of mesoporous silica nanoparticles with an average diameter of 60 nm. Both doxorubicin and ICG were loaded by a thermally cleavable gatekeeper that was cleaved due to hyperthermia induced by ICG under NIR illumination. For the in vivo evaluation of tumor imaging and targeting, mesoporous silica nanoparticles were injected intravenously into 4T1 tumor-bearing mice. The results showed that the near-infrared fluorescence (NIRF) of ICG in mice treated with mesoporous silica nanoparticles was detected at 8 h in tumor tissues with high potency compared to other tissues. Significant NIRF was detected at 24 and 48 h indicating the tumor-targeting ability and long-term tumor retention of the nanoparticles. Fluorescence imaging of frozen tumor sections showed weak fluorescence of doxorubicin in mice treated with ICG and doxorubicin, indicating poor retention ability and tumor targeting of free doxorubicin. The intensity was also low in tumor tissues of mice treated with mesoporous silica nanoparticles without NIR irradiation. The red fluorescence intensity of doxorubicin in mice treated with mesoporous silica nanoparticles and NIR was much stronger confirming the targeted drug delivery of doxorubicin with efficient drug release. In other words, released ICG made a light-to-heat conversion, and provided both imaging and burst drug release in the cytoplasm. In another study, RGD and ICG-decorated albumin nanoparticles loaded with artemisinin were synthesized. TEM images showed uniform spherical shapes with an average diameter of 141 nm and a zeta potential of –20 mV. Albumin nanoparticles have shown promising biocompatibility, stability, and temperature responsiveness. The particle size of the nanoparticles increased when exposed to high temperatures generated by ICG under NIR irradiation, leading to the drug release. The drug release study revealed that 61.5% of artemisinin was released after six cycles of NIR irradiation, which proved that the drug had a burst release upon NIR irradiation. In vivo fluorescence images showed that albumin nanoparticles accumulated in tumor tissues at a maximum amount at 24 h after intravenous administration. The generated hyperthermia upon NIR irradiation triggered drug release and caused tumor death [200].
6. Dual-Stimuli Responsive Nanomaterials Targeting Integrins
There is evidence suggesting that endogenous stimuli, such as pH, redox, and enzymes, and/or exogenous stimuli, such as temperature and magnet, are not sufficient for bench-to-bed translation of targeted stimuli-responsive nanomaterials [201]. To overcome this barrier, great effort has been made to prepare dual-stimulus responsive nanoparticles to offer unprecedented control over drug delivery and release, leading to superior antitumor potency in vitro and/or in vivo [202]. In this section, we discuss dual-stimuli responsive theranostic nanomaterials in the presence of integrin ligands.
6.1. pH-Redox (ROS)-Responsive Theranostic Nanomaterials
Among stimuli-responsive systems, pH and ROS stimuli are the most appreciated in cancer therapy. Their combination allows promising improvements in the stability of nanoparticles in vivo, as well as activation of drug release and enhanced tumor cell uptake [203]. The concentration of ROS in cancer cells is higher than that in normal cells [204]. The acidic pH present in the endosomal/lysosomal compartments (pH 4–5) is important for the development of pH-responsive nanocarriers. However, complete drug release may also be limited by H+ depletion in the absence of adequate supplementation [205]. Their combination leads to increase in vivo stability of nanoparticles, activation of drug release, and enhanced tumor cell uptake [203]. An example of this synergistic effect was reported by Bahadur et al. in 2012. They prepared polymeric nanoparticles with a disulfide-S-S-linkage, which presented suitable stability in the extracellular matrix. The researchers prepared cRGD-γ-poly(2-(pyridin-2-yldisulfanyl)ethyl acrylate)-γ-polyethylene glycol polymer and functionalized it with cRGD through a thiol-disulfide exchange reaction. Doxorubicin was encapsulated in the pore of the nanoparticles, and HCT-116 colon cancer cells were used for the experiments. The researchers observed enhanced cellular uptake and nuclear localization due to the presence of RGD and a fast release of the drug due to the acidic pH and redox potential of the cancer cells [206]. Another interesting approach was developed by Yao and co-workers who prepared a pH-ROS nanostimuli-responsive system. The nanocarrier was formed by 4-hydroxymethylphenylboronic acid pinacol ester as an ROS-responsive moiety, as well as cyclodextrin. The system was loaded with docetaxel. To enhance cellular uptake, nanoparticles were modified with cRGD. To fully release docetaxel (DTX), nanoparticles must be disassembled. After internalization into cancer cells by cRGD, the aldehyde is released, ROS oxidize the boron atom, inducing the breakdown of the borate ester, and then the chemical bond between cyclodextrin and the phenyl group is broken, resulting in the full release of the drug. The drug delivery system was tested in 4T1 breast cancer cells and showed substantial antitumor activity after 24 h. Activity was maintained after 48 h of incubation. In vivo, DTX/RGD@NP showed lower tumor volume and weight after 20 days of treatment demonstrating significant tumor-inhibition efficiency. The ability of DTX/RGD@NPs to inhibit lung metastasis of breast cancer was also investigated. Treatment with saline and the blank control resulted in a large number of metastatic nodules. While, after treatment with DTX/RGD@NPs, no metastatic clots were observed in the lung tissue of mice [20].
6.2. Temperature-pH-Responsive Theranostic Nanomaterials
An interesting dual approach is to use high temperatures and acidic conditions, both of which are present in the extracellular environment of the cancer cells [207]. For example, Kim and co-workers in 2023 prepared a thermosensitive polymer, N-isopropyl acrylamide-co-N-vinyl-imidazole-co-acrylic acid, based on mesoporous nanomaterial (MSN) coated with RGD sequence, pNIBIm-AA-RGD/MSN, and Dox embedded inside the pores of the MSN. The system consists of a pNIBIm-AA-RGD copolymer shell and an MSN as drug cargo with a diameter of 90 nm [208]. It has been demonstrated that the pNIBIm polymer is able to retain more drug at temperatures below the low critical solution temperature (LCST), whereas the drug is released from the polymer at higher temperatures. The acidic conditions in the cancer cell play a synergic effect with the high temperature for the drug-release [209]. The results of this study showed a rapid release of Dox equal to 90% at 40 °C and pH = 6.5 in 1 day, while at 40 °C and pH = 7.4 the release was equal to 85% in 2 days [208].
6.3. Light-Redox-Responsive Theranostic Nanomaterials
The dual effects of the light and the capacity to reduce glutathione (GSH) have been studied by several researchers. Wang and co-workers developed a gene delivery system using RGD-modified disulfide cross-linked short PEIs (DSPEI) functionalized on gold nanoparticles [210]. In this study, near-infrared (NIR) light was used, with an absorbance range of 650–900 nm. This provides deep tissue penetration and high spatial accuracy without damaging healthy tissues. They exploited the optical and electronic properties of gold nanoparticles (GNPs), where the plasmons present on the surfaces of the nanoparticles decay after excitation, leading to the transfer of excited electrons to the adsorbate before thermalization. Researchers covalently bonded to a carrier on the surfaces of the nanoparticles through an Au-S bond. Finally, a therapeutic molecule was loaded via a weaker interaction to allow the drug to be released after irradiation with a continuous-wave laser. Nanoparticles absorb energy, resulting in a reduction in the interaction between the nanocarrier and therapeutic molecules. This, in turn, leads to drug release at a specific site due to the presence of the RGD motif [210]. In another study, the effect of NIR exogenous stimuli combined with the pH-endogenous was studied by Zhou et al. in 2019 [211]. They prepared a system composed of an amphiphilic polymer PEG-Nbz-PAE-Nbz-PEG called HTMP, containing PEG (as a hydrophilic segment) and pH-sensitive poly(β-aminoester)s-PAE (as a hydrophobic segment) covalently linked via an o-nitrobenzyl (Nbz) linker. Another amphiphilic polymer, iRGD-PAE-iRGD (iPHT) was also prepared. These two parts were used to prepare a hybrid micelle called iPHM. Core-shell structured NaYF4:Yb/Tm@NaYF4 and up-conversion nanoparticles (UCNPs) were introduced to convert NIR to UV-vis, and doxorubicin was loaded into the nanoparticles to form iPHM@UCNP named iPUDN. According to the obtained results, iPUDN showed a different release of doxorubicin at several pH values. At pH 7.4, the system showed a slow release of Dox of approximately 27% in 48 h. Once the pH reached 5.5, Dox release increased to 66% at the same time. Following NIR irradiation, 80 amounts of Dox was released at pH 5.5, confirming the key role of NIR and pH in enhancing the Dox release from iPUDN. To evaluate the ability of iPUDN to penetrate, the authors worked on MCF-7 cells and demonstrated a remarkable uptake due to the presence of iRGD. The effect of NIR is to induce dePEGylation of iPUDN to facilitate exposure of iRGD to cells [211].
6.4. pH-Light-Responsive Theranostic Nanomaterials
The combination of pH and light could be an interesting approach to prepare dual-stimuli-responsive theranostics. For instance, Kuang et al. [212] prepared nanoparticles formed by gadolinium (Gd) and hafnium dioxide (HfO2), to confer high relativity, radio-enhancement ability, and biosafety [213,214]. The authors prepared a system called Gd2Hf2O7@PDA@PEG-Pt-RGD, where polydopamine (PDA) is a photothermal agent and RGD is an integrin ligand, while PEG is used for the biostability and cisplatin is used as a chemotherapeutic agent. Tests were conducted on A549R cells (adenocarcinomic human alveolar basal epithelial cells). The authors did not observe any cytotoxicity of Gd2Hf2O7@PDA@PEG-Pt-RGD after 24 h of incubation at different concentrations. However, after NIR irradiation, the cell viability decreased when the concentration of the nanoparticle system increased. The effects of hyperthermia include the denaturation of cytoplasmic proteins, inhibition of DNA repair, and disturbances in signal transduction. An enhanced release of cisplatin was observed when the pH was reduced from 7.4 to 6.5 ranging from 24.4 ± 0.3% to 46.1 ± 1.6% [212]. Another interesting nanoparticle system was prepared by He et al. in 2021 [215]. They prepared melanin-coated magnetic nanoparticles (MMNs) conjugated with an RGD sequence and co-loaded with Dox and ICG through π−π stacking interactions (RMDI). It was observed that the release of Dox was 26.5% at pH 6 whereas the drug release was 17.3% after 4 h of treatment at pH 7. After NIR irradiation for 5 min, the Dox release was enhanced. Accumulation of RDMI was observed after the treatment of glioblastoma cells (U87MG) owing to the presence of the RGD sequence on the outer shell of the nanoparticles. This cytotoxic effect resulted in a 40% reduction in cellular viability. Therefore, under NIR laser irradiation, the cytotoxic effect was higher owing to the synergistic effect of the photothermal-enhanced chemotherapy. In vivo study conducted on U87MG showed an enhanced temperature in the local tumor after laser irradiation followed by a complete photothermal destruction of tumor cells [215].
6.5. pH-Magnet-Responsive Theranostic Nanomaterials
The preparation of pH-magnet dual-responsive theranostics is another approach that has attracted considerable attention in recent years. Gao et al. developed polymeric micelles with the simultaneous ability of targeted drug delivery and imaging. They used DOX as a model anticancer drug that can be released from the micelles by a pH-responsive mechanism, cRGD as an αvβ3 integrin ligand, superparamagnetic iron oxide as a material for MRI detection, and PEG-block-poly(D,L-lactide) co-polymer as a model carrier polymer. cRGD-SPIO-DOX micelles with the size around 45–47 nm were synthesized, and in vitro tests were conducted on SLK endothelial cells over expressing αvβ3 integrin. These results demonstrated that the micelles have a promising therapeutic and diagnostic abilities [216]. In another study, Wu et al. designed a theranostic nanoparticle model. For this purpose, they utilized DOX as a model chemotherapeutic agent, RGD2 as a ligand for αvβ3 integrin, exceedingly small magnetic iron oxide nanoparticles (ES-MION) as a MRI contrast agents, and poly(ethylene glycol) methyl ether (mPEG) as a model polymer. The size of the DOX@ES-MION@RGD2@mPEG nanoparticles was approximately 13 nm, which can prolong the nanoparticle circulation. For in vitro testing, U-87 MG cells and MCF-7 cells were integrin αvβ3 positive and negative, respectively. The results demonstrated that at neutral pH, RGD2 can be hidden by mPEG, but can be exposed to tumor cells mildly acidic pH, which might improve cell targeting. Additionally, DOX is released at pH of approximately 5.5, which reduces the side effects of the drug in normal cells. In vivo tests performed on nude mice confirmed that the prepared nanoparticles could transfer the drug with high efficiency [217]. In another investigation, Subramanian et al. developed amphipathic chitosan-based targeted nanomicellar theranostic doxorubicin-superparamagnetic iron oxide nanoparticles (SPIONs). In this study, RGD was used as a ligand for targeting integrin and amphipathic chitosan for pH responsiveness. They used DOX and SPIONs as the model anticancer drugs and diagnostic components, respectively. The size was approximately 30–45 nm. In vitro studies were performed on Dox-resistant triple-negative breast cancer human cells (MDA-MB 231) and murine cells (4T1). The results indicated that Ab-CS-Dox-SPION micelles had maximum anti-migration activity with minimal scratch closure in both the cell lines. In vivo studies were carried out on female BALB/c mice where the intratumoral injection of nanomicelles showed higher anti-tumor effect and enhancement in MRI-T2 contrast simultaneously [218].
6.1. pH-Redox (ROS)-Responsive Theranostic Nanomaterials
Among stimuli-responsive systems, pH and ROS stimuli are the most appreciated in cancer therapy. Their combination allows promising improvements in the stability of nanoparticles in vivo, as well as activation of drug release and enhanced tumor cell uptake [203]. The concentration of ROS in cancer cells is higher than that in normal cells [204]. The acidic pH present in the endosomal/lysosomal compartments (pH 4–5) is important for the development of pH-responsive nanocarriers. However, complete drug release may also be limited by H+ depletion in the absence of adequate supplementation [205]. Their combination leads to increase in vivo stability of nanoparticles, activation of drug release, and enhanced tumor cell uptake [203]. An example of this synergistic effect was reported by Bahadur et al. in 2012. They prepared polymeric nanoparticles with a disulfide-S-S-linkage, which presented suitable stability in the extracellular matrix. The researchers prepared cRGD-γ-poly(2-(pyridin-2-yldisulfanyl)ethyl acrylate)-γ-polyethylene glycol polymer and functionalized it with cRGD through a thiol-disulfide exchange reaction. Doxorubicin was encapsulated in the pore of the nanoparticles, and HCT-116 colon cancer cells were used for the experiments. The researchers observed enhanced cellular uptake and nuclear localization due to the presence of RGD and a fast release of the drug due to the acidic pH and redox potential of the cancer cells [206]. Another interesting approach was developed by Yao and co-workers who prepared a pH-ROS nanostimuli-responsive system. The nanocarrier was formed by 4-hydroxymethylphenylboronic acid pinacol ester as an ROS-responsive moiety, as well as cyclodextrin. The system was loaded with docetaxel. To enhance cellular uptake, nanoparticles were modified with cRGD. To fully release docetaxel (DTX), nanoparticles must be disassembled. After internalization into cancer cells by cRGD, the aldehyde is released, ROS oxidize the boron atom, inducing the breakdown of the borate ester, and then the chemical bond between cyclodextrin and the phenyl group is broken, resulting in the full release of the drug. The drug delivery system was tested in 4T1 breast cancer cells and showed substantial antitumor activity after 24 h. Activity was maintained after 48 h of incubation. In vivo, DTX/RGD@NP showed lower tumor volume and weight after 20 days of treatment demonstrating significant tumor-inhibition efficiency. The ability of DTX/RGD@NPs to inhibit lung metastasis of breast cancer was also investigated. Treatment with saline and the blank control resulted in a large number of metastatic nodules. While, after treatment with DTX/RGD@NPs, no metastatic clots were observed in the lung tissue of mice [20].
6.2. Temperature-pH-Responsive Theranostic Nanomaterials
An interesting dual approach is to use high temperatures and acidic conditions, both of which are present in the extracellular environment of the cancer cells [207]. For example, Kim and co-workers in 2023 prepared a thermosensitive polymer, N-isopropyl acrylamide-co-N-vinyl-imidazole-co-acrylic acid, based on mesoporous nanomaterial (MSN) coated with RGD sequence, pNIBIm-AA-RGD/MSN, and Dox embedded inside the pores of the MSN. The system consists of a pNIBIm-AA-RGD copolymer shell and an MSN as drug cargo with a diameter of 90 nm [208]. It has been demonstrated that the pNIBIm polymer is able to retain more drug at temperatures below the low critical solution temperature (LCST), whereas the drug is released from the polymer at higher temperatures. The acidic conditions in the cancer cell play a synergic effect with the high temperature for the drug-release [209]. The results of this study showed a rapid release of Dox equal to 90% at 40 °C and pH = 6.5 in 1 day, while at 40 °C and pH = 7.4 the release was equal to 85% in 2 days [208].
6.3. Light-Redox-Responsive Theranostic Nanomaterials
The dual effects of the light and the capacity to reduce glutathione (GSH) have been studied by several researchers. Wang and co-workers developed a gene delivery system using RGD-modified disulfide cross-linked short PEIs (DSPEI) functionalized on gold nanoparticles [210]. In this study, near-infrared (NIR) light was used, with an absorbance range of 650–900 nm. This provides deep tissue penetration and high spatial accuracy without damaging healthy tissues. They exploited the optical and electronic properties of gold nanoparticles (GNPs), where the plasmons present on the surfaces of the nanoparticles decay after excitation, leading to the transfer of excited electrons to the adsorbate before thermalization. Researchers covalently bonded to a carrier on the surfaces of the nanoparticles through an Au-S bond. Finally, a therapeutic molecule was loaded via a weaker interaction to allow the drug to be released after irradiation with a continuous-wave laser. Nanoparticles absorb energy, resulting in a reduction in the interaction between the nanocarrier and therapeutic molecules. This, in turn, leads to drug release at a specific site due to the presence of the RGD motif [210]. In another study, the effect of NIR exogenous stimuli combined with the pH-endogenous was studied by Zhou et al. in 2019 [211]. They prepared a system composed of an amphiphilic polymer PEG-Nbz-PAE-Nbz-PEG called HTMP, containing PEG (as a hydrophilic segment) and pH-sensitive poly(β-aminoester)s-PAE (as a hydrophobic segment) covalently linked via an o-nitrobenzyl (Nbz) linker. Another amphiphilic polymer, iRGD-PAE-iRGD (iPHT) was also prepared. These two parts were used to prepare a hybrid micelle called iPHM. Core-shell structured NaYF4:Yb/Tm@NaYF4 and up-conversion nanoparticles (UCNPs) were introduced to convert NIR to UV-vis, and doxorubicin was loaded into the nanoparticles to form iPHM@UCNP named iPUDN. According to the obtained results, iPUDN showed a different release of doxorubicin at several pH values. At pH 7.4, the system showed a slow release of Dox of approximately 27% in 48 h. Once the pH reached 5.5, Dox release increased to 66% at the same time. Following NIR irradiation, 80 amounts of Dox was released at pH 5.5, confirming the key role of NIR and pH in enhancing the Dox release from iPUDN. To evaluate the ability of iPUDN to penetrate, the authors worked on MCF-7 cells and demonstrated a remarkable uptake due to the presence of iRGD. The effect of NIR is to induce dePEGylation of iPUDN to facilitate exposure of iRGD to cells [211].
6.4. pH-Light-Responsive Theranostic Nanomaterials
The combination of pH and light could be an interesting approach to prepare dual-stimuli-responsive theranostics. For instance, Kuang et al. [212] prepared nanoparticles formed by gadolinium (Gd) and hafnium dioxide (HfO2), to confer high relativity, radio-enhancement ability, and biosafety [213,214]. The authors prepared a system called Gd2Hf2O7@PDA@PEG-Pt-RGD, where polydopamine (PDA) is a photothermal agent and RGD is an integrin ligand, while PEG is used for the biostability and cisplatin is used as a chemotherapeutic agent. Tests were conducted on A549R cells (adenocarcinomic human alveolar basal epithelial cells). The authors did not observe any cytotoxicity of Gd2Hf2O7@PDA@PEG-Pt-RGD after 24 h of incubation at different concentrations. However, after NIR irradiation, the cell viability decreased when the concentration of the nanoparticle system increased. The effects of hyperthermia include the denaturation of cytoplasmic proteins, inhibition of DNA repair, and disturbances in signal transduction. An enhanced release of cisplatin was observed when the pH was reduced from 7.4 to 6.5 ranging from 24.4 ± 0.3% to 46.1 ± 1.6% [212]. Another interesting nanoparticle system was prepared by He et al. in 2021 [215]. They prepared melanin-coated magnetic nanoparticles (MMNs) conjugated with an RGD sequence and co-loaded with Dox and ICG through π−π stacking interactions (RMDI). It was observed that the release of Dox was 26.5% at pH 6 whereas the drug release was 17.3% after 4 h of treatment at pH 7. After NIR irradiation for 5 min, the Dox release was enhanced. Accumulation of RDMI was observed after the treatment of glioblastoma cells (U87MG) owing to the presence of the RGD sequence on the outer shell of the nanoparticles. This cytotoxic effect resulted in a 40% reduction in cellular viability. Therefore, under NIR laser irradiation, the cytotoxic effect was higher owing to the synergistic effect of the photothermal-enhanced chemotherapy. In vivo study conducted on U87MG showed an enhanced temperature in the local tumor after laser irradiation followed by a complete photothermal destruction of tumor cells [215].
6.5. pH-Magnet-Responsive Theranostic Nanomaterials
The preparation of pH-magnet dual-responsive theranostics is another approach that has attracted considerable attention in recent years. Gao et al. developed polymeric micelles with the simultaneous ability of targeted drug delivery and imaging. They used DOX as a model anticancer drug that can be released from the micelles by a pH-responsive mechanism, cRGD as an αvβ3 integrin ligand, superparamagnetic iron oxide as a material for MRI detection, and PEG-block-poly(D,L-lactide) co-polymer as a model carrier polymer. cRGD-SPIO-DOX micelles with the size around 45–47 nm were synthesized, and in vitro tests were conducted on SLK endothelial cells over expressing αvβ3 integrin. These results demonstrated that the micelles have a promising therapeutic and diagnostic abilities [216]. In another study, Wu et al. designed a theranostic nanoparticle model. For this purpose, they utilized DOX as a model chemotherapeutic agent, RGD2 as a ligand for αvβ3 integrin, exceedingly small magnetic iron oxide nanoparticles (ES-MION) as a MRI contrast agents, and poly(ethylene glycol) methyl ether (mPEG) as a model polymer. The size of the DOX@ES-MION@RGD2@mPEG nanoparticles was approximately 13 nm, which can prolong the nanoparticle circulation. For in vitro testing, U-87 MG cells and MCF-7 cells were integrin αvβ3 positive and negative, respectively. The results demonstrated that at neutral pH, RGD2 can be hidden by mPEG, but can be exposed to tumor cells mildly acidic pH, which might improve cell targeting. Additionally, DOX is released at pH of approximately 5.5, which reduces the side effects of the drug in normal cells. In vivo tests performed on nude mice confirmed that the prepared nanoparticles could transfer the drug with high efficiency [217]. In another investigation, Subramanian et al. developed amphipathic chitosan-based targeted nanomicellar theranostic doxorubicin-superparamagnetic iron oxide nanoparticles (SPIONs). In this study, RGD was used as a ligand for targeting integrin and amphipathic chitosan for pH responsiveness. They used DOX and SPIONs as the model anticancer drugs and diagnostic components, respectively. The size was approximately 30–45 nm. In vitro studies were performed on Dox-resistant triple-negative breast cancer human cells (MDA-MB 231) and murine cells (4T1). The results indicated that Ab-CS-Dox-SPION micelles had maximum anti-migration activity with minimal scratch closure in both the cell lines. In vivo studies were carried out on female BALB/c mice where the intratumoral injection of nanomicelles showed higher anti-tumor effect and enhancement in MRI-T2 contrast simultaneously [218].
7. Stimuli-Responsive Nanomaterials Targeting Integrin for Tumor Diagnosis
The development of an early and efficient diagnostic tool has attracted great interest for the fight against cancer. To date, numerous technologies have been developed, including magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computerized tomography (SPECT). However, they lack efficacy and selectivity in specific tissues. In 2018, a near-infrared (NIR) fluorescence imaging reduced glutathione (GSH)-activated probe (CyA-cRGD) was developed for a rapid and accurate cancer diagnosis, taking advantage of its high affinity for αvβ3 integrin (Figure 4) [219]. NIR is a non-invasive, inexpensive, and easy-to-perform system owing to its molecular fluorescence as an exogenous contrast agent. The probe, prepared by Yuan et al., showed a rapid and significant enhancement of the NIR fluorescence characteristics. Once the system was used in vivo, they observed a high efficiency for early-stage tumor diagnosis within 72 h of its implantation [219]. In 2020, Zhang et al. developed a contrast agent to be use in magnetic resonance imaging (MR) for hepatocellular carcinoma (HCC) [220]. The gadolinium-based (Gd) contrast agent was RWY-dL-(Gd-DOTA)4, the RWY motif was able to recognize the integrin α6. In addition, a PEG4 spacer was used to avoid steric hindrance of Gd-DOTA monoamide. Moreover, a lysine dendrimer was employed to increase the molar ratio of Gd-DOTA monoamide to the peptide. The authors used HCC-LM3 subcutaneous liver tumors and the analysis revealed that RWY-dL-(Gd-DOTA)4 can provide a signal three times higher than Ctrl-dL-(Gd-DOTA)4 [220]. Among external stimuli, the use of ultrasound has attracted great interest in recent years for the sensitive detection of intravascular targets [221]. Contrast ultrasound imaging is inexpensive, and permits real-time anatomical and molecular imaging. This technique has important applications in the imaging of tumor angiogenesis due to the presence of micron-scale microbubbles (MB) acting as contrast agents [222]. The most relevant MB contrast agent requires a hybrid low-high-mechanical-index (MI) pulse. A high MI was used to destroy the circulating MB after injection. In this context, Anderson et al. developed a cRGD-MB capable of working with low-MI pulses for a sensitive clinical imaging system. The cRGD-MB was tested on mouse model of mammary carcinoma and the researchers observed significantly enhanced contrast signals with a high tumor-to-background ratio [223].
8. Future Perspective and Challenges
The clinical translation of integrin stimuli-responsive theranostics depends on several crucial questions that must be answered. These questions can be categorized in two different levels. The first level is the factors associated with the integrin ligand. There are various integrin receptors and targeting ligands. In order to bench-to-bedside translation of integrin-decorated nanoplatforms, the integrin targeted sequence must be designed precisely to enhance the interaction of the ligand with desired receptors. The second point is the density and degree of conjugation which has considerable impact not only on the physicochemical properties of the theranostics agent but also on its pharmacokinetic characteristics such as blood circulation time, metabolism, and excretion. The conjugation degree and number of integrin ligands on the nanoplatform is a determining factor for the affinity of the delivery system to its receptor. Finding the optimum degree of conjugation is a crucial point for the future development of each nanoplatform. Since there is no universal formula to determine the optimum number of ligands on a nanoplatform, this number must be found case-by-case. Another major point is the availability of the ligand for integrin receptor following its conjugation on the platform structure. Successful conjugation of integrin ligands on a delivery system with optimum number does not necessarily lead to efficient targeted delivery. The 3D structure of the receptor and the conformation of the ligand following conjugation determine whether an efficient interaction between the receptor and its ligand occurs or not. In this case, the presence of appropriate linkers facilitates the successful interaction between the integrin receptor and its ligand. The length of the linker, its chemical composition, and the presence of different functional groups must be optimized too.
The second level of crucial question for the clinical translation of integrin-stimuli responsive theranostics is their properties following in vivo administration including pharmacokinetic characteristics and safety as well as their efficiency to accumulate in the desired cells, tissues, or organs. The conjugation of ligand on a nanoplatform not only changes the physicochemical properties of the ligand itself but also the nanoplatform too. These changes may have considerable impact on the pharmacokinetic properties of theranostics following the administration. Since the main route of administration is the injection such as intravenous administration, the interaction of nano-theranostics with blood component is another major concern for their clinical translation. It has been shown that the formation of protein corona around the nano-delivery systems changes their properties and inhibits their recognition by the receptors. The composition of soft and hard corona layers around the nano-theranostics depends on several personalized factors including age, gender, ethnicity, and other diseases in the patient. These factors change the composition of the protein corona leading to different pharmacological responses in different individuals. These inter-individual differences may hamper successful clinical translation of targeted nano-theranostics. Another major point for bench-to-bed translation of these systems is their safety following administration. Although the safety of RGD peptide has been shown in several studies, the toxicity concern related to each new sequence must be determined. In other words, the safety results of previously studied similar sequences cannot be extrapolated to the novel ones.
The last but not the least challenge for their clinical translation is that integrins are ubiquitous receptors, and can be found on normal cells too. This point raises the question on their in vivo specificity following systemic administration of integrin-targeted constructs. Therefore, an appropriate target-to-background ratio must be carefully considered.
9. Conclusions
Considering the fact that various number nano-based platforms are in clinical trials either for diagnostic, therapeutic, or theranostic purposes, the modification of such nanoplatforms is a prerequisite step for their bench-to-bedside translation. These modifications must include, but are not limited to, the improvement of carrier pharmacokinetic behaviors, such as prolonged circulation time and reduced premature release of the cargo as well as their biocompatibility and biodegradability. In this regard, transferring the cargo into a precise site of action, such as tumor cells, is one of the major obstacles hampering the wide clinical application of these platforms. The decoration of nanoplatforms with integrin ligands has attracted considerable attention. However, integrin decoration has certain limitations. Integrins are expressed in cancer and normal cells. Therefore, off-target effects are a major limitation of the integrin-decorated nanoplatforms. The integrin family consists of several receptors that have affinity for various targeting ligands. Thus, finding a specific ligand with the ability to target a particular subfamily is a necessary step towards the clinical translation of such theranostics. On the other hand, various approaches have been employed to improve pharmacokinetic characteristics of the platforms including PEGylation. Although this approach has shown great ability to increase the half-life and circulation time of the delivery system as well as the reduction in its opsonization by the immune system, there are several concerns regarding its combination with targeting strategies. PEG molecules may hide targeting ligands decorated on the surface of nanoplatforms and reduce their recognition by the specific receptors. Thus, the optimization of shielding agents such as PEG in terms of molecular weight and substitution degree is another point that must be considered for clinical translation.
The authors believe that the successful translation of integrin stimuli-responsive theranostics depends on the shoulder-to-shoulder development of two key factors: nano-theranostic platforms must be developed to ensure improved pharmacokinetic properties as well as promising toxicity, biocompatibility, and biodegradability. The second step is to concentrate on the integrin ligands. In this regard, it is suggested that integrin profiling be performed to find specific integrins overexpressed on particular target cancer cells. More precise ligand–receptor interactions reduce the off-target effects, increasing their potential for clinical applications. The combination of these strategies may create novel modalities for targeted stimuli-responsive diagnostic, therapeutic, and theranostic platforms as a prerequisite step for personalized medicine. |
Title: Recovery and Analysis of Bacterial Membrane Vesicle Nanoparticles from Human Plasma Using Dielectrophoresis | Body:
1. Introduction
Bacterial membrane vesicles (BMVs) provide valuable information about the bacteria that secrete them and can carry biomarkers for bacterial detection. BMVs, also known as membrane vesicles (MVs), or outer membrane vesicles (OMVs) when derived from Gram-negative bacteria specifically, are 20–300 nm diameter spherical lipid-bound nanoparticles that pinch off of bacteria and can contain proteins, nucleic acids, polysaccharides, and lipids from their parent bacteria [1,2]. BMVs are produced as a means of inter- and intra-species communication, pathogenesis, and immunomodulation and are naturally secreted throughout the bacterial lifecycle [1,3,4,5]. BMVs contain a similar membrane composition to the parent bacteria cell wall and contain proteins specific to their parent [6,7,8]. BMVs carry useful biomarkers to identify bacteria in wastewater or in food, to analyze microbiomes, to rapidly identify bacterial infections with high specificity in clinical diagnostic applications, and to monitor the treatment of infections. In particular, there is potential for using BMVs to diagnose septicemia, an infection of the blood leading to sepsis. Sepsis is a life-threatening disease causing or contributing to over 11 million deaths per year, concentrated in low- and middle-income countries, and often goes undiagnosed due to difficulties detecting and culturing the bacteria [9]. Rapid and accurate identification of the infecting bacteria is key to prescribing the correct antibiotics. Circulating BMVs are less affected by antibiotics than live bacteria, meaning that even if broad-spectrum antibiotic therapy is started immediately, the sepsis-causing strain can still be identified by BMVs left in circulation. This would allow physicians to cease the harmful delivery of unnecessary antibiotics once the strain is identified. Neisseria meningitidis OMVs have been shown to be stable at 4 °C for a year, although the prevalence and circulation time of BMVs in vivo during infection have not yet been elucidated [10,11].
An extensive review of in-patient sepsis found that in 70.1% of cases, a causal organism was not identified, making rapid and accurate identification a critical unmet clinical need [12]. Further, identification via BMVs eliminates the need to wait for the extended length required for blood cultures, which, despite the time investment of multiple days, may still fail [11]. In addition, BMVs could play a role in cancer detection. Not only is there evidence that host microbiotic changes can affect cancer risk, but tumors infected by bacteria can develop tumor microbiomes, the composition of which has been shown to play a crucial role in tumor growth and metastasis [13,14]. The BMVs released from the tumor into body fluids would be collectable for use as a diagnostic tool to inform treatment decisions.
Due to the valuable diagnostic biomarkers carried by BMVs, there is a broad and growing interest in developing methods to quickly and easily recover BMVs from body fluids in a manner that would be suitable for clinical translation. However, collecting BMVs from complex fluids using traditional methods is a challenge due to their small size and low buoyant density, making it not only time-consuming but also a low-yield endeavor that becomes worse with more complex clinical samples. Currently, ultracentrifugation (UC) is the gold standard for the isolation of BMVs. During BMV purification by UC, the sample is spun at high speeds to pellet BMVs; however, this takes multiple hours, and other cellular fragments and debris will end up in the pellet [15]. Density gradient purification can yield a higher purity, wherein the sample is ultracentrifuged in a tube with layered liquids of increasing density, allowing for better separation of BMVs from cellular debris based on density. Density gradient purification, however, is even more time consuming than standard UC; it dilutes the sample and is not amenable to scaling, so it cannot be translated effectively to the clinical laboratory setting.
To address the challenges of recovering BMVs from plasma, we have developed a new method that utilizes dielectrophoresis (DEP)-based forces which take advantage of differences in the dielectric properties between the particles and the surrounding fluid and have been shown to rapidly recover particles similar to BMVs from complex mixtures [16,17,18]. DEP force generation can be performed using microelectrode arrays integrated into microfluidic devices, which require as little as 20 µL of sample volume (Figure 1A,B). The devices used in this experiment have an array of microelectrodes which are designed to generate electric field gradients that result in electric field factors strong enough to capture sub-micron particles from high-conductance samples by applying an alternating electric field (Figure 1C). The electric field generated on these devices has been extensively modeled and proven capable of generating electric field gradients strong enough to isolate sub-micron particles [19,20]. The full experimental setup for applying the electric field to the devices and subsequent imaging can be seen in the Supplementary Materials (Supplementary Figure S1).
By adjusting the frequency of the applied alternating electric field, a subset of nanoparticles can be selectively isolated from the fluid by drawing them to the region around the electrode edge where the electric field factor is the highest (Figure 1C). Previous experiments have demonstrated that a frequency of 14 kHz is optimal for isolating nanoparticles, including extracellular vesicles and liposomes [21,22,23,24]. Simultaneously, larger particles and fragments of cellular debris are pushed away from regions with a high electric field factor. The DEP force (FDEP) on a given particle can be predicted using the classical DEP equation:(1)FDEP=2πϵmR3RCM*∇E2
where ϵm is the electric permittivity of the media, R is the radius of the particle, RCM* is the real part of the frequency dependent Clausius–Mossotti factor, and ∇E2 is the electric field factor [25]. DEP has successfully been used to collect and analyze many different types of biological particles, including exosomes, live bacteria, cell-free DNA, viruses, and circulating tumor cells [22,26,27,28]. DEP can recover particles from high-conducting buffered solutions and from complex high-conducting fluids such as whole blood, plasma, and undiluted model saliva [22,26,27]. DEP is advantageous because it does not require labeling of the desired particles prior to isolation, which streamlines the isolation protocol and allows for observations of particle properties in their native form.
Another benefit of DEP recovery is that it concentrates the collected particles around the electrode edge. This increases the signal-to-noise ratio, resulting in a greater level of sensitivity compared to UC where the recovered particles need to be resuspended in a wash buffer for recovery and analysis, resulting in particle dilution. Another advantage of DEP is that the particle collection zone is spatially well defined, which is critical for on-chip biomarker quantification through fluorescence staining, something that is not possible with UC. Finally, the DEP method is highly parallelizable and could result in higher levels of throughput than UC.
The BMVs recovered by DEP are co-collected with other nanoparticles of a similar size and material composition, such as eukaryotic extracellular vesicles. This is also the case with UC, where particles with a similar size and buoyant density are co-collected. We demonstrate here that the DEP-recovered BMVs are of sufficient purity to allow for successful immunostaining and detection of bacteria-specific biomarkers. Despite rapidly rising interest in using BMVs for diagnostic applications, the isolation and analysis of BMVs via DEP has not been previously demonstrated.
Herein, we demonstrate the isolation of BMVs from human plasma via DEP performed on an electrokinetic chip. We show that collected BMVs can be analyzed further for bacterial classification via immunostaining and quantified on-chip, which streamlines the analysis process and eliminates any biomarker loss that could occur through fluid transfers. The information provided by recovering BMVs via DEP and analyzing them on-chip demonstrates the potential for using DEP in point-of-care and in-the-field applications by circumventing the large equipment and time-consuming and labor-intensive methods of BMV recovery currently available.
2. Methods
2.1. Materials
BL21 Escherichia coli were purchased from New England Biolabs (Ipswich, MA, USA C2527H). Difco LB Broth, Miller (Luria–Bertani) (Fisher Scientific, Hampton, NH, USA) (244610, lot #1285538), Gibco Dulbecco’s phosphate-buffered saline (14190-144, lot #2717594), glutaraldehyde 50% aqueous solution (A10500-0E, lot #10242942), bovine serum albumin (B14, 2975507), Vybrant DiO cell-labeling solution (V22886, lot #2491432), and Invitrogen donkey anti-goat IgG (H + L) cross-absorbed secondary antibody, AlexaFluor647 (A21235, lot #2482945), were all purchased from Thermo Fisher Scientific (Waltham, MA, USA). The Millipore Stericup quick-release vacuum filtration systems of 0.45 µm pore size and 500 mL capacity units were purchased from Millipore Sigma (Dramstadt, Germany; S2HVU05RE, lot #MP224708G2). Amicon stirred cells, 200 mL, were purchased from Millipore Sigma (Darmstadt, Germany; UFSC20001). Ultracel 30 kDa ultracentrifugation disks were purchased from Millipore Sigma (Darmstadt, Germany; PLTK07610, lot #C3BB90755). Goat anti-lipid A LPS antibody was purchased from Bio Rad (Hercules, CA, USA, OBT1844, lot #163258). Polyclonal rabbit anti-CD9 antibodies and goat anti-rabbit HRP secondary antibodies for Western blot analysis were purchased from System Biosciences Innovation (Palo Alto, CA, USA; EXOS-CD9A-1, lot#190911-002, dilution 1:1000; EXOSAB-HRP, lot#190408-015, dilution 1:20,000). Donkey anti-goat HRP antibodies were purchased from Promega Corporation (Madison, WI, USA; V805A, lot#547026, dilution 1:5000). SuperSignal Pico PLUS Chemiluminescent Substrate and Pierce BCA Protein Assay Kits were purchased from Thermo Scientific (Waltham, MA, USA; 34580, 23225). BD Difco skim milk was purchased from Midland Scientific (Omaha, NE, USA; 232100). Bolt Sample Reducing Agent was purchased from Invitrogen (Waltham, WA, USA; B0009). Formvar/carbon 300 mesh support grids were purchased from Ted Pella Inc. (Redding, CA, USA; 01753). Uranyl acetate was purchased from Electron Microscopy Sciences (Hatfield, PA, USA; 22400, lot #170809-03).
De-identified pooled human plasma (blood derived) was purchased from Innovative Research (Novi, MI, USA; lot #HMN923275) which obtained the plasma from consented donors as part of an IRB. All experimental protocols were carried out in accordance with relevant guidelines and regulations outlined by the OHSU Institutional Review Board.
2.2. Preparation and Characterization of Bacterial Membrane Vesicles
2.2.1. Isolation of Bacterial Membrane Vesicles from Culture
An overnight culture of BL21(DE3) Escherichia coli grown in Luria–Bertani broth (LB) was diluted 1:1000 into 1 L of fresh LB and grown for 24 h at 37 °C, 250 RPM (Figure 2A). The culture was then centrifuged for 45 min at 3000× g at 4 °C, and the supernatant was poured off and filtered through a 0.45 µm filter (Figure 2A). The filtered supernatant was then condensed in an Amicon filtration unit with a 10kDa filter. The condensed supernatant was ultracentrifuged at 150,000× g for 3 h at 4 °C. The supernatant was poured off, and the BMV pellet was resuspended in sterile 1x PBS and then filtered through a 0.45 µm filter. The purified BMVs were stored at 4 °C for a maximum of one week, or frozen at −80 °C if they were to be used more than a week from collection.
2.2.2. Nanoparticle Tracking Analysis of Bacterial Membrane Vesicles
Size distribution and concentration measurements of the BMVs were obtained using a ZetaView PMX-420 nanoparticle tracking analysis system from Particle Metrix (Inning am Ammersee) (Starnberg, Germany). For each measurement, 11 fluid cell positions were scanned at 30 frames/s with a shutter setting of 100 using a 488 nm laser at 40 mW. The cell temperature was regulated at 25 °C. Analysis was performed using the ZetaView software version 8.05.12 SP2. Samples were diluted 1:1000 in 0.5X PBS before each measurement.
2.2.3. Transmission Electron Microscopy
Purified BMVs were blotted onto glow-discharged 300-mesh formvar/carbon-coated copper grids. The grid-adhered BMVs were then incubated with goat anti-lipopolysaccharide (LPS) antibody (Bio Rad, lot #163258) (Hercules, CA, USA) diluted 1:10 in 3% BSA in PBS and subsequently incubated with anti-goat antibody conjugated to 10 nm gold beads diluted 1:8 in 3% BSA in PBS. Samples were then fixed with 2% glutaraldehyde and negative-stained with 0.4% uranyl acetate. Imaging of the grids was performed at 120 keV on a Tecnai Spirit TEM system (FEI Company, Hillsboro, OR, USA) using the AMT software interface, AMT Capture Engine v7.00, on a NanoSprint12S-B CMOS camera system (AMT).
2.2.4. Western Blot Analysis
The protein concentration of samples containing isolated Henrietta Lacks (HeLa) cell line-derived EVs and BL21 Escherichia coli BMVs were determined using the BCA assay and then normalized to equal concentrations of 4.33 μg/mL. Normalized samples were lysed by the addition of 30% NP-40 with protease inhibitors, set on ice for 30 min, and then passed through a 30G needle up to 6 times. Samples were then reduced by DTT and then incubated in 95 °C for 5 min. Samples were then loaded into a 4–12% Tris-Bis gel and run for 22 min at 200V in 1xMEPS buffer. Mini Blot Module was then used to transfer protein to a 0.45 μm PVDF membrane by applying 20 V for 1 h. Protein-transferred membranes were blocked in 5% fat-free milk in 1xTBST for 1 h and then incubated with primary anti-lipid A LPS and anti-CD9 antibodies in 5% fat-free milk in 1xTBST with agitation in 4 °C overnight. The membranes were washed three times with 1xTBST, incubated with secondary HRP-conjugated antibodies in 5% fat-free milk in 1xTBST for 1 h at room temperature, and then washed three times with 1xTBST. Membranes were developed using chemiluminescent substrate and imaged using the iBright imaging system.
2.3. Dielectrophoresis Isolation and Analysis of Bacterial Membrane Vesicles
2.3.1. Dielectrophoresis Isolation of Bacterial Membrane Vesicles
An aliquot of BMVs prepared in Section 2.2.1 was thawed and diluted 1:10 in pooled healthy human plasma. The BMV-spiked plasma solution was introduced to three- or eight-channel ExoVerita microfluidic chips purchased from Biological Dynamics (San Diego, CA, USA). Each channel of the microfluidic chips holds approximately 20 µL of sample and has 780 electrodes each 60 µm in diameter. A 25 µL sample of plasma was loaded into the inlet ports of the chip and a KD Scientific syringe pump was used to flow in the sample at a rate of 7 µL/min to a total volume of 20 µL. Once all channels were filled with the sample, the flow was stopped, and an alternating electric signal was applied. The electric signal was generated using an Agilent waveform generator with impedance set to High Z and a Newtons4th Ltd., Leicester, UK amplifier set to AC coupling, low bandwidth, and 4X amplification to apply a signal of 10 Vpp at 14 kHz. This signal was applied to the chip for a duration of 10 min. After collection, the applied alternating current was turned off and the microfluidic channel was rinsed with 50 µL of 0.5X PBS at a flow rate of 7 µL/min to remove uncollected particles.
2.3.2. Immunofluorescence Staining of Dielectrophoresis-Isolated Bacterial Membrane Vesicles
After DEP recovery, the applied voltage signal was turned off for the remainder of the experiment and a 100 µL blocking solution of 2% milk (in 0.5X PBS) was loaded into the inlet ports and a syringe pump was used to flow in the solution at a rate of 7 µL/min to a total volume of 50 µL. The solution was incubated on-chip for 20 min and then a wash step of 50 µL of 0.5X PBS buffer at a flow rate of 7 µL/min rinsed away the remaining milk. The collected BMVs on-chip were stained for 1 h with goat anti-lipid A LPS antibody suspended in 2% milk (1:200). After incubation, a wash step of 50 µL of 2% milk at a flow rate of 7 µL/min rinsed away all the unbound antibodies. The chip was then incubated for 1 h with Alexa Fluor 647 donkey anti-goat antibody suspended in 2% milk (1:500). After incubation, a wash step of 50 µL 0.5X PBS at a flow rate of 7 µL/min rinsed away the unbound antibody and 2% milk. After rinsing, fluorescence imaging was performed using a Zeiss Axio Imager Vario A2 microscope (Zeiss, Oberkochen, Germany).
2.3.3. Scanning Electron Microscopy
A microfluidic chip was used to collect BMVs, as described in Section 2.3.1. The chip was then washed with DI water and stored in a −80 °C freezer overnight to freeze the fluid. The cold temperature also allowed for the removal of the glass covering and adhesive tape, leaving the frozen water over the electrode array exposed. The chip was then lyophilized to remove the frozen water to expose the collected BMVs. The array was then adhered to an 18 mm aluminum pin-style SEM stub with carbon tape. A Leica EM ACE600 High Vacuum Sputter Coater (Leica Microsystems, Wetzlar, Germany) equipped with a planetary stage was used to conductively coat the surface with 8 nm carbon. SEM imaging was performed on a Thermo Scientific Helios UC5 NanoLab DualBeam SEM (Thermo Fisher Scientific, Waltham, MA, USA) using a 1 keV and 13 pA beam, 4 mm working distance, and the TLD detector (TLD, New York, NY, USA) in secondary electron mode. Images were collected at both 0° and 45° stage tilt.
2.3.4. Statistical Analysis
To assess system variability, three replicates were performed using the protocol described in Section 2.3.1 and Section 2.3.2 staining with either an anti-LPS antibody for BMVs isolated from plasma or a lipid intercalating dye, DiO for BMVs, isolated from 0.5X PBS. The stained collected material was quantified using a custom MATLAB program [21]. The coefficient of variation was calculated by dividing the standard deviation by the mean for each staining method. A negative control was also performed by applying the DEP parameters to a plasma sample without added BMVs. GraphPad Prism v9.4.0 was used to generate the bar graphs and conduct a Student’s two-tailed unpaired t-test where **** represents a p value less than 0.0001. Lower limit of detection (LLD) analysis was performed using GraphPad Prism v9.4.0. The LLD value was determined by measuring the mean background fluorescence signal and adding 3 times the standard deviation (x¯ ± 3 × SD). Then, the line of best fit for the BMV fluorescence dilution curve was calculated and the intersection with the LLD was determined.
2.1. Materials
BL21 Escherichia coli were purchased from New England Biolabs (Ipswich, MA, USA C2527H). Difco LB Broth, Miller (Luria–Bertani) (Fisher Scientific, Hampton, NH, USA) (244610, lot #1285538), Gibco Dulbecco’s phosphate-buffered saline (14190-144, lot #2717594), glutaraldehyde 50% aqueous solution (A10500-0E, lot #10242942), bovine serum albumin (B14, 2975507), Vybrant DiO cell-labeling solution (V22886, lot #2491432), and Invitrogen donkey anti-goat IgG (H + L) cross-absorbed secondary antibody, AlexaFluor647 (A21235, lot #2482945), were all purchased from Thermo Fisher Scientific (Waltham, MA, USA). The Millipore Stericup quick-release vacuum filtration systems of 0.45 µm pore size and 500 mL capacity units were purchased from Millipore Sigma (Dramstadt, Germany; S2HVU05RE, lot #MP224708G2). Amicon stirred cells, 200 mL, were purchased from Millipore Sigma (Darmstadt, Germany; UFSC20001). Ultracel 30 kDa ultracentrifugation disks were purchased from Millipore Sigma (Darmstadt, Germany; PLTK07610, lot #C3BB90755). Goat anti-lipid A LPS antibody was purchased from Bio Rad (Hercules, CA, USA, OBT1844, lot #163258). Polyclonal rabbit anti-CD9 antibodies and goat anti-rabbit HRP secondary antibodies for Western blot analysis were purchased from System Biosciences Innovation (Palo Alto, CA, USA; EXOS-CD9A-1, lot#190911-002, dilution 1:1000; EXOSAB-HRP, lot#190408-015, dilution 1:20,000). Donkey anti-goat HRP antibodies were purchased from Promega Corporation (Madison, WI, USA; V805A, lot#547026, dilution 1:5000). SuperSignal Pico PLUS Chemiluminescent Substrate and Pierce BCA Protein Assay Kits were purchased from Thermo Scientific (Waltham, MA, USA; 34580, 23225). BD Difco skim milk was purchased from Midland Scientific (Omaha, NE, USA; 232100). Bolt Sample Reducing Agent was purchased from Invitrogen (Waltham, WA, USA; B0009). Formvar/carbon 300 mesh support grids were purchased from Ted Pella Inc. (Redding, CA, USA; 01753). Uranyl acetate was purchased from Electron Microscopy Sciences (Hatfield, PA, USA; 22400, lot #170809-03).
De-identified pooled human plasma (blood derived) was purchased from Innovative Research (Novi, MI, USA; lot #HMN923275) which obtained the plasma from consented donors as part of an IRB. All experimental protocols were carried out in accordance with relevant guidelines and regulations outlined by the OHSU Institutional Review Board.
2.2. Preparation and Characterization of Bacterial Membrane Vesicles
2.2.1. Isolation of Bacterial Membrane Vesicles from Culture
An overnight culture of BL21(DE3) Escherichia coli grown in Luria–Bertani broth (LB) was diluted 1:1000 into 1 L of fresh LB and grown for 24 h at 37 °C, 250 RPM (Figure 2A). The culture was then centrifuged for 45 min at 3000× g at 4 °C, and the supernatant was poured off and filtered through a 0.45 µm filter (Figure 2A). The filtered supernatant was then condensed in an Amicon filtration unit with a 10kDa filter. The condensed supernatant was ultracentrifuged at 150,000× g for 3 h at 4 °C. The supernatant was poured off, and the BMV pellet was resuspended in sterile 1x PBS and then filtered through a 0.45 µm filter. The purified BMVs were stored at 4 °C for a maximum of one week, or frozen at −80 °C if they were to be used more than a week from collection.
2.2.2. Nanoparticle Tracking Analysis of Bacterial Membrane Vesicles
Size distribution and concentration measurements of the BMVs were obtained using a ZetaView PMX-420 nanoparticle tracking analysis system from Particle Metrix (Inning am Ammersee) (Starnberg, Germany). For each measurement, 11 fluid cell positions were scanned at 30 frames/s with a shutter setting of 100 using a 488 nm laser at 40 mW. The cell temperature was regulated at 25 °C. Analysis was performed using the ZetaView software version 8.05.12 SP2. Samples were diluted 1:1000 in 0.5X PBS before each measurement.
2.2.3. Transmission Electron Microscopy
Purified BMVs were blotted onto glow-discharged 300-mesh formvar/carbon-coated copper grids. The grid-adhered BMVs were then incubated with goat anti-lipopolysaccharide (LPS) antibody (Bio Rad, lot #163258) (Hercules, CA, USA) diluted 1:10 in 3% BSA in PBS and subsequently incubated with anti-goat antibody conjugated to 10 nm gold beads diluted 1:8 in 3% BSA in PBS. Samples were then fixed with 2% glutaraldehyde and negative-stained with 0.4% uranyl acetate. Imaging of the grids was performed at 120 keV on a Tecnai Spirit TEM system (FEI Company, Hillsboro, OR, USA) using the AMT software interface, AMT Capture Engine v7.00, on a NanoSprint12S-B CMOS camera system (AMT).
2.2.4. Western Blot Analysis
The protein concentration of samples containing isolated Henrietta Lacks (HeLa) cell line-derived EVs and BL21 Escherichia coli BMVs were determined using the BCA assay and then normalized to equal concentrations of 4.33 μg/mL. Normalized samples were lysed by the addition of 30% NP-40 with protease inhibitors, set on ice for 30 min, and then passed through a 30G needle up to 6 times. Samples were then reduced by DTT and then incubated in 95 °C for 5 min. Samples were then loaded into a 4–12% Tris-Bis gel and run for 22 min at 200V in 1xMEPS buffer. Mini Blot Module was then used to transfer protein to a 0.45 μm PVDF membrane by applying 20 V for 1 h. Protein-transferred membranes were blocked in 5% fat-free milk in 1xTBST for 1 h and then incubated with primary anti-lipid A LPS and anti-CD9 antibodies in 5% fat-free milk in 1xTBST with agitation in 4 °C overnight. The membranes were washed three times with 1xTBST, incubated with secondary HRP-conjugated antibodies in 5% fat-free milk in 1xTBST for 1 h at room temperature, and then washed three times with 1xTBST. Membranes were developed using chemiluminescent substrate and imaged using the iBright imaging system.
2.2.1. Isolation of Bacterial Membrane Vesicles from Culture
An overnight culture of BL21(DE3) Escherichia coli grown in Luria–Bertani broth (LB) was diluted 1:1000 into 1 L of fresh LB and grown for 24 h at 37 °C, 250 RPM (Figure 2A). The culture was then centrifuged for 45 min at 3000× g at 4 °C, and the supernatant was poured off and filtered through a 0.45 µm filter (Figure 2A). The filtered supernatant was then condensed in an Amicon filtration unit with a 10kDa filter. The condensed supernatant was ultracentrifuged at 150,000× g for 3 h at 4 °C. The supernatant was poured off, and the BMV pellet was resuspended in sterile 1x PBS and then filtered through a 0.45 µm filter. The purified BMVs were stored at 4 °C for a maximum of one week, or frozen at −80 °C if they were to be used more than a week from collection.
2.2.2. Nanoparticle Tracking Analysis of Bacterial Membrane Vesicles
Size distribution and concentration measurements of the BMVs were obtained using a ZetaView PMX-420 nanoparticle tracking analysis system from Particle Metrix (Inning am Ammersee) (Starnberg, Germany). For each measurement, 11 fluid cell positions were scanned at 30 frames/s with a shutter setting of 100 using a 488 nm laser at 40 mW. The cell temperature was regulated at 25 °C. Analysis was performed using the ZetaView software version 8.05.12 SP2. Samples were diluted 1:1000 in 0.5X PBS before each measurement.
2.2.3. Transmission Electron Microscopy
Purified BMVs were blotted onto glow-discharged 300-mesh formvar/carbon-coated copper grids. The grid-adhered BMVs were then incubated with goat anti-lipopolysaccharide (LPS) antibody (Bio Rad, lot #163258) (Hercules, CA, USA) diluted 1:10 in 3% BSA in PBS and subsequently incubated with anti-goat antibody conjugated to 10 nm gold beads diluted 1:8 in 3% BSA in PBS. Samples were then fixed with 2% glutaraldehyde and negative-stained with 0.4% uranyl acetate. Imaging of the grids was performed at 120 keV on a Tecnai Spirit TEM system (FEI Company, Hillsboro, OR, USA) using the AMT software interface, AMT Capture Engine v7.00, on a NanoSprint12S-B CMOS camera system (AMT).
2.2.4. Western Blot Analysis
The protein concentration of samples containing isolated Henrietta Lacks (HeLa) cell line-derived EVs and BL21 Escherichia coli BMVs were determined using the BCA assay and then normalized to equal concentrations of 4.33 μg/mL. Normalized samples were lysed by the addition of 30% NP-40 with protease inhibitors, set on ice for 30 min, and then passed through a 30G needle up to 6 times. Samples were then reduced by DTT and then incubated in 95 °C for 5 min. Samples were then loaded into a 4–12% Tris-Bis gel and run for 22 min at 200V in 1xMEPS buffer. Mini Blot Module was then used to transfer protein to a 0.45 μm PVDF membrane by applying 20 V for 1 h. Protein-transferred membranes were blocked in 5% fat-free milk in 1xTBST for 1 h and then incubated with primary anti-lipid A LPS and anti-CD9 antibodies in 5% fat-free milk in 1xTBST with agitation in 4 °C overnight. The membranes were washed three times with 1xTBST, incubated with secondary HRP-conjugated antibodies in 5% fat-free milk in 1xTBST for 1 h at room temperature, and then washed three times with 1xTBST. Membranes were developed using chemiluminescent substrate and imaged using the iBright imaging system.
2.3. Dielectrophoresis Isolation and Analysis of Bacterial Membrane Vesicles
2.3.1. Dielectrophoresis Isolation of Bacterial Membrane Vesicles
An aliquot of BMVs prepared in Section 2.2.1 was thawed and diluted 1:10 in pooled healthy human plasma. The BMV-spiked plasma solution was introduced to three- or eight-channel ExoVerita microfluidic chips purchased from Biological Dynamics (San Diego, CA, USA). Each channel of the microfluidic chips holds approximately 20 µL of sample and has 780 electrodes each 60 µm in diameter. A 25 µL sample of plasma was loaded into the inlet ports of the chip and a KD Scientific syringe pump was used to flow in the sample at a rate of 7 µL/min to a total volume of 20 µL. Once all channels were filled with the sample, the flow was stopped, and an alternating electric signal was applied. The electric signal was generated using an Agilent waveform generator with impedance set to High Z and a Newtons4th Ltd., Leicester, UK amplifier set to AC coupling, low bandwidth, and 4X amplification to apply a signal of 10 Vpp at 14 kHz. This signal was applied to the chip for a duration of 10 min. After collection, the applied alternating current was turned off and the microfluidic channel was rinsed with 50 µL of 0.5X PBS at a flow rate of 7 µL/min to remove uncollected particles.
2.3.2. Immunofluorescence Staining of Dielectrophoresis-Isolated Bacterial Membrane Vesicles
After DEP recovery, the applied voltage signal was turned off for the remainder of the experiment and a 100 µL blocking solution of 2% milk (in 0.5X PBS) was loaded into the inlet ports and a syringe pump was used to flow in the solution at a rate of 7 µL/min to a total volume of 50 µL. The solution was incubated on-chip for 20 min and then a wash step of 50 µL of 0.5X PBS buffer at a flow rate of 7 µL/min rinsed away the remaining milk. The collected BMVs on-chip were stained for 1 h with goat anti-lipid A LPS antibody suspended in 2% milk (1:200). After incubation, a wash step of 50 µL of 2% milk at a flow rate of 7 µL/min rinsed away all the unbound antibodies. The chip was then incubated for 1 h with Alexa Fluor 647 donkey anti-goat antibody suspended in 2% milk (1:500). After incubation, a wash step of 50 µL 0.5X PBS at a flow rate of 7 µL/min rinsed away the unbound antibody and 2% milk. After rinsing, fluorescence imaging was performed using a Zeiss Axio Imager Vario A2 microscope (Zeiss, Oberkochen, Germany).
2.3.3. Scanning Electron Microscopy
A microfluidic chip was used to collect BMVs, as described in Section 2.3.1. The chip was then washed with DI water and stored in a −80 °C freezer overnight to freeze the fluid. The cold temperature also allowed for the removal of the glass covering and adhesive tape, leaving the frozen water over the electrode array exposed. The chip was then lyophilized to remove the frozen water to expose the collected BMVs. The array was then adhered to an 18 mm aluminum pin-style SEM stub with carbon tape. A Leica EM ACE600 High Vacuum Sputter Coater (Leica Microsystems, Wetzlar, Germany) equipped with a planetary stage was used to conductively coat the surface with 8 nm carbon. SEM imaging was performed on a Thermo Scientific Helios UC5 NanoLab DualBeam SEM (Thermo Fisher Scientific, Waltham, MA, USA) using a 1 keV and 13 pA beam, 4 mm working distance, and the TLD detector (TLD, New York, NY, USA) in secondary electron mode. Images were collected at both 0° and 45° stage tilt.
2.3.4. Statistical Analysis
To assess system variability, three replicates were performed using the protocol described in Section 2.3.1 and Section 2.3.2 staining with either an anti-LPS antibody for BMVs isolated from plasma or a lipid intercalating dye, DiO for BMVs, isolated from 0.5X PBS. The stained collected material was quantified using a custom MATLAB program [21]. The coefficient of variation was calculated by dividing the standard deviation by the mean for each staining method. A negative control was also performed by applying the DEP parameters to a plasma sample without added BMVs. GraphPad Prism v9.4.0 was used to generate the bar graphs and conduct a Student’s two-tailed unpaired t-test where **** represents a p value less than 0.0001. Lower limit of detection (LLD) analysis was performed using GraphPad Prism v9.4.0. The LLD value was determined by measuring the mean background fluorescence signal and adding 3 times the standard deviation (x¯ ± 3 × SD). Then, the line of best fit for the BMV fluorescence dilution curve was calculated and the intersection with the LLD was determined.
2.3.1. Dielectrophoresis Isolation of Bacterial Membrane Vesicles
An aliquot of BMVs prepared in Section 2.2.1 was thawed and diluted 1:10 in pooled healthy human plasma. The BMV-spiked plasma solution was introduced to three- or eight-channel ExoVerita microfluidic chips purchased from Biological Dynamics (San Diego, CA, USA). Each channel of the microfluidic chips holds approximately 20 µL of sample and has 780 electrodes each 60 µm in diameter. A 25 µL sample of plasma was loaded into the inlet ports of the chip and a KD Scientific syringe pump was used to flow in the sample at a rate of 7 µL/min to a total volume of 20 µL. Once all channels were filled with the sample, the flow was stopped, and an alternating electric signal was applied. The electric signal was generated using an Agilent waveform generator with impedance set to High Z and a Newtons4th Ltd., Leicester, UK amplifier set to AC coupling, low bandwidth, and 4X amplification to apply a signal of 10 Vpp at 14 kHz. This signal was applied to the chip for a duration of 10 min. After collection, the applied alternating current was turned off and the microfluidic channel was rinsed with 50 µL of 0.5X PBS at a flow rate of 7 µL/min to remove uncollected particles.
2.3.2. Immunofluorescence Staining of Dielectrophoresis-Isolated Bacterial Membrane Vesicles
After DEP recovery, the applied voltage signal was turned off for the remainder of the experiment and a 100 µL blocking solution of 2% milk (in 0.5X PBS) was loaded into the inlet ports and a syringe pump was used to flow in the solution at a rate of 7 µL/min to a total volume of 50 µL. The solution was incubated on-chip for 20 min and then a wash step of 50 µL of 0.5X PBS buffer at a flow rate of 7 µL/min rinsed away the remaining milk. The collected BMVs on-chip were stained for 1 h with goat anti-lipid A LPS antibody suspended in 2% milk (1:200). After incubation, a wash step of 50 µL of 2% milk at a flow rate of 7 µL/min rinsed away all the unbound antibodies. The chip was then incubated for 1 h with Alexa Fluor 647 donkey anti-goat antibody suspended in 2% milk (1:500). After incubation, a wash step of 50 µL 0.5X PBS at a flow rate of 7 µL/min rinsed away the unbound antibody and 2% milk. After rinsing, fluorescence imaging was performed using a Zeiss Axio Imager Vario A2 microscope (Zeiss, Oberkochen, Germany).
2.3.3. Scanning Electron Microscopy
A microfluidic chip was used to collect BMVs, as described in Section 2.3.1. The chip was then washed with DI water and stored in a −80 °C freezer overnight to freeze the fluid. The cold temperature also allowed for the removal of the glass covering and adhesive tape, leaving the frozen water over the electrode array exposed. The chip was then lyophilized to remove the frozen water to expose the collected BMVs. The array was then adhered to an 18 mm aluminum pin-style SEM stub with carbon tape. A Leica EM ACE600 High Vacuum Sputter Coater (Leica Microsystems, Wetzlar, Germany) equipped with a planetary stage was used to conductively coat the surface with 8 nm carbon. SEM imaging was performed on a Thermo Scientific Helios UC5 NanoLab DualBeam SEM (Thermo Fisher Scientific, Waltham, MA, USA) using a 1 keV and 13 pA beam, 4 mm working distance, and the TLD detector (TLD, New York, NY, USA) in secondary electron mode. Images were collected at both 0° and 45° stage tilt.
2.3.4. Statistical Analysis
To assess system variability, three replicates were performed using the protocol described in Section 2.3.1 and Section 2.3.2 staining with either an anti-LPS antibody for BMVs isolated from plasma or a lipid intercalating dye, DiO for BMVs, isolated from 0.5X PBS. The stained collected material was quantified using a custom MATLAB program [21]. The coefficient of variation was calculated by dividing the standard deviation by the mean for each staining method. A negative control was also performed by applying the DEP parameters to a plasma sample without added BMVs. GraphPad Prism v9.4.0 was used to generate the bar graphs and conduct a Student’s two-tailed unpaired t-test where **** represents a p value less than 0.0001. Lower limit of detection (LLD) analysis was performed using GraphPad Prism v9.4.0. The LLD value was determined by measuring the mean background fluorescence signal and adding 3 times the standard deviation (x¯ ± 3 × SD). Then, the line of best fit for the BMV fluorescence dilution curve was calculated and the intersection with the LLD was determined.
3. Results and Discussion
To demonstrate the recovery of bacterial membrane vesicles (BMVs) using dielectrophoresis (DEP), BMVs were first isolated from BL21 Escherichia coli cultures using standard procedures (Figure 2A) and characterized via nanoparticle tracking analysis and transmission electron microscopy (Figure 2B–E). BMVs were isolated from a liquid bacterial culture via ultracentrifugation and stored at 4 °C if used within one week, or frozen at −80 °C if they were to be used more than a week from collection (Figure 2A). The size and concentration of purified BMVs were assessed using nanoparticle tracking analysis by observing both unlabeled particles (Figure 2B) and BMVs labeled with a lipid intercalating dye (DiO) (Figure 2C). The mean particle diameter for unlabeled particles was 127 ± 54 nm with a concentration of 4.7 × 1010 particles/mL. The stained BMVs showed a mean particle size of 102 ± 31 nm with a concentration of 1.4 × 1010 particles/mL. The presence of purified BMVs was further confirmed via transmission electron microscopy, which shows circular particles around 100 nm in diameter in samples of BMVs unlabeled (Figure 2D) and labeled (Figure 2E) with DiO.
BMVs purified from E. coli culture and labeled with DiO were then isolated using DEP. An alternating electric current frequency of 14 kHz was applied to the electrode array to successfully collect BMVs. This frequency was selected due to previous research showing its use to collect extracellular vesicles, which are eukaryotic analogs of BMVs with similar sizes and dielectric properties [21,22,23,24].
BMVs stained with DiO were diluted 1:1, 1:2, and 1:4 in 0.5X PBS to concentrations of 7 × 109 BMVs/mL, 4.7 × 109 BMVs/mL, and 2.8 × 109 BMVs/mL, respectively, and isolated using DEP on the electrokinetic microfluidic device (Figure 3A–C). The collection of fluorescently labeled particles was observed around the edge of each electrode. Representative images of a nine-electrode subsection from each array are shown. The fluorescence intensity around the electrode edges decreased with the decreasing concentration of BMVs. To determine the background level of aggregated DiO collection, a control solution of 500 nM DiO in 0.5X PBS was exposed to the same alternating electric field as the labeled BMVs, but no collection around the electrode edges was observed (Figure 3D). To further validate the isolation of BMVs using DEP, scanning electron microscopy of an electrode edge was performed to visualize the particles (Figure 3E,F). BMVs are visible in profile collected along the vertical edge around the electrode and from top down on the flat electrode surface. A few of the BMVs are denoted by black arrows. DEP was also applied to a control chip containing only 0.5x PBS, and the chip was then imaged using scanning electron microscopy to confirm that no nanoparticles were collected from the buffer solution (Supplementary Figure S2).
To our knowledge, this is the first demonstration of BMV isolation using DEP. To demonstrate the versatility of this technique to collect BMVs from high-conducting complex body fluids, BMVs were isolated from human plasma (Figure 4). A stock of purified unlabeled BMVs were spiked into pooled healthy human plasma samples. To specifically label BMVs after DEP collection from plasma, an antibody against lipid A lipopolysaccharide (LPS), which is a Gram-negative bacteria-specific biomarker found in BMVs and not expected to be found in healthy human plasma, was used. Validation of the antibody used for immunolabeling was performed using transmission electron microscopy and Western blot analysis. For transmission electron microscopy (Figure 4A), purified BMVs were stained with the primary antibody against LPS and then stained with a secondary antibody labeled with 10 nm gold particles (dark circles), confirming that LPS was present on the surface of the BMVs as expected. A control sample of BMVs stained with just the gold-conjugated secondary antibody confirmed an absence of non-specific staining (Supplementary Figure S3). A Western blot analysis was used to further confirm the presence of LPS on the BMVs and absence of LPS on human-derived nanoparticles (Figure 4B). The Western blot revealed that BMVs stained positive for LPS, as expected when derived from Gram-negative parent bacteria, while human-derived extracellular vesicles (EVs) isolated from the human HeLa cell line did not stain positive. Multiple bands staining positive for LPS can be seen in the BMV sample, likely corresponding to the naturally varying lengths of the polysaccharide chain portion of LPS. Both EVs and BMVs were also stained with an antibody against CD9, an EV surface marker that is not present on BMVs. The isolated BMVs did not stain positive for CD9, while the EVs did stain positive. Uncropped Western blot images can be seen in Supplementary Figure S4. This analysis reveals that the LPS antibody stains LPS-containing BMVs and does not stain human EVs.
Unstained BMVs were spiked into a human plasma sample to a final concentration of 1.8 × 1010 BMVs/mL and were successfully recovered using DEP. The collected material around the electrode edge, which was expected to contain a mixture of BMVs along with endogenous EV particles, was stained with the anti-LPS antibody, and BMVs were observed around the electrode edge via fluorescence imaging (Figure 4C). To further validate the DEP collection of BMVs, a plasma sample without spiked BMVs was run on the DEP chip under the same conditions and stained with the anti-LPS antibody (Figure 4D). No fluorescence signal was observed around the electrode edge, showing that the signal in Figure 4C was from collected BMVs.
To understand the reproducibility of DEP-based BMV collection, multiple replicates of collection and staining were performed with two different staining methods. The first was three replicates of BMV isolation from 0.5X PBS at a concentration of 4.5 × 109 BMVs/mL stained with the lipid intercalating dye DiO. The second was three replicates of spiked BMV isolation from human plasma at a concentration of 4.5 × 109 BMVs/mL stained with an antibody against LPS (Figure 4E). The coefficient of variation for the three replicates was found to be 0.52 for staining BMVs isolated from buffer using DiO and 0.74 for staining BMVs isolated from undiluted plasma using an anti-LPS antibody. This shows reproducible BMV collection by DEP from both buffer and plasma. Variability can be reduced through adjustments to experimental parameters and the use of internal standards to adjust for chip-to-chip variability.
The lower limit of detection of BMVs from a human plasma sample was determined by a serial dilution of BMVs into plasma samples followed by DEP isolation and on-chip LPS staining. The lower limit of detection was determined to be 4.31 × 109 BMVs/mL (Figure 4F).
DEP collection of BMVs from plasma took 10 min followed by a 10 min wash for purification. The on-chip immunostaining process took an additional 3 h, 2 of which consisted of incubation with the staining antibodies. This long staining time was chosen to ensure full staining of the collected BMVs but could be optimized to a shorter time period. Overall, the DEP technique required considerably less time and labor compared to the traditional ultracentrifugation method where just BMV recovery from a buffer can take over 3 h [15,30,31] and even longer when recovering from higher-viscosity fluids like plasma [32]. To immunostain the ultracentrifuged BMVs, the stain needs to be introduced and the particles washed, requiring additional 3 h ultracentrifugation sessions, resulting in a total time of 12 h to produce purified and stained BMVs. Sample loss is also high during these ultracentrifugation washes, a problem not encountered by the DEP technique since the BMVs are held at the electrode edge, resulting in low levels of loss throughout the DEP washing steps.
4. Conclusions
BMV nanoparticles were successfully recovered from buffer and from high-conducting human plasma using the DEP microfluidic chip. These collected particles were then successfully quantified and classified as being derived from Gram-negative bacteria on the chip using immunostaining techniques for the Gram-negative bacteria-specific biomarker lipid A LPS. The lower limit of detection of these BMVs in plasma was found to be 4.31 x 109 BMVs/mL, which is a useful range for sepsis diagnostics. To our knowledge, these experiments demonstrate the first use of DEP to recover BMVs. The isolation and purification of BMVs from a complex plasma sample using DEP can be performed in 20 min, and immunostaining can be performed in 3 h directly on the chip, which is much shorter than the 12 h required to isolate and stain BMVs from a plasma sample using the gold-standard method of ultracentrifugation followed by additional time for biomarker quantification analysis. Furthermore, the dielectrophoretic isolation of BMVs is easier to scale and automate compared to ultracentrifugation, which can enable high-throughput BMV analysis. These exciting results show that DEP technology is a valuable tool to enable the efficient collection and quantification of BMVs with the speed, ease of use, and high automation potential needed for the future clinical translation of BMV-based diagnostics. |
Title: Could elective nodal irradiation for locally advanced rectal cancer be omitted in the context of total neoadjuvant therapy? An analysis of the recurrence sites of rectal cancer | Body:
Introduction
The approach to neoadjuvant therapy for locally advanced rectal cancer (LARC) has entered the era of total neoadjuvant treatment (TNT) after went through the stages of single-chemotherapy, conventional fractionated radiotherapy alone, short-course radiotherapy (SCRT) and long-course chemoradiotherapy (LCRT). From the preferred recommendation of TNT in the National Comprehensive Cancer Network (NCCN) guideline for LARC patients, we can find that the chemotherapy regimen of CAPEOX or FOLFOX, traditionally administered postoperatively, has been arranged before the total mesorectal excision (TME), while LCRT or SCRT regimen unchanged. This shift underscores an enhanced emphasis on chemotherapy in the neoadjuvant treatment for LARC.
Radiation therapy (RT) has been a fundamental component of neoadjuvant treatment for LARC, contributing significantly to the improvement of anal preservation rate and the reduction of local recurrence rate. The delineation of neoadjuvant radiotherapy targets, historically based on Roels et al.’s 2006 analysis of postoperative recurrence sites in rectal cancer patients, did not distinguish between pre- and postoperative radiotherapy settings (1). In 2012, Valentin and his colleagues put forth guidelines for preoperative radiotherapy targeting based in varying T and N stages, later solidified in expert consensus whether the irradiation extents for lateral lymph nodes and inclusion of the ischiorectal fossa should be defined as a target (2, 3). Up to now, in clinical practice, the radiotherapy target setting of both LCRT and SCRT adheres to this consensus.
In the context of TNT mode, neoadjuvant therapy for rectal cancer has greatly increased the weight of chemotherapy. This raises pertinent questions: Will the elective nodal irradiation (ENI) of pelvic field combined with involved-field irradiation (IFI) of increasing dose in high-risk areas lead to an over-treatment? Could the successful IFI strategies used in lung and esophageal cancers be replicated in radiotherapy for LARC? Although there are no definitive answers to these questions, there is no doubt that investigating these concerns holds substantial clinical significance.
In this study, we evaluated 126 rectal cancer patients who experienced local recurrence post-surgery and analyzed the recurrence site and patterns, aiming to inform the optimal preoperative radiotherapy target setting for LARC patients under TNT mode.
Materials and methods
Patients
From January 2009 to July 2023, rectal cancer patients who underwent radical surgery in our hospital and were diagnosed with local recurrence during follow-up were included in this study. The main inclusion criteria were as follows: (1) Diagnosis of rectal cancer, irrespective of gender and age; (2) Complete diagnostic and treatment history in our hospital; (3) Underwent at least one abdominopelvic enhanced CT or MRI at the initial visit; (4) Underwent at least one abdominopelvic enhanced CT or MRI+DWI at the time of local recurrence diagnosis; (5) Pathological confirmation for patient who underwent surgery for local recurrent lesions; (6) Significant reduction in lesion volume and/or symptom relief after radiotherapy and/or chemotherapy in patient who did not undergo surgery. The exclusion criteria included: (1) Patients with a second primary malignancy at initial diagnosis or during follow-up; (2) Patients with both local recurrent lesions and distant organ metastasis; (3) Patients who refuse treatment for recurrent lesions; (4) Patients who do not meet the above inclusion criteria. All participants were identified in an institutional tumor registry through a protocol approved by the institutional review board with waiver of informed consent.
Assessment of local recurrence
Local recurrence was identified by imaging as invasive or asymmetric masses not attributable to postoperative structural changes, or masses visible initially but not meeting diagnostic criteria, which then exhibited growth during follow-up. Lesions confirmed by pathological biopsy after resection or shrunk after RT and/or drug therapy were clinically diagnosed as local recurrence. The above non-pathological assessments were independently performed by two senior radiologists.
Definition of local recurrence site
In this study, recurrence sites were classified into internal pelvic cavity (IPC) and external pelvic cavity (EPC). IPC encompassed the mesenteric regions (MR) (incl. anastomosis and rectal stump), presacral regions (PR) (Defined as: lesions located in front of the sacrum and the distance between the posterior margin of lesion and the anterior margin of the sacrum was within 1 cm), and the lateral lymphatic drainage region (LLDR) (incl. obturator, internal iliac, and external iliac). EPC included the perianal, the inguinal area (IA), and the paravascular area (PA) between the inferior mesenteric artery and the common iliac artery.
Relationship between recurrences and initial lesions
Here, we compared and analyzed the locations of recurrent and initial lesions (incl. confirmed and/or suspicious) to identify patterns between them. A multivariate analysis was conducted to determine factors influencing site consistency.
Statistical analysis
Statistical analysis was performed using SPSS software 25.0 (SPSS, Inc., Chicago, IL, USA) and MedCalc Version 32.0. Chi-square test was used to evaluate the distribution of recurrence sites. A forward stepwise logistic regression was used to analyze factors associated with recurrence site consistency with initial lesions. The Hosmer and Lemeshow test was used to assess the logistic regression model’s goodness of fit, and receiver operating characteristic (ROC) curve analysis was used to evaluate the model’s predictive performance. In this study, P<0.05 was considered statistically significant.
Patients
From January 2009 to July 2023, rectal cancer patients who underwent radical surgery in our hospital and were diagnosed with local recurrence during follow-up were included in this study. The main inclusion criteria were as follows: (1) Diagnosis of rectal cancer, irrespective of gender and age; (2) Complete diagnostic and treatment history in our hospital; (3) Underwent at least one abdominopelvic enhanced CT or MRI at the initial visit; (4) Underwent at least one abdominopelvic enhanced CT or MRI+DWI at the time of local recurrence diagnosis; (5) Pathological confirmation for patient who underwent surgery for local recurrent lesions; (6) Significant reduction in lesion volume and/or symptom relief after radiotherapy and/or chemotherapy in patient who did not undergo surgery. The exclusion criteria included: (1) Patients with a second primary malignancy at initial diagnosis or during follow-up; (2) Patients with both local recurrent lesions and distant organ metastasis; (3) Patients who refuse treatment for recurrent lesions; (4) Patients who do not meet the above inclusion criteria. All participants were identified in an institutional tumor registry through a protocol approved by the institutional review board with waiver of informed consent.
Assessment of local recurrence
Local recurrence was identified by imaging as invasive or asymmetric masses not attributable to postoperative structural changes, or masses visible initially but not meeting diagnostic criteria, which then exhibited growth during follow-up. Lesions confirmed by pathological biopsy after resection or shrunk after RT and/or drug therapy were clinically diagnosed as local recurrence. The above non-pathological assessments were independently performed by two senior radiologists.
Definition of local recurrence site
In this study, recurrence sites were classified into internal pelvic cavity (IPC) and external pelvic cavity (EPC). IPC encompassed the mesenteric regions (MR) (incl. anastomosis and rectal stump), presacral regions (PR) (Defined as: lesions located in front of the sacrum and the distance between the posterior margin of lesion and the anterior margin of the sacrum was within 1 cm), and the lateral lymphatic drainage region (LLDR) (incl. obturator, internal iliac, and external iliac). EPC included the perianal, the inguinal area (IA), and the paravascular area (PA) between the inferior mesenteric artery and the common iliac artery.
Relationship between recurrences and initial lesions
Here, we compared and analyzed the locations of recurrent and initial lesions (incl. confirmed and/or suspicious) to identify patterns between them. A multivariate analysis was conducted to determine factors influencing site consistency.
Statistical analysis
Statistical analysis was performed using SPSS software 25.0 (SPSS, Inc., Chicago, IL, USA) and MedCalc Version 32.0. Chi-square test was used to evaluate the distribution of recurrence sites. A forward stepwise logistic regression was used to analyze factors associated with recurrence site consistency with initial lesions. The Hosmer and Lemeshow test was used to assess the logistic regression model’s goodness of fit, and receiver operating characteristic (ROC) curve analysis was used to evaluate the model’s predictive performance. In this study, P<0.05 was considered statistically significant.
Results
Characteristics of enrolled patients
According to the inclusion and exclusion criteria, a total of 126 patients were selected for this study, as shown in
Figure 1
. Among them, 10 cases were confirmed by postoperative pathology, while 116 cases were validated based on the efficacy of nonoperative treatment. The baseline characteristics of patients are shown in
Table 1
.
Table 1
Baseline characteristics of patients.
Items
Characteristics
N (%)
Gender
Male
85 (67.46)
Female
41 (32.54)
Age (years)
<30
1 (0.79)
30-60
60 (47.62)
≥60
65 (51.59)
Initial CEA value (ng/ml)
<5
58 (46.03)
5-10
27 (21.43)
10-20
22 (17.46)
20-30
5 (3.97)
30-40
3 (2.38)
40-50
4 (3.17)
≥50
7 (5.56)
Distance between the lower margin of the tumor and the anal margin (cm)
≤3
29 (23.02)
3-5
30 (23.81)
5-10
54 (42.86)
>10
13 (10.32)
Surgical methods
Miles
44 (34.92)
Dixon
70 (55.56)
Hartamann
12 (9.52)
Lateral pelvic lymphadenectomy
no
123 (97.62)
yes
3 (2.38)
Pathological type
Adenocarcinoma
98 (77.78)
Mucinous carcinoma
28 (22.22)
CRM
negative
104 (82.54)
positive
22 (17.46)
Blood vessel invasion
negative
112 (88.89)
positive
14 (11.11)
Nerve invasion
negative
103 (81.75)
positive
23 (18.25)
Pathological T staging
pT1
3 (2.38)
pT2
16 (12.70)
pT3
62 (49.21)
pT4
45 (35.71)
Pathological N staging
pN0
53 (42.06)
pN1
37 (29.37)
pN2
36 (28.57)
Preoperative adjuvant therapy
no
109 (86.51)
Chemotherapy alone
4 (3.17)
chemotherapy+target therapy
0 (0)
chemotherapy+radiotherapy
13 (10.32)
chemotherapy+target therapy+radiotherapy
0 (0)
Postoperative adjuvant therapy
no
45 (35.71)
Chemotherapy alone
52 (41.70)
chemotherapy+target therapy
4 (3.17)
chemotherapy+radiotherapy
24 (19.05)
chemotherapy+target therapy+radiotherapy
1 (0.79)
Perioperative adjuvant therapy
no
36 (28.57)
Chemotherapy alone
49 (38.89)
chemotherapy+target therapy
4 (3.18)
chemotherapy+radiotherapy
36 (28.57)
chemotherapy+target therapy+radiotherapy
1 (0.79)
Recurrence period (months)
≤12
38 (30.16)
12-24
43 (34.13)
24-36
26 (20.63)
36-48
9 (7.14)
48-60
2 (1.59)
>60
8 (6.35)
Treatment of recurrent lesions
Operation
6 (4.76)
Drug therapy
30 (23.81)
Radiotherapy
10 (7.94)
Radiotherapy combined with drug therapy
74 (58.73)
Drug therapy combined with operation
2 (1.59)
Radiotherapy combined with drug therapy and operation
4 (3.17)
Figure 1
Patients selection process.
Local recurrence sites
Among the 126 patients, 95 cases (75.40%) underwent IPC recurrence, with the breakdown as follows: MR (42, 33.33%), PR (28, 22.22%), MR+PR (13, 10.32%), LLDR (3, 2.38%), and combined LLDR with MR and/or PR (9, 7.14%). There were 5 cases (3.97%) of EPC recurred, including perianal (3, 2.38%), perianal and IA (1, 0.79%), PA (1, 0.79%). Additionally, 26 cases (20.63%) had both IPC and EPC recurrences, detailed in
Table 2
. In this study, all PR lesions were below the anterior inferior margin of the 2nd sacrum, and no external iliac lesions were observed in patients with LLDR recurrence. Of the 186 recurrent lesions in total, MR and PR were the most common recurrence sites, accounting for 40.86% (76/186) and 32.26% (60/186), respectively. In addition, the recurrence rates for LLDR, IA, perianal, and PA were 10.22%, 9.14%, 3.76%, and 3.76%, respectively, as shown in
Table 3
. Distribution of recurrence lesions was obviously unbalanced, which embodied that the occurrence frequency of lesions in MP and PR was much higher than that in other areas. Due to their anatomical proximity, MR and PR were collectively defined as high-frequency recurrence area (HRA) for this study.
Table 2
Location of local recurrent lesions of rectal cancer patients.
Location
N (%)
Internal pelvic cavity
95 (75.40)
MR
42 (33.33)
PR
28 (22.22)
MR and PR
13 (10.32)
LLDR (obturator)
3 (2.38)
MR+LLDR (obturator)
2 (1.59)
MR+LLDR (internal iliac)
2 (1.59)
PR+LLDR (internal iliac)
1 (0.79)
PR+LLDR (obturator+internal iliac)
2 (1.59)
MR+PR+LLDR (obturator)
1 (0.79)
MR+PR+LLDR (internal iliac)
1 (0.79)
External pelvic cavity
5 (3.97)
perianal
3 (2.38)
perianal+IA
1 (0.79)
PA
1 (0.79)
Internal pelvic cavity and external pelvic cavity
26 (20.63)
MR+IA
3 (2.38)
MR+PA
1 (0.79)
perianal+PR
1 (0.79)
PR+IA
7 (5.56)
PR+PA
2 (1.59)
MR+PR+IA
1 (0.79)
MR+PR+LLDR (internal iliac)+IA
1 (0.79)
MR+PR+PA
2 (1.59)
MR+PR+perianal+IA
1 (0.79)
MR+LLDR (internal iliac+obturator)
1 (0.79)
MR+LLDR (internal iliac)+PA
1 (0.79)
MR+LLDR (internal iliac+obturator)+IA
1 (0.79)
PR+LLDR (obturator+internal iliac)+IA
1 (0.79)
PR+LLDR (internal iliac)+PA
1(0.79)
PR+perianal+IA
1(0.79)
LLDR (obturator)+PA
1(0.79)
MR, mesenteric regions; PR, presacral regions; LLDR, lateral lymphatic drainage region; IA, inguinal area; PA, paravascular area between the inferior mesenteric artery and the common iliac artery.
Table 3
Distribution difference of local recurrent focus after radical resection of rectal cancer.
Items
Location
X2
P
MR
PR
LLDR
IA
perianal
PA
Observed (N)
76
60
19
17
7
7
140.58
0.000
Expected (N)
31
31
31
31
31
31
Residual
45
29
-12
-14
-24
-24
MR, mesenteric regions; PR, presacral regions; LLDR, lateral lymphatic drainage region; IA, inguinal area; PA, paravascular area between the inferior mesenteric artery and the common iliac artery.
Pattern analysis of local recurrence sites
Comparing the initial and recurrence imaging data, we found that in addition to 83 patients with the recurrent lesions located in HRA, 26 cases’ lesions located in areas suspected in initial imaging (Incl. 9 cases in LLDR, 8 in IA, 4 in PA, 2 in LLDR+IA, 1 in LLDR+PA, and 2 in IA+PA), as shown in
Figure 2
and
Supplementary Table 1
. Beyond the 109 patients (86.51%) mentioned above, 17 (13.49%) patients had with new lesion locations not identified in initial imaging, with the breakdown as follows: 3 cases with simple perianal recurrence, 1 case with perianal+IA, 2 cases with MR+PR+perianal+IA, 2 cases with simple obturator, 1 case with MR+PR+obturator, 2 cases with MR+PR+obturator+internal iliac, 1 case with MR+PR+internal iliac, 1 case with simple IA, 1 case with IA+internal iliac, 1 case with simple PA, and 2 cases with PA+internal iliac recurrence.
Figure 2
(A) A suspected lesion in the obturator lymphatic drainage region in initial imaging; (B) Recurrent lesion in the obturator lymphatic drainage region; (C) A suspected lesion in the inguinal lymphatic drainage region in initial imaging; (D) Recurrent lesion in the inguinal lymphatic drainage region; (E) A suspected lesion in the para-aortic lymphatic drainage region in initial imaging; (F) Recurrent lesion in the para-aortic lymphatic drainage region.
Among the 186 locally recurrent lesions, 136 lesions were located in HRA, and 31 lesions located in areas suspected in initial imaging (Incl. 12 in LLDR, 12 in IA, 7 in PA), as shown in
Supplementary Table 2
. In addition to the 167 (90.76%) lesions mentioned above, 19 (10.22%) new lesions were not identified in the initial imaging, including 7 in LLDR, 5 in IA, and 7 lesions in perianal region.
Given that 86.51% of patients had recurrent lesions in HRA and the suspected lesions areas, we conducted a Logistic analysis of the relationship between 16 clinical factors and this condition (the variable assignment were detailed in
Supplementary Table 3
). In order to reduce the distortion of model evaluation, we conducted collinearity statistics for all factors before performing the logistic analysis. A variance inflation factor (VIF) of predictors ≥10, including the postoperative and perioperative treatment methods, was thought to be highly correlated with at least one of the other predictors in the aforementioned model. When excluding the factor of postoperative treatment methods from the model, we observed that the VIF of all factors was <2. Then the logistic analysis identified initial CEA values and perioperative treatment methods as a negative and a positive predictor, respectively (
Table 4
), and the Logistic regression model was
Z=sigmoid(X)=11+e1.946−0.457X3+0.907X15
. The predictive performances of the CEA and Logistic regression model were obtained by ROC analysis, yielding the Area Under Curve (AUC) of 0.713 (95% CI 0.626-0.790) and 0.747 (95% CI 0.661-0.820), respectively. On the basis of the optimal cut-off values of 6.54 ng/ml and 0.534, the sensitivity, specificity, positive predictive value and negative predictive values were 59.63% and 56.88%, 82.35% and 82.35%, 95.59% and 95.38%, 24.13% and 22.95%, respectively (
Figure 3
).
Table 4
Multivariate analysis of consistency between the location of recurrent lesions and the initial lesions.
Variable
B
SE
Wald
Exp(B)
P
95%CI
Initial CEA value
-0.456
0.139
10.688
0.634
0.001
0.482-0.833
Perioperative treatment methods
0.907
0.415
4.784
2.477
0.029
1.099-5.586
Constant
1.939
0.469
17.062
6.954
0.000
Figure 3
ROC analysis of initial CEA value and Logistic regression model.
Characteristics of enrolled patients
According to the inclusion and exclusion criteria, a total of 126 patients were selected for this study, as shown in
Figure 1
. Among them, 10 cases were confirmed by postoperative pathology, while 116 cases were validated based on the efficacy of nonoperative treatment. The baseline characteristics of patients are shown in
Table 1
.
Table 1
Baseline characteristics of patients.
Items
Characteristics
N (%)
Gender
Male
85 (67.46)
Female
41 (32.54)
Age (years)
<30
1 (0.79)
30-60
60 (47.62)
≥60
65 (51.59)
Initial CEA value (ng/ml)
<5
58 (46.03)
5-10
27 (21.43)
10-20
22 (17.46)
20-30
5 (3.97)
30-40
3 (2.38)
40-50
4 (3.17)
≥50
7 (5.56)
Distance between the lower margin of the tumor and the anal margin (cm)
≤3
29 (23.02)
3-5
30 (23.81)
5-10
54 (42.86)
>10
13 (10.32)
Surgical methods
Miles
44 (34.92)
Dixon
70 (55.56)
Hartamann
12 (9.52)
Lateral pelvic lymphadenectomy
no
123 (97.62)
yes
3 (2.38)
Pathological type
Adenocarcinoma
98 (77.78)
Mucinous carcinoma
28 (22.22)
CRM
negative
104 (82.54)
positive
22 (17.46)
Blood vessel invasion
negative
112 (88.89)
positive
14 (11.11)
Nerve invasion
negative
103 (81.75)
positive
23 (18.25)
Pathological T staging
pT1
3 (2.38)
pT2
16 (12.70)
pT3
62 (49.21)
pT4
45 (35.71)
Pathological N staging
pN0
53 (42.06)
pN1
37 (29.37)
pN2
36 (28.57)
Preoperative adjuvant therapy
no
109 (86.51)
Chemotherapy alone
4 (3.17)
chemotherapy+target therapy
0 (0)
chemotherapy+radiotherapy
13 (10.32)
chemotherapy+target therapy+radiotherapy
0 (0)
Postoperative adjuvant therapy
no
45 (35.71)
Chemotherapy alone
52 (41.70)
chemotherapy+target therapy
4 (3.17)
chemotherapy+radiotherapy
24 (19.05)
chemotherapy+target therapy+radiotherapy
1 (0.79)
Perioperative adjuvant therapy
no
36 (28.57)
Chemotherapy alone
49 (38.89)
chemotherapy+target therapy
4 (3.18)
chemotherapy+radiotherapy
36 (28.57)
chemotherapy+target therapy+radiotherapy
1 (0.79)
Recurrence period (months)
≤12
38 (30.16)
12-24
43 (34.13)
24-36
26 (20.63)
36-48
9 (7.14)
48-60
2 (1.59)
>60
8 (6.35)
Treatment of recurrent lesions
Operation
6 (4.76)
Drug therapy
30 (23.81)
Radiotherapy
10 (7.94)
Radiotherapy combined with drug therapy
74 (58.73)
Drug therapy combined with operation
2 (1.59)
Radiotherapy combined with drug therapy and operation
4 (3.17)
Figure 1
Patients selection process.
Local recurrence sites
Among the 126 patients, 95 cases (75.40%) underwent IPC recurrence, with the breakdown as follows: MR (42, 33.33%), PR (28, 22.22%), MR+PR (13, 10.32%), LLDR (3, 2.38%), and combined LLDR with MR and/or PR (9, 7.14%). There were 5 cases (3.97%) of EPC recurred, including perianal (3, 2.38%), perianal and IA (1, 0.79%), PA (1, 0.79%). Additionally, 26 cases (20.63%) had both IPC and EPC recurrences, detailed in
Table 2
. In this study, all PR lesions were below the anterior inferior margin of the 2nd sacrum, and no external iliac lesions were observed in patients with LLDR recurrence. Of the 186 recurrent lesions in total, MR and PR were the most common recurrence sites, accounting for 40.86% (76/186) and 32.26% (60/186), respectively. In addition, the recurrence rates for LLDR, IA, perianal, and PA were 10.22%, 9.14%, 3.76%, and 3.76%, respectively, as shown in
Table 3
. Distribution of recurrence lesions was obviously unbalanced, which embodied that the occurrence frequency of lesions in MP and PR was much higher than that in other areas. Due to their anatomical proximity, MR and PR were collectively defined as high-frequency recurrence area (HRA) for this study.
Table 2
Location of local recurrent lesions of rectal cancer patients.
Location
N (%)
Internal pelvic cavity
95 (75.40)
MR
42 (33.33)
PR
28 (22.22)
MR and PR
13 (10.32)
LLDR (obturator)
3 (2.38)
MR+LLDR (obturator)
2 (1.59)
MR+LLDR (internal iliac)
2 (1.59)
PR+LLDR (internal iliac)
1 (0.79)
PR+LLDR (obturator+internal iliac)
2 (1.59)
MR+PR+LLDR (obturator)
1 (0.79)
MR+PR+LLDR (internal iliac)
1 (0.79)
External pelvic cavity
5 (3.97)
perianal
3 (2.38)
perianal+IA
1 (0.79)
PA
1 (0.79)
Internal pelvic cavity and external pelvic cavity
26 (20.63)
MR+IA
3 (2.38)
MR+PA
1 (0.79)
perianal+PR
1 (0.79)
PR+IA
7 (5.56)
PR+PA
2 (1.59)
MR+PR+IA
1 (0.79)
MR+PR+LLDR (internal iliac)+IA
1 (0.79)
MR+PR+PA
2 (1.59)
MR+PR+perianal+IA
1 (0.79)
MR+LLDR (internal iliac+obturator)
1 (0.79)
MR+LLDR (internal iliac)+PA
1 (0.79)
MR+LLDR (internal iliac+obturator)+IA
1 (0.79)
PR+LLDR (obturator+internal iliac)+IA
1 (0.79)
PR+LLDR (internal iliac)+PA
1(0.79)
PR+perianal+IA
1(0.79)
LLDR (obturator)+PA
1(0.79)
MR, mesenteric regions; PR, presacral regions; LLDR, lateral lymphatic drainage region; IA, inguinal area; PA, paravascular area between the inferior mesenteric artery and the common iliac artery.
Table 3
Distribution difference of local recurrent focus after radical resection of rectal cancer.
Items
Location
X2
P
MR
PR
LLDR
IA
perianal
PA
Observed (N)
76
60
19
17
7
7
140.58
0.000
Expected (N)
31
31
31
31
31
31
Residual
45
29
-12
-14
-24
-24
MR, mesenteric regions; PR, presacral regions; LLDR, lateral lymphatic drainage region; IA, inguinal area; PA, paravascular area between the inferior mesenteric artery and the common iliac artery.
Pattern analysis of local recurrence sites
Comparing the initial and recurrence imaging data, we found that in addition to 83 patients with the recurrent lesions located in HRA, 26 cases’ lesions located in areas suspected in initial imaging (Incl. 9 cases in LLDR, 8 in IA, 4 in PA, 2 in LLDR+IA, 1 in LLDR+PA, and 2 in IA+PA), as shown in
Figure 2
and
Supplementary Table 1
. Beyond the 109 patients (86.51%) mentioned above, 17 (13.49%) patients had with new lesion locations not identified in initial imaging, with the breakdown as follows: 3 cases with simple perianal recurrence, 1 case with perianal+IA, 2 cases with MR+PR+perianal+IA, 2 cases with simple obturator, 1 case with MR+PR+obturator, 2 cases with MR+PR+obturator+internal iliac, 1 case with MR+PR+internal iliac, 1 case with simple IA, 1 case with IA+internal iliac, 1 case with simple PA, and 2 cases with PA+internal iliac recurrence.
Figure 2
(A) A suspected lesion in the obturator lymphatic drainage region in initial imaging; (B) Recurrent lesion in the obturator lymphatic drainage region; (C) A suspected lesion in the inguinal lymphatic drainage region in initial imaging; (D) Recurrent lesion in the inguinal lymphatic drainage region; (E) A suspected lesion in the para-aortic lymphatic drainage region in initial imaging; (F) Recurrent lesion in the para-aortic lymphatic drainage region.
Among the 186 locally recurrent lesions, 136 lesions were located in HRA, and 31 lesions located in areas suspected in initial imaging (Incl. 12 in LLDR, 12 in IA, 7 in PA), as shown in
Supplementary Table 2
. In addition to the 167 (90.76%) lesions mentioned above, 19 (10.22%) new lesions were not identified in the initial imaging, including 7 in LLDR, 5 in IA, and 7 lesions in perianal region.
Given that 86.51% of patients had recurrent lesions in HRA and the suspected lesions areas, we conducted a Logistic analysis of the relationship between 16 clinical factors and this condition (the variable assignment were detailed in
Supplementary Table 3
). In order to reduce the distortion of model evaluation, we conducted collinearity statistics for all factors before performing the logistic analysis. A variance inflation factor (VIF) of predictors ≥10, including the postoperative and perioperative treatment methods, was thought to be highly correlated with at least one of the other predictors in the aforementioned model. When excluding the factor of postoperative treatment methods from the model, we observed that the VIF of all factors was <2. Then the logistic analysis identified initial CEA values and perioperative treatment methods as a negative and a positive predictor, respectively (
Table 4
), and the Logistic regression model was
Z=sigmoid(X)=11+e1.946−0.457X3+0.907X15
. The predictive performances of the CEA and Logistic regression model were obtained by ROC analysis, yielding the Area Under Curve (AUC) of 0.713 (95% CI 0.626-0.790) and 0.747 (95% CI 0.661-0.820), respectively. On the basis of the optimal cut-off values of 6.54 ng/ml and 0.534, the sensitivity, specificity, positive predictive value and negative predictive values were 59.63% and 56.88%, 82.35% and 82.35%, 95.59% and 95.38%, 24.13% and 22.95%, respectively (
Figure 3
).
Table 4
Multivariate analysis of consistency between the location of recurrent lesions and the initial lesions.
Variable
B
SE
Wald
Exp(B)
P
95%CI
Initial CEA value
-0.456
0.139
10.688
0.634
0.001
0.482-0.833
Perioperative treatment methods
0.907
0.415
4.784
2.477
0.029
1.099-5.586
Constant
1.939
0.469
17.062
6.954
0.000
Figure 3
ROC analysis of initial CEA value and Logistic regression model.
Discussion
With the advancement of radiotherapy technology and the renewal of treatment concept, the radiotherapy of malignant tumors has entered an era of precision. On the premise of ensuring the curative effect of patients, reasonable minimize of irradiation field is of great value in reducing radiotherapy-related damage and ensuring smooth treatment execution. This approach is evident in the shift from ENI to IFI in treating esophageal and lung cancer (4–9). As a local treatment modality, RT plays an vital role in preventing local recurrence of tumors. Consequently, it is crucial to obtain the pattern and regularity of local recurrence sites for for setting accurate radiotherapy targets.
For the recurrence site of rectal cancer, Roels et al., in their comprehensive analysis of 17 studies, reported higher local recurrence rates in the mesorectal and presacral site, at 87% and 49%, respectively (1). Our study aligns with these findings: 95 of the 126 patients had recurrent lesions located in the pelvic cavity, of which 87.37% (83/95) occurred in MR and/or PR, and 73.12% of the 186 recurrent lesions occurred in these two areas. These results underscore the MR and PR as high-risk recurrence areas in rectal cancer, warranting their inclusion in neoadjuvant radiotherapy target areas.
In clinical practice, lymph node properties of rectal cancer are generally evaluated from three aspects: whether its short diameter is greater than 0.5cm, whether the margin is regular and whether the signal is uniform. Although the introduction of the new PET functional probe of Ga68 PSMA-11 and the application of magnetic resonance lymphography have significantly improved the diagnostic accuracy, widespread application of these new technologies still requires a time and experience (10, 11). Therefore, the study of determining the properties of lymph nodes by imaging technology is a research focus. In this study, 10.22%, 9.14%, and 3.76% of 186 recurrent lesions were found in the LLDR, IA and PA, respectively. Among them, 63.16% (12/19), 70.59% (12/17) and 100% (7/7) of the recurrent lesions showed suspicious lesions at the corresponding locations on the initial imaging, while new recurrent lesions in these areas only accounted for 6.45% (12/186). Therefore, we believe that strengthening the intensity of imaging examination in initial patients and incorporating suspicious lesions into the preoperative radiotherapy target could potentially reduce the local recurrence in rectal cancer patients.
As an important tumor biomarker, CEA has been widely recognized for its significant value in evaluating the disease progression and prognosis of colorectal cancer patients (12, 13). In this study, we further analyzed factors that may influence the high rate of recurrence in HRA and suspected lesion areas and found that the initial CEA value was a negative predictor of this status. Analysis of its predictive value indicated that when the cut-off value was less than or equal to 6.54 ng/ml, its positive predictive value and negative predictive value were 95.59% and 24.13%, respectively, which further indicated that low initial CEA (≤6.54 ng/ml) correlate with less aggressive tumor behavior. Additionally, we also observed that undergoing multiple perioperative treatment methods positively impacted the recurrence lesion location, emphasizing the important of intensive perioperative treatment in inhibiting tumor migration. However, the predictive model for the recurrent lesions location constructed in this study, despite its high positive predictive value (95.38%), exhibited a low negative prediction value (22.95%). Therefore, we thought that this model had limited efficacy in predicting whether new lesions would occur in the non-HRA and the sites outside the initial suspected lesion area.
At present, the TNT mode is the only preoperative regimen recommended by the NCCN guidelines for LARC patients with pMMR/MSS status (14). This regimen marks a significant shift from the traditional LCRT and SCRT, primarily by intensifying the chemotherapy component to a duration of 12-16 weeks. Previous studies have underscored that pivotal role of chemotherapy in the neoadjuvant setting for rectal cancer. For example, the EORTC 22921 trial involving 1011 patients demonstrated that adding chemotherapy to preoperative radiotherapy in cT3-4 resectable rectal cancer patients enhanced tumor downstaging and downsizing (15), with patients showing ypT0-2 status gaining benefits in disease-free survival and overall survival (16). Maas and colleagues analyzed 3313 patients from 13 datasets based on their response to neoadjuvant chemoradiotherapy (nCRT) and found that although adding chemotherapy during the interval between radiotherapy and surgery did not benefit the prognosis of patients who obtained pathological complete response (pCR), it was beneficial for patients with ypT1-2 and ypT3-4 (17). In the recent PROSPECT trial, researchers even mentioned the concept of omitting radiotherapy in neoadjuvant therapy of LARC patients, although they recruited patients with only cT1-3 and not anxious about preserving sphincter. This idea further underscores the growing importance of chemotherapy in treatment (18).
While the impact of pCR on long-term prognosis remains a subject of debate (19–22), it is clear that neoadjuvant chemoradiotherapy offers high-response patients the opportunity for anal preservation, low recurrence, and adoption of “Wait and Watch”. In the exploration of LCRT based TNT mode, Garcia-Aguilar and colleagues found that patients receiving sequential 6-cycle mFOLFOX6 following nCRT exhibited a pCR rate more than double that of those who received only nCRT (38% vs.18%) (23). Similarly, in the SCRT-based TNT model, the RAPIDO Trial reported significantly higher pCR rates with the SCRT sequential 6-cycle CapeOX or 9-cycle FOLFOX4 regimen compared to nCRT (28.37% vs. 14.32%) (24, 25). These results highlight that the increased weight of chemotherapy in neoadjuvant therapy significantly improves the responsiveness of LARC patients to neoadjuvant therapy.
According to the requirements of ICRU 83 (26), the target area of neoadjuvant radiotherapy for rectal cancer needs to include MR, PR, LLDR, and the ischiorectal fossa of patients with levator ani muscle invasion. Patients undergoing long-course nCRT also need to implement additional irradiation on the MR and the affected lymph drainage area, that is, a combination of ENI and IFI. In addition, previous research had demonstrated that intensified radiotherapy directed at the primary tumor (not on the lymph nodes) could also enhance tumor regression rates, attain higher pCR rate and reduce the local recurrence rate. Especially for patients with difficulty in preserving anus or with high-risk factors (MRF+, T4, etc.) (27–29).
At present, there are still few studies on IFI in the target setting of pelvic malignancies. YANG et al. analyzed the prognostic factors affecting patients with recurrent ovarian cancer and found that the use of ENI or IFI target setting mode did not lead to differences in survival (30). Li et al. compared elderly bladder cancer patients receiving IFI and ENI, noting no significant difference in overall survival and local progression-free survival between the two groups. However, the acute toxicity rate in the IFI group was significantly lower than that in the ENI group (45.23% vs 72.00%, P=0.008) (31). In our study, a substantial majority of patients (86.51%, 109/126) and relapses (90.86%, 167/186) occurred in HAR and regions with preoperatively suspicious positive lymph nodes. Therefore, we believe that exploring IFI for LARC patients in the context of TNT treatment has important clinical value.
Although we obtained complete data on local recurrence in patients with rectal cancer, our retrospective analysis spanning 14 years has limitations. Firstly, to ensure the completeness of the clinical data, this study only reviewed 126 patients in our center and did not conduct a multi-center review. Secondly, in the analysis of factors that affect the location of recurrent lesions in HRA and suspicious lesion locations, due to the complexity of patient treatment plans and courses, we only assigned orderly values to the treatment methods. While our analysis indicated a higher correlation of recurrence location with intensive treatment, further research is needed to validate these findings, particularly in the context of IFI. Thirdly, we did not perform internal or external validation on the predictive model due to a limited number of patients with new recurrent lesions outside HRA and suspicious lesions. In the future, we will continue to accumulate data or conduct multi-center reviews to complete this work.
In summary, this study classified the sites of recurrent lesions of rectal cancer and compared them with the initial images, and preliminarily discovered the patterns of recurrence sites. Based on this, we believe that exploring IFI for LARC patients is feasible, especially in the context of TNT mode. Should IFI demonstrate similar efficacy and prognosis to ENI, it could significantly reduce radiotherapy adverse events and treatment delay in LARC patients. |
Title: Bee-Inspired Healing: Apitherapy in Veterinary Medicine for Maintenance and Improvement Animal Health and Well-Being | Body:
1. Introduction
Honey bee products, used by humans for centuries, have been gathering increasing attention in the scientific and professional public due to their therapeutic potential. Their use for numerous health problems, known as apitherapy, steadily increases in humans. However, the application of bee products in veterinary medicine is much less known to the professional and general public, although evidence shows their effectiveness in treating various diseases in animals.
Apitherapy refers to the use of honey bee (Apis mellifera) products for the treatment or prevention of health problems in humans and animals and is considered a branch of complementary and alternative medicine. Honey bee products encompass a diverse range of substances, including those derived primarily from plants (nectar, pollen, and plant resins collected and processed by bees with their secretions into honey, bee pollen, and propolis) and those synthesized by bees from their glands (such as royal jelly and bee venom). It is less known that bee larvae, primarily drone larvae, also hold promise for health maintenance and improvement [1,2].
This work distinguishes itself through its original focus, emphasizing findings from in vivo studies, namely those conducted on animals. Furthermore, the included studies are meticulously selected, adhering to three key criteria: they must have been published within the last 15 years, reported in peer-reviewed journals, and available in full-text format.
2. Honey
Clinical trials have extensively investigated the effects of honey on a wide range of animals, including dogs, horses, cats, cattle, and pigs, with preclinical trials involving rats, mice, and rabbits [3]. Once overshadowed by antibiotics, medical-grade honey is regaining popularity due to its antimicrobial and pro-healing properties, making it valuable for cutaneous wound healing [4]. Honey’s best-known and proven efficacy in animals is in the treatment of burns and wounds, especially those refractory to conventional treatments [3,4,5,6,7]. Among the various types of honey, mānuka honey, derived from the Leptospermum scoparium shrub native to southeastern Australia and New Zealand, has proven particularly effective. The advantage of mānuka honey over other honey types is attributed to its unusually high methylglyoxal (MGO) content, considered a major antibacterial compound in mānuka honey [8]. However, there are other mechanisms of antimicrobial action characteristic of most honey types: enzymatic production of hydrogen peroxide (H2O2), high osmolarity due to high sugar concentration, low pH level, and the presence of the bee antimicrobial peptide defensin-1 [9]. In a study on horses, contaminated wounds on the hind limbs healed faster and more successfully when treated with mānuka honey gel or just mānuka honey compared to untreated control wounds [5]. However, in an experiment on mice, chestnut honey showed better efficacy than mānuka honey in terms of angiogenesis and reepithelialization on the seventh day of treatment, while after 14 days, the effect of both kinds of honey was equally effective and significantly better compared to the control [7]. Importantly, mānuka honey may be less effective in treating wounds because its high MGO content adversely affects two of the mechanisms of antimicrobial action by modifying protein components; MGO inhibits glucose oxidase (the enzyme responsible for H2O2 generation), so the accumulation of H2O2 is disabled [10], while defensin-1, after modification by MGO, loses its antibacterial activity [11].
Interestingly, MGO concentration in mānuka honey is still considered one of its main quality factors (presented as an important potency measure) incorporated in the ”Unique Mānuka Factor” (UMF™) grading system. Originally, the UMF grade reflected only the level of antibacterial activity of honey without information about the components responsible for it [9]. With the discovery of MGO and its antibacterial potency, its concentration became the key signature marker for UMF [12]; however, MGO alone cannot explain the entirety of the antimicrobial effectiveness of mānuka honey, as this honey inhibits the growth of pathogenic bacteria at concentrations well below the minimum inhibitory concentration (MIC) of MGO alone [13,14,15,16]. The relationship between the MGO content of honey and its antibacterial effect varies and depends on the bacterial species [13,14,15,16,17]. For example, the positive correlation was strong in cases of Enterococcus faecalis and Escherichia coli and moderate in the case of Staphylococcus aureus, but no relationship was recorded between MGO content and efficacy against Pseudomonas aeruginosa [17]. The latest is in accordance with the previous evidence that MGO is not solely responsible for the efficacy of mānuka honey against P. aeruginosa [13,14,15,16]. These findings explain the result of an earlier study in which manuka honey with a lower UMF value showed greater antimicrobial activity than honey with a higher UMF value [18]. Overall, neither the MGO level nor the UMF value represents a reliable indicator of the antibacterial effect.
Excellent effects in wound healing with honey were demonstrated in cats [6,19] (Figure 1—not previously published) and dogs [20,21] (Figure 2—not previously published) regardless of whether a commercial product with medical-grade honey (L-Mesitran®) was used [6,19,21] or raw honey was applied to the gauze and then to the wound [20]. L-Mesitran® showed exceptional efficacy in treating cutaneous injuries. For instance, in a case where a cat had complete skin loss across 100% of its leg with a fractured distal ulna, the leg healed entirely, including the bone, with the regrowth of new skin and hair [6] (Figure 3).
Full-thickness skin wounds of cats treated with medical grade honey exhibited superior outcomes (reduced edema, enhanced angiogenesis, and increased fibroblast concentration) than those treated with Hypericum perforatum; the latter significantly improved only tissue perfusion compared to the untreated controls [4]. This is a very important result since H. perforatum is traditionally used for wound healing, and it achieves healing effects through the stimulation of epithelialization and granulation [22,23], collagen accumulation [22], and the expression of genes related to tissue repair and regeneration [24].
An extensive leg wound in a 5-year-old female cattle was also successfully treated with raw honey, further proving honey’s remarkable healing properties, although the wound did not fully heal due to the cow being slaughtered before complete recovery (Figure 4). The wound was rinsed with tap water, covered with raw honey, and bandaged daily for the first two weeks. Thereafter, the bandage was changed, and honey was applied every second day. No antiseptics were used during the wound healing.
In addition to wound treatment, experiments on rats showed the efficacy of honey in the prevention and treatment of gastrointestinal problems, especially in the healing of gastric ulcers [25,26,27,28] and disorders related to metabolic syndrome, such as hyperglycemia, dyslipidemia, and hyperlipidemia [29,30,31,32,33].
3. Propolis
There is much evidence of the successful treatment of animals with propolis products. Propolis-based products have proven to be a good alternative to conventional antimicrobials, with ample evidence of successful treatment in animals. For example, propolis has shown an excellent antifungal effect in preclinical studies on laboratory mice with vulvovaginal candidiasis; mucoadhesive propolis-based gel demonstrated antifungal efficacy similar to clotrimazole cream [34], while propolis extract incorporated in mucoadhesive thermoresponsive systems showed antifungal action similar and even superior, depending on the propolis concentration, to those with nystatin [35].
The great attention of veterinarians has been attracted by the possibility of using propolis in the treatment of bovine mastitis caused by microorganisms resistant to conventional antibiotics because of the promising efficacy that propolis ethanolic extracts demonstrated against etiological agents of bovine mastitis, Staphylococcus aureus and Escherichia coli. However, that efficacy was demonstrated indirectly, e.g., in vitro, against standard strains and wild-types of S. aureus and E. coli isolated from mastitic milk [36], against S. aureus cultivated in complex media or milk [37], and against damages induced by S. aureus and E-coli in bovine mammary epithelial cells [38]. However, negative effect of propolis on mammary tissue was also reported, as it reduced the viability of bovine mammary gland explants [36]. Thus, before the introduction of propolis into the veterinary practice of mastitis treatment in cattle, clinical studies should confirm the effectiveness, dosage, and safety of propolis use in mastitis control.
The antiviral effects of propolis have been evidenced in some in vivo studies, mostly against herpes simplex viruses HSV-1 and HSV-2 [39,40]. The antiparasitic effect has been evidenced in animal models for some types of propolis. After oral administration, propolis extracts effectively decreased the infection level of the microsporidian parasite Nosema ceranae in honey bee intestine [41,42,43]. Propolis extracts were also highly effective against other protistan parasites, such as Trypanosoma brucei [44], Plasmodium falciparum, P. berghei [45], and P. chabaudi [46], as evidenced in experiments on rats applying doses 400 and 600 mg/kg against T. brucei [44] and much lower doses against Plasmodium sp. in mice [45,46]. The antiparasitic properties of propolis have also been recorded against helminths, such as against Schistosoma mansoni. A single dose of propolis (400 mg/kg) administered to mice significantly reduced the burden of worms and the number of eggs, both immature and those in feces [47].
Exceptional success in wound healing with propolis products has been evidenced in several animal species [48]. In rats, all tested formulations of propolis gel, containing 1.2%, 2.4%, and 3.6% w/v (dry matter) of propolis, showed good activity in healing skin lesions, and the best results were achieved in the group treated with a gel containing 3.6% propolis [49]. In dogs, propolis paste 30% significantly reduced the healing time of wounds due to enhanced reepithelization and contraction of wounds [50]. Healing burn wounds with ointments containing propolis was very successful in pigs [51,52]. The healing effect is explained by the prevention of fibronectin biosynthesis and its degradation in the wound area [51], as well as by the broad-spectrum antibacterial activity, acceleration of neoangiogenesis and epithelialization [52].
Interestingly, propolis, as an aqueous or ethanolic extract, was shown to be effective as an adjuvant in veterinary vaccines and even superior to traditional vaccine adjuvants [53]. For example, propolis significantly increased antibody production when added to canine parvovirus and canine coronavirus vaccines [54], a vaccine against porcine parvovirus [53], and a vaccine against the bacterium Aeromonas salmonicida, the cause of furunculosis in fish [55].
Hydroalcoholic extracts of red propolis have shown gastroprotective properties in rats. The basis of this effect was a significant inhibition of the development of acute ulcers, a significant decrease in the volume of gastric secretion, and a significant increase in the production of gastric mucus, as well as antioxidant, and anti-Helicobacter pylori activities [56].
As added to broiler feed, even at low doses (10 mg/kg), propolis effectively protected the liver and blood vessels by inhibiting the formation of pathological lesions; with an increase in the dose to 50 mg/kg, the positive effects were even better [57].
Propolis has also been found to have anticancer potential [58] and alleviate the toxic effects of chemotherapy agents [59]. The antitumor activity of propolis observed in in vivo experiments (on rats and mice) indicates its potential application in the treatment of oncological cases in veterinary medicine [58,59,60].
Propolis is effective in healing numerous inflammatory and painful conditions in the oral cavity, such as candidiasis, gingivitis, periodontitis, ulcers, pulp mummification, supragingival plaque, caries, herpes, wounds after surgical interventions, and cancers, as evidenced in experiments on laboratory animals [61,62,63,64]. Besides, employing propolis for tooth storage before reimplantation in healthy mixed-breed dogs proved to be effective in enhancing the success rate of the procedure [65]. It is also worth noting the excellent effectiveness of topically applied propolis ear drops in otitis externa in dogs [66] and propolis eye drops in corneal wound healing in rats [67].
When administered as a dietary supplement to alloxan-induced diabetic mice, propolis preparations mitigated hepatotoxicity and nephrotoxicity by reducing oxidative stress and minimizing the deleterious effects of free radicals on tissue [68].
Propolis achieves therapeutic properties thanks to its complex composition, but primarily to numerous phenolic compounds, of which flavonoids, phenolic acids, and esters are the most biologically active [69,70]. For propolis activities, many underlying mechanisms have been discovered and reviewed [69,70,71]. For example, the antimicrobial effect of propolis is based on stimulating macrophages to activate Th1 cells, which enhances the cellular immune response (release of cytokines that are essential coordinators of the immune response to intracellular pathogens); in some microorganisms, propolis disrupts the permeability of the cell membrane leading to leakage of cellular components [71]. The anti-inflammatory activity of propolis is accomplished by inhibiting cyclooxygenase and prostaglandin biosynthesis, neutralizing free radicals, reducing cytokines, nitric oxide synthesis, and immunosuppression [70]. Various mechanisms are involved in the anticancer effect of propolis, among which are those already mentioned, including its powerful antioxidant and immunomodulatory potential, as well as its ability to stimulate cell cycle arrest, induce apoptosis, inhibit angiogenesis and metastasis of tumors, inhibit specific oncogene signaling pathways, inhibit glucose uptake and metabolism in the cancer cell, and possibly participate in epigenetic regulation [71].
4. Bee Venom (Apitoxin)
Bee venom has been used since ancient times in the treatment of rheumatoid arthritis, and its success in therapy lies in its anti-inflammatory and antinociceptive effects, reducing swelling and pain, as evidenced by numerous preclinical and clinical studies [72,73,74]. Melittin, the dominant component of bee venom (≥50% w/w of bee venom), is responsible for its anti-inflammatory effect and also exhibits anticancer potential [75,76]. For melittin, as well as other components of bee venom (phospholipase A2 and apamin), a neuroprotective effect has been demonstrated, explaining the positive effects of bee stings on neurodegenerative diseases such as Parkinson’s disease, multiple sclerosis, and intervertebral disc degeneration recorded in experiments on dogs [77] and mice [78,79,80]. In experimental animals, bee venom was effective in the therapy of neuropathies caused by nerve injuries or chemotherapeutics [81,82,83].
Bee venom acupuncture has proven useful in the case of a dog diagnosed with idiopathic facial paralysis; such a therapy led to a gradual improvement of clinical signs, and complete recovery of sensory and neurological facial signs in a dog was noted after eight weeks [84].
Thanks to its antioxidant potential, bee venom improves reproductive performance, immune response, and general health status, as demonstrated in rabbits [85,86]. Antioxidant, anti-inflammatory, and anti-apoptotic mechanisms were underlying the gastroprotective effect of bee venom in an experiment on mice [87]. When added as a supplement to food or water, bee venom has improved feed conversion and increased body weight in broiler chickens without adverse side effects [88,89], while administering bee venom through a live bee sting or by injection positively affected the growth, survival, and immunity of young pigs [90]. In vivo studies on dogs [91], broiler chickens [92], mice [93], and pigs [94] have also shown that bee venom acts as an immunoprophylactic agent, as well as having antibacterial, antifungal, and antiviral effects. The positive therapeutic effect of melittin in animals infected with methicillin-resistant Staphylococcus aureus (MRSA) opens up the possibility of its use in treating MRSA infections [95]. Finally, the antidiabetic potential of bee venom was studied on laboratory rats [96,97]. A single-dose injection (0.5 mg/kg) can be considered an initial step in demonstrating its antidiabetic effects [97]. Additionally, the selection of solvent for the use of dry bee venom in injectable and/or topical forms is crucial. This is because the degradation/dysfunction of a specific peptide and the more complex enzymatic bioactivity of the main components in the venom should be prevented, thereby preserving the desired effectiveness of the treatment. To ensure optimal antioxidant activity, we recommend to dissolve the venom in physiological saline [98].
5. Pollen
Due to its exceptional nutritional composition, bee-collected pollen (known as “bee pollen”) is considered a “functional food” and is most commonly recommended as a dietary supplement [99]. The potential of bee pollen in animal husbandry was mainly investigated in terms of its effect on growth performance, meat performance and slaughter yield. Adding bee pollen or its ethanolic extract to poultry feed in a range of 400–800 mg/kg feed effectively stimulated animal growth and improved broiler gut microflora [100]. However, this supplementation did not significantly improve meat performance and slaughter yield [101]. Even a ten-times higher concentration of bee pollen (7.5–20 g/kg feed) given to broilers did not affect slaughter yield despite significant improvement in the growth performance, the immune response [102,103,104], and the microbiological composition of the intestine [104].
The medicinal effects of bee pollen have been demonstrated only in treating benign prostatic hyperplasia and inflammation [105,106,107] and diabetes-induced testicular dysfunction [108] in experiments on rats.
From the above, one gets the impression that the evidence of the apitherapeutic effects of pollen on animals is scarce. This may be due to the poor bioavailability of pollen nutrients (due to the strong protective layers of pollen grains) and the extreme susceptibility of pollen to microbial spoilage. That is why it is necessary to work on the development of new technologies that will prevent pollen spoilage, protect sensitive pollen compounds, and ensure a higher degree of accessibility [99].
6. Royal Jelly
Very little data exists on the clinical effects of royal jelly. Here, we show only those that meet the criteria set for this review paper. In in vivo experiments (on laboratory rats), royal jelly was shown to protect the body from the toxic effects of chemotherapy, including nephrotoxicity, hepatotoxicity, and pulmonary fibrosis [109,110,111]. The anticancer potential of royal jelly has been demonstrated in experiments on mice [112,113]. Royal jelly is also attributed to anti-aging effects, supported by scientific evidence obtained in experiments on mice [114]. In immature rats, royal jelly has been found to promote folliculogenesis and increase ovarian hormones [115], and in ovariectomized rats, it improved bone strength [116] and showed potential in the prevention of osteoporosis [117]. It has been shown in rabbits to alleviate neurological disorders by increasing estrogen levels and the activity of the cholinergic and antioxidative systems while reducing cholesterol and restoring the autonomic nervous system [118]. Interestingly, royal jelly showed beneficial effects related to male fertility and reproductive success in rats [119,120,121], mice [122,123], and rabbits [124,125]. Bioactive components responsible for royal jelly biological activities are fatty acids (e.g., 10-hydroxy-2-decenoic acid), free amino acids, peptides, proteins, and phenolic compounds, among which the most numerous are flavonoids [126].
7. Drone Larvae
It is well known that animals have been eating honey bee larvae to provide significant amounts of energy and supplement their protein needs [2,127]. However, due to its importance for the bee colony, worker brood should not be taken, while drone brood can be used in cases where it is obtained as a by-product of the usual beekeeping procedure known as ’drone brood removal’ aimed at controlling the Varroa destructor mite. This is a biotechnical procedure in which beekeepers insert a special ’trap frame’ (frame with comb cells of a size corresponding to drone brood) into the hive to attract V. destructor mites (because drone brood is eight times more attractive to them than worker brood) [128]. When removing the ’trap frames’ from the hive, the entire drone brood is discarded. This product is generally considered waste and remains unused despite its exceptional chemical composition and high nutritional value (due to the presence of proteins, fatty acids, vitamins, hormones, and antioxidants) [127], as well as pharmacological properties [2] and potential that justify its use in maintaining health. In veterinary medicine, drone larvae are primarily valued for their androgenic effects and positive impact on reproductive performance, fertility, and/or productivity. Androgenic effects have been demonstrated in various animals (laboratory and farm animals), and the studies almost always used drone larvae homogenate (DLH), either freshly made or in the form of the commercial preparation ‘Apilarnil’ invented by Nicolae Iliesiu in 1981 in Romania. ’Apilarnil’ is made from drone larvae through a process that includes homogenization, filtration, and lyophilization [2]. For example, the androgenic effects of DLH have been demonstrated in rams when given as a dietary supplement (at doses of 10, 15, and 20 mg/kg body weight); all doses improved ram reproductive functions, reflected in increased offspring numbers. The optimal effect on sperm quality was achieved with a dose of 15 mg/kg of body weight administered once daily, which resulted in a 30.4% increase in ejaculate volume, a 14.3% increase in sperm concentration in ejaculate, and a sperm motility score of 9.2/10 points [129]. The androgenic effect of DLH in castrated laboratory rats was evidenced through a significant increase in the expression of Spot14-like androgen-inducible protein (SLAP) in rat prostate [130]. In male broilers, food supplementation with DLH during the growth period also led to androgenic effects; after 20 days of administration of DLH (4 g per broiler per day), secondary sexual characteristics (comb size, wattle size, and aggression) were more pronounced than in the control group [131]. When DLH was added to the feed of broilers aged 28 to 55 days, at doses of 2.5 and 7.5 g per individual, it also stimulated the development of secondary sexual characteristics and sexual maturation in males (increased testis weight, increased testosterone concentration, and comb size), but also led to a decrease in blood glucose and cholesterol levels and a reduction in fear [132]. Feed supplemented with DLH in doses of 2.5 and 7.5 g per individual did not stimulate the development of secondary sexual characteristics in female broilers, and anabolic effects were absent regardless of the sex of the broilers [132].
In females, the effect of DLH is different; it exhibited anabolic and actoprotective effects in young pigs when administered as a dietary supplement. Namely, given to gilts at a concentration of 25 mg of dry matter per 1 kg of food, DLH significantly increased in production parameters (live weight, average daily gain, and slaughter yield) [133]. Besides, DLH significantly affected hormone levels (increased cortisol and decreased testosterone), whereby a significantly better effect was achieved when selenopyran was introduced into the food along with DLH at a concentration of 1.2 mg per 1 kg of food [133]. An anabolic effect was also recorded in another experiment on gilts, where DLH was added to the feed (25 mg/kg of forage) for 180 days [134]; the results motivated the authors to suggest DLH as a valuable dietary additive in livestock farming for increasing meat production. However, in the same experiment, DLH significantly affected folliculogenesis in the ovaries; it stimulated the early stages of folliculogenesis but caused a decrease in the size of Graafian follicles with signs of atresia in the final stage of follicular development, along with changes in the expression of growth factors GDF9 and BMP15 responsible for proper follicle development and ovulation. In another study, supplementary feeding with 0.5 g of DLH daily enhanced the reproductive function of gilts during puberty by reducing the time to their first estrous cycle for artificial insemination [135]. Therefore, care should be taken when treating gilts with DHL (can be given to those intended for slaughter but not to those planned for reproduction).
Finally, in experimental rats, DLH showed neuroprotective potential and therapeutic potential in sepsis. Namely, it prevents the apoptosis of Purkinje cells by lowering the levels of pro-inflammatory cytokines (IL-6, TNF-α, IL-1β) that are elevated during sepsis and thus prevents sepsis-induced apoptosis in the brain [136]. It also protects the liver from lipopolysaccharide (LSP)-induced damage by reducing tissue damage, inhibiting the TLR4/HMGB-1/NF-κB signaling pathway, and protecting liver cells from DNA damage and oxidative stress [137].
8. Precautions and Final Remarks
It is important to state that products from the honey bee hive can be contaminated, both with chemicals (primarily acaricides used by beekeepers to control the honey bee mite Varroa destructor, but also with other pesticides commonly used in agriculture) and other environmental pollutants [138,139]. Fat-rich products such as wax and propolis, as well as hive air, are particularly prone to contamination [140,141,142]. Therefore, for the protection of health, strength, and immunity of honeybee colonies, it is advisable to either utilize products derived from natural sources, such as plants, algae, and fungi [143,144,145,146,147,148,149,150,151,152,153], or employ traditional beekeeping methods that minimize stress, reduce pathogens exposure, and negate the need for chemical treatments [154,155].
Finally, it should be emphasized that apitherapy in veterinary practice can only be applied after examination by a licensed veterinarian. Additionally, when using any bee product, care should be taken regarding the possibility of allergic reactions, though they are less commonly reported compared to humans. If an allergic reaction such as envenomation with honey bee venom is suspected, prompt veterinary attention should be sought to manage symptoms and prevent further complications [156,157].
Apitherapy has great potential for application in veterinary medicine, but officially, it can only be used as a complementary method of treatment. Growing evidence underscores the value of apitherapy in promoting animal health and vitality (Table 1). This leads to an increasing interest of animal owners and veterinarians in using bee products. However, they must recognize the potential risks associated with apitherapy, including potentially fatal anaphylactic reactions in certain cases [158,159]. However, further preclinical and clinical trials are necessary to comprehensively understand the basic mechanisms of action of bee products and determine the optimal doses and methods of their application in animals. |
Title: Co-occurrence of unhealthy lifestyle behaviours in middle-aged adults: findings from the Swedish CArdioPulmonary bioImage Study (SCAPIS) | Body:
Introduction
The prevalence of unhealthy lifestyle behaviours, such as excessive alcohol consumption, smoking, physical inactivity, and poor adherence to dietary recommendations, has been a growing concern in public health1,2. These unhealthy lifestyle behaviours can have significant negative impacts on individuals' health and increase the risk of chronic diseases1,2. Unhealthy lifestyle behaviours have been estimated to account for almost two-thirds of cardiovascular deaths in low-, middle- and high-income countries3, and approximately one-third of cancer cases could be prevented by improving lifestyle behaviours. Co-occurrence of unhealthy lifestyle behaviours reduces considerably the number of remaining years of life expected to live without disability and without chronic conditions4.
While studies have examined the co-occurrence of unhealthy lifestyle behaviours in the general adult population5,6, there is a limited understanding of the co-occurrence of multiple unhealthy behaviours in middle-aged adults. Based on studies, 15% of adults do not engage in unhealthy lifestyle behaviours5. In middle-aged and older adults, a higher percentage of participants avoid these behaviours, 28% and 31%, respectively7,8. The highest co-occurrence in the adult population was observed between physical inactivity and non-adherence to dietary recommendations (approximately 50%). Additionally, non-adherence to dietary recommendations and smoking were identified in 23–38% of the adult population5. One study7 in middle-aged adults in Australia studied the co-occurrence of six unhealthy lifestyle behaviours (smoking, unhealthy alcohol behaviour, unhealthy dietary behaviour, physical inactivity, sedentary behaviour, and insufficient sleep). This study showed that 31% of participants reported no risk behaviour, 37% had one unhealthy lifestyle behaviour, and 21%, 8%, 2%, 0.4%, and 0.04% had respectively two, three, four, five and six unhealthy lifestyle behaviours. Another Swedish study8 examined the co-occurrence of unhealthy lifestyle behaviours (smoking, risky alcohol consumption, insufficient physical activity, and poor diet) in middle-aged adults and found that 50% of the middle-aged adults had two unhealthy lifestyle behaviours and 18% had three.
A systematic literature review on the co-occurrence of unhealthy lifestyle behaviours in the adult population described the co-occurrence of harmful alcohol consumption and smoking as well as unhealthy diet and smoking5. Inconsistencies with respect to age groups were found in the literature, with one study9 showing that individuals aged 45–64 demonstrated lower likelihoods of engaging in two, three, or four risk behaviours compared to those aged 16–24, while another study revealed a contrasting pattern, as older participants (25–34 years, 35–44 years, 45–54 years) reported engaging in more risk behaviours than their younger counterparts (16–24 years)10. Socio-economic status and social support are well-known factors influencing the co-occurrence of unhealthy lifestyle behaviours. Socio-economic status is considered the strongest predictor of engaging in multiple health risk behaviours5,11. Additionally, good social support helps protect against adopting unhealthy lifestyle behaviours8,12. Overall, the literature is limited in middle-aged adults in being able to describe the prevalence of co-occurring unhealthy lifestyle behaviours, as well as how the prevalence differentiates between groups in society.
To develop targeted interventions for middle-aged adults which promote multiple lifestyle behaviours, we need to expand upon what is known about the prevalence of co-occurrence in this population. In addition, in order to tailor intervention content to specific target groups, it is necessary to estimate co-occurrence’s association with individuals’ characteristics13.
Therefore, this study investigated the co-occurrence of unhealthy lifestyle behaviours in a study population based of a random sample of middle-aged adults and the associations among unhealthy behaviours (alcohol consumption, diet, physical inactivity, and smoking). A secondary aim of this study was to investigate the associations between the co-occurrence of unhealthy lifestyle behaviours and individual level variables which are relatively easy to monitor and could, therefore, be used to defined groups of individuals who may benefit from, and be targets for, health behaviour interventions. These individual level variables included sociodemographic characteristics, socioeconomic factors, social support, and history of disease.
Methods
This study used data from the Swedish CArdioPulmonary bioImage Study (SCAPIS)14, a Swedish nationwide population-based cohort mainly designed to research cardiovascular and chronic obstructive pulmonary diseases. All Swedish residents have a unique personal identification number, which allowed unbiased and randomized recruitment from the Swedish population register. Initial contact was made by sending out an informational brochure asking recipient to contact the study centre. If the centre was not contacted, the recipient was reminded by up to three telephone calls and finally by letter. If the centre was contacted and the recipient was willing to participate in the study, an appointment was arranged at the study centre. No exclusion criteria applied except the inability to understand written and spoken Swedish for informed consent.
A protocol of the statistical analysis plan, was registered on the open science framework before start of the analysis15. The Regional Ethical Review Board approved SCAPIS as a multicentre trial (2014-33-32 M). All participants provided written informed consent, and the research was conducted in accordance with the Declaration of Helsinki. This study received ethical approval on 2021-05-18 from the Swedish Ethical Review Authority (Dnr 2021-02,121).
Outcomes and measures
Participants of SCAPIS were asked to complete a questionnaire, comprising of 140 questions, including questions on biological sex, age, socioeconomic factors, social support, history of disease, family history of disease, alcohol consumption, smoking, physical inactivity, and intake of food and drinks.
Socioeconomic factors assessed included educational level, employment status, marital status, financial preparedness for unforeseen expenses, budget management difficulties, living situation, and own and parents’ country of birth. Social support was evaluated using a validated condensed version of the Interview Schedule for Social Interaction16, with each of the twelve items assessed separately. Analysing each item separately allowed us to study which parts of social support specifically is associated with health behaviours, rather than only looking at social support as a whole, summarised into a single score. Self-reported medical history encompassed various conditions, including heart-related conditions (e.g. myocardial infarction and heart failure), lung diseases (e.g. chronic obstructive pulmonary disease (COPD) and chronic bronchitis), diabetes, and cancers. Family history of diseases in first-degree relatives was also queried.
Unhealthy alcohol consumption was assessed using the alcohol use disorders identification test (AUDIT)17. In this study, the last two questions (“Have you or someone else been injured because of your drinking?” and “Has a relative, friend, doctor, or other health care worker been concerned about your drinking or suggested you cut down?”) had only two response options (No/Yes) instead of the usual three (No/Yes, but not in the last year/Yes, during the last year). The AUDIT score ranges from 0 to 40, with higher scores indicating more harmful alcohol use. A score of 8 or higher typically indicates hazardous or harmful alcohol consumption. The Cronbach’s alpha in this study was 0.73.
Nonadherence to dietary recommendations was determined through food intake assessments using the MiniMeal-Q questionnaire18. This questionnaire covered 75 to 126 food items and inquired about dietary habits over the past few months. Energy (kJ/day) and macronutrient (g/day) intake were calculated using the national nutrient content database (www.slv.se). The Swedish Healthy Eating Index score (SHEI-score) was utilised to gauge adherence to dietary recommendations19. This index reflects the ratio between the recommended consumption of nine specified foods and the nutrient intake outlined in the 2012 Nordic Recommendations. These nine specified foods and the nutrient intake were:At least 500 g vegetables and fruit per day (potatoes not included)At least 2.5 g fibre/MJAt least 75 g wholemeal/10 MJAt least 45 g fish and shellfish per day (frequency of 2–3 per week and portion size 125 g)Polyunsaturated Fatty Acids (PUFA) minimum 7.5 E%Monounsaturated fatty acids (MUFA) minimum 15 E%Saturated Fatty Acids (SFA) maximum 10 E%Maximum 500 g red and processed meat per weekMaximum 10 E% added sugar
Scores for each item range from 0 (nonadherence to the dietary recommendation) to 1 (adherent to the dietary recommendation), and the total score ranges from 0 to 9, with lower scores indicating poorer adherence to dietary recommendations. It’s worth noting that, for consistency with other behaviour measures, the scale was reversed in the analyses enclosed, so higher scores indicate lower adherence and vice versa. Participants in the first, second, and third quartiles were considered non-adherent to dietary guidelines.
Physical inactivity was defined following the definition by the World Health Organization as insufficient physical activity to meet current recommendations20. For adults aged 18–64, this entails at least 150–300 min of moderate-intensity aerobic activity or at least 75–150 min of vigorous-intensity aerobic activity per week. It should also include muscle-strengthening activities involving all major muscle groups two or more days per week. Physical inactivity was assessed with a question regarding exercise frequency over the past three months to improve endurance and well-being. Response options included: Never; Sometimes, but not regularly; 1–2 times a week; 2–3 times a week; More than 3 times a week; Not willing/able to reply. “Never” or “Sometimes, but not regularly” indicated physical inactivity as these participants were considered to not have sufficient physical activity to meet current recommendations.
Smoking status was determined with a single question: “Do you smoke?” Response options included: No, I never smoked; No, I quit smoking; Yes, I smoke occasionally; Yes, I smoke regularly; Not willing/able to reply. Participants who stated they were current smokers during an oral inquiry at a study visit, but answered on the questionnaire that they did not smoke, were considered current smokers.
Data analyses
We used multilevel multinomial regression to estimate the cohort prevalence of co-occurrence of two and three unhealthy lifestyle behaviours and the prevalence of all four unhealthy lifestyle behaviours. The levels were the six different study centres for which adaptive intercepts were added to the models. ‘No unhealthy behaviours’ was used as the reference category, and adaptive intercepts were added for study site. We added covariates to the multinomial regression models to estimate conditional associations between the co-occurrence of unhealthy lifestyle behaviours and sociodemographic characteristics (including age and biological sex), socioeconomic factors, social support, and history of disease. To estimate the associations among unhealthy lifestyle behaviours, we regressed each health behaviour measure (AUDIT, SHEI-score, physical inactivity, and smoking) against the other health behaviour measures, e.g., for AUDIT, we used SHEI-score, physical inactivity, and smoking as covariates. Interactions among the covariates were also added. We used negative binomial regression for AUDIT, linear regression for SHEI-scores, and logistic regression for physical inactivity and smoking. These models also included adaptive intercepts for study site.
We used Bayesian inference to estimate posterior distributions of associations21. Unlike maximum likelihood inference with null hypothesis testing, Bayesian inference results in a probability distribution over quantities of interest (in this case, associations). Thus, rather than focusing only on rejecting or keeping the narrow null hypothesis, Bayesian inference assigns a probability to each possible value of the associative measure (e.g. an odds ratio). This allows for a more straightforward inspection of the relative compatibility between the data and different association estimates. The distribution of estimates is referred to as the posterior distribution since it incorporates our prior belief (prior distribution) and the data observed. For reporting it is convenient to communicate a point estimate of the association rather than the full posterior distribution, and we used the median of the posterior distribution for this purpose. To portray the posterior distribution and the inherent uncertainty of the estimates, we used 95% compatibility intervals (CI) defined as the 2.5% and 97.5% percentiles of the posterior distributions. We used standard normal priors for all parameters in the multinomial cohort prevalence models. In models with covariates, we used Cauchy priors to induce sparsity, with a half-normal hyperprior for the Cauchy scale parameter. Data were analysed using the R statistical software version 4.0.4 and Stan 2.30.1 (CmdStan).
Outcomes and measures
Participants of SCAPIS were asked to complete a questionnaire, comprising of 140 questions, including questions on biological sex, age, socioeconomic factors, social support, history of disease, family history of disease, alcohol consumption, smoking, physical inactivity, and intake of food and drinks.
Socioeconomic factors assessed included educational level, employment status, marital status, financial preparedness for unforeseen expenses, budget management difficulties, living situation, and own and parents’ country of birth. Social support was evaluated using a validated condensed version of the Interview Schedule for Social Interaction16, with each of the twelve items assessed separately. Analysing each item separately allowed us to study which parts of social support specifically is associated with health behaviours, rather than only looking at social support as a whole, summarised into a single score. Self-reported medical history encompassed various conditions, including heart-related conditions (e.g. myocardial infarction and heart failure), lung diseases (e.g. chronic obstructive pulmonary disease (COPD) and chronic bronchitis), diabetes, and cancers. Family history of diseases in first-degree relatives was also queried.
Unhealthy alcohol consumption was assessed using the alcohol use disorders identification test (AUDIT)17. In this study, the last two questions (“Have you or someone else been injured because of your drinking?” and “Has a relative, friend, doctor, or other health care worker been concerned about your drinking or suggested you cut down?”) had only two response options (No/Yes) instead of the usual three (No/Yes, but not in the last year/Yes, during the last year). The AUDIT score ranges from 0 to 40, with higher scores indicating more harmful alcohol use. A score of 8 or higher typically indicates hazardous or harmful alcohol consumption. The Cronbach’s alpha in this study was 0.73.
Nonadherence to dietary recommendations was determined through food intake assessments using the MiniMeal-Q questionnaire18. This questionnaire covered 75 to 126 food items and inquired about dietary habits over the past few months. Energy (kJ/day) and macronutrient (g/day) intake were calculated using the national nutrient content database (www.slv.se). The Swedish Healthy Eating Index score (SHEI-score) was utilised to gauge adherence to dietary recommendations19. This index reflects the ratio between the recommended consumption of nine specified foods and the nutrient intake outlined in the 2012 Nordic Recommendations. These nine specified foods and the nutrient intake were:At least 500 g vegetables and fruit per day (potatoes not included)At least 2.5 g fibre/MJAt least 75 g wholemeal/10 MJAt least 45 g fish and shellfish per day (frequency of 2–3 per week and portion size 125 g)Polyunsaturated Fatty Acids (PUFA) minimum 7.5 E%Monounsaturated fatty acids (MUFA) minimum 15 E%Saturated Fatty Acids (SFA) maximum 10 E%Maximum 500 g red and processed meat per weekMaximum 10 E% added sugar
Scores for each item range from 0 (nonadherence to the dietary recommendation) to 1 (adherent to the dietary recommendation), and the total score ranges from 0 to 9, with lower scores indicating poorer adherence to dietary recommendations. It’s worth noting that, for consistency with other behaviour measures, the scale was reversed in the analyses enclosed, so higher scores indicate lower adherence and vice versa. Participants in the first, second, and third quartiles were considered non-adherent to dietary guidelines.
Physical inactivity was defined following the definition by the World Health Organization as insufficient physical activity to meet current recommendations20. For adults aged 18–64, this entails at least 150–300 min of moderate-intensity aerobic activity or at least 75–150 min of vigorous-intensity aerobic activity per week. It should also include muscle-strengthening activities involving all major muscle groups two or more days per week. Physical inactivity was assessed with a question regarding exercise frequency over the past three months to improve endurance and well-being. Response options included: Never; Sometimes, but not regularly; 1–2 times a week; 2–3 times a week; More than 3 times a week; Not willing/able to reply. “Never” or “Sometimes, but not regularly” indicated physical inactivity as these participants were considered to not have sufficient physical activity to meet current recommendations.
Smoking status was determined with a single question: “Do you smoke?” Response options included: No, I never smoked; No, I quit smoking; Yes, I smoke occasionally; Yes, I smoke regularly; Not willing/able to reply. Participants who stated they were current smokers during an oral inquiry at a study visit, but answered on the questionnaire that they did not smoke, were considered current smokers.
Data analyses
We used multilevel multinomial regression to estimate the cohort prevalence of co-occurrence of two and three unhealthy lifestyle behaviours and the prevalence of all four unhealthy lifestyle behaviours. The levels were the six different study centres for which adaptive intercepts were added to the models. ‘No unhealthy behaviours’ was used as the reference category, and adaptive intercepts were added for study site. We added covariates to the multinomial regression models to estimate conditional associations between the co-occurrence of unhealthy lifestyle behaviours and sociodemographic characteristics (including age and biological sex), socioeconomic factors, social support, and history of disease. To estimate the associations among unhealthy lifestyle behaviours, we regressed each health behaviour measure (AUDIT, SHEI-score, physical inactivity, and smoking) against the other health behaviour measures, e.g., for AUDIT, we used SHEI-score, physical inactivity, and smoking as covariates. Interactions among the covariates were also added. We used negative binomial regression for AUDIT, linear regression for SHEI-scores, and logistic regression for physical inactivity and smoking. These models also included adaptive intercepts for study site.
We used Bayesian inference to estimate posterior distributions of associations21. Unlike maximum likelihood inference with null hypothesis testing, Bayesian inference results in a probability distribution over quantities of interest (in this case, associations). Thus, rather than focusing only on rejecting or keeping the narrow null hypothesis, Bayesian inference assigns a probability to each possible value of the associative measure (e.g. an odds ratio). This allows for a more straightforward inspection of the relative compatibility between the data and different association estimates. The distribution of estimates is referred to as the posterior distribution since it incorporates our prior belief (prior distribution) and the data observed. For reporting it is convenient to communicate a point estimate of the association rather than the full posterior distribution, and we used the median of the posterior distribution for this purpose. To portray the posterior distribution and the inherent uncertainty of the estimates, we used 95% compatibility intervals (CI) defined as the 2.5% and 97.5% percentiles of the posterior distributions. We used standard normal priors for all parameters in the multinomial cohort prevalence models. In models with covariates, we used Cauchy priors to induce sparsity, with a half-normal hyperprior for the Cauchy scale parameter. Data were analysed using the R statistical software version 4.0.4 and Stan 2.30.1 (CmdStan).
Results
A total of 59,909 individuals were invited to participate in SCAPIS, with 30,154 (50.3%) choosing to join. On average, participants were 58 years old (SD = 4), and 51% were female (n = 15,508). The majority were married (71%, n = 21,514), possessed at least a secondary school education (88%, n = 26,553), were employed (81%, n = 24,483), and were born in Europe (91%, n = 27,519). Prevalent diseases, affecting ten percent or more of the population, included hypertension (22%, n = 6,620) and hyperlipidaemia (11%, n = 3,425). Over a quarter of participants had a family history of myocardial infarction (27%, n = 8,299), diabetes (26%, n = 7,929), and stroke (26%, n = 7,766) (Supplementary Table 1).
The median posterior AUDIT score in the cohort was 3.8 (95% CI = 3.44; 4.08), and the median posterior prevalence of hazardous and harmful alcohol consumption was 7.7% (95% CI = 6.1%; 10.8%). The median posterior Swedish Healthy Eating Index score (reversed) was 6.0 (95% CI = 5.7; 6.2). The median posterior prevalence of physical inactivity was 50% (95% CI = 46.3%; 53.5%), and the median posterior prevalence of current smokers was 12.8% (95% CI = 9.2%; 18.8%).
Prevalence of co-occurrence of unhealthy lifestyle behaviours
Figure 1 depicts the prevalence of co-occurrence of unhealthy lifestyle behaviours. Data was not available to calculate the number of unhealthy behaviours for 17% (n = 5130) of participants; therefore, when estimating prevalences of the number of unhealthy behaviours we included participants with data available for all behaviours. In total, 14% of participants did not engage in unhealthy lifestyle behaviours (n = 3500). A single unhealthy behaviour was observed among 38% of participants (n = 9613), while two unhealthy behaviours were observed among 36% (n = 9051). Ten per cent of participants were observed to have three unhealthy behaviours (n = 2497), and only 2% were observed to have all four (n = 363).Fig. 1Prevalence of (co)occurrence of unhealthy lifestyle behaviours (unhealthy alcohol consumption, smoking, sedentary lifestyle and non-adherence to dietary recommendations).
Prevalence and conditional associations of the co-occurrence of unhealthy lifestyle behaviours
In Supplementary Table 2, the estimated prevalence of combined unhealthy lifestyle behaviours from multilevel logistic regression are presented. The most prevalent combination of unhealthy lifestyle behaviours was physical inactivity paired with non-adherence to dietary recommendations, estimated to 38% (n = 10,573). The prevalence of smoking in conjunction with nonadherence to dietary recommendations was estimated to 9% (n = 2742), while the prevalence of smoking combined with physical inactivity was estimated to 8% (n = 2522). The prevalence of unhealthy alcohol consumption alongside nonadherence to dietary guidelines was estimated to 7% (n = 2093), and the prevalence of unhealthy alcohol consumption paired with physical inactivity was estimated to 5% (n = 1576). Finally, the prevalence of both unhealthy alcohol consumption and smoking was estimated to 2% (n = 643).
Figures 2, 3, 4, and 5 depict the posterior distributions of the estimated associations among unhealthy lifestyle behaviours. Participants who were smokers scored higher on AUDIT, after having adjusted for the other two lifestyle behaviours. Similarly, participants with less adherence to dietary recommendations also scored higher on AUDIT. Smoking was more likely among physically inactive participants and participants who were less adherent to dietary recommendations. The combination of both physical inactivity and non-adherence to dietary recommendations further increased the likelihood of smoking. Being less adherent to dietary recommendations was also associated with being physically inactive. Please see Supplementary Tables 3 through 6 for full numerical details.Fig. 2 Posterior distributions of conditional associations between AUDIT scores and diet (SHEI), physical inactivity, and smoking. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 3 Posterior distributions of conditional associations between SHEI scores and AUDIT, physical inactivity, and smoking. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 4 Posterior distributions of conditional associations between physical inactivity and AUDIT, SHEI, and smoking. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 5Posterior distributions of conditional associations between smoking and AUDIT, SHEI, and physical inactivity. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.
Factors associated with co-occurrence of unhealthy lifestyle behaviours
In Figs. 6, 7, and 8, posterior distributions are presented of estimated conditional associations between the number of unhealthy lifestyle behaviours and: socioeconomic factors (Fig. 6), social support (Fig. 7), and history of disease and family history of disease (Fig. 8). For full numerical details, please see Supplementary Tables 7 to 9. Men, even when accounting for socioeconomic factors, social support, and history of disease and family history of disease, tended to have more often co-occurrence of unhealthy lifestyle behaviours compared to women. Age did not appear to play a major role in the co-occurrence of these behaviours, noting that the age-range in the study population was narrow.Fig. 6Conditional associations between socioeconomic factors and the number of unhealthy lifestyle behaviours. Associations are given in odds ratios with no unhealthy lifestyle behaviours as reference category. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 7Conditional associations between social support and the number of unhealthy lifestyle behaviours. Associations are given in odds ratios with no unhealthy lifestyle behaviours as reference category. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 8Conditional associations between history of disease and family history of disease and the number of unhealthy lifestyle behaviours. Associations are given in odds ratios with no unhealthy lifestyle behaviours as reference category. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.
We found that participants with a university or college degree were less likely to have three or four co-occurrent unhealthy lifestyle behaviours than participants with low education those who only finished elementary school, or those with a high school degree. We also found that participants who had difficulties managing regular expenses were more likely to have three or four co-occurring unhealthy lifestyle behaviours. There was also a marked association between reporting having no one around who appreciated one’s efforts and a higher number of unhealthy lifestyle behaviours. Finally, we found that participants with a lung disease (such as COPD, chronic bronchitis, emphysema, tuberculosis, or other lung diseases) were more likely to have three or four co-occurring unhealthy lifestyle behaviours.
Prevalence of co-occurrence of unhealthy lifestyle behaviours
Figure 1 depicts the prevalence of co-occurrence of unhealthy lifestyle behaviours. Data was not available to calculate the number of unhealthy behaviours for 17% (n = 5130) of participants; therefore, when estimating prevalences of the number of unhealthy behaviours we included participants with data available for all behaviours. In total, 14% of participants did not engage in unhealthy lifestyle behaviours (n = 3500). A single unhealthy behaviour was observed among 38% of participants (n = 9613), while two unhealthy behaviours were observed among 36% (n = 9051). Ten per cent of participants were observed to have three unhealthy behaviours (n = 2497), and only 2% were observed to have all four (n = 363).Fig. 1Prevalence of (co)occurrence of unhealthy lifestyle behaviours (unhealthy alcohol consumption, smoking, sedentary lifestyle and non-adherence to dietary recommendations).
Prevalence and conditional associations of the co-occurrence of unhealthy lifestyle behaviours
In Supplementary Table 2, the estimated prevalence of combined unhealthy lifestyle behaviours from multilevel logistic regression are presented. The most prevalent combination of unhealthy lifestyle behaviours was physical inactivity paired with non-adherence to dietary recommendations, estimated to 38% (n = 10,573). The prevalence of smoking in conjunction with nonadherence to dietary recommendations was estimated to 9% (n = 2742), while the prevalence of smoking combined with physical inactivity was estimated to 8% (n = 2522). The prevalence of unhealthy alcohol consumption alongside nonadherence to dietary guidelines was estimated to 7% (n = 2093), and the prevalence of unhealthy alcohol consumption paired with physical inactivity was estimated to 5% (n = 1576). Finally, the prevalence of both unhealthy alcohol consumption and smoking was estimated to 2% (n = 643).
Figures 2, 3, 4, and 5 depict the posterior distributions of the estimated associations among unhealthy lifestyle behaviours. Participants who were smokers scored higher on AUDIT, after having adjusted for the other two lifestyle behaviours. Similarly, participants with less adherence to dietary recommendations also scored higher on AUDIT. Smoking was more likely among physically inactive participants and participants who were less adherent to dietary recommendations. The combination of both physical inactivity and non-adherence to dietary recommendations further increased the likelihood of smoking. Being less adherent to dietary recommendations was also associated with being physically inactive. Please see Supplementary Tables 3 through 6 for full numerical details.Fig. 2 Posterior distributions of conditional associations between AUDIT scores and diet (SHEI), physical inactivity, and smoking. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 3 Posterior distributions of conditional associations between SHEI scores and AUDIT, physical inactivity, and smoking. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 4 Posterior distributions of conditional associations between physical inactivity and AUDIT, SHEI, and smoking. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 5Posterior distributions of conditional associations between smoking and AUDIT, SHEI, and physical inactivity. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.
Factors associated with co-occurrence of unhealthy lifestyle behaviours
In Figs. 6, 7, and 8, posterior distributions are presented of estimated conditional associations between the number of unhealthy lifestyle behaviours and: socioeconomic factors (Fig. 6), social support (Fig. 7), and history of disease and family history of disease (Fig. 8). For full numerical details, please see Supplementary Tables 7 to 9. Men, even when accounting for socioeconomic factors, social support, and history of disease and family history of disease, tended to have more often co-occurrence of unhealthy lifestyle behaviours compared to women. Age did not appear to play a major role in the co-occurrence of these behaviours, noting that the age-range in the study population was narrow.Fig. 6Conditional associations between socioeconomic factors and the number of unhealthy lifestyle behaviours. Associations are given in odds ratios with no unhealthy lifestyle behaviours as reference category. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 7Conditional associations between social support and the number of unhealthy lifestyle behaviours. Associations are given in odds ratios with no unhealthy lifestyle behaviours as reference category. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.Fig. 8Conditional associations between history of disease and family history of disease and the number of unhealthy lifestyle behaviours. Associations are given in odds ratios with no unhealthy lifestyle behaviours as reference category. Points represent the median of the posterior distributions, orange bars the interquartile range, and blue bars the 2.5% and 97.5% percentiles.
We found that participants with a university or college degree were less likely to have three or four co-occurrent unhealthy lifestyle behaviours than participants with low education those who only finished elementary school, or those with a high school degree. We also found that participants who had difficulties managing regular expenses were more likely to have three or four co-occurring unhealthy lifestyle behaviours. There was also a marked association between reporting having no one around who appreciated one’s efforts and a higher number of unhealthy lifestyle behaviours. Finally, we found that participants with a lung disease (such as COPD, chronic bronchitis, emphysema, tuberculosis, or other lung diseases) were more likely to have three or four co-occurring unhealthy lifestyle behaviours.
Discussion
Overall, 14% of middle-aged adults did not report engaging in any of the unhealthy lifestyle behaviours measured in this study, which included harmful alcohol consumption, not adhering to dietary recommendations, physical inactivity, and smoking. A similar finding of the proportion of individuals abstaining from these behaviours was noted in population-based Eurobarometer data (15%)6. The Eurobarometer data study examined the prevalence of five unhealthy lifestyle behaviours, specifically smoking, excessive alcohol consumption, infrequent consumption of fresh fruits, physical inactivity, and irregular dental check-ups. However, two studies involving middle-aged and older adults found twice as many individuals not engaging in unhealthy lifestyle behaviours (28 and 31%)7,8.
Around one-third engaged in one or two unhealthy lifestyle behaviours, respectively. This finding aligns with population-based data from Eurobarometer6, which observed a prevalence of 29% for a single unhealthy lifestyle behaviour and 30% for two such behaviours among participants. Similar results for engaging in one unhealthy lifestyle behaviour (37%) was found in an Australian study, but it noted that one-fifth of the participants reported two unhealthy lifestyle behaviours7. Another Swedish study in middle-aged adults found that a higher amount that reported two unhealthy lifestyle behaviours (50%)8. Finally, we found that 10% of middle-aged adults engaged in three unhealthy lifestyle behaviours, while only 2% engaged in four. These findings are in line with Australian findings, which found that 8% of participants engaged in three unhealthy lifestyle behaviours and 2% in four7. A Swedish study reported approximately twice the amount of middle-aged adults engaged in three unhealthy lifestyle behaviours (18%) three, and similar amount of middle-aged adults engaged in this study in four unhealthy lifestyle behaviours (3%)8.
In our study, the highest co-occurrence was observed between physical inactivity and non-adherence to dietary recommendations. These findings align with studies included in a literature review involving the adult population5, where the prevalence of co-occurrence was found to be within the range of 47% to 54% and with a Swedish study where the co-occurrence was found in 46%8. Non-adherence to dietary recommendations and smoking were identified in 23–38% of the adult population5, whereas in our study, this co-occurrence was only prevalent in 9% of the middle-aged population. Another Swedish study showed an even lower percentage for this co-occurrence (0.4%)8. These results are not surprising, as the prevalence of smoking has reduced drastically in Sweden over the past decade22. The odds of smoking were increased among those with higher AUDIT and SHEI scores and those who were physically inactive, suggesting that smokers were more likely than not to have other unhealthy lifestyle behaviours. This was confirmed in another Swedish study, where they found that most smokers (88%) reported three or four unhealthy lifestyle behaviour.
We found that participants with a lower educational level and having difficulties managing expenses were more likely to have 3 or 4 co-occurring unhealthy behaviours. These findings are corroborated by studies included in a systematic review5, showing that socio-economic status was the strongest predictor of engaging in multiple health risk behaviours. We also found that middle-aged adults who felt that they had no one around who appreciated their efforts were more likely to be unhealthy with respect to all four behaviours included in this study. The importance of the quality of social support for health was confirmed by a Swedish cohort study23, including older adults, showing that a rich social network protected participants from having multiple unhealthy lifestyle behaviours8.
Most studies of public health interventions have been on interventions targeting one unhealthy lifestyle behaviour, even if it is recognised that interventions targeting more than one behaviour could be more effective24. A scoping review showed that it is rare that interventions target all four health behaviours studied here, and of those interventions that target multiple behaviours, more than half target unhealthy diet and insufficient physical activity (the most prevalent co-occurrence found in this study)25. However, several ongoing trials in a range of populations will provide evidence of the benefits and drawbacks of simultaneously targeting multiple behaviours in the near future26–28. Our study showed that the efficacy of multi-behavioural interventions in middle-aged adults should be focussing on simultaneous targeting physical inactivity and unhealthy diet, given their high co-occurrence in this population. Future research should also assess the underlying mechanisms in middle-aged adults driving the associations observed in our study, for example why in middle-aged adults that have lower quality of social support lead to multiple unhealthy lifestyle behaviours and assess which public health policies could impact the co-occurrence of unhealthy lifestyles. Longitudinal studies are needed to establish causal relationships between sociodemographic characteristics, socioeconomic factors, social support, and history of disease on the co-occurrence of unhealthy lifestyle behaviours.
Methodological considerations
This cross-sectional study used statistical methods that estimated conditional associations between variables. Therefore, estimates of associations should not be considered causal as we cannot rule out confounding and collider bias. In addition, the study relied on self-reported variables, which may introduce bias29–32. The findings are nevertheless helpful in describing the state of the study population, as this gives insights into where targeted interventions are needed. A related limitation is that the study cannot explain the underlying reasons for the observed associations. Socio-demographic disparities persist partially due to the uneven distribution of health-related behaviours, though this study cannot explain why this continues to be the case.
Another limitation of this study is that the sample is selective, consisting of those who could and chose to participate in SCAPIS, making it less representative of all middle-aged adults in Sweden. A discrepancy can, for instance, be seen when considering harmful alcohol consumption, which has been found to be much more prevalent in general population surveys. However, the estimated associations between factors are still valid if participation was not selectively conditional on them. For instance, the association between education and the co-occurrence of unhealthy behaviours is valid if it is not different in those who participated and those who did not. While this is less likely to be the case than selectiveness based on health behaviours alone, it is still a limitation to be borne in mind when interpreting findings.
Methodological considerations
This cross-sectional study used statistical methods that estimated conditional associations between variables. Therefore, estimates of associations should not be considered causal as we cannot rule out confounding and collider bias. In addition, the study relied on self-reported variables, which may introduce bias29–32. The findings are nevertheless helpful in describing the state of the study population, as this gives insights into where targeted interventions are needed. A related limitation is that the study cannot explain the underlying reasons for the observed associations. Socio-demographic disparities persist partially due to the uneven distribution of health-related behaviours, though this study cannot explain why this continues to be the case.
Another limitation of this study is that the sample is selective, consisting of those who could and chose to participate in SCAPIS, making it less representative of all middle-aged adults in Sweden. A discrepancy can, for instance, be seen when considering harmful alcohol consumption, which has been found to be much more prevalent in general population surveys. However, the estimated associations between factors are still valid if participation was not selectively conditional on them. For instance, the association between education and the co-occurrence of unhealthy behaviours is valid if it is not different in those who participated and those who did not. While this is less likely to be the case than selectiveness based on health behaviours alone, it is still a limitation to be borne in mind when interpreting findings.
Conclusions
This study highlights the need for targeted interventions addressing multiple unhealthy lifestyle behaviours, especially among socio-economically disadvantaged individuals and those with low-quality social support. Future research and public health efforts should consider strategies to simultaneously address unhealthy lifestyle behaviours and promote a comprehensive approach to healthy ageing in middle-aged adults.
Supplementary Information
Supplementary Information. |
Title: Interdisciplinary cooperation with solution-focused brief therapy to reduce job stress, burnout, and coping in Chinese nurses: A randomised controlled trial | Body:
1
Introduction
As China's social and economic development progresses and material living standards improve, its healthcare needs are changing, driving the continuous expansion and innovative development of nursing service content and service models. In recent years, the rapid advancements in medical diagnostics and treatment technologies have placed heightened demands on nurses' continuous learning capabilities and nursing work quality [1]. However, there is a widespread shortage of nursing personnel in Chinese hospitals. China's nursing personnel per thousand people and the bed-to-nurse ratio are remarkably lower than those in developed nations, substantially increasing the intensity of nursing work and the work pressure on nurses. In Chinese urban public hospitals, 74.73 % of nurses report experiencing high work stress [2], remarkably surpassing the 62 % rate observed in a work stress survey of American nurses conducted by Warshawsky and Havens [3]. Several studies have indicated that 37.2 % of Chinese nurses exhibit more than moderate job stress, nearly 37.5 % of nurses experience medium to high levels of job burnout, and the score for Chinese nurses' active coping abilities is low [4,5]. Within China's public healthcare environment, there may be major discrepancies in the comprehension of the same medical information and examination results between nurses and patients, leading to patient mistrust, lack of understanding, and disrespect for nurses' work, further intensifying the public opinion pressure on nurses [6]. The chasm between expectations and reality has greatly eroded many nurses' confidence, potentially leading to issues such as physical discomfort, increased susceptibility to illnesses and diseases, inattention, and reduced work efficiency. In severe cases, this may contribute to anxiety, depression, and other mental health disorders [7,8].
This situation has raised concerns among managers. The implementation plan of health policies and service improvement action plans includes the promotion of patients' inclination to seek medical treatment, as well as the provision of care for nurses and improvement of their practice environment. Therefore, managers must reduce work intensity and allocate nursing resources effectively to alleviate nurses' workload. Additionally, effective measures are needed to mitigate nurses' job burnout, enhance their ability to cope with stress, and harness their resilience. However, research has been hampered by a lack of psychological knowledge among nursing staff. In such cases, the involvement of psychological professionals becomes crucial. However, there is currently no reported research on interdisciplinary cooperation for carrying out such intervention activities.
Solution-focused brief therapy (SFBT) is a psychological intervention model that builds upon the traditional problem-solving approach, providing individuals with a more positive and respectful framework. SFBT helps individuals explore constructive solutions by focusing on their existing resources and how to use them for positive transformation [9]. In 1970, De Shazer [10] introduced the concept of the brief solution-focused model, which later evolved into SFBT. SFBT has been widely used in psychiatric treatment, clinical psychological intervention, and school psychological counselling, demonstrating remarkable short-term effects [11]. Franklin and Hai [12] first reviewed the results of SFBT in substance use treatment. They found that SFBT can improve substance use and comorbid mental health and psychosocial problems such as depression, trauma, and school- and work-related behaviour problems. In addition, a review of a 2021 study found that incorporating emotion-centred communication into SFBT intervention programmes would provide more assistance to a wider range of participants [13].
In nursing research, SFBT was initially applied to mental health nurses involved in patient care [14,15]. Subsequently, SFBT was gradually used in intervention studies targeting adolescents, patients, and nurses. SFBT has been proven to exert a positive impact on the mental health of adolescents and patients with cancer [16,17]. Zak et al. [18] reviewed SFBT effectiveness in different populations; high confidence in evidence of effectiveness was established for depression, overall mental health, and progress towards individual goals for the adult population. For nurses, the current SFBT interventions are based on a psychological perspective, using different group counselling activities aimed at helping nurses manage emotions and develop coping skills, thereby reducing their anxiety levels, improving their psychological resilience and reporting rates of minor adverse events, and producing positive results [[19], [20], [21]]. However, there are currently no research reports on SFBT interventions for nurses’ job stress from a nursing perspective.
Therefore, this study aimed to compare the potential differences in job stress, job burnout, and coping style between an experimental group receiving SFBT and a control group receiving routine psychological intervention among nurses before and after SFBT intervention. This study also explored the application of SFBT in the context of interdisciplinary collaboration.
2
Material and methods
2.1
Ethical considerations
This study received approval from the Ethics Committee of Dalian Municipal Central Hospital (approval number: YN2022-109-01). Before the survey, clinical nurses participating in this study were informed about the purpose, content, and time commitment. They were given informed consent forms and questionnaires to ensure voluntary participation. This ensured that individuals were aware of their right to freely participate or withdraw from the study without any harm to their interests.
2.2
Study design
This study was a single-blinded randomised controlled trial with a pre-test, post-test design. The study was conducted between 1 May and December 1, 2022 in a tertiary general hospital in Dalian, Liaoning Province, China.
2.3
Team construction
A research team consisting of mentors from nursing and psychology disciplines was established. The nursing team comprised a supervisor with a master's degree in nursing and two master's degree in nursing students. Their responsibilities included preliminary research, participant selection, assistance in treatment activities, questionnaire collection, and result analysis. The psychology team consisted of a supervisor with a master's degree in psychology and three graduate students majoring in psychology, all qualified as second-class psychological counsellors. The psychology team was responsible for the overall planning and development of treatment activities, ensuring standardised implementation of weekly intervention activities, quality control during the process, and the proper application of SFBT. The research plan, objectives, and implementation rules were set by the double-mentor team. An open and effective information exchange model was established between the two teams and team members to ensure successful study execution.
2.4
Sample size
This was a randomised controlled intervention study. The sample size for this study was determined based on the following formula: according to standard statistical methods [22,23], in which n1 and n2 are the number of participants in groups 1 and 2. Assuming standard α and β values of 0.05 and 0.10, respectively, the values for Zα and Zβ were 1.64 and 1.28, respectively. According to the results of the preliminary experiment in this study, the difference between the mean values of the two groups of variables (δ) was 5.40 and the standard deviation of the two sample populations (σ) was 5.81. This yielded a sample size estimate of 20 per group after rounding. Assuming a maximum loss rate of 30 %, the final number of participants in the experimental and control groups was determined to be 30. Therefore, 60 eligible nurses were recruited and randomly assigned to the control (n = 30) and an experimental (n = 30) group using a random number method. Nurses in the control group received a routine psychological intervention, whereas nurses in the experimental group received an SFBT intervention conducted by the teachers and students of the psychology team.n1=n2=2[(Zα+Zβ)σδ]2
2.5
Participants
The inclusion criteria were as follows: possessing a Chinese nurse practice qualification certificate, working in a clinical nursing role, experiencing burnout in two or more dimensions according to the Maslach Burnout Inventory, and having an average score of ≥2.5 on the Chinese Nurses Stressor Scale. All participants were required to provide voluntary informed consent. The exclusion criteria included severe physical or mental illness, maternity leave, sick leave, retirement, or pursuing further study during the study period. The participants who discontinued their involvement after the second activity were also excluded. Written informed consent was obtained from all participants, and they were thoroughly briefed on the study's purpose, importance, and methods.
2.6
Study intervention
This study was conducted from May 2022 to December 2022. Before commencing the activities, a meeting was held to establish the intervention goal and implementation plan, agree on the cooperation model and work content between the two parties, and clarify the division of tasks, responsibilities, and implementation rules for each discipline team and its members.
2.7
SFBT intervention measures
In this study, the SFBT programme included six sets of activities, each scheduled at 1-week intervals (Table 1). Week 1 focused on establishing a group contract and a ‘warm-up’ unit. The 30 nurses in the experimental group were divided into five groups of six people each. The members introduced themselves, built trust, and engaged in ice-breaking activities to foster a positive atmosphere of team cooperation. The aim was to help participants relieve psychosomatic pressure, acquire stress management skills, and explore internal and external resources for sustainable mental and physical well-being.Table 1Solution-focused brief therapy activities.Table 1WeekThemeContentTimeWeek1Stress management1.The concept of "stress"120 min2.”Massage”3.Hello, team.4.Pass the parce-Introduce myself5.Pressure circle activityWeek2Emotional rainbow1.The passions120 min2.Our emotions3.What's my anger?4.Emotional test5.Meditate and relax6.Emotional icebergWeek3emotional balloon1.Large and small watermelons120 min2.The master of emotions3.Creative emotional balloon4.Cognitive Behavioral Therapy intervention in anger Control5.Know anger, watch anger, control anger!Week4emotional change1."A different me."120 min2."Talk about your discomfort."3."Picking emotional mushrooms"4."emotional sculpture"5.Emotional shieldWeek5Stop your anger1.”Lost mind”120 min2.The power of the group3.Situational performance4.”Crystal ball”5.”Live it up”Week6Colourful life1.Turn back time120 min2.A fresh start3.Advantage bombing4.”Colourful apples”
Week 2 involved the ‘emotional rainbow’ activity. The team members were guided to identify their adverse emotional reactions and actively understand and explore their own potential for change. They were encouraged to set goal plans, analyse the feasibility of problem-solving methods, and share examples and successful experiences of managing negative emotions in their work. The ‘iceberg theory’ was introduced to help nurses explore their unconscious emotions related to recent anger events.
Week 3 focused on ‘emotional balloons’. The team members were encouraged to approach recurring problems by identifying ‘exceptions’ based on past positive experiences. This approach aimed to stimulate creativity, enhance confidence, and develop effective strategies for dealing with negative emotions.
Week 4 centred around ‘mood changes’. The team members were guided to realise that they are not controlled by their emotions and possess various resources to regulate anger. They were also encouraged to express their uncomfortable feelings appropriately.
Week 5 involved the activity ‘anger can also be furious’. Assistance was provided to team members to evaluate the overall effectiveness of problem-solving, summarise successful experiences, and develop effective ways to express and manage anger. The goal was to help nurses understand themselves better, master techniques for expressing and controlling anger, and transfer these skills to work-life conflicts through continuous effort.
Week 6 included a review group and the ‘colourful life’ module. The team members discussed and summarised the entire SFBT intervention, emphasising its focus on problem-solving, emotional improvement, and reducing the likelihood of problems and negative emotions.
2.8
Activity organisation
Before initiating the intervention, the nursing team conducted a questionnaire survey among nurses in the entire hospital. Sixty nurses who met the inclusion criteria were selected to participate in the study. The purpose, significance, and content of the experiment were explained to the participants, and their informed consent was obtained. Before each weekly activity, the nursing team notified the participants and worked with the psychology team to arrange and inspect the activity site, ensuring smooth progress. At the end of the 6-week intervention, the nursing team distributed the same questionnaire to both groups to assess job stress, job burnout, and coping styles. The collected questionnaires were numbered and checked, and the data were entered into a database for subsequent empirical analysis.
The participants in the control group received daily psychological training from the nursing department but no training in the SFBT content.
2.9
Instruments
2.9.1
Demographic questionnaire
The study used several instruments. The demographic questionnaire included items such as gender, age, marital status, years of work experience, department, highest educational background, mode of employment, and average monthly income.
2.9.2
Chinese Nurse Stressor Scale
The Chinese Nurse Stressor Scale (CNSS) was developed by Li and Liu [24] to assess the levels and sources of work-related stress among Chinese clinical nurses. It comprises 34 items covering five dimensions: (1) nursing profession and work, (2) workload and time distribution, (3) working environment and resources, (4) patient nursing, and (5) management and interpersonal relationships. The CNSS uses a 4-point Likert-type scoring system, yielding a total score ranging from 34 to 136. A higher score indicates greater work-related stress among clinical nurses. In this study, the Cronbach's alpha reliability coefficient was 0.98.
2.9.3
Maslach Burnout Inventory
The Maslach Burnout Inventory (MBI), translated and modified by Feng et al. [25] based on the original scale developed by Maslach and Jackson [26], measures the severity of job burnout among study participants. The Chinese version includes 22 items across three dimensions: (1) emotional exhaustion, (2) depersonalisation, and (3) personal accomplishment, with Cronbach's alpha coefficients of 0.86, 0.76, and 0.76, respectively. According to the criteria of Ye et al. [27], scores above 27 for emotional exhaustion, 8 for depersonalisation, and 24 for personal accomplishment (reverse-scored) indicate burnout in the corresponding dimensions. Higher scores for emotional exhaustion and depersonalisation and lower scores for personal accomplishment suggest more severe job burnout. In this study, Cronbach's alpha for the MBI was 0.88.
2.9.4
Simplified Coping Style Questionnaire
The Simplified Coping Style Questionnaire (SCSQ), adapted and revised by Xie [28] from the Coping Style Questionnaire developed by Folkman et al. [29], is a widely used tool for assessing individual coping styles in China. Comprising 20 items across two dimensions, positive coping and negative coping, the SCSQ uses a 4-point Likert-type scoring system. Higher scores in either dimension indicate a stronger inclination towards the corresponding coping style. In this study, Cronbach's alpha for the SCSQ was 0.90.
2.10
Data collection
Two groups of nurses were assessed using demographic questionnaires, the CNSS, the MBI, and the SCSQ 1 month before and 1 month after the intervention. The questionnaires were distributed and collected onsite. Once collected, the questionnaires were checked for completeness and then sorted and reviewed by two nursing graduate students to ensure data accuracy.
2.11
Data analysis
Data entry was performed using a two-person input method. Following double-person examination and verification, the database was established and analysed using IBM SPSS 25.0 (IBM Corp., Armonk, NY, USA). The distribution of all variables was checked for normality. Demographic data are reported as frequency and percentage for categorical variables and mean and standard deviation (SD) for continuous variables. The baseline differences between the intervention and control groups were compared using independent sample t-tests or chi-square tests. The scores of the CNSS, MBI, and SCSQ, and their changes were described using mean and SD. Paired sample t-tests were performed to compare the results before and after the intervention within each of the two groups. P-values of <0.05 were considered statistically significant.
2.1
Ethical considerations
This study received approval from the Ethics Committee of Dalian Municipal Central Hospital (approval number: YN2022-109-01). Before the survey, clinical nurses participating in this study were informed about the purpose, content, and time commitment. They were given informed consent forms and questionnaires to ensure voluntary participation. This ensured that individuals were aware of their right to freely participate or withdraw from the study without any harm to their interests.
2.2
Study design
This study was a single-blinded randomised controlled trial with a pre-test, post-test design. The study was conducted between 1 May and December 1, 2022 in a tertiary general hospital in Dalian, Liaoning Province, China.
2.3
Team construction
A research team consisting of mentors from nursing and psychology disciplines was established. The nursing team comprised a supervisor with a master's degree in nursing and two master's degree in nursing students. Their responsibilities included preliminary research, participant selection, assistance in treatment activities, questionnaire collection, and result analysis. The psychology team consisted of a supervisor with a master's degree in psychology and three graduate students majoring in psychology, all qualified as second-class psychological counsellors. The psychology team was responsible for the overall planning and development of treatment activities, ensuring standardised implementation of weekly intervention activities, quality control during the process, and the proper application of SFBT. The research plan, objectives, and implementation rules were set by the double-mentor team. An open and effective information exchange model was established between the two teams and team members to ensure successful study execution.
2.4
Sample size
This was a randomised controlled intervention study. The sample size for this study was determined based on the following formula: according to standard statistical methods [22,23], in which n1 and n2 are the number of participants in groups 1 and 2. Assuming standard α and β values of 0.05 and 0.10, respectively, the values for Zα and Zβ were 1.64 and 1.28, respectively. According to the results of the preliminary experiment in this study, the difference between the mean values of the two groups of variables (δ) was 5.40 and the standard deviation of the two sample populations (σ) was 5.81. This yielded a sample size estimate of 20 per group after rounding. Assuming a maximum loss rate of 30 %, the final number of participants in the experimental and control groups was determined to be 30. Therefore, 60 eligible nurses were recruited and randomly assigned to the control (n = 30) and an experimental (n = 30) group using a random number method. Nurses in the control group received a routine psychological intervention, whereas nurses in the experimental group received an SFBT intervention conducted by the teachers and students of the psychology team.n1=n2=2[(Zα+Zβ)σδ]2
2.5
Participants
The inclusion criteria were as follows: possessing a Chinese nurse practice qualification certificate, working in a clinical nursing role, experiencing burnout in two or more dimensions according to the Maslach Burnout Inventory, and having an average score of ≥2.5 on the Chinese Nurses Stressor Scale. All participants were required to provide voluntary informed consent. The exclusion criteria included severe physical or mental illness, maternity leave, sick leave, retirement, or pursuing further study during the study period. The participants who discontinued their involvement after the second activity were also excluded. Written informed consent was obtained from all participants, and they were thoroughly briefed on the study's purpose, importance, and methods.
2.6
Study intervention
This study was conducted from May 2022 to December 2022. Before commencing the activities, a meeting was held to establish the intervention goal and implementation plan, agree on the cooperation model and work content between the two parties, and clarify the division of tasks, responsibilities, and implementation rules for each discipline team and its members.
2.7
SFBT intervention measures
In this study, the SFBT programme included six sets of activities, each scheduled at 1-week intervals (Table 1). Week 1 focused on establishing a group contract and a ‘warm-up’ unit. The 30 nurses in the experimental group were divided into five groups of six people each. The members introduced themselves, built trust, and engaged in ice-breaking activities to foster a positive atmosphere of team cooperation. The aim was to help participants relieve psychosomatic pressure, acquire stress management skills, and explore internal and external resources for sustainable mental and physical well-being.Table 1Solution-focused brief therapy activities.Table 1WeekThemeContentTimeWeek1Stress management1.The concept of "stress"120 min2.”Massage”3.Hello, team.4.Pass the parce-Introduce myself5.Pressure circle activityWeek2Emotional rainbow1.The passions120 min2.Our emotions3.What's my anger?4.Emotional test5.Meditate and relax6.Emotional icebergWeek3emotional balloon1.Large and small watermelons120 min2.The master of emotions3.Creative emotional balloon4.Cognitive Behavioral Therapy intervention in anger Control5.Know anger, watch anger, control anger!Week4emotional change1."A different me."120 min2."Talk about your discomfort."3."Picking emotional mushrooms"4."emotional sculpture"5.Emotional shieldWeek5Stop your anger1.”Lost mind”120 min2.The power of the group3.Situational performance4.”Crystal ball”5.”Live it up”Week6Colourful life1.Turn back time120 min2.A fresh start3.Advantage bombing4.”Colourful apples”
Week 2 involved the ‘emotional rainbow’ activity. The team members were guided to identify their adverse emotional reactions and actively understand and explore their own potential for change. They were encouraged to set goal plans, analyse the feasibility of problem-solving methods, and share examples and successful experiences of managing negative emotions in their work. The ‘iceberg theory’ was introduced to help nurses explore their unconscious emotions related to recent anger events.
Week 3 focused on ‘emotional balloons’. The team members were encouraged to approach recurring problems by identifying ‘exceptions’ based on past positive experiences. This approach aimed to stimulate creativity, enhance confidence, and develop effective strategies for dealing with negative emotions.
Week 4 centred around ‘mood changes’. The team members were guided to realise that they are not controlled by their emotions and possess various resources to regulate anger. They were also encouraged to express their uncomfortable feelings appropriately.
Week 5 involved the activity ‘anger can also be furious’. Assistance was provided to team members to evaluate the overall effectiveness of problem-solving, summarise successful experiences, and develop effective ways to express and manage anger. The goal was to help nurses understand themselves better, master techniques for expressing and controlling anger, and transfer these skills to work-life conflicts through continuous effort.
Week 6 included a review group and the ‘colourful life’ module. The team members discussed and summarised the entire SFBT intervention, emphasising its focus on problem-solving, emotional improvement, and reducing the likelihood of problems and negative emotions.
2.8
Activity organisation
Before initiating the intervention, the nursing team conducted a questionnaire survey among nurses in the entire hospital. Sixty nurses who met the inclusion criteria were selected to participate in the study. The purpose, significance, and content of the experiment were explained to the participants, and their informed consent was obtained. Before each weekly activity, the nursing team notified the participants and worked with the psychology team to arrange and inspect the activity site, ensuring smooth progress. At the end of the 6-week intervention, the nursing team distributed the same questionnaire to both groups to assess job stress, job burnout, and coping styles. The collected questionnaires were numbered and checked, and the data were entered into a database for subsequent empirical analysis.
The participants in the control group received daily psychological training from the nursing department but no training in the SFBT content.
2.9
Instruments
2.9.1
Demographic questionnaire
The study used several instruments. The demographic questionnaire included items such as gender, age, marital status, years of work experience, department, highest educational background, mode of employment, and average monthly income.
2.9.2
Chinese Nurse Stressor Scale
The Chinese Nurse Stressor Scale (CNSS) was developed by Li and Liu [24] to assess the levels and sources of work-related stress among Chinese clinical nurses. It comprises 34 items covering five dimensions: (1) nursing profession and work, (2) workload and time distribution, (3) working environment and resources, (4) patient nursing, and (5) management and interpersonal relationships. The CNSS uses a 4-point Likert-type scoring system, yielding a total score ranging from 34 to 136. A higher score indicates greater work-related stress among clinical nurses. In this study, the Cronbach's alpha reliability coefficient was 0.98.
2.9.3
Maslach Burnout Inventory
The Maslach Burnout Inventory (MBI), translated and modified by Feng et al. [25] based on the original scale developed by Maslach and Jackson [26], measures the severity of job burnout among study participants. The Chinese version includes 22 items across three dimensions: (1) emotional exhaustion, (2) depersonalisation, and (3) personal accomplishment, with Cronbach's alpha coefficients of 0.86, 0.76, and 0.76, respectively. According to the criteria of Ye et al. [27], scores above 27 for emotional exhaustion, 8 for depersonalisation, and 24 for personal accomplishment (reverse-scored) indicate burnout in the corresponding dimensions. Higher scores for emotional exhaustion and depersonalisation and lower scores for personal accomplishment suggest more severe job burnout. In this study, Cronbach's alpha for the MBI was 0.88.
2.9.4
Simplified Coping Style Questionnaire
The Simplified Coping Style Questionnaire (SCSQ), adapted and revised by Xie [28] from the Coping Style Questionnaire developed by Folkman et al. [29], is a widely used tool for assessing individual coping styles in China. Comprising 20 items across two dimensions, positive coping and negative coping, the SCSQ uses a 4-point Likert-type scoring system. Higher scores in either dimension indicate a stronger inclination towards the corresponding coping style. In this study, Cronbach's alpha for the SCSQ was 0.90.
2.9.1
Demographic questionnaire
The study used several instruments. The demographic questionnaire included items such as gender, age, marital status, years of work experience, department, highest educational background, mode of employment, and average monthly income.
2.9.2
Chinese Nurse Stressor Scale
The Chinese Nurse Stressor Scale (CNSS) was developed by Li and Liu [24] to assess the levels and sources of work-related stress among Chinese clinical nurses. It comprises 34 items covering five dimensions: (1) nursing profession and work, (2) workload and time distribution, (3) working environment and resources, (4) patient nursing, and (5) management and interpersonal relationships. The CNSS uses a 4-point Likert-type scoring system, yielding a total score ranging from 34 to 136. A higher score indicates greater work-related stress among clinical nurses. In this study, the Cronbach's alpha reliability coefficient was 0.98.
2.9.3
Maslach Burnout Inventory
The Maslach Burnout Inventory (MBI), translated and modified by Feng et al. [25] based on the original scale developed by Maslach and Jackson [26], measures the severity of job burnout among study participants. The Chinese version includes 22 items across three dimensions: (1) emotional exhaustion, (2) depersonalisation, and (3) personal accomplishment, with Cronbach's alpha coefficients of 0.86, 0.76, and 0.76, respectively. According to the criteria of Ye et al. [27], scores above 27 for emotional exhaustion, 8 for depersonalisation, and 24 for personal accomplishment (reverse-scored) indicate burnout in the corresponding dimensions. Higher scores for emotional exhaustion and depersonalisation and lower scores for personal accomplishment suggest more severe job burnout. In this study, Cronbach's alpha for the MBI was 0.88.
2.9.4
Simplified Coping Style Questionnaire
The Simplified Coping Style Questionnaire (SCSQ), adapted and revised by Xie [28] from the Coping Style Questionnaire developed by Folkman et al. [29], is a widely used tool for assessing individual coping styles in China. Comprising 20 items across two dimensions, positive coping and negative coping, the SCSQ uses a 4-point Likert-type scoring system. Higher scores in either dimension indicate a stronger inclination towards the corresponding coping style. In this study, Cronbach's alpha for the SCSQ was 0.90.
2.10
Data collection
Two groups of nurses were assessed using demographic questionnaires, the CNSS, the MBI, and the SCSQ 1 month before and 1 month after the intervention. The questionnaires were distributed and collected onsite. Once collected, the questionnaires were checked for completeness and then sorted and reviewed by two nursing graduate students to ensure data accuracy.
2.11
Data analysis
Data entry was performed using a two-person input method. Following double-person examination and verification, the database was established and analysed using IBM SPSS 25.0 (IBM Corp., Armonk, NY, USA). The distribution of all variables was checked for normality. Demographic data are reported as frequency and percentage for categorical variables and mean and standard deviation (SD) for continuous variables. The baseline differences between the intervention and control groups were compared using independent sample t-tests or chi-square tests. The scores of the CNSS, MBI, and SCSQ, and their changes were described using mean and SD. Paired sample t-tests were performed to compare the results before and after the intervention within each of the two groups. P-values of <0.05 were considered statistically significant.
3
Results
3.1
Nurse characteristics
The mean age of the experimental group was 37.53 (SD = 5.62) years and that of the control group was 36.20 (SD = 7.16) years. The two groups did not significantly differ in age (p = 0.426). The mean work experience of the experimental group was 15.07 (SD = 6.42) years and that of the control group was 14.20 (SD = 8.26) years. The two groups did not differ significantly in work experience (p = 0.652) or other demographic characteristics (Table 2).Table 2Baseline demographic characteristics and group assignment.Table 2CategoryControl group（n = 30）Intervention group（n = 30）t/X2PAge (mean ± standard deviation)36.20 ± 7.1637.53 ± 5.620.8010.426Gentle n(%)0.0001.000Man1(3.3)1(3.3)Woman29(96.7)29(96.7)Working Years (mean ± standard deviation)14.20 ± 8.2615.07 ± 6.420.4540.652Marital status n(%)0.7930.428Single75Married2223Divorced12Section−1.3700.171Internal Medicine Department812Surgery109Gynecology Department01Emergency treatment12Auxiliary department07Other115The degree at the time of obtaining the license n(%)−0.6040.546Technical secondary school49Junior college116Undergraduate course1515The highest academic qualifications n(%)0.1850.667Junior college42Undergraduate course2628Personnel relationship n(%)0.1110.739Contract system2425Organization65Professional title n(%)1.3820.167Nurses31Primary nurse45Supervising nurse1712Co-chief superintendent nurse612Night shift n(%)0.0770.781Yes2120No910Salary n(%)0.0820.935<3000103000–5000525001–700012207001–10000127>1000001
3.2
Nurse CNSS scores
Before the intervention, the mean total CNSS score for nurses in the experimental group was 85.83 (SD = 15.38) points and that for nurses in the control group was 85.53 (SD = 15.18) points. Both groups exhibited moderate to high levels of work-related stress. After the intervention, the mean total CNSS score for nurses in the experimental group decreased to 65.93 (SD = 14.55) points, representing a significant improvement compared with the pre-intervention score (t = 7.166, p < 0.001). In the control group, there was no significant change in the total CNSS score after the intervention (t = 3.182, p = 0.003) (Table 3).Table 3Outcomes of paired sample t-tests for job stress, burnout, and coping between the experimental group (n = 30) and control group (n = 30).Table 3OutcomeIntervention group (Mean ± Standard Deviation)Control group(Mean ± Standard Deviation)BaselinePost-testtPBaselinePost-testtPCNSS85.83 ± 15.3865.93 ± 14.557.166P < 0.00185.53 ± 15.1874.50 ± 20.903.1820.003Nursing profession and working property19.30 ± 4.5613.90 ± 4.177.003P < 0.00119.10 ± 4.2215.97 ± 5.403.7620.001Workload and time distribution15.27 ± 3.6412.10 ± 3.854.525P < 0.00115.50 ± 4.0413.20 ± 4.633.0270.005Working environment and equipment7.23 ± 2.455.50 ± 1.852.642P < 0.0017.43 ± 2.256.63 ± 2.771.9220.065Patient care25.47 ± 4.4020.77 ± 4.154.620P < 0.00125.43 ± 4.9321.97 ± 5.232.7730.010Management and interpersonal relation18.63 ± 4.4713.67 ± 3.507.140P < 0.00118.07 ± 4.0316.73 ± 5.051.4000.172Emotional exhaustion34.23 ± 9.4617.33 ± 12.697.568P < 0.00135.33 ± 10.9322.37 ± 14.665.016P < 0.001Depersonalisation12.17 ± 5.866.00 ± 5.955.634P < 0.00113.90 ± 7.417.77 ± 8.194.110P < 0.001Diminished personal accomplishment29.83 ± 9.8335.70 ± 10.15−3.3570.00228.27 ± 7.3731.10 ± 10.10−1.4990.145Positive coping21.17 ± 7.6123.93 ± 7.33−2.1420.04118.27 ± 6.8318.27 ± 5.270.000P > 0.999Negative coping11.03 ± 4.1410.40 ± 4.090.7450.46211.83 ± 4.3711.40 ± 5.110.4080.687
3.3
Nurse MBI scores
After the intervention, the mean emotional exhaustion and depersonalisation scores for nurses in the experimental group decreased significantly compared with the pre-intervention scores (t = 7.568, p < 0.001; t = 5.634, p < 0.001). Additionally, the mean personal accomplishment score increased significantly after the intervention (t = −3.357, p = 0.002). In the control group, the mean emotional exhaustion and depersonalisation scores also decreased after the intervention (t = 5.016, p < 0.001; t = 4.110, p < 0.001). However, no significant difference was observed in the mean personal accomplishment score (t = −1.499, p = 0.145) (Table 3).
3.4
Nurse SCSQ scores
After the intervention, the mean active coping score for nurses in the experimental group increased significantly compared with the pre-intervention score (t = −2.142, p = 0.041). However, the mean negative coping score did not change significantly after the intervention (t = 0.745, p = 0.462). In the control group, there were no significant differences in the mean coping scores before and after the intervention (t = 0.000, p > 0.999; t = 0.408, p = 0.687) (Table 3).
3.5
Differences between the two groups before and after the intervention
There were no significant differences between the control and the experimental groups at baseline. After the intervention, the positive coping dimension differed significantly between the two groups (p = 0.001), whereas there were no significant differences in the CNSS, MBI, and negative coping dimensions (Table 3).
3.1
Nurse characteristics
The mean age of the experimental group was 37.53 (SD = 5.62) years and that of the control group was 36.20 (SD = 7.16) years. The two groups did not significantly differ in age (p = 0.426). The mean work experience of the experimental group was 15.07 (SD = 6.42) years and that of the control group was 14.20 (SD = 8.26) years. The two groups did not differ significantly in work experience (p = 0.652) or other demographic characteristics (Table 2).Table 2Baseline demographic characteristics and group assignment.Table 2CategoryControl group（n = 30）Intervention group（n = 30）t/X2PAge (mean ± standard deviation)36.20 ± 7.1637.53 ± 5.620.8010.426Gentle n(%)0.0001.000Man1(3.3)1(3.3)Woman29(96.7)29(96.7)Working Years (mean ± standard deviation)14.20 ± 8.2615.07 ± 6.420.4540.652Marital status n(%)0.7930.428Single75Married2223Divorced12Section−1.3700.171Internal Medicine Department812Surgery109Gynecology Department01Emergency treatment12Auxiliary department07Other115The degree at the time of obtaining the license n(%)−0.6040.546Technical secondary school49Junior college116Undergraduate course1515The highest academic qualifications n(%)0.1850.667Junior college42Undergraduate course2628Personnel relationship n(%)0.1110.739Contract system2425Organization65Professional title n(%)1.3820.167Nurses31Primary nurse45Supervising nurse1712Co-chief superintendent nurse612Night shift n(%)0.0770.781Yes2120No910Salary n(%)0.0820.935<3000103000–5000525001–700012207001–10000127>1000001
3.2
Nurse CNSS scores
Before the intervention, the mean total CNSS score for nurses in the experimental group was 85.83 (SD = 15.38) points and that for nurses in the control group was 85.53 (SD = 15.18) points. Both groups exhibited moderate to high levels of work-related stress. After the intervention, the mean total CNSS score for nurses in the experimental group decreased to 65.93 (SD = 14.55) points, representing a significant improvement compared with the pre-intervention score (t = 7.166, p < 0.001). In the control group, there was no significant change in the total CNSS score after the intervention (t = 3.182, p = 0.003) (Table 3).Table 3Outcomes of paired sample t-tests for job stress, burnout, and coping between the experimental group (n = 30) and control group (n = 30).Table 3OutcomeIntervention group (Mean ± Standard Deviation)Control group(Mean ± Standard Deviation)BaselinePost-testtPBaselinePost-testtPCNSS85.83 ± 15.3865.93 ± 14.557.166P < 0.00185.53 ± 15.1874.50 ± 20.903.1820.003Nursing profession and working property19.30 ± 4.5613.90 ± 4.177.003P < 0.00119.10 ± 4.2215.97 ± 5.403.7620.001Workload and time distribution15.27 ± 3.6412.10 ± 3.854.525P < 0.00115.50 ± 4.0413.20 ± 4.633.0270.005Working environment and equipment7.23 ± 2.455.50 ± 1.852.642P < 0.0017.43 ± 2.256.63 ± 2.771.9220.065Patient care25.47 ± 4.4020.77 ± 4.154.620P < 0.00125.43 ± 4.9321.97 ± 5.232.7730.010Management and interpersonal relation18.63 ± 4.4713.67 ± 3.507.140P < 0.00118.07 ± 4.0316.73 ± 5.051.4000.172Emotional exhaustion34.23 ± 9.4617.33 ± 12.697.568P < 0.00135.33 ± 10.9322.37 ± 14.665.016P < 0.001Depersonalisation12.17 ± 5.866.00 ± 5.955.634P < 0.00113.90 ± 7.417.77 ± 8.194.110P < 0.001Diminished personal accomplishment29.83 ± 9.8335.70 ± 10.15−3.3570.00228.27 ± 7.3731.10 ± 10.10−1.4990.145Positive coping21.17 ± 7.6123.93 ± 7.33−2.1420.04118.27 ± 6.8318.27 ± 5.270.000P > 0.999Negative coping11.03 ± 4.1410.40 ± 4.090.7450.46211.83 ± 4.3711.40 ± 5.110.4080.687
3.3
Nurse MBI scores
After the intervention, the mean emotional exhaustion and depersonalisation scores for nurses in the experimental group decreased significantly compared with the pre-intervention scores (t = 7.568, p < 0.001; t = 5.634, p < 0.001). Additionally, the mean personal accomplishment score increased significantly after the intervention (t = −3.357, p = 0.002). In the control group, the mean emotional exhaustion and depersonalisation scores also decreased after the intervention (t = 5.016, p < 0.001; t = 4.110, p < 0.001). However, no significant difference was observed in the mean personal accomplishment score (t = −1.499, p = 0.145) (Table 3).
3.4
Nurse SCSQ scores
After the intervention, the mean active coping score for nurses in the experimental group increased significantly compared with the pre-intervention score (t = −2.142, p = 0.041). However, the mean negative coping score did not change significantly after the intervention (t = 0.745, p = 0.462). In the control group, there were no significant differences in the mean coping scores before and after the intervention (t = 0.000, p > 0.999; t = 0.408, p = 0.687) (Table 3).
3.5
Differences between the two groups before and after the intervention
There were no significant differences between the control and the experimental groups at baseline. After the intervention, the positive coping dimension differed significantly between the two groups (p = 0.001), whereas there were no significant differences in the CNSS, MBI, and negative coping dimensions (Table 3).
4
Discussion
This study aimed to investigate the effect of SFBT interventions, implemented through a collaborative approach between nursing and psychology majors, on nurses' job stress, burnout, and coping styles. The findings supported our hypothesis that SFBT effectively reduces job stress and burnout and enhances nurses’ positive coping mechanisms.
The study demonstrated that SFBT effectively diminishes nurses' job burnout, aligning with previous findings [19,30]. Our approach differed from that of previous studies by incorporating teamwork activities into the intervention process, using nurses’ real-world experiences to foster emotion regulation abilities and mobilise their resilience.
The study results revealed a substantial direct effect of SFBT on nurses' work stress. SFBT can alleviate stress levels in patients with breast cancer and college students [31,32]. Mache et al. [33] reported that SFBT assisted clinicians in managing work stress, actively seeking suitable strategies to manage emotions, developing effective communication skills, and enhancing their overall mental well-being. Additionally, our treatment effectively improved nurses’ positive coping strategies, consistent with previous research. Wang et al. [34] conducted a 7-day SFBT intervention focused on the coping styles of hospitalised patients with leukaemia, observing an increase in subjective initiative among patients following SFBT, leading to more positive approaches toward disease management and enhanced chemotherapy success rates. Moreover, in the Chinese healthcare setting, nurses often struggle to establish effective communication and empathy with patients and their families, fostering patient mistrust and misunderstandings regarding nursing care [6,35]. SFBT can empower nurses to enhance their active coping abilities, encouraging them to use subjectivity and enthusiasm, address communication challenges with patients, and adopt positive attitudes and effective communication skills from a patient-centred perspective. This, in turn, can help patients comprehend health conditions and treatment information, contributing to a more supportive healthcare environment. However, further research is necessary to comprehensively assess the effect of SFBT on negative coping styles.
The primary objective of the SFBT programme adopted in this study was to alleviate stress and regulate emotions, enabling nurses to confront their challenges, mobilise their psychological strengths, and approach problems from a positive perspective. This, in turn, aims to empower nurses to effectively manage their own issues and the pressures imposed by their external work environment, seeking positive social support systems to manage their emotions. Therefore, the ultimate goal is to reduce nurses’ work stress and job burnout and enhance their ability to cope positively.
In addition, nursing managers have identified various issues in nursing management practices, including psychology [36], education [37], and development [38]. However, nursing disciplines often lack knowledge and sufficient scientific and professional response measures to address these problems effectively. Consequently, a single nursing discipline cannot effectively and scientifically resolve nursing issues. Currently, nursing managers need to collaborate with other disciplines to integrate resources from different fields and provide comprehensive solutions to nursing problems [39,40].
This study involved the cooperation of a professional psychology team to establish an interdisciplinary team. The interdisciplinary team implemented SFBT for nurses experiencing high levels of job stress and burnout. This approach allowed for the analysis and resolution of problems faced by nurses from different professions using a shared perspective. The psychology team provided nurse-centred, professional, and continuous SFBT intervention, with the ability to adjust and adapt the intervention content timely and dynamically, while consistently monitoring nurses’ well-being. Additionally, the nursing team facilitated the entire process, breaking through the limitations of a single-discipline management model. Through an empirical analysis of the research process and feedback from the nursing team, the interdisciplinary background experts in nursing and psychology could effectively communicate and discuss issues encountered throughout the activities, thus enabling better adjustment of the intervention content. These two factors enhanced SFBT effectiveness, leading to improvements in nurses' job stress, job burnout, and coping styles.
4.1
Limitations
One limitation of this study is that the comparison between the experimental group receiving SFBT and the control group receiving routine psychological intervention only shows an important difference in active coping. This discrepancy may be attributed to the short intervention time and limited number of sessions in this study. However, compared with the control group, the experimental group showed greater changes in scores on other scales. It can thus be predicted that long-term SFBT interventions are likely to significantly improve nurses' job stress, job burnout, and coping styles. Additionally, another limitation of our study is the selection of only clinically employed nurses with moderate job stress and burnout, resulting in limited generalizability of our sample to the general population. In the future, we will conduct long-term interdisciplinary cooperation in SFBT intervention for nurses with mild to severe levels of job stress and burnout. This will further verify the effectiveness of the intervention and its applicability to nurses with different levels of job stress and burnout.
4.1
Limitations
One limitation of this study is that the comparison between the experimental group receiving SFBT and the control group receiving routine psychological intervention only shows an important difference in active coping. This discrepancy may be attributed to the short intervention time and limited number of sessions in this study. However, compared with the control group, the experimental group showed greater changes in scores on other scales. It can thus be predicted that long-term SFBT interventions are likely to significantly improve nurses' job stress, job burnout, and coping styles. Additionally, another limitation of our study is the selection of only clinically employed nurses with moderate job stress and burnout, resulting in limited generalizability of our sample to the general population. In the future, we will conduct long-term interdisciplinary cooperation in SFBT intervention for nurses with mild to severe levels of job stress and burnout. This will further verify the effectiveness of the intervention and its applicability to nurses with different levels of job stress and burnout.
5
Conclusions
This study delves into the psychological issues faced by nurses in nursing management and implements SFBT intervention through the collaboration of nursing and psychology. The research findings indicate that the interdisciplinary application of SFBT effectively resolves the psychological problems experienced by nurses, considerably improving their levels of job stress and burnout. This intervention encourages nurses to approach work-related problems with a positive mindset and provides new insights for training nursing and psychology postgraduates. Future research should explore the efficacy of SFBT for nurses experiencing moderate and severe levels of job stress and burnout, expand the range of research participants, and conduct continuous SFBT experiments to further validate the approaches for mitigating job stress and burnout while improving coping mechanisms. Additionally, further investigation should explore the feasibility of interdisciplinary collaboration between nursing management or nursing researchers and other disciplines.AbbreviationsAbbreviationFull termCNSSChinese Nurse Stressor ScaleMBIMaslach Burnout InventorySCSQSimplified Coping Style QuestionnaireSDStandard deviationSFBTSolution-focused brief therapy
5.1
Data availability statement
Not applicable.
5.1
Data availability statement
Not applicable.
CRediT authorship contribution statement
Yan Kong: Writing – original draft, Visualization, Investigation, Formal analysis. YanQi Zhang: Validation, Project administration, Methodology, Conceptualization. Ping Sun: Validation, Supervision, Project administration, Conceptualization. JinShu Zhang: Project administration, Investigation, Formal analysis, Data curation. Yang Lu: Project administration, Investigation. Juan Li: Project administration, Investigation. YingHua Zheng: Writing – review & editing, Supervision, Project administration, Conceptualization.
Funding
This research did not receive any specific funding.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: Optimizing terminology for pancreatectomy: Introducing a new notation system | Body:
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest in association with the present study. |
Title: Illuminating the dark kinome: utilizing multiplex peptide activity arrays to functionally annotate understudied kinases | Body:
Introduction
Protein kinases are integral components of various biological processes and act as master regulators of critical signaling pathways. Disruption of these important enzymes may result in unchecked signaling patterns leading to abnormal cellular behavior. As a result, perturbed kinase signaling, at the genetic or proteomic level, is strongly linked to various diseases. Gain or loss of function mutations in kinase genes are found in several diseases, including developmental and metabolic disorders, as well as some types of cancer [1]. Further, the active kinome, which is the broad-based activity of the complete set of protein kinases, is altered in cancer, psychiatric and neurodegenerative disorders [2–4]. These observations have shifted the focus towards protein kinases as promising druggable targets, resulting in an increase of Food and Drug Administration (FDA) approved small molecules targeting protein kinases over the past ten years [5].
However, while protein kinases have been a central focus of research for several decades, a large portion of the kinome is considered understudied or “dark.” The dark kinome is a consequence of signaling studies being primarily dedicated to a subset of kinases with well-established roles in cellular processes and signaling [6]. “Dark” status is given to a kinase when it is poorly studied compared to the rest of kinome [7, 8]. This disparity of knowledge has left much of the kinome untargeted. For example, less than 10% of kinases have FDA approved small molecule inhibitors or activators [9]. Despite the presence of more than 500 protein kinases in the human kinome, only 76 FDA approved kinase inhibitors have been developed [10]. This is in part due to the complexity of designing selective kinase inhibitors, but also due to poorly studied kinases and kinase networks. Investigating dark kinases will provide insights into this gap of knowledge, highlighted by the Illuminating the Druggable Genome program (IDG) [11]. The purpose of this program is to “illuminate” and target dark kinases and other understudied proteins.
Two important components are needed for dark protein kinases: 1) functional annotation of their downstream substrates, and 2) protein kinase-disease associations. Annotations are key to uncover related cascades and pathways, while disease associations reveal therapeutic opportunities and novel drug targets. We present a hybrid empirical and in-silico workflow to extend the functional knowledge of dark kinases. To empirically identify novel kinase substrates, the PamStation12 (PamGene International B.V.) platform was utilized to profile purified recombinant kinases. The same platform was used to assess the active kinome in disease substrates and to deconvolve their kinomic signatures utilizing the recombinant kinase screening profiles (Fig. 1).Fig. 1Extending knowledge of understudied kinase by combining empirical and in-silico approaches. An in-vitro/in-silico hybrid workflow extending functional knowledge of understudied or “dark” kinases was developed. To empirically identify novel kinase substrates, the PamStation12 platform was utilized to profile purified recombinant kinases. In-silico databases and tools were used to analyze and visualize the graph knowledge of dark kinases, as well as to explore connections with diseases and biological pathways. KRSA: Kinome Random Sampling Analyzer, iLINCS: Integrative Library of Integrated Network-Based Cellular Signatures
In the central nervous system (CNS), protein kinases are involved in many critical processes such as long-term potentiation, nutrient sensing, and neuroplasticity [4, 12, 13]. Moreover, perturbed protein kinase networks are associated with several psychiatric and neurological disorders [4]. In this study, we used our hybrid workflow to extend the functional knowledge of five dark tyrosine kinases from different kinase families and their associations with Schizophrenia (SCZ), Alzheimer’s Dementia (AD), and Major Depressive Disorder (MDD).
Methods
Dark kinome
To determine membership in the “dark kinome,” a curated list of all human protein kinases was obtained [14]. This list was queried against the Integrated Network and Dynamical Reasoning Assembler (INDRA) database, a bibliometric knowledge aggregator consisting of unique, de-duplicated statements of causal biological mechanisms drawn from natural language processing of PubMed abstracts, PubMedCentral full texts, and a host of other resources such as Pathway Commons and BioGRID [15]. Consistent with the NIH Illuminating the Druggable Genome (IDG) consortium and prevailing literature, dark kinases were defined as those belonging to the bottom third percentile of the kinome ranked by standardized log-transformed INDRA statements (Supplementary Table 1) [16]. Of these dark kinases, 19 tyrosine kinases were identified from the INDRA database, of which five tyrosine kinases were selected based on recombinant kinase availability, reported tyrosine kinase activity, expression in the brain, and relevance to schizophrenia.
Sample preparation
Tyrosine kinase non-receptor 1 (TNK1, AA range G106-D390) and EPH receptor A6 (EPHA6, AA range R683-V1130) were purchased from ReactionBiology. Leukocyte tyrosine kinase receptor (LTK, AA range S20-P424) and Insulin receptor-related receptor (INSRR, AA range L24-A747) were purchased from R&Dsystems. Apoptosis associated tyrosine kinase (AATK, AA range E1216-A1374) was purchased from LSBio. The recombinant kinases were prepped in fresh 50 mM Tris-HCI, pH 7.5, 150 mM NaCl, 0.25 mM DTT, 0.1 mM EGTA, 0.1 mM EDTA, 0.1 mM PMSF, 25% Glycerol for the Pamstation12 as well as the benchtop activity assay validation experiment.
Postmortem dorsolateral prefrontal cortex from non-psychiatric control (17, M:8 and F:9) and schizophrenia (16, M:8 and F:9) subjects (Maryland Brain Collection) were homogenized using Kimble Biomasher II Closed System Tissue Grinders (DWK Life Sciences, NIPPI Inc., Tokyo, Japan) in M-PER Mammalian Protein Extraction Reagent (ThermoFisher, Rockford, IL) and 100X HALT Protease and Phosphatase Inhibitors. After homogenization, samples were spun down at 13xG for 15 min at 4º C. The supernatant fraction was separated from the pellet fraction, stored and aliquoted in 0.5 mL tubes, and stored at -80º C. Protein concentration was determined using the Pierce BCA Assay Kit (ThermoFisher, Rockford, IL). After the protein concentration was determined for each subject, the supernatant fraction was then pooled by sex and diagnosis with equal protein content for each subject. Each of the four pooled samples was diluted to 2.0 ug/uL. Samples were stored at -80º C until the day of the experiment. On the day of the experiment, 10ug of lysates were loaded onto phospho-tyrosine (PTK) chips and run in technical triplicate at. The PTK chips have a total of 196 unique peptides (3 controls, 193 reporter peptides); these peptides are standardized on the chips (ie these are not custom arrays) and are commercially available from the Pamgene Corporation. Prediction algorithms suggests that the PTK chip covers about 96% of protein tyrosine kinases in the human genome [17].
Instruments and experimental conditions
A Pamstation12 (PamGene International B.V., ‘s-Hertogenbosch,
North Brabant, The Netherlands) was used to screen recombinant kinases and tissue homogenate on a PTK PamChip (catalog number 86402, 4 arrays/chip). Each array contains 196 immobilized peptides from which phosphorylated tyrosine residues are used as a readout of kinase activity. The PamChips are blocked with 2% BSA for 30 cycles followed washing of the arrays using PK buffer. The PTK assay master mix consists of PK buffer, 100 × BSA, DTT, 10 × PTK additive, 4 mM ATP and a phospho-tyrosine antibody. The total protein used for tissue homogenate was 10 ug/array and a concentration–response was used to determine adequate total protein for recombinant kinases. For our human studies, samples were pooled by diagnosis and sex. Pooled samples were run in technical triplicate to account for variability. After the sample is loaded a CCD camera takes a picture every five cycles for 60 cycles with images taken 2 cycles before and after the real-time kinetics determination.
Immunoblotting
30 uL of EPHA6 (250 ng, 250 ng heat inactivated) and GAB1 (0 ng, 200 ng, 300 ng, 400 ng) were taken from a 96-well plate (Corning) used to co-incubate the proteins at 37 °C and prepared for SDS-PAGE using 6 uL of Laemmli SDS sample buffer (Boston BioProducts). The protein samples were run on a 1.0 mm thick, 4 − 12% Bis–Tris gradient gel (Invitrogen) and transferred to a PVDF membrane using a semi-dry transfer cell (Bio-Rad). The membrane was blocked using TBS Odyssey Blocking Buffer (Li-Cor) for 20 min at room temperature, then probed overnight at 4 °C with a primary pan-p-Tyr antibody (1:250, Cell Signaling, catalogue # 9411S). Next, the membrane was incubated for 1 h at room temperature with an anti-mouse secondary antibody (1:5000, Li-Cor, 926–68,070). Following secondary incubation, the membrane was scanned using Odyssey Infrared Imaging System (Li-Cor). Additional samples (1.25ug EPHA6, 2ug GAB1) were sent to the University of Cincinnati Core Research Facility for Mass Spectrometry (Supplementary Material 12).
Data analysis
Kinome array profiling
The raw PamChip images are pre-processed using BioNavigator (PamGene International B.V.) to generate numerical values of the signal intensity for each reporter peptide on the chip. To analyze the kinome array profiles, the Kinome Random Sampling Analyzer (KRSA) R package was used to pre-process, apply quality control checks, and select differentially phosphorylated peptides [18]. KRSA was used to analyze the kinome profiles of postmortem dorsolateral prefrontal cortex from control and schizophrenia subjects. The analysis starts with calculating the linear regression slope (signal intensity as a function of camera exposure time) followed by scaling the signal by multiplying by 100 and then log transforming the scaled values. The derived values from the previous steps are used as the final signal (i.e., peptide phosphorylation intensity) in the comparative analyses [19]. Additional quality control filtration steps were carried out to remove peptides with either very low signal intensity (signal < 5) or R2 of less than 0.9 in the linear model. The signal ratios between pairs of groups (control versus schizophrenia) were used to calculate log2 fold change (LFC) for each peptide. The LFC was calculated per chip and by sex, and then averaged across chips. To compare the kinase activity, peptides with an average LFC of at least 0.2 were carried forward to the upstream kinase analysis.
To investigate implicated upstream kinases, three different software packages were used: KRSA, upstream kinase analysis (UKA), and Kinase Enrichment Analysis (KEA3). To look at associated upstream kinase families, KRSA takes the list of differentially phosphorylated peptides and uses a random resampling approach to assign scores for each kinase family [19]. The Upstream Kinase Analysis (UKA) tool from BioNavigator was used to look at individual upstream kinases. The default settings of the standard STK analysis protocol were used with the additional step of upstream kinase analysis. UKA reports the final score as a metric for ranking implicated kinases, which is calculated based on the specificity of the peptides mapped to the kinases and the significance of phosphorylation changes of the peptides between the compared groups. The Kinase Enrichment Analysis Version 3 (KEA3) web tool was also used to perform kinase set enrichment analysis using the corresponding proteins of the top differentially phosphorylated reporter peptides as the input [20].
We used the Creedenzymatic R package to aggregate the results from these three different analytic tools. The Creedenzymatic R package is a pipeline software package that combines, scores, and visualizes the results from multiple upstream kinase analytic tools (https://github.com/CogDisResLab/creedenzymatic) [3]. The Creedenzymatic package harmonizes the different metrics used in KRSA, UKA, and KEA3 with percentile rank normalization. This harmonization approach results in a standardized percentile score for each kinase under each tool. Next, the mean and median percentile score for each kinase is calculated by averaging the normalized scores across the three analytic tools. Additionally, kinases are mapped to the official HUGO Gene Nomenclature Committee (HGNC) symbols and subfamilies, ensuring the naming convention is consistent across the three different tools.
For the recombinant kinase screening, semi-supervised clustering was used to categorize peptides into four different groups. An unsupervised hierarchical clustering was first performed followed by extracting the top four clusters. The first cluster, titled “No Affinity,” is a list of peptides that show no phosphorylation activity across all tested protein concentrations. The remaining peptides are clustered under three groups based on their dose–response signal activity: low, medium, and high affinity. The “High Affinity” group is a list of reporter peptides that show phosphorylation activity at the lowest protein concentration. “Medium Affinity” are peptides that only start showing phosphorylation activity at the medium protein concentration. Finally, the “Low Affinity” group is a list of peptides that only show activity at the highest protein concentration.
Principal component analysis (PCA) was also performed to visualize the clusters of peptides and determine the component loading for the first four principal components (PC1, PC2, PC3, and PC4). To examine the consensus of the amino acid sequence for each cluster of peptides, we first extracted the main tyrosine phosphosite and the ten neighboring amino acids (five amino acids from each side) of all reporter peptides on the PTK PamChip. We then used the ggseqlogo R package to plot the amino acid sequence logos of the three clusters of peptides and for each recombinant kinase screening profile [21].
To investigate the association of the recombinant kinase profiles with the schizophrenia kinome dataset, we performed a peptide set enrichment analysis using the tmod R package [22, 23]. Using the average peptide LFC, by comparing control to schizophrenia subjects, we ranked all the peptides that passed quality controls filtration steps based on the absolute values of LFCs. Next, we used the makeTmod function from the tmod package to create peptide modules based on the five kinase screening profiles and using the three main clusters (low, medium, and high affinity). We also generated combined peptide lists by combining either all peptides from the three clusters or just the medium and high affinity peptides. The combined peptides lists are used to expand the list of peptides for the more selective kinases, mainly INSRR and LTK. To run the enrichment analysis, we utilized the tmodUtest function to perform a Mann–Whitney U test (U-test) on the ranks of peptides belonging to the defined clusters and the peptides that do not belong to the peptides sets. We used a modified version of the evidencePlot function to visualize the results and highlight the significant enriched peptide sets (p-value < 0.05 and area under the curve (AUC) > 0.5). Additionally, we used a waterfall plot to visualize the peptide LFCs from the schizophrenia kinome dataset.
Pathway Analysis and In-silico Data Exploration
The Enrichr web tool was used to perform multiple gene set enrichment analyses across different get set libraries [24]. The peptides were first mapped to their corresponding proteins, then to the official HGNC gene symbols. The gene set library used in the pathway analysis is the Gene Ontology (GO) Biological Process 2021. The ARCHS4 tool was also used to visualize gene expression levels across various tissues. The Cell Types Database from the Allen Brain Map was used to extract cell-level gene expression in multiple brain regions [25]. The Human Protein Atlas (HPA) web portal was utilized to explore protein expression across tissues, cluster genes based on co-expression, and determine subcellular localization patterns [26, 27]. The web tool Kaleidoscope was used to query multiple curated databases such as STRING, Genotype-Tissue Expression (GTEx), Brain RNASeq, Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS), and Pharos from the Illuminating the Druggable Genome (IDG) program [28–34].
Dark kinome
To determine membership in the “dark kinome,” a curated list of all human protein kinases was obtained [14]. This list was queried against the Integrated Network and Dynamical Reasoning Assembler (INDRA) database, a bibliometric knowledge aggregator consisting of unique, de-duplicated statements of causal biological mechanisms drawn from natural language processing of PubMed abstracts, PubMedCentral full texts, and a host of other resources such as Pathway Commons and BioGRID [15]. Consistent with the NIH Illuminating the Druggable Genome (IDG) consortium and prevailing literature, dark kinases were defined as those belonging to the bottom third percentile of the kinome ranked by standardized log-transformed INDRA statements (Supplementary Table 1) [16]. Of these dark kinases, 19 tyrosine kinases were identified from the INDRA database, of which five tyrosine kinases were selected based on recombinant kinase availability, reported tyrosine kinase activity, expression in the brain, and relevance to schizophrenia.
Sample preparation
Tyrosine kinase non-receptor 1 (TNK1, AA range G106-D390) and EPH receptor A6 (EPHA6, AA range R683-V1130) were purchased from ReactionBiology. Leukocyte tyrosine kinase receptor (LTK, AA range S20-P424) and Insulin receptor-related receptor (INSRR, AA range L24-A747) were purchased from R&Dsystems. Apoptosis associated tyrosine kinase (AATK, AA range E1216-A1374) was purchased from LSBio. The recombinant kinases were prepped in fresh 50 mM Tris-HCI, pH 7.5, 150 mM NaCl, 0.25 mM DTT, 0.1 mM EGTA, 0.1 mM EDTA, 0.1 mM PMSF, 25% Glycerol for the Pamstation12 as well as the benchtop activity assay validation experiment.
Postmortem dorsolateral prefrontal cortex from non-psychiatric control (17, M:8 and F:9) and schizophrenia (16, M:8 and F:9) subjects (Maryland Brain Collection) were homogenized using Kimble Biomasher II Closed System Tissue Grinders (DWK Life Sciences, NIPPI Inc., Tokyo, Japan) in M-PER Mammalian Protein Extraction Reagent (ThermoFisher, Rockford, IL) and 100X HALT Protease and Phosphatase Inhibitors. After homogenization, samples were spun down at 13xG for 15 min at 4º C. The supernatant fraction was separated from the pellet fraction, stored and aliquoted in 0.5 mL tubes, and stored at -80º C. Protein concentration was determined using the Pierce BCA Assay Kit (ThermoFisher, Rockford, IL). After the protein concentration was determined for each subject, the supernatant fraction was then pooled by sex and diagnosis with equal protein content for each subject. Each of the four pooled samples was diluted to 2.0 ug/uL. Samples were stored at -80º C until the day of the experiment. On the day of the experiment, 10ug of lysates were loaded onto phospho-tyrosine (PTK) chips and run in technical triplicate at. The PTK chips have a total of 196 unique peptides (3 controls, 193 reporter peptides); these peptides are standardized on the chips (ie these are not custom arrays) and are commercially available from the Pamgene Corporation. Prediction algorithms suggests that the PTK chip covers about 96% of protein tyrosine kinases in the human genome [17].
Instruments and experimental conditions
A Pamstation12 (PamGene International B.V., ‘s-Hertogenbosch,
North Brabant, The Netherlands) was used to screen recombinant kinases and tissue homogenate on a PTK PamChip (catalog number 86402, 4 arrays/chip). Each array contains 196 immobilized peptides from which phosphorylated tyrosine residues are used as a readout of kinase activity. The PamChips are blocked with 2% BSA for 30 cycles followed washing of the arrays using PK buffer. The PTK assay master mix consists of PK buffer, 100 × BSA, DTT, 10 × PTK additive, 4 mM ATP and a phospho-tyrosine antibody. The total protein used for tissue homogenate was 10 ug/array and a concentration–response was used to determine adequate total protein for recombinant kinases. For our human studies, samples were pooled by diagnosis and sex. Pooled samples were run in technical triplicate to account for variability. After the sample is loaded a CCD camera takes a picture every five cycles for 60 cycles with images taken 2 cycles before and after the real-time kinetics determination.
Immunoblotting
30 uL of EPHA6 (250 ng, 250 ng heat inactivated) and GAB1 (0 ng, 200 ng, 300 ng, 400 ng) were taken from a 96-well plate (Corning) used to co-incubate the proteins at 37 °C and prepared for SDS-PAGE using 6 uL of Laemmli SDS sample buffer (Boston BioProducts). The protein samples were run on a 1.0 mm thick, 4 − 12% Bis–Tris gradient gel (Invitrogen) and transferred to a PVDF membrane using a semi-dry transfer cell (Bio-Rad). The membrane was blocked using TBS Odyssey Blocking Buffer (Li-Cor) for 20 min at room temperature, then probed overnight at 4 °C with a primary pan-p-Tyr antibody (1:250, Cell Signaling, catalogue # 9411S). Next, the membrane was incubated for 1 h at room temperature with an anti-mouse secondary antibody (1:5000, Li-Cor, 926–68,070). Following secondary incubation, the membrane was scanned using Odyssey Infrared Imaging System (Li-Cor). Additional samples (1.25ug EPHA6, 2ug GAB1) were sent to the University of Cincinnati Core Research Facility for Mass Spectrometry (Supplementary Material 12).
Data analysis
Kinome array profiling
The raw PamChip images are pre-processed using BioNavigator (PamGene International B.V.) to generate numerical values of the signal intensity for each reporter peptide on the chip. To analyze the kinome array profiles, the Kinome Random Sampling Analyzer (KRSA) R package was used to pre-process, apply quality control checks, and select differentially phosphorylated peptides [18]. KRSA was used to analyze the kinome profiles of postmortem dorsolateral prefrontal cortex from control and schizophrenia subjects. The analysis starts with calculating the linear regression slope (signal intensity as a function of camera exposure time) followed by scaling the signal by multiplying by 100 and then log transforming the scaled values. The derived values from the previous steps are used as the final signal (i.e., peptide phosphorylation intensity) in the comparative analyses [19]. Additional quality control filtration steps were carried out to remove peptides with either very low signal intensity (signal < 5) or R2 of less than 0.9 in the linear model. The signal ratios between pairs of groups (control versus schizophrenia) were used to calculate log2 fold change (LFC) for each peptide. The LFC was calculated per chip and by sex, and then averaged across chips. To compare the kinase activity, peptides with an average LFC of at least 0.2 were carried forward to the upstream kinase analysis.
To investigate implicated upstream kinases, three different software packages were used: KRSA, upstream kinase analysis (UKA), and Kinase Enrichment Analysis (KEA3). To look at associated upstream kinase families, KRSA takes the list of differentially phosphorylated peptides and uses a random resampling approach to assign scores for each kinase family [19]. The Upstream Kinase Analysis (UKA) tool from BioNavigator was used to look at individual upstream kinases. The default settings of the standard STK analysis protocol were used with the additional step of upstream kinase analysis. UKA reports the final score as a metric for ranking implicated kinases, which is calculated based on the specificity of the peptides mapped to the kinases and the significance of phosphorylation changes of the peptides between the compared groups. The Kinase Enrichment Analysis Version 3 (KEA3) web tool was also used to perform kinase set enrichment analysis using the corresponding proteins of the top differentially phosphorylated reporter peptides as the input [20].
We used the Creedenzymatic R package to aggregate the results from these three different analytic tools. The Creedenzymatic R package is a pipeline software package that combines, scores, and visualizes the results from multiple upstream kinase analytic tools (https://github.com/CogDisResLab/creedenzymatic) [3]. The Creedenzymatic package harmonizes the different metrics used in KRSA, UKA, and KEA3 with percentile rank normalization. This harmonization approach results in a standardized percentile score for each kinase under each tool. Next, the mean and median percentile score for each kinase is calculated by averaging the normalized scores across the three analytic tools. Additionally, kinases are mapped to the official HUGO Gene Nomenclature Committee (HGNC) symbols and subfamilies, ensuring the naming convention is consistent across the three different tools.
For the recombinant kinase screening, semi-supervised clustering was used to categorize peptides into four different groups. An unsupervised hierarchical clustering was first performed followed by extracting the top four clusters. The first cluster, titled “No Affinity,” is a list of peptides that show no phosphorylation activity across all tested protein concentrations. The remaining peptides are clustered under three groups based on their dose–response signal activity: low, medium, and high affinity. The “High Affinity” group is a list of reporter peptides that show phosphorylation activity at the lowest protein concentration. “Medium Affinity” are peptides that only start showing phosphorylation activity at the medium protein concentration. Finally, the “Low Affinity” group is a list of peptides that only show activity at the highest protein concentration.
Principal component analysis (PCA) was also performed to visualize the clusters of peptides and determine the component loading for the first four principal components (PC1, PC2, PC3, and PC4). To examine the consensus of the amino acid sequence for each cluster of peptides, we first extracted the main tyrosine phosphosite and the ten neighboring amino acids (five amino acids from each side) of all reporter peptides on the PTK PamChip. We then used the ggseqlogo R package to plot the amino acid sequence logos of the three clusters of peptides and for each recombinant kinase screening profile [21].
To investigate the association of the recombinant kinase profiles with the schizophrenia kinome dataset, we performed a peptide set enrichment analysis using the tmod R package [22, 23]. Using the average peptide LFC, by comparing control to schizophrenia subjects, we ranked all the peptides that passed quality controls filtration steps based on the absolute values of LFCs. Next, we used the makeTmod function from the tmod package to create peptide modules based on the five kinase screening profiles and using the three main clusters (low, medium, and high affinity). We also generated combined peptide lists by combining either all peptides from the three clusters or just the medium and high affinity peptides. The combined peptides lists are used to expand the list of peptides for the more selective kinases, mainly INSRR and LTK. To run the enrichment analysis, we utilized the tmodUtest function to perform a Mann–Whitney U test (U-test) on the ranks of peptides belonging to the defined clusters and the peptides that do not belong to the peptides sets. We used a modified version of the evidencePlot function to visualize the results and highlight the significant enriched peptide sets (p-value < 0.05 and area under the curve (AUC) > 0.5). Additionally, we used a waterfall plot to visualize the peptide LFCs from the schizophrenia kinome dataset.
Pathway Analysis and In-silico Data Exploration
The Enrichr web tool was used to perform multiple gene set enrichment analyses across different get set libraries [24]. The peptides were first mapped to their corresponding proteins, then to the official HGNC gene symbols. The gene set library used in the pathway analysis is the Gene Ontology (GO) Biological Process 2021. The ARCHS4 tool was also used to visualize gene expression levels across various tissues. The Cell Types Database from the Allen Brain Map was used to extract cell-level gene expression in multiple brain regions [25]. The Human Protein Atlas (HPA) web portal was utilized to explore protein expression across tissues, cluster genes based on co-expression, and determine subcellular localization patterns [26, 27]. The web tool Kaleidoscope was used to query multiple curated databases such as STRING, Genotype-Tissue Expression (GTEx), Brain RNASeq, Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS), and Pharos from the Illuminating the Druggable Genome (IDG) program [28–34].
Kinome array profiling
The raw PamChip images are pre-processed using BioNavigator (PamGene International B.V.) to generate numerical values of the signal intensity for each reporter peptide on the chip. To analyze the kinome array profiles, the Kinome Random Sampling Analyzer (KRSA) R package was used to pre-process, apply quality control checks, and select differentially phosphorylated peptides [18]. KRSA was used to analyze the kinome profiles of postmortem dorsolateral prefrontal cortex from control and schizophrenia subjects. The analysis starts with calculating the linear regression slope (signal intensity as a function of camera exposure time) followed by scaling the signal by multiplying by 100 and then log transforming the scaled values. The derived values from the previous steps are used as the final signal (i.e., peptide phosphorylation intensity) in the comparative analyses [19]. Additional quality control filtration steps were carried out to remove peptides with either very low signal intensity (signal < 5) or R2 of less than 0.9 in the linear model. The signal ratios between pairs of groups (control versus schizophrenia) were used to calculate log2 fold change (LFC) for each peptide. The LFC was calculated per chip and by sex, and then averaged across chips. To compare the kinase activity, peptides with an average LFC of at least 0.2 were carried forward to the upstream kinase analysis.
To investigate implicated upstream kinases, three different software packages were used: KRSA, upstream kinase analysis (UKA), and Kinase Enrichment Analysis (KEA3). To look at associated upstream kinase families, KRSA takes the list of differentially phosphorylated peptides and uses a random resampling approach to assign scores for each kinase family [19]. The Upstream Kinase Analysis (UKA) tool from BioNavigator was used to look at individual upstream kinases. The default settings of the standard STK analysis protocol were used with the additional step of upstream kinase analysis. UKA reports the final score as a metric for ranking implicated kinases, which is calculated based on the specificity of the peptides mapped to the kinases and the significance of phosphorylation changes of the peptides between the compared groups. The Kinase Enrichment Analysis Version 3 (KEA3) web tool was also used to perform kinase set enrichment analysis using the corresponding proteins of the top differentially phosphorylated reporter peptides as the input [20].
We used the Creedenzymatic R package to aggregate the results from these three different analytic tools. The Creedenzymatic R package is a pipeline software package that combines, scores, and visualizes the results from multiple upstream kinase analytic tools (https://github.com/CogDisResLab/creedenzymatic) [3]. The Creedenzymatic package harmonizes the different metrics used in KRSA, UKA, and KEA3 with percentile rank normalization. This harmonization approach results in a standardized percentile score for each kinase under each tool. Next, the mean and median percentile score for each kinase is calculated by averaging the normalized scores across the three analytic tools. Additionally, kinases are mapped to the official HUGO Gene Nomenclature Committee (HGNC) symbols and subfamilies, ensuring the naming convention is consistent across the three different tools.
For the recombinant kinase screening, semi-supervised clustering was used to categorize peptides into four different groups. An unsupervised hierarchical clustering was first performed followed by extracting the top four clusters. The first cluster, titled “No Affinity,” is a list of peptides that show no phosphorylation activity across all tested protein concentrations. The remaining peptides are clustered under three groups based on their dose–response signal activity: low, medium, and high affinity. The “High Affinity” group is a list of reporter peptides that show phosphorylation activity at the lowest protein concentration. “Medium Affinity” are peptides that only start showing phosphorylation activity at the medium protein concentration. Finally, the “Low Affinity” group is a list of peptides that only show activity at the highest protein concentration.
Principal component analysis (PCA) was also performed to visualize the clusters of peptides and determine the component loading for the first four principal components (PC1, PC2, PC3, and PC4). To examine the consensus of the amino acid sequence for each cluster of peptides, we first extracted the main tyrosine phosphosite and the ten neighboring amino acids (five amino acids from each side) of all reporter peptides on the PTK PamChip. We then used the ggseqlogo R package to plot the amino acid sequence logos of the three clusters of peptides and for each recombinant kinase screening profile [21].
To investigate the association of the recombinant kinase profiles with the schizophrenia kinome dataset, we performed a peptide set enrichment analysis using the tmod R package [22, 23]. Using the average peptide LFC, by comparing control to schizophrenia subjects, we ranked all the peptides that passed quality controls filtration steps based on the absolute values of LFCs. Next, we used the makeTmod function from the tmod package to create peptide modules based on the five kinase screening profiles and using the three main clusters (low, medium, and high affinity). We also generated combined peptide lists by combining either all peptides from the three clusters or just the medium and high affinity peptides. The combined peptides lists are used to expand the list of peptides for the more selective kinases, mainly INSRR and LTK. To run the enrichment analysis, we utilized the tmodUtest function to perform a Mann–Whitney U test (U-test) on the ranks of peptides belonging to the defined clusters and the peptides that do not belong to the peptides sets. We used a modified version of the evidencePlot function to visualize the results and highlight the significant enriched peptide sets (p-value < 0.05 and area under the curve (AUC) > 0.5). Additionally, we used a waterfall plot to visualize the peptide LFCs from the schizophrenia kinome dataset.
Pathway Analysis and In-silico Data Exploration
The Enrichr web tool was used to perform multiple gene set enrichment analyses across different get set libraries [24]. The peptides were first mapped to their corresponding proteins, then to the official HGNC gene symbols. The gene set library used in the pathway analysis is the Gene Ontology (GO) Biological Process 2021. The ARCHS4 tool was also used to visualize gene expression levels across various tissues. The Cell Types Database from the Allen Brain Map was used to extract cell-level gene expression in multiple brain regions [25]. The Human Protein Atlas (HPA) web portal was utilized to explore protein expression across tissues, cluster genes based on co-expression, and determine subcellular localization patterns [26, 27]. The web tool Kaleidoscope was used to query multiple curated databases such as STRING, Genotype-Tissue Expression (GTEx), Brain RNASeq, Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS), and Pharos from the Illuminating the Druggable Genome (IDG) program [28–34].
Results
Identifying dark tyrosine protein kinases
Utilizing the knowledge base INDRA database to assign normalized knowledge scores for all protein kinases, we identified 19 understudied or “dark” protein tyrosine kinases (Supplementary Fig. 1A). Out of these 19 “dark” kinases, we selected five kinases to screen using a multiplex functional kinase activity profiling technology (PamStation12 platform). The kinases we profiled include EPHA6, AATK, INSRR, LTK, and TNK1. The normalized ranks for these kinases range from 0.20 to 0.28, where 0 represents the most understudied kinase and 1 represents the most studied kinase (Supplementary Fig. 1B).
Functional profiling of dark tyrosine protein kinases
Screening purified recombinant “dark” tyrosine protein kinases under four conditions (low, medium, high protein concentrations, and heat inactivation as negative control) revealed a set of clusters of concentration-dependent reporter peptides. Based on concentration–response patterns and a semi-supervised clustering approach, we grouped peptides into four different categories: zero or no affinity, low, medium, and high affinity (Supplementary Table 2). This analysis identified 18 high affinity peptides, 31 medium affinity peptides, and 4 low affinity peptides for EPHA6 (Fig. 2B). This analysis identified 26, 9, 3 and 3 high affinity peptides, 31, 46, 4 and 1 medium affinity peptides, and 6, 10, 8 and 1 low affinity peptides for TNK1, AATK, INSRR and LTK respectively (Supplementary Fig. 2A, 2G, 2M, 2S). Signal intensity of phosphosites with increasing total protein (2.5 ng – 25 ng) of recombinant protein shows concentration specific increases in reporter peptide phosphorylation (Fig. 2C, Supplementary Fig. 2D, 2J, 2P, 2V).Fig. 2PamStation12 phospho-tyrosine profiles of purified recombinant dark protein kinases identify novel substrates. A Highlighting the five dark kinases (LTK, TNK1, INSRR, AATK, EPHA6) selected for profiling on a kinome phylogenetic tree. B 2.5 ng, 25 ng, 250 ng of EPHA6 total protein was screened along with a 250 ng EPHA6 heat inactivated (denatured protein) control. Red indicates increased phosphorylation and yellow indicates less phosphorylation at that peptide. A semi-supervised clustering and principal component analysis (PCA) is visualized for PamChip peptides reporting on EPHA6. C Signal intensity plot illustrating increased activity with increased total protein of EPHA6. D Peptide sequence logos for EPHA6 peptides identified on the phosphor-tyrosine (PTK) Pamchip. The color of the single letter amino acid code denotes the chemical features of each amino acid map against relative position. High Affinity: list of peptides that show phosphorylation activity at the lowest protein concentration. Medium Affinity: list of peptides that only start showing phosphorylation activity at the medium protein concentration. Low Affinity: a list of peptides that only show phosphorylation activity at the highest protein concentration. No Affinity: a list of peptides that show no phosphorylation activity across all tested protein concentrations. Ephrin Type-A Receptor 6 (EPHA6)
Chip coverage was calculated by dividing the number of peptide hits by the total number of peptides present on the PTK PamChip (193 peptides). We observed 34%, 27%, 8%, 3%, and 33% chip coverage for AATK, EPHA6, INSRR, LTK, and TNK1 kinases, respectively (Fig. 3A). Next, we examined the intersection of peptide hits across all of the kinase profiles and visualized the overlap as an UpSet plot (Fig. 3B) [35]. The chip coverage and peptide overlap analysis reveals the degree of selectivity of our list of kinases to the reporter peptides printed on the PTK PamChip. For example, the relatively low chip coverage for INSRR and LTK suggests that these kinases are more selective than AATK, EPHA6, and TNK1. Additionally, peptide overlap analysis revealed which reporter peptides are uniquely phosphorylated by the recombinant dark protein kinases. For instance, the peptide overlap analysis shows that the majority of the peptide hits of AATK are exclusively phosphorylated by AATK and not by the other kinases. On the other hand, the majority of peptides hits of EPHA6 and TNK1 are shared among the two kinases.Fig. 3Chip coverage and peptide overlap analyses reveal differential kinase-substrate selectivity. A Pie charts of chip coverage of AATK, EPHA6, INSRR, LTK, and TNK1. The chip coverage is calculated by dividing the number of peptide hits for each kinase by the total number of peptides present on the protein tyrosine kinase (PTK) PamChip (193 peptides). “Low” denotes low affinity peptides, “Medium” denotes medium affinity peptides, “High” denotes high affinity peptides. B UpSet plot showing the overlap of peptide hits across all recombinant kinase profiles and peptide clusters
Next, we examined the consensus of the amino acid sequence for each cluster of peptides by extracting the main tyrosine phosphosite and the ten neighboring amino acids (five amino acids from each side) of all peptide hits. We visualized the amino acid sequence logos of the three clusters of peptides for each recombinant kinase screening profile (Fig. 2C, Supplementary Fig. 2E, 2K, 2Q, 2W). Pathway analyses were performed using the list of peptide hits for each recombinant kinase screening profile using Enrichr. Using the Gene Ontology (GO) Biological Process 2021 gene set library, we extracted and visualized the top ten pathways (Fig. 2D, Supplementary Fig. 2F, 2L, 2Q, 2X).
In-silico data exploration of dark kinases
Known and predicted functional pathways
We explored several publicly available biological databases to examine the current functional knowledge of our list of kinases in terms of their role as enzymes or substrates, tissue-specific and brain-specific expression at the mRNA and protein levels, known and predicted pathways, subcellular localization, and protein–protein interaction (PPI) networks.
To investigate known downstream substrates for kinases, we queried the iPTMnet database [36]. Unsurprisingly, there are currently no annotated downstream substrates for our list of kinases (Supplementary Table 3). Next, we examined gene expression of our list of targets at the mRNA and protein level using the GTEx and HPA databases across different tissues and specifically in the brain. The results from these databases indicate that AATK, INSRR, and EPHA6 are highly enriched in the brain. Additionally, the HPA database shows INSRR is enriched in the kidney as well. Interestingly, TNK1 shows low brain expression at the mRNA level but high abundance at the protein level. LTK shows overall low expression across all tissues with the highest being in the lung and intestine (Supplementary Table 4).
In terms of subcellular localization, the HPA database also contains immunofluorescent staining images of human cell lines aiming to fully annotate the subcellular localization of the human proteome. Examining the subcellular localization of our selected dark protein kinases reveals differential localization patterns. AATK is localized to mitochondria, EPHA6 to the nucleoplasm, LTK to vesicles such as endosomes and lysosomes, and TNK1 to cell junctions. No data was available for INSRR.
Next, we explored known and predicted functional pathways of our selected dark protein kinases. For known pathways, we utilized Enrichr and the Gene Ontology (GO) Biological Process 2021 gene set library using the HGNC symbol of the kinase as the single input. The results from this analysis showed several brain-specific pathways for some of our kinases. For example, AATK is involved in brain development (GO:0007420) and central nervous system development (GO:0007417). EPHA6 is involved in axon guidance (GO:0007411) and axonogenesis (GO:0007409). LTK is involved in regulation of neuron differentiation (GO:0045664) and regulation of neuron projection development (GO:0010976 and GO:0010975). INSRR and TNK1 have other pathways that include cellular response to pH (GO:0071467) and actin cytoskeleton reorganization (GO:0031532) for INSRR and innate immune response (GO:0045087) for TNK1 (Supplementary Table 5).
Given that the functional knowledge of understudied kinases is relatively low, it is more suitable to examine predicted functional annotations. To investigate predicted functional pathways, we used three approaches: co-expression clustering, protein–protein interaction networks, and genetic perturbation analysis. The co-expression clustering analysis examines the top pathways of other well-studied genes that are highly co-expressed with our list of kinases. We utilized the co-expression clustering analyses deployed by the HPA and ARCHS web tools to associate predicted pathways with our targets. The HPA tissue and single-cell co-expression analysis for AATK and INSRR revealed that these protein kinases are co-expressed with genes that are functionally involved in central nervous system myelination, axon guidance, synaptic transmission, and microtubule cytoskeleton organization. Additionally, ARCHS shows central nervous system myelination (GO:0022010) as one of the top predicted pathways for AATK. Other predicted pathways for AATK include regulation of short-term neuronal synaptic plasticity (GO:0048172), synaptic vesicle maturation and exocytosis (GO:0016188, GO:0016079, and GO:2,000,300), and neuronal action potential propagation (GO:0019227 and GO:2,000,463). The predicted pathways for INSRR in ARCHS include Golgi transport vesicle coating (GO:0048200), insulin secretion (GO:0030073), and protein maturation by protein folding (GO:0022417). EPHA6 is part of the “Brain – Ion transport” cluster in the tissue expression clustering analysis in HPA and the “Neurons & Oligodendrocytes—Synaptic function” cluster in the single-cell co-expression analysis. These gene clusters also include other brain-specific pathways such as synaptic transmission, AMPA receptor activity, neuronal action potential, presynaptic membrane assembly, glutamate secretion, and regulation of NMDA receptor activity. ARCHS reveals similar pathways such as glutamate receptor signaling pathways (GO:0035235, GO:0007215, and GO:1,900,449) and additional pathways including synaptic plasticity (GO:0048172) and protein localization to synapse (GO:0035418). In HPA, LTK is a member of a cluster of genes that are involved in various immune response pathways such as adaptive and innate immune response, complement activation, and interferon-gamma-mediated signaling pathways and protein assembly and transport (Supplementary Table 6).
The second approach to associate functional pathways to our list of dark kinases is protein–protein interaction networks. STRING is a curated database of known and predicted protein–protein associations based on multiple resources including gene co-expression, experimental data, and text mining. We used STRING to extract the top 25 protein interactors with each of our list of kinases, using the default minimum required interaction score (medium confidence = 0.400) as the threshold. Next, we used the built-in functional network enrichment analysis in the STRING web tool. Using the previously described parameters, the AATK network had 24 interactors and only three enriched GO (Molecular Function) terms, including Ser-tRNA (Ala) hydrolase activity, alanine-tRNA ligase activity, and Rab guanyl-nucleotide exchange factor activity. EPHA6 network has 24 interactors and 317 enriched GO terms (Biological Process). The top enriched terms for the EPHA6 network include ephrin receptor signaling pathway, axon guidance, and neuron projection development and morphogenesis. The INSRR network contains 24 interactors and 252 enriched GO terms. The top enriched pathways include regulation of protein kinase B signaling, insulin receptor signaling pathway, phosphatidylinositol 3-kinase signaling, and MAPK cascade. There are 12 protein interactors in the LTK network and 85 enriched pathways. The top enriched terms include epidermal growth factor receptor signaling pathway, ERBB2 signaling pathway, generation of neurons, and insulin receptor signaling pathway. Finally, the TNK1 network has 12 interactors and only three enriched GO terms, including regulation of metalloendopeptidase and aspartic-type endopeptidase activity involved in amyloid precursor protein catabolic process (Supplementary Table 7).
To explore the results of our GO analyses, we performed “lookup” validation literature searches for select kinases and associated GO pathways (Supplementary Table 8). Notably, we found that the GO pathways axon guidance (GO:0007411) and innate immune response (GO:0045087) were associated with EPHA6 and TNK1 kinases, respectively. EPHA6 was highly expressed and localized to ganglion cells of the developing human retina. Patterns of EPHA6 expression in the macaque retina related to fovea development and ganglion cell projections suggest a role in the postnatal maintenance of neuronal projections [37]. A group investigating the impact of EPHA6 deletion on neuronal cell morphology in LacZ/LacZ mice discovered extensive impairments in the structure and function of both cells in the brain and spinal cord. These impairments include defects in memory and learning, changes in cellular morphology upon golgi staining at 2 months of age presenting aggregation of cells in the frontal cortical and mid-cortical regions [38]. Similarly, TNK1 was identified as a unique regulator of the ISG (interferon stimulated genes) pathway of the antiviral innate immune response in a high-throughput, genome wide cDNA screening assay used to identify genes regulating the ISG expression. Functionally, activated IFN-receptor complex recruits TNK1 from the cytoplasm, where upon phosphorylation it potentiates the JAK STAT signaling pathway. The authors noted there was a change in the phosphorylation state of STAT1 at two sites by western blot after 24 h of interferon beta exposure: tyrosine 701, and serine 727, however functional characterization of TNK1 activity was not conducted. Interestingly, the phosphosite containing tyrosine 701 maps to our medium affinity peptide logo (Supplementary Fig. 2E) corroborating the findings in our study [39].
The third approach to assess functional annotation of understudied kinases is with controlled genetic manipulation. The LINCS database hosts over a million transcriptional signatures of various cell lines with either genetic or chemical perturbations. We queried the iLINCS web portal to find and analyze gene knockdown or overexpression signatures of our list of dark kinases. Four of our five dark kinases have at least one gene knockdown signature, while TNK1 only has a gene overexpression signature. To keep the comparisons consistent, we selected the VCAP cell line as the primary cell line for the connectivity analysis because it was the only cell line that has gene knockdown signatures for all four kinases (AATK, EPHA6, INSRR, and LTK). To examine and functionally annotate the transcriptional “echo” of knocking down these kinases, we extracted the top differentially expressed genes (p-value < 0.05 and log2 fold change > 0.5 or < -0.5) from the LINCS knockdown signatures and performed gene set enrichment analysis using Enrichr. Using methods described in Sect. " Data analysis" of the dark kinome supplement, we selected top differentially expressed genes and extracted 99 genes from the AATK knockdown signature (LINCSKD_29946). Sorting by Enrichr’s Combined Score, the top associated pathways of this list of genes include regulation of astrocyte differentiation (GO:0048711, GO:0048710), regulation of insulin receptor signaling pathway (GO:0046627, GO:1,900,077), and regulation of extrinsic apoptotic signaling pathway via death domain receptors (GO:1,902,041). We extracted 113 differentially expressed genes from the EPHA6 knockdown signature (LINCSKD_30785). The top implicated pathways for these genes include protein insertion into mitochondrial membrane (GO:0001844, GO:0097345, and GO:0051204), regulation of inositol trisphosphate biosynthetic process (GO:0032960), and glutamate receptor signaling pathway (GO:0035235). For the INSRR knockdown signature (LINCSKD_31354), we extracted 98 differentially expressed genes and revealed pathways involved in regulation of hormone metabolic process (GO:0032352), sequestering of NF-kappaB (GO:0007253), and regulation of glial cell proliferation (GO:0060251). We extracted 100 differentially expressed genes from the LTK knockdown signature (LINCSKD_31563). The top implicated pathways for this list of genes include DNA ligation (GO:0006266), central nervous system projection neuron axonogenesis (GO:0021952), and acetyl-CoA metabolic process (GO:0006084). For the TNK1 overexpression signature (LINCSOE_9396), we extracted 278 differentially expressed genes and revealed pathways involved in regulation of protein import (GO:1,904,589), axo-dendritic transport (GO:0008088), and glycolytic process (GO:0006096) (Supplementary Table 8).
Connectivity analysis with psychiatric and neurogenerative diseases
To elucidate the association of our list of dark kinases with diseases, more specifically with psychiatric and neurogenerative disorders, we performed a transcriptional connectivity analysis between gene perturbation signatures (from LINCS) and previously published disease signatures from schizophrenia (SCZ), major depressive disorder (MDD), and Alzheimer’s Disease (AD) datasets. Utilizing the disease signatures available in Kaleidoscope, we performed a Pearson correlation analysis to calculate the concordance scores between the LINCS gene perturbation and disease signatures using the top differentially expressed genes in each kinase knockdown or overexpression signature (Fig. 4A). Each node represents a transcriptional signature, and the edges are the Pearson correlation coefficients (Fig. 4B). Overall, the EPHA6 gene signature showed the most concordance with the disease groups, most notably with the SCZ and AD datasets. Some kinases showed relatively higher correlation with one disease group compared to the other diseases. For example, the AATK knockdown signature showed similarity with only the AD datasets. Additionally, we observed that the INSRR gene signature showed exclusively higher concordance with the SCZ datasets. The LTK knockdown signature showed the least amount of connectivity with the disease groups. The TNK1 overexpression signature also had low connectivity, especially with the SCZ and AD datasets.Fig. 4Transcriptional connectivity analysis explores concordance between AATK, EPHA6, TNK1, LTK, INSRR gene knockdown signatures and schizophrenia and Alzheimer’s Disease. A Pearson correlation analysis of LINCS knockdown signatures of AATK, EPHA6, INSRR, LTK, and TNK1 against 31 schizophrenia (SCZ), 21 Alzheimer’s Disease (AD), and 33 major depressive disorder (MDD) datasets extracted from the Kaleidoscope web application. The values represent correlation coefficients and color represent the sign of the coefficients (blue: positive correlation, red: negative correlation). The “*” within each node denotes the significance of the correlation analysis where the corrected p-value was under 0.05. B The connectivity analysis visualized as networks where each node represents a transcriptional signature and edges represent the correlation coefficients between signatures. The central node in each network represents the gene knockdown or overexpression signature of the corresponding kinase (AATK, EPHA6, INSRR, LTK, and TNK1) retrieved from iLINCS
Identifying dark tyrosine protein kinases
Utilizing the knowledge base INDRA database to assign normalized knowledge scores for all protein kinases, we identified 19 understudied or “dark” protein tyrosine kinases (Supplementary Fig. 1A). Out of these 19 “dark” kinases, we selected five kinases to screen using a multiplex functional kinase activity profiling technology (PamStation12 platform). The kinases we profiled include EPHA6, AATK, INSRR, LTK, and TNK1. The normalized ranks for these kinases range from 0.20 to 0.28, where 0 represents the most understudied kinase and 1 represents the most studied kinase (Supplementary Fig. 1B).
Functional profiling of dark tyrosine protein kinases
Screening purified recombinant “dark” tyrosine protein kinases under four conditions (low, medium, high protein concentrations, and heat inactivation as negative control) revealed a set of clusters of concentration-dependent reporter peptides. Based on concentration–response patterns and a semi-supervised clustering approach, we grouped peptides into four different categories: zero or no affinity, low, medium, and high affinity (Supplementary Table 2). This analysis identified 18 high affinity peptides, 31 medium affinity peptides, and 4 low affinity peptides for EPHA6 (Fig. 2B). This analysis identified 26, 9, 3 and 3 high affinity peptides, 31, 46, 4 and 1 medium affinity peptides, and 6, 10, 8 and 1 low affinity peptides for TNK1, AATK, INSRR and LTK respectively (Supplementary Fig. 2A, 2G, 2M, 2S). Signal intensity of phosphosites with increasing total protein (2.5 ng – 25 ng) of recombinant protein shows concentration specific increases in reporter peptide phosphorylation (Fig. 2C, Supplementary Fig. 2D, 2J, 2P, 2V).Fig. 2PamStation12 phospho-tyrosine profiles of purified recombinant dark protein kinases identify novel substrates. A Highlighting the five dark kinases (LTK, TNK1, INSRR, AATK, EPHA6) selected for profiling on a kinome phylogenetic tree. B 2.5 ng, 25 ng, 250 ng of EPHA6 total protein was screened along with a 250 ng EPHA6 heat inactivated (denatured protein) control. Red indicates increased phosphorylation and yellow indicates less phosphorylation at that peptide. A semi-supervised clustering and principal component analysis (PCA) is visualized for PamChip peptides reporting on EPHA6. C Signal intensity plot illustrating increased activity with increased total protein of EPHA6. D Peptide sequence logos for EPHA6 peptides identified on the phosphor-tyrosine (PTK) Pamchip. The color of the single letter amino acid code denotes the chemical features of each amino acid map against relative position. High Affinity: list of peptides that show phosphorylation activity at the lowest protein concentration. Medium Affinity: list of peptides that only start showing phosphorylation activity at the medium protein concentration. Low Affinity: a list of peptides that only show phosphorylation activity at the highest protein concentration. No Affinity: a list of peptides that show no phosphorylation activity across all tested protein concentrations. Ephrin Type-A Receptor 6 (EPHA6)
Chip coverage was calculated by dividing the number of peptide hits by the total number of peptides present on the PTK PamChip (193 peptides). We observed 34%, 27%, 8%, 3%, and 33% chip coverage for AATK, EPHA6, INSRR, LTK, and TNK1 kinases, respectively (Fig. 3A). Next, we examined the intersection of peptide hits across all of the kinase profiles and visualized the overlap as an UpSet plot (Fig. 3B) [35]. The chip coverage and peptide overlap analysis reveals the degree of selectivity of our list of kinases to the reporter peptides printed on the PTK PamChip. For example, the relatively low chip coverage for INSRR and LTK suggests that these kinases are more selective than AATK, EPHA6, and TNK1. Additionally, peptide overlap analysis revealed which reporter peptides are uniquely phosphorylated by the recombinant dark protein kinases. For instance, the peptide overlap analysis shows that the majority of the peptide hits of AATK are exclusively phosphorylated by AATK and not by the other kinases. On the other hand, the majority of peptides hits of EPHA6 and TNK1 are shared among the two kinases.Fig. 3Chip coverage and peptide overlap analyses reveal differential kinase-substrate selectivity. A Pie charts of chip coverage of AATK, EPHA6, INSRR, LTK, and TNK1. The chip coverage is calculated by dividing the number of peptide hits for each kinase by the total number of peptides present on the protein tyrosine kinase (PTK) PamChip (193 peptides). “Low” denotes low affinity peptides, “Medium” denotes medium affinity peptides, “High” denotes high affinity peptides. B UpSet plot showing the overlap of peptide hits across all recombinant kinase profiles and peptide clusters
Next, we examined the consensus of the amino acid sequence for each cluster of peptides by extracting the main tyrosine phosphosite and the ten neighboring amino acids (five amino acids from each side) of all peptide hits. We visualized the amino acid sequence logos of the three clusters of peptides for each recombinant kinase screening profile (Fig. 2C, Supplementary Fig. 2E, 2K, 2Q, 2W). Pathway analyses were performed using the list of peptide hits for each recombinant kinase screening profile using Enrichr. Using the Gene Ontology (GO) Biological Process 2021 gene set library, we extracted and visualized the top ten pathways (Fig. 2D, Supplementary Fig. 2F, 2L, 2Q, 2X).
In-silico data exploration of dark kinases
Known and predicted functional pathways
We explored several publicly available biological databases to examine the current functional knowledge of our list of kinases in terms of their role as enzymes or substrates, tissue-specific and brain-specific expression at the mRNA and protein levels, known and predicted pathways, subcellular localization, and protein–protein interaction (PPI) networks.
To investigate known downstream substrates for kinases, we queried the iPTMnet database [36]. Unsurprisingly, there are currently no annotated downstream substrates for our list of kinases (Supplementary Table 3). Next, we examined gene expression of our list of targets at the mRNA and protein level using the GTEx and HPA databases across different tissues and specifically in the brain. The results from these databases indicate that AATK, INSRR, and EPHA6 are highly enriched in the brain. Additionally, the HPA database shows INSRR is enriched in the kidney as well. Interestingly, TNK1 shows low brain expression at the mRNA level but high abundance at the protein level. LTK shows overall low expression across all tissues with the highest being in the lung and intestine (Supplementary Table 4).
In terms of subcellular localization, the HPA database also contains immunofluorescent staining images of human cell lines aiming to fully annotate the subcellular localization of the human proteome. Examining the subcellular localization of our selected dark protein kinases reveals differential localization patterns. AATK is localized to mitochondria, EPHA6 to the nucleoplasm, LTK to vesicles such as endosomes and lysosomes, and TNK1 to cell junctions. No data was available for INSRR.
Next, we explored known and predicted functional pathways of our selected dark protein kinases. For known pathways, we utilized Enrichr and the Gene Ontology (GO) Biological Process 2021 gene set library using the HGNC symbol of the kinase as the single input. The results from this analysis showed several brain-specific pathways for some of our kinases. For example, AATK is involved in brain development (GO:0007420) and central nervous system development (GO:0007417). EPHA6 is involved in axon guidance (GO:0007411) and axonogenesis (GO:0007409). LTK is involved in regulation of neuron differentiation (GO:0045664) and regulation of neuron projection development (GO:0010976 and GO:0010975). INSRR and TNK1 have other pathways that include cellular response to pH (GO:0071467) and actin cytoskeleton reorganization (GO:0031532) for INSRR and innate immune response (GO:0045087) for TNK1 (Supplementary Table 5).
Given that the functional knowledge of understudied kinases is relatively low, it is more suitable to examine predicted functional annotations. To investigate predicted functional pathways, we used three approaches: co-expression clustering, protein–protein interaction networks, and genetic perturbation analysis. The co-expression clustering analysis examines the top pathways of other well-studied genes that are highly co-expressed with our list of kinases. We utilized the co-expression clustering analyses deployed by the HPA and ARCHS web tools to associate predicted pathways with our targets. The HPA tissue and single-cell co-expression analysis for AATK and INSRR revealed that these protein kinases are co-expressed with genes that are functionally involved in central nervous system myelination, axon guidance, synaptic transmission, and microtubule cytoskeleton organization. Additionally, ARCHS shows central nervous system myelination (GO:0022010) as one of the top predicted pathways for AATK. Other predicted pathways for AATK include regulation of short-term neuronal synaptic plasticity (GO:0048172), synaptic vesicle maturation and exocytosis (GO:0016188, GO:0016079, and GO:2,000,300), and neuronal action potential propagation (GO:0019227 and GO:2,000,463). The predicted pathways for INSRR in ARCHS include Golgi transport vesicle coating (GO:0048200), insulin secretion (GO:0030073), and protein maturation by protein folding (GO:0022417). EPHA6 is part of the “Brain – Ion transport” cluster in the tissue expression clustering analysis in HPA and the “Neurons & Oligodendrocytes—Synaptic function” cluster in the single-cell co-expression analysis. These gene clusters also include other brain-specific pathways such as synaptic transmission, AMPA receptor activity, neuronal action potential, presynaptic membrane assembly, glutamate secretion, and regulation of NMDA receptor activity. ARCHS reveals similar pathways such as glutamate receptor signaling pathways (GO:0035235, GO:0007215, and GO:1,900,449) and additional pathways including synaptic plasticity (GO:0048172) and protein localization to synapse (GO:0035418). In HPA, LTK is a member of a cluster of genes that are involved in various immune response pathways such as adaptive and innate immune response, complement activation, and interferon-gamma-mediated signaling pathways and protein assembly and transport (Supplementary Table 6).
The second approach to associate functional pathways to our list of dark kinases is protein–protein interaction networks. STRING is a curated database of known and predicted protein–protein associations based on multiple resources including gene co-expression, experimental data, and text mining. We used STRING to extract the top 25 protein interactors with each of our list of kinases, using the default minimum required interaction score (medium confidence = 0.400) as the threshold. Next, we used the built-in functional network enrichment analysis in the STRING web tool. Using the previously described parameters, the AATK network had 24 interactors and only three enriched GO (Molecular Function) terms, including Ser-tRNA (Ala) hydrolase activity, alanine-tRNA ligase activity, and Rab guanyl-nucleotide exchange factor activity. EPHA6 network has 24 interactors and 317 enriched GO terms (Biological Process). The top enriched terms for the EPHA6 network include ephrin receptor signaling pathway, axon guidance, and neuron projection development and morphogenesis. The INSRR network contains 24 interactors and 252 enriched GO terms. The top enriched pathways include regulation of protein kinase B signaling, insulin receptor signaling pathway, phosphatidylinositol 3-kinase signaling, and MAPK cascade. There are 12 protein interactors in the LTK network and 85 enriched pathways. The top enriched terms include epidermal growth factor receptor signaling pathway, ERBB2 signaling pathway, generation of neurons, and insulin receptor signaling pathway. Finally, the TNK1 network has 12 interactors and only three enriched GO terms, including regulation of metalloendopeptidase and aspartic-type endopeptidase activity involved in amyloid precursor protein catabolic process (Supplementary Table 7).
To explore the results of our GO analyses, we performed “lookup” validation literature searches for select kinases and associated GO pathways (Supplementary Table 8). Notably, we found that the GO pathways axon guidance (GO:0007411) and innate immune response (GO:0045087) were associated with EPHA6 and TNK1 kinases, respectively. EPHA6 was highly expressed and localized to ganglion cells of the developing human retina. Patterns of EPHA6 expression in the macaque retina related to fovea development and ganglion cell projections suggest a role in the postnatal maintenance of neuronal projections [37]. A group investigating the impact of EPHA6 deletion on neuronal cell morphology in LacZ/LacZ mice discovered extensive impairments in the structure and function of both cells in the brain and spinal cord. These impairments include defects in memory and learning, changes in cellular morphology upon golgi staining at 2 months of age presenting aggregation of cells in the frontal cortical and mid-cortical regions [38]. Similarly, TNK1 was identified as a unique regulator of the ISG (interferon stimulated genes) pathway of the antiviral innate immune response in a high-throughput, genome wide cDNA screening assay used to identify genes regulating the ISG expression. Functionally, activated IFN-receptor complex recruits TNK1 from the cytoplasm, where upon phosphorylation it potentiates the JAK STAT signaling pathway. The authors noted there was a change in the phosphorylation state of STAT1 at two sites by western blot after 24 h of interferon beta exposure: tyrosine 701, and serine 727, however functional characterization of TNK1 activity was not conducted. Interestingly, the phosphosite containing tyrosine 701 maps to our medium affinity peptide logo (Supplementary Fig. 2E) corroborating the findings in our study [39].
The third approach to assess functional annotation of understudied kinases is with controlled genetic manipulation. The LINCS database hosts over a million transcriptional signatures of various cell lines with either genetic or chemical perturbations. We queried the iLINCS web portal to find and analyze gene knockdown or overexpression signatures of our list of dark kinases. Four of our five dark kinases have at least one gene knockdown signature, while TNK1 only has a gene overexpression signature. To keep the comparisons consistent, we selected the VCAP cell line as the primary cell line for the connectivity analysis because it was the only cell line that has gene knockdown signatures for all four kinases (AATK, EPHA6, INSRR, and LTK). To examine and functionally annotate the transcriptional “echo” of knocking down these kinases, we extracted the top differentially expressed genes (p-value < 0.05 and log2 fold change > 0.5 or < -0.5) from the LINCS knockdown signatures and performed gene set enrichment analysis using Enrichr. Using methods described in Sect. " Data analysis" of the dark kinome supplement, we selected top differentially expressed genes and extracted 99 genes from the AATK knockdown signature (LINCSKD_29946). Sorting by Enrichr’s Combined Score, the top associated pathways of this list of genes include regulation of astrocyte differentiation (GO:0048711, GO:0048710), regulation of insulin receptor signaling pathway (GO:0046627, GO:1,900,077), and regulation of extrinsic apoptotic signaling pathway via death domain receptors (GO:1,902,041). We extracted 113 differentially expressed genes from the EPHA6 knockdown signature (LINCSKD_30785). The top implicated pathways for these genes include protein insertion into mitochondrial membrane (GO:0001844, GO:0097345, and GO:0051204), regulation of inositol trisphosphate biosynthetic process (GO:0032960), and glutamate receptor signaling pathway (GO:0035235). For the INSRR knockdown signature (LINCSKD_31354), we extracted 98 differentially expressed genes and revealed pathways involved in regulation of hormone metabolic process (GO:0032352), sequestering of NF-kappaB (GO:0007253), and regulation of glial cell proliferation (GO:0060251). We extracted 100 differentially expressed genes from the LTK knockdown signature (LINCSKD_31563). The top implicated pathways for this list of genes include DNA ligation (GO:0006266), central nervous system projection neuron axonogenesis (GO:0021952), and acetyl-CoA metabolic process (GO:0006084). For the TNK1 overexpression signature (LINCSOE_9396), we extracted 278 differentially expressed genes and revealed pathways involved in regulation of protein import (GO:1,904,589), axo-dendritic transport (GO:0008088), and glycolytic process (GO:0006096) (Supplementary Table 8).
Connectivity analysis with psychiatric and neurogenerative diseases
To elucidate the association of our list of dark kinases with diseases, more specifically with psychiatric and neurogenerative disorders, we performed a transcriptional connectivity analysis between gene perturbation signatures (from LINCS) and previously published disease signatures from schizophrenia (SCZ), major depressive disorder (MDD), and Alzheimer’s Disease (AD) datasets. Utilizing the disease signatures available in Kaleidoscope, we performed a Pearson correlation analysis to calculate the concordance scores between the LINCS gene perturbation and disease signatures using the top differentially expressed genes in each kinase knockdown or overexpression signature (Fig. 4A). Each node represents a transcriptional signature, and the edges are the Pearson correlation coefficients (Fig. 4B). Overall, the EPHA6 gene signature showed the most concordance with the disease groups, most notably with the SCZ and AD datasets. Some kinases showed relatively higher correlation with one disease group compared to the other diseases. For example, the AATK knockdown signature showed similarity with only the AD datasets. Additionally, we observed that the INSRR gene signature showed exclusively higher concordance with the SCZ datasets. The LTK knockdown signature showed the least amount of connectivity with the disease groups. The TNK1 overexpression signature also had low connectivity, especially with the SCZ and AD datasets.Fig. 4Transcriptional connectivity analysis explores concordance between AATK, EPHA6, TNK1, LTK, INSRR gene knockdown signatures and schizophrenia and Alzheimer’s Disease. A Pearson correlation analysis of LINCS knockdown signatures of AATK, EPHA6, INSRR, LTK, and TNK1 against 31 schizophrenia (SCZ), 21 Alzheimer’s Disease (AD), and 33 major depressive disorder (MDD) datasets extracted from the Kaleidoscope web application. The values represent correlation coefficients and color represent the sign of the coefficients (blue: positive correlation, red: negative correlation). The “*” within each node denotes the significance of the correlation analysis where the corrected p-value was under 0.05. B The connectivity analysis visualized as networks where each node represents a transcriptional signature and edges represent the correlation coefficients between signatures. The central node in each network represents the gene knockdown or overexpression signature of the corresponding kinase (AATK, EPHA6, INSRR, LTK, and TNK1) retrieved from iLINCS
Known and predicted functional pathways
We explored several publicly available biological databases to examine the current functional knowledge of our list of kinases in terms of their role as enzymes or substrates, tissue-specific and brain-specific expression at the mRNA and protein levels, known and predicted pathways, subcellular localization, and protein–protein interaction (PPI) networks.
To investigate known downstream substrates for kinases, we queried the iPTMnet database [36]. Unsurprisingly, there are currently no annotated downstream substrates for our list of kinases (Supplementary Table 3). Next, we examined gene expression of our list of targets at the mRNA and protein level using the GTEx and HPA databases across different tissues and specifically in the brain. The results from these databases indicate that AATK, INSRR, and EPHA6 are highly enriched in the brain. Additionally, the HPA database shows INSRR is enriched in the kidney as well. Interestingly, TNK1 shows low brain expression at the mRNA level but high abundance at the protein level. LTK shows overall low expression across all tissues with the highest being in the lung and intestine (Supplementary Table 4).
In terms of subcellular localization, the HPA database also contains immunofluorescent staining images of human cell lines aiming to fully annotate the subcellular localization of the human proteome. Examining the subcellular localization of our selected dark protein kinases reveals differential localization patterns. AATK is localized to mitochondria, EPHA6 to the nucleoplasm, LTK to vesicles such as endosomes and lysosomes, and TNK1 to cell junctions. No data was available for INSRR.
Next, we explored known and predicted functional pathways of our selected dark protein kinases. For known pathways, we utilized Enrichr and the Gene Ontology (GO) Biological Process 2021 gene set library using the HGNC symbol of the kinase as the single input. The results from this analysis showed several brain-specific pathways for some of our kinases. For example, AATK is involved in brain development (GO:0007420) and central nervous system development (GO:0007417). EPHA6 is involved in axon guidance (GO:0007411) and axonogenesis (GO:0007409). LTK is involved in regulation of neuron differentiation (GO:0045664) and regulation of neuron projection development (GO:0010976 and GO:0010975). INSRR and TNK1 have other pathways that include cellular response to pH (GO:0071467) and actin cytoskeleton reorganization (GO:0031532) for INSRR and innate immune response (GO:0045087) for TNK1 (Supplementary Table 5).
Given that the functional knowledge of understudied kinases is relatively low, it is more suitable to examine predicted functional annotations. To investigate predicted functional pathways, we used three approaches: co-expression clustering, protein–protein interaction networks, and genetic perturbation analysis. The co-expression clustering analysis examines the top pathways of other well-studied genes that are highly co-expressed with our list of kinases. We utilized the co-expression clustering analyses deployed by the HPA and ARCHS web tools to associate predicted pathways with our targets. The HPA tissue and single-cell co-expression analysis for AATK and INSRR revealed that these protein kinases are co-expressed with genes that are functionally involved in central nervous system myelination, axon guidance, synaptic transmission, and microtubule cytoskeleton organization. Additionally, ARCHS shows central nervous system myelination (GO:0022010) as one of the top predicted pathways for AATK. Other predicted pathways for AATK include regulation of short-term neuronal synaptic plasticity (GO:0048172), synaptic vesicle maturation and exocytosis (GO:0016188, GO:0016079, and GO:2,000,300), and neuronal action potential propagation (GO:0019227 and GO:2,000,463). The predicted pathways for INSRR in ARCHS include Golgi transport vesicle coating (GO:0048200), insulin secretion (GO:0030073), and protein maturation by protein folding (GO:0022417). EPHA6 is part of the “Brain – Ion transport” cluster in the tissue expression clustering analysis in HPA and the “Neurons & Oligodendrocytes—Synaptic function” cluster in the single-cell co-expression analysis. These gene clusters also include other brain-specific pathways such as synaptic transmission, AMPA receptor activity, neuronal action potential, presynaptic membrane assembly, glutamate secretion, and regulation of NMDA receptor activity. ARCHS reveals similar pathways such as glutamate receptor signaling pathways (GO:0035235, GO:0007215, and GO:1,900,449) and additional pathways including synaptic plasticity (GO:0048172) and protein localization to synapse (GO:0035418). In HPA, LTK is a member of a cluster of genes that are involved in various immune response pathways such as adaptive and innate immune response, complement activation, and interferon-gamma-mediated signaling pathways and protein assembly and transport (Supplementary Table 6).
The second approach to associate functional pathways to our list of dark kinases is protein–protein interaction networks. STRING is a curated database of known and predicted protein–protein associations based on multiple resources including gene co-expression, experimental data, and text mining. We used STRING to extract the top 25 protein interactors with each of our list of kinases, using the default minimum required interaction score (medium confidence = 0.400) as the threshold. Next, we used the built-in functional network enrichment analysis in the STRING web tool. Using the previously described parameters, the AATK network had 24 interactors and only three enriched GO (Molecular Function) terms, including Ser-tRNA (Ala) hydrolase activity, alanine-tRNA ligase activity, and Rab guanyl-nucleotide exchange factor activity. EPHA6 network has 24 interactors and 317 enriched GO terms (Biological Process). The top enriched terms for the EPHA6 network include ephrin receptor signaling pathway, axon guidance, and neuron projection development and morphogenesis. The INSRR network contains 24 interactors and 252 enriched GO terms. The top enriched pathways include regulation of protein kinase B signaling, insulin receptor signaling pathway, phosphatidylinositol 3-kinase signaling, and MAPK cascade. There are 12 protein interactors in the LTK network and 85 enriched pathways. The top enriched terms include epidermal growth factor receptor signaling pathway, ERBB2 signaling pathway, generation of neurons, and insulin receptor signaling pathway. Finally, the TNK1 network has 12 interactors and only three enriched GO terms, including regulation of metalloendopeptidase and aspartic-type endopeptidase activity involved in amyloid precursor protein catabolic process (Supplementary Table 7).
To explore the results of our GO analyses, we performed “lookup” validation literature searches for select kinases and associated GO pathways (Supplementary Table 8). Notably, we found that the GO pathways axon guidance (GO:0007411) and innate immune response (GO:0045087) were associated with EPHA6 and TNK1 kinases, respectively. EPHA6 was highly expressed and localized to ganglion cells of the developing human retina. Patterns of EPHA6 expression in the macaque retina related to fovea development and ganglion cell projections suggest a role in the postnatal maintenance of neuronal projections [37]. A group investigating the impact of EPHA6 deletion on neuronal cell morphology in LacZ/LacZ mice discovered extensive impairments in the structure and function of both cells in the brain and spinal cord. These impairments include defects in memory and learning, changes in cellular morphology upon golgi staining at 2 months of age presenting aggregation of cells in the frontal cortical and mid-cortical regions [38]. Similarly, TNK1 was identified as a unique regulator of the ISG (interferon stimulated genes) pathway of the antiviral innate immune response in a high-throughput, genome wide cDNA screening assay used to identify genes regulating the ISG expression. Functionally, activated IFN-receptor complex recruits TNK1 from the cytoplasm, where upon phosphorylation it potentiates the JAK STAT signaling pathway. The authors noted there was a change in the phosphorylation state of STAT1 at two sites by western blot after 24 h of interferon beta exposure: tyrosine 701, and serine 727, however functional characterization of TNK1 activity was not conducted. Interestingly, the phosphosite containing tyrosine 701 maps to our medium affinity peptide logo (Supplementary Fig. 2E) corroborating the findings in our study [39].
The third approach to assess functional annotation of understudied kinases is with controlled genetic manipulation. The LINCS database hosts over a million transcriptional signatures of various cell lines with either genetic or chemical perturbations. We queried the iLINCS web portal to find and analyze gene knockdown or overexpression signatures of our list of dark kinases. Four of our five dark kinases have at least one gene knockdown signature, while TNK1 only has a gene overexpression signature. To keep the comparisons consistent, we selected the VCAP cell line as the primary cell line for the connectivity analysis because it was the only cell line that has gene knockdown signatures for all four kinases (AATK, EPHA6, INSRR, and LTK). To examine and functionally annotate the transcriptional “echo” of knocking down these kinases, we extracted the top differentially expressed genes (p-value < 0.05 and log2 fold change > 0.5 or < -0.5) from the LINCS knockdown signatures and performed gene set enrichment analysis using Enrichr. Using methods described in Sect. " Data analysis" of the dark kinome supplement, we selected top differentially expressed genes and extracted 99 genes from the AATK knockdown signature (LINCSKD_29946). Sorting by Enrichr’s Combined Score, the top associated pathways of this list of genes include regulation of astrocyte differentiation (GO:0048711, GO:0048710), regulation of insulin receptor signaling pathway (GO:0046627, GO:1,900,077), and regulation of extrinsic apoptotic signaling pathway via death domain receptors (GO:1,902,041). We extracted 113 differentially expressed genes from the EPHA6 knockdown signature (LINCSKD_30785). The top implicated pathways for these genes include protein insertion into mitochondrial membrane (GO:0001844, GO:0097345, and GO:0051204), regulation of inositol trisphosphate biosynthetic process (GO:0032960), and glutamate receptor signaling pathway (GO:0035235). For the INSRR knockdown signature (LINCSKD_31354), we extracted 98 differentially expressed genes and revealed pathways involved in regulation of hormone metabolic process (GO:0032352), sequestering of NF-kappaB (GO:0007253), and regulation of glial cell proliferation (GO:0060251). We extracted 100 differentially expressed genes from the LTK knockdown signature (LINCSKD_31563). The top implicated pathways for this list of genes include DNA ligation (GO:0006266), central nervous system projection neuron axonogenesis (GO:0021952), and acetyl-CoA metabolic process (GO:0006084). For the TNK1 overexpression signature (LINCSOE_9396), we extracted 278 differentially expressed genes and revealed pathways involved in regulation of protein import (GO:1,904,589), axo-dendritic transport (GO:0008088), and glycolytic process (GO:0006096) (Supplementary Table 8).
Connectivity analysis with psychiatric and neurogenerative diseases
To elucidate the association of our list of dark kinases with diseases, more specifically with psychiatric and neurogenerative disorders, we performed a transcriptional connectivity analysis between gene perturbation signatures (from LINCS) and previously published disease signatures from schizophrenia (SCZ), major depressive disorder (MDD), and Alzheimer’s Disease (AD) datasets. Utilizing the disease signatures available in Kaleidoscope, we performed a Pearson correlation analysis to calculate the concordance scores between the LINCS gene perturbation and disease signatures using the top differentially expressed genes in each kinase knockdown or overexpression signature (Fig. 4A). Each node represents a transcriptional signature, and the edges are the Pearson correlation coefficients (Fig. 4B). Overall, the EPHA6 gene signature showed the most concordance with the disease groups, most notably with the SCZ and AD datasets. Some kinases showed relatively higher correlation with one disease group compared to the other diseases. For example, the AATK knockdown signature showed similarity with only the AD datasets. Additionally, we observed that the INSRR gene signature showed exclusively higher concordance with the SCZ datasets. The LTK knockdown signature showed the least amount of connectivity with the disease groups. The TNK1 overexpression signature also had low connectivity, especially with the SCZ and AD datasets.Fig. 4Transcriptional connectivity analysis explores concordance between AATK, EPHA6, TNK1, LTK, INSRR gene knockdown signatures and schizophrenia and Alzheimer’s Disease. A Pearson correlation analysis of LINCS knockdown signatures of AATK, EPHA6, INSRR, LTK, and TNK1 against 31 schizophrenia (SCZ), 21 Alzheimer’s Disease (AD), and 33 major depressive disorder (MDD) datasets extracted from the Kaleidoscope web application. The values represent correlation coefficients and color represent the sign of the coefficients (blue: positive correlation, red: negative correlation). The “*” within each node denotes the significance of the correlation analysis where the corrected p-value was under 0.05. B The connectivity analysis visualized as networks where each node represents a transcriptional signature and edges represent the correlation coefficients between signatures. The central node in each network represents the gene knockdown or overexpression signature of the corresponding kinase (AATK, EPHA6, INSRR, LTK, and TNK1) retrieved from iLINCS
Profiling the activity of the tyrosine subkinome in schizophrenia
Utilizing the KRSA R package [19], the kinome array profiles of postmortem dorsolateral prefrontal cortex from pooled samples of control and SCZ subjects were fully analyzed starting from preprocessing and quality control filtering to clustering and extracting differentially phosphorylated peptides. To account for the technical variability between chips and biological variation between male and female subjects, the analysis was performed per chip and separated by sex. To determine the global phosphorylation signals in the control and SCZ subjects, we used KRSA to generate global heatmaps with unsupervised hierarchical clustering of pooled samples run in technical triplicate (Fig. 5A, D). To better highlight the differences between the two groups, the values are scaled by row/peptide (Z score transformation). The clustering analysis shows a clear difference between the control and SCZ subjects. Overall, we observed a strong reduction of global phosphorylation in both male and female subjects. To investigate implicated upstream kinases, we used KRSA, UKA, and KEA3 to perform upstream kinase enrichment analysis and the Creedenzymatic R package to aggregate and harmonize the results from each analytic tool. We used the quartile plot function from the Creedenzymatic R package to plot the top associated kinases that explain the differences between the control and SCZ subjects (Fig. 5C, F). The upstream kinases analysis revealed a list of differentially active kinases in SCZ. Moreover, we observed a list of kinases that were differentially active in only the male subjects, only female subjects, and in both male and female subjects. For example, ABL1, DDR2, FYN, LCK, SYK, and NTRK1 kinases were differentially active in male and female SCZ subjects. TYRO3, IGF1R, INSR, MET, PDGFRA, HCK, and SRC kinases were only differentially active in the female subjects, whereas ALK, MERTK, PTK2, PTK2B, JAK2, and FLT3 kinases were only differentially active in the male subjects. Next, we used KinMap to plot the top implicated upstream kinases on the kinome phylogenetic tree (Fig. 5B, E) [40].Fig. 5Sex specific kinase perturbation in the DLPFC of schizophrenia subjects. A-C Pooled technical triplicate females, D-F Pooled technical triplicate males: Unsupervised clustering of the active kinome profiles of postmortem dorsolateral prefrontal cortex (DLPFC) samples from control and SCZ subjects. Color represents the relative phosphorylation activity at each reporter peptide on the PTK PamChip. Yellow represents relatively lower phosphorylation activity and red indicates relatively higher phosphorylation activity. The phosphorylation signals are scaled by row/peptide (Z score transformation) to better highlight the differences between the two groups. This approach shows the upstream kinase analysis on the kinome phylogenetic tree with an emphasis on the tyrosine kinase subgroup. Creedenzymatic plots featuring the quartile scores of upstream kinases using three analytic tools: Kinome Random Sampling Analyzer (KRSA), upstream kinase analysis (UKA), and Kinase Enrichment Analysis (KEA3)
Connecting the recombinant kinase profiles with the schizophrenia active kinome
To connect the recombinant kinase profiles of our dark kinases with the SCZ kinome dataset, we performed a peptide set enrichment analysis using the tmod R package (Supplementary Fig. 3). Similar to the upstream kinase analysis, the recombinant kinase profile enrichment analysis also revealed sex differences in peptide set enrichment scores and directionality of activity. The AATK, EPHA6, INSRR, LTK and TNK1 peptide sets were enriched in the female subjects and mostly show decreased activity, whereas the same peptides sets were not enriched in the male kinome profiles (Supplementary Fig. 3A-B).
Downstream target validation
To validate EPHA6 phosphorylation of novel substrates identified on the array, kinome array experimental conditions were replicated using a benchtop assay with recombinant EPHA6 and one of the “hits” for this dark protein kinase from the PTK array, Growth Factor Receptor Bound Protein 2-Associated Protein 1 (GAB1). Following incubation with an in vitro reaction assay, samples were run on an SDS-PAGE gel and sent for mass spectrometry (Fig. 6A). A pan-phospho-tyrosine antibody was used to identify autophosphorylation sites for EPHA6 as well as novel and predicted phosphorylation sites on GAB1. Interestingly, a p-Tyr band was observed at the expected GAB1 molecular weight only with active EPHA6 and not in the heat inactivated EPHA6 control. Additionally, a p-Tyr band was observed with active EPHA6 alone and wasn’t observed in the heat inactivated control suggesting EPHA6 autophosphorylation at a Tyrosine residue (Fig. 6B).Fig. 6Downstream Target Validation of EPHA6 predicted phosphosites. A Experimental workflow for the benchtop kinase activity assay, western blot validation experiment, and mass spectrometry phospho-site identification. B Representative western blot for two recombinant proteins, GAB1 (substrate) and EPHA6 (kinase). All western blot samples were collected from the benchtop activity assay. 250 ng of active or heat inactivated EPHA6 was run on a gel with and without increasing total protein of GAB1 (0 ng, 200 ng, 300 ng, 400 ng). A pan-phospho-tyrosine antibody (1:250, Cell Signaling, catalogue # 9411S) was used to identify any tyrosine site phosphorylated by EPHA6. C Relative abundance of peptides following protease digestion of the kinase substrate reaction was analyzed by LC-MSMS over a 60-min gradient. D Peptide sequences shown were identified from mass spectrometry and mapped to EPHA6 and GAB1. The underlined sequence corresponding to phosphosite pY627 is present as a substrate on the PTK Pamchip. E Placement of GAB1 and EPHA6 phosphosites on full protein sequence domain maps
Mass spectrometry of co-incubated recombinant proteins revealed phosphorylation of GAB1 and EPHA6 at several tyrosine residues. Tyrosine residue pY627 was initially identified as a PTK array reporter peptide (from GAB1) for recombinant EPHA6 on the array (Fig. 2B). This site and two others were identified on recombinant GAB1 via mass spectrometry. Additionally, two putative autophosphoryation sites on EPHA6 were identified at pY259 and pY484 (Fig. 6C, D). Autophosphorylation of EPHA6 occurred at the ATP binding N-lobe between the EPH ligand binding domain and the fibronectin type III conserved regions. GAB1 was phosphorylated mostly at the C-terminus, along several intrinsically disordered regions suggesting changes in localization and protein–protein interactions (Fig. 6E). Changes in GAB1 function and localization are yet to be determined.
Connecting the recombinant kinase profiles with the schizophrenia active kinome
To connect the recombinant kinase profiles of our dark kinases with the SCZ kinome dataset, we performed a peptide set enrichment analysis using the tmod R package (Supplementary Fig. 3). Similar to the upstream kinase analysis, the recombinant kinase profile enrichment analysis also revealed sex differences in peptide set enrichment scores and directionality of activity. The AATK, EPHA6, INSRR, LTK and TNK1 peptide sets were enriched in the female subjects and mostly show decreased activity, whereas the same peptides sets were not enriched in the male kinome profiles (Supplementary Fig. 3A-B).
Downstream target validation
To validate EPHA6 phosphorylation of novel substrates identified on the array, kinome array experimental conditions were replicated using a benchtop assay with recombinant EPHA6 and one of the “hits” for this dark protein kinase from the PTK array, Growth Factor Receptor Bound Protein 2-Associated Protein 1 (GAB1). Following incubation with an in vitro reaction assay, samples were run on an SDS-PAGE gel and sent for mass spectrometry (Fig. 6A). A pan-phospho-tyrosine antibody was used to identify autophosphorylation sites for EPHA6 as well as novel and predicted phosphorylation sites on GAB1. Interestingly, a p-Tyr band was observed at the expected GAB1 molecular weight only with active EPHA6 and not in the heat inactivated EPHA6 control. Additionally, a p-Tyr band was observed with active EPHA6 alone and wasn’t observed in the heat inactivated control suggesting EPHA6 autophosphorylation at a Tyrosine residue (Fig. 6B).Fig. 6Downstream Target Validation of EPHA6 predicted phosphosites. A Experimental workflow for the benchtop kinase activity assay, western blot validation experiment, and mass spectrometry phospho-site identification. B Representative western blot for two recombinant proteins, GAB1 (substrate) and EPHA6 (kinase). All western blot samples were collected from the benchtop activity assay. 250 ng of active or heat inactivated EPHA6 was run on a gel with and without increasing total protein of GAB1 (0 ng, 200 ng, 300 ng, 400 ng). A pan-phospho-tyrosine antibody (1:250, Cell Signaling, catalogue # 9411S) was used to identify any tyrosine site phosphorylated by EPHA6. C Relative abundance of peptides following protease digestion of the kinase substrate reaction was analyzed by LC-MSMS over a 60-min gradient. D Peptide sequences shown were identified from mass spectrometry and mapped to EPHA6 and GAB1. The underlined sequence corresponding to phosphosite pY627 is present as a substrate on the PTK Pamchip. E Placement of GAB1 and EPHA6 phosphosites on full protein sequence domain maps
Mass spectrometry of co-incubated recombinant proteins revealed phosphorylation of GAB1 and EPHA6 at several tyrosine residues. Tyrosine residue pY627 was initially identified as a PTK array reporter peptide (from GAB1) for recombinant EPHA6 on the array (Fig. 2B). This site and two others were identified on recombinant GAB1 via mass spectrometry. Additionally, two putative autophosphoryation sites on EPHA6 were identified at pY259 and pY484 (Fig. 6C, D). Autophosphorylation of EPHA6 occurred at the ATP binding N-lobe between the EPH ligand binding domain and the fibronectin type III conserved regions. GAB1 was phosphorylated mostly at the C-terminus, along several intrinsically disordered regions suggesting changes in localization and protein–protein interactions (Fig. 6E). Changes in GAB1 function and localization are yet to be determined.
Discussion
In this study, we used a hybrid in silico and in vitro approach to extend the functional knowledge of five understudied (i.e., dark) tyrosine kinases (AATK, EPHA6, INSRR, LTK, and TNK1) and their associations with schizophrenia (SCZ), Alzheimer’s dementia (AD), and major depressive disorder (MDD). Our hybrid approach to identifying and characterizing novel substrates for dark kinases is unique compared to traditional methods. In contrast to traditional approaches, we used a multiplex platform that allows for a large number of substrates to be examined at the same time and we identified peptide substrates based on kinase activity profiles. Most approaches to kinase substrate characterization require prior selection of a kinase of interest. This is followed by knockdown, overexpression, and/or drug inhibition studies of the targeted kinase with biochemical assays and/or mass spectrometry. While these approaches are tried and true they may be difficult to scale up to the subkinome or whole kinome level. Further, with mass spectrometry-based phospho-peptide detection, low abundance may mask subtle changes in peptide abundance, making inference of kinase activity based on this technique challenging. Our kinome array profiling platform avoids these potential limitations, directly measuring kinase activity, rather than peptide abundance.
In silico gene expression enrichment and pathway prediction analyses revealed that AATK, INSRR, and EPHA6 have high expression levels in the brain and are associated with important CNS biological pathways such as neurodevelopment and axon guidance. Transcriptional connectivity analyses show that the EPHA6 gene signature has the most concordance with the disease groups (SCZ, AD, and MDD). EPHA6 is a member of the ephrin receptor family which is the largest family of receptor tyrosine kinases. The ephrin receptor family is involved in various cellular processes during development and is a key regulator of adult tissue homeostasis [41]. More specifically, pathway analysis of our recombinant kinase screening of EPHA6 is consistent with what is known about its role in axon guidance and development. Moreover, the high concordance of an EPHA6 knockdown signature with the AD datasets complements a known association of EPHA6 with learning and memory [42]. Further, a genome-wide analysis of an early-onset familial Alzheimer’s disease (EO-FAD) case identified a pathogenic copy number variation (CNV) located in close proximity to EPHA6 [43]. Finally, axon guidance pathway genes, including EPHA6, are associated with schizophrenia based on genome‑wide association studies (GWAS) [44].
Interestingly, some kinases had more connections with one disease group than others. For instance, the AATK knockdown signature showed similarity with only AD datasets, whereas the INSRR gene signature showed exclusively higher concordance with the SCZ datasets. AATK has recently been identified as part of a novel mechanism controlling dendritic spine formation via endosome trafficking [12]. Two novel potential loci for frontotemporal dementia (FTD), which is the second most prevalent form of early onset dementia after Alzheimer's dementia (AD), exert pathological effects by decreasing expression of AATK [45].
INSRR belongs to the insulin receptor family alongside the insulin and insulin-like growth factor receptors [46]. Impaired glucose metabolism and insulin resistance are two well-known hallmarks of schizophrenia [47]. INSRR functions as an extracellular pH sensor and plays an important role in pH homeostasis [48]. It is well documented that the pH in brains of schizophrenia patients are lower than normal controls and possibly altering dopamine release and uptake at synapses, NMDA receptor activity, and glutamate release [49]. However, the exact cause for lower pH in schizophrenia still remains unclear [49]. Dysregulation of INSRR in schizophrenia, as we observed in this study, may offer a potential explanation of the impaired pH sensing in this illness.
The co-expression clustering analysis we performed for our list of dark kinases grouped AATK and INSRR in clusters of genes that are functionally involved in central nervous system myelination, axon guidance, and synaptic transmission. All of these biological processes have been associated with schizophrenia [50]. Abnormalities of oligodendrocytes and myelin sheath surrounding axons have been observed in postmortem studies in schizophrenia [51, 52]. While there is evidence for the role of insulin related proteins such as insulin-like growth factor-I in oligodendrocyte development and altered myelin content, the role of INSRR in myelination is unclear [53]. Interestingly, given the fact that INSRR might act as pH sensor, research suggests that extracellular acidic pH decreases survival of oligodendrocyte precursor cells (OPCs) and reduces their differentiation into oligodendrocytes adding to impaired myelination in schizophrenia [54]. The co-expression clustering analysis of AATK also validates the proposed role of AATK as regulator of oligodendrocyte differentiation and myelination [13].
The chip coverage and peptide overlap analysis of the recombinant kinase screening profiles revealed a variable degree of substrate selectivity. This was expected as some kinases target a wide range of downstream targets, whereas other kinases are very selective [55]. Additionally, peptide overlap analysis revealed limited overlap between the five kinases, consistent with what we expected given that these kinases are members of five different kinase families based on phylogeny. One interesting observation from this study is that AATK, despite being labeled as a serine/threonine kinase, had strong phosphorylation activity on the tyrosine PamChip suggesting that this kinase is a dual-specificity kinase. This observation suggests that the other kinases in the subfamily, including LMTK2 and LMTK3, may be dual-specificity as well.
Notably, our five dark kinases were not screened for serine/threonine substrate activity. Provided the limited knowledgebase of substrate selectivity for dark kinases, it is possible for these kinases to have dual specificity for substrates. Future studies should include functional assessment of axon generation in response to perturbation of EPHA6 expression. Such studies will leverage the workflow established in this study to determine novel substrates for dark kinases. Of note, the observed sex-effect of dark kinases showing decreased activity in female, but not male, schizophrenia subjects will require further examination. Broadly decreased kinase activity in female, but not male, schizophrenia subjects suggest a sex-specific signaling alteration for this often-devastating illness. Mechanistic studies examining EPHA6’s role in axon guidance and development along with projects focused on sex-specific kinase signaling networks would provide important insight to the functional roles of this kinase.
Our study is not without limitations. Reporter peptides on the PTK chip were preselected and limited to 193 unique sequences, making conclusions about consensus peptide target sequences limited, as this is only a small fraction of the entire proteome. Further, the predicted coverage for the PTK chip is 96% for the tyrosine kinase subkinome, which may leave some kinases untargeted. Interactions between recombinant proteins may differ compared to cell-based systems; our promising findings, particularly for EPHA6, need to be confirmed using cell culture or animal models.
In conclusion, we identified 195 putative novel kinase-substrate interactions with variable degrees of affinity for five understudied protein tyrosine kinases. The hybrid workflow we deployed corroborated existing knowledge for these kinases and revealed potentially novel functional associations. We posit that this approach may be deployed to provide strong functional annotation leads for understudied serine/threonine kinases as well. Extending the substrate annotations of dark kinases will close the gap of knowledge and disparity in cell signaling and kinase networks, offsetting the bias of existing biological databases towards well-studied “light” protein kinases.
Supplementary Information
Supplementary Material 1. Supplementary Figure 1: Identification of dark kinases. A) Utilizing the knowledge-based INDRA database to assign normalized knowledge scores for all protein kinases. 19 understudied or“dark” protein tyrosine kinases were identified. Of these 19 dark protein tyrosine kinases, the five shown in blue were the ones of interest in our study. B) Normalized INDRA statements of our five dark kinases amongst light and dark kinases. LTK (Leukocyte Receptor Tyrosine Kinase), AATK (Apoptosis Associated Tyrosine Kinase), INSRR (Insulin Receptor Related Receptor), TNK1 (Tyrosine Kinase Non-Receptor 1), and EPHA6 (EPH Receptor A6).Supplementary Material 2. Supplementary Figure 2: PamStation12 kinase profiles of purified recombinant kinases identify novel substrates. Four of the remaining understudied or “dark” kinases were screened using the PamStation12 (PamGene International B.V.) platform: A, G, M, and S) 2.5-ng, 25-ng, 250-ng of recombinant total protein was screened along with a 250-ng heat inactivated (denatured protein) control. Red indicates increased phosphorylation and yellow indicates less phosphorylation at that peptide. A semi-supervised clustering and principal component analysis (PCA) is visualized for PamChip peptides reporting on kinase activity. D, J, P and V) Signal intensity plot illustrating increased activity with increased total protein of recombinant kinase. E, K, Q and W) Peptide sequence logos for each recombinant protein peptide sequence identified on the PTK Pamchip. F, L, R and X) The color of the single letter amino acid code denotes the chemical features of each amino acid map against relative position. High Affinity: list of peptides that show phosphorylation activity at the lowest protein concentration. Medium Affinity: list of peptides that only start showing phosphorylation activity at the medium protein concentration. Low Affinity: a list of peptides that only show phosphorylation activity at the highest protein concentration. No Affinity: a list of peptides that show no phosphorylation activity across all tested protein concentrations. Apoptosis-associated tyrosine kinase (AATK), Insulin Receptor-related Protein (INSRR), Leukocyte Tyrosine Kinase Receptor (LTK), and Non-receptor Tyrosine Protein Kinase (TNK1).Supplementary Material 3. Supplementary Figure 3: A) Females B) Males. Peptides reporting on dark kinase activity in schizophrenia versus controls postmortem samples. The peptides are ranked by log2 fold change (LFC) calculated by comparing control to schizophrenia subjects. SCZ – Schizophrenia, CTRL – Control, AATK - Apoptosis-associated tyrosine kinase, EPHA6 - EPH Receptor A6, INSRR - Insulin Receptor-related Protein, LTK - Leukocyte Tyrosine Kinase Receptor, and TNK1 - Non-receptor Tyrosine Protein Kinase’. Supplementary Material 4.Supplementary Material 5.Supplementary Material 6.Supplementary Material 7.Supplementary Material 8.Supplementary Material 9.Supplementary Material 10.Supplementary Material 11.Supplementary Material 12.Supplementary Material 13.Supplementary Material 14.Supplementary Material 15.Supplementary Material 16. |
Title: PP84 Evaluation Of The Evidence Level Of Scrambler Therapy For Musculoskeletal Pain Relief: A Systematic Literature Review | Body: |
Title: Aromatase Inhibitor Monotherapy to Augment Height in Boys: Does It Work and Is It Safe? | Body: |
Title: House
Dust Mite Proteins Adsorb on Multiwalled Carbon
Nanotubes Forming an Allergen Corona That Intensifies Allergic Lung
Disease in Mice | Body:
Results
Pulmonary Coexposure to MWCNTs and HDM Extract
Synergistically
Enhance Allergic Lung Inflammation in Mice
The physicochemical
characteristics of the MWCNTs used in this study (NC7000 from Nanocyl,
Inc.) were previously characterized.32 Selected
physicochemical characteristics of NC7000 MWCNTs are summarized in Table 1.
Table 1
Selected Physicochemical Characterization
of NC7000 MWCNTsa
physicochemical
characteristic
amorphous carbon
4.68%
carbonb
92.05% (EDX); 97.8% (XPS)
oxygen
3.19% (EDX); 1.4 (XPS)
trace metals (ICP–MS)
4.43%
Al
0.54%
TEc
avg. diameter (TEM)
12 nm
length (TEM, SEM)
1350 nm
BET surface area
24 m2/g
pore
volume
61 mL/g
a
Adapted with permission
from ref (32). Copyright
2020, Springer-Nature.
b
Carbon
measured by EDX (energy dispersive
X-ray analysis) or XPS (X-ray Photoelectron spectroscopy).
c
TE = transition elements (Co and
Fe).
Transmission electron
microscopy (TEM) demonstrated that these
MWCNTs have a tangled morphology (Figure 1A). Using the sensitization and challenge
protocol illustrated in Figure 1B, male and female C57BL/6 mice were exposed by oropharyngeal
aspiration (OPA) a total of 6 times over a period of 3 weeks to the
following treatments in 50 μL vehicle each dosing session: Dulbecco’s
phosphate buffered saline (DPBS) solution as the vehicle control,
0.5 mg/kg of MWCNTs (12.5 μg), 0.02 mg/kg of HDM extract (0.5
μg), or combination of MWCNTs and HDM extract. In this experiment,
the cumulative dose of MWCNTs was 3 mg/kg and the cumulative dose
of HDM extract was 0.12 mg/kg. In both male and female mice, coexposure
to MWCNTs and HDM extract significantly increased total protein, lactate
dehydrogenase (LDH) and total cell counts in BALF compared to the
vehicle control group (Figure 1C–E). Photomicrographs of cytospin slides of BALF showed
a marked increase in lung inflammatory cells that were primarily eosinophils
(Figure 1F). Differential
counting of BALF cells from cytospin slides showed that coexposure
to MWCNTs and HDM extract significantly reduced the relative proportion
of macrophages (Figure 1G), and this was due primarily to a marked increase in the numbers
of eosinophils in both male and female mice (Figure 1H). MWCNT exposure alone produced a marginal
yet significant increase in the number of neutrophils in BALF in both
sexes (Figure 1I).
The absolute numbers of macrophages, eosinophils, and neutrophils
in each treatment group from which the percentage of each cell type
was derived is shown in Figure S1.
Figure 1
Cellularity
and biomarkers of lung injury in BALF collected from
mice exposed to HDM extract, MWCNTs or both. (A) TEM image of NC7000
MWCNTs. (B) Illustration of the exposure protocol using oropharyngeal
aspiration of MWCNT and/or HDM extract. (C–E) Total protein,
LDH and total cell counts in BALF. (F) Cytospin images from BALF showing
eosinophilic inflammation after coexposure to MWCNTs and HDM extract.
Red arrows indicate macrophages with MWCNT inclusions. Black bars
= 10 μm. (G–I) Differential cell counts from cytospins
showing macrophages, eosinophils, and neutrophils. **p < 0.01,
****p < 0.0001 compared to vehicle; ###p < 0.001, ####p <
0.0001 compared to MWCNT; &p <
0.05, &&p < 0.01, &&&p < 0.001, &&&&p < 0.0001 compared to HDM; ∧∧∧∧p < 0.0001 compared to HDM + MWCNT; ap < 0.05, aap <
0.01, aaap < 0.001 between sexes determined
by two-way ANOVA with Tukey’s post hoc analysis.
Coexposure to MWCNTs and HDM Extract Modulate the Pathogenesis
of Allergic Lung Disease in Mice
Lung sections of male and
female mice were prepared and stained for histopathology. Lung sections
were stained with hematoxylin and eosin (H&E) to assess inflammation
in the lungs. The relatively low dose of HDM extract used in this
study did not result in any significant increase in focal inflammation
in either male or females (Figure 2A). MWCNTs alone caused focal inflammation in the alveolar
region along with granuloma formation (Figure 2A). Inflammation and granulomas were further
increased in the lungs of mice exposed to the coexposures of MWCNTs
and HDM extract (Figure 2A). The inflammation score was increased in both male and female
mice treated with coexposure compared to MWCNTs alone, HDM alone,
or vehicle (Figure 2B). The evaluation of fibrosis in the lungs of male and female mice
was conducted using Masson’s trichrome staining. Morphometric
analysis of photomicrographs from trichrome-stained lung sections
showed that the coexposure of MWCNTs and HDM extract increased airway
fibrosis in the lungs of both male and female mice as compared to
MWCNTs alone, HDM alone, or vehicle (Figure 2C). Fibrosis was quantified by measuring
the area-perimeter ratio of trichrome-positive collagen around airways
(Figure 2D). Photomicrographs
of AB-PAS-stained lung sections showed a marked increase in mucous
cell metaplasia in the airways of male and female mice after coexposure
to HDM extract and MWCNTs, but not any of the other treatment groups
(Figure 2E). Quantitative
morphometry showed significant increases in the percentage of AB-PAS
positive stained cells based on the area of respective airways in
both male and female coexposure groups compared to the other treatment
groups (Figure 2F).
Figure 2
Allergic
lung disease in male and female mice exposed to HDM extract,
MWCNTs or both. (A) Representative images of H&E-stained lungs
sections. Arrows indicate MWCNTs. Black bars = 100 μm. (B) Inflammation
scores derived from H&E-stained sections from all animals. (C)
Representative images of Masson’s trichrome-stained lung sections
showing blue-stained collagen (arrowheads). Black arrows indicate
MWCNTs. Black bars = 100 μm. (D) Quantification of trichrome-positive
airway collagen from all animals using area-perimeter ratio method.
(E) Representative images of AB-PAS -stained lung tissue sections
showing purple PAS + mucins (arrowheads). Arrows indicate MWCNTs.
Black bars = 100 μm. (F) Morphometric quantification of AB-PAS
+ airway mucin. *p < 0.05, **p < 0.01, ****p < 0.0001 compared to vehicle
treatment; ##p < 0.01, ###p < 0.001, ####p <
0.0001 when compared to MWCNT treatment; &p < 0.05, &&p < 0.01, &&&&p < 0.0001 when compared
to HDM treatment as determined by two-way ANOVA with Tukey’s
post hoc analysis.
MWCNT and HDM Extract Coexposures
Exacerbate mRNA Expression
of Pro-Inflammatory and Pro-Fibrotic Mediators
Gene expression
analysis via qRT-PCR revealed significantly increased Arg-1 mRNA expression in male or female mice coexposed
to MWCNTs and HDM extract (Figure 3A). Coexposure to MWCNTs and HDM extract also increased Col1a1 mRNA, which encodes collagen proteins that contribute
to lung fibrosis (Figure 3B). Pro-inflammatory cytokine mRNAs Il6, Ccl11, Il13 and Il33 were
also measured. Coexposure to HDM extract and MWCNTs significantly
increased Il6 mRNA in male mice but not female mice
(Figure 3C). Gene expression
of Ccl11, a primary eosinophil chemokine, was significantly
increased by coexposure to HDM extract and MWCNTs in both male and
female mice, yet no sex differences were observed (Figure 3D). Coexposure to HDM extract
and MWCNTs significantly increased Il13 mRNA in female
mice but not male mice (Figure 3E), whereas coexposure did not significantly change Il33 mRNA (Figure 3F).
Figure 3
qRT-PCR of cytokines and cell signaling mediators in the lungs
of male and female mice exposed to MWCNTs with or without HDM extract.
(A) Arg-1 mRNA. (B) Col1a1 mRNA.
(C) Il6 mRNA.(D) Ccl11 mRNA. (E) Il13 mRNA. (F) Il33 mRNA. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 compared to vehicle
treatment; #p < 0.05, ###p < 0.001, ####p <
0.0001 when compared to MWCNT treatment; &&p < 0.01, &&&p < 0.001, &&&&p <
0.0001 when compared to HDM treatment; ap < 0.05 or aap < 0.01 when comparing
sexes as determined by two-way ANOVA with Tukey’s post hoc
analysis.
MWCNT and HDM Extract Coexposure
Increases the Phosphorylation
of STAT6 in Lung Tissue
Lung protein lysates were analyzed
by Western blot analysis to measure phosphorylated STAT6 and total
STAT6 protein levels in mice exposed to MWCNTs, HDM extract, or a
combination of both (Figure 4). Western blots of 4 male or female mice for each treatment
group are shown in Figure 4A and semiquantitative densitometry of the p-STAT6 signal
from each group of mice normalized to either total STAT6 or β-actin
is shown in Figure 4B. The original uncropped Western blot data are shown in Supporting
Information Figure S2. Coexposures of HDM
extract and MWCNTs increased p-STAT6 compared to other treatment groups
(Figure 4A). Densitometric
analysis showed that male mice exhibited higher levels of p-STAT6
compared to female mice when normalized to either total STAT6 or β-actin
(Figure 4B).
Figure 4
(A) Western
blots of whole lung lysates from male and female mice
exposed to vehicle, HDM extract, MWCNTs, or both. Each lane represents
an individual animal. (B) Densitometry of p-STAT6 normalized against
total STAT6 or β-Actin. ***p < 0.001 compared
to vehicle treatment; ###p < 0.001
compared to MWCNT treatment; &&&p < 0.001 compared to HDM treatment; aap < 0.01 comparing sexes as determined by two-way ANOVA with Tukey’s
post hoc analysis.
Key Allergenic Proteins
Found in HDM Extract Adsorb to the Surface
of MWCNTs to form a HDM Allergen Corona
To characterize the
HDM-MWCNT allergen corona, cell-free samples were prepared and evaluated
by BCA and Western blot analysis. Figure 5A depicts the process by which the HDM-MWCNTs
were formed, and details are described in the Method section. We previously reported the results of a proteomic analysis
using liquid chromatography with tandem mass spectrometry (LC–MS/MS)
to show the normalized abundance (% of total protein) of the top 10
proteins identified in HDM extract and the HDM corona formed on MWCNTs
(HDM-MWCNTs).29 The same lot number of
HDM extract was used for proteomic analysis and animal exposures. Figure 5B shows the normalized
abundance (% of total protein) of the top 10 proteins identified in
HDM extract and the HDM-MWCNTs from the proteomic analysis. The proteomic
analysis identified both Der p 1 and Der p 2 in the HDM-MWCNT corona
and showed that der p 2, but not Der p 1, was enriched (Figure 5B). Total protein quantification
via BCA showed that approximately 7% of original concentration of
HDM extract was adsorbed on the surface of the MWCNTs following incubation
and rinsing (Figure 5C). Two well-known HDM proteins that are linked to asthma in humans,
Der p 1 and der p 2, were previously identified in the HDM-MWCNTs
by proteomics29 and were therefore measured
and further quantified by Western blot analysis (Figure 5D,E). Compared to the initial
concentration of HDM extract, it was found that Der p 2 protein was
significantly enriched upon the surface of MWCNTs (Figure 5D). Der p 1 was also identified
as a corona protein but was not enriched compared to the initial concentration
in HDM extract (Figure 5E). Dynamic light scattering (DLS) was performed to determine whether
the HDM corona altered the agglomeration and dispersion of MWCNTs
in aqueous media (DPBS). DLS showed that HDM-MWCNTs had decreased
hydrodynamic diameter and polydispersity index (PDI) compared to pristine
MWCNTs, but not to a significant extent (Figure S3).
Figure 5
Characterization of the HDM-MWCNT allergen corona. (A) Schematic
showing the preparation of MWCNT-HDM coronas (see details in Methods). (B) Normalized abundance (% of total protein)
of the top-10 proteins identified in HDM extract and the HDM corona
formed on MWCNTs (HDM-MWCNT) identified by proteomic analysis using
liquid chromatography with tandem mass spectrometry (LC–MS/MS).
UC denotes uncharacterized protein. Adapted with permission from ref (29), Table S3. Copyright 2024, Royal Society of Chemistry. (C) Total protein
in HDM extract (HDM) or extracted from HDM-MWCNT corona with lysis
buffer. (D,E) Western blots of Der p 2 and Der p 1 extracted from
HDM-MWCNTs along with densitometry from two replicates. **p < 0.01 between HDM extract and HDM-MWCNTs.
Pulmonary Exposure to HDM-MWCNT Allergen Corona Mimics HDM Extract
and MWCNT Coexposure Treatment Responses in Mice
As illustrated
in Figure 6A, mice
were exposed by OPA a total of 6 times over a period of 3 weeks to
the following treatments in 50 μL vehicle for each dosing session:
DPBS solution as the vehicle control, 0.5 mg/kg MWCNTs (12.5 μg
for males; 10 μg for females), or HDM-MWCNTs prepared by the
incubation of MWCNTs with HDM extract described in Methods and Materials. Both male and female mice had significantly
higher total BALF protein after exposure to the HDM-MWCNTs compared
to pristine MWCNTs (Figure 6B). Female mice also had significantly higher total BALF protein
than male mice after exposure to HDM-MWCNTs (Figure 6B). A significantly higher level of LDH in
the BALF of male mice was observed after exposure to MWCNTs alone
when compared to the vehicle treatment, yet exposure to HDM-MWCNTs
did not further increase LDH levels compared to pristine MWCNTs (Figure 6C). Moreover, female
mice exposed to HDM-MWCNTs had a significant increase in LDH compared
to control treatments and was also significantly higher than male
mice exposed to HDM-MWCNTs. Furthermore, both male and female mice
exposed to HDM-MWCNTs had a significantly higher total BALF cell count
than either the MWCNT treatment group or the vehicle control group,
while treated female mice had significantly higher cell counts than
the male mice (Figure 6D). Cytospin slide images of BALF cells revealed a marked increase
in the number of lung inflammatory cells after exposure to HDM-MWCNTs
(Figure 6E). Differential
cell counting to quantify the relative proportion of cell types showed
that the percentage of alveolar macrophages significantly decreased
after exposure to the HDM-MWCNTs (Figure 6F) and this was due primarily to an increase
in the relative numbers of eosinophils (Figure 6G). Neutrophil cell counts showed no differences
among treatment groups; however, there was a significant increase
when comparing male mice to female mice after HDM-MWCNT exposure (Figure 6H). The absolute
numbers of macrophages, eosinophils, and neutrophils in each treatment
group from which the percentage of each cell type was derived is shown
in Figure S4.
Figure 6
Cellularity analysis
and biomarkers of lung injury in BALF collected
from mice exposed to MWCNTs or HDM-MWCNTs. (A) Schematic of the exposure
protocol in mice using oropharyngeal aspiration (OPA) of MWCNTs or
HDM-MWCNTs. (B–D) Total protein, LDH and total cell counts
in BALF. (E) Cytospin images from BALF from mice showing eosinophilic
inflammation after exposure to HDM-MWCNTs. Red arrows indicate macrophages
with MWCNT inclusions. Black bars = 10 μm. (F–H) BALF
differential cell counts showing macrophages, eosinophils and neutrophils.
*p < 0.05, ****p < 0.0001
compared to vehicle treatment; ####p <
0.0001 when compared to MWCNT treatment; ∧∧∧∧p < 0.0001 when compared to MWCNT-HDM corona
treatment; ap <0.05 when comparing sexes as determined
by two-way ANOVA with Tukey’s post hoc analysis.
HDM-MWCNT Allergen Corona Mediates the Pathogenesis of Allergic
Lung Disease in Mice
Lung histopathology
sections from male and female mice were prepared and stained
with hematoxylin and eosin (H&E) to highlight inflammation in
the lungs. Pristine MWCNTs or HDM-MWCNTs caused focal inflammation
in the alveolar region along with some granuloma formation, with HDM-MWCNTs
producing more severe pathology (Figure 7A). Semiquantitative scoring of inflammation
scoring on a scale of 1–5 showed that both male and female
mice treated with MWCNTs had a significant increase in lung inflammation
that was further increased by HDM-MWCNTs (Figure 7B). The evaluation of fibrosis in the lungs
of mice was conducted using Masson’s trichrome staining followed
by morphometry of trichrome-positive airway collagen using the area-perimeter
ratio method. Photomicrographs of trichrome-stained lung sections
are shown in Figure 7C. Semiquantitative morphometry using the area-perimeter method revealed
that exposure to HDM-MWCNTs increased airway fibrosis in the lungs
of both male and female mice as compared to pristine MWCNTs or vehicle
(Figure 7D). Photomicrographs
of AB-PAS-stained lung sections showed a marked increase in mucous
cell metaplasia in the airways of male and female mice exposed to
HDM-MWCNTs, but not pristine MWCNTs or vehicle control (Figure 7E). Quantitative morphometry
showed that both male and female mice exposed to HDM-MWCNTs had greater
airway mucin compared to pristine MWCNTs or vehicle (Figure 7F).
Figure 7
Allergic lung disease
in mice exposed to MWCNTs or HDM-MWCNTs.
(A) Representative images of H&E-stained lungs sections. Arrows
indicate MWCNTs. Black bars = 100 μm. (B) Inflammation scores
derived from H&E-stained lung sections from all animals. (C) Representative
images of Masson’s trichrome-stained lung sections showing
blue-stained collagen (arrowheads). Black arrows indicate MWCNTs.
Black bars = 100 μm. (D) Quantification of trichrome-positive
airway collagen from all animals using area-perimeter ratio method.
(E) Representative images of AB-PAS- stained lung tissue sections
showing purple PAS + mucins (arrowheads). Arrows indicate MWCNTs.
Black bars = 100 μm. (F) Morphometric quantification of AB-PAS
+ airway mucin. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 compared to vehicle; ##p < 0.01, ####p < 0.0001 compared
to MWCNT; ap < 0.05 comparing sexes
as determined by two-way ANOVA with Tukey’s post hoc analysis.
The HDM-MWCNT Allergen Corona Mediates Pro-Fibrotic
and Pro-Inflammatory
Mediator Gene Expression
Lung lysates were collected from
male and female mice that were exposed to a vehicle solution, pristine
MWCNTs, or MWCNT-HDMs. The level of Arg1 mRNA was
not significantly increased by pristine MWCNTs compared to the vehicle
control in either male or female mice. However, Arg1 mRNA expression in male and female mice exposed to HDM-MWCNTs was
significantly increased by > 20-fold (Figure 8A). Col1a1 mRNA was also
significantly increased in the lungs of male and female mice exposed
to the HDM-MWCNTs (Figure 8B). In addition, mRNAs encoding the pro-inflammatory mediators Il6 and Ccl11 were significantly increased
after exposure to HDM-MWCNTs in lung tissue from male and female mice,
but not with pristine MWCNTs (Figure 8C–D). HDM-MWCNTs significantly increased Il13 mRNA in female mice but not male mice (Figure 8E). Additionally, HDM-MWCNTs
increased Il33 mRNA in female mice but not in male
mice (Figure 8F).
Figure 8
qRT-PCR
of cell signaling mediators in the lungs of male and female
mice exposed to MWCNTs or HDM-MWCNTs. (A) Arg1 mRNA.
(B) Col1a1 mRNA. (C) Il6 mRNA. (D) Ccl11 mRNA. (E) Il13 mRNA. (F) Il33 mRNA. *p < 0.05, **p < 0.01, ***p < 0.001 compared to vehicle
treatment; #p < 0.05, ##p < 0.01, ###p <
0.001 compared to MWCNT treatment as determined by two-way ANOVA with
Tukey’s post hoc analysis.
The HDM-MWCNT Allergen Corona Increases the Phosphorylation
of STAT6 in the Lungs of Mice
Protein samples were also collected
from the lung lysates from male and female mice after exposures to
a vehicle control, MWCNTs, or HDM-MWCNTs and evaluated by Western
blot analysis for phosphorylation of STAT6 relative to total STAT6.
Western blots from 4 male and 4 female mice evaluated from each treatment
group are shown in Figure 9A. The original uncropped Western blots are shown in Figure S5. HDM-MWCNTs increased p-STAT6 in lung
tissue from both male and female mice whereas pristine MWCNTs did
not (Figure 9A). Semiquantitative
densitometry confirmed that HDM-MWCNTs caused a significant increase
in p-STAT6 protein compared to a vehicle or pristine MWCNTs in female
mice when normalized to total STAT6 or β-actin. Male mice also
had elevated p-STAT6 after exposure to HDM-MWCNTs when normalized
to β-actin and was nearly significant (p =
0.06) when normalized to total STAT6 (Figure 9B).
Figure 9
(A) Western blots of p-STAT6, STAT6 and |3-actin
in lung lysates
from mice exposed to MWCNTs or HDM-MWCNTs. (B) Quantitative densitometry
of pSTAT6 normalized against total STAT6 or p-actin. **p < 0.01, ****p < 0.0001 compared to vehicle; #p < 0.05, ##p < 0.01, ####p < 0.0001 when compared
to MWCNT treatment; aap < 0.01 when
comparing sexes as determined by two-way ANOVA with Tukey’s
post hoc analysis.
Pulmonary Coexposure to MWCNTs and HDM Extract
Synergistically
Enhance Allergic Lung Inflammation in Mice
The physicochemical
characteristics of the MWCNTs used in this study (NC7000 from Nanocyl,
Inc.) were previously characterized.32 Selected
physicochemical characteristics of NC7000 MWCNTs are summarized in Table 1.
Table 1
Selected Physicochemical Characterization
of NC7000 MWCNTsa
physicochemical
characteristic
amorphous carbon
4.68%
carbonb
92.05% (EDX); 97.8% (XPS)
oxygen
3.19% (EDX); 1.4 (XPS)
trace metals (ICP–MS)
4.43%
Al
0.54%
TEc
avg. diameter (TEM)
12 nm
length (TEM, SEM)
1350 nm
BET surface area
24 m2/g
pore
volume
61 mL/g
a
Adapted with permission
from ref (32). Copyright
2020, Springer-Nature.
b
Carbon
measured by EDX (energy dispersive
X-ray analysis) or XPS (X-ray Photoelectron spectroscopy).
c
TE = transition elements (Co and
Fe).
Transmission electron
microscopy (TEM) demonstrated that these
MWCNTs have a tangled morphology (Figure 1A). Using the sensitization and challenge
protocol illustrated in Figure 1B, male and female C57BL/6 mice were exposed by oropharyngeal
aspiration (OPA) a total of 6 times over a period of 3 weeks to the
following treatments in 50 μL vehicle each dosing session: Dulbecco’s
phosphate buffered saline (DPBS) solution as the vehicle control,
0.5 mg/kg of MWCNTs (12.5 μg), 0.02 mg/kg of HDM extract (0.5
μg), or combination of MWCNTs and HDM extract. In this experiment,
the cumulative dose of MWCNTs was 3 mg/kg and the cumulative dose
of HDM extract was 0.12 mg/kg. In both male and female mice, coexposure
to MWCNTs and HDM extract significantly increased total protein, lactate
dehydrogenase (LDH) and total cell counts in BALF compared to the
vehicle control group (Figure 1C–E). Photomicrographs of cytospin slides of BALF showed
a marked increase in lung inflammatory cells that were primarily eosinophils
(Figure 1F). Differential
counting of BALF cells from cytospin slides showed that coexposure
to MWCNTs and HDM extract significantly reduced the relative proportion
of macrophages (Figure 1G), and this was due primarily to a marked increase in the numbers
of eosinophils in both male and female mice (Figure 1H). MWCNT exposure alone produced a marginal
yet significant increase in the number of neutrophils in BALF in both
sexes (Figure 1I).
The absolute numbers of macrophages, eosinophils, and neutrophils
in each treatment group from which the percentage of each cell type
was derived is shown in Figure S1.
Figure 1
Cellularity
and biomarkers of lung injury in BALF collected from
mice exposed to HDM extract, MWCNTs or both. (A) TEM image of NC7000
MWCNTs. (B) Illustration of the exposure protocol using oropharyngeal
aspiration of MWCNT and/or HDM extract. (C–E) Total protein,
LDH and total cell counts in BALF. (F) Cytospin images from BALF showing
eosinophilic inflammation after coexposure to MWCNTs and HDM extract.
Red arrows indicate macrophages with MWCNT inclusions. Black bars
= 10 μm. (G–I) Differential cell counts from cytospins
showing macrophages, eosinophils, and neutrophils. **p < 0.01,
****p < 0.0001 compared to vehicle; ###p < 0.001, ####p <
0.0001 compared to MWCNT; &p <
0.05, &&p < 0.01, &&&p < 0.001, &&&&p < 0.0001 compared to HDM; ∧∧∧∧p < 0.0001 compared to HDM + MWCNT; ap < 0.05, aap <
0.01, aaap < 0.001 between sexes determined
by two-way ANOVA with Tukey’s post hoc analysis.
Coexposure to MWCNTs and HDM Extract Modulate the Pathogenesis
of Allergic Lung Disease in Mice
Lung sections of male and
female mice were prepared and stained for histopathology. Lung sections
were stained with hematoxylin and eosin (H&E) to assess inflammation
in the lungs. The relatively low dose of HDM extract used in this
study did not result in any significant increase in focal inflammation
in either male or females (Figure 2A). MWCNTs alone caused focal inflammation in the alveolar
region along with granuloma formation (Figure 2A). Inflammation and granulomas were further
increased in the lungs of mice exposed to the coexposures of MWCNTs
and HDM extract (Figure 2A). The inflammation score was increased in both male and female
mice treated with coexposure compared to MWCNTs alone, HDM alone,
or vehicle (Figure 2B). The evaluation of fibrosis in the lungs of male and female mice
was conducted using Masson’s trichrome staining. Morphometric
analysis of photomicrographs from trichrome-stained lung sections
showed that the coexposure of MWCNTs and HDM extract increased airway
fibrosis in the lungs of both male and female mice as compared to
MWCNTs alone, HDM alone, or vehicle (Figure 2C). Fibrosis was quantified by measuring
the area-perimeter ratio of trichrome-positive collagen around airways
(Figure 2D). Photomicrographs
of AB-PAS-stained lung sections showed a marked increase in mucous
cell metaplasia in the airways of male and female mice after coexposure
to HDM extract and MWCNTs, but not any of the other treatment groups
(Figure 2E). Quantitative
morphometry showed significant increases in the percentage of AB-PAS
positive stained cells based on the area of respective airways in
both male and female coexposure groups compared to the other treatment
groups (Figure 2F).
Figure 2
Allergic
lung disease in male and female mice exposed to HDM extract,
MWCNTs or both. (A) Representative images of H&E-stained lungs
sections. Arrows indicate MWCNTs. Black bars = 100 μm. (B) Inflammation
scores derived from H&E-stained sections from all animals. (C)
Representative images of Masson’s trichrome-stained lung sections
showing blue-stained collagen (arrowheads). Black arrows indicate
MWCNTs. Black bars = 100 μm. (D) Quantification of trichrome-positive
airway collagen from all animals using area-perimeter ratio method.
(E) Representative images of AB-PAS -stained lung tissue sections
showing purple PAS + mucins (arrowheads). Arrows indicate MWCNTs.
Black bars = 100 μm. (F) Morphometric quantification of AB-PAS
+ airway mucin. *p < 0.05, **p < 0.01, ****p < 0.0001 compared to vehicle
treatment; ##p < 0.01, ###p < 0.001, ####p <
0.0001 when compared to MWCNT treatment; &p < 0.05, &&p < 0.01, &&&&p < 0.0001 when compared
to HDM treatment as determined by two-way ANOVA with Tukey’s
post hoc analysis.
MWCNT and HDM Extract Coexposures
Exacerbate mRNA Expression
of Pro-Inflammatory and Pro-Fibrotic Mediators
Gene expression
analysis via qRT-PCR revealed significantly increased Arg-1 mRNA expression in male or female mice coexposed
to MWCNTs and HDM extract (Figure 3A). Coexposure to MWCNTs and HDM extract also increased Col1a1 mRNA, which encodes collagen proteins that contribute
to lung fibrosis (Figure 3B). Pro-inflammatory cytokine mRNAs Il6, Ccl11, Il13 and Il33 were
also measured. Coexposure to HDM extract and MWCNTs significantly
increased Il6 mRNA in male mice but not female mice
(Figure 3C). Gene expression
of Ccl11, a primary eosinophil chemokine, was significantly
increased by coexposure to HDM extract and MWCNTs in both male and
female mice, yet no sex differences were observed (Figure 3D). Coexposure to HDM extract
and MWCNTs significantly increased Il13 mRNA in female
mice but not male mice (Figure 3E), whereas coexposure did not significantly change Il33 mRNA (Figure 3F).
Figure 3
qRT-PCR of cytokines and cell signaling mediators in the lungs
of male and female mice exposed to MWCNTs with or without HDM extract.
(A) Arg-1 mRNA. (B) Col1a1 mRNA.
(C) Il6 mRNA.(D) Ccl11 mRNA. (E) Il13 mRNA. (F) Il33 mRNA. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 compared to vehicle
treatment; #p < 0.05, ###p < 0.001, ####p <
0.0001 when compared to MWCNT treatment; &&p < 0.01, &&&p < 0.001, &&&&p <
0.0001 when compared to HDM treatment; ap < 0.05 or aap < 0.01 when comparing
sexes as determined by two-way ANOVA with Tukey’s post hoc
analysis.
MWCNT and HDM Extract Coexposure
Increases the Phosphorylation
of STAT6 in Lung Tissue
Lung protein lysates were analyzed
by Western blot analysis to measure phosphorylated STAT6 and total
STAT6 protein levels in mice exposed to MWCNTs, HDM extract, or a
combination of both (Figure 4). Western blots of 4 male or female mice for each treatment
group are shown in Figure 4A and semiquantitative densitometry of the p-STAT6 signal
from each group of mice normalized to either total STAT6 or β-actin
is shown in Figure 4B. The original uncropped Western blot data are shown in Supporting
Information Figure S2. Coexposures of HDM
extract and MWCNTs increased p-STAT6 compared to other treatment groups
(Figure 4A). Densitometric
analysis showed that male mice exhibited higher levels of p-STAT6
compared to female mice when normalized to either total STAT6 or β-actin
(Figure 4B).
Figure 4
(A) Western
blots of whole lung lysates from male and female mice
exposed to vehicle, HDM extract, MWCNTs, or both. Each lane represents
an individual animal. (B) Densitometry of p-STAT6 normalized against
total STAT6 or β-Actin. ***p < 0.001 compared
to vehicle treatment; ###p < 0.001
compared to MWCNT treatment; &&&p < 0.001 compared to HDM treatment; aap < 0.01 comparing sexes as determined by two-way ANOVA with Tukey’s
post hoc analysis.
Key Allergenic Proteins
Found in HDM Extract Adsorb to the Surface
of MWCNTs to form a HDM Allergen Corona
To characterize the
HDM-MWCNT allergen corona, cell-free samples were prepared and evaluated
by BCA and Western blot analysis. Figure 5A depicts the process by which the HDM-MWCNTs
were formed, and details are described in the Method section. We previously reported the results of a proteomic analysis
using liquid chromatography with tandem mass spectrometry (LC–MS/MS)
to show the normalized abundance (% of total protein) of the top 10
proteins identified in HDM extract and the HDM corona formed on MWCNTs
(HDM-MWCNTs).29 The same lot number of
HDM extract was used for proteomic analysis and animal exposures. Figure 5B shows the normalized
abundance (% of total protein) of the top 10 proteins identified in
HDM extract and the HDM-MWCNTs from the proteomic analysis. The proteomic
analysis identified both Der p 1 and Der p 2 in the HDM-MWCNT corona
and showed that der p 2, but not Der p 1, was enriched (Figure 5B). Total protein quantification
via BCA showed that approximately 7% of original concentration of
HDM extract was adsorbed on the surface of the MWCNTs following incubation
and rinsing (Figure 5C). Two well-known HDM proteins that are linked to asthma in humans,
Der p 1 and der p 2, were previously identified in the HDM-MWCNTs
by proteomics29 and were therefore measured
and further quantified by Western blot analysis (Figure 5D,E). Compared to the initial
concentration of HDM extract, it was found that Der p 2 protein was
significantly enriched upon the surface of MWCNTs (Figure 5D). Der p 1 was also identified
as a corona protein but was not enriched compared to the initial concentration
in HDM extract (Figure 5E). Dynamic light scattering (DLS) was performed to determine whether
the HDM corona altered the agglomeration and dispersion of MWCNTs
in aqueous media (DPBS). DLS showed that HDM-MWCNTs had decreased
hydrodynamic diameter and polydispersity index (PDI) compared to pristine
MWCNTs, but not to a significant extent (Figure S3).
Figure 5
Characterization of the HDM-MWCNT allergen corona. (A) Schematic
showing the preparation of MWCNT-HDM coronas (see details in Methods). (B) Normalized abundance (% of total protein)
of the top-10 proteins identified in HDM extract and the HDM corona
formed on MWCNTs (HDM-MWCNT) identified by proteomic analysis using
liquid chromatography with tandem mass spectrometry (LC–MS/MS).
UC denotes uncharacterized protein. Adapted with permission from ref (29), Table S3. Copyright 2024, Royal Society of Chemistry. (C) Total protein
in HDM extract (HDM) or extracted from HDM-MWCNT corona with lysis
buffer. (D,E) Western blots of Der p 2 and Der p 1 extracted from
HDM-MWCNTs along with densitometry from two replicates. **p < 0.01 between HDM extract and HDM-MWCNTs.
Pulmonary Exposure to HDM-MWCNT Allergen Corona Mimics HDM Extract
and MWCNT Coexposure Treatment Responses in Mice
As illustrated
in Figure 6A, mice
were exposed by OPA a total of 6 times over a period of 3 weeks to
the following treatments in 50 μL vehicle for each dosing session:
DPBS solution as the vehicle control, 0.5 mg/kg MWCNTs (12.5 μg
for males; 10 μg for females), or HDM-MWCNTs prepared by the
incubation of MWCNTs with HDM extract described in Methods and Materials. Both male and female mice had significantly
higher total BALF protein after exposure to the HDM-MWCNTs compared
to pristine MWCNTs (Figure 6B). Female mice also had significantly higher total BALF protein
than male mice after exposure to HDM-MWCNTs (Figure 6B). A significantly higher level of LDH in
the BALF of male mice was observed after exposure to MWCNTs alone
when compared to the vehicle treatment, yet exposure to HDM-MWCNTs
did not further increase LDH levels compared to pristine MWCNTs (Figure 6C). Moreover, female
mice exposed to HDM-MWCNTs had a significant increase in LDH compared
to control treatments and was also significantly higher than male
mice exposed to HDM-MWCNTs. Furthermore, both male and female mice
exposed to HDM-MWCNTs had a significantly higher total BALF cell count
than either the MWCNT treatment group or the vehicle control group,
while treated female mice had significantly higher cell counts than
the male mice (Figure 6D). Cytospin slide images of BALF cells revealed a marked increase
in the number of lung inflammatory cells after exposure to HDM-MWCNTs
(Figure 6E). Differential
cell counting to quantify the relative proportion of cell types showed
that the percentage of alveolar macrophages significantly decreased
after exposure to the HDM-MWCNTs (Figure 6F) and this was due primarily to an increase
in the relative numbers of eosinophils (Figure 6G). Neutrophil cell counts showed no differences
among treatment groups; however, there was a significant increase
when comparing male mice to female mice after HDM-MWCNT exposure (Figure 6H). The absolute
numbers of macrophages, eosinophils, and neutrophils in each treatment
group from which the percentage of each cell type was derived is shown
in Figure S4.
Figure 6
Cellularity analysis
and biomarkers of lung injury in BALF collected
from mice exposed to MWCNTs or HDM-MWCNTs. (A) Schematic of the exposure
protocol in mice using oropharyngeal aspiration (OPA) of MWCNTs or
HDM-MWCNTs. (B–D) Total protein, LDH and total cell counts
in BALF. (E) Cytospin images from BALF from mice showing eosinophilic
inflammation after exposure to HDM-MWCNTs. Red arrows indicate macrophages
with MWCNT inclusions. Black bars = 10 μm. (F–H) BALF
differential cell counts showing macrophages, eosinophils and neutrophils.
*p < 0.05, ****p < 0.0001
compared to vehicle treatment; ####p <
0.0001 when compared to MWCNT treatment; ∧∧∧∧p < 0.0001 when compared to MWCNT-HDM corona
treatment; ap <0.05 when comparing sexes as determined
by two-way ANOVA with Tukey’s post hoc analysis.
HDM-MWCNT Allergen Corona Mediates the Pathogenesis of Allergic
Lung Disease in Mice
Lung histopathology
sections from male and female mice were prepared and stained
with hematoxylin and eosin (H&E) to highlight inflammation in
the lungs. Pristine MWCNTs or HDM-MWCNTs caused focal inflammation
in the alveolar region along with some granuloma formation, with HDM-MWCNTs
producing more severe pathology (Figure 7A). Semiquantitative scoring of inflammation
scoring on a scale of 1–5 showed that both male and female
mice treated with MWCNTs had a significant increase in lung inflammation
that was further increased by HDM-MWCNTs (Figure 7B). The evaluation of fibrosis in the lungs
of mice was conducted using Masson’s trichrome staining followed
by morphometry of trichrome-positive airway collagen using the area-perimeter
ratio method. Photomicrographs of trichrome-stained lung sections
are shown in Figure 7C. Semiquantitative morphometry using the area-perimeter method revealed
that exposure to HDM-MWCNTs increased airway fibrosis in the lungs
of both male and female mice as compared to pristine MWCNTs or vehicle
(Figure 7D). Photomicrographs
of AB-PAS-stained lung sections showed a marked increase in mucous
cell metaplasia in the airways of male and female mice exposed to
HDM-MWCNTs, but not pristine MWCNTs or vehicle control (Figure 7E). Quantitative morphometry
showed that both male and female mice exposed to HDM-MWCNTs had greater
airway mucin compared to pristine MWCNTs or vehicle (Figure 7F).
Figure 7
Allergic lung disease
in mice exposed to MWCNTs or HDM-MWCNTs.
(A) Representative images of H&E-stained lungs sections. Arrows
indicate MWCNTs. Black bars = 100 μm. (B) Inflammation scores
derived from H&E-stained lung sections from all animals. (C) Representative
images of Masson’s trichrome-stained lung sections showing
blue-stained collagen (arrowheads). Black arrows indicate MWCNTs.
Black bars = 100 μm. (D) Quantification of trichrome-positive
airway collagen from all animals using area-perimeter ratio method.
(E) Representative images of AB-PAS- stained lung tissue sections
showing purple PAS + mucins (arrowheads). Arrows indicate MWCNTs.
Black bars = 100 μm. (F) Morphometric quantification of AB-PAS
+ airway mucin. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 compared to vehicle; ##p < 0.01, ####p < 0.0001 compared
to MWCNT; ap < 0.05 comparing sexes
as determined by two-way ANOVA with Tukey’s post hoc analysis.
The HDM-MWCNT Allergen Corona Mediates Pro-Fibrotic
and Pro-Inflammatory
Mediator Gene Expression
Lung lysates were collected from
male and female mice that were exposed to a vehicle solution, pristine
MWCNTs, or MWCNT-HDMs. The level of Arg1 mRNA was
not significantly increased by pristine MWCNTs compared to the vehicle
control in either male or female mice. However, Arg1 mRNA expression in male and female mice exposed to HDM-MWCNTs was
significantly increased by > 20-fold (Figure 8A). Col1a1 mRNA was also
significantly increased in the lungs of male and female mice exposed
to the HDM-MWCNTs (Figure 8B). In addition, mRNAs encoding the pro-inflammatory mediators Il6 and Ccl11 were significantly increased
after exposure to HDM-MWCNTs in lung tissue from male and female mice,
but not with pristine MWCNTs (Figure 8C–D). HDM-MWCNTs significantly increased Il13 mRNA in female mice but not male mice (Figure 8E). Additionally, HDM-MWCNTs
increased Il33 mRNA in female mice but not in male
mice (Figure 8F).
Figure 8
qRT-PCR
of cell signaling mediators in the lungs of male and female
mice exposed to MWCNTs or HDM-MWCNTs. (A) Arg1 mRNA.
(B) Col1a1 mRNA. (C) Il6 mRNA. (D) Ccl11 mRNA. (E) Il13 mRNA. (F) Il33 mRNA. *p < 0.05, **p < 0.01, ***p < 0.001 compared to vehicle
treatment; #p < 0.05, ##p < 0.01, ###p <
0.001 compared to MWCNT treatment as determined by two-way ANOVA with
Tukey’s post hoc analysis.
The HDM-MWCNT Allergen Corona Increases the Phosphorylation
of STAT6 in the Lungs of Mice
Protein samples were also collected
from the lung lysates from male and female mice after exposures to
a vehicle control, MWCNTs, or HDM-MWCNTs and evaluated by Western
blot analysis for phosphorylation of STAT6 relative to total STAT6.
Western blots from 4 male and 4 female mice evaluated from each treatment
group are shown in Figure 9A. The original uncropped Western blots are shown in Figure S5. HDM-MWCNTs increased p-STAT6 in lung
tissue from both male and female mice whereas pristine MWCNTs did
not (Figure 9A). Semiquantitative
densitometry confirmed that HDM-MWCNTs caused a significant increase
in p-STAT6 protein compared to a vehicle or pristine MWCNTs in female
mice when normalized to total STAT6 or β-actin. Male mice also
had elevated p-STAT6 after exposure to HDM-MWCNTs when normalized
to β-actin and was nearly significant (p =
0.06) when normalized to total STAT6 (Figure 9B).
Figure 9
(A) Western blots of p-STAT6, STAT6 and |3-actin
in lung lysates
from mice exposed to MWCNTs or HDM-MWCNTs. (B) Quantitative densitometry
of pSTAT6 normalized against total STAT6 or p-actin. **p < 0.01, ****p < 0.0001 compared to vehicle; #p < 0.05, ##p < 0.01, ####p < 0.0001 when compared
to MWCNT treatment; aap < 0.01 when
comparing sexes as determined by two-way ANOVA with Tukey’s
post hoc analysis.
Discussion
We
previously reported that the allergic lung response to relatively
low doses of HDM extract is highly amplified by coexposure to MWCNTs
in male C57BL6 mice.8 The allergic lung
response was characterized by eosinophilic lung inflammation, airway
fibrosis and mucous cell metaplasia. In the present study, we first
confirmed that coexposure to MWCNTs and HDM extract synergistically
increased eosinophilic lung inflammation compared to MWCNTs or HDM
extract alone in both male and female mice. However, the underlying
mechanism through which MWCNTs exacerbated HDM extract-induced allergic
lung disease remained unknown. Recently, we demonstrated that a variety
of HDM proteins adsorb to MWCNTs to form an allergen corona and Der
p 2 was identified as a dominant protein that was enriched in the
allergen corona.29 In the current study,
we discovered that all aspects of allergic lung disease, including
eosinophilia, airway fibrosis and mucous cell metaplasia, were significantly
enhanced by HDM-MWCNTs compared to pristine MWCNTs. Therefore, our
findings demonstrate that the allergen corona is a key mechanism through
which MWCNTs exacerbate HDM extract-induced allergic lung disease
in mice. To our knowledge, the current study is the only report assessing
the pathological consequences of a nanoparticle-HDM allergen corona
delivered to the lungs of mice.
The enhanced allergic lung inflammatory
response to MWCNTs with
HDM corona in mice was almost as robust as that achieved with coexposure
to HDM extract and MWCNTs, even though only a small fraction of the
HDM extract protein (∼7%) adsorbed to MWCNTs to form a corona.
We previously reported that Der p 2 is enriched on the surface of
HDM-MWCNTs as part of the allergen corona.29 For example, Der p 2 comprised ∼25% of the total protein
in HDM extract yet made up ∼52% of the total protein in the
HDM-MWCNTs (Figure 5B). This finding has important implications for allergic airway disease
since Der p 2 is the major human allergen in HDM extract that is linked
to the pathogenesis of asthma.33 Studies
with mice also support a role for Der p 2 as a causative agent of
HDM extract that produces pathological outcomes of allergic lung disease.
For example, the intratracheal instillation of Der p 2 into the lungs
of mice produced eosinophilic inflammation, mucous cell metaplasia
and airway fibrosis.34 All of these pathological
outcomes were observed in the present study after oropharyngeal aspiration
of HDM-MWCNTs or coexposure to HDM extract and MWCNTs. Moreover, der
p 2-derived peptide immunotherapy abrogates asthma-related features
in mice exposed to HDM extract.35 These
findings support Der p 2 as a major allergen in HDM extract that mediates
allergic lung disease. The reason for enrichment of Der p 2 in HDM-MWCNTs
is unknown but could be due, at least in part, to the hydrophobicity
of MWCNTs. We previously reported that the MWCNTs used in this study
have a negative zeta potential in aqueous media.32 Hydrophobicity has been linked to negative zeta potential
and is a major factor that mediates cellular uptake of pristine MWCNTs.36 Der p 2 also binds to hydrophobic molecules.
For example, Der p 2 has been identified as a major cholesterol binding
protein.37 Der p 1 is another major human
allergen in HDM extract yet was identified in the HDM-MWCNT allergen
corona at relatively low abundance.29 In
the present study, we were able to demonstrate both Der p 1 and Der
p 2 in the HDM-MWCNTs by Western blot analysis. It is noteworthy that
we used the same source and lot number of HDM extract as described
previously.29 This is an important issue,
since the pathologic and physiologic response to HDM extract exposure
in mice is dependent on lot characteristics.38 While Der p 2 is likely the most important HDM allergen identified
in the HDM-MWCNTs, other proteins that we previously identified by
proteomic analysis such as der p 30 (ferritin), der p 36 (C2 domain
containing protein), and Der p 1 (cysteine protease), could be contributory
allergic lung disease.
The mechanism through which HDM-MWCNTs
amplify allergic lung disease
in mice remains to be elucidated. As mentioned above, Der p 2 was
previously identified as the most abundant HDM protein in the allergen
corona and this may provide an explanation for the increased inflammatory
responses in mice exposed to the HDM-MWCNTs.29 Der p 2 is a functional mimic of the toll-like receptor 4 (TLR4)
cofactor MD-2 that drives lung conventional DC activation and airway
inflammation.39 TLR4 is a highly conserved
receptor found on macrophages, monocytes, endothelial cells, and monocyte-derived
dendritic cells that recognizes pathogen-associated molecular patterns
(PAMPs), such as LPS.40−42 Moreover, low doses of LPS have been shown to enhance
Th2 responses and allergic airway inflammation via TLR4 in mice after
intranasal ovalbumin administration.43 Both
Der p 1 and Der p 2 have previously been reported to bind to the endocytic
receptor, promoting endocytosis of the allergen.44 TLR4 is unique among other toll-like receptors due to its
ability to activate signaling from the cell–surface as well
as intracellularly due to functional TRAM-TRIF signaling following
endocytosis of the receptor.45,46 This could be a potential
mechanism of action, where HDM-MWCNTs are taken up by macrophages
and the engulfed MWCNTs could interact with TLR4 intracellularly.
LPS is a ubiquitous contaminant in many types of biomaterials and
has been found to bind to the surface of nanoparticles.47−50 Relevant to the current study, LPS is a constituent of HDM extract
and therefore macrophages could interact directly with MWCNT-bound
LPS via cell–surface TLR4.
Another potential mechanism
through which HDM-MWCNTs could exacerbate
allergic lung disease is lysosomal membrane permeabilization (LMP)
after phagocytosis and compartmentalization of the HDM-MWCNTs within
lysosomes. LMP is the loss of membrane integrity, which allows the
release of luminal contents of the lysosome into the cytosol.51 Lysosomes also act as signaling hubs in apoptosis
and in different forms of regulated necrosis; these play a critical
role in cellular responses to nanoparticles.52,53 For example, exposure to carbon black nanoparticles induced cathepsin
B-dependent pyroptosis with LMP in alveolar and RAW264.7 macrophages.54,55 It has also been suggested that distinct cathepsins control necrotic
cell death mediated by pyroptosis inducers and lysosome destabilizing
agents.56 It should be noted that the intrinsic
property of the MWCNT may influence LMP. Rigidity, which is described
as the ability of an MWCNT to resist deformation, may refute the idea
that NC7000 MWCNTs used in the present study can elicit the same effect
as long and rod-like MWCNTs on lysosomal-dependent inflammation. For
example, previous studies found that long and rigid MWCNTs (NM401)
triggered pyroptosis in macrophages with gasdermin D-dependent IL-1β
secretion, as compared to tangled MWCNTs (NM400 and NM402) that are
more similar to the tangled NC7000 MWCNTs used in the present study.52 Similarly, it was found that while both tangled
and rod-like MWCNTs both trigger acute inflammation, only rod-like
MWCNTs caused frustrated phagocytosis by macrophages and chronic inflammation
in mice.57 It is argued that the strong
toxicity of these rod-like MWCNTs could be due to cellular “piercing”,
in which MWCNT-containing phago-lysosomes (i.e., frustrated phagocytosis)
result in physical damage to cell membranes.58 LMP has also been shown to trigger activation of the NLRP3 inflammasome,
where the assembled inflammasome complex within the cytoplasm generates
active caspase-1 to cleave pro-IL-1β and pro-IL-18 to mature
forms that stimulate pyroptosis, resulting in inflammation.59,60 Additionally, NLRP3 downregulation has also been found to inhibit
phagocytosis in vitro.61 However, our previous work with tangled MWCNTs suggests that the
NLRP3 inflammasome and consequent IL-1β release by macrophages
would not likely play a role in the exacerbation of allergic lung
disease observed in the present study, since pre-exposure of mice
to HDM extract suppressed MWCNT-induced IL-1β in BALF.62 Further study is needed to determine whether
HDM-MWCNTs trigger LMP more effectively than pristine MWCNTs.
Regarding allergic lung disease, it is important to make the distinction
between tangled and rigid MWCNTs. The NC7000 MWCNTs used in the present
study are tangled and alone did not produce an asthma-like phenotype
when delivered to the lungs of mice by oropharyngeal aspiration, yet
these MWCNTs strongly exacerbated allergic lung disease induced by
HDM extract. This is consistent with other tangled MWCNTs that have
been reported to exacerbate allergen-induced lung inflammation and
fibrosis.63 Also, as demonstrated in the
present study, tangled MWCNTs produce neutrophilic inflammation in
the lungs of mice yet exacerbate eosinophilic lung inflammation initiated
by allergen exposure. In contrast, rigid MWCNTs (most notably Mitsui-7)
produce an asthma-like phenotype in the lungs of mice, including mucous
cell metaplasia, in the absence of any allergen.57,64 However, tangled MWCNTs such as NC7000 are representative of products
currently on the market that are being sold in bulk quantities, whereas
rigid MWCNTs are either are being phased out due to their carcinogenic
potential or are no longer in production.
Lastly, as we previously
found, Der p 2 is the most abundant protein
from HDM extract that was adsorbed to MWCNTs.29 As a lipid-binding family protein,65 Der
p 2 was found to preferentially bind to cholesterol in a dose-dependent
manner.66 Lipid cargo of other lipid binding
allergens, like the potential cholesterol bound to der p 2, have been
found to affect allergenicity via the suppression or exacerbation
of host Th2 responses.67−69 Cholesterol itself has been shown to enhance allergic
responses in sensitized individuals, with increases in IgE, reduction
in Th1 cytokines, and promotion of Th2 cytokine production.70,71 It is possible that native Der p 2 could contain bound cholesterol
that originates from the dietary sources of D. pteronyssinus or inhaled Der p 2 may interact with extracellular murine cholesterol
and promote allergic responses. Further studies may provide clarity
on the potential involvement of Der p 2 in the allergen corona as
a lipid-binding protein that may exacerbate inhaled MWCNT responses.
An important aspect of the current study was the evaluation of
potential sex differences in the exacerbation of HDM extract-induced
lung disease by MWCNTs. There were no significant differences in pathological
outcomes between sexes (inflammation scores, mucous cell metaplasia,
airway fibrosis). However, female mice had higher numbers of total
BALF cells after coexposure to HDM extract and MWCNTs or HDM-MWCNTs
and this was due to higher absolute numbers of eosinophils in BALF
after coexposure to MWCNTs and HDM extract or after exposure to HDM-MWCNTs.
Moreover, female mice exhibited higher levels of LDH and total protein
in BALF after exposure to HDM-MWCNTs but not after coexposure to HDM
extract and MWCNTs. Female mice also had a significantly higher level
of Il13 mRNA compared to male mice after coexposure
to HDM extract and MWCNTs, although we did not observe a significant
difference in Il13 mRNA expression between sexes
after exposure to HDM-MWCNTs. IL-13 is a central mediator of allergic
asthma that activates the STAT6 pathway to cause eosinophilic inflammation
and mucous cell metaplasia.72,73 Overall, the data in
the present study suggests that female mice could be more susceptible
than males in terms of cellular inflammatory responses to HDM extract
and MWCNTs or HDM-MWCNTs. We previously reported that male C57BL6
mice, the same strain used in current study, were more susceptible
to lung inflammation induced by oropharyngeal aspiration of nickel
nanoparticles and LPS.74 Others have reported
that allergic airway lung inflammation was higher in female mice than
male mice upon exposure to OVA sensitization and challenge, due to
higher eosinophilic count, OVA-specific IgE, and production of Th2
cytokines, while having lower percentages of regulatory CD4(+)/CD25(+)
T cells than males.75 Another study concluded
that airway inflammation was greater in female mice than males in
response to OVA and IL-33 exposure, which is a well-known marker for
a Th2 microenvironment.76 IL-33 has been
shown to amplify IL-13-induced M2a phenotype development and eotaxin
2 (CCL24) production in vitro and MWCNTs were demonstrated
to also induce greater eosinophil recruitment in female mice upon
both IL-33 and IL-13 signaling.77−80 We did not observe any major changes in Il33 mRNA expression in either male or female mice after coexposure to
HDM extract and MWCNTs or exposure to HDM-MWCNTs in the present study.
The occupational and commercial exposures of MWCNTs have yet to
be determined to exacerbate asthma in humans. While our findings do
support the exacerbation of allergic lung disease in mice, a lack
of epidemiological data hinder the potential reflection of these findings
in real world applications. Humans are exposed to MWCNTs through inhalation
and dermal exposure, and potentially through contaminated food and
water.81 It has also been reported that
about 35% of patients with allergic diseases are sensitized to HDM.82 In occupational settings, it has been reported
that concentrations of 1050 μg/m3 of MWCNTs were
found during secondary manufacturing processes such as packaging and
bagging of them, while also being detected in the air of manufacturing
plants ranging from 0.002 to 24.9 μg/m3.83,84 Moreover, a cross-sectional study of 12 U.S. facilities producing
carbon nanotubes and nanofibers (CNT/F) showed that 18% of participants
had CNT/F in induced sputum and respiratory allergy development was
positively associated with inhalable elemental carbon and number of
years worked.85 Exposure to HDM allergens
can also occur in the workplace. Matuka and colleagues reported that
the percentage sensitization among workers referred to the National
Institute for Occupational Health (NIOH) Occupational Allergy clinic
in South Africa was 41.6% for D. farinae and 33.8% for D. pteronyssinus.86 Moreover, 19% of patients who reported work-related
sickness tested positive for HDM allergens, suggesting that workplace
sensitization is plausible. To our knowledge there are no reports
of coexposures to MWCNTs and HDM in the workplace, although it is
likely that such coexposures occur. Further epidemiological studies
should provide clarity on the significance of MWCNTs in exacerbating
allergic asthma in humans.
Conclusion
The current study explored
the mechanism through which MWCNTs exacerbate
HDM extract-induced allergic lung disease in mice. We discovered that
the HDM-MWCNT allergen corona mediates the exacerbation of HDM-induced
allergic lung disease by MWCNTs in a coexposure scenario. Our findings
demonstrated that even in trace amounts, allergenic proteins from
HDM extract (e.g., der p 2) adsorb on the surface of MWCNTs forming
an allergen corona that amplifies biochemical, cellular and pathological
aspects of allergic lung disease (STAT6 activation, induction of pro-inflammatory
cytokines, eosinophilic inflammation, airway fibrosis, and mucous
cell metaplasia). The increasing use of MWCNTs could increase the
risk of occupational or consumer exposures, including the exacerbation
of allergic asthma in environments where individuals are coexposed
to nanomaterials and allergens.
Materials
and Methods
House Dust Mite Extract
HDM extract from D. pteronyssinus was purchased from Greer Laboratories
Inc. (Lenoir, NC). Lyophilized HDM extract was dissolved in DPBS to
achieve a stock total HDM extract protein concentration of 1 mg/mL,
with a total yield of 4.57 mg, as measured by Bradford assay. The
HDM extract [item #XPB91D3A2.5; lot #390991] contained 1610 endotoxin
units, measured by amoebocyte lysate test, according to the manufacturer.
Stock solution was further diluted in DPBS to achieve the necessary
working concentrations for dosing.
Multiwalled Carbon Nanotubes
NC7000 MWCNTs were purchased
from Nanocyl, Inc. (Sambreville, Belgium). We previously performed
a thorough physicochemical characterization of these MWCNTs.32 The physicochemical characteristics of the MWCNTs
from this previous analysis are summarized in Table 1 and a TEM image is shown in Figure S1. MWCNTs were suspended in DPBS (Sigma,
St. Louis, MO) to achieve a stock concentration of 3.3 mg/mL. Prepared
stock of MWCNTs suspension was sonicated in a cup horn sonicator (Q500,
Qsonica, Newtown, CT) for 10 min at 60 A. Stock solution was further
diluted with DPBS to achieve the necessary working conditions for
dosing. The dosing strategy for delivering MWCNTs to the lungs of
mice by oropharyngeal aspiration in the absence or presence of HDM
extract is illustrated in Figure 1. Dynamic light scattering (DLS; Zetasizer, Malvern
Instruments, Worcestershire, England) was used to measure the hydrodynamic
diameter and polydispersity index of MWCNTs (500 mg/mL in 0.01X PBS
in water) and HDM-MWCNT protein coronas (500 mg/mL of HDM incubated
with 500 mg/mL MWCNT in 0.01X PBS in water). Measurements were carried
out in triplicate with three distinct samples. Average and standard
deviation are reported for all measurements. The DLS results are shown
in Figure S3.
Preparation and Characterization
of HDM-MWCNT Allergen Corona
Samples
Concentrations of 10 μg/mL MWCNTs and 1.84
μg/mL HDM extract were incubated in DPBS on an Aliquot Mixer
(Ames model #4651) in 10 mL collection tubes for 30 min. Following
rocking, samples were vortexed for 10 s at highest speed and centrifuged
at 12,000 rpm for 20 min. Supernatant was replaced with equal volume
of DPBS. The pellets were vortexed for 10 s at highest speed and centrifuged
at 12,000 rpm for 20 min. The cycle was completed for a total of 3
times. The pristine MWCNTs used as a comparison to the HDM-MWCNTs
went through the same washing and resuspension protocol after centrifugation.
The final samples were resuspended in 50 μL of DPBS for dosing
mice. We previously conducted a thorough proteomic analysis of the
HDM-MWCNT corona proteins using liquid chromatography with tandem
mass spectrometry (LC–MS/MS) to identify proteins in HDM extract
and the HDM corona formed on MWCNTs (HDM-MWCNTs).29 Moreover, the same lot of HDM extract that was used in
our previous proteomic analysis was used in the present study. The
normalized abundance (% of total protein) of top 10 proteins in HDM
extract and in the HDM-MWCNT corona from this analysis are shown in Figure 5B.
Analysis of
Total Protein and der p Allergens in HDM-MWCNT Coronas
HDM-MWCNTs
were prepared as described above, with concentrations
of 1.84 μg/mL of HDM extract and 10 μg/mL of MWCNTs. After
the final centrifugation step, samples were resuspended in 1 mL of
lysis buffer (20 mM Tris–HCl, 150 mM NaCl, 1 mM EDTA, 1 mM
EGTA, 1% triton X-100, 1 mM Na3VO4, 1×
halt protease inhibitor cocktail, in DPBS). The resulting mixture
was centrifuged at 2000 rpm for 5 min. The resulting supernatant was
removed from the pelleted MWCNTs. 1.84 μg/mL of HDM extract
was diluted in lysis buffer for HDM-alone positive control. The protein
concentration of the supernatant and HDM positive control was determined
using the Pierce BCA Protein Assay Kit (ThermoFisher Scientific, Waltham,
MA). Absorbance was read at 450 nm with a correction at 540 nm using
the Multiskan EX microplate spectrophotometer (ThermoFisher, Waltham,
MA). Samples were loaded onto a Criterion TGX stain-free precast 4–15%
SDS-PAGE gel (Bio-Rad Laboratories Inc., Hercules, CA), and separated
by electrophoresis and transferred onto PVDF membranes. Membranes
were blocked for 1 h and incubated overnight in 1:1000 dilution of
primary antibodies purchased from RayBiotech (Norcross, GA) der P1,
#130-10,020 and antibodies-online.com (Limerick, PA) (der P2, #ABIN7141165). Following primary antibody
incubation, transferred membranes were washed and incubated in 1:2500
dilution (Cell Signaling Technology, Danvers, MA) with horseradish
peroxidase-conjugated secondary antirabbit antibody. Enhanced chemiluminescence
(ECL) Prime Western Blotting Detection Reagent (Cytiva, Marlborough,
MA) was used to facilitate HRP-induced chemiluminescence according
to the manufacturer’s instructions. Resulting signals were
captured using Amersham Imager 680 (GE Life Sciences, Marlborough,
MA) and semiquantitative densitometry was performed using ImageQuant
software (GE Life Sciences, Marlborough, MA).
Animal Care
Wildtype
(WT) male and female C57BL/6J
mice (8-10 weeks) were purchased from The Jackson Laboratory (Bar
Harbor, ME). Mice were housed in an AAALAC (association for assessment
and accreditation of Laboratory Animal Care) accredited animal facility,
which was humidity/temperature controlled, pathogen-free, and supplied
with food and water ad libitum. All animal procedures were approved
by the NC State University Institutional Animal Care and Committee
(IACUC). Mice were housed 5 per cage according to their respective
treatment groups and sex—vehicle control, MWCNTs, HDM extract,
MWCNTs + HDM extract, and MWCNT-HDM allergen corona.
Exposure of
Mice to MWCNTs and HDM Extract
Mice were
exposed by oropharyngeal aspiration (OPA) to 50 μL of the following
treatments: vehicle solution control, MWCNTs, HDM extract, both, or
by MWCNT-HDM allergen corona. Dosing strategy included exposure strategies
in both the sensitization phase (days 1, 3, and 5) and the challenge
phase (days 15, 17, and 19). All treatments were prepared in a DPBS
(Sigma) vehicle solution and were vortexed immediately before delivery
to mice by oropharyngeal aspiration (OPA) under isoflurane anesthesia.
For the first experiment using MWCNTs in the presence or absence of
HDM extract, male and female WT mice were exposed to vehicle or 0.02
mg/kg body weight of HDM per dosing session (0.4 μg/female mouse;
0.5 μg/male mouse) with or without 0.5 mg/kg body weight of
MWCNTs (10 μg/female mouse; 12.5 μg/male mouse). In the
second experiment introducing the MWCNT-HDM allergen corona, male
and female WT mice were exposed to vehicle or 0.5 mg/kg body weight
(10 μg/female mouse; 12.5 μg/male mouse) MWCNTs with or
without 0.092 mg/kg body weight (1.84 μg/female mouse; 2.3 μg/male
mouse) of HDM extract per dosing session.
Necropsy and Sample Collection
Necropsy was performed
on day 22 following euthanization with an intraperitoneal injection
of pentobarbital. Bronchoalveolar lavage fluid (BALF) was collected
from each mouse by cannulating the trachea and conducting lavages
of the lungs with 0.5 mL of chilled DPBS two times. 100 μL of
the BALF was collected for differential cell counts and 10 μL
was collected for total BALF cell counts. The remaining BALF was centrifuged
at 2000 rpm for 5 min and the resulting supernatant was transferred
to a separate set of tubes and utilized for analysis of protein, LDH,
and cytokines/chemokines. The cell pellet was resuspended in 200 μL
DPBS and used to analyze inflammatory cells. For histopathology, the
left lung lobe was fixed in neutral buffered formalin (VWR, Radnor,
PA) for 24 h, then transferred to 70% ethanol for a week before being
embedded in paraffin. For mRNA analysis, a right superior lung lobe
was stored in RNAlater (Fisher Scientific, Waltham, MA) and stored
at −80 °C. For protein analysis, the right medial and
inferior lung lobes were snap frozen in liquid nitrogen and stored
at −80 °C.
BALF Inflammatory Cell Counts
Total
BALF cell counts
were performed using a hemocytometer. For differential cell counts,
100 μL of BALF was centrifuged onto glass slides using a Cytospin
4 centrifuge (ThermoFisher, Waltham, MA) and the slides were then
fixed and stained with the Diff-Quik stain set (Epredia, Kalamazoo,
MI). Cell differentials were quantified by counting up to 500 cells
per slide using an Olympus light microscope BX41 (Center Valley, PA)
to determine relative numbers of macrophages, neutrophils, eosinophils,
and lymphocytes. Data was presented as a proportion of total cells
on slide.
Cytokine Analysis in BALF
DuoSet enzyme-linked immunosorbent
assay (ELISA) kits (R&D Systems, Minneapolis, MN) were used according
to the manufacturer’s protocol to quantify protein levels of
cytokines C-X-C motif chemokine ligand 2 (CXCL2), interleukin 1 beta
(IL-1β), interleukin 33 (IL-33), and tumor necrosis factor alpha
(TNF-α) from BALF. Concentrations of cytokines were resulting
from the absorbance values measured at 450 nm with a background correction
at 540 nm using the Multiskan EX microplate spectrophotometer (ThermoFisher,
Waltham, MA). Standard curves were used to derive cytokine concentrations
based on the manufacturer’s protocol (GraphPad Prism, version
10.0, La Jolla, CA).
Cytotoxicity and Total Protein in BALF
LDH activity
in BALF was assayed as an indicator for pulmonary cytotoxicity with
the “pierce LDH Cytotoxicity Assay Kit” (ThermoFisher,
Waltham, MA), according to the manufacturer’s instructions.
Absorbance values were measured at 450 nm using a Multiskan EX microplate
spectrophotometer (ThermoFisher). Total protein concentration in BALF
was determined using the “Pierce BCA Protein Assay Kit”
(ThermoFisher), according to the manufacturer’s instructions.
qRT-PCR
The right superior lungs were frozen and stored
in RNAlater (Fisher Scientific, Waltham, MA). Frozen lung samples
were thawed to 4 °C. Thawed lung samples were moved to 1.5 mL
centrifugation tubes containing RNA lysis buffer (Zymo Research, Irvine,
CA). Two 35 mm stainless steel beads were inserted into each 1.5 mL
collection tube containing lung sample and RNA lysis buffer to facilitate
tissue homogenization. Mini Bead Mill Homogenizer (VWR International)
was used with a speed setting of 4 for 1 min. The resulting mixture
was centrifuged at 5000 rpm for 5 min. Applied Biosystems high-capacity
cDNA reverse transcription kit (ThermoFisher Scientific, Waltham,
MA) was used to create cDNA from the mRNA isolated from the right
lung lobe using Quick-RNA MiniPrep (Zymo Research, Irvine, CA) according
to the manufacturer’s instructions. The FastStart Universal
Probe Master (Rox) (Roche, Basel, Switzerland) was used to run Taqman
qPCR on the Applied Biosystems QuantStudio3 Real-Time PCR System Thermal
Cycling Block (ABI, Foster City, CA) to determine the comparative
CT (ΔΔCT) fold change expression
of specific mRNAs (Col1a1, Arg1, IL-6, Ccl-11) normalized to B2M as the endogenous control.
Immunoblotting
Snap-frozen right lung lobe samples
were transferred to 1.5 mL centrifugation tubes containing lysis buffer
(20 mM Tris–HCl, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% triton
X-100, 1 mM Na3VO4, 1 × halt protease inhibitor
cocktail, in DPBS). Two 35 mm stainless steel beads were inserted
into each 1.5 mL centrifugation tube containing right lung sample
and lysis buffer to assist tissue homogenization. Mini Bead Mill Homogenizer
(VWR International) was used with a speed setting of 4 for 1 min.
The resulting lysate was spun down at 2000 rpm for 5 min. The protein
concentration of the supernatant was determined using the Pierce BCA
Protein Assay Kit (ThermoFisher Scientific, Waltham, MA). Absorbance
was read at 450 nm with a correction at 540 nm using the Multiskan
EX microplate spectrophotometer (ThermoFisher, Waltham, MA). Samples
were loaded onto a Criterion TGX stain-free precast 4–15% SDS-PAGE
gel (Bio-Rad Laboratories Inc., Hercules, CA), and separated by electrophoresis
and transferred onto PVDF membranes. Membranes were blocked for 1
h and incubated overnight in 1:1000 dilution of rabbit or mouse primary
antibodies purchased from Cell Signaling Technology (phosphorylated
STAT6 at Tyr640, #56554S; STAT6, #5397S; and β-actin, #4967L).
Following primary antibody incubation, transferred membranes were
washed and incubated in 1:2500 dilution (Cell Signaling Technology,
Danvers, MA) with horseradish peroxidase-conjugated secondary antirabbit
antibody. Enhanced chemiluminescence (ECL) Prime Western Blotting
Detection Reagent (Cytiva, Marlborough, MA) was used to facilitate
HRP-induced chemiluminescence according to the manufacturer’s
instructions. Resulting signals were captured using Amersham Imager
680 (GE Life Sciences, Marlborough, MA) and semiquantitative densitometry
was performed using ImageQuant software (GE Life Sciences, Marlborough,
MA).
Histopathology
The left lung was cut into three cross
sections, which were embedded in paraffin, and 5 μm histologic
sections were mounted on charged glass slides. Sections were stained
with the following: Hematoxylin and eosin (H&E) to assess pro-inflammatory
tissue reactions, Masson’s trichrome for collagen deposition,
and Alcian blue/periodic acid-Schiff (AB/PAS) for mucus production.
Quantitative Scoring of Airway Inflammation
Microscope
slides with lung tissue sections stained with hematoxylin and eosin
were evaluated for inflammation using a 4× objective (inflammatory
cells and thickness of the alveolar walls). Inflammation scores were
assigned by three independent observers using the following scale:
1 - normal lung tissue as seen in the WT control group, 2 - minimal
change, 3 - mild change, 4 - moderate change, and 5 - marked change.
Data were presented as the mean ± SEM of the inflammation score
for each dose group in both genotypes.
Quantitative Morphometry
of Airway Fibrosis and Mucous Cell
Metaplasia
Airway fibrosis, based on Gomori’s trichrome-stained
slides, was assessed by measuring thickness of the collagen layer,
surrounding the airways, using an area/perimeter ratio method, as
described previously.8,74,75 Approximately 10 airways per lung cross sections per mouse (3 cross
sections per mouse, resulting in a total of 30 photomicrographs per
mouse) that fit to our criteria (circular airways that fit in the
field of view) were photographed at 100× magnification using
an Olympus BX41 light microscope (Center Valley, PA). To determine
the area/perimeter ratio, round to oval shaped airways under 500 ×
500 μm (H × W) were imaged at 100 ×. The lasso tool
in Adobe Photoshop CS5 was used to surround trichrome positive collagen
around the airways, giving the outer area, and to surround the basement
membrane, giving the inner area and circumference (perimeter). The
difference between the outer and inner area was divided by the circumference
giving the area/perimeter ratio. All measurements were performed in
a blinded manner. Mucous cell metaplasia and airway mucus production
assessed by imaging all airways under approximately 500 × 500
μm (H × W) in each AB/PAS-stained sample and quantifying
the area of positive staining in ImageJ (National Institutes of Health)
as a percent area.
Statistical Analysis
One-way ANOVA
with Tukey’s
post hoc test or Student’s t-test was used
to evaluate differences between treatment groups (GraphPad Prism,
version 10.0, La Jolla, CA). Two-way ANOVA with a Tukey’s post
hoc test was utilized to evaluate differences among treatment and
sex groups. All data represent the mean ± SEM of five animal
replicates.
House Dust Mite Extract
HDM extract from D. pteronyssinus was purchased from Greer Laboratories
Inc. (Lenoir, NC). Lyophilized HDM extract was dissolved in DPBS to
achieve a stock total HDM extract protein concentration of 1 mg/mL,
with a total yield of 4.57 mg, as measured by Bradford assay. The
HDM extract [item #XPB91D3A2.5; lot #390991] contained 1610 endotoxin
units, measured by amoebocyte lysate test, according to the manufacturer.
Stock solution was further diluted in DPBS to achieve the necessary
working concentrations for dosing.
Multiwalled Carbon Nanotubes
NC7000 MWCNTs were purchased
from Nanocyl, Inc. (Sambreville, Belgium). We previously performed
a thorough physicochemical characterization of these MWCNTs.32 The physicochemical characteristics of the MWCNTs
from this previous analysis are summarized in Table 1 and a TEM image is shown in Figure S1. MWCNTs were suspended in DPBS (Sigma,
St. Louis, MO) to achieve a stock concentration of 3.3 mg/mL. Prepared
stock of MWCNTs suspension was sonicated in a cup horn sonicator (Q500,
Qsonica, Newtown, CT) for 10 min at 60 A. Stock solution was further
diluted with DPBS to achieve the necessary working conditions for
dosing. The dosing strategy for delivering MWCNTs to the lungs of
mice by oropharyngeal aspiration in the absence or presence of HDM
extract is illustrated in Figure 1. Dynamic light scattering (DLS; Zetasizer, Malvern
Instruments, Worcestershire, England) was used to measure the hydrodynamic
diameter and polydispersity index of MWCNTs (500 mg/mL in 0.01X PBS
in water) and HDM-MWCNT protein coronas (500 mg/mL of HDM incubated
with 500 mg/mL MWCNT in 0.01X PBS in water). Measurements were carried
out in triplicate with three distinct samples. Average and standard
deviation are reported for all measurements. The DLS results are shown
in Figure S3.
Preparation and Characterization
of HDM-MWCNT Allergen Corona
Samples
Concentrations of 10 μg/mL MWCNTs and 1.84
μg/mL HDM extract were incubated in DPBS on an Aliquot Mixer
(Ames model #4651) in 10 mL collection tubes for 30 min. Following
rocking, samples were vortexed for 10 s at highest speed and centrifuged
at 12,000 rpm for 20 min. Supernatant was replaced with equal volume
of DPBS. The pellets were vortexed for 10 s at highest speed and centrifuged
at 12,000 rpm for 20 min. The cycle was completed for a total of 3
times. The pristine MWCNTs used as a comparison to the HDM-MWCNTs
went through the same washing and resuspension protocol after centrifugation.
The final samples were resuspended in 50 μL of DPBS for dosing
mice. We previously conducted a thorough proteomic analysis of the
HDM-MWCNT corona proteins using liquid chromatography with tandem
mass spectrometry (LC–MS/MS) to identify proteins in HDM extract
and the HDM corona formed on MWCNTs (HDM-MWCNTs).29 Moreover, the same lot of HDM extract that was used in
our previous proteomic analysis was used in the present study. The
normalized abundance (% of total protein) of top 10 proteins in HDM
extract and in the HDM-MWCNT corona from this analysis are shown in Figure 5B.
Analysis of
Total Protein and der p Allergens in HDM-MWCNT Coronas
HDM-MWCNTs
were prepared as described above, with concentrations
of 1.84 μg/mL of HDM extract and 10 μg/mL of MWCNTs. After
the final centrifugation step, samples were resuspended in 1 mL of
lysis buffer (20 mM Tris–HCl, 150 mM NaCl, 1 mM EDTA, 1 mM
EGTA, 1% triton X-100, 1 mM Na3VO4, 1×
halt protease inhibitor cocktail, in DPBS). The resulting mixture
was centrifuged at 2000 rpm for 5 min. The resulting supernatant was
removed from the pelleted MWCNTs. 1.84 μg/mL of HDM extract
was diluted in lysis buffer for HDM-alone positive control. The protein
concentration of the supernatant and HDM positive control was determined
using the Pierce BCA Protein Assay Kit (ThermoFisher Scientific, Waltham,
MA). Absorbance was read at 450 nm with a correction at 540 nm using
the Multiskan EX microplate spectrophotometer (ThermoFisher, Waltham,
MA). Samples were loaded onto a Criterion TGX stain-free precast 4–15%
SDS-PAGE gel (Bio-Rad Laboratories Inc., Hercules, CA), and separated
by electrophoresis and transferred onto PVDF membranes. Membranes
were blocked for 1 h and incubated overnight in 1:1000 dilution of
primary antibodies purchased from RayBiotech (Norcross, GA) der P1,
#130-10,020 and antibodies-online.com (Limerick, PA) (der P2, #ABIN7141165). Following primary antibody
incubation, transferred membranes were washed and incubated in 1:2500
dilution (Cell Signaling Technology, Danvers, MA) with horseradish
peroxidase-conjugated secondary antirabbit antibody. Enhanced chemiluminescence
(ECL) Prime Western Blotting Detection Reagent (Cytiva, Marlborough,
MA) was used to facilitate HRP-induced chemiluminescence according
to the manufacturer’s instructions. Resulting signals were
captured using Amersham Imager 680 (GE Life Sciences, Marlborough,
MA) and semiquantitative densitometry was performed using ImageQuant
software (GE Life Sciences, Marlborough, MA).
Animal Care
Wildtype
(WT) male and female C57BL/6J
mice (8-10 weeks) were purchased from The Jackson Laboratory (Bar
Harbor, ME). Mice were housed in an AAALAC (association for assessment
and accreditation of Laboratory Animal Care) accredited animal facility,
which was humidity/temperature controlled, pathogen-free, and supplied
with food and water ad libitum. All animal procedures were approved
by the NC State University Institutional Animal Care and Committee
(IACUC). Mice were housed 5 per cage according to their respective
treatment groups and sex—vehicle control, MWCNTs, HDM extract,
MWCNTs + HDM extract, and MWCNT-HDM allergen corona.
Exposure of
Mice to MWCNTs and HDM Extract
Mice were
exposed by oropharyngeal aspiration (OPA) to 50 μL of the following
treatments: vehicle solution control, MWCNTs, HDM extract, both, or
by MWCNT-HDM allergen corona. Dosing strategy included exposure strategies
in both the sensitization phase (days 1, 3, and 5) and the challenge
phase (days 15, 17, and 19). All treatments were prepared in a DPBS
(Sigma) vehicle solution and were vortexed immediately before delivery
to mice by oropharyngeal aspiration (OPA) under isoflurane anesthesia.
For the first experiment using MWCNTs in the presence or absence of
HDM extract, male and female WT mice were exposed to vehicle or 0.02
mg/kg body weight of HDM per dosing session (0.4 μg/female mouse;
0.5 μg/male mouse) with or without 0.5 mg/kg body weight of
MWCNTs (10 μg/female mouse; 12.5 μg/male mouse). In the
second experiment introducing the MWCNT-HDM allergen corona, male
and female WT mice were exposed to vehicle or 0.5 mg/kg body weight
(10 μg/female mouse; 12.5 μg/male mouse) MWCNTs with or
without 0.092 mg/kg body weight (1.84 μg/female mouse; 2.3 μg/male
mouse) of HDM extract per dosing session.
Necropsy and Sample Collection
Necropsy was performed
on day 22 following euthanization with an intraperitoneal injection
of pentobarbital. Bronchoalveolar lavage fluid (BALF) was collected
from each mouse by cannulating the trachea and conducting lavages
of the lungs with 0.5 mL of chilled DPBS two times. 100 μL of
the BALF was collected for differential cell counts and 10 μL
was collected for total BALF cell counts. The remaining BALF was centrifuged
at 2000 rpm for 5 min and the resulting supernatant was transferred
to a separate set of tubes and utilized for analysis of protein, LDH,
and cytokines/chemokines. The cell pellet was resuspended in 200 μL
DPBS and used to analyze inflammatory cells. For histopathology, the
left lung lobe was fixed in neutral buffered formalin (VWR, Radnor,
PA) for 24 h, then transferred to 70% ethanol for a week before being
embedded in paraffin. For mRNA analysis, a right superior lung lobe
was stored in RNAlater (Fisher Scientific, Waltham, MA) and stored
at −80 °C. For protein analysis, the right medial and
inferior lung lobes were snap frozen in liquid nitrogen and stored
at −80 °C.
BALF Inflammatory Cell Counts
Total
BALF cell counts
were performed using a hemocytometer. For differential cell counts,
100 μL of BALF was centrifuged onto glass slides using a Cytospin
4 centrifuge (ThermoFisher, Waltham, MA) and the slides were then
fixed and stained with the Diff-Quik stain set (Epredia, Kalamazoo,
MI). Cell differentials were quantified by counting up to 500 cells
per slide using an Olympus light microscope BX41 (Center Valley, PA)
to determine relative numbers of macrophages, neutrophils, eosinophils,
and lymphocytes. Data was presented as a proportion of total cells
on slide.
Cytokine Analysis in BALF
DuoSet enzyme-linked immunosorbent
assay (ELISA) kits (R&D Systems, Minneapolis, MN) were used according
to the manufacturer’s protocol to quantify protein levels of
cytokines C-X-C motif chemokine ligand 2 (CXCL2), interleukin 1 beta
(IL-1β), interleukin 33 (IL-33), and tumor necrosis factor alpha
(TNF-α) from BALF. Concentrations of cytokines were resulting
from the absorbance values measured at 450 nm with a background correction
at 540 nm using the Multiskan EX microplate spectrophotometer (ThermoFisher,
Waltham, MA). Standard curves were used to derive cytokine concentrations
based on the manufacturer’s protocol (GraphPad Prism, version
10.0, La Jolla, CA).
Cytotoxicity and Total Protein in BALF
LDH activity
in BALF was assayed as an indicator for pulmonary cytotoxicity with
the “pierce LDH Cytotoxicity Assay Kit” (ThermoFisher,
Waltham, MA), according to the manufacturer’s instructions.
Absorbance values were measured at 450 nm using a Multiskan EX microplate
spectrophotometer (ThermoFisher). Total protein concentration in BALF
was determined using the “Pierce BCA Protein Assay Kit”
(ThermoFisher), according to the manufacturer’s instructions.
qRT-PCR
The right superior lungs were frozen and stored
in RNAlater (Fisher Scientific, Waltham, MA). Frozen lung samples
were thawed to 4 °C. Thawed lung samples were moved to 1.5 mL
centrifugation tubes containing RNA lysis buffer (Zymo Research, Irvine,
CA). Two 35 mm stainless steel beads were inserted into each 1.5 mL
collection tube containing lung sample and RNA lysis buffer to facilitate
tissue homogenization. Mini Bead Mill Homogenizer (VWR International)
was used with a speed setting of 4 for 1 min. The resulting mixture
was centrifuged at 5000 rpm for 5 min. Applied Biosystems high-capacity
cDNA reverse transcription kit (ThermoFisher Scientific, Waltham,
MA) was used to create cDNA from the mRNA isolated from the right
lung lobe using Quick-RNA MiniPrep (Zymo Research, Irvine, CA) according
to the manufacturer’s instructions. The FastStart Universal
Probe Master (Rox) (Roche, Basel, Switzerland) was used to run Taqman
qPCR on the Applied Biosystems QuantStudio3 Real-Time PCR System Thermal
Cycling Block (ABI, Foster City, CA) to determine the comparative
CT (ΔΔCT) fold change expression
of specific mRNAs (Col1a1, Arg1, IL-6, Ccl-11) normalized to B2M as the endogenous control.
Immunoblotting
Snap-frozen right lung lobe samples
were transferred to 1.5 mL centrifugation tubes containing lysis buffer
(20 mM Tris–HCl, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% triton
X-100, 1 mM Na3VO4, 1 × halt protease inhibitor
cocktail, in DPBS). Two 35 mm stainless steel beads were inserted
into each 1.5 mL centrifugation tube containing right lung sample
and lysis buffer to assist tissue homogenization. Mini Bead Mill Homogenizer
(VWR International) was used with a speed setting of 4 for 1 min.
The resulting lysate was spun down at 2000 rpm for 5 min. The protein
concentration of the supernatant was determined using the Pierce BCA
Protein Assay Kit (ThermoFisher Scientific, Waltham, MA). Absorbance
was read at 450 nm with a correction at 540 nm using the Multiskan
EX microplate spectrophotometer (ThermoFisher, Waltham, MA). Samples
were loaded onto a Criterion TGX stain-free precast 4–15% SDS-PAGE
gel (Bio-Rad Laboratories Inc., Hercules, CA), and separated by electrophoresis
and transferred onto PVDF membranes. Membranes were blocked for 1
h and incubated overnight in 1:1000 dilution of rabbit or mouse primary
antibodies purchased from Cell Signaling Technology (phosphorylated
STAT6 at Tyr640, #56554S; STAT6, #5397S; and β-actin, #4967L).
Following primary antibody incubation, transferred membranes were
washed and incubated in 1:2500 dilution (Cell Signaling Technology,
Danvers, MA) with horseradish peroxidase-conjugated secondary antirabbit
antibody. Enhanced chemiluminescence (ECL) Prime Western Blotting
Detection Reagent (Cytiva, Marlborough, MA) was used to facilitate
HRP-induced chemiluminescence according to the manufacturer’s
instructions. Resulting signals were captured using Amersham Imager
680 (GE Life Sciences, Marlborough, MA) and semiquantitative densitometry
was performed using ImageQuant software (GE Life Sciences, Marlborough,
MA).
Histopathology
The left lung was cut into three cross
sections, which were embedded in paraffin, and 5 μm histologic
sections were mounted on charged glass slides. Sections were stained
with the following: Hematoxylin and eosin (H&E) to assess pro-inflammatory
tissue reactions, Masson’s trichrome for collagen deposition,
and Alcian blue/periodic acid-Schiff (AB/PAS) for mucus production.
Quantitative Scoring of Airway Inflammation
Microscope
slides with lung tissue sections stained with hematoxylin and eosin
were evaluated for inflammation using a 4× objective (inflammatory
cells and thickness of the alveolar walls). Inflammation scores were
assigned by three independent observers using the following scale:
1 - normal lung tissue as seen in the WT control group, 2 - minimal
change, 3 - mild change, 4 - moderate change, and 5 - marked change.
Data were presented as the mean ± SEM of the inflammation score
for each dose group in both genotypes.
Quantitative Morphometry
of Airway Fibrosis and Mucous Cell
Metaplasia
Airway fibrosis, based on Gomori’s trichrome-stained
slides, was assessed by measuring thickness of the collagen layer,
surrounding the airways, using an area/perimeter ratio method, as
described previously.8,74,75 Approximately 10 airways per lung cross sections per mouse (3 cross
sections per mouse, resulting in a total of 30 photomicrographs per
mouse) that fit to our criteria (circular airways that fit in the
field of view) were photographed at 100× magnification using
an Olympus BX41 light microscope (Center Valley, PA). To determine
the area/perimeter ratio, round to oval shaped airways under 500 ×
500 μm (H × W) were imaged at 100 ×. The lasso tool
in Adobe Photoshop CS5 was used to surround trichrome positive collagen
around the airways, giving the outer area, and to surround the basement
membrane, giving the inner area and circumference (perimeter). The
difference between the outer and inner area was divided by the circumference
giving the area/perimeter ratio. All measurements were performed in
a blinded manner. Mucous cell metaplasia and airway mucus production
assessed by imaging all airways under approximately 500 × 500
μm (H × W) in each AB/PAS-stained sample and quantifying
the area of positive staining in ImageJ (National Institutes of Health)
as a percent area.
Statistical Analysis
One-way ANOVA
with Tukey’s
post hoc test or Student’s t-test was used
to evaluate differences between treatment groups (GraphPad Prism,
version 10.0, La Jolla, CA). Two-way ANOVA with a Tukey’s post
hoc test was utilized to evaluate differences among treatment and
sex groups. All data represent the mean ± SEM of five animal
replicates. |
Title: Use of the PRECEDE-PROCEED Model in Piloting Vaccine Promotion and Infection Self-Protection: Intervention Development and Effectiveness Examination | Body:
1. Introduction
The outbreak of COVID-19 caused global burdens for all continents, posing critical challenges for healthcare systems, social order, and economic development. Countries worldwide adopted a series of prevention and control methods to combat the spread of COVID-19. The introduction of vaccines at the national level and the cultivation of self-protective behaviors at the individual level took central stage in the containment of the COVID-19 pandemic [1,2]. Moreover, the lack of effective treatment available for COVID-19 emphasized the importance of preventive practices [3]. The World Health Organization (WHO) recommended social distancing, hand washing, face masking, and vaccine uptake as effective preventive practices for COVID-19 containment [4,5]. An ecological study, which included 1,908,197 COVID-19 cases from 190 countries, found that the use of any type of preventive practices could decrease COVID-19 transmission and that the combined use of multiple preventive practices resulted in increased effectiveness [6].
Since vaccines became available, countries worldwide have launched mass COVID-19 vaccination drives. The high uptake rates of COVID-19 vaccination provided immunological protection for vaccinated individuals and whole populations. A global survey reported a 79% rate of COVID-19 vaccine acceptance and a 12% rate of COVID-19 vaccine hesitancy among 23,000 respondents in 23 countries over 3 years [7]. A survey conducted during two waves of the COVID-19 epidemic in Hong Kong from February 2020 to September 2020 found that the rates of COVID-19 vaccine acceptance decreased from 44.2% in the first wave (February 2020) to 34.8% in the third wave (September 2020), and the rates of vaccine hesitancy increased from 55.8% to 65.2% between the two waves [8]. How to improve the vaccination rate remained a big challenge in Hong Kong during the COVID-19 pandemic and post-pandemic era.
On the other hand, the introduction of the COVID-19 vaccine could not negate the importance of other preventive practices, such as self-protective behaviors. A study from the United States found that eliminating mandates on the use of masks after reaching a 50% COVID-19 vaccination rate caused several outbreaks of delta variants, which emphasized the importance of the combined use of the COVID-19 vaccine and self-protective behaviors [9]. Health education took advantage of empowering individuals to accumulate health knowledge and adopt prevention practices, which hold the potential to promote COVID-19 vaccination and foster self-protective behavior. Previous studies have shown the effectiveness of educational interventions in the containment of the COVID-19 pandemic and recommended educational interventions as effective strategies for future outbreak responses [10,11].
The PRECEDE-PROCEED model is a widely used and thoroughly evaluated theoretical framework to plan, conduct, and evaluate health education and promotion programs [12,13,14]. It provides an evidence-based framework to identify intervention targets in consideration of health needs and health resources, develops intervention programs under the integration of individual characteristics and context factors, and implements intervention programs incorporating organizational and administrative impediments and supports [15,16,17]. The PRECEDE-PROCEED model has been applied to design diverse interventions to address various health issues, such as chronic disease management and health behavior promotion [18,19]. It was also used to systematically assess the determinants of HPV and influenza vaccination [20,21,22,23]. The application of the PRECEDE-PROCEED model in the context of COVID-19 is unique due to the unprecedented nature of the pandemic, the rapid development and deployment of novel mRNA vaccines, and the widespread vaccine hesitancy. The mRNA-based COVID-19 vaccine candidate BNT162b2. (Pfizer BionTec), as one of the most widely and most effective vaccines, both brought enthusiasm and skepticism. While the rapid development of these vaccines showcased the potential of biotech in responding to global health crises, it also contributed to vaccine hesitancy due to concerns about the long-term effects and new technology involved. These factors created a distinct set of challenges and opportunities, making this a novel case for the model’s application in promoting public health measures. Considering high vaccine hesitancy and the urgent need for COVID-19 prevention in the Hong Kong population, we piloted a health education program by using the PRECEDE-PROCEED model as the theoretical framework to develop and implement the intervention package for promoting COVID-19 vaccination and fostering self-protective behaviors in terms of early testing, hand washing, mask wearing, and social distancing. We also performed a quasi-experimental study to preliminarily evaluate the effect of the intervention package.
2. Methods
2.1. Study Design
The present study was reported in accordance with the CONSORT 2010 statement extension to randomized pilot and feasibility trials. It was approved by the Human Subjects Ethical Subcommittee of the Hong Kong Polytechnic University (reference number: HSEARS20210809004) and performed in accordance with the Helsinki Declaration. Written informed consent was obtained from each participant after a verbal and written explanation (via an information sheet) of the purpose and procedures of this study.
This study had the following two parts: (1) tailoring an intervention package with health education contents based on Phases 1–4 of the PRECEDE-PROCEED model and (2) utilizing a quasi-experimental study to test the effect of this health education intervention package on promoting COVID-19 vaccination, increasing early testing and fostering self-protective behaviors based on Phases 5 and 8 of the PRECEDE-PROCEED model. Phases 6 and 7 were examined in the formal study with a larger sample size and thus not reported here.
2.2. Part I: Development of the Intervention Package
We developed an adaptive PRECEDE-PROCEED model under the pandemic context of Hong Kong (Figure 1). The PRECEDE part constituted four assessment phases, which included a social assessment, epidemiological assessment, educational and ecological assessment, administrative and policy assessment, and intervention alignment [15].
Phase 1: Social assessment
The social assessment started with a review of the literature on the social influences of Hong Kong residents during the COVID-19 pandemic. Their quality of life and well-being during the pandemic period were assessed [24].
Phase 2: Epidemiological assessment
The epidemiological assessment was conducted through a critical review of the literature and government reports [25,26,27,28]. The number of infections, infection rate, mortality rate of COVID-19 in Hong Kong, vaccination rate and the vaccine hesitancy of COVID-19 in Hong Kong populations, and probability and adherence of self-protective behavior during the pandemic period were all assessed [25,26,27,28].
Phase 3: Educational and ecological assessment
The educational and ecological assessment was performed through a comprehensive review of the relevant literature, which identified the predisposing, reinforcing, and enabling factors of the COVID-19 vaccine and self-protective behaviors [15,25,26,27,28]. Predisposing factors were intellectual and emotional “givens” that made individuals more or less likely to adopt healthy behavior, which included knowledge, attitudes, beliefs, and confidence in the COVID-19 vaccine, early testing, and self-protective behavior [15,25,26,27,28]. Enabling factors were internal and external conditions directly related to issues that help individuals adopt and maintain healthy behavior, which included the availability and accessibility of the COVID-19 vaccine, early testing kits, and personal protective equipment [15,25,26,27,28]. Reinforcing factors followed a behavior, and provided incentives and rewards for the persistence or repetition of the behavior, which included social support and community mobilization for COVID-19 prevention [15,25,26,27,28].
Phase 4: Administrative and policy assessment and intervention alignment
The literature review, along with a government report, were used to perform administrative and policy assessment and intervention alignment, which assessed government-disseminated educational materials, government-issued vaccination policies, and government-implemented anti-epidemic policies [29]. An integrative health education package was then developed based on the evidence summarized in Phases 1 to 4.
2.3. Part II: Implementation and Evaluation of the Intervention Package
Phases 5: Implementation and Phase 8: Outcome evaluation
According to Phase 5 of the PRECEDE-PROCEED model, a single-arm pre–post experimental study was conducted among 50 participants; then, the effects of the intervention package were evaluated based on Phase 8 of the PRECEDE-PROCEED model [15]. The COVID-19 vaccination rates and self-protective behaviors against COVID-19 were measured as the outcomes at the midterm (1.5 months) and endpoint (3 months) during the 3-month intervention period. The whole experimental study was conducted from February 2022 to May 2022.
2.3.1. Study Subjects and Sample Size Calculation
A convenience sampling was adopted to enlist 50 individuals residing in noninstitutional settings within Hong Kong. The inclusion criteria were as follows: (1) a resident of Hong Kong, (2) 18 years of age or older, (3) qualified to receive at least one additional dose of COVID-19 vaccine, and (4) willfully consenting to participate in the research by providing written informed consent. The exclusion criteria were as follows: (1) contraindications to the COVID-19 vaccine and (2) an inability to provide vaccination records.
Based on a previous study, a four-month health education intervention increased the vaccination rate from 35% to 76% [30]. To detect a significant difference with a power of 0.90 and an α of 0.05, we estimated a required sample size of 39 participants for the initial group. Considering an anticipated dropout rate of 15%, the final sample size required was 46 participants.
2.3.2. Recruitment
Participants were recruited through various channels, such as a social network tool (WhatsApp, https://web.whatsapp.com/), the university’s internal email system, the school’s alumni network, and the distribution of fliers and posters in communities and clinics. These posters included details about this study, along with a contact phone number, email address, and QR code. Individuals who were interested in participating could get in touch with the research team for initial registration. Trained research assistants provided interested individuals with information about this study’s objectives and logistical aspects. The initial eligibility of the participants was also assessed. Those who fulfilled the eligible criteria were then invited to participate in this study.
2.3.3. Intervention Implementation
Qualified participants received a 3-month health education program, which was developed based on the evidence from Phase 1 to 4 assessments. The contents of the intervention package and the implementation protocol over the 3-month duration are presented in the Results section as the results for Part 1 of this study.
2.3.4. Outcome Measures
The primary outcome of the experiment was the COVID-19 vaccination rate, which was calculated by dividing the number of new vaccine doses administered to participants during the intervention period by the total number of participants. To confirm vaccine uptake, we relied on the Hong Kong government’s LeaveHomeSafe software (https://www.fehd.gov.hk/english/licensing/guide_general_reference/COVID19_LeaveHomeSafe.html, accessed on 19 August 2024) or the official vaccine records provided by the participants themselves. The secondary outcomes encompassed early testing, handwashing, mask wearing, and adherence to social distancing. Early testing was determined by asking whether participants or their family members had undergone early rapid testing in the past month, with responses categorized as “Yes” (1) or “No” (0). Handwashing and mask-wearing behaviors were evaluated through a series of 10 and 6 items, respectively, using dichotomous choices (1 for “Yes” and 0 for “No”). These items gauged the correctness of handwashing and mask-wearing practices over the previous month. We then summed the score of each item to create the overall scores ranging from 0 to 10 for handwashing and 0 to 6 for mask wearing, where higher scores indicated more accurate behavior. These items were formulated in accordance with the Department of Health of Hong Kong’s recommendations for proper handwashing and mask wearing [31,32]. Social distancing compliance was assessed by asking participants to rate their adherence to the government’s social distancing regulations over the past month on a 10-point scale, where 0 indicated no adherence and 10 represented the highest adherence. We collected data for all these secondary outcomes at baseline, 1.5 months, and 3 months throughout the study period.
2.3.5. Statistical Analysis
Baseline characteristics, including demographics (sex, age, education, marital status, employment, occupation, living condition, residential area, and income), lifestyle factors (smoking, drinking, and exercise), chronic diseases, and influenza vaccine status, were presented as mean ± standard deviation (SD) for continuous variables and numbers and proportions (%) for categorical variables. Independent t-tests and chi-squared tests were employed when appropriate to compare the differences between participants who took the Comirnaty vaccine and those who took the Sinovac vaccine at baseline.
The raw vaccination rates at 1.5 and 3 months were calculated as the number of new vaccine doses taken by the participants during the 1.5-month and 3-month intervention period divided by the total number of participants remaining in the trial at the end of 1.5 and 3 months, respectively. According to the Hong Kong government’s policy on COVID-19 [33], people were not allowed to receive a new vaccine dose within 3 months after COVID-19 infection. Thus, a modified vaccination rate was computed by excluding participants infected with COVID-19 during the intervention period. Subgroup analyses of the vaccination rates were also performed on the basis of the participants’ baseline vaccination dose (first dose vs. second dose).
Meanwhile, given that this was a single-arm quasi-experimental study, we compared the raw vaccination rates of our participants to that of Hong Kong general populations with the same gender (women) and similar age (30–75 years) during the same time period (February 2022 to May 2022) (Ntotal = 2,766,600) [34,35]. The new dose of vaccine per 1000 person-days was calculated and compared between women in our sample and the corresponding Hong Kong population. A scatter plot was established to show the vaccination rates for new doses (per week) in our study and Hong Kong general populations; a loess smooth curve with 95% CI was used to visually demonstrate the rising tendency of the vaccination rate.
Moreover, McNemar’s test was applied to examine the pre–post change in the early testing rates (self and family). A paired t-test was used to evaluate the pre–post change in the score of self-protective behavior (hand washing, mask wearing, and social distancing). All statistical analyses were performed using SPSS (version 26.0) and R (version 4.1.3). A two-sided p < 0.05 was considered statistically significant.
2.1. Study Design
The present study was reported in accordance with the CONSORT 2010 statement extension to randomized pilot and feasibility trials. It was approved by the Human Subjects Ethical Subcommittee of the Hong Kong Polytechnic University (reference number: HSEARS20210809004) and performed in accordance with the Helsinki Declaration. Written informed consent was obtained from each participant after a verbal and written explanation (via an information sheet) of the purpose and procedures of this study.
This study had the following two parts: (1) tailoring an intervention package with health education contents based on Phases 1–4 of the PRECEDE-PROCEED model and (2) utilizing a quasi-experimental study to test the effect of this health education intervention package on promoting COVID-19 vaccination, increasing early testing and fostering self-protective behaviors based on Phases 5 and 8 of the PRECEDE-PROCEED model. Phases 6 and 7 were examined in the formal study with a larger sample size and thus not reported here.
2.2. Part I: Development of the Intervention Package
We developed an adaptive PRECEDE-PROCEED model under the pandemic context of Hong Kong (Figure 1). The PRECEDE part constituted four assessment phases, which included a social assessment, epidemiological assessment, educational and ecological assessment, administrative and policy assessment, and intervention alignment [15].
Phase 1: Social assessment
The social assessment started with a review of the literature on the social influences of Hong Kong residents during the COVID-19 pandemic. Their quality of life and well-being during the pandemic period were assessed [24].
Phase 2: Epidemiological assessment
The epidemiological assessment was conducted through a critical review of the literature and government reports [25,26,27,28]. The number of infections, infection rate, mortality rate of COVID-19 in Hong Kong, vaccination rate and the vaccine hesitancy of COVID-19 in Hong Kong populations, and probability and adherence of self-protective behavior during the pandemic period were all assessed [25,26,27,28].
Phase 3: Educational and ecological assessment
The educational and ecological assessment was performed through a comprehensive review of the relevant literature, which identified the predisposing, reinforcing, and enabling factors of the COVID-19 vaccine and self-protective behaviors [15,25,26,27,28]. Predisposing factors were intellectual and emotional “givens” that made individuals more or less likely to adopt healthy behavior, which included knowledge, attitudes, beliefs, and confidence in the COVID-19 vaccine, early testing, and self-protective behavior [15,25,26,27,28]. Enabling factors were internal and external conditions directly related to issues that help individuals adopt and maintain healthy behavior, which included the availability and accessibility of the COVID-19 vaccine, early testing kits, and personal protective equipment [15,25,26,27,28]. Reinforcing factors followed a behavior, and provided incentives and rewards for the persistence or repetition of the behavior, which included social support and community mobilization for COVID-19 prevention [15,25,26,27,28].
Phase 4: Administrative and policy assessment and intervention alignment
The literature review, along with a government report, were used to perform administrative and policy assessment and intervention alignment, which assessed government-disseminated educational materials, government-issued vaccination policies, and government-implemented anti-epidemic policies [29]. An integrative health education package was then developed based on the evidence summarized in Phases 1 to 4.
2.3. Part II: Implementation and Evaluation of the Intervention Package
Phases 5: Implementation and Phase 8: Outcome evaluation
According to Phase 5 of the PRECEDE-PROCEED model, a single-arm pre–post experimental study was conducted among 50 participants; then, the effects of the intervention package were evaluated based on Phase 8 of the PRECEDE-PROCEED model [15]. The COVID-19 vaccination rates and self-protective behaviors against COVID-19 were measured as the outcomes at the midterm (1.5 months) and endpoint (3 months) during the 3-month intervention period. The whole experimental study was conducted from February 2022 to May 2022.
2.3.1. Study Subjects and Sample Size Calculation
A convenience sampling was adopted to enlist 50 individuals residing in noninstitutional settings within Hong Kong. The inclusion criteria were as follows: (1) a resident of Hong Kong, (2) 18 years of age or older, (3) qualified to receive at least one additional dose of COVID-19 vaccine, and (4) willfully consenting to participate in the research by providing written informed consent. The exclusion criteria were as follows: (1) contraindications to the COVID-19 vaccine and (2) an inability to provide vaccination records.
Based on a previous study, a four-month health education intervention increased the vaccination rate from 35% to 76% [30]. To detect a significant difference with a power of 0.90 and an α of 0.05, we estimated a required sample size of 39 participants for the initial group. Considering an anticipated dropout rate of 15%, the final sample size required was 46 participants.
2.3.2. Recruitment
Participants were recruited through various channels, such as a social network tool (WhatsApp, https://web.whatsapp.com/), the university’s internal email system, the school’s alumni network, and the distribution of fliers and posters in communities and clinics. These posters included details about this study, along with a contact phone number, email address, and QR code. Individuals who were interested in participating could get in touch with the research team for initial registration. Trained research assistants provided interested individuals with information about this study’s objectives and logistical aspects. The initial eligibility of the participants was also assessed. Those who fulfilled the eligible criteria were then invited to participate in this study.
2.3.3. Intervention Implementation
Qualified participants received a 3-month health education program, which was developed based on the evidence from Phase 1 to 4 assessments. The contents of the intervention package and the implementation protocol over the 3-month duration are presented in the Results section as the results for Part 1 of this study.
2.3.4. Outcome Measures
The primary outcome of the experiment was the COVID-19 vaccination rate, which was calculated by dividing the number of new vaccine doses administered to participants during the intervention period by the total number of participants. To confirm vaccine uptake, we relied on the Hong Kong government’s LeaveHomeSafe software (https://www.fehd.gov.hk/english/licensing/guide_general_reference/COVID19_LeaveHomeSafe.html, accessed on 19 August 2024) or the official vaccine records provided by the participants themselves. The secondary outcomes encompassed early testing, handwashing, mask wearing, and adherence to social distancing. Early testing was determined by asking whether participants or their family members had undergone early rapid testing in the past month, with responses categorized as “Yes” (1) or “No” (0). Handwashing and mask-wearing behaviors were evaluated through a series of 10 and 6 items, respectively, using dichotomous choices (1 for “Yes” and 0 for “No”). These items gauged the correctness of handwashing and mask-wearing practices over the previous month. We then summed the score of each item to create the overall scores ranging from 0 to 10 for handwashing and 0 to 6 for mask wearing, where higher scores indicated more accurate behavior. These items were formulated in accordance with the Department of Health of Hong Kong’s recommendations for proper handwashing and mask wearing [31,32]. Social distancing compliance was assessed by asking participants to rate their adherence to the government’s social distancing regulations over the past month on a 10-point scale, where 0 indicated no adherence and 10 represented the highest adherence. We collected data for all these secondary outcomes at baseline, 1.5 months, and 3 months throughout the study period.
2.3.5. Statistical Analysis
Baseline characteristics, including demographics (sex, age, education, marital status, employment, occupation, living condition, residential area, and income), lifestyle factors (smoking, drinking, and exercise), chronic diseases, and influenza vaccine status, were presented as mean ± standard deviation (SD) for continuous variables and numbers and proportions (%) for categorical variables. Independent t-tests and chi-squared tests were employed when appropriate to compare the differences between participants who took the Comirnaty vaccine and those who took the Sinovac vaccine at baseline.
The raw vaccination rates at 1.5 and 3 months were calculated as the number of new vaccine doses taken by the participants during the 1.5-month and 3-month intervention period divided by the total number of participants remaining in the trial at the end of 1.5 and 3 months, respectively. According to the Hong Kong government’s policy on COVID-19 [33], people were not allowed to receive a new vaccine dose within 3 months after COVID-19 infection. Thus, a modified vaccination rate was computed by excluding participants infected with COVID-19 during the intervention period. Subgroup analyses of the vaccination rates were also performed on the basis of the participants’ baseline vaccination dose (first dose vs. second dose).
Meanwhile, given that this was a single-arm quasi-experimental study, we compared the raw vaccination rates of our participants to that of Hong Kong general populations with the same gender (women) and similar age (30–75 years) during the same time period (February 2022 to May 2022) (Ntotal = 2,766,600) [34,35]. The new dose of vaccine per 1000 person-days was calculated and compared between women in our sample and the corresponding Hong Kong population. A scatter plot was established to show the vaccination rates for new doses (per week) in our study and Hong Kong general populations; a loess smooth curve with 95% CI was used to visually demonstrate the rising tendency of the vaccination rate.
Moreover, McNemar’s test was applied to examine the pre–post change in the early testing rates (self and family). A paired t-test was used to evaluate the pre–post change in the score of self-protective behavior (hand washing, mask wearing, and social distancing). All statistical analyses were performed using SPSS (version 26.0) and R (version 4.1.3). A two-sided p < 0.05 was considered statistically significant.
2.3.1. Study Subjects and Sample Size Calculation
A convenience sampling was adopted to enlist 50 individuals residing in noninstitutional settings within Hong Kong. The inclusion criteria were as follows: (1) a resident of Hong Kong, (2) 18 years of age or older, (3) qualified to receive at least one additional dose of COVID-19 vaccine, and (4) willfully consenting to participate in the research by providing written informed consent. The exclusion criteria were as follows: (1) contraindications to the COVID-19 vaccine and (2) an inability to provide vaccination records.
Based on a previous study, a four-month health education intervention increased the vaccination rate from 35% to 76% [30]. To detect a significant difference with a power of 0.90 and an α of 0.05, we estimated a required sample size of 39 participants for the initial group. Considering an anticipated dropout rate of 15%, the final sample size required was 46 participants.
2.3.2. Recruitment
Participants were recruited through various channels, such as a social network tool (WhatsApp, https://web.whatsapp.com/), the university’s internal email system, the school’s alumni network, and the distribution of fliers and posters in communities and clinics. These posters included details about this study, along with a contact phone number, email address, and QR code. Individuals who were interested in participating could get in touch with the research team for initial registration. Trained research assistants provided interested individuals with information about this study’s objectives and logistical aspects. The initial eligibility of the participants was also assessed. Those who fulfilled the eligible criteria were then invited to participate in this study.
2.3.3. Intervention Implementation
Qualified participants received a 3-month health education program, which was developed based on the evidence from Phase 1 to 4 assessments. The contents of the intervention package and the implementation protocol over the 3-month duration are presented in the Results section as the results for Part 1 of this study.
2.3.4. Outcome Measures
The primary outcome of the experiment was the COVID-19 vaccination rate, which was calculated by dividing the number of new vaccine doses administered to participants during the intervention period by the total number of participants. To confirm vaccine uptake, we relied on the Hong Kong government’s LeaveHomeSafe software (https://www.fehd.gov.hk/english/licensing/guide_general_reference/COVID19_LeaveHomeSafe.html, accessed on 19 August 2024) or the official vaccine records provided by the participants themselves. The secondary outcomes encompassed early testing, handwashing, mask wearing, and adherence to social distancing. Early testing was determined by asking whether participants or their family members had undergone early rapid testing in the past month, with responses categorized as “Yes” (1) or “No” (0). Handwashing and mask-wearing behaviors were evaluated through a series of 10 and 6 items, respectively, using dichotomous choices (1 for “Yes” and 0 for “No”). These items gauged the correctness of handwashing and mask-wearing practices over the previous month. We then summed the score of each item to create the overall scores ranging from 0 to 10 for handwashing and 0 to 6 for mask wearing, where higher scores indicated more accurate behavior. These items were formulated in accordance with the Department of Health of Hong Kong’s recommendations for proper handwashing and mask wearing [31,32]. Social distancing compliance was assessed by asking participants to rate their adherence to the government’s social distancing regulations over the past month on a 10-point scale, where 0 indicated no adherence and 10 represented the highest adherence. We collected data for all these secondary outcomes at baseline, 1.5 months, and 3 months throughout the study period.
2.3.5. Statistical Analysis
Baseline characteristics, including demographics (sex, age, education, marital status, employment, occupation, living condition, residential area, and income), lifestyle factors (smoking, drinking, and exercise), chronic diseases, and influenza vaccine status, were presented as mean ± standard deviation (SD) for continuous variables and numbers and proportions (%) for categorical variables. Independent t-tests and chi-squared tests were employed when appropriate to compare the differences between participants who took the Comirnaty vaccine and those who took the Sinovac vaccine at baseline.
The raw vaccination rates at 1.5 and 3 months were calculated as the number of new vaccine doses taken by the participants during the 1.5-month and 3-month intervention period divided by the total number of participants remaining in the trial at the end of 1.5 and 3 months, respectively. According to the Hong Kong government’s policy on COVID-19 [33], people were not allowed to receive a new vaccine dose within 3 months after COVID-19 infection. Thus, a modified vaccination rate was computed by excluding participants infected with COVID-19 during the intervention period. Subgroup analyses of the vaccination rates were also performed on the basis of the participants’ baseline vaccination dose (first dose vs. second dose).
Meanwhile, given that this was a single-arm quasi-experimental study, we compared the raw vaccination rates of our participants to that of Hong Kong general populations with the same gender (women) and similar age (30–75 years) during the same time period (February 2022 to May 2022) (Ntotal = 2,766,600) [34,35]. The new dose of vaccine per 1000 person-days was calculated and compared between women in our sample and the corresponding Hong Kong population. A scatter plot was established to show the vaccination rates for new doses (per week) in our study and Hong Kong general populations; a loess smooth curve with 95% CI was used to visually demonstrate the rising tendency of the vaccination rate.
Moreover, McNemar’s test was applied to examine the pre–post change in the early testing rates (self and family). A paired t-test was used to evaluate the pre–post change in the score of self-protective behavior (hand washing, mask wearing, and social distancing). All statistical analyses were performed using SPSS (version 26.0) and R (version 4.1.3). A two-sided p < 0.05 was considered statistically significant.
3. Results
3.1. Assessment Results Based on Phases 1–4 of the PRECEDE-PROCEED Model
In accordance with four phases’ assessments, 14 articles or government reports were reviewed to identify the health problems and needs of Hong Kong residents during the COVID-19 pandemic [24,25,26,36,37,38,39,40,41,42,43,44,45,46] (Supplementary Table S1). We also evaluated the decision matrix for each factor in Phase 3 in Supplementary Table S2. In summary, the COVID-19 pandemic significantly impacted the quality of life and well-being of the Hong Kong population with worrisome infection and mortality rates, and insufficient adherence to early testing and self-protective behaviors. Acceptance toward the vaccine and self-protective behaviors was unsatisfactory. Insufficient availability and accessibility toward personal protective equipment coupled with a lack of social support were noticed. Misinformation from unofficial media sources, misunderstanding of official information and health education materials, combined with insufficient explanation efforts by authorities, hampered the public understanding of government policies and strategies against COVID-19.
3.2. Structure and Contents of the Intervention Package
The intervention package comprised six components targeting predisposing, reinforcing, and enabling factors. The contents of the intervention package and the implementation protocol are shown in Table 1.
To address predisposing factors, health education materials, including 16 health education videos and 36 health tips, were employed. The videos transformed information from Hong Kong government health education materials about the COVID-19 pandemic, vaccines, rapid testing, and self-protection behaviors into more easily understandable descriptions for residents. These videos were disseminated two to three times per week over the first six weeks and redistributed twice during booster sessions (weeks 7–12). Health tips, which showcased the latest updates on the COVID-19-related situation in Hong Kong, were provided three times per week (Supplementary Table S3).
To address reinforcing factors, individual consultations and reminders of vaccination were instituted. The individual consultations provided one-on-one services to discern and mitigate participants’ hesitancy toward vaccination. The reminders of vaccination encompassed prompts urging participants to get vaccinated. These individual consultations and vaccination reminders alternated on a biweekly basis.
To address enabling factors, anti-epidemic health packages and vaccine booking services were provided. The anti-epidemic health package comprised hand sanitizer, masks, and rapid test kits. The vaccine booking service assisted participants in scheduling appointments through Hong Kong’s online vaccination booking system.
3.3. Effectiveness of the Intervention Package on Vaccination Promotion and Self-Protective Behaviors
3.3.1. Baseline Characteristics of the Participants
As shown in Figure 2, 194 participants were assessed for eligibility at the beginning of the trial. Of these, 144 were excluded for not meeting the inclusion criteria. The remaining 50 participants were allocated to the intervention group and received baseline. The flowchart then shows the progression through this study, with assessments conducted at baseline (T0), after the first follow-up (T1), with 46 participants remaining, and the final follow-up (T2), with 45 participants completing this study.
Table 2 displays the baseline characteristics of the 50 participants. The majority were female (96.0%), with a mean (SD) age of 57.1 (9.3) years. Notably, 68% had at least one chronic disease. Most participants were nonsmokers (86%) and did not consume alcohol regularly (58%). Nearly half did not engage in regular exercise (48%) and were unemployed (48%). A high proportion had education levels of secondary or higher (94%). Marital status showed that 60% were married or cohabiting. Geographically, 54% resided in the New Territories, 32% in Kowloon, and 14% on Hong Kong Island. Income data were disclosed by 74%, with 64.8% reporting a monthly income between 10,000 and 49,999 HKD. Regarding influenza vaccination, 24% had ever received it, and 16% had received it within the past year.
A total of 48 participants had been administered the first or second dose of either the Sinovac or Comirnaty COVID-19 vaccines before the baseline assessment (Table 2). No significant difference in baseline characteristics was observed between participants who had received the Sinovac or Comirnaty COVID-19 vaccines (all p > 0.05).
3.3.2. Vaccination Rate after Intervention
Changes in vaccination rates are shown in Table 3 and Figure 3. Between the baseline and 1.5 months, 28 participants received a new vaccine dose, while 4 participants withdrew. The raw vaccination rate was 60.9% (28 out of 46, 95% CI: 45.4% to 74.9%) at the 1.5-month assessment. During this period, 10 participants were infected with COVID-19, leading to a modified vaccination rate of 73.35% (28 out of 36, 95% CI: 58.1% to 85.4%). Among the 28 participants who received a new dose of the vaccine at 1.5 months, the majority obtained a new third dose (25 out of 28, 89.3%). As illustrated in Figure 3, 43 participants had already received their second dose at the baseline enrollment and were eligible for the third dose during the intervention period. Excluding three participants who withdrew at the 1.5-month point, the raw vaccination rate for the new third dose was 62.5% (25 out of 40, 95% CI: 45.8% to 77.3%). In consideration of the 7 individuals who contracted the virus, the modified vaccination rate for the new third dose was 76.9% (25 out of 33, 95% CI: 60.7% to 88.9%).
Over the course of 3 months, 33 participants received an additional vaccine dose, while 5 participants opted to withdraw from this study, and 10 participants contracted COVID-19. The raw vaccination rates and modified vaccination rates were 73.3% (33 out of 45, 95% CI: 58.1% to 85.4%) and 94.3% (33 out of 35, 95% CI: 80.8% to 99.3%), respectively. Among those who received a new vaccine dose, a substantial majority, i.e., 31 out of 33 (91.2%), received the third dose, with 1 person withdrawing at the 3-month mark. The raw vaccination rates and modified vaccination rates for individuals receiving the third dose of the vaccine were 76.9% (30 out of 39, 95% CI: 60.7% to 88.9%) and 93.8% (30 out of 32, 95%: 79.2% to 99.2%), respectively.
Figure 4 illustrates the weekly increase in the vaccination rate. In our study, the upward trend amounted to 9.97 doses per 1000 person-days among females, surpassing the rate of 1.36 doses per 1000 person-days observed in the same age- and gender-specific (30–75 years, women) Hong Kong population.
3.3.3. Early Testing and Self-Protection Behavior
Table 4 shows the results of early testing and self-protection behaviors. At baseline, 59.2% of all participants had undergone early rapid testing, and 57.1% reported that their family members had done the same. After the 1.5-month intervention, both of these percentages notably increased to 78.3% (with p < 0.05 for both comparisons). After 3 months, these percentages further rose to 86.70% and 84.40%, respectively, again with p-values less than 0.05 for both.
Participants also showed improvements in their self-protective behaviors. The average score (SD) for handwashing behavior saw a slight increase from 9.1 (1.6) to 9.4 (1.3) at 1.5 months (p = 0.088), and it significantly climbed to 9.5 (1.0) at 3 months (p < 0.05). Similarly, the mean score (SD) for mask-wearing behavior demonstrated improvement, rising from a baseline average of 4.9 (1.3) to 5.2 (1.0) at 1.5 months and 5.3 (1.2) at 3 months (p < 0.05 for both comparisons). However, the mean score (SD) for social distancing behavior was 7.6 (1.3) at baseline, significantly increasing to 8.0 (1.1) at 1.5 months (p = 0.017) and then slightly decreasing to 7.5 (1.1) at 3 months (p = 0.901).
3.1. Assessment Results Based on Phases 1–4 of the PRECEDE-PROCEED Model
In accordance with four phases’ assessments, 14 articles or government reports were reviewed to identify the health problems and needs of Hong Kong residents during the COVID-19 pandemic [24,25,26,36,37,38,39,40,41,42,43,44,45,46] (Supplementary Table S1). We also evaluated the decision matrix for each factor in Phase 3 in Supplementary Table S2. In summary, the COVID-19 pandemic significantly impacted the quality of life and well-being of the Hong Kong population with worrisome infection and mortality rates, and insufficient adherence to early testing and self-protective behaviors. Acceptance toward the vaccine and self-protective behaviors was unsatisfactory. Insufficient availability and accessibility toward personal protective equipment coupled with a lack of social support were noticed. Misinformation from unofficial media sources, misunderstanding of official information and health education materials, combined with insufficient explanation efforts by authorities, hampered the public understanding of government policies and strategies against COVID-19.
3.2. Structure and Contents of the Intervention Package
The intervention package comprised six components targeting predisposing, reinforcing, and enabling factors. The contents of the intervention package and the implementation protocol are shown in Table 1.
To address predisposing factors, health education materials, including 16 health education videos and 36 health tips, were employed. The videos transformed information from Hong Kong government health education materials about the COVID-19 pandemic, vaccines, rapid testing, and self-protection behaviors into more easily understandable descriptions for residents. These videos were disseminated two to three times per week over the first six weeks and redistributed twice during booster sessions (weeks 7–12). Health tips, which showcased the latest updates on the COVID-19-related situation in Hong Kong, were provided three times per week (Supplementary Table S3).
To address reinforcing factors, individual consultations and reminders of vaccination were instituted. The individual consultations provided one-on-one services to discern and mitigate participants’ hesitancy toward vaccination. The reminders of vaccination encompassed prompts urging participants to get vaccinated. These individual consultations and vaccination reminders alternated on a biweekly basis.
To address enabling factors, anti-epidemic health packages and vaccine booking services were provided. The anti-epidemic health package comprised hand sanitizer, masks, and rapid test kits. The vaccine booking service assisted participants in scheduling appointments through Hong Kong’s online vaccination booking system.
3.3. Effectiveness of the Intervention Package on Vaccination Promotion and Self-Protective Behaviors
3.3.1. Baseline Characteristics of the Participants
As shown in Figure 2, 194 participants were assessed for eligibility at the beginning of the trial. Of these, 144 were excluded for not meeting the inclusion criteria. The remaining 50 participants were allocated to the intervention group and received baseline. The flowchart then shows the progression through this study, with assessments conducted at baseline (T0), after the first follow-up (T1), with 46 participants remaining, and the final follow-up (T2), with 45 participants completing this study.
Table 2 displays the baseline characteristics of the 50 participants. The majority were female (96.0%), with a mean (SD) age of 57.1 (9.3) years. Notably, 68% had at least one chronic disease. Most participants were nonsmokers (86%) and did not consume alcohol regularly (58%). Nearly half did not engage in regular exercise (48%) and were unemployed (48%). A high proportion had education levels of secondary or higher (94%). Marital status showed that 60% were married or cohabiting. Geographically, 54% resided in the New Territories, 32% in Kowloon, and 14% on Hong Kong Island. Income data were disclosed by 74%, with 64.8% reporting a monthly income between 10,000 and 49,999 HKD. Regarding influenza vaccination, 24% had ever received it, and 16% had received it within the past year.
A total of 48 participants had been administered the first or second dose of either the Sinovac or Comirnaty COVID-19 vaccines before the baseline assessment (Table 2). No significant difference in baseline characteristics was observed between participants who had received the Sinovac or Comirnaty COVID-19 vaccines (all p > 0.05).
3.3.2. Vaccination Rate after Intervention
Changes in vaccination rates are shown in Table 3 and Figure 3. Between the baseline and 1.5 months, 28 participants received a new vaccine dose, while 4 participants withdrew. The raw vaccination rate was 60.9% (28 out of 46, 95% CI: 45.4% to 74.9%) at the 1.5-month assessment. During this period, 10 participants were infected with COVID-19, leading to a modified vaccination rate of 73.35% (28 out of 36, 95% CI: 58.1% to 85.4%). Among the 28 participants who received a new dose of the vaccine at 1.5 months, the majority obtained a new third dose (25 out of 28, 89.3%). As illustrated in Figure 3, 43 participants had already received their second dose at the baseline enrollment and were eligible for the third dose during the intervention period. Excluding three participants who withdrew at the 1.5-month point, the raw vaccination rate for the new third dose was 62.5% (25 out of 40, 95% CI: 45.8% to 77.3%). In consideration of the 7 individuals who contracted the virus, the modified vaccination rate for the new third dose was 76.9% (25 out of 33, 95% CI: 60.7% to 88.9%).
Over the course of 3 months, 33 participants received an additional vaccine dose, while 5 participants opted to withdraw from this study, and 10 participants contracted COVID-19. The raw vaccination rates and modified vaccination rates were 73.3% (33 out of 45, 95% CI: 58.1% to 85.4%) and 94.3% (33 out of 35, 95% CI: 80.8% to 99.3%), respectively. Among those who received a new vaccine dose, a substantial majority, i.e., 31 out of 33 (91.2%), received the third dose, with 1 person withdrawing at the 3-month mark. The raw vaccination rates and modified vaccination rates for individuals receiving the third dose of the vaccine were 76.9% (30 out of 39, 95% CI: 60.7% to 88.9%) and 93.8% (30 out of 32, 95%: 79.2% to 99.2%), respectively.
Figure 4 illustrates the weekly increase in the vaccination rate. In our study, the upward trend amounted to 9.97 doses per 1000 person-days among females, surpassing the rate of 1.36 doses per 1000 person-days observed in the same age- and gender-specific (30–75 years, women) Hong Kong population.
3.3.3. Early Testing and Self-Protection Behavior
Table 4 shows the results of early testing and self-protection behaviors. At baseline, 59.2% of all participants had undergone early rapid testing, and 57.1% reported that their family members had done the same. After the 1.5-month intervention, both of these percentages notably increased to 78.3% (with p < 0.05 for both comparisons). After 3 months, these percentages further rose to 86.70% and 84.40%, respectively, again with p-values less than 0.05 for both.
Participants also showed improvements in their self-protective behaviors. The average score (SD) for handwashing behavior saw a slight increase from 9.1 (1.6) to 9.4 (1.3) at 1.5 months (p = 0.088), and it significantly climbed to 9.5 (1.0) at 3 months (p < 0.05). Similarly, the mean score (SD) for mask-wearing behavior demonstrated improvement, rising from a baseline average of 4.9 (1.3) to 5.2 (1.0) at 1.5 months and 5.3 (1.2) at 3 months (p < 0.05 for both comparisons). However, the mean score (SD) for social distancing behavior was 7.6 (1.3) at baseline, significantly increasing to 8.0 (1.1) at 1.5 months (p = 0.017) and then slightly decreasing to 7.5 (1.1) at 3 months (p = 0.901).
3.3.1. Baseline Characteristics of the Participants
As shown in Figure 2, 194 participants were assessed for eligibility at the beginning of the trial. Of these, 144 were excluded for not meeting the inclusion criteria. The remaining 50 participants were allocated to the intervention group and received baseline. The flowchart then shows the progression through this study, with assessments conducted at baseline (T0), after the first follow-up (T1), with 46 participants remaining, and the final follow-up (T2), with 45 participants completing this study.
Table 2 displays the baseline characteristics of the 50 participants. The majority were female (96.0%), with a mean (SD) age of 57.1 (9.3) years. Notably, 68% had at least one chronic disease. Most participants were nonsmokers (86%) and did not consume alcohol regularly (58%). Nearly half did not engage in regular exercise (48%) and were unemployed (48%). A high proportion had education levels of secondary or higher (94%). Marital status showed that 60% were married or cohabiting. Geographically, 54% resided in the New Territories, 32% in Kowloon, and 14% on Hong Kong Island. Income data were disclosed by 74%, with 64.8% reporting a monthly income between 10,000 and 49,999 HKD. Regarding influenza vaccination, 24% had ever received it, and 16% had received it within the past year.
A total of 48 participants had been administered the first or second dose of either the Sinovac or Comirnaty COVID-19 vaccines before the baseline assessment (Table 2). No significant difference in baseline characteristics was observed between participants who had received the Sinovac or Comirnaty COVID-19 vaccines (all p > 0.05).
3.3.2. Vaccination Rate after Intervention
Changes in vaccination rates are shown in Table 3 and Figure 3. Between the baseline and 1.5 months, 28 participants received a new vaccine dose, while 4 participants withdrew. The raw vaccination rate was 60.9% (28 out of 46, 95% CI: 45.4% to 74.9%) at the 1.5-month assessment. During this period, 10 participants were infected with COVID-19, leading to a modified vaccination rate of 73.35% (28 out of 36, 95% CI: 58.1% to 85.4%). Among the 28 participants who received a new dose of the vaccine at 1.5 months, the majority obtained a new third dose (25 out of 28, 89.3%). As illustrated in Figure 3, 43 participants had already received their second dose at the baseline enrollment and were eligible for the third dose during the intervention period. Excluding three participants who withdrew at the 1.5-month point, the raw vaccination rate for the new third dose was 62.5% (25 out of 40, 95% CI: 45.8% to 77.3%). In consideration of the 7 individuals who contracted the virus, the modified vaccination rate for the new third dose was 76.9% (25 out of 33, 95% CI: 60.7% to 88.9%).
Over the course of 3 months, 33 participants received an additional vaccine dose, while 5 participants opted to withdraw from this study, and 10 participants contracted COVID-19. The raw vaccination rates and modified vaccination rates were 73.3% (33 out of 45, 95% CI: 58.1% to 85.4%) and 94.3% (33 out of 35, 95% CI: 80.8% to 99.3%), respectively. Among those who received a new vaccine dose, a substantial majority, i.e., 31 out of 33 (91.2%), received the third dose, with 1 person withdrawing at the 3-month mark. The raw vaccination rates and modified vaccination rates for individuals receiving the third dose of the vaccine were 76.9% (30 out of 39, 95% CI: 60.7% to 88.9%) and 93.8% (30 out of 32, 95%: 79.2% to 99.2%), respectively.
Figure 4 illustrates the weekly increase in the vaccination rate. In our study, the upward trend amounted to 9.97 doses per 1000 person-days among females, surpassing the rate of 1.36 doses per 1000 person-days observed in the same age- and gender-specific (30–75 years, women) Hong Kong population.
3.3.3. Early Testing and Self-Protection Behavior
Table 4 shows the results of early testing and self-protection behaviors. At baseline, 59.2% of all participants had undergone early rapid testing, and 57.1% reported that their family members had done the same. After the 1.5-month intervention, both of these percentages notably increased to 78.3% (with p < 0.05 for both comparisons). After 3 months, these percentages further rose to 86.70% and 84.40%, respectively, again with p-values less than 0.05 for both.
Participants also showed improvements in their self-protective behaviors. The average score (SD) for handwashing behavior saw a slight increase from 9.1 (1.6) to 9.4 (1.3) at 1.5 months (p = 0.088), and it significantly climbed to 9.5 (1.0) at 3 months (p < 0.05). Similarly, the mean score (SD) for mask-wearing behavior demonstrated improvement, rising from a baseline average of 4.9 (1.3) to 5.2 (1.0) at 1.5 months and 5.3 (1.2) at 3 months (p < 0.05 for both comparisons). However, the mean score (SD) for social distancing behavior was 7.6 (1.3) at baseline, significantly increasing to 8.0 (1.1) at 1.5 months (p = 0.017) and then slightly decreasing to 7.5 (1.1) at 3 months (p = 0.901).
4. Discussion
Our study first utilized the PRECEDE-PROCEED model to assess the health needs of Hong Kong residents during the COVID-19 pandemic and develop a health education package. The intervention package significantly improved COVID-19 vaccination rates, surpassing that of the same age- and gender-specific Hong Kong population during the same period. Additionally, the intervention resulted in a significant rise in the proportion of early testing and improvements in correct mask use and handwashing behaviors. Our findings demonstrate the PRECEDE-PROCEED model’s feasibility and efficacy in developing health education programs for promoting vaccination and enhancing self-protective behaviors, offering a practicable framework for future efforts to control infectious disease transmission.
The success of immunization programs highly depends on extensive public vaccination acceptance, making it critical to develop educational programs to increase vaccine uptake during pandemics like COVID-19 [47]. Previous studies have shown varying improvements in vaccination rates with health education. For instance, a pre–post study reported a 32.0% increase in HPV vaccination rate after a 6-month video-based health intervention [48]. A recent meta-analysis of 14 studies found a 39% increase in vaccination rates through community-engaged education [49]. Our study demonstrated a higher increase in vaccination rates than these prior findings, likely due to the multi-component nature of our intervention package and the context of the pandemic. Our intervention included videos, consultations, reminders, incentives, and support services [13]. A meta-analysis found that multi-component health communication campaigns generally lead to greater behavior changes than single-component ones [50]. Furthermore, we implemented targeted educational interventions to tackle the challenges of low vaccination coverage in Hong Kong. At the beginning of our study, the low vaccine coverage observed in Hong Kong was largely due to factors such as vaccine hesitancy and the spread of misinformation. Vaccine hesitancy, particularly during the early stages of the pandemic, was driven by concerns regarding the safety and efficacy of vaccines, which were exacerbated by misinformation circulating on social media and other informal platforms. These interventions were designed to dispel misinformation and deliver personalized communication aimed at addressing the specific concerns of the Hong Kong population. By identifying and addressing the underlying causes of low vaccine uptake, our intervention successfully improved vaccination rates in Hong Kong.
Self-protective behaviors stand as a cornerstone in pandemic control strategies [51]. Our health education intervention significantly enhanced self-protective behaviors during the COVID-19 pandemic. Previous studies also showed improvements following health education. For example, a randomized controlled trial with 18,223 adults found that physician-delivered COVID-19 health messages increased the self-reported protective behavior [52]. A quasi-experimental trial with 219 university students reported increased self-protective behavior scores with a digital-based self-learned educational intervention [11]. Unlike vaccination, which is typically a one-time action, protective behaviors require continuous effort and vigilance, making them more susceptible to changes in public perception and compliance over time. A population-based survey found fluctuating adherence to voluntary protective behavior in Hong Kong populations over two consecutive COVID-19 epidemic waves [53]. The PRECEDE-PROCEED model enabled the development of a holistic intervention tailored to the specific needs within a specific context, helping maintain and enhance protective behaviors over time.
Our pilot study demonstrated a successful example that utilized the PRECEDE-PROCEED model to develop a health education package for vaccination promotion. A study used the first four phases of the model to address predisposing, enabling, and reinforcing factors to COVID-19 vaccination, resulting in 20,792 vaccinations administered at a neighborhood site over 16 weeks [54]. A meta-analysis found that interventions using the PRECEDE-PROCEED model could significantly improve health knowledge [13]. This evidence supports the PRECEDE-PROCEED model as an effective framework for health education. The model’s strengths included its systematic approach to planning and evaluating health education programs, ensuring evidence-based and population-centered interventions [55]. Another key advantage of the PRECEDE-PROCEED model was its flexibility in responding to dynamic changes in community health needs [56]. During a pandemic, this model allowed for continuous assessment and adaptation of strategies, ensuring interventions remain relevant and effective over time. Additionally, its step-by-step methodology facilitated an easy adoption and implementation, even for those unfamiliar with the model, showcasing its potential for future community-based health promotion programs [57].
Several limitations of the present study should be noted. Firstly, the sample size of the quasi-experimental study was relatively small, with only 50 subjects included. Nevertheless, this was just a pilot study to assess the preliminary effects of the health education program against COVID-19 infection. A formal randomized controlled trial with more participants should be conducted to validate further the findings of this pilot study in the future. Secondly, a single-arm design was employed. Since no control group for comparison, it might lead to uncertainty in assessing the intervention effects to some extent. Alternatively, we compared our results with the Hong Kong general populations with the same gender (women) and similar age group (30–75 years) during the same period. This comparison strongly verified the positive effect of the intervention on COVID-19 vaccination rate. Nonetheless, given the data on potential confounders, the results of the quasi-experimental study should be interpreted with caution.
5. Conclusions
The present study tailored and piloted a health education intervention based on the PRECEDE-PROCEED model to improve vaccination rates and enhance self-protective behaviors against COVID-19 in Hong Kong. The PRECEDE-PROCEED model facilitated the development of the intervention package, which allowed the integration of individual and environmental factors and enable the consideration of organizational, administrative, and policy barriers or support. Our study findings support the feasibility and effectiveness of using the PRECEDE-PROCEED model to develop a comprehensive intervention for promoting vaccination and enhancing self-protection behaviors, which provides a referential approach for future studies in controlling the transmission of infectious diseases in the population. |
Title: Identification and validation of basement membrane-related genes predicting prognosis and immune infiltration associated with bladder cancer | Body:
1. Introduction
Bladder cancer (BC) is the most common malignant tumor of the urinary system, with the incidence ranking first among the malignant tumors of the urinary system.[1,2] About 3-quarters of bladder malignancies are non-muscle-invasive.[3] In contrast, 20% to 25% of bladder malignancies are muscle-invasive at the initial onset.[4,5] Patients with localized disease can be cured by surgical resection or radiotherapy, but this treatment is limited if the disease is recurrent or spread over long distances.[6] Despite advances in chemotherapy regimens such as platinum and 5-FU therapy for patients with advanced BC, the efficacy of chemotherapy remains unsatisfactory, the overall survival (OS) rate at 5 years was barely >50%.[7,8] Targeted therapy is therefore the future of targeted BC. Targeted agents have been developed in recent years, but overall results remain disappointing. There is a need to develop valuable biomarkers to predict the possibility of reducing late-stage disease.
The basement membrane (BM) is a characteristic structural formation of the extracellular matrix and is found in a variety of tissues.[9] BM is highly bioactive because it is rich in components that promote cell adhesion, growth, migration, invasion, and differentiation through a variety of cell surface receptors.[10] A recent study identified up to 200 genes described to act differently at different locations in the BM.[11] These genes are involved in tumorigenesis development and metastasis.[12] Therefore, the measurement of these BM-related genes provides a new method for the prediction of the long-term prognosis of cancer patients.
In this study, we explored the potential clinical utility of BM-related genes prognostic stratification and targeted BC immunotherapy. We analyzed BM-related gene expression and corresponding clinical information to develop personalized prognostic models for BC patients. Bioinformatics analysis of BM-related genes was performed to explore its potential regulatory mechanisms. Our findings provide a basis for developing personalized therapies for BC patients.
2. Materials and methods
2.1. BC data source
We obtained RNA sequencing data (RNA-seq FPKM), somatic mutation data, copy number variation data, and associated clinicopathological information for breast cancer from the UCSC Xena website (UCSC Xena at xenabrowser.net). To validate the Cancer Genome Atlas (TCGA) dataset results, we additionally procured expression profiling data from both breast cancer patients and normal tissues through “GSE32548”[13] and 10X Genomics single-cell RNA sequencing data from “GSE129845”[14] via the gene expression omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) (see Fig. S1, supplementary material, Article Flowchart). http://links.lww.com/MD/N195
2.2. Identification of differentially expressed genes (DEGs)
We download 222 BM genes from (https://bmbase.manchester.ac.uk/). To identify DEGs in normal and tumor BM genes, we used criteria for were identified using the “limma” package in R with inclusion criteria of adjusted P value < .01 and |log Fold-Change| ≥ 1 for differential genes.
2.3. Functional enrichment analysis of DEGs
Based on these DEGs, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using the “clusterProfiler,”“enrichment plot,” and “ggplot 2” software package. The screening criteria adjusted P value < .05 for GO and KEGG enrichment analysis were statistically significant for enrichment. using GSEA (version 4.3.2) analysis to assess the KEGG enrichment pathway in patients in high and low-risk groups.
2.4. Construction of the BM-related gene prognostic and risk model
Univariate cox-regression analysis for DEGs was used to screen for prognosis-related genes with prognostic significance at a P value < .01. The screened prognosis BM-related genes were then included in the least absolute shrinkage and selection operator (LASSO) regression model, which was performed using the R package “glmnet”， in which all prognosis BM-related genes were penalized to prevent the overfitting effects of the model. The penalty parameter (λ) of the model was determined by 10-fold cross-validation that followed the minimum criterion. Thereafter, genes with independent prognosis were screened by multivariate cox-regression analysis and risk scores were calculated. The risk signature was constructed by multiplying the linear combination of the BM-related gene expression levels following algorithm: (expression level of gene1 × corresponding coefficient1 + expression level of gene2 × corresponding coefficient2+... expression level of gene n × corresponding coefficient n). Based on the results of the calculated risk score, patients were divided into high-risk and low-risk groups according to the median risk score. Validation of candidate genes at the protein level was performed through the HPA database (https://www.proteinatlas.org/)
2.5. Evaluation of a nomogram
For the purpose of comparing OS between the high- and low-risk groups, the TCGA database constructed the Kaplan–Meier (KM) curve with a log-rank test using the R package “survminer.” The receiver operating characteristic (ROC) curve analysis was utilized to evaluate the prediction accuracy of the BM gene via the R package “timeROC.” The validations were performed simultaneously in the GEO database. a nomogram was constructed using the R packages “rms” and “regplot” and the independent prognostic factors found in the TCGA database by means of univariate and multivariate cox-regression analysis. The availability of this nomogram was evaluated by the concordance index and calibration curve. The ROC analysis was also used to determine the nomogram accuracy in OS prediction.
2.6. Immunohistochemistry (IHC)
For IHC, At Chengdu Second People Hospital, tissues from 12 BC patients were obtained along with tissues from paracarcinomas. Following dewaxing and hydration, tissue slices were put in a PBS buffer. After soaking the regions in hydrogen peroxide at ambient temperature, the primary antibody was left to incubate for an entire night at 4°C. After that, the slices were tethered with hematoxylin and treated with a secondary antibody. Subsequently, the slices were sealed with neutral gum after being dehydrated using ethanol at varying concentration gradients. Next, we used a microscope to study the tissue morphology and take pictures of it. The details for the 4 antibodies used in this study are provided in Table S1(see Table S1, supplementary material, the 3 immunohistochemical antibodies). http://links.lww.com/MD/N195 The indexes of IHC include the expression intensity and the percentage of positive cells. The expression intensity is divided into 4 grades: 0 = negative, 1 = weak positive, 2 = moderate positive, and 3 = strong positive, and the percentage of positive cells refers to the proportion of positive cells in the same type of cells. The whole section was viewed under low magnification (4×) to observe the coloration of tumor parenchyma cells and the distribution of positive, select the areas of high expression, and count the percentage of positive cells among the same type of cells in 5 high magnification (20 × or 40×) fields as the result of the whole section, and the results of the interpretation were reported in percentage.
2.7. Single-cell data download and organization
R software and the correlation package were used to evaluate BC single-cell sequencing data. In GSE129845, single-cell sequencing data of BC were read and Seurat objects were generated. Several low-quality cells were eliminated, including those with gene expression counts < 500, mitochondrial gene expression >5%, erythrocyte gene expression >5%, and removal of the cell cycle influence on the outcomes. The data was normalized using the “LogNormalize” method. 2000 extremely distinct genes were chosen using the “FindVariableFeatures” approach. The 2000 extremely distinct genes’ principal components analysis (PCA) was downscaled using the “RunPCA” tool. The uniform manifold approximation and projection (UMAP) dimensionality reduction approach was used to show the clustered cells on a 2-dimensional map.
2.8. Immune infiltration and drug sensitivity analysis
The abundance of the 22 immune cells was calculated by the CIBERSORT algorithm with 1000 permutations. P < .05 there were statistically significant. We assessed the variations between the high- and low-risk groups in the expression levels of immune-related markers and the quantity of 22 immune cells. Immune scores and stromal scores of patients with BC were calculated by the R package “Estimation” using the ESTIMATE algorithm. relationship between assessment and risk score prognostic characteristics using immune checkpoints (ICPs) and immune suppressive cytokines (ISC). To investigate the high and low-risk groups in the clinical efficacy of chemotherapeutic agents in patients, Using the “pRRophetic” package, we were able to determine the half-inhibitory concentration (IC50) values of widely used drugs. A statistically significant P value was defined as <.05.
2.1. BC data source
We obtained RNA sequencing data (RNA-seq FPKM), somatic mutation data, copy number variation data, and associated clinicopathological information for breast cancer from the UCSC Xena website (UCSC Xena at xenabrowser.net). To validate the Cancer Genome Atlas (TCGA) dataset results, we additionally procured expression profiling data from both breast cancer patients and normal tissues through “GSE32548”[13] and 10X Genomics single-cell RNA sequencing data from “GSE129845”[14] via the gene expression omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) (see Fig. S1, supplementary material, Article Flowchart). http://links.lww.com/MD/N195
2.2. Identification of differentially expressed genes (DEGs)
We download 222 BM genes from (https://bmbase.manchester.ac.uk/). To identify DEGs in normal and tumor BM genes, we used criteria for were identified using the “limma” package in R with inclusion criteria of adjusted P value < .01 and |log Fold-Change| ≥ 1 for differential genes.
2.3. Functional enrichment analysis of DEGs
Based on these DEGs, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using the “clusterProfiler,”“enrichment plot,” and “ggplot 2” software package. The screening criteria adjusted P value < .05 for GO and KEGG enrichment analysis were statistically significant for enrichment. using GSEA (version 4.3.2) analysis to assess the KEGG enrichment pathway in patients in high and low-risk groups.
2.4. Construction of the BM-related gene prognostic and risk model
Univariate cox-regression analysis for DEGs was used to screen for prognosis-related genes with prognostic significance at a P value < .01. The screened prognosis BM-related genes were then included in the least absolute shrinkage and selection operator (LASSO) regression model, which was performed using the R package “glmnet”， in which all prognosis BM-related genes were penalized to prevent the overfitting effects of the model. The penalty parameter (λ) of the model was determined by 10-fold cross-validation that followed the minimum criterion. Thereafter, genes with independent prognosis were screened by multivariate cox-regression analysis and risk scores were calculated. The risk signature was constructed by multiplying the linear combination of the BM-related gene expression levels following algorithm: (expression level of gene1 × corresponding coefficient1 + expression level of gene2 × corresponding coefficient2+... expression level of gene n × corresponding coefficient n). Based on the results of the calculated risk score, patients were divided into high-risk and low-risk groups according to the median risk score. Validation of candidate genes at the protein level was performed through the HPA database (https://www.proteinatlas.org/)
2.5. Evaluation of a nomogram
For the purpose of comparing OS between the high- and low-risk groups, the TCGA database constructed the Kaplan–Meier (KM) curve with a log-rank test using the R package “survminer.” The receiver operating characteristic (ROC) curve analysis was utilized to evaluate the prediction accuracy of the BM gene via the R package “timeROC.” The validations were performed simultaneously in the GEO database. a nomogram was constructed using the R packages “rms” and “regplot” and the independent prognostic factors found in the TCGA database by means of univariate and multivariate cox-regression analysis. The availability of this nomogram was evaluated by the concordance index and calibration curve. The ROC analysis was also used to determine the nomogram accuracy in OS prediction.
2.6. Immunohistochemistry (IHC)
For IHC, At Chengdu Second People Hospital, tissues from 12 BC patients were obtained along with tissues from paracarcinomas. Following dewaxing and hydration, tissue slices were put in a PBS buffer. After soaking the regions in hydrogen peroxide at ambient temperature, the primary antibody was left to incubate for an entire night at 4°C. After that, the slices were tethered with hematoxylin and treated with a secondary antibody. Subsequently, the slices were sealed with neutral gum after being dehydrated using ethanol at varying concentration gradients. Next, we used a microscope to study the tissue morphology and take pictures of it. The details for the 4 antibodies used in this study are provided in Table S1(see Table S1, supplementary material, the 3 immunohistochemical antibodies). http://links.lww.com/MD/N195 The indexes of IHC include the expression intensity and the percentage of positive cells. The expression intensity is divided into 4 grades: 0 = negative, 1 = weak positive, 2 = moderate positive, and 3 = strong positive, and the percentage of positive cells refers to the proportion of positive cells in the same type of cells. The whole section was viewed under low magnification (4×) to observe the coloration of tumor parenchyma cells and the distribution of positive, select the areas of high expression, and count the percentage of positive cells among the same type of cells in 5 high magnification (20 × or 40×) fields as the result of the whole section, and the results of the interpretation were reported in percentage.
2.7. Single-cell data download and organization
R software and the correlation package were used to evaluate BC single-cell sequencing data. In GSE129845, single-cell sequencing data of BC were read and Seurat objects were generated. Several low-quality cells were eliminated, including those with gene expression counts < 500, mitochondrial gene expression >5%, erythrocyte gene expression >5%, and removal of the cell cycle influence on the outcomes. The data was normalized using the “LogNormalize” method. 2000 extremely distinct genes were chosen using the “FindVariableFeatures” approach. The 2000 extremely distinct genes’ principal components analysis (PCA) was downscaled using the “RunPCA” tool. The uniform manifold approximation and projection (UMAP) dimensionality reduction approach was used to show the clustered cells on a 2-dimensional map.
2.8. Immune infiltration and drug sensitivity analysis
The abundance of the 22 immune cells was calculated by the CIBERSORT algorithm with 1000 permutations. P < .05 there were statistically significant. We assessed the variations between the high- and low-risk groups in the expression levels of immune-related markers and the quantity of 22 immune cells. Immune scores and stromal scores of patients with BC were calculated by the R package “Estimation” using the ESTIMATE algorithm. relationship between assessment and risk score prognostic characteristics using immune checkpoints (ICPs) and immune suppressive cytokines (ISC). To investigate the high and low-risk groups in the clinical efficacy of chemotherapeutic agents in patients, Using the “pRRophetic” package, we were able to determine the half-inhibitory concentration (IC50) values of widely used drugs. A statistically significant P value was defined as <.05.
3. Result
3.1. Differentially expressed BM-related genes
We compared the differential expression of 222 BM-related genes in BC in 19 normal samples and 409 tumor samples (see Table S2, supplementary material, which shows the BM-related genes) http://links.lww.com/MD/N195, and we obtained a total of 48 differential genes, of which 15 were upregulated and 33 were down-regulated (Fig. 1A–B).
Figure 1.
Identification and functional enrichment enrichment analysis the differentially expressed BM-related genes. (A) Histogram of differentially genetically up- and down-regulated genes in BM-related genes. (B) Heatmap of the differentially expressed BM-related genes. (C) Top 5 of GO functional enrichment analysis. (D) Top KEGG pathway analysis. BM = basement membrane, GO = gene ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.
3.2. GO and KEGG pathway enrichment
Based on these deg for GO enrichment analysis and KEGG pathway analysis, the result is that there are 1150 genes enriched in GO (Table S3). http://links.lww.com/MD/N195 The results of the GO enrichment analysis of the top 5 genes showed that in the biological processes, cellular components, and molecular function (Fig. 1C). The results of KEGG pathway enrichment analysis showed that 20 enrichment pathways were mainly involved and (Table S4). http://links.lww.com/MD/N195 The top 10 ranked major pathways: ECM-receptor interaction, focal adhesion, human papillomavirus infection, PI3K-Akt signaling pathway, arrhythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, dilated cardiomyopathy, small cell lung cancer, protein digestion and absorption, and proteoglycans in cancer (Fig. 1D).
3.3. Construction of BM-related genes prognostic model
Univariate cox-regression analysis revealed a significant association between 25 DEGs and BC prognosis (Fig. 2A–C, see Table S5, supplementary material, http://links.lww.com/MD/N195 which shows univariate analysis). only 6 DEGs (AGRN, COL6A1, EFEMP1, GPC2, ITGA3, LAMA2) were identified as predictors after LASSO regression analysis (Fig. 2D and E). In addition, multivariate cox-regression analysis identified only 3 DEGs (EFEMP1[hazard ratio {HR}: 1.187, 95%CI: 1.088–1.296, P < .01], GPC2 [HR: 0.706, 95%CI: 0.550–0.907, P < .01], and ITGA3 [HR: 0.797, 95%CI: 0.716–0.888, P < .01]) used to construct prognostic models of BC patients (Fig. 2F, see Fig. S2, supplementary material, shows the KM survival analysis of 3 patient genes) http://links.lww.com/MD/N195. Following a median risk score, patients were categorized into high- and low-risk groups and subjected to a KM survival analysis (Table 1). The risk score was calculated as: Risk score = (0.171585498921286) * EFEMP1 + (−0.347995962870904) * GPC2 + (−0.226752721489659) * ITGA3. The KM survival curves showed poor prognostics for high-risk patients compared to low-risk patients (Fig. 3A). PCA, the results showed that patients in both high and low-risk groups had a significant dispersion (Fig. 3B). The risk of death increased with the increase in risk score, but patients’ survival time decreases continuously (Fig. 3C–E). The area under the curve (AUC) values for the ROC curve predicted 3-year and 5-year survival were 0.638 and 0.662, respectively (Fig. 3F).
Table 1
The correlations between risk groups and clinical parameters in the TCGA database.
Characteristic
Risk groups
P value
High
Low
Age
<.001
≤60
10 (13.5%)
38 (38.8%)
>60
64 (86.5%)
60 (61.2%)
Gender
.317
Female
20 (27.0%)
19 (19.4%)
Male
54 (73.0%)
79 (80.6%)
T_stage
.12
T1
0 (0%)
1 (1.0%)
T2
17 (23.0%)
38 (38.8%)
T3
46 (62.2%)
47 (48.0%)
T4
11 (14.9%)
12 (12.2%)
N_stage
<.001
N0
36 (48.6%)
82 (83.7%)
N1
14 (18.9%)
8 (8.2%)
N2
23 (31.1%)
7 (7.1%)
N3
1 (1.4%)
1 (1.0%)
M_stage
.246
M0
69 (93.2%)
96 (98.0%)
M1
5 (6.8%)
2 (2.0%)
Fustat
<.001
Alive
24 (32.4%)
72 (73.5%)
Death
50 (67.6%)
26 (26.5%)
TCGA = The Cancer Genome Atlas.
Figure 2.
Genes associated with BM and prognosis. (A) Univariate analysis result of a network correlations in the TCGA cohort (B) Frequencies of CNV gain, loss in BM-related gene. (C) Circus plots of chromosome distributions of BM-related gene. (D, E) LASSO regression analysis of the genes from univariate analysis. (F) Results of the multivariate analysis. BM = basement membrane, CNV = copy number variation, LASSO = Least absolute shrinkage and selection operator, TCGA = The Cancer Genome Atlas.
Figure 3.
Construction of the risk score. (A) Survival probability based on the high and low-risk group. (B) PCA analysis in patients with high and low-risk groups of TCGA database. (C, D) Distribution of the survival, risk score, and survival of patients with BC. (E) Heatmap of the expression of 3 BM-related genes in BC. (F) The ROC curve for the 3- and 5-yr overall survival. BC = bladder cancer, BM = basement membrane, PCA = principal components analysis, ROC = receiver operating characteristic, TCGA = The Cancer Genome Atlas.
3.4. Prediction model
In consideration of the clinical and pathological characteristics and risk score, a prediction nomogram was created. The risk score (HR = 2.062, 95%CI:1.597–2.663, P < .01), T-stage (HR = 1.629, 95%CI:1.153–2.302, P < .01), N-stage (HR = 1.577, 95%CI:1.243–2.001, P < .01) and gender (HR = 0.608, 95%CI:0.375–0.988, P = .044) were strongly correlated with prognosis, according to the univariate analysis (Fig. 4A). The risk score (HR = 1.818, 95%CI:1.315–2.513, P < .01), and gender (HR = 0.558, 95%CI:0.339–0.918, P < .05) were independent indicators that could accurately predict individuals with BC who would have a poor prognosis, according to multivariate analysis (Fig. 4B). The age, gender, N-stage, T-stage, M-stage, and risk score p values for the Schoenfeld individual test were, in order, 0.4444, 0.9848, 0.6198, 0.7672, 0.4514, and 0.7793. The global Schoenfeld test generated a P value of .6823 (see Table S6, supplementary material, http://links.lww.com/MD/N195 shows the global Schoenfeld test, see Fig. S3 supplementary material, http://links.lww.com/MD/N195 shows the Schoenfeld individual test). The results indicated that all of the factors were satisfied for the PH assumption. Compared to the other clinicopathological parameters, the predictive nomogram showed a higher ability to predict 3- and 5-year OS (Concordance = 0.697, Fig. 4C). The nomogram can reliably predict survival, according to the calibration curve for 1-, 3-, and 5-year survival (Fig. 4D). In addition, we did ROC curves for multiple clinical factors age, TNM staging, and risk score and calculated the area under the ROC curve (AUC) for 1-,3-, and 5 years. We found that the AUC values of risk score and other clinical factors were compared and found significantly higher AUC values for the mentioned risk score, which implies that risk score is a good prognostic predictor (Fig. 4E–G).
Figure 4.
Model predictions. (A) Univariate analysis of the clinicopathologic features and the risk score. (B) Multivariate analysis of the clinicopathologic features and the risk score. (C) Nomogram to predict the survival of the BC patients. (D) Calibration curve for 1-, 3-, and 5-yr survival. (E-G) The ROC curves for multiple clinical factors and risk scores compared for 1-, 3-, and 5-yr OS in BC. BC = bladder cancer, OS = overall survival, ROC = receiver operating characteristic
3.5. Validation of risk score and immunohistochemical analysis
The validity of the model was verified by GEO validation data. The validation dataset KM survival curve revealed that high-risk groups had a noticeably worse prognosis than low-risk groups (see Fig. S4A, supplementary material, survival probability based on the high and low-risk group) http://links.lww.com/MD/N195. In the validation dataset, 3 prognostic genes showed significant expression. The GEO validation dataset showed a drop in survival time but an increase in death risk with a higher risk score, similar to the TCGA dataset (see Fig. S4B–D, supplementary material, which shows the validation of the risk score) http://links.lww.com/MD/N195. The ROC curve areas for predicting OS patient at 3 and 5 years were 0.698 and 0.709 (see Fig. S4E, supplementary material, which shows the area under the OR curve). http://links.lww.com/MD/N195 BC patients of high and low-risk groups could be completely separated by the PCA (see Fig. S4F, supplementary material, http://links.lww.com/MD/N195 which shows the PCA analysis) based on these 3 BM-related gene with differential mRNA levels. Immunohistochemical staining showed that the GPC2 and ITG3 genes were predominantly expressed in the cytoplasm and cell membrane and were more highly expressed in BC samples than in normal samples. The EFEMP1 gene was predominantly expressed in the cytoplasm and nucleus and was also more highly expressed in BC samples than in normal samples (Fig. 5A, see Table S7, supplementary material, http://links.lww.com/MD/N195 which shows immunohistochemical staining results).
Figure 5.
The 3 genes of immunohistochemical staining expression and expression of single-cell RNA types. (A) Expression of 3 genes in normal and tumor sample in the BC. (B) Gene expression in 3 bladder cancer single-cell RNA samples. (C-E) Tthe 3 genes of expression of single-cell RNA types. BC = bladder cancer.
3.6. Single-cell RNA-Seq data analysis
To move forward investigate the identification of 3 BM-related genes in BC cells and determine their expression in specific cell types, we analyzed single-cell data (Fig. 5B). The findings showed that single-cell analysis was unable to detect the expression of the GPC2 gene, and that ITG3 was mostly expressed in epithelial cells and fibroblasts. (Fig. 5C–E).
3.7. Relationship between BM-related gene signature and immune cell infiltration
Using the CIBERSORT algorithm estimation, we assessed the infiltration of the 22 different types of immune cells in the TCGA data and found B cells naïve, Macrophages M2, Dendritic cells resting, Dendritic cells activated, Mast cells resting were significantly different between the high- and low-risk groups (Fig. 6A). the correlation between risk score and immune cell abundance. As depicted in Figure 6B to I, the risk score was positively correlated with B cell naïve, Macrophages M2, Mast cells resting, while risk score showed the opposite relationship with follicular Dendritic cells activated, Dendritic cells resting, NK cells resting and T cells follicular helper. The ESTIMATE score analysis result showed stromal score, immune score, and ESTIMATE scores were significantly higher in the high-risk group (Fig. 6J). Next, the relationship between ICPs and this prognostic feature was evaluated. Figure 6K shows that 30 ICPs behave differently in 2 risk subgroups, PD-1, PDL-1, and CTLA4. In addition, the relationship between ISCs and high and low-risk groups was evaluated and IL10, IL13, TGFB2 and TGFB3 were found to be statistically different in the 2 groups (see Fig. S5, supplementary material, http://links.lww.com/MD/N195 which shows expression of ISCs in the high and low-risk groups).
Figure 6.
Evaluation of the TME and checkpoints. (A) Relation of the immune cells with a risk score. (B-I) Correlations between risk score and immune cell types. (J) Correlations between risk score and both immune and stromal scores. (K) Correlations between the ICPs in the high and low-risk groups (P < .05 *; P < .01 **; P < .001 ***). ICPs = immune checkpoints, TME = tumor microenvironment.
3.8. Cancer-related gene mutation and drug sensitivity analysis
To evaluate differences in associated with cancer mutations comparing high-risk and low-risk groups, we first estimated the mutation frequency of each group genes. Figure 7A to B describe in general the representative gene mutations found in all of the groups. Genes such as TP53 (53%), TNN (42%), KMT2D (27%), MUC16(27%), and ARID1A (26%) had the top 5 mutation frequencies in the high-risk group. TP53 (45%), TNN (46%), KMT2D (26%), MUC16(24%), and ARID1A (23%) consisted among the top 5 genes in the low-risk category with the greatest frequency of mutations. The TCGA dataset estimation of the chemotherapy response in the 2 distinct groups of risks. We identified BC most often used medications, such as Cisplatin, Gemcitabine, Camptothecin, Doxorubicin, Thapsigargan, and Methotrexate. The results show that the IC50 of Cisplatin, Gemcitabine, and Methotrexate was considerably greater within the high-risk group from the TCGA dataset than that observed in the low-risk group (Fig. 6C–E), This indicates that individuals in both categories can respond differently to these chemotherapeutic drugs. Likewise, the variations in IC50 for camptothecin, Doxorubicin, Thapsigargan, and Vinblastine in both groups were not significant (see Fig. S6, supplementary material, shows the risk score and chemotherapeutic sensitivity) http://links.lww.com/MD/N195.
Figure 7.
The gene mutation, drug sensitivity and GSEA analysis. (A, B) The waterfall plot of top 15 gene somatic mutation features established with high and low-risk groups. (C-E) Relationships between risk score and chemotherapeutic sensitivity. (F) The top 10 pathways of GSEA enrichment analysis in the high-risk groups. (G) The top 10 pathways of GSEA enrichment analysis in the low-risk groups.
3.9. GSEA enrichment analysis
To investigate any variations in signaling pathways comparing the 2 patient groups that have different risk. GSEA was performed. Our results demonstrated that the majority of high-risk patients enriched in metabolic signaling pathways (Fig. 7F), such as glycine serine and threonine metabolism, nicotinate and nicotinamide metabolism, purine metabolism, pyrimidine metabolism, and tryptophan metabolism. Moreover, the low-risk group was enriched with ABC transporters, alpha-linolenic acid metabolism, basal cell carcinoma, ether lipid metabolism, glycosylphosphatidylinositol GPI anchor biosynthesis, hedgehog signaling pathway, histidine metabolism, other glycan degradation, steroid hormone biosynthesis, TGF beta signaling pathway (Fig. 7G).
3.1. Differentially expressed BM-related genes
We compared the differential expression of 222 BM-related genes in BC in 19 normal samples and 409 tumor samples (see Table S2, supplementary material, which shows the BM-related genes) http://links.lww.com/MD/N195, and we obtained a total of 48 differential genes, of which 15 were upregulated and 33 were down-regulated (Fig. 1A–B).
Figure 1.
Identification and functional enrichment enrichment analysis the differentially expressed BM-related genes. (A) Histogram of differentially genetically up- and down-regulated genes in BM-related genes. (B) Heatmap of the differentially expressed BM-related genes. (C) Top 5 of GO functional enrichment analysis. (D) Top KEGG pathway analysis. BM = basement membrane, GO = gene ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.
3.2. GO and KEGG pathway enrichment
Based on these deg for GO enrichment analysis and KEGG pathway analysis, the result is that there are 1150 genes enriched in GO (Table S3). http://links.lww.com/MD/N195 The results of the GO enrichment analysis of the top 5 genes showed that in the biological processes, cellular components, and molecular function (Fig. 1C). The results of KEGG pathway enrichment analysis showed that 20 enrichment pathways were mainly involved and (Table S4). http://links.lww.com/MD/N195 The top 10 ranked major pathways: ECM-receptor interaction, focal adhesion, human papillomavirus infection, PI3K-Akt signaling pathway, arrhythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, dilated cardiomyopathy, small cell lung cancer, protein digestion and absorption, and proteoglycans in cancer (Fig. 1D).
3.3. Construction of BM-related genes prognostic model
Univariate cox-regression analysis revealed a significant association between 25 DEGs and BC prognosis (Fig. 2A–C, see Table S5, supplementary material, http://links.lww.com/MD/N195 which shows univariate analysis). only 6 DEGs (AGRN, COL6A1, EFEMP1, GPC2, ITGA3, LAMA2) were identified as predictors after LASSO regression analysis (Fig. 2D and E). In addition, multivariate cox-regression analysis identified only 3 DEGs (EFEMP1[hazard ratio {HR}: 1.187, 95%CI: 1.088–1.296, P < .01], GPC2 [HR: 0.706, 95%CI: 0.550–0.907, P < .01], and ITGA3 [HR: 0.797, 95%CI: 0.716–0.888, P < .01]) used to construct prognostic models of BC patients (Fig. 2F, see Fig. S2, supplementary material, shows the KM survival analysis of 3 patient genes) http://links.lww.com/MD/N195. Following a median risk score, patients were categorized into high- and low-risk groups and subjected to a KM survival analysis (Table 1). The risk score was calculated as: Risk score = (0.171585498921286) * EFEMP1 + (−0.347995962870904) * GPC2 + (−0.226752721489659) * ITGA3. The KM survival curves showed poor prognostics for high-risk patients compared to low-risk patients (Fig. 3A). PCA, the results showed that patients in both high and low-risk groups had a significant dispersion (Fig. 3B). The risk of death increased with the increase in risk score, but patients’ survival time decreases continuously (Fig. 3C–E). The area under the curve (AUC) values for the ROC curve predicted 3-year and 5-year survival were 0.638 and 0.662, respectively (Fig. 3F).
Table 1
The correlations between risk groups and clinical parameters in the TCGA database.
Characteristic
Risk groups
P value
High
Low
Age
<.001
≤60
10 (13.5%)
38 (38.8%)
>60
64 (86.5%)
60 (61.2%)
Gender
.317
Female
20 (27.0%)
19 (19.4%)
Male
54 (73.0%)
79 (80.6%)
T_stage
.12
T1
0 (0%)
1 (1.0%)
T2
17 (23.0%)
38 (38.8%)
T3
46 (62.2%)
47 (48.0%)
T4
11 (14.9%)
12 (12.2%)
N_stage
<.001
N0
36 (48.6%)
82 (83.7%)
N1
14 (18.9%)
8 (8.2%)
N2
23 (31.1%)
7 (7.1%)
N3
1 (1.4%)
1 (1.0%)
M_stage
.246
M0
69 (93.2%)
96 (98.0%)
M1
5 (6.8%)
2 (2.0%)
Fustat
<.001
Alive
24 (32.4%)
72 (73.5%)
Death
50 (67.6%)
26 (26.5%)
TCGA = The Cancer Genome Atlas.
Figure 2.
Genes associated with BM and prognosis. (A) Univariate analysis result of a network correlations in the TCGA cohort (B) Frequencies of CNV gain, loss in BM-related gene. (C) Circus plots of chromosome distributions of BM-related gene. (D, E) LASSO regression analysis of the genes from univariate analysis. (F) Results of the multivariate analysis. BM = basement membrane, CNV = copy number variation, LASSO = Least absolute shrinkage and selection operator, TCGA = The Cancer Genome Atlas.
Figure 3.
Construction of the risk score. (A) Survival probability based on the high and low-risk group. (B) PCA analysis in patients with high and low-risk groups of TCGA database. (C, D) Distribution of the survival, risk score, and survival of patients with BC. (E) Heatmap of the expression of 3 BM-related genes in BC. (F) The ROC curve for the 3- and 5-yr overall survival. BC = bladder cancer, BM = basement membrane, PCA = principal components analysis, ROC = receiver operating characteristic, TCGA = The Cancer Genome Atlas.
3.4. Prediction model
In consideration of the clinical and pathological characteristics and risk score, a prediction nomogram was created. The risk score (HR = 2.062, 95%CI:1.597–2.663, P < .01), T-stage (HR = 1.629, 95%CI:1.153–2.302, P < .01), N-stage (HR = 1.577, 95%CI:1.243–2.001, P < .01) and gender (HR = 0.608, 95%CI:0.375–0.988, P = .044) were strongly correlated with prognosis, according to the univariate analysis (Fig. 4A). The risk score (HR = 1.818, 95%CI:1.315–2.513, P < .01), and gender (HR = 0.558, 95%CI:0.339–0.918, P < .05) were independent indicators that could accurately predict individuals with BC who would have a poor prognosis, according to multivariate analysis (Fig. 4B). The age, gender, N-stage, T-stage, M-stage, and risk score p values for the Schoenfeld individual test were, in order, 0.4444, 0.9848, 0.6198, 0.7672, 0.4514, and 0.7793. The global Schoenfeld test generated a P value of .6823 (see Table S6, supplementary material, http://links.lww.com/MD/N195 shows the global Schoenfeld test, see Fig. S3 supplementary material, http://links.lww.com/MD/N195 shows the Schoenfeld individual test). The results indicated that all of the factors were satisfied for the PH assumption. Compared to the other clinicopathological parameters, the predictive nomogram showed a higher ability to predict 3- and 5-year OS (Concordance = 0.697, Fig. 4C). The nomogram can reliably predict survival, according to the calibration curve for 1-, 3-, and 5-year survival (Fig. 4D). In addition, we did ROC curves for multiple clinical factors age, TNM staging, and risk score and calculated the area under the ROC curve (AUC) for 1-,3-, and 5 years. We found that the AUC values of risk score and other clinical factors were compared and found significantly higher AUC values for the mentioned risk score, which implies that risk score is a good prognostic predictor (Fig. 4E–G).
Figure 4.
Model predictions. (A) Univariate analysis of the clinicopathologic features and the risk score. (B) Multivariate analysis of the clinicopathologic features and the risk score. (C) Nomogram to predict the survival of the BC patients. (D) Calibration curve for 1-, 3-, and 5-yr survival. (E-G) The ROC curves for multiple clinical factors and risk scores compared for 1-, 3-, and 5-yr OS in BC. BC = bladder cancer, OS = overall survival, ROC = receiver operating characteristic
3.5. Validation of risk score and immunohistochemical analysis
The validity of the model was verified by GEO validation data. The validation dataset KM survival curve revealed that high-risk groups had a noticeably worse prognosis than low-risk groups (see Fig. S4A, supplementary material, survival probability based on the high and low-risk group) http://links.lww.com/MD/N195. In the validation dataset, 3 prognostic genes showed significant expression. The GEO validation dataset showed a drop in survival time but an increase in death risk with a higher risk score, similar to the TCGA dataset (see Fig. S4B–D, supplementary material, which shows the validation of the risk score) http://links.lww.com/MD/N195. The ROC curve areas for predicting OS patient at 3 and 5 years were 0.698 and 0.709 (see Fig. S4E, supplementary material, which shows the area under the OR curve). http://links.lww.com/MD/N195 BC patients of high and low-risk groups could be completely separated by the PCA (see Fig. S4F, supplementary material, http://links.lww.com/MD/N195 which shows the PCA analysis) based on these 3 BM-related gene with differential mRNA levels. Immunohistochemical staining showed that the GPC2 and ITG3 genes were predominantly expressed in the cytoplasm and cell membrane and were more highly expressed in BC samples than in normal samples. The EFEMP1 gene was predominantly expressed in the cytoplasm and nucleus and was also more highly expressed in BC samples than in normal samples (Fig. 5A, see Table S7, supplementary material, http://links.lww.com/MD/N195 which shows immunohistochemical staining results).
Figure 5.
The 3 genes of immunohistochemical staining expression and expression of single-cell RNA types. (A) Expression of 3 genes in normal and tumor sample in the BC. (B) Gene expression in 3 bladder cancer single-cell RNA samples. (C-E) Tthe 3 genes of expression of single-cell RNA types. BC = bladder cancer.
3.6. Single-cell RNA-Seq data analysis
To move forward investigate the identification of 3 BM-related genes in BC cells and determine their expression in specific cell types, we analyzed single-cell data (Fig. 5B). The findings showed that single-cell analysis was unable to detect the expression of the GPC2 gene, and that ITG3 was mostly expressed in epithelial cells and fibroblasts. (Fig. 5C–E).
3.7. Relationship between BM-related gene signature and immune cell infiltration
Using the CIBERSORT algorithm estimation, we assessed the infiltration of the 22 different types of immune cells in the TCGA data and found B cells naïve, Macrophages M2, Dendritic cells resting, Dendritic cells activated, Mast cells resting were significantly different between the high- and low-risk groups (Fig. 6A). the correlation between risk score and immune cell abundance. As depicted in Figure 6B to I, the risk score was positively correlated with B cell naïve, Macrophages M2, Mast cells resting, while risk score showed the opposite relationship with follicular Dendritic cells activated, Dendritic cells resting, NK cells resting and T cells follicular helper. The ESTIMATE score analysis result showed stromal score, immune score, and ESTIMATE scores were significantly higher in the high-risk group (Fig. 6J). Next, the relationship between ICPs and this prognostic feature was evaluated. Figure 6K shows that 30 ICPs behave differently in 2 risk subgroups, PD-1, PDL-1, and CTLA4. In addition, the relationship between ISCs and high and low-risk groups was evaluated and IL10, IL13, TGFB2 and TGFB3 were found to be statistically different in the 2 groups (see Fig. S5, supplementary material, http://links.lww.com/MD/N195 which shows expression of ISCs in the high and low-risk groups).
Figure 6.
Evaluation of the TME and checkpoints. (A) Relation of the immune cells with a risk score. (B-I) Correlations between risk score and immune cell types. (J) Correlations between risk score and both immune and stromal scores. (K) Correlations between the ICPs in the high and low-risk groups (P < .05 *; P < .01 **; P < .001 ***). ICPs = immune checkpoints, TME = tumor microenvironment.
3.8. Cancer-related gene mutation and drug sensitivity analysis
To evaluate differences in associated with cancer mutations comparing high-risk and low-risk groups, we first estimated the mutation frequency of each group genes. Figure 7A to B describe in general the representative gene mutations found in all of the groups. Genes such as TP53 (53%), TNN (42%), KMT2D (27%), MUC16(27%), and ARID1A (26%) had the top 5 mutation frequencies in the high-risk group. TP53 (45%), TNN (46%), KMT2D (26%), MUC16(24%), and ARID1A (23%) consisted among the top 5 genes in the low-risk category with the greatest frequency of mutations. The TCGA dataset estimation of the chemotherapy response in the 2 distinct groups of risks. We identified BC most often used medications, such as Cisplatin, Gemcitabine, Camptothecin, Doxorubicin, Thapsigargan, and Methotrexate. The results show that the IC50 of Cisplatin, Gemcitabine, and Methotrexate was considerably greater within the high-risk group from the TCGA dataset than that observed in the low-risk group (Fig. 6C–E), This indicates that individuals in both categories can respond differently to these chemotherapeutic drugs. Likewise, the variations in IC50 for camptothecin, Doxorubicin, Thapsigargan, and Vinblastine in both groups were not significant (see Fig. S6, supplementary material, shows the risk score and chemotherapeutic sensitivity) http://links.lww.com/MD/N195.
Figure 7.
The gene mutation, drug sensitivity and GSEA analysis. (A, B) The waterfall plot of top 15 gene somatic mutation features established with high and low-risk groups. (C-E) Relationships between risk score and chemotherapeutic sensitivity. (F) The top 10 pathways of GSEA enrichment analysis in the high-risk groups. (G) The top 10 pathways of GSEA enrichment analysis in the low-risk groups.
3.9. GSEA enrichment analysis
To investigate any variations in signaling pathways comparing the 2 patient groups that have different risk. GSEA was performed. Our results demonstrated that the majority of high-risk patients enriched in metabolic signaling pathways (Fig. 7F), such as glycine serine and threonine metabolism, nicotinate and nicotinamide metabolism, purine metabolism, pyrimidine metabolism, and tryptophan metabolism. Moreover, the low-risk group was enriched with ABC transporters, alpha-linolenic acid metabolism, basal cell carcinoma, ether lipid metabolism, glycosylphosphatidylinositol GPI anchor biosynthesis, hedgehog signaling pathway, histidine metabolism, other glycan degradation, steroid hormone biosynthesis, TGF beta signaling pathway (Fig. 7G).
4. Discussion
BC is one of the most lethal tumors in man.[15,16] Although diagnostic and treatment techniques have advanced, the overall incidence of BC has been progressively increasing.[17] it remains a disease on which human beings need concentrate. BM cleavage is critical for tumor invasion and progression, clearing the way for tumor breakthroughs and crossing mechanical barriers in the first place.[18] The early phases of malignant epithelial transition in the BC are marked by deformation of normal structures and loss of cell polarity, which is connected with tumor aggressiveness.[19,20] This is caused by changes to the BM protein half-chromosomes and receptors, which are found in the membranes of malignant cells. Altered integrins lose their role as determinants and maintainers of tissue integrity and facilitate further progression. These receptors are reassigned to membrane protrusions in order to promote adhesion, migration, and survival in invading tissues.[21]
In the present investigation, we determined the transcriptional alterations and expression of BM-based genes in the TCGA-BLCA cohort. Our findings revealed that prognostic models based on EFEMP1, GPC2 and ITGA3 could reliably forecast BC patients’ prognosis, and constructed correlation models and risk scores for prognosticating breast cancer, which will aid in the comprehension of the disease molecular pathophysiology and improve comprehension of diagnosis, therapy, and prognosis. The fact that individuals with a high BM score also had an unfavorable OS is noteworthy, as it implies that a high BM score could be a predictor of an unfavorable outcome. BM is connected to the harmful actions of several tumors.
The epidermal growth factor-containing fibulin-like extracellular matrix protein 1 (EFEMP1) high expression correlated with adverse pathologic characteristics of UC and independently predicted adverse outcomes.[22] EFEMP1 promotes angiogenesis and promotes cervical cancer growth through VEGF upregulation.[23] GPC2 is a prognostic marker for numerous cancers and an adjuvant to early tumor diagnosis.[24] Researchers found that GPC2 expression was upregulated in prostate cancer and small cell lung cancer suggesting poor patient prognosis.[25,26] Furthermore, experience suggests that high expression of GPC2 in duct adenocarcinoma after pancreaticoduodenectomy may lead to favorable prognosis.[27] One study found that by stimulating the miR-1184/ITGA3 axis, circBC048201 encourages the growth, migration, and invasion of BC cells.[28] In breast cancer, ITGA3 is a novel biomarker to evaluate breast cancer diagnosis and prognosis. In addition, ECM modulation and immune cell infiltration are mediated by ITGA3.[29] MiR-223 downregulation improved ITGA3/ITGB1 signaling and aided in PCa cells’ ability to migrate and invade cancer cells.[30] Our immunohistochemical staining showed that these 3 genes were expressed higher in bladder tumor samples than in normal samples. The findings of the studies we mentioned above support our results.
The application of risk scores to predict the prognosis of prognostic tumors has been shown,[31,32] and In BC patients, our risk score was substantially correlated with survival. The findings imply that among BC patients, the risk score is a reliable indicator of survival outcomes. Its 3- and 5-year OS prediction robustness was proven using ROC. Furthermore. univariate and multifactorial cox-regression analyses of BC pathological clinical characteristics and risk scores were significantly correlated. The multifactor ROC curve area was predictive of 1-, 3-, and 5-year. Consequently, risk ratings might be a good indicator of a patient prognosis. Finally, the risk score prognosis was validated using the GEO dataset, and the outcomes demonstrated that the risk score could predict prognosis with accuracy. We, therefore, think that this could be a novel, highly accurate predictive model.
Immune cell infiltration is associated with BC occurrence, prognosis, and treatment. For a number of years, immunotherapy has considerably extended the period that patients with advanced BC survive,[33] The immune system and stromal cells are essential elements of the tumor microenvironment, and immune and stromal scores associated with clinical characteristics and prognosis in BC.[34,35] The stromal and immunological scores of the high-risk group were greater than those of the low-risk group, according to our calculations using the ESTIMATE program execution. This suggested that BM could be associated with tumor microenvironment involvement to regulate neoplastic invasion and migration, disease progression. Treatment for clear cell renal carcinoma includes ICP inhibition, and progress in this area is still happening quickly.[36,37] ICP analysis showed that most high-risk groups were significantly higher than low-risk groups, which may suggest a correlation between BM and ICP inhibitor therapy, such as for advanced bladder urothelial carcinoma, either with or without active PI3K pathway, the combination of ICB with Pan-PI3K inhibition exerts strong antitumor effects.[38] study shows that muscle-invasive urothelial carcinoma treated with ICP inhibitor nivolumab is more beneficial in disease-free survival patients compared to controls.[39] Cisplatin, Gemcitabine, and Methotrexate which are chemotherapeutic agents for oncology, are standard treatments for patients with advanced tumors and can improve survival and progression in BC patients.
The PI3K-Akt signaling pathway and ECM-receptor interaction are linked to the results of the functional enrichment analysis of the BMs-related genes using KEGG. Receptor interaction has been shown to have a critical role in the development of cancer and the production of metastases.[40,41] Studies have also been reported in BC the ECM-receptor interaction pathway plays a role in the development of BC and may be involved in the progression of BC.[42] An essential signaling pathway in tumors is the PI3K-Akt pathway, and a variety of tumor development is associated with this pathway,[43–45] and has been demonstrated to play a part in the onset of BC in BC.[46,47]
This study has several limitations. First, we did not perform in vivo and in vitro experiments to validate the results obtained from our study. Second, we did not validate BC specimens and additional in vivo and in vitro studies will be needed to confirm our findings. Despite these limitations, we believe that our results will be useful and informative for future studies on BM-related genes in BC.
5. Conclusions
It is possible that EPEMP1, GPC2, and ITGA3 are predictive biomarkers that are useful and that have a significant role in immune cell infiltration in people with BC. Further clinical research on this biomarker model is necessary to confirm its clinical relevance.
Acknowledgments
We acknowledge TCGA and GEO database for providing its platforms.
Author contributions
Conceptualization: Lin He, Zhen Yang.
Data curation: Fei Lai, Zhen Yang, Chaoyou Huang.
Formal analysis: Thongher Lia.
Investigation: Lin He.
Methodology: Lin He, Thongher Lia, Chaoyou Huang.
Project administration: Fei Lai, Thongher Lia, Chaoyou Huang.
Resources: Chaoyou Huang.
Software: Lin He, Zhen Yang.
Validation: Lin He, Zhen Yang.
Writing – original draft: Fei Lai, Thongher Lia, Zhen Yang, Chaoyou Huang.
Writing – review & editing: Fei Lai.
Supplementary Material |
Title: SOX2, JAGGED1, β-Catenin, and Vitamin D Receptor Expression Patterns during Early Development and Innervation of the Human Inner Ear | Body:
1. Introduction
Hearing loss is one of the most common disabilities and affects approximately 360 million people worldwide [1]. It can be divided into sensorineural, conductive, or mixed. Sensorineural hearing loss is the most common type and can be caused by lesions in the inner ear, cochlear (auditory) nerve, or neurons of the auditory pathway [2]. Congenital hearing loss may stem from environmental factors such as congenital infections, while genetic factors likely predominate in developed countries [3].
Human inner ear development begins at 4 weeks after fertilization (W) with the appearance of otic placodes at the level of the hindbrain [4]. The otic placode invaginates and forms the otocyst, which gives rise to the entire membranous labyrinth of the inner ear. The dorsal part of the otocyst will form the utricle and semicircular ducts, while the ventral part will become the saccule and cochlear duct [5]. The anteromedial region of the otocyst is characterized by SRY-box 2 (SOX2) expression and represents the proneurosensory domain. As neural development begins, prospective neuroblasts from this domain will delaminate and differentiate into bipolar neurons which will form the vestibulocochlear ganglion (VCG) [6]. After neuroblast delamination, the SOX2-positive region is referred to as the prosensory domain and contains precursors for hair cells (HCs), the sensory cells of the inner ear [7]. Specification of the prosensory domain relies on lateral induction mediated by JAGGED1. JAGGED1 induces its own expression in adjacent cells and the expression of SOX2, resulting in the homogenous commitment of otic progenitors to the prosensory fate [8]. Interestingly, there seems to exist an interspecies difference between mice and primates regarding the expression of these factors [9]. While JAGGED1 is expressed early in the mouse inner ear and is essential for SOX2 expression [10], in the common marmoset, SOX2 expression precedes JAGGED1 expression [11]. The relationship between the expression patterns of SOX2 and JAGGED1 has not been described during early human inner ear development. Under the effect of WNT and fibroblast growth factor signaling, cells of the prosensory domain exit the cell cycle and begin expressing Atoh1 and other NOTCH ligands, such as JAGGED2 and DELTA-LIKE 1 [12]. Atoh1 has a key role in HC differentiation, maturation, and survival, while the NOTCH ligands suppress HC fate in adjacent cells through lateral inhibition, resulting in the differentiation of supporting cells [13]. The differentiation of HCs begins between 7 and 8 W in the vestibular maculae [14] and at 8 W in the basal turn of the cochlear duct [15], becoming visible by light microscopy between 10 and 12 W [16]. With the differentiation of HCs, the sensory domains are formed in distinct regions of the membranous labyrinth, three cristae ampullares in the semicircular ducts, two maculae in the utricle and saccule, and the organ of Corti in the cochlear duct [17].
Several investigators showed that VCG neurons send their peripheral processes back to the sensory domains from which they originated from and form synapses with HCs [18,19,20]. Spiral ganglion neurons and their processes are surrounded by peripheral glial cells at 7 W [21]. A light microscopy study described the presence of nerve fibers in the human inner ear epithelium around 4 W [22], but this was refuted by other studies [23,24]. Electron microscopy studies have demonstrated nerve fibers in the macular epithelium at 7 W [25] and penetrating the basal turn of the cochlear duct at 9 W [26,27]. The nerve fibers reach the HCs of the cochlear duct between 11 and 12 W [16] and become abundant by 13 W [28]. The supporting cells secrete ATP, neurotrophin-3, and brain-derived neurotrophic factor, which engage in reciprocal interactions with the HCs and VCG neurons, regulating the formation of synaptic connections [29]. HCs do not require innervation for differentiation and survival as loss of synaptic formation does not alter the structure of inner ear sensory epithelia [30]. Furthermore, the differentiation of epithelial cells in the sensory and non-sensory components of the inner ear occurs simultaneously and is driven by a specific set of genes and transcription factors [31].
WNT signaling is important in the development of the inner ear where it participates in otic specification, the formation of vestibular structures, and the development of the cochlea. The canonical WNT pathway acts through FRIZZLED receptors, with β-catenin (CTNNB1) serving as a major second messenger, culminating in the transcriptional activation of genes [32]. CTNNB1 is normally expressed in the membranes of epithelial cells; however, it can also be ectopically expressed in the cytoplasm and/or nucleus, which marks the activation of canonical WNT signaling [33].
The vitamin D receptor (VDR) is a member of the nuclear receptor superfamily that is highly conserved within vertebrates. It is located predominantly in the cytoplasm; however, upon interacting with the active form of vitamin D, it forms heterodimers with the retinoid X receptor and translocates to the nucleus. After binding with various transcription factors VDR causes up- or downregulation of numerous genes controlled by vitamin D [34,35]. It was described that VDR can upregulate the expression of brain natriuretic peptide which promotes VCG neuron survival and neurite growth in the rat inner ear [36]. Also, loss of VDR expression is associated with early-onset sensorineural hearing loss and vestibular dysfunction in mice [37,38]. Several studies have shown an association between vitamin D deficiency and sensorineural hearing loss and vestibular disorders in humans [39,40,41]. Two studies have even found that treatment outcomes of hearing loss correlate with vitamin D serum levels [42,43]. It is also interesting to note that vitamin D-mediated signaling displays mutual antagonism with canonical WNT signaling in cancers [44]; however, their relationship and expression patterns during inner ear development have not been explored.
While the innervation and role of canonical WNT signaling during mammalian inner ear development have been extensively studied on mouse models, studies examining human inner ear development are sparse, particularly during the earliest stages. The expression of VDR during human inner ear development has not been described to the best of the authors’ knowledge, while animal models demonstrate that VDR deficiency leads to inner ear defects. Therefore, the aim of this study was to analyze the innervation and distribution of SOX2, JAGGED1, CTNNB1, and VDR during early human inner ear development. Additionally, we aimed to explore the relationship between SOX2 and JAGGED1, as well as CTNNB1 and VDR, by comparing their expression patterns throughout the analyzed developmental stages.
2. Results
Sections of human conceptuses between 5 and 10 W were analyzed to determine the innervation, SOX2, JAGGED1, CTNNB1, and VDR expression in developing inner ears. The cochlear region (developing cochlear duct) and vestibular apparatus (developing utricle, saccule, and semicircular ducts) were analyzed separately to determine the depth of innervation and protein expression. DAPI staining was used to visualize all cell nuclei.
2.1. Morphology of the Developing Human Inner Ear
During the 5th W, the inner ear is in the stage of otic vesicle (otocyst), which shows initial signs of elongation (to form vestibular region dorsally and cochlear region ventrally). The endolymphatic duct extends from the otocyst, approaching the nearby hindbrain which is surrounded by the head mesenchyme (Figure 1A). During further development, the semicircular ducts grow from the utricle and the cochlear duct from the saccule, while the surrounding mesenchyme shows initial signs of chondrification (Figure 1B,C). In the 7th and 8th W, the cochlear duct is coiling and the epithelial cells in its wall will gradually differentiate into the organ of Corti, while the ganglion cells of the 8th nerve form the spiral ganglion. The cartilaginous otic capsule surrounds the cochlear duct (Figure 1D). Further differentiation is also observed in the region of developing semicircular ducts, the junction between the utricle and saccule, and in the developing maculae (Figure 1E,F). In the 9th and 10th W of human inner ear development, the cartilaginous otic capsule is well defined, and contains the coiled cochlear duct and spiral ganglion cells which approach the ventral wall of cochlear duct (Figure 1G). The cochlear duct is continuous with the nearby saccule (Figure 1H). Communication of the utricle and saccule, with mutually perpendicular maculae and semicircular ducts close to the utricle are also enclosed by a cartilaginous shell. Vacuolization is observed in the surrounding mesenchyme, which will gradually develop into the perilymphatic space (Figure 1I).
2.2. SOX2 and JAGGED1 Expression Characterizes Prosensory Domains of the Developing Human Inner Ear
SOX2 expression characterized prosensory domains in the developing cochlear duct and vestibular apparatus, which will eventually become the organ of Corti, maculae, and cristae ampullares. SOX2 expression was predominately nuclear, with some cells displaying intense cytoplasmic staining. JAGGED1 membranous expression was co-localized with SOX2 expression in the developing inner ear at 5–6 W (Figure 2A). The expression pattern of SOX2 was more extensive compared to JAGGED1. The same pattern was observed at 7–8 W (Figure 2B) and 9–10 W (Figure 2C,D), with SOX2 expression being consistently more extensive. The membranous staining pattern of JAGGED1 was present in both vestibular and cochlear prosensory regions of the developing inner ear, without visible significant differences (Figure 2C,D). Some cells of the spiral ganglion also displayed SOX2 nuclear staining; however, they were negative for JAGGED1 (Figure 2D). These cells likely represent developing glial (satellite) cells.
2.3. Innervation of the Developing Human Inner Ear
Acetylated-α-tubulin (Ac-TUB α) was used to display nerve fibers as it is only present in structures with stabilized microtubules, such as axons, while JAGGED1 marked the prosensory domains of the developing cochlear duct and vestibular apparatus. At 5–6 W, no nerve fibers were present in the cochlear duct (Figure 3A), while the prospective maculae of the vestibular apparatus had some nerve fibers present in the basal half of the epithelium (Figure 3B). At 8 W, the nerve fibers had reached the cochlear duct epithelium (Figure 3C). At the same timepoint, the developing maculae were more densely innervated, and the nerve fibers reached the basal surfaces of differentiating HCs (Figure 3D). At 9–10 W, the developing cochlear duct still had nerve fibers present mostly at the basal surface of the epithelium (Figure 3E), while the vestibular maculae were densely innervated with nerve fibers surrounding the HCs (Figure 3F). Strong Ac-TUB-α immunoexpression was also observed in the apical region of the cochlear duct prosensory domain at 9–10 W, probably representing the processes of developing phalangeal and pillar cells (Figure 3E). When analyzing the maximum depth of innervation, a significant linear trend among developmental periods was found for both the cochlear (R2 = 26.03%, β = 0.358 ± 0.290) and vestibular (R2 = 47.40%, β = 8.294 ± 4.196) prosensory domains (Figure 3G). The innervation of the vestibular apparatus was more extensive (p < 0.0001) compared to the cochlear duct throughout the entire analyzed period (Figure 3H).
2.4. CTNNB1 Expression in the Developing Human Inner Ear
At 5–6 W, CTNNB1 displayed a mostly membranous staining pattern in the epithelium of the developing cochlear duct, meaning that canonical WNT signaling is not active in the majority of cochlear duct cells at this stage (Figure 4A). The cochlear floor region displayed little to no CTNNB1 staining in the SOX2-positive area (Figure 4B), with more apparent membranous staining laterally near the outer sulcus (Figure 4C). Nuclear CTNNB1 staining was visible in the SOX2-positive area on the medial border of the floor region (Figure 4D) and in the epithelium of the roof region (Figure 4E), indicating active canonical WNT signaling in these regions. At 7–8 W, the expression pattern was similar to 5–6 W (Figure 4F). The floor regions containing the SOX2-positive prosensory domain displayed mostly membranous CTNNB1 staining (Figure 4G,H), while nuclear CTNNB1 staining and active canonical WNT signaling characterized the epithelial cells of the roof region and the border between the two regions (Figure 4I,J). At 9–10 W, most cells of the cochlear duct had a membranous CTNNB1 staining pattern, indicating that canonical WNT signaling is not active (Figure 4K–O). The vestibular regions at 5–6 W contained both cells displaying membranous and nuclear CTNNB1 expression (Figure 5A). The maculae and developing cristae ampullares (SOX2-positive) displayed membranous staining (Figure 5B–D), while nuclear expression could be seen in the transitional and dark cell epithelium (Figure 5C), as well as in the endolymphatic duct (Figure 5E), indicating that canonical WNT signaling is active in these regions. A similar pattern was seen at 7–8 W (Figure 5F), with membranous CTNNB1 staining present in the maculae (Figure 5G,H) and nuclear expression in the non-sensory area near the epithelial fold (Figure 5I) and in the semicircular ducts (Figure 5J). Strong membranous CTNNB1 staining was visible at 9–10 W (Figure 5K), especially in the maculae (Figure 5L) and dark cell epithelium (Figure 5O). Nuclear CTNNB1 expression was present in some but not all the epithelial folds (Figure 5M,N). Many cells of the periotic mesenchyme around the cochlear duct and vestibular apparatus showed nuclear CTNNB1 expression in all the observed developmental periods (Figure 4 and Figure 5). Therefore, active canonical WNT signaling is mostly present in the non-sensory areas of the inner ear epithelium and the periotic mesenchyme, while the SOX2-positive prosensory domains show no active canonical WNT signaling during early development. Analysis of the area percentage of CTNNB1 expression did not reveal a significant linear trend among developmental periods in the cochlear duct (R2 = 0.12%, β = −0.078 ± 0.859), while the expression in the vestibular epithelium showed a quadratic trend, with a nadir in expression at 7–8 W (R2 = 11.15%) (Figure 6A). The vestibular regions displayed significantly higher CTNNB1 expression than the developing cochlear duct (p = 0.0151) when analyzing across all observed timepoints (Figure 6B).
2.5. VDR Expression in the Developing Human Inner Ear
At 5–6 W, VDR expression was present in the epithelium of the developing cochlear duct (Figure 7A). The cochlear floor region displayed mostly membranous/cytoplasmatic VDR staining, while nuclear staining could be seen in some SOX2-positive and SOX2-negative cells (Figure 7B,C). VDR expression in the floor region was higher near the border with the roof region (Figure 7C,D), and the highest expression was observed in the roof region (Figure 7E). At 7–8 W, the expression pattern was similar to 5–6 W (Figure 7F), with the floor regions having some cells with nuclear VDR staining, both within and outside the prosensory domain (Figure 7G,H). The expression of VDR was generally higher in the roof regions (Figure 7I,J). The same expression pattern was present at 9–10 W (Figure 7K–O). The vestibular regions at 5–6 W had both membranous/cytoplasmatic and nuclear VDR expression (Figure 8A). The cells of the maculae and developing cristae ampullares (SOX2-positive) displayed both staining patterns (Figure 8B–D), and the same was true for the non-sensory transitional and dark cell epithelium areas (Figure 8D,E). A similar pattern was seen at 7–8 W (Figure 8F) with VDR staining being stronger in the non-sensory dark cell epithelium compared to the prosensory domains (Figure 8G–I), with the exception of a forming epithelial fold that showed weaker VDR expression (Figure 8J). Strong VDR staining was visible at 9–10 W in the dark cell epithelium (Figure 8K). Nuclear VDR expression was present in the maculae (Figure 8L) and some cells of the epithelial folds, while most cells of the folds were devoid of VDR expression (Figure 8M,N). Nuclear VDR staining was present in the dark cell epithelium (Figure 8O). VDR staining was seldom present in cells of the periotic mesenchyme, mostly at 5–6 W, while a majority of the cells showed no VDR expression (Figure 7 and Figure 8). Analysis of the area percentage of VDR expression showed a significant linear trend among developmental periods in the cochlear duct (R2 = 32.79%, β = 0.224 ± 0.120), while the expression in the vestibular epithelium showed a quadratic trend, with a peak in expression at 7–8 W (R2 = 12.46%) (Figure 6C). The vestibular region had significantly higher VDR expression than the developing cochlear duct (p = 0.0348) when analyzing across all observed timepoints (Figure 6D).
2.1. Morphology of the Developing Human Inner Ear
During the 5th W, the inner ear is in the stage of otic vesicle (otocyst), which shows initial signs of elongation (to form vestibular region dorsally and cochlear region ventrally). The endolymphatic duct extends from the otocyst, approaching the nearby hindbrain which is surrounded by the head mesenchyme (Figure 1A). During further development, the semicircular ducts grow from the utricle and the cochlear duct from the saccule, while the surrounding mesenchyme shows initial signs of chondrification (Figure 1B,C). In the 7th and 8th W, the cochlear duct is coiling and the epithelial cells in its wall will gradually differentiate into the organ of Corti, while the ganglion cells of the 8th nerve form the spiral ganglion. The cartilaginous otic capsule surrounds the cochlear duct (Figure 1D). Further differentiation is also observed in the region of developing semicircular ducts, the junction between the utricle and saccule, and in the developing maculae (Figure 1E,F). In the 9th and 10th W of human inner ear development, the cartilaginous otic capsule is well defined, and contains the coiled cochlear duct and spiral ganglion cells which approach the ventral wall of cochlear duct (Figure 1G). The cochlear duct is continuous with the nearby saccule (Figure 1H). Communication of the utricle and saccule, with mutually perpendicular maculae and semicircular ducts close to the utricle are also enclosed by a cartilaginous shell. Vacuolization is observed in the surrounding mesenchyme, which will gradually develop into the perilymphatic space (Figure 1I).
2.2. SOX2 and JAGGED1 Expression Characterizes Prosensory Domains of the Developing Human Inner Ear
SOX2 expression characterized prosensory domains in the developing cochlear duct and vestibular apparatus, which will eventually become the organ of Corti, maculae, and cristae ampullares. SOX2 expression was predominately nuclear, with some cells displaying intense cytoplasmic staining. JAGGED1 membranous expression was co-localized with SOX2 expression in the developing inner ear at 5–6 W (Figure 2A). The expression pattern of SOX2 was more extensive compared to JAGGED1. The same pattern was observed at 7–8 W (Figure 2B) and 9–10 W (Figure 2C,D), with SOX2 expression being consistently more extensive. The membranous staining pattern of JAGGED1 was present in both vestibular and cochlear prosensory regions of the developing inner ear, without visible significant differences (Figure 2C,D). Some cells of the spiral ganglion also displayed SOX2 nuclear staining; however, they were negative for JAGGED1 (Figure 2D). These cells likely represent developing glial (satellite) cells.
2.3. Innervation of the Developing Human Inner Ear
Acetylated-α-tubulin (Ac-TUB α) was used to display nerve fibers as it is only present in structures with stabilized microtubules, such as axons, while JAGGED1 marked the prosensory domains of the developing cochlear duct and vestibular apparatus. At 5–6 W, no nerve fibers were present in the cochlear duct (Figure 3A), while the prospective maculae of the vestibular apparatus had some nerve fibers present in the basal half of the epithelium (Figure 3B). At 8 W, the nerve fibers had reached the cochlear duct epithelium (Figure 3C). At the same timepoint, the developing maculae were more densely innervated, and the nerve fibers reached the basal surfaces of differentiating HCs (Figure 3D). At 9–10 W, the developing cochlear duct still had nerve fibers present mostly at the basal surface of the epithelium (Figure 3E), while the vestibular maculae were densely innervated with nerve fibers surrounding the HCs (Figure 3F). Strong Ac-TUB-α immunoexpression was also observed in the apical region of the cochlear duct prosensory domain at 9–10 W, probably representing the processes of developing phalangeal and pillar cells (Figure 3E). When analyzing the maximum depth of innervation, a significant linear trend among developmental periods was found for both the cochlear (R2 = 26.03%, β = 0.358 ± 0.290) and vestibular (R2 = 47.40%, β = 8.294 ± 4.196) prosensory domains (Figure 3G). The innervation of the vestibular apparatus was more extensive (p < 0.0001) compared to the cochlear duct throughout the entire analyzed period (Figure 3H).
2.4. CTNNB1 Expression in the Developing Human Inner Ear
At 5–6 W, CTNNB1 displayed a mostly membranous staining pattern in the epithelium of the developing cochlear duct, meaning that canonical WNT signaling is not active in the majority of cochlear duct cells at this stage (Figure 4A). The cochlear floor region displayed little to no CTNNB1 staining in the SOX2-positive area (Figure 4B), with more apparent membranous staining laterally near the outer sulcus (Figure 4C). Nuclear CTNNB1 staining was visible in the SOX2-positive area on the medial border of the floor region (Figure 4D) and in the epithelium of the roof region (Figure 4E), indicating active canonical WNT signaling in these regions. At 7–8 W, the expression pattern was similar to 5–6 W (Figure 4F). The floor regions containing the SOX2-positive prosensory domain displayed mostly membranous CTNNB1 staining (Figure 4G,H), while nuclear CTNNB1 staining and active canonical WNT signaling characterized the epithelial cells of the roof region and the border between the two regions (Figure 4I,J). At 9–10 W, most cells of the cochlear duct had a membranous CTNNB1 staining pattern, indicating that canonical WNT signaling is not active (Figure 4K–O). The vestibular regions at 5–6 W contained both cells displaying membranous and nuclear CTNNB1 expression (Figure 5A). The maculae and developing cristae ampullares (SOX2-positive) displayed membranous staining (Figure 5B–D), while nuclear expression could be seen in the transitional and dark cell epithelium (Figure 5C), as well as in the endolymphatic duct (Figure 5E), indicating that canonical WNT signaling is active in these regions. A similar pattern was seen at 7–8 W (Figure 5F), with membranous CTNNB1 staining present in the maculae (Figure 5G,H) and nuclear expression in the non-sensory area near the epithelial fold (Figure 5I) and in the semicircular ducts (Figure 5J). Strong membranous CTNNB1 staining was visible at 9–10 W (Figure 5K), especially in the maculae (Figure 5L) and dark cell epithelium (Figure 5O). Nuclear CTNNB1 expression was present in some but not all the epithelial folds (Figure 5M,N). Many cells of the periotic mesenchyme around the cochlear duct and vestibular apparatus showed nuclear CTNNB1 expression in all the observed developmental periods (Figure 4 and Figure 5). Therefore, active canonical WNT signaling is mostly present in the non-sensory areas of the inner ear epithelium and the periotic mesenchyme, while the SOX2-positive prosensory domains show no active canonical WNT signaling during early development. Analysis of the area percentage of CTNNB1 expression did not reveal a significant linear trend among developmental periods in the cochlear duct (R2 = 0.12%, β = −0.078 ± 0.859), while the expression in the vestibular epithelium showed a quadratic trend, with a nadir in expression at 7–8 W (R2 = 11.15%) (Figure 6A). The vestibular regions displayed significantly higher CTNNB1 expression than the developing cochlear duct (p = 0.0151) when analyzing across all observed timepoints (Figure 6B).
2.5. VDR Expression in the Developing Human Inner Ear
At 5–6 W, VDR expression was present in the epithelium of the developing cochlear duct (Figure 7A). The cochlear floor region displayed mostly membranous/cytoplasmatic VDR staining, while nuclear staining could be seen in some SOX2-positive and SOX2-negative cells (Figure 7B,C). VDR expression in the floor region was higher near the border with the roof region (Figure 7C,D), and the highest expression was observed in the roof region (Figure 7E). At 7–8 W, the expression pattern was similar to 5–6 W (Figure 7F), with the floor regions having some cells with nuclear VDR staining, both within and outside the prosensory domain (Figure 7G,H). The expression of VDR was generally higher in the roof regions (Figure 7I,J). The same expression pattern was present at 9–10 W (Figure 7K–O). The vestibular regions at 5–6 W had both membranous/cytoplasmatic and nuclear VDR expression (Figure 8A). The cells of the maculae and developing cristae ampullares (SOX2-positive) displayed both staining patterns (Figure 8B–D), and the same was true for the non-sensory transitional and dark cell epithelium areas (Figure 8D,E). A similar pattern was seen at 7–8 W (Figure 8F) with VDR staining being stronger in the non-sensory dark cell epithelium compared to the prosensory domains (Figure 8G–I), with the exception of a forming epithelial fold that showed weaker VDR expression (Figure 8J). Strong VDR staining was visible at 9–10 W in the dark cell epithelium (Figure 8K). Nuclear VDR expression was present in the maculae (Figure 8L) and some cells of the epithelial folds, while most cells of the folds were devoid of VDR expression (Figure 8M,N). Nuclear VDR staining was present in the dark cell epithelium (Figure 8O). VDR staining was seldom present in cells of the periotic mesenchyme, mostly at 5–6 W, while a majority of the cells showed no VDR expression (Figure 7 and Figure 8). Analysis of the area percentage of VDR expression showed a significant linear trend among developmental periods in the cochlear duct (R2 = 32.79%, β = 0.224 ± 0.120), while the expression in the vestibular epithelium showed a quadratic trend, with a peak in expression at 7–8 W (R2 = 12.46%) (Figure 6C). The vestibular region had significantly higher VDR expression than the developing cochlear duct (p = 0.0348) when analyzing across all observed timepoints (Figure 6D).
3. Discussion
Our study focused on the innervation of the prosensory domains, as well as the expression of Sox2, JAGGED1, CTNNB1, and VDR in the developing human inner ear. We have analyzed samples from conceptuses aged 5 to 10 W in order to determine when innervation begins and how it proceeds during early development. We have also described the spatiotemporal expression pattern and mutual relationship of SOX2, JAGGED1, CTNNB1, and VDR.
In our samples, SOX2 and JAGGED1 had similar expression patterns in the cells of the developing inner ear as were described in other studies [7,8,45]. These cells belong to the prosensory domain from which HCs will develop. Although SOX2 is a transcription factor, its expression in the cytoplasm has been described in both embryonic and cancer stem cells [46,47]. Acetylation of SOX2 has been described as a post-translational regulatory mechanism that causes nuclear export, accumulation in the cytoplasm, and subsequent proteosomal degradation, leading to a reduction in transcription of its target genes [46]. Given the cytoplasmic expression of SOX2 found in our study, this mechanism might be involved in SOX2 patterning during inner ear development; however, experimental studies are needed to ascertain this. Even though it was previously described that JAGGED1 induces SOX2 expression via lateral induction [8], the expression of SOX2 in our samples was more extensive than that of JAGGED1, suggesting that SOX2 expression is also under the control of other factors. This is in line with studies on primate models which have demonstrated that SOX2 expression precedes JAGGED1 expression during inner ear development [9,11]. Given the differences between murine and primate models for prosensory domain specification [9,10,11], our results indicate that primate models more accurately represent the early stages of inner ear prosensory domain specification. It is important to note that, unlike Hosoya et al. [11], we have not found a developmental stage when SOX2 expression is present and JAGGED1 expression is absent. Therefore, investigation of earlier stages of human inner ear development, if possible, is necessary to better elucidate the process of prosensory domain specification and the relationship between SOX2 and JAGGED1.
When observing the innervation of the prosensory domains, we found that the nerve fibers first reach the developing cochlear duct at 8 W and remain mostly at the basal surface at 9–10 W, which is in line with multiple studies [26,27,31]. The vestibular region (developing maculae) was already visibly innervated at 6 W. Chacko et al. have described the earliest innervation of human vestibular structures at 8 W [48], while Sans and Dechesne have found nerve fibers in the macular epithelium at 7 W; however, their studies did not analyze any conceptuses at earlier developmental periods [25]. The innervation of vestibular prosensory domains became more excessive at later developmental periods and nerve fibers reached the most differentiating HCs at 9–10 W, which is in line with the aforementioned study [48]. Interestingly, even though SOX2 expression was more widespread than JAGGED1, the nerve fibers penetrated the prosensory domain epithelium only in the JAGGED1-positive region, suggesting that JAGGED1 expression is associated with axon guidance.
Membranous CTNNB1 expression was present in the human inner ear epithelium throughout all the observed periods, becoming the strongest at 9–10 W. Nuclear CTNNB1 expression was mostly seen in the non-sensory regions and surrounding mesenchyme. Nuclear CTNNB1 expression usually indicates the activation of canonical WNT signaling, which has been shown to induce the proliferation and differentiation of epithelial cells of the developing murine cochlear duct [33,45,49,50]. It was also described that CTNNB1 may play a role in the proliferation of the vestibular epithelium in rats [51]. It has been shown that CTNNB1 expression is lost after the formation of the otocyst from the otic cup in rats [52]; however, CTNNB1 expression was present in the 5 W otocyst of our samples. Further investigation is needed to determine whether CTNNB1 expression is lost and then regained during human otocyst formation or if the expression is present throughout the entire process of development.
The role of VDR in inner ear development and function has not been extensively studied. It has been described that downregulation of VDR signaling is associated with benign paroxysmal positional vertigo, sensorineural hearing loss, and other inner ear disorders in humans [39,40,41,53]. A study on zebrafish found that the knockdown of VDR resulted in malformed vestibular structures and decreased HC production [54]. Zou et al. found that knockout of VDR in mice had no effects on cochlear morphology but resulted in early-onset sensorineural hearing loss associated with reduced apoptosis [38]. We found that VDR is expressed in the human inner ear during early development, especially in the vestibular regions, which corresponds to the findings of the previously mentioned studies that loss of VDR results in vestibular but not cochlear malformations [38,54]. Considering that the expression was mostly membranous or cytoplasmatic, and seldom nuclear, we presume that vitamin D affects the developing human inner ear mostly through non-genomic pathways [55]. Studies on VDR expression in the human inner ear at later stages of development and postnatally are needed to further elucidate the distribution and possible role of VDR for inner ear function.
When analyzing the relationship between CTNNB1 and VDR expression in the developing inner ear, we have discovered that nuclear CTNNB1 expression and VDR expression display opposite patterns when observing the same regions (Supplementary Figure S2). This relationship between nuclear CTNNB1 expression and VDR has been previously described as mutually antagonistic in several studies [44,56,57]. Considering that nuclear CTNNB1 expression is associated with proliferation and VDR signaling with apoptosis in the inner ear [38,45], we presume that these factors are needed to properly regulate the fine balance between proliferation and apoptosis, which is necessary for normal human inner ear development [58].
The main limitations of our study are the small sample size and the fact that we cannot perform quantitative studies of protein expression such as Western blotting or flow cytometry since our samples are archived sections of embryonic/fetal tissue that was formalin-fixed and paraffin-embedded. An additional limitation is that immunofluorescent staining can have varying results depending on the experimental conditions, and that non-specific staining is often present and can interfere with the interpretation of results. With this in mind, we consider the results of our study to be valuable as we provide insight about the innervation as well as SOX2, JAGGED1, CTNNB1, and VDR expression in the human inner ear at the earliest stages of development. Additional experimental and observational studies on animal models are needed to better understand the interplays of these factors during inner ear development.
4. Materials and Methods
4.1. Human Samples
In our study, we investigated a total of 12 human developing inner ear samples which belong to the archive collection of human embryos and fetuses in the Department of Anatomy, Histology, and Embryology, at the University of Split School of Medicine. The embryonic and fetal tissue specimens were obtained after spontaneous abortions or tubal pregnancies, with the permission of the Ethical and Drug Committee of the University Hospital in Split in accordance with the Helsinki Declaration (class: 003-08/16-03/0001, approval number: 2181-198-03-04-16-0024), and only conceptuses with no signs of maceration, abnormalities, or morphological changes were included in the study. The ages of conceptuses, stated as weeks after fertilization (W), were estimated by crown–rump length and menstrual data and correlated with the Carnegie stages. Each developmental age group (5–6 W, 7–8 W, and 9–10 W) contained four conceptuses.
4.2. Immunofluorescence Staining
Following the fixation in 4% paraformaldehyde in phosphate buffer saline (PBS), tissue was dehydrated in graded ethanol solutions. The tissue was then embedded in paraffin blocks, cut serially as 5 µm thick sections, and mounted on glass slides. Every tenth section was stained with hematoxylin and eosin to describe the stages in normal inner ear development and to confirm the preservation of tissue morphology. The immunofluorescence staining protocol was performed as described previously [59]. Briefly, the tissue slides were deparaffinized in xylol and rehydrated in graded ethanol solutions. The samples were then heated in a sodium citrate buffer (pH 6.0) using a steam cooker for 30 min. After a round of washing in phosphate-buffered saline (PBS), protein blocking buffer (Protein Block ab64226, Abcam, Cambridge, UK) was applied for 20 min in a humid chamber. Combinations of primary antibodies (Table 1) were applied and incubated overnight in the humid chamber. The primary antibodies were rinsed by washing the samples in PBS, and appropriate secondary antibodies (Table 1) were applied for 1 h in the humid chamber. Afterward, samples were once again washed in PBS, counterstained with 4′,6-diamidino-2-phenylindole (DAPI) for 2 min, and cover-slipped using a mounting medium (ImmuMount, Thermo Shandon, Pittsburgh, PA, USA). The specificity of staining was controlled by omitting primary or secondary antibodies from the staining protocol and capturing images using identical microscope settings (Supplementary Figure S1). The stained samples were analyzed using an epifluorescence microscope (Olympus BX61, Tokyo, Japan), and images were captured with a mounted digital camera (Nikon Ri-D2, Nikon, Tokyo, Japan) using NIS-Elements F software version 3.0 (Nikon, Tokyo, Japan).
4.3. Innervation Analysis
The innervation of prosensory domains was estimated by determining the maximum depth of nerve fiber penetration into the inner ear epithelium. It was calculated as the ratio of the intraepithelial length of the farthest-reaching nerve fiber and the epithelial thickness at the same point.
4.4. Immunofluorescence Signal Quantification
In order to quantify the immunofluorescence signal of the analyzed proteins, we calculated the area percentage that the signal took up in the captured images as described previously [60]. Briefly, for each sample, we captured images of the developing inner ear epithelium using a 40× objective lens. Each image underwent identical processing steps. Using the Adobe Photoshop version 21.0.2 (Adobe, San Jose, CA, USA) Lasso tool, the regions of interest were selected and extracted from the images. Afterwards, the fluorescent signal of the analyzed protein was isolated with the median filter and triangle thresholding methods in ImageJ software version 1.53o (NIH, Bethesda, MD, USA). The area percentage of the isolated signal was calculated and corrected relative to the original image from which it was extracted. The figures displayed were assembled in Adobe Photoshop after subtracting the background and slightly enhancing the contrast of the original captured images.
4.5. Statistical Analysis
Statistical analysis was performed using GraphPad Prism version 9.0.0 software (GraphPad Software, San Diego, CA, USA). All results are presented as the mean and standard deviation of the calculated percentages. The normality of distribution of the data was determined using the Shapiro–Wilk test. Two-way analysis of variance (ANOVA) with Tukey’s post hoc test was used to determine the statistical significance of the difference in protein expression and maximum depth of nerve fiber penetration between the analyzed groups of samples. Analyses of trends for innervation and expression of CTNNB1 and VDR were performed by regression modeling. Linear trends were described by the slope (β) of the regression line. The goodness of fit measure used was the determination coefficient (R2). Statistical significance was set at p < 0.05.
4.1. Human Samples
In our study, we investigated a total of 12 human developing inner ear samples which belong to the archive collection of human embryos and fetuses in the Department of Anatomy, Histology, and Embryology, at the University of Split School of Medicine. The embryonic and fetal tissue specimens were obtained after spontaneous abortions or tubal pregnancies, with the permission of the Ethical and Drug Committee of the University Hospital in Split in accordance with the Helsinki Declaration (class: 003-08/16-03/0001, approval number: 2181-198-03-04-16-0024), and only conceptuses with no signs of maceration, abnormalities, or morphological changes were included in the study. The ages of conceptuses, stated as weeks after fertilization (W), were estimated by crown–rump length and menstrual data and correlated with the Carnegie stages. Each developmental age group (5–6 W, 7–8 W, and 9–10 W) contained four conceptuses.
4.2. Immunofluorescence Staining
Following the fixation in 4% paraformaldehyde in phosphate buffer saline (PBS), tissue was dehydrated in graded ethanol solutions. The tissue was then embedded in paraffin blocks, cut serially as 5 µm thick sections, and mounted on glass slides. Every tenth section was stained with hematoxylin and eosin to describe the stages in normal inner ear development and to confirm the preservation of tissue morphology. The immunofluorescence staining protocol was performed as described previously [59]. Briefly, the tissue slides were deparaffinized in xylol and rehydrated in graded ethanol solutions. The samples were then heated in a sodium citrate buffer (pH 6.0) using a steam cooker for 30 min. After a round of washing in phosphate-buffered saline (PBS), protein blocking buffer (Protein Block ab64226, Abcam, Cambridge, UK) was applied for 20 min in a humid chamber. Combinations of primary antibodies (Table 1) were applied and incubated overnight in the humid chamber. The primary antibodies were rinsed by washing the samples in PBS, and appropriate secondary antibodies (Table 1) were applied for 1 h in the humid chamber. Afterward, samples were once again washed in PBS, counterstained with 4′,6-diamidino-2-phenylindole (DAPI) for 2 min, and cover-slipped using a mounting medium (ImmuMount, Thermo Shandon, Pittsburgh, PA, USA). The specificity of staining was controlled by omitting primary or secondary antibodies from the staining protocol and capturing images using identical microscope settings (Supplementary Figure S1). The stained samples were analyzed using an epifluorescence microscope (Olympus BX61, Tokyo, Japan), and images were captured with a mounted digital camera (Nikon Ri-D2, Nikon, Tokyo, Japan) using NIS-Elements F software version 3.0 (Nikon, Tokyo, Japan).
4.3. Innervation Analysis
The innervation of prosensory domains was estimated by determining the maximum depth of nerve fiber penetration into the inner ear epithelium. It was calculated as the ratio of the intraepithelial length of the farthest-reaching nerve fiber and the epithelial thickness at the same point.
4.4. Immunofluorescence Signal Quantification
In order to quantify the immunofluorescence signal of the analyzed proteins, we calculated the area percentage that the signal took up in the captured images as described previously [60]. Briefly, for each sample, we captured images of the developing inner ear epithelium using a 40× objective lens. Each image underwent identical processing steps. Using the Adobe Photoshop version 21.0.2 (Adobe, San Jose, CA, USA) Lasso tool, the regions of interest were selected and extracted from the images. Afterwards, the fluorescent signal of the analyzed protein was isolated with the median filter and triangle thresholding methods in ImageJ software version 1.53o (NIH, Bethesda, MD, USA). The area percentage of the isolated signal was calculated and corrected relative to the original image from which it was extracted. The figures displayed were assembled in Adobe Photoshop after subtracting the background and slightly enhancing the contrast of the original captured images.
4.5. Statistical Analysis
Statistical analysis was performed using GraphPad Prism version 9.0.0 software (GraphPad Software, San Diego, CA, USA). All results are presented as the mean and standard deviation of the calculated percentages. The normality of distribution of the data was determined using the Shapiro–Wilk test. Two-way analysis of variance (ANOVA) with Tukey’s post hoc test was used to determine the statistical significance of the difference in protein expression and maximum depth of nerve fiber penetration between the analyzed groups of samples. Analyses of trends for innervation and expression of CTNNB1 and VDR were performed by regression modeling. Linear trends were described by the slope (β) of the regression line. The goodness of fit measure used was the determination coefficient (R2). Statistical significance was set at p < 0.05.
5. Conclusions
SOX2 and JAGGED1 are expressed in the prosensory domains of the human inner ear between 5–10 W, with SOX2 expression being consistently more extensive. Nerve fibers can be seen penetrating the vestibular maculae of the human inner ear at 6 W and at the base of the cochlear duct at 8 W. CTNNB1 and VDR are expressed throughout the inner ear epithelium during early development, with non-sensory and vestibular regions demonstrating the strongest expression. |
Title: YAP/TAZ Signalling Controls Epidermal Keratinocyte Fate | Body:
1. Introduction
The skin epidermis is a multilayered and rapidly self-renewing epithelium that protects an organism from dehydration, pathogens, chemicals and physical damage [1]. Keratinocyte (KC) turnover starts in the basal layer, the deepest layer in the epidermis, located at the basal lamina. The basal layer contains epidermal stem cells; their quiescence and cell cycle are regulated by external signals from the stem cell niche that result in the production of amplifying progenitors and differentiated cells. During differentiation, KCs move upward and sequentially form spinous, granular, and cornified layers [2,3,4]. Cells in each epidermal layer have distinctive properties dictated by transcriptional programmes leading to the expression of specific layer markers.
Mitotically active basal KCs are characterised by the expression of keratins 5 (KRT5), 14 (KRT14), and 15 (KRT15). Basal stem cells are also distinguished by high expression of adhesion proteins. Basal KCs are attached to the basal lamina through hemidesmosomes and focal adhesions containing integrins. KCs contact each other through adhesive junctions (AJs) and desmosomes, which connect actin cytoskeletons and keratin filaments, respectively. After leaving the basal layer, cells downregulate the expression of basal cell markers and activate the expression of suprabasal keratins 1 (KRT1) and 10 (KRT10), as well as keratin 9 (KRT9) in the palmoplantar epidermis and 2e (KRT2e) in thickened sites. In the granular layer, even more differentiated KCs start synthesising keratohyalin granules. From the upper spinous to the cornified layer, the cornified cell envelope starts assembling and developing a physical barrier. This process involves structural proteins such as loricrin cornified envelope precursor protein (LORICRIN), involucrin (IVL), and trichohyalin (TCHH). Finally, the terminally differentiated KCs undergo an ‘epidermal programmed cell death’ and desquamate [4,5,6].
Skin derivates include hair follicles (HFs), sebaceous glands, and sweat glands. Throughout life, the HFs undergo cyclic changes that are categorised into stages: anagen (growth), catagen (regression), and telogen (resting). Catagen results in the degeneration of the lower part of the HF. During anagen, HFs regenerate their structure. HFs contain rapidly dividing matrix cells in hair bulbs that undergo differentiation into the hair shaft and inner root sheath (IRS). During the hair cycle, the matrix cell population is replenished by descendants from the stem cell niche located in the bulge area of the outer root sheath (ORS) [7,8].
Therefore, the epidermal cell phenotype could be characterised by belonging to HFs or the interfollicular epidermis (IFE) and by the level of differentiation, which corresponds to cell proliferative ability. During skin injury or in a proinflammatory microenvironment, KCs can undergo partial epithelial–mesenchymal transition (EMT) and upregulate several markers, including keratins 6 (KRT6), 16 (KRT16), and 17 (KRT17), which are not normally expressed in the IFE [9]. The paralogues Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) could participate in the regulation of KCs, acquiring a particular cell phenotype.
YAP and TAZ are typically described as members of the Hippo pathway. Hippo is an evolutionarily highly conserved signalling pathway first identified in Drosophila melanogaster. The mammalian Hippo pathway consists of mammalian STE20-like kinase 1/2 (MST1/2), large tumour suppressor kinases 1/2 (LATS1/2), and two scaffold proteins, Salvador homologue 1 (SAV1) and MOB (monopolar spindle-one-binder proteins) kinase activator 1A and 1B (MOB1A/B). The activation of this signalling pathway results in the phosphorylation of YAP and TAZ. Phosphorylated YAP/TAZ are inactivated, sequestered in membrane-associated protein complexes or undergo proteasomal degradation. Active non-phosphorylated YAP/TAZ can translocate to the nucleus, where they function as transcriptional regulators. They do not directly bind DNA regulatory elements and upregulate the transcriptional activity via transcription factors (TFs) predominantly from the TEA domain (TEAD) family [10,11,12].
The key role of the Hippo pathway, mediated YAP/TAZ activity, was revealed in the cell differentiation of epithelia in various organs [13,14,15,16,17]. Activated YAP and TAZ are essential for proper epidermal development and self-renewal [18,19]. Along with the Hippo pathway, YAP and TAZ activation is regulated by mechanotransduction and signalling pathways that define KCs’ stemness, proliferation, differentiation and injury response (e.g., WNT/β-catenin [CTNNB1], transforming growth factor beta [TGFβ], and epidermal growth factor [EGF] receptor [EGFR]/phosphoinositide 3-kinase [PI3K]) [14,20,21,22,23,24,25]. Due to YAP and TAZ being involved in normal skin epidermis maintenance [26] and the progression of many diseases [27,28], YAP/TAZ activation mechanisms are of particular interest and require comprehensive investigation.
The growing number of review articles on YAP/TAZ involvement in cell differentiation [29,30,31], cancer progression [32,33], organogenesis [34,35,36,37], and ageing [38,39] indicates that evidence of YAP/TAZ functions is rapidly accumulating and must be summarised and analysed. In this review, we discuss the mechanisms of YAP and TAZ activation in the skin epidermis and the transcription programme regulated by YAP and TAZ activity in epidermal KCs.
2. YAP/TAZ Distribution in Normal and Diseased Epidermis
Nuclear YAP appears when the epidermis comprises a single layer of KCs during embryogenesis [18,19,40]. During further development and in adult skin, YAP and TAZ are found in the nuclei of basal KCs and the cytoplasm in the suprabasal layers [14,19]. In aged skin, the number of cells with active YAP decreases along with the proliferation rate [14,40]. KCs with active nuclear YAP exhibit increased expression of proliferation markers and tumour protein p63 (TP63), a key TF that determines epidermal growth [41].
HF development begins with reduced YAP expression in epidermal placodes; however, YAP remains in an active nuclear state [40]. During the subsequent stages of HF development and hair cycle, YAP is distributed in the nuclei in proliferative HF structures—the hair matrix and ORS—while it remains in the cytoplasm in differentiating IRS and hair shaft cells [21,26,42].
During wound healing, YAP and TAZ expression is upregulated in the epidermis and dermal fibroblasts [14,19]. In contrast, ultraviolet radiation inhibits cell proliferation and decreases the expression of YAP and its targets [43,44]. In diseased skin, YAP/TAZ overactivation correlates with a hyperproliferative phenotype: psoriasis [45,46,47], lichen planus [48], and several skin cancers, including basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma [49,50,51,52,53]. Human papillomavirus progression requires YAP/TAZ nuclear recruitment to upregulate KC proliferation [54,55]. In contrast, inherited epidermal abnormalities, such as epidermolysis bullosa, are characterised by defective, low-proliferation KCs, and predominantly cytoplasmic YAP localisation in the epidermis [41].
The correlation between nuclear YAP localisation and proliferation rate is also observed in cultured epidermal KCs. KCs with active YAP signalling exhibit elevated integrin and p63 expression, rapid adhesion, and high colony formation efficiency [19,41,56]. YAP and TAZ expression decreases during KCs passaging [41,57]. In growing KC colonies, nuclear YAP is localised specifically at the colony edges, while interior cells retain only cytoplasmic YAP [19,41]. Establishing this pattern is essential for contact inhibition: the suppression of proliferation upon increasing cell density. Preserving YAP in the cytoplasm is ensured by cell contact stabilisation mediated by E-cadherin and associated 14-3-3 proteins [41,58].
Culture conditions and the application of substrates with specific properties allow the exploration of the mechanosensory nature of YAP/TAZ signalling. The application of ‘soft’ substrates (with an elastic modulus of up to 4 kPa) prevents YAP activation, while ‘stiff’ substrates induce YAP nuclear translocation [25,45,59,60]. Therefore, soft substrates stimulate the expression of KC differentiation markers [59]. Like contact inhibition, mechanosensing relies on cell-contact proteins and associated cytoskeletal filaments. This mechanism likely contributes to the YAP activation pattern in the epidermis, reflecting the varying stiffness experienced by basal and more differentiated layers [45].
In summary, nuclear YAP and TAZ are features of proliferating KCs, while cytoplasmic YAP and TAZ are specific to early differentiating cells.
3. YAP/TAZ Signalling Modulation and Its Influence on Epidermal Cell Fate
3.1. Approaches to Modulating YAP/TAZ Activity
YAP/TAZ signalling is usually enhanced or inhibited to understand YAP/TAZ regulation. YAP/TAZ can be enhanced by overexpression or overactivation. Overexpression increases the amount of YAP and TAZ protein and does not affect the regulation of their activity. Overexpression is primarily used for primary KC cultures [41,57].
Overactivation leads YAP/TAZ to accumulate in the nucleus, where they can regulate gene expression. Constitutively active YAP isoforms are created via genetic modification at serine residues, rendering them resistant to LATS-mediated cytoplasmic sequestration. Mutating serine 127 to alanine (S127A) [18,40,57] or applying YAP lacking serine residues (YAP2-5SA) [14] increases nuclear YAP levels. Overactivated YAP is suitable for animal models, while inserting the constitutively active YAP isoform into primary KCs in culture results in their senescence and death [41]. The YAP2-5SA-ΔC variant also lacks the C-terminal region from Q281, including a transactivation domain. This mutation prevents YAP-driven gene expression in vitro. This mutant protein was expected to block endogenous YAP function and disrupt epidermal homeostasis. However, transgenic mouse lines exhibit typical YAP overactivation phenotypes [21,42]. To ensure skin-specific expression, KRT5 and KRT14 gene promoters were used with or without an inducible system [40,42,52].
Another way to obtain mice with YAP overactivation is by targeting the Hippo pathway. MOB1A/B deficiency strongly activated endogenous YAP1 in mice [61,62]. In addition, 14-3-3 proteins are critical for excluding phosphorylated YAP1 from nuclei; the skin of Er/Er mice carrying the repeated epilation mutation (Er) of 14-3-3σ protein failed to retain YAP1 in the cytoplasm [63]. LATS kinase inhibitors activate YAP/TAZ signalling by preventing their LATS-dependent phosphorylation and consequent degradation [64]. A small-molecule agent, PY-60, activates YAP-driven transcription by blocking the Hippo pathway scaffolding protein annexin A2 (ANXA2) [65,66].
Mice with skin-specific Krt5/14-Cre or CreERT were crossed with YAP/TAZ-floxed mice to create a double conditional knockout (KO) [14,18,67]. To knockdown (KD) YAP/TAZ in the epidermis, cells were infected with a lentiviral vector carrying YAP- or TAZ-specific short hairpin RNA (shRNA) or small interfering RNA (siRNA) [40,41,68]. A genetically encoded dominant-negative protein (TEADi) was designed to inhibit TEAD-dependent YAP/TAZ expression regulation. It contains TEAD-interacting domains that bind to TEADs and prevent their interaction with transcriptional coactivators [68]. Serine 94 in the human YAP and serine 79 in the mouse orthologue are essential for YAP’s interaction with TEADs [59]; changing them to alanine inhibited YAP/TEAD communication and suppressed YAP activity in genetically modified animals [18]. Finally, small-molecule inhibitors of YAP/TAZ signalling have been discovered. Verteporfin (VP) blocks the interaction of YAP with TEAD, preventing the transcriptional activation of YAP downstream targets [69]. VP also induces sequestration of YAP in the cytoplasm by increasing levels of 14-3-3σ and its subsequent proteasomal degradation [70,71]. Peptide 17 is a more selective inhibitor; it similarly disrupts the binding of YAP and TEAD but does not prevent the translocation of YAP into the nucleus [72,73]. The Src family kinase inhibitor dasatinib is known to strongly inhibit YAP activation [52]. Simvastatin has been identified as an efficient agent for inhibiting TAZ [53,67].
3.2. Alterations in KC Marker Expression Under YAP/TAZ Modulation
YAP/TAZ overactivation or inhibition affects KC-specific marker expression and proliferation and the structure of the skin and its derivatives.
Doxycycline-mediated expression of active YAP (S127A) under the KRT14 promoter significantly altered mouse skin structure [40]. After induction, epidermal development demonstrated hypertrophy, hyperproliferation, and increased basal marker expression, whereas expression of the differentiation markers KRT10 and LORICRIN was mostly absent, even during embryogenesis. HF structure was disrupted, although the hair-specific TFs, controlling the specification of the HF’s layered structure, were preserved [18,40]. YAP2-5SA-ΔC mice exhibit milder deviations that gradually develop during the postnatal period. From four to five weeks of age, the skin becomes dry and fragile, with lesions and hair loss. At postnatal day 85 (P85), all epidermal layers are thickened, leading to hypertrophy and hyperkeratinisation [42].
The Yap KO mouse model exhibited thinner and fragile skin with a loss of epidermal tissue on limbs and abnormal epidermal architecture and died at the embryonic stage or shortly after birth. Histological analysis revealed a hypoplastic basal layer with abnormal cell morphology, decreased progenitor cells, and a reduced cornified layer [18]. Like with YAP overactivation, YAP/TAZ double KO mice demonstrate progressive alopecia in neonates or after two weeks post-tamoxifen injection in adults [14].
Below, we summarise the published evidence on the upregulation and downregulation of KC-specific markers under YAP overactivation and suppression conditions (Figure 1).
3.2.1. Proliferation
The most significant changes are related to the proliferation rate. Proliferation marker expression in the epidermis of both YAP (S127A) and YAP2-5SA-ΔC mice indicates a substantial increase in the number of positive cells in the basal layer and an extension into the suprabasal layers [18,21,22,40,42,75,79]. The same effects were observed under treatment with a small-molecule YAP activator [65]. YAP/TAZ KO mice exhibited a significant decrease in proliferating basal cells [14,18].
Cultured KCs are also sensitive to YAP signalling modulation. YAP overexpression enhanced proliferation, immortalisation, senescence escape, and clonal evolution blockage in primary human KCs [41,57]. Indeed, YAP2-5SA- and YAP (S127A)-transduced KCs increased in size, expressed β-galactosidase, and overexpressed cyclin-dependent kinase inhibitor 2A (CDKN2A/p16INK4a) and tumour protein p53 (TP53), indicating cell senescence [41]. Overexpressed YAP escapes the E-cadherin-mediated contact inhibition of proliferation [58]. In contrast, YAP and TAZ KD results in cell differentiation, inhibited proliferation, cell cycle arrest, and apoptosis promotion in primary and immortalised KCs [41,46,49,68]. Therefore, in vivo and in vitro evidence indicates a regulatory role for YAP and a supporting role for TAZ in cell proliferation.
3.2.2. Differentiation
YAP overactivation-induced skin hypertrophy results from increasing numbers of KRT5/14- and p63-positive basal cells and their propagation into suprabasal layers [18,40,42]. The thickness of superior layers depends on YAP modification. In the presence of the YAP2-5SA-∆C mutant, the IFE exhibits an expansion of the spinous and granular layers, expressing KRT10 and IVL, respectively, and thickening of the cornified layer, indicating hyperkeratinisation [42]. In contrast, in the presence of the YAP (S127A) mutant, there is a significant thinning or absence of differentiated layers expressing KRT1/10 and LORICRIN in the epidermis [18,40]. These differences are related to mouse age and the intensity of skin abnormality progression that appeared even in embryogenesis for YAP (S127A) mice but several weeks after birth for YAP2-5SA-∆C mice.
Yap KO and TEADi provoked aberrant distribution of KRT5/14 in the basal layer and the appearance of differentiated KRT1/10 cells in the basal lamina of mouse skin. All the layers became thinner, and the number of KRT10- and LORICRIN-positive cells decreased [18,68]. In contrast, epidermis structure did not differ between Taz KO and control mice [67]. Thus, YAP is the predominant paralog in epidermal self-renewal in skin homeostasis.
In cultured KCs, YAP activation is responsible for differentiation inhibition [45,57,59,68]. The analysis of differentiation markers revealed that YAP maintains a high proliferation rate by preserving the progenitor features of KCs but does not induce cell dedifferentiation and re-entry into the cell cycle of differentiated cells [41].
Collagen XVII (COL17A1), a component of the hemidesmosomes, is involved in cell turnover in the basal layer: its differential expression between the descendants of dividing basal KCs defines the cell’s persistence in the basal layer or movement to the suprabasal layers [80]. Increased COL17A1 expression corresponds to a fraction of slowly dividing basal KCs with stemness properties [81]. Basal KCs in Mob1a/b-deficient (YAP1-activated) mouse skin lose COL17A1 expression. In vitro, Mob1a/b-deficient cells demonstrate impaired adhesive properties and are eliminated from the substrate by wild-type cells [61]. Therefore, it can be assumed that rapidly dividing cells with nuclear YAP are transiently amplifying progenitors rather than stem cells.
3.2.3. Hair Follicles
Both YAP activation and suppression lead to hair loss but via different mechanisms. YAP deletion or TEAD-binding inhibition reduces the proliferative cell fraction in HFs and the IFE, resulting in progressive hair loss [14,68]. YAP (S127A) mutants with pronounced alterations in embryo epidermis exhibit abnormalities is HF structure, but not in HF localisation [40]. The HFs of both YAP (S127A) and YAP2-5SA-ΔC mice strains acquire disorganised structure and predominantly consist of proliferating cell mass positive for bulge and hair matrix markers [40,42]. YAP overactivation regulates HF progenitor cell proliferation and prevents their subsequent differentiation; however, it does not influence cell fate choice to become either HF or IFE based on preserving the expression of TFs specific to the different HF layers [40,42].
HF formation also appears after wounding in mouse skin through HF neogenesis [82]. In this case, the number of HFs was greater under YAP overactivation; they similarly exhibited abnormal morphology [83]. This highlights the common action of YAP with other signalling pathways involved in HF regeneration.
3.2.4. Wound Healing
During skin regeneration, epidermal cells acquire an injured phenotype and undergo partial EMT, which is necessary for KC migration and proliferation. Both YAP and TAZ guide the epidermal restoration after injury. YAP/TAZ KD and KO delayed wound healing in mouse skin [14,84], while their activation promoted wound closure [65,66]. Although Yap overexpression does not initiate EMT [43], YAP and TAZ stimulate cell migration [36,38], proliferation [9] and prevent apoptosis [67,77].
YAP/TAZ also activate the expression of genes involved in inflammation. Direct targets of YAP/TAZ include cysteine-rich angiogenic inducer 61 (CYR61, currently called cellular communication network factor 1 [CCN1]), connective tissue growth factor (CTGF; currently called cellular communication network factor 2 [CCN2]), and TGFβ pathway members [50,84]. CCN1 is involved in KC injury response [43,85,86], and CCN2 is required for re-epithelisation, proliferation, and tissue remodelling [87,88]. YAP1 stimulates KCs to produce the colony-stimulating factor 1 (CSF1) in the wound microenvironment, which supports macrophage attraction in a paracrine manner [89].
YAP and TAZ activity must be transient for successful tissue regeneration. Chronic inflammation or signalling abnormalities prolong the timing of YAP and TAZ activity and result in their nuclear accumulation, which leads to SCC formation [52], EMT [52,53,90] and increased proinflammatory cytokine and growth factor levels [87,88,91,92]. Under YAP overactivation, mouse skin reproduced a psoriatic phenotype with hypertrophic epidermis that was positive for KC injury-associated markers KRT6/16 and a fibrotic state in the dermis with fibroblast hyperproliferation and increased collagen production [75,79,83].
3.1. Approaches to Modulating YAP/TAZ Activity
YAP/TAZ signalling is usually enhanced or inhibited to understand YAP/TAZ regulation. YAP/TAZ can be enhanced by overexpression or overactivation. Overexpression increases the amount of YAP and TAZ protein and does not affect the regulation of their activity. Overexpression is primarily used for primary KC cultures [41,57].
Overactivation leads YAP/TAZ to accumulate in the nucleus, where they can regulate gene expression. Constitutively active YAP isoforms are created via genetic modification at serine residues, rendering them resistant to LATS-mediated cytoplasmic sequestration. Mutating serine 127 to alanine (S127A) [18,40,57] or applying YAP lacking serine residues (YAP2-5SA) [14] increases nuclear YAP levels. Overactivated YAP is suitable for animal models, while inserting the constitutively active YAP isoform into primary KCs in culture results in their senescence and death [41]. The YAP2-5SA-ΔC variant also lacks the C-terminal region from Q281, including a transactivation domain. This mutation prevents YAP-driven gene expression in vitro. This mutant protein was expected to block endogenous YAP function and disrupt epidermal homeostasis. However, transgenic mouse lines exhibit typical YAP overactivation phenotypes [21,42]. To ensure skin-specific expression, KRT5 and KRT14 gene promoters were used with or without an inducible system [40,42,52].
Another way to obtain mice with YAP overactivation is by targeting the Hippo pathway. MOB1A/B deficiency strongly activated endogenous YAP1 in mice [61,62]. In addition, 14-3-3 proteins are critical for excluding phosphorylated YAP1 from nuclei; the skin of Er/Er mice carrying the repeated epilation mutation (Er) of 14-3-3σ protein failed to retain YAP1 in the cytoplasm [63]. LATS kinase inhibitors activate YAP/TAZ signalling by preventing their LATS-dependent phosphorylation and consequent degradation [64]. A small-molecule agent, PY-60, activates YAP-driven transcription by blocking the Hippo pathway scaffolding protein annexin A2 (ANXA2) [65,66].
Mice with skin-specific Krt5/14-Cre or CreERT were crossed with YAP/TAZ-floxed mice to create a double conditional knockout (KO) [14,18,67]. To knockdown (KD) YAP/TAZ in the epidermis, cells were infected with a lentiviral vector carrying YAP- or TAZ-specific short hairpin RNA (shRNA) or small interfering RNA (siRNA) [40,41,68]. A genetically encoded dominant-negative protein (TEADi) was designed to inhibit TEAD-dependent YAP/TAZ expression regulation. It contains TEAD-interacting domains that bind to TEADs and prevent their interaction with transcriptional coactivators [68]. Serine 94 in the human YAP and serine 79 in the mouse orthologue are essential for YAP’s interaction with TEADs [59]; changing them to alanine inhibited YAP/TEAD communication and suppressed YAP activity in genetically modified animals [18]. Finally, small-molecule inhibitors of YAP/TAZ signalling have been discovered. Verteporfin (VP) blocks the interaction of YAP with TEAD, preventing the transcriptional activation of YAP downstream targets [69]. VP also induces sequestration of YAP in the cytoplasm by increasing levels of 14-3-3σ and its subsequent proteasomal degradation [70,71]. Peptide 17 is a more selective inhibitor; it similarly disrupts the binding of YAP and TEAD but does not prevent the translocation of YAP into the nucleus [72,73]. The Src family kinase inhibitor dasatinib is known to strongly inhibit YAP activation [52]. Simvastatin has been identified as an efficient agent for inhibiting TAZ [53,67].
3.2. Alterations in KC Marker Expression Under YAP/TAZ Modulation
YAP/TAZ overactivation or inhibition affects KC-specific marker expression and proliferation and the structure of the skin and its derivatives.
Doxycycline-mediated expression of active YAP (S127A) under the KRT14 promoter significantly altered mouse skin structure [40]. After induction, epidermal development demonstrated hypertrophy, hyperproliferation, and increased basal marker expression, whereas expression of the differentiation markers KRT10 and LORICRIN was mostly absent, even during embryogenesis. HF structure was disrupted, although the hair-specific TFs, controlling the specification of the HF’s layered structure, were preserved [18,40]. YAP2-5SA-ΔC mice exhibit milder deviations that gradually develop during the postnatal period. From four to five weeks of age, the skin becomes dry and fragile, with lesions and hair loss. At postnatal day 85 (P85), all epidermal layers are thickened, leading to hypertrophy and hyperkeratinisation [42].
The Yap KO mouse model exhibited thinner and fragile skin with a loss of epidermal tissue on limbs and abnormal epidermal architecture and died at the embryonic stage or shortly after birth. Histological analysis revealed a hypoplastic basal layer with abnormal cell morphology, decreased progenitor cells, and a reduced cornified layer [18]. Like with YAP overactivation, YAP/TAZ double KO mice demonstrate progressive alopecia in neonates or after two weeks post-tamoxifen injection in adults [14].
Below, we summarise the published evidence on the upregulation and downregulation of KC-specific markers under YAP overactivation and suppression conditions (Figure 1).
3.2.1. Proliferation
The most significant changes are related to the proliferation rate. Proliferation marker expression in the epidermis of both YAP (S127A) and YAP2-5SA-ΔC mice indicates a substantial increase in the number of positive cells in the basal layer and an extension into the suprabasal layers [18,21,22,40,42,75,79]. The same effects were observed under treatment with a small-molecule YAP activator [65]. YAP/TAZ KO mice exhibited a significant decrease in proliferating basal cells [14,18].
Cultured KCs are also sensitive to YAP signalling modulation. YAP overexpression enhanced proliferation, immortalisation, senescence escape, and clonal evolution blockage in primary human KCs [41,57]. Indeed, YAP2-5SA- and YAP (S127A)-transduced KCs increased in size, expressed β-galactosidase, and overexpressed cyclin-dependent kinase inhibitor 2A (CDKN2A/p16INK4a) and tumour protein p53 (TP53), indicating cell senescence [41]. Overexpressed YAP escapes the E-cadherin-mediated contact inhibition of proliferation [58]. In contrast, YAP and TAZ KD results in cell differentiation, inhibited proliferation, cell cycle arrest, and apoptosis promotion in primary and immortalised KCs [41,46,49,68]. Therefore, in vivo and in vitro evidence indicates a regulatory role for YAP and a supporting role for TAZ in cell proliferation.
3.2.2. Differentiation
YAP overactivation-induced skin hypertrophy results from increasing numbers of KRT5/14- and p63-positive basal cells and their propagation into suprabasal layers [18,40,42]. The thickness of superior layers depends on YAP modification. In the presence of the YAP2-5SA-∆C mutant, the IFE exhibits an expansion of the spinous and granular layers, expressing KRT10 and IVL, respectively, and thickening of the cornified layer, indicating hyperkeratinisation [42]. In contrast, in the presence of the YAP (S127A) mutant, there is a significant thinning or absence of differentiated layers expressing KRT1/10 and LORICRIN in the epidermis [18,40]. These differences are related to mouse age and the intensity of skin abnormality progression that appeared even in embryogenesis for YAP (S127A) mice but several weeks after birth for YAP2-5SA-∆C mice.
Yap KO and TEADi provoked aberrant distribution of KRT5/14 in the basal layer and the appearance of differentiated KRT1/10 cells in the basal lamina of mouse skin. All the layers became thinner, and the number of KRT10- and LORICRIN-positive cells decreased [18,68]. In contrast, epidermis structure did not differ between Taz KO and control mice [67]. Thus, YAP is the predominant paralog in epidermal self-renewal in skin homeostasis.
In cultured KCs, YAP activation is responsible for differentiation inhibition [45,57,59,68]. The analysis of differentiation markers revealed that YAP maintains a high proliferation rate by preserving the progenitor features of KCs but does not induce cell dedifferentiation and re-entry into the cell cycle of differentiated cells [41].
Collagen XVII (COL17A1), a component of the hemidesmosomes, is involved in cell turnover in the basal layer: its differential expression between the descendants of dividing basal KCs defines the cell’s persistence in the basal layer or movement to the suprabasal layers [80]. Increased COL17A1 expression corresponds to a fraction of slowly dividing basal KCs with stemness properties [81]. Basal KCs in Mob1a/b-deficient (YAP1-activated) mouse skin lose COL17A1 expression. In vitro, Mob1a/b-deficient cells demonstrate impaired adhesive properties and are eliminated from the substrate by wild-type cells [61]. Therefore, it can be assumed that rapidly dividing cells with nuclear YAP are transiently amplifying progenitors rather than stem cells.
3.2.3. Hair Follicles
Both YAP activation and suppression lead to hair loss but via different mechanisms. YAP deletion or TEAD-binding inhibition reduces the proliferative cell fraction in HFs and the IFE, resulting in progressive hair loss [14,68]. YAP (S127A) mutants with pronounced alterations in embryo epidermis exhibit abnormalities is HF structure, but not in HF localisation [40]. The HFs of both YAP (S127A) and YAP2-5SA-ΔC mice strains acquire disorganised structure and predominantly consist of proliferating cell mass positive for bulge and hair matrix markers [40,42]. YAP overactivation regulates HF progenitor cell proliferation and prevents their subsequent differentiation; however, it does not influence cell fate choice to become either HF or IFE based on preserving the expression of TFs specific to the different HF layers [40,42].
HF formation also appears after wounding in mouse skin through HF neogenesis [82]. In this case, the number of HFs was greater under YAP overactivation; they similarly exhibited abnormal morphology [83]. This highlights the common action of YAP with other signalling pathways involved in HF regeneration.
3.2.4. Wound Healing
During skin regeneration, epidermal cells acquire an injured phenotype and undergo partial EMT, which is necessary for KC migration and proliferation. Both YAP and TAZ guide the epidermal restoration after injury. YAP/TAZ KD and KO delayed wound healing in mouse skin [14,84], while their activation promoted wound closure [65,66]. Although Yap overexpression does not initiate EMT [43], YAP and TAZ stimulate cell migration [36,38], proliferation [9] and prevent apoptosis [67,77].
YAP/TAZ also activate the expression of genes involved in inflammation. Direct targets of YAP/TAZ include cysteine-rich angiogenic inducer 61 (CYR61, currently called cellular communication network factor 1 [CCN1]), connective tissue growth factor (CTGF; currently called cellular communication network factor 2 [CCN2]), and TGFβ pathway members [50,84]. CCN1 is involved in KC injury response [43,85,86], and CCN2 is required for re-epithelisation, proliferation, and tissue remodelling [87,88]. YAP1 stimulates KCs to produce the colony-stimulating factor 1 (CSF1) in the wound microenvironment, which supports macrophage attraction in a paracrine manner [89].
YAP and TAZ activity must be transient for successful tissue regeneration. Chronic inflammation or signalling abnormalities prolong the timing of YAP and TAZ activity and result in their nuclear accumulation, which leads to SCC formation [52], EMT [52,53,90] and increased proinflammatory cytokine and growth factor levels [87,88,91,92]. Under YAP overactivation, mouse skin reproduced a psoriatic phenotype with hypertrophic epidermis that was positive for KC injury-associated markers KRT6/16 and a fibrotic state in the dermis with fibroblast hyperproliferation and increased collagen production [75,79,83].
3.2.1. Proliferation
The most significant changes are related to the proliferation rate. Proliferation marker expression in the epidermis of both YAP (S127A) and YAP2-5SA-ΔC mice indicates a substantial increase in the number of positive cells in the basal layer and an extension into the suprabasal layers [18,21,22,40,42,75,79]. The same effects were observed under treatment with a small-molecule YAP activator [65]. YAP/TAZ KO mice exhibited a significant decrease in proliferating basal cells [14,18].
Cultured KCs are also sensitive to YAP signalling modulation. YAP overexpression enhanced proliferation, immortalisation, senescence escape, and clonal evolution blockage in primary human KCs [41,57]. Indeed, YAP2-5SA- and YAP (S127A)-transduced KCs increased in size, expressed β-galactosidase, and overexpressed cyclin-dependent kinase inhibitor 2A (CDKN2A/p16INK4a) and tumour protein p53 (TP53), indicating cell senescence [41]. Overexpressed YAP escapes the E-cadherin-mediated contact inhibition of proliferation [58]. In contrast, YAP and TAZ KD results in cell differentiation, inhibited proliferation, cell cycle arrest, and apoptosis promotion in primary and immortalised KCs [41,46,49,68]. Therefore, in vivo and in vitro evidence indicates a regulatory role for YAP and a supporting role for TAZ in cell proliferation.
3.2.2. Differentiation
YAP overactivation-induced skin hypertrophy results from increasing numbers of KRT5/14- and p63-positive basal cells and their propagation into suprabasal layers [18,40,42]. The thickness of superior layers depends on YAP modification. In the presence of the YAP2-5SA-∆C mutant, the IFE exhibits an expansion of the spinous and granular layers, expressing KRT10 and IVL, respectively, and thickening of the cornified layer, indicating hyperkeratinisation [42]. In contrast, in the presence of the YAP (S127A) mutant, there is a significant thinning or absence of differentiated layers expressing KRT1/10 and LORICRIN in the epidermis [18,40]. These differences are related to mouse age and the intensity of skin abnormality progression that appeared even in embryogenesis for YAP (S127A) mice but several weeks after birth for YAP2-5SA-∆C mice.
Yap KO and TEADi provoked aberrant distribution of KRT5/14 in the basal layer and the appearance of differentiated KRT1/10 cells in the basal lamina of mouse skin. All the layers became thinner, and the number of KRT10- and LORICRIN-positive cells decreased [18,68]. In contrast, epidermis structure did not differ between Taz KO and control mice [67]. Thus, YAP is the predominant paralog in epidermal self-renewal in skin homeostasis.
In cultured KCs, YAP activation is responsible for differentiation inhibition [45,57,59,68]. The analysis of differentiation markers revealed that YAP maintains a high proliferation rate by preserving the progenitor features of KCs but does not induce cell dedifferentiation and re-entry into the cell cycle of differentiated cells [41].
Collagen XVII (COL17A1), a component of the hemidesmosomes, is involved in cell turnover in the basal layer: its differential expression between the descendants of dividing basal KCs defines the cell’s persistence in the basal layer or movement to the suprabasal layers [80]. Increased COL17A1 expression corresponds to a fraction of slowly dividing basal KCs with stemness properties [81]. Basal KCs in Mob1a/b-deficient (YAP1-activated) mouse skin lose COL17A1 expression. In vitro, Mob1a/b-deficient cells demonstrate impaired adhesive properties and are eliminated from the substrate by wild-type cells [61]. Therefore, it can be assumed that rapidly dividing cells with nuclear YAP are transiently amplifying progenitors rather than stem cells.
3.2.3. Hair Follicles
Both YAP activation and suppression lead to hair loss but via different mechanisms. YAP deletion or TEAD-binding inhibition reduces the proliferative cell fraction in HFs and the IFE, resulting in progressive hair loss [14,68]. YAP (S127A) mutants with pronounced alterations in embryo epidermis exhibit abnormalities is HF structure, but not in HF localisation [40]. The HFs of both YAP (S127A) and YAP2-5SA-ΔC mice strains acquire disorganised structure and predominantly consist of proliferating cell mass positive for bulge and hair matrix markers [40,42]. YAP overactivation regulates HF progenitor cell proliferation and prevents their subsequent differentiation; however, it does not influence cell fate choice to become either HF or IFE based on preserving the expression of TFs specific to the different HF layers [40,42].
HF formation also appears after wounding in mouse skin through HF neogenesis [82]. In this case, the number of HFs was greater under YAP overactivation; they similarly exhibited abnormal morphology [83]. This highlights the common action of YAP with other signalling pathways involved in HF regeneration.
3.2.4. Wound Healing
During skin regeneration, epidermal cells acquire an injured phenotype and undergo partial EMT, which is necessary for KC migration and proliferation. Both YAP and TAZ guide the epidermal restoration after injury. YAP/TAZ KD and KO delayed wound healing in mouse skin [14,84], while their activation promoted wound closure [65,66]. Although Yap overexpression does not initiate EMT [43], YAP and TAZ stimulate cell migration [36,38], proliferation [9] and prevent apoptosis [67,77].
YAP/TAZ also activate the expression of genes involved in inflammation. Direct targets of YAP/TAZ include cysteine-rich angiogenic inducer 61 (CYR61, currently called cellular communication network factor 1 [CCN1]), connective tissue growth factor (CTGF; currently called cellular communication network factor 2 [CCN2]), and TGFβ pathway members [50,84]. CCN1 is involved in KC injury response [43,85,86], and CCN2 is required for re-epithelisation, proliferation, and tissue remodelling [87,88]. YAP1 stimulates KCs to produce the colony-stimulating factor 1 (CSF1) in the wound microenvironment, which supports macrophage attraction in a paracrine manner [89].
YAP and TAZ activity must be transient for successful tissue regeneration. Chronic inflammation or signalling abnormalities prolong the timing of YAP and TAZ activity and result in their nuclear accumulation, which leads to SCC formation [52], EMT [52,53,90] and increased proinflammatory cytokine and growth factor levels [87,88,91,92]. Under YAP overactivation, mouse skin reproduced a psoriatic phenotype with hypertrophic epidermis that was positive for KC injury-associated markers KRT6/16 and a fibrotic state in the dermis with fibroblast hyperproliferation and increased collagen production [75,79,83].
4. Mechanisms of YAP/TAZ Activation in the Epidermis
4.1. Hippo-Dependent YAP/TAZ Activation
The mechanism of YAP/TAZ nuclear shuttling is context-dependent and differs between organs, tissues, and cell types; it involves a group of regulatory elements controlling YAP and TAZ activity directly or via the Hippo pathway. Hippo signalling plays a key role in the differentiation and specification of simple and pseudostratified epithelia [12,36,93,94,95]. Here, we discuss the particular mechanisms of YAP/TAZ activation in stratified cornifying epidermis.
Under Hippo activation, active MST1/2 phosphorylates SAV1 and MOB1/2, which assist MST1/2 in recruiting and phosphorylating LATS1/2. Activated LATS1/2 serine/threonine kinases phosphorylate YAP and TAZ on their serine residues. In humans, inhibition includes phosphorylation of S127 and S381 in YAP and S89 and S311 in TAZ. YAP-S127 and TAZ-S89 phosphorylation is required for 14-3-3 protein binding to establish the molecular complex for cytoplasmic sequestration and subsequent inactivation. S381/S311 phosphorylation of YAP/TAZ, respectively, initiates proteasomal degradation. Dephosphorylases revert active YAP/TAZ conformation and signalling activity. The canonical Hippo pathway has been discussed in detail in several recent reviews [28,96].
Hippo pathway suppression promotes YAP activation during embryonic skin development [40,61] or until confluence is established in KC cultures [97,98], but not after achieving contact inhibition [18,19]. Unlike other epithelial types, Hippo activity is not controlled by cell polarity protein complexes in the epidermis [58]. YAP and TAZ cytoplasmic states are maintained primarily by cell–cell and cell–matrix contacts [97,98]. Junction-associated intracellular proteins 14-3-3 and α-catenin sequester phosphorylated YAP and TAZ in the cytoplasm [63,98,99,100]. Disrupting the α-catenin/14-3-3σ/YAP complex allows YAP to interact with the catalytic subunit of protein phosphatase 2A (PP2AC), triggering its activation [18].
The E-cadherin/AJ and actomyosin complex, including F-actin, myosin II, actin-related proteins 2/3 (ARP2/3), and myosin light chain kinases (either myosin light chain kinase [MLCK] or Rho-associated kinase [ROCK]), mediates mechanosensory YAP/TAZ activation [45,98,101]. This process also involves desmosomal proteins desmoglein 3 (DSG3), adaptor plakophilins (PKPs) [60,97,102], and keratin filaments [103] to create the membrane YAP/TAZ-sequestration complex. YAP/TAZ nuclear shuttling appears under inflammation-induced AJ disassembly [101] or changes in matrix stiffness [59,98,104]. YAP/TAZ is mechanosensory-regulated even at the nuclear level. Nuclear F-actin-dependent binding of YAP and TAZ by the SWI/SNF chromatin-remodelling complex via the AT-rich interaction domain 1A (ARID1A) subunit prevents their interaction with TEADs [105].
Several studies suggest a key role of cell–matrix contacts and associated signalling pathways, such as integrin/Src and focal adhesion kinases (FAKs), in the activation of YAP/TAZ in the epidermis [14,90]. SRC proto-oncogene, non-receptor tyrosine kinase (Src) directly phosphorylates YAP at three binding sites on tyrosine residues (341, 357, and 394) that allow YAP activation and subsequent target gene expression [90]. Dysregulated integrin signalling appears in junctional epidermolysis bullosa due to mutations in laminin 332, COL17A1, or integrin α6β4, leading to YAP suppression as a part of its pathological development [14,41,81]. In skin homeostasis, integrin/Src-mediated YAP activation is negatively controlled by α-catenin [90] and Thy-1 cell surface antigen (THY1), a GPI-anchored protein that also functions as an important part of the AJ complex [83]. Besides epidermal self-renewal, integrin-mediated YAP activation via the FAK/Src [89] and PI3K/pyruvate dehydrogenase kinase 1 (PDK1) pathways [106,107] is involved in wound healing and cancer progression.
Most of the mechanisms of YAP activation in KCs are summarised in Figure 2.
4.2. YAP/TAZ Interaction with Non-Hippo Signalling Pathway Epidermal KCs
YAP/TAZ interacting with other signalling pathways that participate in epithelial morphogenesis [12] and skin pathology, including ageing [39], fibrosis [108,109], and cancer [28], has been accurately discussed in recent reviews. In addition, several articles have focused on particular signalling pathways [110,111,112] and YAP/TAZ involvement in the signalling microenvironment [113,114,115]. Here, we summarise the YAP/TAZ interplay in KCs.
4.2.1. WNT/β-Catenin
The canonical WNT pathway activates β-catenin to regulate epidermal morphogenesis, HF formation and cycling, epidermal self-renewal, and stem cell pool maintenance [8,116,117,118] in KCs. YAP/TAZ and WNT/β-catenin interact in a mutually activating and suppressing manner. YAP and β-catenin expression and nuclear localisation patterns overlap within the epidermis. Increased proliferation in YAP2-5SA-ΔC hyperplastic skin and immortalised KCs (HaCaT cell line) is associated with canonical WNT/β-catenin activation via YAP-mediated Wnt family member 16 (WNT16) expression [21,22]. WNT/β-catenin drive skin abnormality progression induced by YAP overactivation [22,98].
The Hippo/YAP/TAZ and WNT/β-catenin pathways are connected at several levels. Firstly, YAP, TAZ, and β-catenin are sequestered in a common membrane complex that includes LDL receptor-related protein 6 (LRP6), axin 1 (AXIN1), glycogen synthase kinase 3 beta (GSK3β), and dishevelled [98,119,120,121]. YAP depletion significantly increased activated β-catenin via AJ complex destabilisation [68]. Several studies have proposed the existence of nuclear complexes that included YAP/TEAD interacting with LEF/TCF [122,123]. This observation highlights the common action of WNT and YAP in maintaining the undifferentiated and highly proliferative progenitor state.
4.2.2. Sonic Hedgehog Signalling Molecule (SHH)
SHH and WNT/β-catenin are involved in HF development and regeneration; however, both pathways also drive skin tumour progression. SHH upregulation was accompanied by YAP and WNT/β-catenin activation in mouse and human BCC [74,79]. In turn, YAP promotes the expression of SHH GLI family zinc finger 2 (GLI2) by activating β-catenin [79]. SHH and WNT are involved in YAP-derived increases in HF number after wounding in mouse skin [82]. WNT/β-catenin, SHH, and YAP/TAZ support the proliferation of low-differentiation cells, which is essential in epidermal self-renewal and HF cycling. Overactivation of all three signalling pathways in mouse skin leads to highly similar abnormalities in epidermal structure, including epidermal thickening and HF disorganisation, indicating positive feedback and cumulative regulation [42,79,124].
4.2.3. Notch
Unlike the WNT/β-catenin pathway, the Notch pathway induces epidermal cell commitment and differentiation [125]. YAP acts as a Notch antagonist. YAP activation upregulates the expression of Notch pathway inhibitors delta-like canonical Notch ligands 1 (DLL1) and 3 (DLL3) and NEDD4-like E3 ubiquitin-protein ligase (NEDD4L), while cytoplasmic YAP sequestration results in Notch-mediated subsequent KC differentiation [59,126]. YAP/TAZ suppression of Notch activity maintained low differentiation of the hypertrophic epidermis in genetically modified mice [40,59] and BCC [74].
4.2.4. EGF/EGFR
EGF stimulates KC proliferation in the homeostatic epidermis and promotes cell migration during wound healing. EGF/EGFR activity is required for YAP nuclear localisation in epidermal basal KCs [14,23]. The basic EGF/EGFR signalling pathway acts via the Ras/Raf/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) cascade [127]; however, it recruits PI3K/protein kinase B (AKT) [14] and AXL receptor tyrosine kinase (AXL)-mediated Hippo suppression [23,128,129] for YAP activation in the epidermis. EGFR also regulates YAP-derived motility by targeting YAP/DSG3 interplay [102] and via the conjunction of the EGFR/PI3K and EGFR/Src signalling [130]. PDK1, a downstream member of the EGFR/PI3K pathway, was recently identified as a disruptor of the Hippo complex, which is involved in regulating YAP phosphorylation in KCs [14] but not in SCC cells [23,131].
The EGF/EGFR pathway is one of the most upregulated under YAP/TAZ activation. Amphiregulin (AREG), a direct target of YAP and TAZ, is an EGFR ligand that promotes extensive cell proliferation after UV damage [67] and in multiple skin diseases [46,47]. This observation indicates a positive feedback between YAP/TAZ and EGFR activation [132].
4.2.5. Fibroblast Growth Factors (FGFs)
The interplay of FGF7/10 with WNTs regulates basal cell proliferation and subsequent epidermal stratification during development [133]. In adult skin, FGF10 produced by dermal fibroblasts binds fibroblast growth factor receptor 2 (FGFR2b) in KCs to stimulate ERK-mediated YAP activity to induce skin regeneration after injury [44]. FGFs cooperate with other signalling pathways during wound healing, including TGFβ, a well-known YAP/TAZ partner in skin regeneration [134]. FGFR/YAP/TAZ interplay requires in-depth future investigation because their cooperation has already been explored in breast and liver cancer [135,136].
4.2.6. G-Protein Coupled Receptors (GPCRs)
GPCRs are a family of membrane proteins involved in key physiological skin functions in homeostasis and pathology. Agonist activation of GPCRs promotes the exchange of GTP with GDP from the guanine nucleotide-binding protein alpha subunit (Gα). GTP-bound Gα dissociates from the guanine nucleotide-binding protein beta/gamma complex (Gβγ), and Gα and Gβγ separately activate a series of downstream effectors. The interplay between GPCRs and YAP in the skin epidermis has been carefully described in a recent review [137]. Briefly, the G protein subunit alpha S (Gαs) and its effector protein kinase A (PKA) suppress YAP1 and the SHH pathway to control skin development and act as an antioncogene [138]. In contrast, GPCR-G protein subunit alpha i (Gαi) activation can increase KC proliferation and suppress differentiation, leading to epidermal hyperplasia via upregulation of YAP but not SHH [139]. Uridine 5′-diphosphate-specific G protein-coupled pyrimidinergic receptor P2Y6 (P2RY6) was found to be involved in activating proinflammatory responses, including YAP and WNT upregulation, and could be involved in skin regeneration [140].
4.2.7. TGFβ/Bone Morphogenetic Protein (BMP)/SMAD
TGFβ receptor (TGFβR) signalling activation results in type I and type II receptor dimerisation and the phosphorylation of receptor-regulated SMADs (R-SMADs), which form a complex with SMAD family member 4 (SMAD4), translocate to the nucleus, and activate the transcription programme. SMAD family members 1/5/8 (SMAD1/5/8) are usually related to BMP ligands, while SMAD family members 2/3 (SMAD2/3) are associated with TGFβ. SMAD family member 7 (SMAD7) has mostly inhibitory functions [141].
TGFβs are a major group of inflammation drivers in skin regeneration [142]. The control of the expression of TGFβ pathway members by YAP/TAZ has been explored in genetically modified mouse models and cultured human KCs [75,84]. YAP and TAZ work with TGFβ1 by creating molecular complexes with SMAD2/3 [24,25] to promote EMT, wound healing and carcinogenesis [24,25,53].
In contrast, BMP/SMAD1/5/8 and SMAD7 are involved in epidermal and HF development during embryogenesis in crosstalk with WNTs and FGFs [133,143,144]. In adult skin, BMPs regulate KC differentiation in the IFE and cell specification during HF regeneration [145]. The possible interplay of BMP/YAP/TAZ in the epidermis is indicated by their upregulation in several cancer types [146,147]. SMAD7 is involved in stabilising E-cadherin/β-catenin [148] and can form complexes with YAP [148], thus preventing its activation and preserving it in the cytoplasm. However, SMAD7 is involved in upregulating KC proliferation in psoriasis and carcinogenesis, indicating more complicated signalling regulation [149,150]. As YAP overexpression/overactivation destabilises the balance between proliferation and differentiation in the epidermis and HFs, BMPs are expected to be downregulated or incapable of realising their target expression.
4.2.8. Nuclear Factor Kappa B (NF-κB)
The NF-κB pathway integrates the extracellular inflammation signals and transmits them via phosphorylation of the IκB kinase (IKK) complex, activating proinflammatory gene expression [151]. During skin development and postnatal homeostasis, the NF-κB pathway is involved not only in injury-related responses but also in the differentiation processes of HF patterning and morphogenesis [152].
YAP and NF-κB mutually regulate each other’s activity in inflammation but not in a morphogenetic manner. YAP-mediated positive regulation of NF-κB/p65 was found in HaCaT cells [46], while interleukin 17A (IL17A) upregulates actin-related gene 1 (ACT1)/NF-κB and subsequent YAP activation, resulting in psoriatic-like alterations in KCs [47]. NF-κB is one of the most upregulated signals in psoriasis [153], as well as YAP and TAZ [154].
In conclusion, YAP/TAZ sustain a series of positive feedback loops with proinflammatory signalling pathways (TGFβ, EGF, and NF-kB), WNT/β-catenin, and SHH that are involved in preventing the differentiation and maintaining proliferation. YAP suppresses differentiation, while TAZ accompanies YAP in case of injury or disease. This highlights the multi-level control of YAP and TAZ activity in homeostatic skin.
4.1. Hippo-Dependent YAP/TAZ Activation
The mechanism of YAP/TAZ nuclear shuttling is context-dependent and differs between organs, tissues, and cell types; it involves a group of regulatory elements controlling YAP and TAZ activity directly or via the Hippo pathway. Hippo signalling plays a key role in the differentiation and specification of simple and pseudostratified epithelia [12,36,93,94,95]. Here, we discuss the particular mechanisms of YAP/TAZ activation in stratified cornifying epidermis.
Under Hippo activation, active MST1/2 phosphorylates SAV1 and MOB1/2, which assist MST1/2 in recruiting and phosphorylating LATS1/2. Activated LATS1/2 serine/threonine kinases phosphorylate YAP and TAZ on their serine residues. In humans, inhibition includes phosphorylation of S127 and S381 in YAP and S89 and S311 in TAZ. YAP-S127 and TAZ-S89 phosphorylation is required for 14-3-3 protein binding to establish the molecular complex for cytoplasmic sequestration and subsequent inactivation. S381/S311 phosphorylation of YAP/TAZ, respectively, initiates proteasomal degradation. Dephosphorylases revert active YAP/TAZ conformation and signalling activity. The canonical Hippo pathway has been discussed in detail in several recent reviews [28,96].
Hippo pathway suppression promotes YAP activation during embryonic skin development [40,61] or until confluence is established in KC cultures [97,98], but not after achieving contact inhibition [18,19]. Unlike other epithelial types, Hippo activity is not controlled by cell polarity protein complexes in the epidermis [58]. YAP and TAZ cytoplasmic states are maintained primarily by cell–cell and cell–matrix contacts [97,98]. Junction-associated intracellular proteins 14-3-3 and α-catenin sequester phosphorylated YAP and TAZ in the cytoplasm [63,98,99,100]. Disrupting the α-catenin/14-3-3σ/YAP complex allows YAP to interact with the catalytic subunit of protein phosphatase 2A (PP2AC), triggering its activation [18].
The E-cadherin/AJ and actomyosin complex, including F-actin, myosin II, actin-related proteins 2/3 (ARP2/3), and myosin light chain kinases (either myosin light chain kinase [MLCK] or Rho-associated kinase [ROCK]), mediates mechanosensory YAP/TAZ activation [45,98,101]. This process also involves desmosomal proteins desmoglein 3 (DSG3), adaptor plakophilins (PKPs) [60,97,102], and keratin filaments [103] to create the membrane YAP/TAZ-sequestration complex. YAP/TAZ nuclear shuttling appears under inflammation-induced AJ disassembly [101] or changes in matrix stiffness [59,98,104]. YAP/TAZ is mechanosensory-regulated even at the nuclear level. Nuclear F-actin-dependent binding of YAP and TAZ by the SWI/SNF chromatin-remodelling complex via the AT-rich interaction domain 1A (ARID1A) subunit prevents their interaction with TEADs [105].
Several studies suggest a key role of cell–matrix contacts and associated signalling pathways, such as integrin/Src and focal adhesion kinases (FAKs), in the activation of YAP/TAZ in the epidermis [14,90]. SRC proto-oncogene, non-receptor tyrosine kinase (Src) directly phosphorylates YAP at three binding sites on tyrosine residues (341, 357, and 394) that allow YAP activation and subsequent target gene expression [90]. Dysregulated integrin signalling appears in junctional epidermolysis bullosa due to mutations in laminin 332, COL17A1, or integrin α6β4, leading to YAP suppression as a part of its pathological development [14,41,81]. In skin homeostasis, integrin/Src-mediated YAP activation is negatively controlled by α-catenin [90] and Thy-1 cell surface antigen (THY1), a GPI-anchored protein that also functions as an important part of the AJ complex [83]. Besides epidermal self-renewal, integrin-mediated YAP activation via the FAK/Src [89] and PI3K/pyruvate dehydrogenase kinase 1 (PDK1) pathways [106,107] is involved in wound healing and cancer progression.
Most of the mechanisms of YAP activation in KCs are summarised in Figure 2.
4.2. YAP/TAZ Interaction with Non-Hippo Signalling Pathway Epidermal KCs
YAP/TAZ interacting with other signalling pathways that participate in epithelial morphogenesis [12] and skin pathology, including ageing [39], fibrosis [108,109], and cancer [28], has been accurately discussed in recent reviews. In addition, several articles have focused on particular signalling pathways [110,111,112] and YAP/TAZ involvement in the signalling microenvironment [113,114,115]. Here, we summarise the YAP/TAZ interplay in KCs.
4.2.1. WNT/β-Catenin
The canonical WNT pathway activates β-catenin to regulate epidermal morphogenesis, HF formation and cycling, epidermal self-renewal, and stem cell pool maintenance [8,116,117,118] in KCs. YAP/TAZ and WNT/β-catenin interact in a mutually activating and suppressing manner. YAP and β-catenin expression and nuclear localisation patterns overlap within the epidermis. Increased proliferation in YAP2-5SA-ΔC hyperplastic skin and immortalised KCs (HaCaT cell line) is associated with canonical WNT/β-catenin activation via YAP-mediated Wnt family member 16 (WNT16) expression [21,22]. WNT/β-catenin drive skin abnormality progression induced by YAP overactivation [22,98].
The Hippo/YAP/TAZ and WNT/β-catenin pathways are connected at several levels. Firstly, YAP, TAZ, and β-catenin are sequestered in a common membrane complex that includes LDL receptor-related protein 6 (LRP6), axin 1 (AXIN1), glycogen synthase kinase 3 beta (GSK3β), and dishevelled [98,119,120,121]. YAP depletion significantly increased activated β-catenin via AJ complex destabilisation [68]. Several studies have proposed the existence of nuclear complexes that included YAP/TEAD interacting with LEF/TCF [122,123]. This observation highlights the common action of WNT and YAP in maintaining the undifferentiated and highly proliferative progenitor state.
4.2.2. Sonic Hedgehog Signalling Molecule (SHH)
SHH and WNT/β-catenin are involved in HF development and regeneration; however, both pathways also drive skin tumour progression. SHH upregulation was accompanied by YAP and WNT/β-catenin activation in mouse and human BCC [74,79]. In turn, YAP promotes the expression of SHH GLI family zinc finger 2 (GLI2) by activating β-catenin [79]. SHH and WNT are involved in YAP-derived increases in HF number after wounding in mouse skin [82]. WNT/β-catenin, SHH, and YAP/TAZ support the proliferation of low-differentiation cells, which is essential in epidermal self-renewal and HF cycling. Overactivation of all three signalling pathways in mouse skin leads to highly similar abnormalities in epidermal structure, including epidermal thickening and HF disorganisation, indicating positive feedback and cumulative regulation [42,79,124].
4.2.3. Notch
Unlike the WNT/β-catenin pathway, the Notch pathway induces epidermal cell commitment and differentiation [125]. YAP acts as a Notch antagonist. YAP activation upregulates the expression of Notch pathway inhibitors delta-like canonical Notch ligands 1 (DLL1) and 3 (DLL3) and NEDD4-like E3 ubiquitin-protein ligase (NEDD4L), while cytoplasmic YAP sequestration results in Notch-mediated subsequent KC differentiation [59,126]. YAP/TAZ suppression of Notch activity maintained low differentiation of the hypertrophic epidermis in genetically modified mice [40,59] and BCC [74].
4.2.4. EGF/EGFR
EGF stimulates KC proliferation in the homeostatic epidermis and promotes cell migration during wound healing. EGF/EGFR activity is required for YAP nuclear localisation in epidermal basal KCs [14,23]. The basic EGF/EGFR signalling pathway acts via the Ras/Raf/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) cascade [127]; however, it recruits PI3K/protein kinase B (AKT) [14] and AXL receptor tyrosine kinase (AXL)-mediated Hippo suppression [23,128,129] for YAP activation in the epidermis. EGFR also regulates YAP-derived motility by targeting YAP/DSG3 interplay [102] and via the conjunction of the EGFR/PI3K and EGFR/Src signalling [130]. PDK1, a downstream member of the EGFR/PI3K pathway, was recently identified as a disruptor of the Hippo complex, which is involved in regulating YAP phosphorylation in KCs [14] but not in SCC cells [23,131].
The EGF/EGFR pathway is one of the most upregulated under YAP/TAZ activation. Amphiregulin (AREG), a direct target of YAP and TAZ, is an EGFR ligand that promotes extensive cell proliferation after UV damage [67] and in multiple skin diseases [46,47]. This observation indicates a positive feedback between YAP/TAZ and EGFR activation [132].
4.2.5. Fibroblast Growth Factors (FGFs)
The interplay of FGF7/10 with WNTs regulates basal cell proliferation and subsequent epidermal stratification during development [133]. In adult skin, FGF10 produced by dermal fibroblasts binds fibroblast growth factor receptor 2 (FGFR2b) in KCs to stimulate ERK-mediated YAP activity to induce skin regeneration after injury [44]. FGFs cooperate with other signalling pathways during wound healing, including TGFβ, a well-known YAP/TAZ partner in skin regeneration [134]. FGFR/YAP/TAZ interplay requires in-depth future investigation because their cooperation has already been explored in breast and liver cancer [135,136].
4.2.6. G-Protein Coupled Receptors (GPCRs)
GPCRs are a family of membrane proteins involved in key physiological skin functions in homeostasis and pathology. Agonist activation of GPCRs promotes the exchange of GTP with GDP from the guanine nucleotide-binding protein alpha subunit (Gα). GTP-bound Gα dissociates from the guanine nucleotide-binding protein beta/gamma complex (Gβγ), and Gα and Gβγ separately activate a series of downstream effectors. The interplay between GPCRs and YAP in the skin epidermis has been carefully described in a recent review [137]. Briefly, the G protein subunit alpha S (Gαs) and its effector protein kinase A (PKA) suppress YAP1 and the SHH pathway to control skin development and act as an antioncogene [138]. In contrast, GPCR-G protein subunit alpha i (Gαi) activation can increase KC proliferation and suppress differentiation, leading to epidermal hyperplasia via upregulation of YAP but not SHH [139]. Uridine 5′-diphosphate-specific G protein-coupled pyrimidinergic receptor P2Y6 (P2RY6) was found to be involved in activating proinflammatory responses, including YAP and WNT upregulation, and could be involved in skin regeneration [140].
4.2.7. TGFβ/Bone Morphogenetic Protein (BMP)/SMAD
TGFβ receptor (TGFβR) signalling activation results in type I and type II receptor dimerisation and the phosphorylation of receptor-regulated SMADs (R-SMADs), which form a complex with SMAD family member 4 (SMAD4), translocate to the nucleus, and activate the transcription programme. SMAD family members 1/5/8 (SMAD1/5/8) are usually related to BMP ligands, while SMAD family members 2/3 (SMAD2/3) are associated with TGFβ. SMAD family member 7 (SMAD7) has mostly inhibitory functions [141].
TGFβs are a major group of inflammation drivers in skin regeneration [142]. The control of the expression of TGFβ pathway members by YAP/TAZ has been explored in genetically modified mouse models and cultured human KCs [75,84]. YAP and TAZ work with TGFβ1 by creating molecular complexes with SMAD2/3 [24,25] to promote EMT, wound healing and carcinogenesis [24,25,53].
In contrast, BMP/SMAD1/5/8 and SMAD7 are involved in epidermal and HF development during embryogenesis in crosstalk with WNTs and FGFs [133,143,144]. In adult skin, BMPs regulate KC differentiation in the IFE and cell specification during HF regeneration [145]. The possible interplay of BMP/YAP/TAZ in the epidermis is indicated by their upregulation in several cancer types [146,147]. SMAD7 is involved in stabilising E-cadherin/β-catenin [148] and can form complexes with YAP [148], thus preventing its activation and preserving it in the cytoplasm. However, SMAD7 is involved in upregulating KC proliferation in psoriasis and carcinogenesis, indicating more complicated signalling regulation [149,150]. As YAP overexpression/overactivation destabilises the balance between proliferation and differentiation in the epidermis and HFs, BMPs are expected to be downregulated or incapable of realising their target expression.
4.2.8. Nuclear Factor Kappa B (NF-κB)
The NF-κB pathway integrates the extracellular inflammation signals and transmits them via phosphorylation of the IκB kinase (IKK) complex, activating proinflammatory gene expression [151]. During skin development and postnatal homeostasis, the NF-κB pathway is involved not only in injury-related responses but also in the differentiation processes of HF patterning and morphogenesis [152].
YAP and NF-κB mutually regulate each other’s activity in inflammation but not in a morphogenetic manner. YAP-mediated positive regulation of NF-κB/p65 was found in HaCaT cells [46], while interleukin 17A (IL17A) upregulates actin-related gene 1 (ACT1)/NF-κB and subsequent YAP activation, resulting in psoriatic-like alterations in KCs [47]. NF-κB is one of the most upregulated signals in psoriasis [153], as well as YAP and TAZ [154].
In conclusion, YAP/TAZ sustain a series of positive feedback loops with proinflammatory signalling pathways (TGFβ, EGF, and NF-kB), WNT/β-catenin, and SHH that are involved in preventing the differentiation and maintaining proliferation. YAP suppresses differentiation, while TAZ accompanies YAP in case of injury or disease. This highlights the multi-level control of YAP and TAZ activity in homeostatic skin.
4.2.1. WNT/β-Catenin
The canonical WNT pathway activates β-catenin to regulate epidermal morphogenesis, HF formation and cycling, epidermal self-renewal, and stem cell pool maintenance [8,116,117,118] in KCs. YAP/TAZ and WNT/β-catenin interact in a mutually activating and suppressing manner. YAP and β-catenin expression and nuclear localisation patterns overlap within the epidermis. Increased proliferation in YAP2-5SA-ΔC hyperplastic skin and immortalised KCs (HaCaT cell line) is associated with canonical WNT/β-catenin activation via YAP-mediated Wnt family member 16 (WNT16) expression [21,22]. WNT/β-catenin drive skin abnormality progression induced by YAP overactivation [22,98].
The Hippo/YAP/TAZ and WNT/β-catenin pathways are connected at several levels. Firstly, YAP, TAZ, and β-catenin are sequestered in a common membrane complex that includes LDL receptor-related protein 6 (LRP6), axin 1 (AXIN1), glycogen synthase kinase 3 beta (GSK3β), and dishevelled [98,119,120,121]. YAP depletion significantly increased activated β-catenin via AJ complex destabilisation [68]. Several studies have proposed the existence of nuclear complexes that included YAP/TEAD interacting with LEF/TCF [122,123]. This observation highlights the common action of WNT and YAP in maintaining the undifferentiated and highly proliferative progenitor state.
4.2.2. Sonic Hedgehog Signalling Molecule (SHH)
SHH and WNT/β-catenin are involved in HF development and regeneration; however, both pathways also drive skin tumour progression. SHH upregulation was accompanied by YAP and WNT/β-catenin activation in mouse and human BCC [74,79]. In turn, YAP promotes the expression of SHH GLI family zinc finger 2 (GLI2) by activating β-catenin [79]. SHH and WNT are involved in YAP-derived increases in HF number after wounding in mouse skin [82]. WNT/β-catenin, SHH, and YAP/TAZ support the proliferation of low-differentiation cells, which is essential in epidermal self-renewal and HF cycling. Overactivation of all three signalling pathways in mouse skin leads to highly similar abnormalities in epidermal structure, including epidermal thickening and HF disorganisation, indicating positive feedback and cumulative regulation [42,79,124].
4.2.3. Notch
Unlike the WNT/β-catenin pathway, the Notch pathway induces epidermal cell commitment and differentiation [125]. YAP acts as a Notch antagonist. YAP activation upregulates the expression of Notch pathway inhibitors delta-like canonical Notch ligands 1 (DLL1) and 3 (DLL3) and NEDD4-like E3 ubiquitin-protein ligase (NEDD4L), while cytoplasmic YAP sequestration results in Notch-mediated subsequent KC differentiation [59,126]. YAP/TAZ suppression of Notch activity maintained low differentiation of the hypertrophic epidermis in genetically modified mice [40,59] and BCC [74].
4.2.4. EGF/EGFR
EGF stimulates KC proliferation in the homeostatic epidermis and promotes cell migration during wound healing. EGF/EGFR activity is required for YAP nuclear localisation in epidermal basal KCs [14,23]. The basic EGF/EGFR signalling pathway acts via the Ras/Raf/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) cascade [127]; however, it recruits PI3K/protein kinase B (AKT) [14] and AXL receptor tyrosine kinase (AXL)-mediated Hippo suppression [23,128,129] for YAP activation in the epidermis. EGFR also regulates YAP-derived motility by targeting YAP/DSG3 interplay [102] and via the conjunction of the EGFR/PI3K and EGFR/Src signalling [130]. PDK1, a downstream member of the EGFR/PI3K pathway, was recently identified as a disruptor of the Hippo complex, which is involved in regulating YAP phosphorylation in KCs [14] but not in SCC cells [23,131].
The EGF/EGFR pathway is one of the most upregulated under YAP/TAZ activation. Amphiregulin (AREG), a direct target of YAP and TAZ, is an EGFR ligand that promotes extensive cell proliferation after UV damage [67] and in multiple skin diseases [46,47]. This observation indicates a positive feedback between YAP/TAZ and EGFR activation [132].
4.2.5. Fibroblast Growth Factors (FGFs)
The interplay of FGF7/10 with WNTs regulates basal cell proliferation and subsequent epidermal stratification during development [133]. In adult skin, FGF10 produced by dermal fibroblasts binds fibroblast growth factor receptor 2 (FGFR2b) in KCs to stimulate ERK-mediated YAP activity to induce skin regeneration after injury [44]. FGFs cooperate with other signalling pathways during wound healing, including TGFβ, a well-known YAP/TAZ partner in skin regeneration [134]. FGFR/YAP/TAZ interplay requires in-depth future investigation because their cooperation has already been explored in breast and liver cancer [135,136].
4.2.6. G-Protein Coupled Receptors (GPCRs)
GPCRs are a family of membrane proteins involved in key physiological skin functions in homeostasis and pathology. Agonist activation of GPCRs promotes the exchange of GTP with GDP from the guanine nucleotide-binding protein alpha subunit (Gα). GTP-bound Gα dissociates from the guanine nucleotide-binding protein beta/gamma complex (Gβγ), and Gα and Gβγ separately activate a series of downstream effectors. The interplay between GPCRs and YAP in the skin epidermis has been carefully described in a recent review [137]. Briefly, the G protein subunit alpha S (Gαs) and its effector protein kinase A (PKA) suppress YAP1 and the SHH pathway to control skin development and act as an antioncogene [138]. In contrast, GPCR-G protein subunit alpha i (Gαi) activation can increase KC proliferation and suppress differentiation, leading to epidermal hyperplasia via upregulation of YAP but not SHH [139]. Uridine 5′-diphosphate-specific G protein-coupled pyrimidinergic receptor P2Y6 (P2RY6) was found to be involved in activating proinflammatory responses, including YAP and WNT upregulation, and could be involved in skin regeneration [140].
4.2.7. TGFβ/Bone Morphogenetic Protein (BMP)/SMAD
TGFβ receptor (TGFβR) signalling activation results in type I and type II receptor dimerisation and the phosphorylation of receptor-regulated SMADs (R-SMADs), which form a complex with SMAD family member 4 (SMAD4), translocate to the nucleus, and activate the transcription programme. SMAD family members 1/5/8 (SMAD1/5/8) are usually related to BMP ligands, while SMAD family members 2/3 (SMAD2/3) are associated with TGFβ. SMAD family member 7 (SMAD7) has mostly inhibitory functions [141].
TGFβs are a major group of inflammation drivers in skin regeneration [142]. The control of the expression of TGFβ pathway members by YAP/TAZ has been explored in genetically modified mouse models and cultured human KCs [75,84]. YAP and TAZ work with TGFβ1 by creating molecular complexes with SMAD2/3 [24,25] to promote EMT, wound healing and carcinogenesis [24,25,53].
In contrast, BMP/SMAD1/5/8 and SMAD7 are involved in epidermal and HF development during embryogenesis in crosstalk with WNTs and FGFs [133,143,144]. In adult skin, BMPs regulate KC differentiation in the IFE and cell specification during HF regeneration [145]. The possible interplay of BMP/YAP/TAZ in the epidermis is indicated by their upregulation in several cancer types [146,147]. SMAD7 is involved in stabilising E-cadherin/β-catenin [148] and can form complexes with YAP [148], thus preventing its activation and preserving it in the cytoplasm. However, SMAD7 is involved in upregulating KC proliferation in psoriasis and carcinogenesis, indicating more complicated signalling regulation [149,150]. As YAP overexpression/overactivation destabilises the balance between proliferation and differentiation in the epidermis and HFs, BMPs are expected to be downregulated or incapable of realising their target expression.
4.2.8. Nuclear Factor Kappa B (NF-κB)
The NF-κB pathway integrates the extracellular inflammation signals and transmits them via phosphorylation of the IκB kinase (IKK) complex, activating proinflammatory gene expression [151]. During skin development and postnatal homeostasis, the NF-κB pathway is involved not only in injury-related responses but also in the differentiation processes of HF patterning and morphogenesis [152].
YAP and NF-κB mutually regulate each other’s activity in inflammation but not in a morphogenetic manner. YAP-mediated positive regulation of NF-κB/p65 was found in HaCaT cells [46], while interleukin 17A (IL17A) upregulates actin-related gene 1 (ACT1)/NF-κB and subsequent YAP activation, resulting in psoriatic-like alterations in KCs [47]. NF-κB is one of the most upregulated signals in psoriasis [153], as well as YAP and TAZ [154].
In conclusion, YAP/TAZ sustain a series of positive feedback loops with proinflammatory signalling pathways (TGFβ, EGF, and NF-kB), WNT/β-catenin, and SHH that are involved in preventing the differentiation and maintaining proliferation. YAP suppresses differentiation, while TAZ accompanies YAP in case of injury or disease. This highlights the multi-level control of YAP and TAZ activity in homeostatic skin.
5. YAP/TAZ/TEAD in the Nucleus
5.1. YAP and TAZ Structure
Human YAP and TAZ have high primary sequence similarity and, thus, several shared structural features, including domain structure. However, the differences in their structures can influence their activation and interaction mechanisms with TFs in the nucleus to regulate gene expression.
The C-terminal region of both proteins contains the coiled-coil (CC) domain, transactivation domain, and PDZ-binding domain [155,156]. The PDZ-binding domain is responsible for their sequestration by 14-3-3 proteins and thus involved in controlling YAP/TAZ in the cytoplasm [157,158]. The SRC homology 3 domain (SH3)-binding motif (amino acids PVKQPPPLAP) located between the WW and the CC domains is sufficient for interaction with several proteins, including the YES proto-oncogene (YES) and Src kinases [156]. Thus, it indicates mostly common activation mechanisms for YAP and TAZ.
The TEAD-binding domain (TBD) is located in the N-terminal region of both transcriptional coactivators and consists of α-helixes and the Ω-loop with a linker region and contacts the TEADs in the N-terminal region [159,160]. The linker is shorter in TAZ than in YAP since it lacks the PXXFP motif, which is significant for the YAP/TEAD interaction [160]. However, this fact allows TEAD interactions with two TAZ molecules and the formation of a heterotetramer instead of a heterodimer [155].
The WW domain is sufficient for the interactions of YAP and TAZ with other TFs, regulatory proteins, and chromatin remodelers. TAZ contains only one WW domain, while the number of WW domains in YAP depends on the isoform: YAP1 contains one WW domain, YAP2 contains two WW domains, and YAP2-L differs from YAP2 in that it contains 16 amino acid residues in the transcription activation domain [161]. The type of isoform was found to be significant in tumour development [162,163,164]; however, it has been poorly investigated in the epidermis.
Several YAP and TAZ domains feature an intrinsically disordered region (IDR), including the CC domain, transactivation domain, and small IDR in the YAP C-terminal region. These domains are involved in liquid–liquid phase separation (LLPS) and non-specific interactions with other IDR-containing TFs during protein condensation [165]. This finding underscores that YAP/TAZ/TEAD can interact specifically and non-specifically with other TFs, and the nature of these interactions may influence the realisation of the cell transcription programme.
5.2. YAP/TAZ-Mediated Transcription Regulation
YAP and TAZ primarily interact with TFs of TEAD family members in the nucleus. All four members of the TEAD family are paralogues and have the same domain structure. Their N terminal region contains a variable region and DNA-binding TEA/ATTS domain. The TEA domain binds to DNA elements and TFs necessary for target transcription activation [166]. The C-terminal transactivation YAP binding domain (YBD) recruits transcription coactivators (YAP, TAZ, and vestigial-like family members 1–4 [VGLL1/2/3/4]) [167,168]. The TEA domain and YBD are separated by a proline-rich domain. Differences in the last two domain structures are presumably responsible for TEAD functional specialisation [169].
Complexes of YAP and TAZ with TEA domain transcription factor 1–4 (TEAD1/2/3/4) have different functions in skin development, homeostasis, and tumorigenesis [170]. TEAD1 is the predominant paralog expressed in epidermal KCs [171]. Along with TEAD3, it is involved in KC self-renewal and proliferation [19,172]. TEAD1 is also detected in various cancer types, including SCC [173,174,175]. TEAD4 expression correlates with KC activation and EMT and, thus, promotes psoriasis and tumour formation [176,177,178,179,180]. TEAD2 appears to play a critical role during development and neural tube closure [181]. Elevated TEAD2 expression has also been associated with cancer progression and tumour drug resistance [182,183].
YAP/TAZ/TEAD bind mostly distant regulatory sequences (enhancers and super-enhancers) regulating target gene expression [184]. To drive the transcription, the YAP/TAZ/TEAD complex recruits bromodomain-containing protein 4 (BRD4) [185] and the Mediator complex to promote cyclin-dependent kinase 9 (CDK9)-dependent transcriptional activity [186]. In addition, YAP associates with the MYB proto-oncogene-like 2 (MYBL2/B-MYB) and MuvB TF complex, leading to the formation of the MMB complex, chromatin looping, and the induction of target gene expression, particularly cell cycle-associated genes [187]. YAP overactivation increases chromatin accessibility at TEAD motifs [188]. YAP activity correlates with CCCTC-binding factor (CTCF)-mediated chromatin reorganisation. It has been found that Hippo pathway activation results in both YAP and CTCF phosphorylation, which reduces the ability of CTCF to form chromatin loops [189]. Figure 3 describes the cytoplasmic and nuclear interactions of YAP/TAZ with signalling pathways, TFs, and chromatin-associated proteins.
5.3. YAP/TAZ/TEAD Interactions with TFs
5.3.1. Activator Protein-1 (AP-1)
AP-1 is the second most abundant TF complex interacting with YAP/TAZ after TEADs. AP1 are dimers of the JUN (JUN, JUNB, JUND) and FOS (FOS, FOSB, FOSL1, and FOSL2) families of leucine-zipper proteins. AP-1 physically interacts with YAP/TAZ/TEAD, promoting target gene expression [184]. AP-1 binds up to 85% of enhancers involved in controlling YAP/TAZ target gene expression: TEADs and AP-1 occupy very similar regions [184]. AP-1 predominantly binds TEADs, although YAP and TAZ may also be involved in the organisation of the transcriptional complex via their WW and TB domains [190,191]. YAP-mediated chromatin reorganisation facilitates genome-wide AP-1 accessibility [188]. AP-1 has been identified as one of the primary factors that recruits SWI/SNF complexes [192]. YAP and TAZ could participate in this complex by binding ARID1A [105].
In the epidermis, AP-1 is one of the most critical TF complexes that regulate KC-specific gene expression and proper KC differentiation [193,194]. Interactions with AP-1 could be one of the key mechanisms by which YAP/TAZ/TEAD regulate the KC state.
5.3.2. TEAD-Binding Proteins
Besides YAP and TAZ, VGLL1, VGLL2, VGLL3, VGLL4, and p160 can interact with TEADs to form transcription complexes [170]. The VGLL-TEAD interaction antagonises YAP-driven transcription in several cancer cell lines [195,196,197]. In the skin, VGLL4 inhibits YAP/TEAD-dependent transcription [197]; however, VGLL4 deficiency does not result in skin abnormalities [198]. Other VGLL proteins are associated with hyperproliferative phenotypes [199,200].
Members of the p160/SRC family, nuclear receptor coactivators 1 (NCOA1/SRC1), 2 (NCOA2/SRC2/TIF2/GRIP1), and 3 (NCOA3/SRC3/AIB1/ACTR), can interact with TEAD2 [201]. VGLL3 and p160/SRCs complexes are involved in skin immunity regulation and wound healing [202].
Since VGLLs and p160 antagonise YAP and TAZ through their ability to form complexes with TEADs and their expression is found in the skin under homeostasis and pathology, these proteins could be involved in modulating YAP/TAZ nuclear activity to control KCs’ fate.
5.3.3. p63
p63 is a TF belonging to a p53 family involved in epidermal development, KC proliferation and differentiation, as well as apoptosis, stem cell maintenance, and adhesion [203,204] by activating and suppressing the expression of target genes, alongside mediating epigenetic regulation [204,205,206]. p63 has different isoforms created by alternative splicing: the TAp63 isoform has an N-terminal transcriptional activation (TA) domain, while dNp63 does not [207]. Both isoforms balance each other and act cooperatively during the stratification process [208,209]. dNp63 is predominantly expressed in the basal layer of the skin epidermis, stimulating cell proliferation and maintaining cell stemness [207]. TAp63 is involved in differentiation and stratification initiation [204].
In the epidermis, YAP/TAZ signalling and p63 are closely interconnected, mutually regulating each other [18,40,41,57,77,210]. dNp63α colocalises with activated nuclear YAP in proliferating basal KCs, while in dNp63α-negative suprabasal layers, YAP is located in the cytoplasm [41]. YAP and dNp63α act jointly to maintain the undifferentiated state of SCC cells [106,107,211,212].
YAP physically bound and stabilised dNp63 in HaCaT cells [77,213,214]. YAP and dNp63 could jointly regulate chromatin remodelling. YAP influences the accessibility of dNp63 target genes [215]. dNp63 interacts with the SWI/SNF complex, resulting in dNp63 target gene expression [206]. p63 cooperates in CTCF-mediated chromatin 3D structure reorganisation [216]. Since dNp63α and YAP/TAZ stimulate the expression of very similar targets and are involved in CTCF-mediated chromatin remodelling [189], this predisposes complex regulation of the availability of transcription regulatory elements in chromatin by YAP/TAZ and p63.
5.3.4. Tumour Protein p73 (TP73)
TF p73 can interact with its homologues p53 and p63. Like p63, it is expressed in human KCs in vivo and in vitro [217]. Nuclear p73 can bind YAP through YAP’s WW domain and its poly-proline (PPPPY) motif [213,218]. YAP/p73 complexes are abundant in colon and breast cancer cell lines [218,219]; the YAP/p73 complex promoted apoptosis [77] and induced differentiation [220] in several cell types, the opposite effect of the traditionally mentioned YAP/TEAD complex. In epidermal KCs, YAP predominantly forms complexes with p63 rather than p73 [77], explaining the observed YAP-mediated effects on KCs.
5.3.5. KLF Transcription Factor 4 (KLF4)
KLF4 is a master gene regulating epidermal cell fate [221]. It regulates the transcription of epidermal differentiation genes involved in stratification and cornification [222]. The YAP/TAZ/TEAD complex negatively regulates KLF4 expression. KC differentiation induced by TEADi requires KLF4, while KLF4 KO increases transcriptional activity downstream of YAP1. KLF4 mediates YAP/TAZ/TEAD transcriptional activity via direct binding by its activation domain [68]. In cancer cells, KLF4 is suppressed, and YAP promotes snail family transcriptional repressor 2 (SNAI2) expression, which is involved in downregulating KLF4 expression [74]. The interplay between KLF4 and the YAP/TAZ/TEAD complex regulates the balance between KC proliferation and differentiation.
5.3.6. WW Domain Binding Protein 2 (WBP2)
WBP2 was recently identified as a TF connected to the YAP/TAZ/TEAD complex. WBP2 acts as a cofactor of YAP, enhancing TEAD-mediated transcription. WBP2 partially implements the YAP transcription programme. In SCC cells, WBP2 KD suppressed proliferation, while YAP KD also induced cell differentiation. WBP2 KO reduced basal KC proliferation and nuclear YAP accumulation during mouse embryogenesis [19].
5.3.7. EMT-Related TFs
EMT is required for tissue regeneration and correct wound healing. It involves partial epithelial cell dedifferentiation, increased proliferation, and cell motility that enables the delivery of rapidly dividing epithelial progenitors to the injury site. After wound-closing, cells undergo redifferentiation and mesenchymal-to-epithelial transformation or cell senescence, followed by elimination by the immune system. Prolonged or chronic EMT disorganises the regeneration processes and arrests tissue architecture reconstruction due to extensive fibrosis or tumorigenesis [223]. YAP and TAZ behaviour follows all these principles: they are essential for correct wound healing and support cell proliferation and migration [14,84], with their overactivation in normal cells resulting in cell senescence under culture conditions [41], while YAP overactivation in mouse skin promoted tumour development with SCC characteristics [18,52]. YAP and TAZ do not promote cell de-differentiation [41] and thus only support EMT but do not induce it.
EMT is known to be driven by E-box DNA-binding TFs such as TWIST (mammalian twist family bHLH transcription factor 1 [TWIST1]), SNAIL (mammalian snail family transcriptional repressor 1 [SNAI1] and 2 [SNAI2/SLUG]), and Zfh1/2 (mammalian zinc finger E-box binding homeobox 1 [ZEB1] and 2 [ZEB2]). YAP signalling upregulates the expression of several EMT-associated TFs, including ZEB1 [52,63] and SNAI2 [63,74], and TAZ activation induces TWIST1 [53]. YAP/ZEB1 nuclear binding enhanced tumour metastasis [224]. In mesenchymal cells, YAP/TAZ and SNAI1/2 can form nuclear complexes to maintain stromal cell renewal [224,225,226]. Therefore, these findings indicate that YAP/TAZ could cooperate with EMT TFs to promote mesenchymal cell fate and prevent EMT termination.
5.4. YAP/TAZ in LLPS
Recently, LLPS has emerged as an important mechanism of TF action [227,228]. Through LLPS, TFs form liquid condensates (droplets) that are highly dynamic and spherical, coalesce, and exhibit wetting behaviour. These condensates facilitate transcription, stabilise enhancer–promoter loops, and contribute to the specificity of transcriptional control, allowing its precise spatiotemporal regulation. LLPS depends on multivalent interactions between the unstructured parts of proteins, IDRs, and sometimes between other domains, such as CC or prion-like domains. Multiple TFs and core components of the transcriptional machinery contain IDRs and undergo LLPS [229,230].
Pioneering studies on YAP/TAZ LLPS have shown that TAZ-green fluorescent protein (GFP) and YAP-enhanced GFP (EGFP) form droplets in cells, the former only in the nucleus and the latter also in the cytoplasm [231,232]. Nuclear condensates of both YAP and TAZ concentrate the transcriptional machinery, open chromatin regions, and nascent RNA transcripts [231,233,234,235]. However, these studies relied heavily on the exogenous protein expression that could lead to overexpression artefacts [236]. Endogenous YAP and TAZ form puncta (foci in the nuclei) in cells, as revealed by immunofluorescence, which colocalise with nascent RNAs and depend on the same cues as YAP/TAZ-GFP droplets [231,232]. Further evidence that YAP/TAZ regulate transcription via LLPS comes from genetic engineering experiments. The CC and WW domains are both important for TAZ LLPS [165,232], whereas the IDR and CC domains are responsible for YAP LLPS [231,236]. Mutations in these domains abolish YAP/TAZ condensation in vitro. Consistently, these mutations disrupt endogenous YAP/TAZ puncta in cells and impair transcriptional regulation [231,232]. Notably, the IDR of YAP contains a pattern of positively charged amino acids, which appears to be essential for Mediator complex and RNA polymerase II (PolII) recruitment [237]. The Mediator complex and PolII attract additional YAP, facilitating condensate formation via a positive feedback loop [237]. The resulting condensates are further enhanced after treatment with transcription elongation inhibitor 5,6-dichloro-1-β-D-ribofuranosylbenzimidazole [237], indicating a possible antagonism between condensate growth and mRNA accumulation, consistent with previous observations [238]. This mechanism may provide a negative feedback loop for YAP condensation.
Hyperosmotic stress [231,239] and interferon-γ (IFNG) [236] induce YAP condensation in various cell types, which correlates with the upregulation of its target gene expression. However, YAP also forms smaller condensates in the nucleus under physiological conditions [233]. The number of such condensates depends on cell confluence and actin filaments. Interactions with TEAD proteins significantly enhance YAP condensation [233]. Single-particle tracking experiments conducted on YAP-TEAD condensates in vitro indicated that condensates slow YAP diffusion, possibly facilitating its target search [233]. Another consequence of YAP condensation is the formation of active transcriptional hubs [231,236]. In the ependymoma cells, the chimeric TFs YAP-mastermind-like domain-containing 1 (MAMLD1) and zinc finger translocation-associated (ZFTA/C11ORF95)-YAP form nuclear condensates and substantially reconfigure spatial genomic contacts as revealed by HiChIP analysis [240].
Unlike YAP, its paralog TAZ does not require crowding agents to undergo LLPS in vitro and forms liquid droplets in nuclei under normal conditions [232]. While TAZ is recruited into YAP condensates upon hyperosmotic treatment, a subset of genes are independently regulated by TAZ. The fact that TAZ more readily forms condensates than YAP may account for their regulatory specificity [232]. YAP/TAZ condensate formation is stimulated by matrix stiffness and actin filament polymerisation and is downregulated by the Hippo pathway [241]. Condensates also modulate TAZ activity through changes in viscoelasticity. The FUS RNA binding protein (FUS) associates with TAZ in nuclear condensates, maintaining their liquid state and thus facilitating TAZ-mediated transcriptional regulation [165]. When phosphorylated, FUS is no longer recruited to the TAZ condensates, resulting in their gelation and TAZ inactivation [165].
Despite the growing evidence that LLPS is essential for YAP/TAZ function, the quantitative contribution of their condensates to gene regulation remains an open question. Various drugs can modulate condensate formation. The most commonly used is 1,6-hexanediol (1,6-HD), which interferes with hydrophobic interactions [242]. However, YAP condensates are insensitive to 1,6-HD treatment [233], unlike TAZ condensates [165,232]. 1,6-HD also exhibits many side effects on chromatin architecture and cell viability [243,244,245]. Drugs such as VP, K-975, or peptide 17 can selectively dissolve YAP condensates by interfering with the YAP-TEAD interaction [233]. Nevertheless, developing strategies to specifically target LLPS without interfering with protein–protein interactions (as in mutation experiments) and general protein abundance (as in various depletion techniques) is crucial. Recently developed approaches that allow the on-demand recruitment of highly soluble proteins to condensates may be useful for further research [246,247,248]. These methods do not require any YAP/TAZ mutation or depletion while selectively disrupting condensate formation.
5.1. YAP and TAZ Structure
Human YAP and TAZ have high primary sequence similarity and, thus, several shared structural features, including domain structure. However, the differences in their structures can influence their activation and interaction mechanisms with TFs in the nucleus to regulate gene expression.
The C-terminal region of both proteins contains the coiled-coil (CC) domain, transactivation domain, and PDZ-binding domain [155,156]. The PDZ-binding domain is responsible for their sequestration by 14-3-3 proteins and thus involved in controlling YAP/TAZ in the cytoplasm [157,158]. The SRC homology 3 domain (SH3)-binding motif (amino acids PVKQPPPLAP) located between the WW and the CC domains is sufficient for interaction with several proteins, including the YES proto-oncogene (YES) and Src kinases [156]. Thus, it indicates mostly common activation mechanisms for YAP and TAZ.
The TEAD-binding domain (TBD) is located in the N-terminal region of both transcriptional coactivators and consists of α-helixes and the Ω-loop with a linker region and contacts the TEADs in the N-terminal region [159,160]. The linker is shorter in TAZ than in YAP since it lacks the PXXFP motif, which is significant for the YAP/TEAD interaction [160]. However, this fact allows TEAD interactions with two TAZ molecules and the formation of a heterotetramer instead of a heterodimer [155].
The WW domain is sufficient for the interactions of YAP and TAZ with other TFs, regulatory proteins, and chromatin remodelers. TAZ contains only one WW domain, while the number of WW domains in YAP depends on the isoform: YAP1 contains one WW domain, YAP2 contains two WW domains, and YAP2-L differs from YAP2 in that it contains 16 amino acid residues in the transcription activation domain [161]. The type of isoform was found to be significant in tumour development [162,163,164]; however, it has been poorly investigated in the epidermis.
Several YAP and TAZ domains feature an intrinsically disordered region (IDR), including the CC domain, transactivation domain, and small IDR in the YAP C-terminal region. These domains are involved in liquid–liquid phase separation (LLPS) and non-specific interactions with other IDR-containing TFs during protein condensation [165]. This finding underscores that YAP/TAZ/TEAD can interact specifically and non-specifically with other TFs, and the nature of these interactions may influence the realisation of the cell transcription programme.
5.2. YAP/TAZ-Mediated Transcription Regulation
YAP and TAZ primarily interact with TFs of TEAD family members in the nucleus. All four members of the TEAD family are paralogues and have the same domain structure. Their N terminal region contains a variable region and DNA-binding TEA/ATTS domain. The TEA domain binds to DNA elements and TFs necessary for target transcription activation [166]. The C-terminal transactivation YAP binding domain (YBD) recruits transcription coactivators (YAP, TAZ, and vestigial-like family members 1–4 [VGLL1/2/3/4]) [167,168]. The TEA domain and YBD are separated by a proline-rich domain. Differences in the last two domain structures are presumably responsible for TEAD functional specialisation [169].
Complexes of YAP and TAZ with TEA domain transcription factor 1–4 (TEAD1/2/3/4) have different functions in skin development, homeostasis, and tumorigenesis [170]. TEAD1 is the predominant paralog expressed in epidermal KCs [171]. Along with TEAD3, it is involved in KC self-renewal and proliferation [19,172]. TEAD1 is also detected in various cancer types, including SCC [173,174,175]. TEAD4 expression correlates with KC activation and EMT and, thus, promotes psoriasis and tumour formation [176,177,178,179,180]. TEAD2 appears to play a critical role during development and neural tube closure [181]. Elevated TEAD2 expression has also been associated with cancer progression and tumour drug resistance [182,183].
YAP/TAZ/TEAD bind mostly distant regulatory sequences (enhancers and super-enhancers) regulating target gene expression [184]. To drive the transcription, the YAP/TAZ/TEAD complex recruits bromodomain-containing protein 4 (BRD4) [185] and the Mediator complex to promote cyclin-dependent kinase 9 (CDK9)-dependent transcriptional activity [186]. In addition, YAP associates with the MYB proto-oncogene-like 2 (MYBL2/B-MYB) and MuvB TF complex, leading to the formation of the MMB complex, chromatin looping, and the induction of target gene expression, particularly cell cycle-associated genes [187]. YAP overactivation increases chromatin accessibility at TEAD motifs [188]. YAP activity correlates with CCCTC-binding factor (CTCF)-mediated chromatin reorganisation. It has been found that Hippo pathway activation results in both YAP and CTCF phosphorylation, which reduces the ability of CTCF to form chromatin loops [189]. Figure 3 describes the cytoplasmic and nuclear interactions of YAP/TAZ with signalling pathways, TFs, and chromatin-associated proteins.
5.3. YAP/TAZ/TEAD Interactions with TFs
5.3.1. Activator Protein-1 (AP-1)
AP-1 is the second most abundant TF complex interacting with YAP/TAZ after TEADs. AP1 are dimers of the JUN (JUN, JUNB, JUND) and FOS (FOS, FOSB, FOSL1, and FOSL2) families of leucine-zipper proteins. AP-1 physically interacts with YAP/TAZ/TEAD, promoting target gene expression [184]. AP-1 binds up to 85% of enhancers involved in controlling YAP/TAZ target gene expression: TEADs and AP-1 occupy very similar regions [184]. AP-1 predominantly binds TEADs, although YAP and TAZ may also be involved in the organisation of the transcriptional complex via their WW and TB domains [190,191]. YAP-mediated chromatin reorganisation facilitates genome-wide AP-1 accessibility [188]. AP-1 has been identified as one of the primary factors that recruits SWI/SNF complexes [192]. YAP and TAZ could participate in this complex by binding ARID1A [105].
In the epidermis, AP-1 is one of the most critical TF complexes that regulate KC-specific gene expression and proper KC differentiation [193,194]. Interactions with AP-1 could be one of the key mechanisms by which YAP/TAZ/TEAD regulate the KC state.
5.3.2. TEAD-Binding Proteins
Besides YAP and TAZ, VGLL1, VGLL2, VGLL3, VGLL4, and p160 can interact with TEADs to form transcription complexes [170]. The VGLL-TEAD interaction antagonises YAP-driven transcription in several cancer cell lines [195,196,197]. In the skin, VGLL4 inhibits YAP/TEAD-dependent transcription [197]; however, VGLL4 deficiency does not result in skin abnormalities [198]. Other VGLL proteins are associated with hyperproliferative phenotypes [199,200].
Members of the p160/SRC family, nuclear receptor coactivators 1 (NCOA1/SRC1), 2 (NCOA2/SRC2/TIF2/GRIP1), and 3 (NCOA3/SRC3/AIB1/ACTR), can interact with TEAD2 [201]. VGLL3 and p160/SRCs complexes are involved in skin immunity regulation and wound healing [202].
Since VGLLs and p160 antagonise YAP and TAZ through their ability to form complexes with TEADs and their expression is found in the skin under homeostasis and pathology, these proteins could be involved in modulating YAP/TAZ nuclear activity to control KCs’ fate.
5.3.3. p63
p63 is a TF belonging to a p53 family involved in epidermal development, KC proliferation and differentiation, as well as apoptosis, stem cell maintenance, and adhesion [203,204] by activating and suppressing the expression of target genes, alongside mediating epigenetic regulation [204,205,206]. p63 has different isoforms created by alternative splicing: the TAp63 isoform has an N-terminal transcriptional activation (TA) domain, while dNp63 does not [207]. Both isoforms balance each other and act cooperatively during the stratification process [208,209]. dNp63 is predominantly expressed in the basal layer of the skin epidermis, stimulating cell proliferation and maintaining cell stemness [207]. TAp63 is involved in differentiation and stratification initiation [204].
In the epidermis, YAP/TAZ signalling and p63 are closely interconnected, mutually regulating each other [18,40,41,57,77,210]. dNp63α colocalises with activated nuclear YAP in proliferating basal KCs, while in dNp63α-negative suprabasal layers, YAP is located in the cytoplasm [41]. YAP and dNp63α act jointly to maintain the undifferentiated state of SCC cells [106,107,211,212].
YAP physically bound and stabilised dNp63 in HaCaT cells [77,213,214]. YAP and dNp63 could jointly regulate chromatin remodelling. YAP influences the accessibility of dNp63 target genes [215]. dNp63 interacts with the SWI/SNF complex, resulting in dNp63 target gene expression [206]. p63 cooperates in CTCF-mediated chromatin 3D structure reorganisation [216]. Since dNp63α and YAP/TAZ stimulate the expression of very similar targets and are involved in CTCF-mediated chromatin remodelling [189], this predisposes complex regulation of the availability of transcription regulatory elements in chromatin by YAP/TAZ and p63.
5.3.4. Tumour Protein p73 (TP73)
TF p73 can interact with its homologues p53 and p63. Like p63, it is expressed in human KCs in vivo and in vitro [217]. Nuclear p73 can bind YAP through YAP’s WW domain and its poly-proline (PPPPY) motif [213,218]. YAP/p73 complexes are abundant in colon and breast cancer cell lines [218,219]; the YAP/p73 complex promoted apoptosis [77] and induced differentiation [220] in several cell types, the opposite effect of the traditionally mentioned YAP/TEAD complex. In epidermal KCs, YAP predominantly forms complexes with p63 rather than p73 [77], explaining the observed YAP-mediated effects on KCs.
5.3.5. KLF Transcription Factor 4 (KLF4)
KLF4 is a master gene regulating epidermal cell fate [221]. It regulates the transcription of epidermal differentiation genes involved in stratification and cornification [222]. The YAP/TAZ/TEAD complex negatively regulates KLF4 expression. KC differentiation induced by TEADi requires KLF4, while KLF4 KO increases transcriptional activity downstream of YAP1. KLF4 mediates YAP/TAZ/TEAD transcriptional activity via direct binding by its activation domain [68]. In cancer cells, KLF4 is suppressed, and YAP promotes snail family transcriptional repressor 2 (SNAI2) expression, which is involved in downregulating KLF4 expression [74]. The interplay between KLF4 and the YAP/TAZ/TEAD complex regulates the balance between KC proliferation and differentiation.
5.3.6. WW Domain Binding Protein 2 (WBP2)
WBP2 was recently identified as a TF connected to the YAP/TAZ/TEAD complex. WBP2 acts as a cofactor of YAP, enhancing TEAD-mediated transcription. WBP2 partially implements the YAP transcription programme. In SCC cells, WBP2 KD suppressed proliferation, while YAP KD also induced cell differentiation. WBP2 KO reduced basal KC proliferation and nuclear YAP accumulation during mouse embryogenesis [19].
5.3.7. EMT-Related TFs
EMT is required for tissue regeneration and correct wound healing. It involves partial epithelial cell dedifferentiation, increased proliferation, and cell motility that enables the delivery of rapidly dividing epithelial progenitors to the injury site. After wound-closing, cells undergo redifferentiation and mesenchymal-to-epithelial transformation or cell senescence, followed by elimination by the immune system. Prolonged or chronic EMT disorganises the regeneration processes and arrests tissue architecture reconstruction due to extensive fibrosis or tumorigenesis [223]. YAP and TAZ behaviour follows all these principles: they are essential for correct wound healing and support cell proliferation and migration [14,84], with their overactivation in normal cells resulting in cell senescence under culture conditions [41], while YAP overactivation in mouse skin promoted tumour development with SCC characteristics [18,52]. YAP and TAZ do not promote cell de-differentiation [41] and thus only support EMT but do not induce it.
EMT is known to be driven by E-box DNA-binding TFs such as TWIST (mammalian twist family bHLH transcription factor 1 [TWIST1]), SNAIL (mammalian snail family transcriptional repressor 1 [SNAI1] and 2 [SNAI2/SLUG]), and Zfh1/2 (mammalian zinc finger E-box binding homeobox 1 [ZEB1] and 2 [ZEB2]). YAP signalling upregulates the expression of several EMT-associated TFs, including ZEB1 [52,63] and SNAI2 [63,74], and TAZ activation induces TWIST1 [53]. YAP/ZEB1 nuclear binding enhanced tumour metastasis [224]. In mesenchymal cells, YAP/TAZ and SNAI1/2 can form nuclear complexes to maintain stromal cell renewal [224,225,226]. Therefore, these findings indicate that YAP/TAZ could cooperate with EMT TFs to promote mesenchymal cell fate and prevent EMT termination.
5.3.1. Activator Protein-1 (AP-1)
AP-1 is the second most abundant TF complex interacting with YAP/TAZ after TEADs. AP1 are dimers of the JUN (JUN, JUNB, JUND) and FOS (FOS, FOSB, FOSL1, and FOSL2) families of leucine-zipper proteins. AP-1 physically interacts with YAP/TAZ/TEAD, promoting target gene expression [184]. AP-1 binds up to 85% of enhancers involved in controlling YAP/TAZ target gene expression: TEADs and AP-1 occupy very similar regions [184]. AP-1 predominantly binds TEADs, although YAP and TAZ may also be involved in the organisation of the transcriptional complex via their WW and TB domains [190,191]. YAP-mediated chromatin reorganisation facilitates genome-wide AP-1 accessibility [188]. AP-1 has been identified as one of the primary factors that recruits SWI/SNF complexes [192]. YAP and TAZ could participate in this complex by binding ARID1A [105].
In the epidermis, AP-1 is one of the most critical TF complexes that regulate KC-specific gene expression and proper KC differentiation [193,194]. Interactions with AP-1 could be one of the key mechanisms by which YAP/TAZ/TEAD regulate the KC state.
5.3.2. TEAD-Binding Proteins
Besides YAP and TAZ, VGLL1, VGLL2, VGLL3, VGLL4, and p160 can interact with TEADs to form transcription complexes [170]. The VGLL-TEAD interaction antagonises YAP-driven transcription in several cancer cell lines [195,196,197]. In the skin, VGLL4 inhibits YAP/TEAD-dependent transcription [197]; however, VGLL4 deficiency does not result in skin abnormalities [198]. Other VGLL proteins are associated with hyperproliferative phenotypes [199,200].
Members of the p160/SRC family, nuclear receptor coactivators 1 (NCOA1/SRC1), 2 (NCOA2/SRC2/TIF2/GRIP1), and 3 (NCOA3/SRC3/AIB1/ACTR), can interact with TEAD2 [201]. VGLL3 and p160/SRCs complexes are involved in skin immunity regulation and wound healing [202].
Since VGLLs and p160 antagonise YAP and TAZ through their ability to form complexes with TEADs and their expression is found in the skin under homeostasis and pathology, these proteins could be involved in modulating YAP/TAZ nuclear activity to control KCs’ fate.
5.3.3. p63
p63 is a TF belonging to a p53 family involved in epidermal development, KC proliferation and differentiation, as well as apoptosis, stem cell maintenance, and adhesion [203,204] by activating and suppressing the expression of target genes, alongside mediating epigenetic regulation [204,205,206]. p63 has different isoforms created by alternative splicing: the TAp63 isoform has an N-terminal transcriptional activation (TA) domain, while dNp63 does not [207]. Both isoforms balance each other and act cooperatively during the stratification process [208,209]. dNp63 is predominantly expressed in the basal layer of the skin epidermis, stimulating cell proliferation and maintaining cell stemness [207]. TAp63 is involved in differentiation and stratification initiation [204].
In the epidermis, YAP/TAZ signalling and p63 are closely interconnected, mutually regulating each other [18,40,41,57,77,210]. dNp63α colocalises with activated nuclear YAP in proliferating basal KCs, while in dNp63α-negative suprabasal layers, YAP is located in the cytoplasm [41]. YAP and dNp63α act jointly to maintain the undifferentiated state of SCC cells [106,107,211,212].
YAP physically bound and stabilised dNp63 in HaCaT cells [77,213,214]. YAP and dNp63 could jointly regulate chromatin remodelling. YAP influences the accessibility of dNp63 target genes [215]. dNp63 interacts with the SWI/SNF complex, resulting in dNp63 target gene expression [206]. p63 cooperates in CTCF-mediated chromatin 3D structure reorganisation [216]. Since dNp63α and YAP/TAZ stimulate the expression of very similar targets and are involved in CTCF-mediated chromatin remodelling [189], this predisposes complex regulation of the availability of transcription regulatory elements in chromatin by YAP/TAZ and p63.
5.3.4. Tumour Protein p73 (TP73)
TF p73 can interact with its homologues p53 and p63. Like p63, it is expressed in human KCs in vivo and in vitro [217]. Nuclear p73 can bind YAP through YAP’s WW domain and its poly-proline (PPPPY) motif [213,218]. YAP/p73 complexes are abundant in colon and breast cancer cell lines [218,219]; the YAP/p73 complex promoted apoptosis [77] and induced differentiation [220] in several cell types, the opposite effect of the traditionally mentioned YAP/TEAD complex. In epidermal KCs, YAP predominantly forms complexes with p63 rather than p73 [77], explaining the observed YAP-mediated effects on KCs.
5.3.5. KLF Transcription Factor 4 (KLF4)
KLF4 is a master gene regulating epidermal cell fate [221]. It regulates the transcription of epidermal differentiation genes involved in stratification and cornification [222]. The YAP/TAZ/TEAD complex negatively regulates KLF4 expression. KC differentiation induced by TEADi requires KLF4, while KLF4 KO increases transcriptional activity downstream of YAP1. KLF4 mediates YAP/TAZ/TEAD transcriptional activity via direct binding by its activation domain [68]. In cancer cells, KLF4 is suppressed, and YAP promotes snail family transcriptional repressor 2 (SNAI2) expression, which is involved in downregulating KLF4 expression [74]. The interplay between KLF4 and the YAP/TAZ/TEAD complex regulates the balance between KC proliferation and differentiation.
5.3.6. WW Domain Binding Protein 2 (WBP2)
WBP2 was recently identified as a TF connected to the YAP/TAZ/TEAD complex. WBP2 acts as a cofactor of YAP, enhancing TEAD-mediated transcription. WBP2 partially implements the YAP transcription programme. In SCC cells, WBP2 KD suppressed proliferation, while YAP KD also induced cell differentiation. WBP2 KO reduced basal KC proliferation and nuclear YAP accumulation during mouse embryogenesis [19].
5.3.7. EMT-Related TFs
EMT is required for tissue regeneration and correct wound healing. It involves partial epithelial cell dedifferentiation, increased proliferation, and cell motility that enables the delivery of rapidly dividing epithelial progenitors to the injury site. After wound-closing, cells undergo redifferentiation and mesenchymal-to-epithelial transformation or cell senescence, followed by elimination by the immune system. Prolonged or chronic EMT disorganises the regeneration processes and arrests tissue architecture reconstruction due to extensive fibrosis or tumorigenesis [223]. YAP and TAZ behaviour follows all these principles: they are essential for correct wound healing and support cell proliferation and migration [14,84], with their overactivation in normal cells resulting in cell senescence under culture conditions [41], while YAP overactivation in mouse skin promoted tumour development with SCC characteristics [18,52]. YAP and TAZ do not promote cell de-differentiation [41] and thus only support EMT but do not induce it.
EMT is known to be driven by E-box DNA-binding TFs such as TWIST (mammalian twist family bHLH transcription factor 1 [TWIST1]), SNAIL (mammalian snail family transcriptional repressor 1 [SNAI1] and 2 [SNAI2/SLUG]), and Zfh1/2 (mammalian zinc finger E-box binding homeobox 1 [ZEB1] and 2 [ZEB2]). YAP signalling upregulates the expression of several EMT-associated TFs, including ZEB1 [52,63] and SNAI2 [63,74], and TAZ activation induces TWIST1 [53]. YAP/ZEB1 nuclear binding enhanced tumour metastasis [224]. In mesenchymal cells, YAP/TAZ and SNAI1/2 can form nuclear complexes to maintain stromal cell renewal [224,225,226]. Therefore, these findings indicate that YAP/TAZ could cooperate with EMT TFs to promote mesenchymal cell fate and prevent EMT termination.
5.4. YAP/TAZ in LLPS
Recently, LLPS has emerged as an important mechanism of TF action [227,228]. Through LLPS, TFs form liquid condensates (droplets) that are highly dynamic and spherical, coalesce, and exhibit wetting behaviour. These condensates facilitate transcription, stabilise enhancer–promoter loops, and contribute to the specificity of transcriptional control, allowing its precise spatiotemporal regulation. LLPS depends on multivalent interactions between the unstructured parts of proteins, IDRs, and sometimes between other domains, such as CC or prion-like domains. Multiple TFs and core components of the transcriptional machinery contain IDRs and undergo LLPS [229,230].
Pioneering studies on YAP/TAZ LLPS have shown that TAZ-green fluorescent protein (GFP) and YAP-enhanced GFP (EGFP) form droplets in cells, the former only in the nucleus and the latter also in the cytoplasm [231,232]. Nuclear condensates of both YAP and TAZ concentrate the transcriptional machinery, open chromatin regions, and nascent RNA transcripts [231,233,234,235]. However, these studies relied heavily on the exogenous protein expression that could lead to overexpression artefacts [236]. Endogenous YAP and TAZ form puncta (foci in the nuclei) in cells, as revealed by immunofluorescence, which colocalise with nascent RNAs and depend on the same cues as YAP/TAZ-GFP droplets [231,232]. Further evidence that YAP/TAZ regulate transcription via LLPS comes from genetic engineering experiments. The CC and WW domains are both important for TAZ LLPS [165,232], whereas the IDR and CC domains are responsible for YAP LLPS [231,236]. Mutations in these domains abolish YAP/TAZ condensation in vitro. Consistently, these mutations disrupt endogenous YAP/TAZ puncta in cells and impair transcriptional regulation [231,232]. Notably, the IDR of YAP contains a pattern of positively charged amino acids, which appears to be essential for Mediator complex and RNA polymerase II (PolII) recruitment [237]. The Mediator complex and PolII attract additional YAP, facilitating condensate formation via a positive feedback loop [237]. The resulting condensates are further enhanced after treatment with transcription elongation inhibitor 5,6-dichloro-1-β-D-ribofuranosylbenzimidazole [237], indicating a possible antagonism between condensate growth and mRNA accumulation, consistent with previous observations [238]. This mechanism may provide a negative feedback loop for YAP condensation.
Hyperosmotic stress [231,239] and interferon-γ (IFNG) [236] induce YAP condensation in various cell types, which correlates with the upregulation of its target gene expression. However, YAP also forms smaller condensates in the nucleus under physiological conditions [233]. The number of such condensates depends on cell confluence and actin filaments. Interactions with TEAD proteins significantly enhance YAP condensation [233]. Single-particle tracking experiments conducted on YAP-TEAD condensates in vitro indicated that condensates slow YAP diffusion, possibly facilitating its target search [233]. Another consequence of YAP condensation is the formation of active transcriptional hubs [231,236]. In the ependymoma cells, the chimeric TFs YAP-mastermind-like domain-containing 1 (MAMLD1) and zinc finger translocation-associated (ZFTA/C11ORF95)-YAP form nuclear condensates and substantially reconfigure spatial genomic contacts as revealed by HiChIP analysis [240].
Unlike YAP, its paralog TAZ does not require crowding agents to undergo LLPS in vitro and forms liquid droplets in nuclei under normal conditions [232]. While TAZ is recruited into YAP condensates upon hyperosmotic treatment, a subset of genes are independently regulated by TAZ. The fact that TAZ more readily forms condensates than YAP may account for their regulatory specificity [232]. YAP/TAZ condensate formation is stimulated by matrix stiffness and actin filament polymerisation and is downregulated by the Hippo pathway [241]. Condensates also modulate TAZ activity through changes in viscoelasticity. The FUS RNA binding protein (FUS) associates with TAZ in nuclear condensates, maintaining their liquid state and thus facilitating TAZ-mediated transcriptional regulation [165]. When phosphorylated, FUS is no longer recruited to the TAZ condensates, resulting in their gelation and TAZ inactivation [165].
Despite the growing evidence that LLPS is essential for YAP/TAZ function, the quantitative contribution of their condensates to gene regulation remains an open question. Various drugs can modulate condensate formation. The most commonly used is 1,6-hexanediol (1,6-HD), which interferes with hydrophobic interactions [242]. However, YAP condensates are insensitive to 1,6-HD treatment [233], unlike TAZ condensates [165,232]. 1,6-HD also exhibits many side effects on chromatin architecture and cell viability [243,244,245]. Drugs such as VP, K-975, or peptide 17 can selectively dissolve YAP condensates by interfering with the YAP-TEAD interaction [233]. Nevertheless, developing strategies to specifically target LLPS without interfering with protein–protein interactions (as in mutation experiments) and general protein abundance (as in various depletion techniques) is crucial. Recently developed approaches that allow the on-demand recruitment of highly soluble proteins to condensates may be useful for further research [246,247,248]. These methods do not require any YAP/TAZ mutation or depletion while selectively disrupting condensate formation.
6. Conclusions
The functioning of YAP/TAZ in human skin under normal and pathological conditions is a cutting-edge area in investigative dermatology, embryology, and regenerative medicine, as these multifunctional proteins are associated with various skin diseases [41,45,46,47,48,49,50,51,52,53]. In addition, YAP and TAZ are paralogues; their functions are different in epidermis (Table 1). Published data indicate that YAP activation is associated with high proliferation and low differentiation independently in the IFE or HFs [40,42]. YAP is more important in homeostatic epidermal self-renewal than TAZ [67]. In addition, YAP acts with TAZ in epidermal injured phenotype formation [14,65,66,84].
The nuclear activity of YAP and TAZ should be transient for proper skin physiology. Many mechanisms can inactivate YAP/TAZ and sequester them in the cytoplasm, including the Hippo pathway and contact inhibition during proliferation [18,19,40,61,97,98]. YAP activation in basal KCs is mediated via organisation cell–matrix contacts with the basal lamina by integrin and subsequent SRC activation [14,90]. YAP cooperates with the WNT, SHH, and EGF pathways and p63, SNAI2, and WBP2 TFs to prevent differentiation and stimulate basal KC proliferation [14,18,19,21,22,23,40,41,57,77,79,82,98,210]. Furthermore, YAP must be inhibited. The possible YAP-induced decrease in COL17A1 in KCs [61] results in the loss of adhesiveness with the basal membrane, their movement to the suprabasal layers with decreased microenvironment stiffness. Excluding KCs from the epidermal stem cell niche decreases WNT and SHH levels. These conditions suppress YAP activity; thus, YAP is cytoplasmic in spinous KCs and absent in superficial layers. KLF4 and NOTCH, which are inhibited by YAP, stimulate KC differentiation [59,68,74,126] (Figure 4).
During wound healing, YAP and TAZ are insufficient for partial KC EMT but support it by promoting proliferation and inhibiting differentiation [43]. During injury, YAP/TAZ activation is mediated by escaping contact inhibition due to epidermis disruption and mechanotransduction due to increased matrix stiffness in the wound [59,98,101,104]; TGFβ, EGF, FGF, and other signalling pathways are also involved [23,24,25,44,53,131,132]. YAP/TAZ interact with EMT-associated TFs and stimulate KCs to initiate the inflammation process, including KRT6/16/17 expression, dedifferentiation, migration, and synthesis of cytokines that attract immune cells [52,63,74,224]. YAP and TAZ activity must be arrested after the regeneration process is complete. Establishing AJs and subsequent contact inhibition could suppress YAP/TAZ or, under prolonged activation, could upregulate the expression of cell senescence markers, leading to the removal of cells from the tissue by immune cells [41]. Chronic inflammation leads to the accumulation of YAP and TAZ in nuclei, causing hyperproliferation of the epidermis in several skin diseases [45,46,47,48]. Tumour formation is also often linked to the dysregulation of YAP/TAZ inhibition mechanisms [49,50,51,52,53] (Figure 4).
YAP/TAZ regulate KC phenotype-specific gene expression by creating protein complexes at enhancers and super-enhancers and reorganising chromatin structure to upregulate the availability of DNA sites for TF binding. YAP and TAZ primarily interact with TEADs [170] and AP-1 [184], which directly binds DNA, to promote target gene expression by recruiting BRD4/Mediator [185] and MMB complexes [187]. YAP cooperates with β-catenin/LEF/TCF and p63 to upregulate stemness genes in homeostatic skin [41,122,123] and with TAZ, SMAD2/3, SNAI1/2, and ZEB1/2 in the injury response [52,63,74]. Under chronic inflammation or tumour progression, accumulated nuclear TAZ could stabilise the complexes between YAP/TAZ and cancer-associated TFs by forming condensates via LLPS [232], thus promoting cell senescence escape and cancer therapy resistance. KLF4 suppresses the YAP/TAZ-mediated transcription machinery and induces KC differentiation. VGLLs are also predicted to participate in YAP/TAZ protein complexes; however, their influence on KC differentiation must be explored. Similarly, studies must determine the roles of TEAD isoforms in YAP/TAZ-mediated target gene expression, particularly since the formation of different TFs with YAP/TAZ can provoke different effects in epidermal skin cells under different conditions. The ability of YAP and TAZ to create molecular condensates via LLPS highlights a gap in understanding how the organisation of protein complexes regulates transcription activity. Understanding the specific details of the mechanisms of YAP/TAZ activation and suppression and their transcription promotion in epidermal KCs would support drug development for hyperproliferative epidermal diseases and anti-cancer therapy, as well as for maintaining KC proliferation and stimulating wound healing in epidermolysis bullosa or chronic ischemic wounds due to diabetes. |
Title: Unrevealing Lithium Repositioning in the Hallmarks of Cancer: Effects of Lithium Salts (LiCl and Li | Body:
1. Introduction
Cervical cancer (CC) is the fourth most common malignancy in women. In 2022, a total of 662,301 new cases were reported by the International Agency for Research on Cancer, causing 348,874 deaths [1]. Low-income and middle-income countries reported 90% of CC cases mainly due to the lack of screening and vaccination initiatives. In contrast, in high-income nations, CC rates and fatalities have decreased by over 50% in the last three decades following the implementation of structured screening programs [2,3,4]. Most of the CC cases arise from infection with the Human Papillomavirus (HPV), with HPV DNA detected in about 95% of cervical lesions [5].
Most HPV infections are transient and disappear naturally. In some cases, persistent infection may lead to premalignant conditions such as cervical intraepithelial neoplasia or adenocarcinoma in situ. In most women, without treatment, progression from dysplasia to invasive carcinoma may take several years to decades. However, in approximately 10% of cases, this progression can occur in less than a year [6]. Diagnosis through Papanicolaou testing is challenging when adenocarcinoma in situ appears, which is believed to contribute to the rising incidence of this specific subtype of CC [7].
Cervical cancer treatment is based on the guidelines of the International Federation of Gynecology and Obstetrics (FIGO) [8]. Treatment options depend on the stage of the disease at diagnosis and local resources. It generally includes cone biopsy, radical trachelectomy, pelvic lymph node dissection, radical hysterectomy, pelvic radiotherapy, brachytherapy, chemoradiation, or a combination thereof [8,9,10]. However, these treatment options have disadvantages such as the narrow application scope, drug resistance, acute and long-term side effects, damage to proliferating healthy tissues, structural deformities, systemic toxicity, and psychological problems [11,12].
Lithium is a natural element used as a mental stabilizer of bipolar disorders since the 20th century and to help against suicide [13,14]. Evidence in vitro and in vivo proposes lithium salts for cancer treatment while reducing side effects [15,16]. Recently, our research group conducted a review on lithium as an antitumor agent [17]. Several investigations revealed that the salt LiCl increased apoptosis in lymph node carcinoma of the prostate cells [18], decreased the proliferation of ameloblastoma cells [19], and, in human multiple myeloma cell lines, inhibited cell proliferation and induced cell cycle arrest [20]. In addition, LiCl reduced the proliferation and colony formation capabilities of human head and neck squamous cell carcinoma [21] and improved the efficacy of chemotherapeutic agents by enhancing non-apoptotic cell death in a colorectal carcinoma model [22]. When LiCl is combined with temozolomide, it induces cell death via NFAT1/FasL signaling in human glioblastoma cells [23] and, combined with mitomycin C, induces autophagy in breast cancer cells [24]. Moreover, LiCl sensitized colon cancer cells to radiation therapy [25], inhibited GSK-3β, and decreased the expression of markers of the mesenchymal phenotype in triple-negative breast cancer lines [26]. Lithium citrate (Li3C6H5O7) induced apoptosis in hepatocarcinoma in vitro [27]. Lithium carbonate (Li2CO3) induced apoptosis and autophagic cell death in HCC-29 cells [27] and arrested the cell cycle in the G2/M phase in the HCC-29 cell line [28]. Li2CO3 also inhibited cell proliferation and stimulated cell death in skin melanoma cells through induction of autophagy and apoptosis [29]. Although there is enough evidence of the effect of lithium salts on different in vitro cancer models, the effect of lithium on cervical cancer is lacking. This evidence has opened up the possibility of repurposing lithium for the treatment of cancer. In this research, we conducted experiments using two lithium salts (Li2CO3 and LiCl) on two cervical cancer cell lines (HeLa and SiHa) and on a non-tumoral cell line (HaCaT) to investigate the effects of lithium on proliferation, DNA fragmentation, migration, and cell cycle arrest.
Together, our results suggest that both lithium salts affected cervical cell proliferation in a time-dependent manner and induced DNA fragmentation in CC cell lines. Moreover, both salts induced the basal expression of PARP-1 and proCAS-3 as proteins associated with apoptotic cell death biomarkers. Finally, LiCl and Li2CO3 arrested the cell cycle of HeLa, SiHa, and HaCaT cells at the G1 phase and inhibited cell migration. Our findings give light to the potential anticancer effect of lithium salts by targeting the hallmarks of cancer, specifically focusing on cell proliferation, pro-apoptotic, anti-migratory, and cell cycle arrest mechanisms.
2. Results
2.1. Effect of Lithium Salts over Cell Proliferation of CC
The crystal violet assay measures cell viability and indirectly determines cell proliferation in groups of cells treated with substances that promote cell death. Adherent cells are detected by crystal violet stain that binds to proteins and DNA. Dead cells lose their adherence and are subtracted from the cell population, reducing the violet stain in the culture [30].
Thus, the cell proliferation percentage at 24 h of exposure to several LiCl and Li2CO3 concentrations was obtained (Figure 1). Both lithium salts induce cell proliferation in a concentration-dependent manner in HeLa (Figure 1A,B), SiHa (Figure 1C,D), and HaCaT (Figure 1E,F) cells. Table 1 shows the IC50 values that depend on the genotype of cells and the lithium salts. The IC50 values calculated for LiCl are in the same range for SiHa and HeLa cell lines. In contrast, for HeLa cells, the IC50 values for Li2CO3 are different, and the IC50 from SiHa is 1.79-fold higher than that of HeLa cells. For the non-tumorigenic cell line (HaCaT) the IC50 values were lower than tumorigenic cell lines. Considering these data, the IC50 values were selected for further analysis.
2.2. Effect of Lithium Salts over the DNA Fragmentation of CC Cell Lines
2.2.1. Effect of Lithium Salts over the Apoptotic Protein’s Expression of CC Cell Lines
To further clarify the effect of lithium on apoptosis, we detected by Western Blot the levels of biomarkers of the execution phase of apoptosis: CAS-3 and PARP-1. CAS-3 in its active form weighs 17 kDa, and sometimes only its precursor form can be detected: proCAS-3 (36 kDa). PARP-1 is used to determine if there is proteolytic activity of CAS-3. When there is apoptosis, the active form of PARP-1 (110 kDa) is cleaved by caspases, generating an 89 kDa fragment. If PARP-1 (110 kDa) increases, it indicates that a response to genetic damage is activated [31,32].
In all cases, proCAS-3 had no significance between cells, groups, and time. Full-length PARP-1 and cleaved-PARP-1 expressions were observed in SiHa, HeLa, and HaCaT cells at 24 h (Figure 2) and 48 h (Figure 3). The expression of PARP-1 was slightly increased after LiCl or Li2CO3 treatment compared to untreated cells or cells treated with Dox at 48 h of stimuli. Statistical analysis of normalized PARP-1 expression levels in HeLa, SiHa, and HaCaT showed a difference significantly according to one-way ANOVA used to compare between groups (* p < 0.05). Interestingly, in LiCl-treated cells, the expression of PARP-1 is higher than Li2CO3-treated cells (* p < 0.05, ** p < 0.002, *** p < 0.001). β-actin was used as a loading control in all samples. It is worth mentioning that in HaCaT cells treated with Doxorubicin (10 µM) for 48 h, β-actin protein was not detected since Doxorubicin is a powerful cell death agent that causes the degradation of cytoskeleton components; this does not compromise the Western Blot results since it demonstrates the effect of different stimuli on the tumor cell phenotypes of greatest interest [33]. The accumulation of the active form of PARP-1 and its 89 kDa fragment varied depending on the cell line and the stimulation time, where in HeLa, there was a significant reduction at 24 h, while, at 48 h, the active form of PARP-1 had a considerable increase. In SiHa, there was only a trend in the reduction of PARP-1 levels with Li2CO3, and in HaCaT there were no significant differences. This shows that lithium salt activity is only in HeLa during the execution phase.
2.2.2. TUNEL Assay of LiCl- and Li2CO3-Treated Cells
The TUNEL assay was performed to detect the 3′-OH ends generated by DNA fragmentation due to apoptosis by activating endonucleases through enzymatic labeling with nucleotides conjugated with FITC. Altogether, DAPI staining is performed to detect DNA and locate the nuclei, evaluate their morphology and nuclear integrity, and determine if the TUNEL signal corresponds to the location of the genetic material within the cells. Thus, this experiment was assessed to determine the lithium-induced DNA fragmentation (Figure 4 and Figure 5) [32,34].
After 24 h of treatment, DNA fragmentation was observed in the lithium-treated cells. In SiHa and HeLa cells, the mean fluorescence intensity after lithium treatment increased compared to untreated cells [C(-) and TdT(-)]. The percentages of positive TUNEL apoptotic cell ratios were 69.8%, 63.8%, and 24.7% for HeLa, SiHa, and HaCaT cells treated with LiCl, respectively. On the other hand, for Li2CO3 treatment, the ratios of total TUNEL-positive cells were 73.9%, 40.4%, and 8.7% for HeLa, SiHa, and HaCaT cells, respectively.
A morphology analysis showed in Figure 5 was performed for the cell count determination in bright fields and for DAPI/TUNEL tinction. Apoptosis morphology characteristics are seen in HeLa, SiHa, and HaCaT cells treated with LiCl and Li2CO3, where cell round morphology and nuclear condensation were observed. DNA fragmentation marked by TUNEL-positive staining within the nucleus, cell integrity, and membrane blebbing were also analyzed. Other types of observed cells maintained the cell line morphology and still showed TUNEL-positive staining.
2.2.3. Flow Cytometry Apoptosis Assay (Annexin V/IP)
The annexin V/PI assay allows for determining the viability and processes of cell death due to apoptosis and necrosis of cells by evaluating the integrity and polarity of their membrane. Viable cells do not present Annexin V/PI staining; necrotic cells present staining with only PI, while cells in early apoptosis only present staining with Annexin V, and late apoptosis staining with both markers is present. Annexin V staining indicates the translocation of phosphatidyl serine, a phenomenon that occurs during apoptosis [35].
As shown in Figure 6, cells treated with lithium displayed apoptosis compared to the control group. Nevertheless, the striking results focus on Li2CO3 in HeLa Cells (panels A and B), which exhibit more apoptosis and less necrosis than the positive control. It is interesting that lithium salts do not show high necrosis levels against cervical cells but do show apoptosis at different levels, favoring HeLa cells.
2.3. Effect of Lithium Salts on Cellular Migration of CC Cell Lines (Wound-Healing Assay)
Wound-healing assays were conducted to investigate whether LiCl or Li2CO3 can influence cell migration in CC cells (Figure 7) since metastasis is known to be a big challenge for cancer patients, and these experiments currently represent a solid basis for state-of-the-art research focused on understanding metastatic potential and the development of possible targeted anti-metastatic therapies [36]. According to Bouchalova et al., 2022 [36], processes such as migration and cell invasion capacity are evaluated for the study of metastasis in in vitro models. The wound assay evaluates basic cell migration parameters by forming a monolayer and generating a wound. The ability of the cells to fill the wound is measured using software image J to determine the level of cell migration.
Cell migration of SiHa, HeLa, and HaCaT cell lines was significantly enhanced after TGF-β treatment (positive control) compared to untreated cells [negative control, C(-)]. Conversely, in Hela (Figure 7, Panels A–C), SiHa (Figure 7, Panels D–F), and HaCaT (Figure 7, Panels G–I) cells, the presence of lithium salts significantly reduced cell migration evaluated at 24, 48, and 72 h (** p > 0.001). In SiHa cells, the relative migration was 16.52% at 24 h and 13.34% at 48–72 h after LiCl treatment, while for Li2CO3 treatment, the relative migration was 10.51% at 24 h to 18.29% at 48–72 h. In HeLa cells, relative migration was 6.44% for 24, 48, and 72 h after LiCl treatment and 5.75%, 8.58%, and 5% for 24, 48, and 72 h after Li2CO3 treatment, respectively. In HaCaT cells, relative migration was 6.21% for 24, 48, and 72 h after LiCl treatment, and for Li2CO3 treatment, the relative migration was 5.73%, 6.54%, and 3.4% for 24, 48, and 72 h, respectively. In negative controls [C(-)] for SiHa, HeLa, and HaCaT cells, the relative migration was 20.51% for 24 h and 35.02% for 48–72 h, while in positive controls (TGF-β), the relative migration was 28.95–34.5% for 24 h, 42.31–52.31% for 48 h and 97–98% for 72 h.
For the statistical data, a one-way ANOVA test was performed, followed by a Tukey test. All experiments were assessed in triplicate, and the values are expressed as the Mean ± SD with * p < 0.01 and ** p < 0.001 compared to the control group with the LiCl and Li2CO3 treatments.
2.4. Effect of Lithium Salts over the Cell Cycle Progression of CC Cell Lines
The amount of DNA in cells varies depending on the phase of the cell cycle, and by staining with PI, which binds proportionally to the amount of DNA within a cell, the phase of the cycle in which it occurs can be analyzed. In this experiment, we used asynchronous cultures, which are essential for conducting a comprehensive cell cycle analysis, allowing for a general overview of cell behavior across all cell cycle stages under normal growth conditions. In a tumor, cells proliferate asynchronously, simultaneously occupying various cell cycle phases. Thus, an asynchronous culture replicates this dynamic, providing a more realistic model to evaluate cellular responses to anticancer treatments [37].
To determine the effect of lithium salt over the cell cycle progression, we analyzed the cell cycle distribution of SiHa, HeLa, and HaCaT cells treated with LiCl or Li2CO3 (Figure 8).
The proportion of HeLa cells in the G1 phase was 29.89% ± 12.61% with LiCl treatment and 39.11% ± 5.24% with Li2CO3 treatment, in contrast to 65.53% ± 7.87% for untreated cells. For the S phase, HeLa cell percentages were 36.66% ± 19.57% with LiCl and 25.06% ± 1.98% with Li2CO3, compared to 30.01% ± 11.90% for untreated cells. Following lithium treatments, there was an increase in the proportion of HeLa cells in the G2 phase. Specifically, 33.45% ± 10.21% of HeLa cells treated with LiCl and 35.83% ± 4.86% with Li2CO3 accumulated in the G2 phase, whereas only 4.47% ± 4.05% of untreated cells accumulated in this phase (Figure 8, panel A).
The percentage of SiHa cells in the G1 phase was 60.96% ± 5.23% for LiCl treatment and 71.54% ± 6.73% for Li2CO3 treatment, compared to 44.36% ± 5.01% of untreated cells. The percentage of SiHa cells in the S phase for LiCl and Li2CO3 treatments was 33.08% ± 3.53% and 22.62% ± 6.77%, respectively, and 33.59% ± 9.86% for untreated cells. The percentage of SiHa cells in the G2 phase was dramatically decreased after lithium treatments. We found that 5.97% ± 1.72% of SiHa cells treated with LiCl or 5.85% ± 0.04% of SiHa cells treated with Li2CO3 accumulated in the G2 phase, while untreated cells that accumulated in the G2 phase were 21.92% ± 4.36% (Figure 8, panel A).
An ANOVA analysis of cells at the G1 and G2 phase showed that LiCl or Li2CO3 treatments had a statistically significant difference with control (untreated cells) and with cells treated with colchicine (positive control) (p < 0.05) (Figure 8, panel B). For HeLa cells, we found a statistically significant difference between cells in the G1 phase after LiCl or Li2CO3 treatment compared to untreated SiHa cells, suggesting that LiCl or Li2CO3 treatment inhibited the progression of the cell cycle at the G1 phase. According to statistical analysis by one-way ANOVA, we found a statistically significant difference between cells in the G1 phase after LiCl or Li2CO3 treatment compared to colchicine-treated SiHa cells. No statistical differences were observed for cells accumulating in the S phase in SiHa or HeLa cell lines. Evidence from the HeLa cell line suggested that LiCl arrested the cell cycle at the S phase, whereas Li2CO3 arrested the cell cycle at the G1 phase. On the other hand, SiHa cells treated with LiCl or Li2CO3 arrested the cell cycle at the G1 phase. The non-tumoral cell line (HaCaT) cell cycle was arrested at the G1 phase after LiCl or Li2CO3 treatments.
To determine if there is a difference between CC cell lines and the lithium treatment, we compared HeLa and SiHa cells treated with LiCl or Li2CO3. We found that cells distributed differently compared to cell lines. When cells were exposed to LiCl, SiHa cells exhibited a G1 phase accumulation of 60.96% ± 5.23%, whereas HeLa cells showed a lower accumulation of 29.89% ± 12.61%. Similarly, treatment with Li2CO3 resulted in 71.54% ± 6.73% of SiHa cells accumulating in G1, compared to only 39.11% ± 5.24% of HeLa cells (Figure 8, Panel C). In contrast, no statistically significant difference was found when LiCl treatment was compared with Li2CO3 treatment in HeLa or SiHa cells (Figure 8, panel C).
2.1. Effect of Lithium Salts over Cell Proliferation of CC
The crystal violet assay measures cell viability and indirectly determines cell proliferation in groups of cells treated with substances that promote cell death. Adherent cells are detected by crystal violet stain that binds to proteins and DNA. Dead cells lose their adherence and are subtracted from the cell population, reducing the violet stain in the culture [30].
Thus, the cell proliferation percentage at 24 h of exposure to several LiCl and Li2CO3 concentrations was obtained (Figure 1). Both lithium salts induce cell proliferation in a concentration-dependent manner in HeLa (Figure 1A,B), SiHa (Figure 1C,D), and HaCaT (Figure 1E,F) cells. Table 1 shows the IC50 values that depend on the genotype of cells and the lithium salts. The IC50 values calculated for LiCl are in the same range for SiHa and HeLa cell lines. In contrast, for HeLa cells, the IC50 values for Li2CO3 are different, and the IC50 from SiHa is 1.79-fold higher than that of HeLa cells. For the non-tumorigenic cell line (HaCaT) the IC50 values were lower than tumorigenic cell lines. Considering these data, the IC50 values were selected for further analysis.
2.2. Effect of Lithium Salts over the DNA Fragmentation of CC Cell Lines
2.2.1. Effect of Lithium Salts over the Apoptotic Protein’s Expression of CC Cell Lines
To further clarify the effect of lithium on apoptosis, we detected by Western Blot the levels of biomarkers of the execution phase of apoptosis: CAS-3 and PARP-1. CAS-3 in its active form weighs 17 kDa, and sometimes only its precursor form can be detected: proCAS-3 (36 kDa). PARP-1 is used to determine if there is proteolytic activity of CAS-3. When there is apoptosis, the active form of PARP-1 (110 kDa) is cleaved by caspases, generating an 89 kDa fragment. If PARP-1 (110 kDa) increases, it indicates that a response to genetic damage is activated [31,32].
In all cases, proCAS-3 had no significance between cells, groups, and time. Full-length PARP-1 and cleaved-PARP-1 expressions were observed in SiHa, HeLa, and HaCaT cells at 24 h (Figure 2) and 48 h (Figure 3). The expression of PARP-1 was slightly increased after LiCl or Li2CO3 treatment compared to untreated cells or cells treated with Dox at 48 h of stimuli. Statistical analysis of normalized PARP-1 expression levels in HeLa, SiHa, and HaCaT showed a difference significantly according to one-way ANOVA used to compare between groups (* p < 0.05). Interestingly, in LiCl-treated cells, the expression of PARP-1 is higher than Li2CO3-treated cells (* p < 0.05, ** p < 0.002, *** p < 0.001). β-actin was used as a loading control in all samples. It is worth mentioning that in HaCaT cells treated with Doxorubicin (10 µM) for 48 h, β-actin protein was not detected since Doxorubicin is a powerful cell death agent that causes the degradation of cytoskeleton components; this does not compromise the Western Blot results since it demonstrates the effect of different stimuli on the tumor cell phenotypes of greatest interest [33]. The accumulation of the active form of PARP-1 and its 89 kDa fragment varied depending on the cell line and the stimulation time, where in HeLa, there was a significant reduction at 24 h, while, at 48 h, the active form of PARP-1 had a considerable increase. In SiHa, there was only a trend in the reduction of PARP-1 levels with Li2CO3, and in HaCaT there were no significant differences. This shows that lithium salt activity is only in HeLa during the execution phase.
2.2.2. TUNEL Assay of LiCl- and Li2CO3-Treated Cells
The TUNEL assay was performed to detect the 3′-OH ends generated by DNA fragmentation due to apoptosis by activating endonucleases through enzymatic labeling with nucleotides conjugated with FITC. Altogether, DAPI staining is performed to detect DNA and locate the nuclei, evaluate their morphology and nuclear integrity, and determine if the TUNEL signal corresponds to the location of the genetic material within the cells. Thus, this experiment was assessed to determine the lithium-induced DNA fragmentation (Figure 4 and Figure 5) [32,34].
After 24 h of treatment, DNA fragmentation was observed in the lithium-treated cells. In SiHa and HeLa cells, the mean fluorescence intensity after lithium treatment increased compared to untreated cells [C(-) and TdT(-)]. The percentages of positive TUNEL apoptotic cell ratios were 69.8%, 63.8%, and 24.7% for HeLa, SiHa, and HaCaT cells treated with LiCl, respectively. On the other hand, for Li2CO3 treatment, the ratios of total TUNEL-positive cells were 73.9%, 40.4%, and 8.7% for HeLa, SiHa, and HaCaT cells, respectively.
A morphology analysis showed in Figure 5 was performed for the cell count determination in bright fields and for DAPI/TUNEL tinction. Apoptosis morphology characteristics are seen in HeLa, SiHa, and HaCaT cells treated with LiCl and Li2CO3, where cell round morphology and nuclear condensation were observed. DNA fragmentation marked by TUNEL-positive staining within the nucleus, cell integrity, and membrane blebbing were also analyzed. Other types of observed cells maintained the cell line morphology and still showed TUNEL-positive staining.
2.2.3. Flow Cytometry Apoptosis Assay (Annexin V/IP)
The annexin V/PI assay allows for determining the viability and processes of cell death due to apoptosis and necrosis of cells by evaluating the integrity and polarity of their membrane. Viable cells do not present Annexin V/PI staining; necrotic cells present staining with only PI, while cells in early apoptosis only present staining with Annexin V, and late apoptosis staining with both markers is present. Annexin V staining indicates the translocation of phosphatidyl serine, a phenomenon that occurs during apoptosis [35].
As shown in Figure 6, cells treated with lithium displayed apoptosis compared to the control group. Nevertheless, the striking results focus on Li2CO3 in HeLa Cells (panels A and B), which exhibit more apoptosis and less necrosis than the positive control. It is interesting that lithium salts do not show high necrosis levels against cervical cells but do show apoptosis at different levels, favoring HeLa cells.
2.2.1. Effect of Lithium Salts over the Apoptotic Protein’s Expression of CC Cell Lines
To further clarify the effect of lithium on apoptosis, we detected by Western Blot the levels of biomarkers of the execution phase of apoptosis: CAS-3 and PARP-1. CAS-3 in its active form weighs 17 kDa, and sometimes only its precursor form can be detected: proCAS-3 (36 kDa). PARP-1 is used to determine if there is proteolytic activity of CAS-3. When there is apoptosis, the active form of PARP-1 (110 kDa) is cleaved by caspases, generating an 89 kDa fragment. If PARP-1 (110 kDa) increases, it indicates that a response to genetic damage is activated [31,32].
In all cases, proCAS-3 had no significance between cells, groups, and time. Full-length PARP-1 and cleaved-PARP-1 expressions were observed in SiHa, HeLa, and HaCaT cells at 24 h (Figure 2) and 48 h (Figure 3). The expression of PARP-1 was slightly increased after LiCl or Li2CO3 treatment compared to untreated cells or cells treated with Dox at 48 h of stimuli. Statistical analysis of normalized PARP-1 expression levels in HeLa, SiHa, and HaCaT showed a difference significantly according to one-way ANOVA used to compare between groups (* p < 0.05). Interestingly, in LiCl-treated cells, the expression of PARP-1 is higher than Li2CO3-treated cells (* p < 0.05, ** p < 0.002, *** p < 0.001). β-actin was used as a loading control in all samples. It is worth mentioning that in HaCaT cells treated with Doxorubicin (10 µM) for 48 h, β-actin protein was not detected since Doxorubicin is a powerful cell death agent that causes the degradation of cytoskeleton components; this does not compromise the Western Blot results since it demonstrates the effect of different stimuli on the tumor cell phenotypes of greatest interest [33]. The accumulation of the active form of PARP-1 and its 89 kDa fragment varied depending on the cell line and the stimulation time, where in HeLa, there was a significant reduction at 24 h, while, at 48 h, the active form of PARP-1 had a considerable increase. In SiHa, there was only a trend in the reduction of PARP-1 levels with Li2CO3, and in HaCaT there were no significant differences. This shows that lithium salt activity is only in HeLa during the execution phase.
2.2.2. TUNEL Assay of LiCl- and Li2CO3-Treated Cells
The TUNEL assay was performed to detect the 3′-OH ends generated by DNA fragmentation due to apoptosis by activating endonucleases through enzymatic labeling with nucleotides conjugated with FITC. Altogether, DAPI staining is performed to detect DNA and locate the nuclei, evaluate their morphology and nuclear integrity, and determine if the TUNEL signal corresponds to the location of the genetic material within the cells. Thus, this experiment was assessed to determine the lithium-induced DNA fragmentation (Figure 4 and Figure 5) [32,34].
After 24 h of treatment, DNA fragmentation was observed in the lithium-treated cells. In SiHa and HeLa cells, the mean fluorescence intensity after lithium treatment increased compared to untreated cells [C(-) and TdT(-)]. The percentages of positive TUNEL apoptotic cell ratios were 69.8%, 63.8%, and 24.7% for HeLa, SiHa, and HaCaT cells treated with LiCl, respectively. On the other hand, for Li2CO3 treatment, the ratios of total TUNEL-positive cells were 73.9%, 40.4%, and 8.7% for HeLa, SiHa, and HaCaT cells, respectively.
A morphology analysis showed in Figure 5 was performed for the cell count determination in bright fields and for DAPI/TUNEL tinction. Apoptosis morphology characteristics are seen in HeLa, SiHa, and HaCaT cells treated with LiCl and Li2CO3, where cell round morphology and nuclear condensation were observed. DNA fragmentation marked by TUNEL-positive staining within the nucleus, cell integrity, and membrane blebbing were also analyzed. Other types of observed cells maintained the cell line morphology and still showed TUNEL-positive staining.
2.2.3. Flow Cytometry Apoptosis Assay (Annexin V/IP)
The annexin V/PI assay allows for determining the viability and processes of cell death due to apoptosis and necrosis of cells by evaluating the integrity and polarity of their membrane. Viable cells do not present Annexin V/PI staining; necrotic cells present staining with only PI, while cells in early apoptosis only present staining with Annexin V, and late apoptosis staining with both markers is present. Annexin V staining indicates the translocation of phosphatidyl serine, a phenomenon that occurs during apoptosis [35].
As shown in Figure 6, cells treated with lithium displayed apoptosis compared to the control group. Nevertheless, the striking results focus on Li2CO3 in HeLa Cells (panels A and B), which exhibit more apoptosis and less necrosis than the positive control. It is interesting that lithium salts do not show high necrosis levels against cervical cells but do show apoptosis at different levels, favoring HeLa cells.
2.3. Effect of Lithium Salts on Cellular Migration of CC Cell Lines (Wound-Healing Assay)
Wound-healing assays were conducted to investigate whether LiCl or Li2CO3 can influence cell migration in CC cells (Figure 7) since metastasis is known to be a big challenge for cancer patients, and these experiments currently represent a solid basis for state-of-the-art research focused on understanding metastatic potential and the development of possible targeted anti-metastatic therapies [36]. According to Bouchalova et al., 2022 [36], processes such as migration and cell invasion capacity are evaluated for the study of metastasis in in vitro models. The wound assay evaluates basic cell migration parameters by forming a monolayer and generating a wound. The ability of the cells to fill the wound is measured using software image J to determine the level of cell migration.
Cell migration of SiHa, HeLa, and HaCaT cell lines was significantly enhanced after TGF-β treatment (positive control) compared to untreated cells [negative control, C(-)]. Conversely, in Hela (Figure 7, Panels A–C), SiHa (Figure 7, Panels D–F), and HaCaT (Figure 7, Panels G–I) cells, the presence of lithium salts significantly reduced cell migration evaluated at 24, 48, and 72 h (** p > 0.001). In SiHa cells, the relative migration was 16.52% at 24 h and 13.34% at 48–72 h after LiCl treatment, while for Li2CO3 treatment, the relative migration was 10.51% at 24 h to 18.29% at 48–72 h. In HeLa cells, relative migration was 6.44% for 24, 48, and 72 h after LiCl treatment and 5.75%, 8.58%, and 5% for 24, 48, and 72 h after Li2CO3 treatment, respectively. In HaCaT cells, relative migration was 6.21% for 24, 48, and 72 h after LiCl treatment, and for Li2CO3 treatment, the relative migration was 5.73%, 6.54%, and 3.4% for 24, 48, and 72 h, respectively. In negative controls [C(-)] for SiHa, HeLa, and HaCaT cells, the relative migration was 20.51% for 24 h and 35.02% for 48–72 h, while in positive controls (TGF-β), the relative migration was 28.95–34.5% for 24 h, 42.31–52.31% for 48 h and 97–98% for 72 h.
For the statistical data, a one-way ANOVA test was performed, followed by a Tukey test. All experiments were assessed in triplicate, and the values are expressed as the Mean ± SD with * p < 0.01 and ** p < 0.001 compared to the control group with the LiCl and Li2CO3 treatments.
2.4. Effect of Lithium Salts over the Cell Cycle Progression of CC Cell Lines
The amount of DNA in cells varies depending on the phase of the cell cycle, and by staining with PI, which binds proportionally to the amount of DNA within a cell, the phase of the cycle in which it occurs can be analyzed. In this experiment, we used asynchronous cultures, which are essential for conducting a comprehensive cell cycle analysis, allowing for a general overview of cell behavior across all cell cycle stages under normal growth conditions. In a tumor, cells proliferate asynchronously, simultaneously occupying various cell cycle phases. Thus, an asynchronous culture replicates this dynamic, providing a more realistic model to evaluate cellular responses to anticancer treatments [37].
To determine the effect of lithium salt over the cell cycle progression, we analyzed the cell cycle distribution of SiHa, HeLa, and HaCaT cells treated with LiCl or Li2CO3 (Figure 8).
The proportion of HeLa cells in the G1 phase was 29.89% ± 12.61% with LiCl treatment and 39.11% ± 5.24% with Li2CO3 treatment, in contrast to 65.53% ± 7.87% for untreated cells. For the S phase, HeLa cell percentages were 36.66% ± 19.57% with LiCl and 25.06% ± 1.98% with Li2CO3, compared to 30.01% ± 11.90% for untreated cells. Following lithium treatments, there was an increase in the proportion of HeLa cells in the G2 phase. Specifically, 33.45% ± 10.21% of HeLa cells treated with LiCl and 35.83% ± 4.86% with Li2CO3 accumulated in the G2 phase, whereas only 4.47% ± 4.05% of untreated cells accumulated in this phase (Figure 8, panel A).
The percentage of SiHa cells in the G1 phase was 60.96% ± 5.23% for LiCl treatment and 71.54% ± 6.73% for Li2CO3 treatment, compared to 44.36% ± 5.01% of untreated cells. The percentage of SiHa cells in the S phase for LiCl and Li2CO3 treatments was 33.08% ± 3.53% and 22.62% ± 6.77%, respectively, and 33.59% ± 9.86% for untreated cells. The percentage of SiHa cells in the G2 phase was dramatically decreased after lithium treatments. We found that 5.97% ± 1.72% of SiHa cells treated with LiCl or 5.85% ± 0.04% of SiHa cells treated with Li2CO3 accumulated in the G2 phase, while untreated cells that accumulated in the G2 phase were 21.92% ± 4.36% (Figure 8, panel A).
An ANOVA analysis of cells at the G1 and G2 phase showed that LiCl or Li2CO3 treatments had a statistically significant difference with control (untreated cells) and with cells treated with colchicine (positive control) (p < 0.05) (Figure 8, panel B). For HeLa cells, we found a statistically significant difference between cells in the G1 phase after LiCl or Li2CO3 treatment compared to untreated SiHa cells, suggesting that LiCl or Li2CO3 treatment inhibited the progression of the cell cycle at the G1 phase. According to statistical analysis by one-way ANOVA, we found a statistically significant difference between cells in the G1 phase after LiCl or Li2CO3 treatment compared to colchicine-treated SiHa cells. No statistical differences were observed for cells accumulating in the S phase in SiHa or HeLa cell lines. Evidence from the HeLa cell line suggested that LiCl arrested the cell cycle at the S phase, whereas Li2CO3 arrested the cell cycle at the G1 phase. On the other hand, SiHa cells treated with LiCl or Li2CO3 arrested the cell cycle at the G1 phase. The non-tumoral cell line (HaCaT) cell cycle was arrested at the G1 phase after LiCl or Li2CO3 treatments.
To determine if there is a difference between CC cell lines and the lithium treatment, we compared HeLa and SiHa cells treated with LiCl or Li2CO3. We found that cells distributed differently compared to cell lines. When cells were exposed to LiCl, SiHa cells exhibited a G1 phase accumulation of 60.96% ± 5.23%, whereas HeLa cells showed a lower accumulation of 29.89% ± 12.61%. Similarly, treatment with Li2CO3 resulted in 71.54% ± 6.73% of SiHa cells accumulating in G1, compared to only 39.11% ± 5.24% of HeLa cells (Figure 8, Panel C). In contrast, no statistically significant difference was found when LiCl treatment was compared with Li2CO3 treatment in HeLa or SiHa cells (Figure 8, panel C).
3. Discussion
Cancer treatment encounters several challenges, including drug resistance, metastasis, exacerbated proliferation, and disease relapse. Thus, this work focuses on lithium salts as a new alternative for targeting some of the hallmarks of cancer, exhorting its repurposing as anticancer agents. Previous studies point out that lithium has antitumor properties over several cancer types, such as prostate [18], ameloblastoma [19], multiple myeloma [20], human head and neck squamous cell carcinoma [21], hepatocarcinoma [27], and skin melanoma [29]. However, there is a lack of evidence of the potential antitumoral effect of different lithium salts in cervical cancer. We provide data showing that the type of lithium salt acts differently depending on the CC genotype.
Cell proliferation of SiHa, HeLa, and HaCaT cells decreased along with the increase in LiCl or Li2CO3 concentrations, suggesting that lithium salts inhibited cell proliferation in a concentration-dependent manner.
Although the IC50 values of LiCl are similar for SiHa and HeLa cells, when treated with Li2CO3, the IC50 value for SiHa cell line was twice that for HeLa cells, signifying that lithium’s nature may be relevant to the repositioning of this drug in addition to low toxicity and high bioavailability of organic lithium salts [38]. Earlier publications indicate that 50 and 100 mM LiCl inhibited the tumor cell growth rate and activated the Wnt/β-catenin signaling pathway in the human CC CaSki cell line [39], supporting the statement of the difference in activity of lithium salts among genotypes.
The effects of lithium on the execution phase of apoptosis were mostly seen in HeLa cells, where alterations in PARP-1 are observed at 24 and 48 hours. The decrease in PARP-1 forms (110 and 89 kDa) at 24 h would indicate that there could be proCAS-3 or some other protease activity and that DNA repair mechanisms are being inhibited. The increase in the active form of PARP-1 at 48 hours in Hela suggests the activation of the damage response due to the occurring DNA fragmentation. In SiHa, on the other hand, the low activity of lithium salts on PARP-1 and proCAS-3 and the high viability on annexin V/PI, together with the presence of morphological changes and DNA fragmentation that were observed, suggests that there could be a programmed cell death independent of caspases, where characteristics of apoptosis with lower kinetics are observed. In HaCaT, the apoptotic effects of lithium salts on PARP-1 and proCAS-3 are consistent with the results in the other experiments, where a low killing effect on viability and induction of DNA fragmentation is observed. Thus, induction of apoptosis is evident except when analyzing the pro-apoptotic proteins by WB (PARP-1 and proCAS-3). This could mean that these cells’ programmed cell-death process is caspase-independent. A similar phenomenon was described in the neuroblastoma cell line B65, where lithium treatments increased cell death by apoptosis without activating CAS-3 activity. Even with caspase inhibitors, apoptosis still occurred, indicating a caspase-independent pathway [40]. Similarly, Karlovic and colleagues (2007) found that LiCl treatments (20 mmol/L) in A1235 glioblastoma cells altered the levels of proteins such as Bcl-2 and proCAS-3, and there was no cleavage of PARP1 [40], reinforcing with our work that lithium does not affect this pathway.
In addition, the translocation of phosphatidylserine from the inner side of the cell membrane to the outer side is associated with apoptosis. This translocation typically occurs due to caspase activation. On the other hand, we evaluated apoptosis by flow cytometry with the Annexin V/PI assay using HeLa cells treated and untreated with lithium salts, where the externalization of phosphatidylserine does occur, indicating cell death by apoptosis. In SiHa cells treated with lithium salts, this effect is observed to a lesser extent, with greater cell viability. Still, the lithium salts exhibited significantly less necrosis in all cases than the reference positive control drug. We also observed DNA fragmentation in the TUNEL assay by LiCl in SiHa (63.8%), HeLa (69.8%), and HaCaT (24.7%) cell lines, while for Li2CO3, the DNA fragmentation was for SiHa (40.4%), HeLa (73.9%), and HaCaT (8.7%). Moreover, Figure 5 shows morphological changes related to apoptosis. The integrity of the membrane, cell shrinkage and round morphology, blebbing, nuclear condensation, and DNA fragmentation were considered for the cell count. Thus, it is demonstrated that lithium salts induce DNA fragmentation and morphological apoptotic alterations in HeLa, SiHa, and, to a lesser extent, HaCaT cells. DNA fragmentation is the main feature of apoptosis caused by the caspase-activated DNA fragmentation factor (DFF). However, other caspase-independent DNA fragmentation factors could be activated, as ENDO-G and AIF (allows other endonuclease mechanisms to be activated) [41], suggesting that Lithium induces a caspase-independent cell death (CICD) by these factors (Figure 9). According to Tait, S.W.G., & Green, D.R. (2008) [42], in CICD, there is slower kinetics compared to normal apoptosis, so this would explain why, despite there being fragmentation and morphological changes, viability remains high in the annexin V/PI assay.
To study the anti-metastatic potential of lithium salts over cervical tumor cells we assessed an anti-migratory assay (wound-healing assay), since migration is the initial step of the complex metastatic process [36]. Both lithium salts had an anti-migratory effect on SiHa, HeLa, and HaCaT cells. LiCl inhibited SiHa, HeLa, and HaCaT migration in 86.66%, 93.56%, and 93.79%, respectively. On the other hand, Li2CO3 inhibited 81.71%, 95%, and 96.6% of the migration of SiHa, HeLa, and HaCaT, respectively. The percentage of the inhibition of migration was 10.5%-fold higher for HeLa cells compared to SiHa at 24, 48, and 72 h. No differences were observed between LiCl and Li2CO3 treatments regarding the anti-migratory effect.
Our results suggested that LiCl and Li2CO3 inhibited the migration and cell proliferation of SiHa and HeLa cell lines more selectively than HaCaT cells used as a non-tumoral genotype. Although the specific mechanism of the inhibitory effect of LiCl and Li2CO3 is unknown, we propose, in Figure 9, the mechanistic actions of lithium salts in the functional experiments conducted in this work. However, further molecular evidence is needed to demonstrate these hypotheses.
Based on the literature review and Figure 9, lithium might inhibit both GSK-3β and IMPase by entering the cell via MCT1; it reduces PIP2, PIP3, and IP3 levels, affecting AKT activation and calcium release from the endoplasmic reticulum, and possibly inhibiting cell migration [43]. Additionally, IP3 is a precursor of the 5PP-IP5 isoform, whose depleted levels may also be associated with the inhibition of cell migration due to the lack of lamellipodia formation [44].
Finally, the observed cell cycle arrest in the G1 phase may be caused by preventing the activation of the NF-κB pathway, which affects the transcription of genes involved in the cell cycle [45].
For instance, when lithium inhibits GSK-3β, it prevents the phosphorylation of β-catenin, causing its accumulation and translocation to the nucleus along with TCF-3, resulting in an anti-proliferative effect by inhibiting DNA synthesis [46]. Simultaneously, GSK-3β is a positive regulator of the NF-κB pathway. When GSK-3β is active, it regulates the IKK protein, which is responsible for phosphorylating IκB, leading to its degradation and the release of NF-κB. This release allows NF-κB to translocate to the nucleus and target various genes, including EMT activation (SNAIL and TWIST) and cell cycle regulation (C-Myc and Cyclin D). Therefore, lithium’s inhibition of GSK-3β would inhibit this signaling pathway and all its downstream targets, favoring the inhibition of cell migration and cell cycle arrest [47].
4. Materials and Methods
4.1. Cell Culture
Human cervical cancer HeLa (infected with HPV-18), SiHa (infected with HPV-16), and HaCaT (non-tumorigenic cell line) cell lines were cultured in RPMI medium (LABORATORIOS MICROLAB S.A. DE C.V., catalog number: M-221P) containing 10% heat-inactivated neonatal calf serum (In vitro, catalog number S-02) and antibiotic–antimycotic (In vitro, catalog number A-07) and incubated at 37 °C and 5% CO2 in an incubator (NUARE, Brossard, QC, Canada, NU-8600). When the cell reached 80–90% confluence, the culture was treated with 0.25% versene (Sigma-Aldrich; Milwaukee, WI, USA) and centrifuged (6000× g) at 4 °C for 10 min. Pellet cells were counted using a hemocytometer and immediately used in the assays.
4.2. Reactive and Materials
LiCl (423.9 mg, Sigma Aldrich; Milwaukee, WI, USA) and Li2CO3 (442.4 mg, Sigma Aldrich; Milwaukee, WI, USA) were dissolved in 50 mL fresh sterile medium and filtrated (2 µm) to obtain a stock solution of each salt: 200 mM for LiCl and 120mM for Li2CO3. Sterile lithium salts were added to the culture medium at the desired final concentrations for each experiment.
Doxorubicin (136 µL, Zuclodox, Zurich Pharma; Tepeji del Río, Mexico) was dissolved in 50 mL fresh sterile medium and filtrated (2 µm) to obtain a solution of 10 µM. At this concentration, the stimuli were added directly to the cells.
H2O2 (1.27 µL, Alcomex; Mexico City, Mexico) was added to 1 mL of fresh sterile medium and filtrated (2 µm) to obtain a final concentration of 125 µM H2O2 directly into the cell culture.
Cytarabine (10 mg, Santa Cruz Biotechnology; Dallas, TX, USA) was dissolved in H2O Mili Q sterile and filtrated (2 µm) to obtain a stock solution 510.78 µM. Sterile Cytarabine (ara-C) was added to the stock solution at 10 µM.
TGF-β (0.21 µM, Sigma Aldrich) was prepared as a work solution at 1.05 nM added in each well.
4.3. Proliferation Assay
Cells (7 × 103 cells/well) were seeded in a 96-well plate for 24 h at 37 °C in a 5% CO2 incubator for the incubation period. Then, the treated-with-HeLa cells were treated with LiCl at different concentrations (0, 12, 14, 17, 19, 21, 24, 35, 47, 59, 71, 83, 94, 106, and 118 mM) and Li2CO3 (0, 7, 8, 9, 11, 12, 14, 20, 27, 34, 41, 47, 54, 61, and 68 mM), SiHa treated with at different concentrations LiCl (0, 12, 14, 17, 19, 21, 24, 35, 47, 59, 71, 83, 94, 106, and 118 mM) and Li2CO3 (0, 7, 8, 9, 11, 12, 14, 20, 27, 34, 41, 47, 54, 61, and 68 mM), and HaCaT with LiCl at different concentrations (0, 6.7, 13.5, 27, 40.5, 54, 67.5, and 81 mM) and Li2CO3 (0, 6, 12, 18, 20, 24, 30, and 36 mM). Times of incubation were 24 h. At the end of the incubation period, cells were washed with PBS pH 7.0, and 50 µL of 0.5% crystal violet staining solution was added and kept at room temperature for 20 m. Finally, the optical density of each well was determined at 570 nm with the Bio Tek EPOCH Microplate Spectrophotometer (Agilent; Santa Clara, CA, USA). The percentage of cytotoxicity was reported considering non-stimulated cells as control and expressed as the mean of three independent biological replicate measurements with technical triplicates that were performed. Data were analyzed with the Graphpad PRISM software (version 9.0.2) [48], to determine the IC50 value of each lithium salt for HeLa, SiHa, and HaCaT cells.
4.4. Determination of IC50
The mean IC50 values are the concentrations of lithium salts needed to inhibit cell viability by 50%, and these values were calculated using the graphical method. Cellular cytotoxicity (%) was plotted on the Y-axis directly against the lithium salt concentration on the X-axis. By extrapolation using the Graphpad PRISM software (version 9.0.2) [48], we determined the IC50 value of each lithium salt for HeLa, SiHa, and HaCaT cells.
4.5. Western Blot Assay (Apoptosis)
HeLa, SiHa, and HaCaT cells treated and untreated with lithium salts and Dox were lysed with RIPA buffer (Santa Cruz Biotechnology, Inc., sc-24948) containing 0.1% protease inhibitors (Complete Protease Inhibitor Cocktail, Roche, catalog number 11697498008) and incubated at 4 °C for 30 min, following which samples were centrifugated at 13,000× g at 4 °C for 25 min. Soluble protein concentrations of cell lysates were determined spectrophotometrically at 280 nm using the Bio Tek EPOCH Microplate Spectrophotometer (Agilent, Santa Clara, CA, USA). Samples with equal amounts of protein were mixed with 5X Laemmli sample buffer (10% SDS, 50% glycerol, 0.02% bromophenol blue, and 0.3125 M Tris HCl, pH 6.8.) supplemented with β-mercaptoethanol and boiled for 10 min. Then, 50 µg protein of each sample was separated into 8% and 12% SDS-PAGE gels for visualization of PARP-1 and Caspase-3 (Cas-3) proteins, respectively, in a vertical electrophoresis system (Mini Trans-Blot® Cell, Bio-Rad, catalog number 1703810; Hercules, CA, USA). Gels were transferred onto 0.45 µm polyvinylidene difluoride (PVDF) membranes (Thermo Scientific, catalog number 88518; Rockford, IL, USA) in a mini Transblot®-Cell module (Bio-Rad; Hercules, CA, USA) at constant 400 mA for 2 h. Then, membranes were blocked with 5% non-fat milk in TBS pH 7.0 containing 0.1% Tween-20 for 2 h at room temperature. Then, membranes were incubated with antibodies against PARP-1 Rabbit pAb (1:3000 dilution, ABclonal, catalog number A0942; Woburn, MA, USA), Caspase-3 mouse mAb (1:1000 dilution, Santa Cruz Biotechnology, Inc., sc-7272; Dallas, TX, USA), and β-actin mouse mAb (1:10,000 dilution, ABclonal, catalog number AC004; Woburn, MA, USA) for 18 h at 4 °C on a rocking platform. After incubation period, membranes were washed five times with TBS pH 7.0 containing 0.1% Tween-20 and incubated for 1 h at 25 °C with a peroxidase-conjugated secondary antibodies anti-rabbit IgG (1:10,000 dilution, Cell Signaling Biotechnology, catalog number 7074s; Danvers, MA, USA) for PARP-1 detection or anti-mouse IgG (1:10,000 dilution, Cell Signaling Biotechnology, catalog number 7076s; Danvers, MA, USA) for Cas-3 and β-actin detection. Finally, membranes were washed with TBS pH 7.0 containing 0.1% Tween-20 and developed by chemiluminescence using Clarity MaxTM Western ECL (Bio-Rad, catalog number 1705062; Hercules, USA) following the instructions from the manufacturer. Finally, image analysis was performed using the C-DiGit Blot (LI-COR; Lincoln, IL, USA). All experiments were performed in triplicate. In all samples, β-actin was detected as a loading control and used to normalize target protein detection for densitometric analysis using the Image J software (version 7). Data normalization was performed by dividing densitometry data from the target protein with densitometry data from the loading control protein (β-actin).
4.6. TUNEL Assay (Apoptosis)
HeLa, SiHa, and HaCaT cells (3 × 104 cells/well) were seeded in 6-well plates with built-in glass coverslips and incubated for 24 h at 37 °C in a 5% CO2 incubator. Then, HeLa cells were treated with 23.14 mM LiCl and 11.52 mM Li2CO3; SiHa cells were treated with 23.43 mM LiCl and 20.57 mM Li2CO3; and HaCaT cells were treated with 15.10 mM LiCl and 10.52 mM Li2CO3, for 24 h. All cell cultures were treated as controls with 125 µM H2O2 or 10 µM Doxorubicin (Zuclodox, Zurich Pharma; Tepeji del Río, Mexico). Cell cultures were incubated for 4 days, and the culture medium was replaced every other day. At the end of the treatment, coverslips containing a monolayer of HeLa, SiHa, or HaCaT cells were fixed in 4% paraformaldehyde/0.1 M PBS (pH 7.0) at room temperature for 30 min and washed with PBS. Then, cells were stained with the In Situ Cell Death Detection Kit following the manufacturer’s instructions (Cat. No. 11 684 795 910, Roche; Manheim, Germany). Negative control cells with medium alone (C(-)) and without addition of TdT enzyme (TdT(-)) were included. All Cells were counterstained with DAPI (1:500 in PBS; Calbiochem; Gibbstown, NJ, USA) and all conditions were analyzed under a confocal microscope TC SP8 (DMI8, Leica, Wetzlar, Germany), with an excitation wavelength of 450–500 nm and a detection wavelength of 490–600 nm. All photos were taken with the same conditions of a 40× objective and had the 40 µm scale bar in the lower left corner, indicating homogeneity. Images were analyzed in LAS X Office software (version 1.4.5.27713, Leica microsystems; version 1.4.5.27713) to determine the fragmentation of cellular DNA, a hallmark of apoptosis.
4.7. Flow Cytometry (Annexin V/IP)
HeLa, SiHa, and HaCaT cells (5 × 105 cells/well) were seeded in 6-well plates and after 24 h of growing the cells were stimulated with the IC50 of lithium salts: HeLa cells were treated with 23.14 mM LiCl, 11.52 mM Li2CO3, SiHa cells were treated with 23.43 mM LiCl, 20.57 mM Li2CO3, and HaCaT cells were treated with 15.10 mM LiCl, 10.52 mM Li2CO3 for 24 h. After that time, according to the manufacturer, the Annexin V/IP assay (FITC Annexin V Apoptosis Detection Kit II, No. 556547, BD BIOSCIENCES; San Diego, CA, USA) was assessed. Finally, the lectures were taken in Beckman Coulter Cytoflex S and data were analyzed in the Kaluza C versión 1.1 software.
4.8. Wound-Healing Assay
A wound-healing assay was performed to evaluate cell migration. In sterile conditions, an adhesive tape (0.5 mm width) was placed in 96-well plates, and a 2 mm division mark was made in the center of the wells to delimit the area for photographic analysis. Then, HeLa, SiHa, and HaCaT cells were seeded (3 × 104 cells/well) in 100 µL of RPMI medium supplemented with 10% neonatal serum and incubated for 24 h at 37 °C with 5% CO2. At the end of the incubation period, an inhibitor of proliferation 10 µM cytarabine (ara-C) was added and incubated for 2 h at the same conditions described above. Subsequently, the tape was removed to form the wound. Samples were gently washed with PBS pH 7.0 and 23.14 mM and 23.43 mM LiCl, and 11.52 mM and 20.57 mM Li2CO3 were added to HeLa and SiHa cells, respectively. HaCaT cells were incubated with 15.10 mM LiCl and 10.52 mM Li2CO3. Negative control (untreated cells) and cells treated with 0.105 µM TGF-β were included in each experiment as a positive migration control. Cells were monitored at several incubations (0, 24, 48, and 72 h) and documented using a Canon EOS Rebel T100 camera. Three biological replicates with technical triplicates were performed for each measurement. Wound thickness was analyzed by bioinformatics tools (Image J) using images taken at different times in the predetermined area and quantifying the migration rate of the cells. The migration percentage (Wound Closure %) was calculated using the following formula:Wound Closure %=At=0−At=∆tAt=0×100%
where At=0 is the initial wound area, and At=Δt is the wound area after n hours of the initial scratch. Relative migration was obtained in μm.
4.9. Flow Cytometry (Cell Cycle Analysis)
Cells (3 × 105) were plated in a 6-well plate and incubated at 24 h at 37 °C with 5% CO2. Then, cells were untreated and treated with 20 µL colchicine (1 mg/mL, as a positive control) and lithium. HeLa cells were treated with 23.14 mM LiCl and 11.52 mM Li2CO3, whereas SiHa cells were treated with 23.43 mM LiCl and 20.57 mM Li2CO3, and HaCaT cells were treated with 15.10 mM LiCl and 10.52 mM Li2CO3. Cells were grown for 24 h at 37 °C with 5% CO2. After trypsinization, cells were collected by centrifugation at 1000× g (1500 rpm) for 3 min at room temperature. Then, cells were fixed in ice-cold methanol (500 µL) with PBS pH 7.0 (500 µL) at 4 °C for 1h. Cells were washed thrice in PBS pH 7.0 (1 mL) and centrifuged. Then, cells were treated with 30 µL RNase A (100 U/mL) and incubated for 30 min at 37 °C. Samples were centrifuged and resuspended in 500 µL of PBS with 5 µL propidium iodide (20 µg/mL) and analyzed using the Beckman Coulter Cytoflex S and data were analyzed in the ModFit Lt software (https://www.vsh.com/products/mflt/, accessed on 18 September 2024). We chose the colchicine concentration based on reported values known to arrest the cell cycle at the G2 phase. Three biological replicates with technical duplicates were performed for each measurement.
4.10. Statistical Analysis
All data were expressed as mean ± standard error of the mean (SEM). Differences between experimental groups were compared using ANOVA or t-student test using GraphPad 9 software. A confidence interval of 95% and a p = 0.05 for statistical significance were considered.
4.1. Cell Culture
Human cervical cancer HeLa (infected with HPV-18), SiHa (infected with HPV-16), and HaCaT (non-tumorigenic cell line) cell lines were cultured in RPMI medium (LABORATORIOS MICROLAB S.A. DE C.V., catalog number: M-221P) containing 10% heat-inactivated neonatal calf serum (In vitro, catalog number S-02) and antibiotic–antimycotic (In vitro, catalog number A-07) and incubated at 37 °C and 5% CO2 in an incubator (NUARE, Brossard, QC, Canada, NU-8600). When the cell reached 80–90% confluence, the culture was treated with 0.25% versene (Sigma-Aldrich; Milwaukee, WI, USA) and centrifuged (6000× g) at 4 °C for 10 min. Pellet cells were counted using a hemocytometer and immediately used in the assays.
4.2. Reactive and Materials
LiCl (423.9 mg, Sigma Aldrich; Milwaukee, WI, USA) and Li2CO3 (442.4 mg, Sigma Aldrich; Milwaukee, WI, USA) were dissolved in 50 mL fresh sterile medium and filtrated (2 µm) to obtain a stock solution of each salt: 200 mM for LiCl and 120mM for Li2CO3. Sterile lithium salts were added to the culture medium at the desired final concentrations for each experiment.
Doxorubicin (136 µL, Zuclodox, Zurich Pharma; Tepeji del Río, Mexico) was dissolved in 50 mL fresh sterile medium and filtrated (2 µm) to obtain a solution of 10 µM. At this concentration, the stimuli were added directly to the cells.
H2O2 (1.27 µL, Alcomex; Mexico City, Mexico) was added to 1 mL of fresh sterile medium and filtrated (2 µm) to obtain a final concentration of 125 µM H2O2 directly into the cell culture.
Cytarabine (10 mg, Santa Cruz Biotechnology; Dallas, TX, USA) was dissolved in H2O Mili Q sterile and filtrated (2 µm) to obtain a stock solution 510.78 µM. Sterile Cytarabine (ara-C) was added to the stock solution at 10 µM.
TGF-β (0.21 µM, Sigma Aldrich) was prepared as a work solution at 1.05 nM added in each well.
4.3. Proliferation Assay
Cells (7 × 103 cells/well) were seeded in a 96-well plate for 24 h at 37 °C in a 5% CO2 incubator for the incubation period. Then, the treated-with-HeLa cells were treated with LiCl at different concentrations (0, 12, 14, 17, 19, 21, 24, 35, 47, 59, 71, 83, 94, 106, and 118 mM) and Li2CO3 (0, 7, 8, 9, 11, 12, 14, 20, 27, 34, 41, 47, 54, 61, and 68 mM), SiHa treated with at different concentrations LiCl (0, 12, 14, 17, 19, 21, 24, 35, 47, 59, 71, 83, 94, 106, and 118 mM) and Li2CO3 (0, 7, 8, 9, 11, 12, 14, 20, 27, 34, 41, 47, 54, 61, and 68 mM), and HaCaT with LiCl at different concentrations (0, 6.7, 13.5, 27, 40.5, 54, 67.5, and 81 mM) and Li2CO3 (0, 6, 12, 18, 20, 24, 30, and 36 mM). Times of incubation were 24 h. At the end of the incubation period, cells were washed with PBS pH 7.0, and 50 µL of 0.5% crystal violet staining solution was added and kept at room temperature for 20 m. Finally, the optical density of each well was determined at 570 nm with the Bio Tek EPOCH Microplate Spectrophotometer (Agilent; Santa Clara, CA, USA). The percentage of cytotoxicity was reported considering non-stimulated cells as control and expressed as the mean of three independent biological replicate measurements with technical triplicates that were performed. Data were analyzed with the Graphpad PRISM software (version 9.0.2) [48], to determine the IC50 value of each lithium salt for HeLa, SiHa, and HaCaT cells.
4.4. Determination of IC50
The mean IC50 values are the concentrations of lithium salts needed to inhibit cell viability by 50%, and these values were calculated using the graphical method. Cellular cytotoxicity (%) was plotted on the Y-axis directly against the lithium salt concentration on the X-axis. By extrapolation using the Graphpad PRISM software (version 9.0.2) [48], we determined the IC50 value of each lithium salt for HeLa, SiHa, and HaCaT cells.
4.5. Western Blot Assay (Apoptosis)
HeLa, SiHa, and HaCaT cells treated and untreated with lithium salts and Dox were lysed with RIPA buffer (Santa Cruz Biotechnology, Inc., sc-24948) containing 0.1% protease inhibitors (Complete Protease Inhibitor Cocktail, Roche, catalog number 11697498008) and incubated at 4 °C for 30 min, following which samples were centrifugated at 13,000× g at 4 °C for 25 min. Soluble protein concentrations of cell lysates were determined spectrophotometrically at 280 nm using the Bio Tek EPOCH Microplate Spectrophotometer (Agilent, Santa Clara, CA, USA). Samples with equal amounts of protein were mixed with 5X Laemmli sample buffer (10% SDS, 50% glycerol, 0.02% bromophenol blue, and 0.3125 M Tris HCl, pH 6.8.) supplemented with β-mercaptoethanol and boiled for 10 min. Then, 50 µg protein of each sample was separated into 8% and 12% SDS-PAGE gels for visualization of PARP-1 and Caspase-3 (Cas-3) proteins, respectively, in a vertical electrophoresis system (Mini Trans-Blot® Cell, Bio-Rad, catalog number 1703810; Hercules, CA, USA). Gels were transferred onto 0.45 µm polyvinylidene difluoride (PVDF) membranes (Thermo Scientific, catalog number 88518; Rockford, IL, USA) in a mini Transblot®-Cell module (Bio-Rad; Hercules, CA, USA) at constant 400 mA for 2 h. Then, membranes were blocked with 5% non-fat milk in TBS pH 7.0 containing 0.1% Tween-20 for 2 h at room temperature. Then, membranes were incubated with antibodies against PARP-1 Rabbit pAb (1:3000 dilution, ABclonal, catalog number A0942; Woburn, MA, USA), Caspase-3 mouse mAb (1:1000 dilution, Santa Cruz Biotechnology, Inc., sc-7272; Dallas, TX, USA), and β-actin mouse mAb (1:10,000 dilution, ABclonal, catalog number AC004; Woburn, MA, USA) for 18 h at 4 °C on a rocking platform. After incubation period, membranes were washed five times with TBS pH 7.0 containing 0.1% Tween-20 and incubated for 1 h at 25 °C with a peroxidase-conjugated secondary antibodies anti-rabbit IgG (1:10,000 dilution, Cell Signaling Biotechnology, catalog number 7074s; Danvers, MA, USA) for PARP-1 detection or anti-mouse IgG (1:10,000 dilution, Cell Signaling Biotechnology, catalog number 7076s; Danvers, MA, USA) for Cas-3 and β-actin detection. Finally, membranes were washed with TBS pH 7.0 containing 0.1% Tween-20 and developed by chemiluminescence using Clarity MaxTM Western ECL (Bio-Rad, catalog number 1705062; Hercules, USA) following the instructions from the manufacturer. Finally, image analysis was performed using the C-DiGit Blot (LI-COR; Lincoln, IL, USA). All experiments were performed in triplicate. In all samples, β-actin was detected as a loading control and used to normalize target protein detection for densitometric analysis using the Image J software (version 7). Data normalization was performed by dividing densitometry data from the target protein with densitometry data from the loading control protein (β-actin).
4.6. TUNEL Assay (Apoptosis)
HeLa, SiHa, and HaCaT cells (3 × 104 cells/well) were seeded in 6-well plates with built-in glass coverslips and incubated for 24 h at 37 °C in a 5% CO2 incubator. Then, HeLa cells were treated with 23.14 mM LiCl and 11.52 mM Li2CO3; SiHa cells were treated with 23.43 mM LiCl and 20.57 mM Li2CO3; and HaCaT cells were treated with 15.10 mM LiCl and 10.52 mM Li2CO3, for 24 h. All cell cultures were treated as controls with 125 µM H2O2 or 10 µM Doxorubicin (Zuclodox, Zurich Pharma; Tepeji del Río, Mexico). Cell cultures were incubated for 4 days, and the culture medium was replaced every other day. At the end of the treatment, coverslips containing a monolayer of HeLa, SiHa, or HaCaT cells were fixed in 4% paraformaldehyde/0.1 M PBS (pH 7.0) at room temperature for 30 min and washed with PBS. Then, cells were stained with the In Situ Cell Death Detection Kit following the manufacturer’s instructions (Cat. No. 11 684 795 910, Roche; Manheim, Germany). Negative control cells with medium alone (C(-)) and without addition of TdT enzyme (TdT(-)) were included. All Cells were counterstained with DAPI (1:500 in PBS; Calbiochem; Gibbstown, NJ, USA) and all conditions were analyzed under a confocal microscope TC SP8 (DMI8, Leica, Wetzlar, Germany), with an excitation wavelength of 450–500 nm and a detection wavelength of 490–600 nm. All photos were taken with the same conditions of a 40× objective and had the 40 µm scale bar in the lower left corner, indicating homogeneity. Images were analyzed in LAS X Office software (version 1.4.5.27713, Leica microsystems; version 1.4.5.27713) to determine the fragmentation of cellular DNA, a hallmark of apoptosis.
4.7. Flow Cytometry (Annexin V/IP)
HeLa, SiHa, and HaCaT cells (5 × 105 cells/well) were seeded in 6-well plates and after 24 h of growing the cells were stimulated with the IC50 of lithium salts: HeLa cells were treated with 23.14 mM LiCl, 11.52 mM Li2CO3, SiHa cells were treated with 23.43 mM LiCl, 20.57 mM Li2CO3, and HaCaT cells were treated with 15.10 mM LiCl, 10.52 mM Li2CO3 for 24 h. After that time, according to the manufacturer, the Annexin V/IP assay (FITC Annexin V Apoptosis Detection Kit II, No. 556547, BD BIOSCIENCES; San Diego, CA, USA) was assessed. Finally, the lectures were taken in Beckman Coulter Cytoflex S and data were analyzed in the Kaluza C versión 1.1 software.
4.8. Wound-Healing Assay
A wound-healing assay was performed to evaluate cell migration. In sterile conditions, an adhesive tape (0.5 mm width) was placed in 96-well plates, and a 2 mm division mark was made in the center of the wells to delimit the area for photographic analysis. Then, HeLa, SiHa, and HaCaT cells were seeded (3 × 104 cells/well) in 100 µL of RPMI medium supplemented with 10% neonatal serum and incubated for 24 h at 37 °C with 5% CO2. At the end of the incubation period, an inhibitor of proliferation 10 µM cytarabine (ara-C) was added and incubated for 2 h at the same conditions described above. Subsequently, the tape was removed to form the wound. Samples were gently washed with PBS pH 7.0 and 23.14 mM and 23.43 mM LiCl, and 11.52 mM and 20.57 mM Li2CO3 were added to HeLa and SiHa cells, respectively. HaCaT cells were incubated with 15.10 mM LiCl and 10.52 mM Li2CO3. Negative control (untreated cells) and cells treated with 0.105 µM TGF-β were included in each experiment as a positive migration control. Cells were monitored at several incubations (0, 24, 48, and 72 h) and documented using a Canon EOS Rebel T100 camera. Three biological replicates with technical triplicates were performed for each measurement. Wound thickness was analyzed by bioinformatics tools (Image J) using images taken at different times in the predetermined area and quantifying the migration rate of the cells. The migration percentage (Wound Closure %) was calculated using the following formula:Wound Closure %=At=0−At=∆tAt=0×100%
where At=0 is the initial wound area, and At=Δt is the wound area after n hours of the initial scratch. Relative migration was obtained in μm.
4.9. Flow Cytometry (Cell Cycle Analysis)
Cells (3 × 105) were plated in a 6-well plate and incubated at 24 h at 37 °C with 5% CO2. Then, cells were untreated and treated with 20 µL colchicine (1 mg/mL, as a positive control) and lithium. HeLa cells were treated with 23.14 mM LiCl and 11.52 mM Li2CO3, whereas SiHa cells were treated with 23.43 mM LiCl and 20.57 mM Li2CO3, and HaCaT cells were treated with 15.10 mM LiCl and 10.52 mM Li2CO3. Cells were grown for 24 h at 37 °C with 5% CO2. After trypsinization, cells were collected by centrifugation at 1000× g (1500 rpm) for 3 min at room temperature. Then, cells were fixed in ice-cold methanol (500 µL) with PBS pH 7.0 (500 µL) at 4 °C for 1h. Cells were washed thrice in PBS pH 7.0 (1 mL) and centrifuged. Then, cells were treated with 30 µL RNase A (100 U/mL) and incubated for 30 min at 37 °C. Samples were centrifuged and resuspended in 500 µL of PBS with 5 µL propidium iodide (20 µg/mL) and analyzed using the Beckman Coulter Cytoflex S and data were analyzed in the ModFit Lt software (https://www.vsh.com/products/mflt/, accessed on 18 September 2024). We chose the colchicine concentration based on reported values known to arrest the cell cycle at the G2 phase. Three biological replicates with technical duplicates were performed for each measurement.
4.10. Statistical Analysis
All data were expressed as mean ± standard error of the mean (SEM). Differences between experimental groups were compared using ANOVA or t-student test using GraphPad 9 software. A confidence interval of 95% and a p = 0.05 for statistical significance were considered.
5. Conclusions
In summary, our research indicates that the cell proliferation of SiHa, HeLa, and HaCaT cells decreased along with the increase in LiCl or Li2CO3 concentrations, suggesting that lithium salts inhibited cell proliferation in a concentration-dependent manner; the IC50 values for Li2CO3 are lower than those obtained for LiCl, showing more sensitivity to the first salt. The data suggest that lithium salts trigger DNA fragmentation in cervical cancer cells, with Li2CO3 exhibiting a more pronounced effect. Additionally, LiCl and Li2CO3 induce apoptosis in a caspase-independent pathway. Finally, both lithium salts inhibit cell migration, leading to the cell cycle arrest in the G1 phase. Altogether, along with the toxicity safety compiled over time for other diseases, it supports further investigations of the lithium salts as anticancer agents, with a promising intervention in cell death, proliferation, and metastasis inhibition. |
Title: DETECTING MULTIPLE REPLICATING SIGNALS USING ADAPTIVE FILTERING PROCEDURES | Body: |
Title: Perianal squamous cell carcinoma with high-grade anal intraepithelial neoplasia in an HIV-positive patient using highly active antiretroviral therapy: case report | Body:
INTRODUCTION
Anogenital condyloma acuminata are benign proliferative lesions caused by human papillomavirus (HPV). Many subtypes of HPV have been identified. Some of them, particularly HPV 16, can lead to premalignant transformation of squamous epithelial cells, resulting in high-grade squamous intraepithelial lesions or even invasive squamous cell carcinoma.1
Diagnosing squamous intraepithelial lesions requires pathological examination. Cytological (anal Pap smears) and histological examination by biopsy under high-resolution anoscopy guidance are useful for internal evaluation of the anus. However, the perianal region, which is also susceptible to malignancy, is not routinely evaluated.
Here, we report on the case of a human immunodeficiency virus (HIV)-positive man who was referred to our service for evaluation of anal warts and presented with perianal microinvasive squamous cell carcinoma.
CASE REPORT
A 54-year-old homosexual male presented to us with a six-year history of recurrent anal warts. He had previously undergone incomplete surgical excision and fulguration of anal and perianal warts in another institution on two occasions. He had tested positive for HIV six years earlier and had been well controlled with highly active antiretroviral therapy (HAART) over the past two years. His CD4 lymphocyte count was 253/mm3 and he had an undetectable HIV viral load. He presented some condylomatous lesions occupying part of the anal canal and the perianal skin, and also a well-demarcated slightly painful perianal erythematous plaque of dimensions 1.0 x 1.0 cm, with an irregular border and surface (Figure 1). Serological tests for herpes simplex virus and syphilis were negative.
Figure 1
Perianal area: preoperative appearance.
The patient's anal Pap smear revealed high-grade squamous intraepithelial lesion and this was confirmed by anal biopsy guided by 3% acetic acid using high-resolution anoscopy. Biopsies were also performed at the border of the suspicious perianal plaque. HPV DNA testing was conducted using a well-established MY09/MY11 polymerase chain reaction assay and HPV-16 type was detected. Pathological examination of the perianal lesion biopsies revealed high-grade squamous intraepithelial lesion. Because of suspected malignancy, we performed local full-thickness excision of the lesion (Figure 2), in addition to ablation of the remaining condyloma. The wound was allowed to heal by secondary intention.
Figure 2
Perianal area: postoperative appearance.
Histological analysis on the excised tissue revealed high-grade squamous intraepithelial lesion with one focus of microinvasive squamous cell carcinoma measuring 1 mm. No lymph vessel or perineural invasion was detected. Despite good postoperative recovery, the patient showed pathological evidence of recurrent anal and perianal high-grade squamous intraepithelial lesions, which were detected by high-resolution anoscopy at the sixth-month follow-up and required further ablation of those lesions. However, no cancer recurrence has been detected so far.
DISCUSSION
Anal cancer shares many biological characteristics with cervical cancer, including similar histopathological appearance and association with HPV infection. Recent studies have shown that both anal squamous intraepithelial lesions and anal HPV infection are more common in HIV-positive homosexual or bisexual men than in HIV-negative men, which suggests that HIV infection may even increase the risk of developing anal cancer.2
Although most of the research on anogenital neoplasia in HIV-infected individuals has focused on lesions of the cervix and anal canal, there is also evidence that similar lesions occur with increased frequency in perianal skin (5 cm radially from the anal verge).3 These lesions are often multifocal and frequently recur after standard treatments. It has been proposed that populations at high risk of developing squamous cell carcinoma of the anus should be screened regularly by means of anal cytology in combination with high-resolution anoscopy.3,4 The perianal area should be routinely examined for suspicious lesions, since perianal dysplasia may present with macroscopic or microscopic lesions. Symptomatic patients complain of perianal bleeding, pruritus, burning and anal discharge, and may present irregular brownish-red, eczematoid lesions. Paucity of symptoms and slow growth often result in delayed diagnosis.
There is a lack of literature regarding the incidence of specifically perianal dysplasia in HIV-infected patients. However, the incidence of anal dysplasia has been well described. In a study covering a four-year period, Palefsky et al.5 observed that HIV-positive men were more likely to develop anal high-grade squamous intraepithelial lesions than were HIV-negative men (relative risk 3.7; 95% confidence interval, CI: 2.6-5.7). High rates of anal high-grade squamous intraepithelial lesions among HIV-positive men, at all CD4 levels but with lower CD4 counts, have been associated with earlier development of high-grade squamous intraepithelial lesions (p = 0.007).
Multiple oncogenic HPV-type infections are very common among HIV-positive homosexual men and show almost a 100% association with anal squamous cell carcinoma, especially HPV type 16.6 Although it is well established that HIV seropositivity and low CD4 T-cell count are positively associated with the overall prevalence of HPV,6 few data have been reported regarding these associations on an individual HPV type-specific basis.
Surgical excision is considered to be the therapy of choice for perianal dysplasia and can be safely performed, especially for patients with limited disease like in our case. However, for more extensive or diffuse perianal high-grade squamous intraepithelial lesions, treatment can be challenging. Alternative treatments for the latter situation includes cryosurgery, 5-fluorouracil cream, photodynamic therapy and imiquimod 5% cream. Wide local excision with free uninvolved margins may leave big reconstructive challenges like extensive flaps or grafts and the risk of incontinence. However, in such cases, conservative management seems to be favored by most surgeons, according to a survey of members of the American Society of Colon and Rectal Surgeons.7 It may not be possible to eliminate the virus, and therefore the goal of treatment is to eliminate macroscopic lesions that may be more likely to progress to invasive carcinoma. Chemoradiation is reserved for patients with invasive squamous cell carcinoma.
In the current case, excisional biopsy was useful for diagnosing and treating very early-stage cancer. Since there was no deep invasive squamous cell carcinoma, we considered it appropriate not to offer adjuvant treatment.
Our patient illustrates a complication for HIV-positive patients that is associated with prolonged survival attributed to HAART. Immune status improvement has effectively decreased some viral infections and malignancies such as molluscum contagiosum and Kaposi's sarcoma. However, HPV infections and associated malignancies have not responded as favorably.8–10 Thus, routine examination of the anal canal in this population is recommended, as described in a screening algorithm.3 Additionally, we recommend careful examination of the perianal skin to rule out malignancies in all HIV-positive patients, especially those who have tested positive for oncogenic anogenital HPV infection. |
Title: Fish Health Altered by Contaminants and Low Water Temperatures Compounded by Prolonged Regional Drought in the Lower Colorado River Basin, USA | Body:
1. Introduction
Lake Mead National Recreation Area (LMNRA) in the Colorado River Basin (CRB) was the first national recreation area created in the United States (U.S.) in 1936 and has the largest area (6053 km2) and largest reservoir, Lake Mead, by volume (max 34.84 km3) (Figure 1) [1]. It is a popular destination for fishing, boating, hiking, and camping, and is close to the City of Las Vegas with a metropolitan population of nearly three million people [2]. Diverse aquatic ecosystems found in riverine sections of the free-flowing Colorado River above and below Lake Mead provide a variety of habitats and shallow waters with aquatic vegetation and deep-water benthos. LMNRA also provides water for 25 million people for urban, industrial, agriculture uses, and hydroelectric power for 1.3 million people in the Southwest U.S. [3]. Below Hoover Dam, the hypolimnetic water released from Lake Mead establishes a ≈21 km stretch of free-flowing river before entering Lake Mohave where unusually cool water temperatures exist within a narrow range of 12–15 °C throughout the year [4].
Lakes Mead and Mohave provide a rich, diverse ecosystem influenced by non-native species, including golden algae (Prymnesium parvum) and three mollusks, including New Zealand mud snail (Potamopyrgus antipodarium), Asian basket clam (Corbicula fluminea), and quagga mussel (Dreissena rostriformis) [5]. Of these invasives, quagga mussels are the most destructive because they colonize in vast numbers on hard surfaces (i.e., biofouling drinking water intakes), and they have established substantial populations since their accidental introduction to Lake Mead in 2007 [6]. With large numbers of quagga mussels in Lake Mead (estimated at 1.5 × 1012 in 2014), with each mussel able to filter ≈1 L water per day, they remove approximately10.5% of environmental organic contaminants (EOCs) from the water column to the benthic ecosystem [7].
Although most of Lake Mead has generally good water quality for nutrients and dissolved oxygen [8], there are several threats to the federally listed Razorback Sucker (Xyrauchen texanus) due to non-native fish previously introduced to establish a sport fishery, reduced habitat from lower lake levels, and increasing regional population growth [9]. Additional stressors, including EOCs in LMNRA, have been previously discussed [4], where the areas of most historical concern are Las Vegas Wash (LVW), Las Vegas Bay (LVB) and Willow Beach (WB) (Figure 1). The LVW is the main drainage for natural and urban runoff from the Las Vegas Valley, that consists of ≈85% wastewater effluent [10]. The stream channel is adjacent to the Black Mountain Industrial complex in Henderson, NV that was developed after World War II for various industrial chemical companies. Early disposal practices included unlined evaporation ponds and lagoons where 483 chemicals including arsenic, polyaromatic hydrocarbons, dichlorobenzene and polychlorinated biphenyls (PCBs) are known or suspected to occur, with many detected in soil and groundwater [4]. LVW discharges directly into LVB, a popular fishing area within Lake Mead.
Water levels have continually dropped in Lake Mead more than 23 m over the past two decades (Figure 2) and reached the lowest recorded level in July 2022 [11]. The long-term mega-drought occurring over the last two decades in the southwestern United States, due to anthropogenic warming, is approaching a severity level of the highest magnitude in the last 12 centuries [12]. With mean yearly water losses from drought estimated at ≈1.4 Gigatons, the CRB was one of the 10 basins globally responsible for 70% of the total world-wide reservoir/lake water storage loss [13]. A continuing drought in the CRB will result in lower water levels in Lake Mead, with a recent study indicating that under a worst-case scenario, a drop in water surface elevation from 325 m (as of September 2023) to dead-pool of 274.3 m (water level where electric power cannot be generated) would increase wastewater discharge concentrations by 114% at the drinking water intake of the Southern Nevada Water Authority in Boulder Basin (Figure 1) [14].
For close to three decades, a multitude of studies of the LMNRA on EOCs and aquatic species, including Common Carp (Cyprinus carpio) (hereafter, carp), Largemouth Bass (Micropterus salmoides), Razorback Suckers (Xyrauchen texanus), and quagga mussels (Dreissena spp.), have documented effects on ecosystem and fish health and condition [3,15,16,17,18,19]. Effects in fish were primarily observed at LVB and LVW (Figure 1), including altered endocrine system function as well as atypical histological and reproductive biomarkers including sperm motility. Another focal site was WB—the only one of 14 sites in the entire CRB where gonadal intersex in carp has been documented (1 of 9 females), and male carp showed poor testicular development including inflammation, calcified deposits, pigmented cell aggregates (PgCA), edema and has some of the highest high polychlorinated biphenyl (PCB) levels (>0.8 µg/g wet weight [ww]) in whole body tissues in the CRB [20]. Poor gonadal condition was found in both male and female carp at WB in 2007/08 [4], and they had the highest PCB whole body levels out of four sites in LMNRA, with 17% of samples >0.8 µg/g ww. These documented effects are a concern for Razorback Suckers as this species is distributed all along the free-flowing Colorado River, from below Hoover Dam to Lake Mohave, including WB [21], and they are reproducing in Lake Mead’s Boulder Basin near the inflow of LVW (Figure 1) [22].
Male carp were used as biomonitors of environmental health, including sperm quality biomarkers, as previous studies from LMNRA have shown their relative utility. They are bottom feeders where contaminated sediment can be ingested and reflect the general conditions of the site, they are collected in. The individual biomarkers chosen include standard measures used to assess general fish health and condition. The goals of this study were to compare fish health indicators in male carp at four LMNRA sites where a gradient of EOC exposure and a substantial range in water temperature regimes have been documented, and to assess how such relationships might be altered by changes in climate and regional drought. Specific objectives were to: (1) Assess potential PCB sources at WB and consider reasons why concentrations were highest at this site; (2) Determine the EOC gradient in water at WB, LVB, LVW and the reference site at Overton Arm (OA); (3) Investigate associations of EOCs and fish health indicators, including reproductive and histological biomarkers; and 4) Assess how changes in climate and regional drought in CRB could influence EOC exposures and consequent fish health/condition, including for the Razorback Sucker, as Lake Mead water levels continue to drop (See Figure 3 for chronology). The overall hypotheses for this study, based on multiple investigations by this team (refer to citations in above paragraph), are that contaminants are negatively influencing fish health, condition, and reproduction, and that projected further reductions in water levels will likely increase these effects.
2. Materials and Methods
2.1. Sample Collection and Chemical Analyses
2.1.1. Passive Samplers
Passive samplers, including Semipermeable Membrane Device (SPMD) and Polar Organic Chemical Integrative Sampler (POCIS), were deployed in protective canisters at each site (Figure 1) using a concrete anchor and a buoy to suspend the samplers ~1 m above the lake bottom at depths ranging from 5 to 8 m. At each site, three SPMDs and ten POCIS, all contained in two deployment canisters, were deployed for ~1 month prior to biota being collected, 14 June–12 July 2010 (Spring; LVB, LVW), 14 October–17 November 2010 (Fall; OA, WB), after which they were retrieved and returned to the laboratory at Columbia Environmental Research Center, Columbia, MO. Methods for the processing and analysis of these samples have been described previously, with a list of individual analytes given in Appendix A (Table A1) [23,24,25]. Briefly, a single SPMD was analyzed for selected polycyclic aromatic hydrocarbons (PAHs), another SPMD was used for organochlorine pesticides (OCs), total polychlorinated biphenyls (tPCBs), five polybrominated diphenyl ether (PBDE) flame retardants, and the final SPMD was for selected wastewater (WW) chemicals [26] which were recovered using a hexane dialysis. Dialysates were treated to a series of sample fractionation and enrichment steps using size exclusion chromatography and adsorption chromatography. Final analyses for PAHs and WW were conducted by using gas chromatography/mass spectrometry (GC/MS), while OCs, tPCBs, and PBDEs were analyzed by using gas chromatography with electron capture detection.
Figure 3
Overview and chronology of experimental activities presented in this manuscript focusing on male Common Carp (Cyprinus carpio), chemistry, and climate at LVB (Las Vegas Bay), LVW (Las Vegas Wash), WB (Willow Beach), OA (Overton Arm) in Lake Mead National Recreation Area, Nevada/Arizona, United States. EOCs 1 (environmental organic contaminants) detected in the water matrix, only; PAHs (polycyclic aromatic hydrocarbons); OCs (organochlorine pesticides); PCB (polychlorinated biphenyls); PBDE (polybrominated diphenyl ether flame retardants); CECs (contaminants of emerging concern). 2 Water quality models predictive of recycled water concentrations incorporating flow conditions, wastewater effluent, water and air temperatures [27,28].
A suite of chemicals related to WW and surface runoff were extracted from POCIS samples using a mixture of dichloromethane:methyl-tert-butyl ether (80:20 v:v) to extract samples for the identification of individual chemicals by GC/MS [24]. Methanol was used to recover select pharmaceuticals from the POCIS prior to analysis using liquid chromatography tandem mass spectrometry (LC/MS/MS). Extracts from two POCIS were composited for both the WW and pharmaceutical samples in order to increase the amount of chemical available in the final extract for analysis. Sample cleanup or fractionation was not done on POCIS extracts prior to analysis. Time-weighted average water concentrations of chemicals sampled by SPMDs and POCIS were determined by using first-order update models as previously described [29,30].
A separate, single POCIS from each site was extracted with methanol and screened for potential estrogenicity using the Yeast Estrogen Screen (YES assay). Estradiol equivalent factors (EEQ) for the water samples were calculated by measuring the effective concentration required to elicit a 50% response (EC50) in the exposed genetically modified yeast cells (Saccharomyces cerevisiae) for the 17β-estradiol (E2)–positive control and determining the percent of sample required to give an equivalent response [23]. The remaining POCIS were archived for future use.
2.1.2. Sediment, Periphyton, Fish Feed and Sealant Samples
At each of the five WB sites (inset Figure 1), sediment was collected using a petite ponar sampler suspended mid-channel from a boat. Attached benthic algae (periphyton) was sampled by scraping rocks in shallow water (<2 m depth) and sediment was rinsed off with native water, excess water shaken off, and placed in chemically clean glass jars. At the Willow Beach National Fish Hatchery (WBNFH), involved in the recovery of the endangered Razorback Sucker [31], additional samples of fish feed and raceway sealant consisting of rubber and commercial gap fillers were sampled to determine if these may be a secondary source of PCBs into WB. All sediment, periphyton, feed, and sealant samples were processed and analyzed for t PCBs using methods previously employed for fish tissue analyses in Lake Mead [4]. This involved accelerated solvent extraction, flash gel permeation and basic alumina fractionation/cleanup, and analysis by GC/MS [32].
2.2. Fish Collection and Sample Processing
Male carp used in this study were collected in July 2010 from LVB and LVW, and in November 2010 from OA and WB. Fish sampling procedures were as previously described [4]. Briefly, fish were captured with an electroshocking boat near the shoreline in water depths of up to 3 m. They were immobilized by pulsed DC current, captured with dipnets, and maintained in a live well until processed on shore. On each sampling date, fish from each site were collected on consecutive days over a 4-day period.
Blood was drawn from the caudal artery into heparinized syringes and centrifuged in capillary tubes on site for hematocrit determinations in duplicate (HematoSTAT II Microhematocrit, Separation Technology, Inc., Altamonte Spring, FL, USA), and the average was reported per fish. Fish were then immersed in a solution of tricaine methanesulfonate (MS-222, 1 g/L of lake water) until all movement ceased, then euthanized by a blunt blow to the head. Fork length (length), and body and testicular mass were measured. Animal handling was conducted in accordance with taxon-specific guidelines [33].
Testis and liver samples were collected in 2010 for analyses. Samples for histology were preserved in 10% buffered formalin, and those for sperm quality analyses were placed in calcium-free Hank’s balanced salt solution at pH 7.5 and 315 milliosmole/kg (HBSS) with 10% v:v 15,000 units of penicillin G sodium, and 5 mg streptomycin sulfate/mL (HBSS/PS). Testicular samples were shipped overnight on wet ice in HBSS/PS to the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center, Lafayette, LA, USA for sperm quality analyses, and livers were transported to the USGS, Texas Cooperative Fish & Wildlife Research Unit, Lubbock, TX, USA for histological analysis.
2.3. Histological Analyses
Gonadal and liver tissue samples were rinsed in 70% ethanol, processed, and embedded in paraffin following standard procedures [34]. Tissues were sectioned to 6 µm thickness and stained with hematoxylin and eosin, and liver samples were also stained with Periodic Acid Schiff (PAS) (Sigma-Aldrich, St. Louis, MO, USA) with or without prior α-amylase digestion (Sigma-Aldrich) for assessment of glycogen deposits. Digital images of histological specimens were taken with an Olympus digital camera (DP70; Tokyo, Japan).
Testes were cut rostro-caudally into 10 equal segments, and a thinner sub-segment from each was processed for histological observations. For large testes, sub-segments were sectioned radially into 2–3 pieces prior to histological processing. Preparations from each segment were scanned for gross anomalies. In mid cross-sections of the testis, the relative area occupied by PgCA was determined using two diagonally aligned, adjacent images of the germinal area at 25×. These images were digitally analyzed with Image-Pro® Express Software, v.4 (Media Cybernetics, Silver Spring, MD, USA) to measure the total area of the two images and the area occupied by PgCAs and reported as percent of total area.
For livers, the percent area occupied by PgCA was evaluated from two digital images as described above. The accumulation of glycogen in hepatocytes was estimated based on analysis of two adjacent tissue sections, one directly stained with PAS and the other digested with α-amylase prior to PAS staining. Glycogen is depolymerized in the presence of α-amylase and washed out of the section but in undigested sections, is stained magenta. The difference in intensity of the magenta coloration between the undigested and digested sections was ranked as: 1 = minimal, 2 = moderate and 3 = high.
2.4. Sperm Quality Analyses
Several tests assessing sperm quality were performed and described below. More details and data are available in [35,36]. Milt from testes arriving after overnight shipment for morning arrival was obtained from the collecting duct at the posterior testis end.
2.4.1. Apoptosis and Viability
Milt (1 µL) was added to 99 µL of cold (8 °C) annexin- binding buffer (BB) [37] and centrifuged at 300 rcf for 5 min, then resuspended in 100 µL BB at 1 × 106 cells/mL. Cells were stained with 5 µL annexin V (cat#A13199, fluorescein conjugate; Life Technologies/ThermoFisher Scientific, Carlsbad, CA, USA) and counterstained with propidium iodide (PI; 2.5 µL of a 2.4 mM stock solution). After incubation in the dark for 15 min, 400 µL of BB was added, and then staining controls and duplicate experimental samples were analyzed by flow cytometry (FCM) with 10,000 events per tube generated in biexponential plots. Data were analyzed with the curly quadrant tool (FlowJo™ Software, v.10.7.0, Ashland, OR, USA, Becton Dickinson and Company). Percentages of total apoptotic, total live, and live apoptotic cells were calculated after initial gating of the singlet population and out-gating cells on cytogram edges.
2.4.2. Mitochondrial Membrane Potential
Cells at 1 × 106 cells/mL were incubated with Rhodamine 123 (5 µL of 0.13 µM solution; Life Technologies) and PI (2.5 µL of a 56 µM solution) and analyzed in duplicate at 10K events each by FCM [9]. Data were later analyzed with FlowJo.
2.4.3. DNA Integrity
Milt at 2 × 106 cells/mL was stained with an equal volume of PI (25 mg/mL) solution for 30 min at 24 °C in the dark [38] and nuclei were analyzed by FCM at fewer than 300/s with 10K nuclei analyzed per sample in duplicate. Data were analyzed with FlowJo for DNA integrity by nuclei outside the main population (NOMP) [37], by percent of haploid and cells with more than haploid DNA content [9,39], and for coefficient of variation by using model 1DAOn_DSD with Modfit LT version 3.3.11 (Verity Software House, Topsham, ME, USA).
2.4.4. Spermatogenic Staging
To assess relative numbers of haploid cells and those in earlier stages of spermatogenic development, a representative testicular cross section of approximately 0.2 g was minced for 1 min in 200 µL HBSS then fixed in 800 µL of 4% paraformaldehyde in phosphate buffered saline (Invitrogen). Days later, fixed testicular cells were diluted to 2 M/mL, filtered through 30-µm nylon mesh (Component Supply, Sparta, TN, USA), stained with PI solution, and incubated at 24 °C for 30 min. In duplicate, 10,000 events were collected by FCM at 300 cells/s or slower, with initial gating on fluorescence 2 area (FL2-A) vs. width, followed by determining the percentage of haploid nuclei out of the total events as displayed with FL2-A histograms [9,38].
2.4.5. Sperm Motility
Aliquots of milt (0.25 µL) were activated with tap water (25 µL) and motilities were measured by computer assisted sperm motion analysis assessed in a chambered slide (Leja 20 catalog number SC20-010040-B, Leja Products, Nieuw-Vennep, The Netherlands). Cells were viewed with phase microscopy (Olympus BX41, Olympus America, Inc, Center Valley, PA, USA) at 200 times total magnification. Data from approximately 500 cells per sample were electronically captured at 60 frames/s and analyzed with SpermVision, v. 3.0 (Minitube of America, Verona, WI, USA). Percent of total- and progressively motile cells were reported.
2.4.6. Sperm Morphology
One µL of milt was diluted with 99 µL HBSS, negatively stained, then viewed at 600× magnification (Olympus BX41) [37]. At least 500 cells per slide, with duplicate slides per individual, were scored for the two most observed abnormalities (macrocephaly and cytoplasmic droplets).
2.4.7. Sperm Counts
Starting with 1 µL of milt fixed in 99 µL of 4% paraformaldehyde (cat. no. FB002; ThermoFisher Scientific, Waltham, MA, USA) having been stored at 8 °C, an aliquot of 0.25 µL was diluted into 500 µL HBSS and analyzed by FCM with Bacteria Counting Kit (B-7277, Molecular Probes, Eugene, OR, USA) [9]. Duplicate counts of 20,000 events were performed on replicates and recounted if replicates varied by more than 15 percent of the standard deviation to generate counts per mL milt.
2.4.8. Adenosine Triphosphate (ATP) Content
The quantity of ATP per 1 M cells was determined by using an ATP-binding luciferase assay (ViaLight Plus Kit, cat. no. LTo7-221, Lot. No. 1309016; Lonza Rockland, Inc., Rockland, ME, USA) according to manufacturer’s instructions and data collected with a TECAN GENios plate reader (Mannedorf, Switzerland). Standard curves were generated each day samples were run. For each sample, 50 µL from a 2 µL live milt in 198 µL initial dilution (also counted later by FCM from fixed suspensions that were processed and luminescence intensities (relative light units; RLUs) were measured and used to calculate ATP concentration per number of cells [36].
2.5. Data Processing and Analyses
Gonadosomatic index (GSI) was determined according to the formula GSI = (gonad mass/body weight) × 100. All biological data were analyzed by Principal Component Analysis (PCA), a multivariate analytical procedure that reduces datasets of multiple variables to a lower number of orthogonal variables (principal components, PCs), and is useful for exploring patterns [40]. Thus, PCA was applied to fish datasets for March 2007, July 2010, and November 2010. Statistical analyses for tPCBs in whole of body carp were performed using data from Patiño et al. (2015) and log transformed to meet the assumptions of normality, with α = 0.05. One-way ANOVA was used for determining statistically significant biological variables between LVB and LVW, and OA and WB, according to seasons sampled as gonadal development and reproductive processes follow seasonal changes [16].
Except for histological measurements, all variables were transformed to normal scores prior to PCA using the Blom method in SAS Proc Rank [41], thereby achieving or improving Gaussian data distributions. Testis and liver PgCA and glycogen rank, not normally distributed, were transformed for PCA using monotonic spline (for quantitative variables) or monotonic/untie (for ordinal variables) options in SAS Proc Prinqual [41]. The prinqual procedure optimizes the properties of the correlation matrix so that PCA can be applied to qualitative, quantitative, or mixed datasets. The identity transformation was applied to the variables previously transformed to normal ranks to keep their values unchanged.
The PCA graphs were generated from the PRINQUAL output using PROC TEMPLATE & SGRENDER in SAS [41]. The number of principal components selected for interpretation was determined by Parallel Analysis which retains only those components with eigenvalues greater than the 95th percentile of random, Monte Carlo-simulated eigenvalues [42,43]. Principal component biplots were generated to graphically explore potential patterns in data distributions. Component scores were used for a rigorous assessment of differences among sampling sites within a selected component using one-way ANOVA and Tukey’s multiple comparison tests (March 2007) or t-tests (July and November 2010; two-sided, α = 0.05).
2.6. Lake Mead Water Quality Model
A full mathematical three-dimensional hydrodynamic and water quality model for Lake Mead was used to simulate probable future scenarios of lowering water levels due to climate change and regional drought. This model is implemented in Aquatic Ecosystem Model 3D (AEM3D), that approximates quantities of interest by solving the Reynolds-averaged Navier-Stokes equations with a turbulent eddy closure and solves for hydrodynamics and water quality [44]. The model has been maintained, calibrated, and updated by SNWA for over 20 years [45]. The model inflows are the Colorado River which accounts for 97% of the inflow volume, with smaller contributions from the Virgin and Muddy Rivers and LVW. Most water is released through Hoover Dam, with minor withdrawals from Southern Nevada Water Authority’s (SNWA) drinking water intake plus model-computed losses to evapotranspiration [46].
Validation of the LM3 (Lakes Mead and Mohave Model) in Lake Mead was performed by sampling sucralose (log Kow 1.67) in the treated effluent (LVW) and then comparing that concentration to the concentration at the drinking water intake in southwestern Lake Mead, Boulder Basin (Figure 1), that agreed with model results of 1.3% recycled water contribution (RWC) [14]. This approach was further validated with data in this study using methyl triclosan (log Kow 4.76), a degradant of an antimicrobial commonly used in soaps, deodorants, and other personal care products where water concentrations in LVW of 15.3 ng/L were compared to 0.15 ng/L at WB station three [26] giving an RWC of 1% that is relatively close to the LM3 model results of 1.3% RWC using the much more soluble sucralose. This gives some confidence that the LM3 model results using sucralose work reasonably well as a surrogate for more hydrophobic EOCs.
The model was run in 2023 for two sites, Hoover Dam and WB, as the two 2010 passive sampling sites in Lake Mead were above the water level. The LM3 was used to approximate dilution of a conservative tracer (sucralose, an artificial sweetener) for previous (2010) and more recent (2022) flow conditions [27]. Dilutions of highly treated WW effluent from LVW reported as RWC in Lake Mead from 2010 and 2022 [27] were compared to determine how measurements may change under current 2023 lake conditions. Hydrodynamic models are not available for LVW; however, mean flow rates were compared for the 12 years between 2010 and 2022 to determine the difference in WW loading into Lake Mead.
For predicting how water temperatures would change at WB, 4.8 °C was added to air temperature [27,28,47]. This is in accordance with RCP 8.5/late century warming projected for Clark County, NV. Wilcoxon rank sum test (MATLAB: rank sum) was used to compare RWC during historic and projected climate scenarios.
2.1. Sample Collection and Chemical Analyses
2.1.1. Passive Samplers
Passive samplers, including Semipermeable Membrane Device (SPMD) and Polar Organic Chemical Integrative Sampler (POCIS), were deployed in protective canisters at each site (Figure 1) using a concrete anchor and a buoy to suspend the samplers ~1 m above the lake bottom at depths ranging from 5 to 8 m. At each site, three SPMDs and ten POCIS, all contained in two deployment canisters, were deployed for ~1 month prior to biota being collected, 14 June–12 July 2010 (Spring; LVB, LVW), 14 October–17 November 2010 (Fall; OA, WB), after which they were retrieved and returned to the laboratory at Columbia Environmental Research Center, Columbia, MO. Methods for the processing and analysis of these samples have been described previously, with a list of individual analytes given in Appendix A (Table A1) [23,24,25]. Briefly, a single SPMD was analyzed for selected polycyclic aromatic hydrocarbons (PAHs), another SPMD was used for organochlorine pesticides (OCs), total polychlorinated biphenyls (tPCBs), five polybrominated diphenyl ether (PBDE) flame retardants, and the final SPMD was for selected wastewater (WW) chemicals [26] which were recovered using a hexane dialysis. Dialysates were treated to a series of sample fractionation and enrichment steps using size exclusion chromatography and adsorption chromatography. Final analyses for PAHs and WW were conducted by using gas chromatography/mass spectrometry (GC/MS), while OCs, tPCBs, and PBDEs were analyzed by using gas chromatography with electron capture detection.
Figure 3
Overview and chronology of experimental activities presented in this manuscript focusing on male Common Carp (Cyprinus carpio), chemistry, and climate at LVB (Las Vegas Bay), LVW (Las Vegas Wash), WB (Willow Beach), OA (Overton Arm) in Lake Mead National Recreation Area, Nevada/Arizona, United States. EOCs 1 (environmental organic contaminants) detected in the water matrix, only; PAHs (polycyclic aromatic hydrocarbons); OCs (organochlorine pesticides); PCB (polychlorinated biphenyls); PBDE (polybrominated diphenyl ether flame retardants); CECs (contaminants of emerging concern). 2 Water quality models predictive of recycled water concentrations incorporating flow conditions, wastewater effluent, water and air temperatures [27,28].
A suite of chemicals related to WW and surface runoff were extracted from POCIS samples using a mixture of dichloromethane:methyl-tert-butyl ether (80:20 v:v) to extract samples for the identification of individual chemicals by GC/MS [24]. Methanol was used to recover select pharmaceuticals from the POCIS prior to analysis using liquid chromatography tandem mass spectrometry (LC/MS/MS). Extracts from two POCIS were composited for both the WW and pharmaceutical samples in order to increase the amount of chemical available in the final extract for analysis. Sample cleanup or fractionation was not done on POCIS extracts prior to analysis. Time-weighted average water concentrations of chemicals sampled by SPMDs and POCIS were determined by using first-order update models as previously described [29,30].
A separate, single POCIS from each site was extracted with methanol and screened for potential estrogenicity using the Yeast Estrogen Screen (YES assay). Estradiol equivalent factors (EEQ) for the water samples were calculated by measuring the effective concentration required to elicit a 50% response (EC50) in the exposed genetically modified yeast cells (Saccharomyces cerevisiae) for the 17β-estradiol (E2)–positive control and determining the percent of sample required to give an equivalent response [23]. The remaining POCIS were archived for future use.
2.1.2. Sediment, Periphyton, Fish Feed and Sealant Samples
At each of the five WB sites (inset Figure 1), sediment was collected using a petite ponar sampler suspended mid-channel from a boat. Attached benthic algae (periphyton) was sampled by scraping rocks in shallow water (<2 m depth) and sediment was rinsed off with native water, excess water shaken off, and placed in chemically clean glass jars. At the Willow Beach National Fish Hatchery (WBNFH), involved in the recovery of the endangered Razorback Sucker [31], additional samples of fish feed and raceway sealant consisting of rubber and commercial gap fillers were sampled to determine if these may be a secondary source of PCBs into WB. All sediment, periphyton, feed, and sealant samples were processed and analyzed for t PCBs using methods previously employed for fish tissue analyses in Lake Mead [4]. This involved accelerated solvent extraction, flash gel permeation and basic alumina fractionation/cleanup, and analysis by GC/MS [32].
2.1.1. Passive Samplers
Passive samplers, including Semipermeable Membrane Device (SPMD) and Polar Organic Chemical Integrative Sampler (POCIS), were deployed in protective canisters at each site (Figure 1) using a concrete anchor and a buoy to suspend the samplers ~1 m above the lake bottom at depths ranging from 5 to 8 m. At each site, three SPMDs and ten POCIS, all contained in two deployment canisters, were deployed for ~1 month prior to biota being collected, 14 June–12 July 2010 (Spring; LVB, LVW), 14 October–17 November 2010 (Fall; OA, WB), after which they were retrieved and returned to the laboratory at Columbia Environmental Research Center, Columbia, MO. Methods for the processing and analysis of these samples have been described previously, with a list of individual analytes given in Appendix A (Table A1) [23,24,25]. Briefly, a single SPMD was analyzed for selected polycyclic aromatic hydrocarbons (PAHs), another SPMD was used for organochlorine pesticides (OCs), total polychlorinated biphenyls (tPCBs), five polybrominated diphenyl ether (PBDE) flame retardants, and the final SPMD was for selected wastewater (WW) chemicals [26] which were recovered using a hexane dialysis. Dialysates were treated to a series of sample fractionation and enrichment steps using size exclusion chromatography and adsorption chromatography. Final analyses for PAHs and WW were conducted by using gas chromatography/mass spectrometry (GC/MS), while OCs, tPCBs, and PBDEs were analyzed by using gas chromatography with electron capture detection.
Figure 3
Overview and chronology of experimental activities presented in this manuscript focusing on male Common Carp (Cyprinus carpio), chemistry, and climate at LVB (Las Vegas Bay), LVW (Las Vegas Wash), WB (Willow Beach), OA (Overton Arm) in Lake Mead National Recreation Area, Nevada/Arizona, United States. EOCs 1 (environmental organic contaminants) detected in the water matrix, only; PAHs (polycyclic aromatic hydrocarbons); OCs (organochlorine pesticides); PCB (polychlorinated biphenyls); PBDE (polybrominated diphenyl ether flame retardants); CECs (contaminants of emerging concern). 2 Water quality models predictive of recycled water concentrations incorporating flow conditions, wastewater effluent, water and air temperatures [27,28].
A suite of chemicals related to WW and surface runoff were extracted from POCIS samples using a mixture of dichloromethane:methyl-tert-butyl ether (80:20 v:v) to extract samples for the identification of individual chemicals by GC/MS [24]. Methanol was used to recover select pharmaceuticals from the POCIS prior to analysis using liquid chromatography tandem mass spectrometry (LC/MS/MS). Extracts from two POCIS were composited for both the WW and pharmaceutical samples in order to increase the amount of chemical available in the final extract for analysis. Sample cleanup or fractionation was not done on POCIS extracts prior to analysis. Time-weighted average water concentrations of chemicals sampled by SPMDs and POCIS were determined by using first-order update models as previously described [29,30].
A separate, single POCIS from each site was extracted with methanol and screened for potential estrogenicity using the Yeast Estrogen Screen (YES assay). Estradiol equivalent factors (EEQ) for the water samples were calculated by measuring the effective concentration required to elicit a 50% response (EC50) in the exposed genetically modified yeast cells (Saccharomyces cerevisiae) for the 17β-estradiol (E2)–positive control and determining the percent of sample required to give an equivalent response [23]. The remaining POCIS were archived for future use.
2.1.2. Sediment, Periphyton, Fish Feed and Sealant Samples
At each of the five WB sites (inset Figure 1), sediment was collected using a petite ponar sampler suspended mid-channel from a boat. Attached benthic algae (periphyton) was sampled by scraping rocks in shallow water (<2 m depth) and sediment was rinsed off with native water, excess water shaken off, and placed in chemically clean glass jars. At the Willow Beach National Fish Hatchery (WBNFH), involved in the recovery of the endangered Razorback Sucker [31], additional samples of fish feed and raceway sealant consisting of rubber and commercial gap fillers were sampled to determine if these may be a secondary source of PCBs into WB. All sediment, periphyton, feed, and sealant samples were processed and analyzed for t PCBs using methods previously employed for fish tissue analyses in Lake Mead [4]. This involved accelerated solvent extraction, flash gel permeation and basic alumina fractionation/cleanup, and analysis by GC/MS [32].
2.2. Fish Collection and Sample Processing
Male carp used in this study were collected in July 2010 from LVB and LVW, and in November 2010 from OA and WB. Fish sampling procedures were as previously described [4]. Briefly, fish were captured with an electroshocking boat near the shoreline in water depths of up to 3 m. They were immobilized by pulsed DC current, captured with dipnets, and maintained in a live well until processed on shore. On each sampling date, fish from each site were collected on consecutive days over a 4-day period.
Blood was drawn from the caudal artery into heparinized syringes and centrifuged in capillary tubes on site for hematocrit determinations in duplicate (HematoSTAT II Microhematocrit, Separation Technology, Inc., Altamonte Spring, FL, USA), and the average was reported per fish. Fish were then immersed in a solution of tricaine methanesulfonate (MS-222, 1 g/L of lake water) until all movement ceased, then euthanized by a blunt blow to the head. Fork length (length), and body and testicular mass were measured. Animal handling was conducted in accordance with taxon-specific guidelines [33].
Testis and liver samples were collected in 2010 for analyses. Samples for histology were preserved in 10% buffered formalin, and those for sperm quality analyses were placed in calcium-free Hank’s balanced salt solution at pH 7.5 and 315 milliosmole/kg (HBSS) with 10% v:v 15,000 units of penicillin G sodium, and 5 mg streptomycin sulfate/mL (HBSS/PS). Testicular samples were shipped overnight on wet ice in HBSS/PS to the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center, Lafayette, LA, USA for sperm quality analyses, and livers were transported to the USGS, Texas Cooperative Fish & Wildlife Research Unit, Lubbock, TX, USA for histological analysis.
2.3. Histological Analyses
Gonadal and liver tissue samples were rinsed in 70% ethanol, processed, and embedded in paraffin following standard procedures [34]. Tissues were sectioned to 6 µm thickness and stained with hematoxylin and eosin, and liver samples were also stained with Periodic Acid Schiff (PAS) (Sigma-Aldrich, St. Louis, MO, USA) with or without prior α-amylase digestion (Sigma-Aldrich) for assessment of glycogen deposits. Digital images of histological specimens were taken with an Olympus digital camera (DP70; Tokyo, Japan).
Testes were cut rostro-caudally into 10 equal segments, and a thinner sub-segment from each was processed for histological observations. For large testes, sub-segments were sectioned radially into 2–3 pieces prior to histological processing. Preparations from each segment were scanned for gross anomalies. In mid cross-sections of the testis, the relative area occupied by PgCA was determined using two diagonally aligned, adjacent images of the germinal area at 25×. These images were digitally analyzed with Image-Pro® Express Software, v.4 (Media Cybernetics, Silver Spring, MD, USA) to measure the total area of the two images and the area occupied by PgCAs and reported as percent of total area.
For livers, the percent area occupied by PgCA was evaluated from two digital images as described above. The accumulation of glycogen in hepatocytes was estimated based on analysis of two adjacent tissue sections, one directly stained with PAS and the other digested with α-amylase prior to PAS staining. Glycogen is depolymerized in the presence of α-amylase and washed out of the section but in undigested sections, is stained magenta. The difference in intensity of the magenta coloration between the undigested and digested sections was ranked as: 1 = minimal, 2 = moderate and 3 = high.
2.4. Sperm Quality Analyses
Several tests assessing sperm quality were performed and described below. More details and data are available in [35,36]. Milt from testes arriving after overnight shipment for morning arrival was obtained from the collecting duct at the posterior testis end.
2.4.1. Apoptosis and Viability
Milt (1 µL) was added to 99 µL of cold (8 °C) annexin- binding buffer (BB) [37] and centrifuged at 300 rcf for 5 min, then resuspended in 100 µL BB at 1 × 106 cells/mL. Cells were stained with 5 µL annexin V (cat#A13199, fluorescein conjugate; Life Technologies/ThermoFisher Scientific, Carlsbad, CA, USA) and counterstained with propidium iodide (PI; 2.5 µL of a 2.4 mM stock solution). After incubation in the dark for 15 min, 400 µL of BB was added, and then staining controls and duplicate experimental samples were analyzed by flow cytometry (FCM) with 10,000 events per tube generated in biexponential plots. Data were analyzed with the curly quadrant tool (FlowJo™ Software, v.10.7.0, Ashland, OR, USA, Becton Dickinson and Company). Percentages of total apoptotic, total live, and live apoptotic cells were calculated after initial gating of the singlet population and out-gating cells on cytogram edges.
2.4.2. Mitochondrial Membrane Potential
Cells at 1 × 106 cells/mL were incubated with Rhodamine 123 (5 µL of 0.13 µM solution; Life Technologies) and PI (2.5 µL of a 56 µM solution) and analyzed in duplicate at 10K events each by FCM [9]. Data were later analyzed with FlowJo.
2.4.3. DNA Integrity
Milt at 2 × 106 cells/mL was stained with an equal volume of PI (25 mg/mL) solution for 30 min at 24 °C in the dark [38] and nuclei were analyzed by FCM at fewer than 300/s with 10K nuclei analyzed per sample in duplicate. Data were analyzed with FlowJo for DNA integrity by nuclei outside the main population (NOMP) [37], by percent of haploid and cells with more than haploid DNA content [9,39], and for coefficient of variation by using model 1DAOn_DSD with Modfit LT version 3.3.11 (Verity Software House, Topsham, ME, USA).
2.4.4. Spermatogenic Staging
To assess relative numbers of haploid cells and those in earlier stages of spermatogenic development, a representative testicular cross section of approximately 0.2 g was minced for 1 min in 200 µL HBSS then fixed in 800 µL of 4% paraformaldehyde in phosphate buffered saline (Invitrogen). Days later, fixed testicular cells were diluted to 2 M/mL, filtered through 30-µm nylon mesh (Component Supply, Sparta, TN, USA), stained with PI solution, and incubated at 24 °C for 30 min. In duplicate, 10,000 events were collected by FCM at 300 cells/s or slower, with initial gating on fluorescence 2 area (FL2-A) vs. width, followed by determining the percentage of haploid nuclei out of the total events as displayed with FL2-A histograms [9,38].
2.4.5. Sperm Motility
Aliquots of milt (0.25 µL) were activated with tap water (25 µL) and motilities were measured by computer assisted sperm motion analysis assessed in a chambered slide (Leja 20 catalog number SC20-010040-B, Leja Products, Nieuw-Vennep, The Netherlands). Cells were viewed with phase microscopy (Olympus BX41, Olympus America, Inc, Center Valley, PA, USA) at 200 times total magnification. Data from approximately 500 cells per sample were electronically captured at 60 frames/s and analyzed with SpermVision, v. 3.0 (Minitube of America, Verona, WI, USA). Percent of total- and progressively motile cells were reported.
2.4.6. Sperm Morphology
One µL of milt was diluted with 99 µL HBSS, negatively stained, then viewed at 600× magnification (Olympus BX41) [37]. At least 500 cells per slide, with duplicate slides per individual, were scored for the two most observed abnormalities (macrocephaly and cytoplasmic droplets).
2.4.7. Sperm Counts
Starting with 1 µL of milt fixed in 99 µL of 4% paraformaldehyde (cat. no. FB002; ThermoFisher Scientific, Waltham, MA, USA) having been stored at 8 °C, an aliquot of 0.25 µL was diluted into 500 µL HBSS and analyzed by FCM with Bacteria Counting Kit (B-7277, Molecular Probes, Eugene, OR, USA) [9]. Duplicate counts of 20,000 events were performed on replicates and recounted if replicates varied by more than 15 percent of the standard deviation to generate counts per mL milt.
2.4.8. Adenosine Triphosphate (ATP) Content
The quantity of ATP per 1 M cells was determined by using an ATP-binding luciferase assay (ViaLight Plus Kit, cat. no. LTo7-221, Lot. No. 1309016; Lonza Rockland, Inc., Rockland, ME, USA) according to manufacturer’s instructions and data collected with a TECAN GENios plate reader (Mannedorf, Switzerland). Standard curves were generated each day samples were run. For each sample, 50 µL from a 2 µL live milt in 198 µL initial dilution (also counted later by FCM from fixed suspensions that were processed and luminescence intensities (relative light units; RLUs) were measured and used to calculate ATP concentration per number of cells [36].
2.4.1. Apoptosis and Viability
Milt (1 µL) was added to 99 µL of cold (8 °C) annexin- binding buffer (BB) [37] and centrifuged at 300 rcf for 5 min, then resuspended in 100 µL BB at 1 × 106 cells/mL. Cells were stained with 5 µL annexin V (cat#A13199, fluorescein conjugate; Life Technologies/ThermoFisher Scientific, Carlsbad, CA, USA) and counterstained with propidium iodide (PI; 2.5 µL of a 2.4 mM stock solution). After incubation in the dark for 15 min, 400 µL of BB was added, and then staining controls and duplicate experimental samples were analyzed by flow cytometry (FCM) with 10,000 events per tube generated in biexponential plots. Data were analyzed with the curly quadrant tool (FlowJo™ Software, v.10.7.0, Ashland, OR, USA, Becton Dickinson and Company). Percentages of total apoptotic, total live, and live apoptotic cells were calculated after initial gating of the singlet population and out-gating cells on cytogram edges.
2.4.2. Mitochondrial Membrane Potential
Cells at 1 × 106 cells/mL were incubated with Rhodamine 123 (5 µL of 0.13 µM solution; Life Technologies) and PI (2.5 µL of a 56 µM solution) and analyzed in duplicate at 10K events each by FCM [9]. Data were later analyzed with FlowJo.
2.4.3. DNA Integrity
Milt at 2 × 106 cells/mL was stained with an equal volume of PI (25 mg/mL) solution for 30 min at 24 °C in the dark [38] and nuclei were analyzed by FCM at fewer than 300/s with 10K nuclei analyzed per sample in duplicate. Data were analyzed with FlowJo for DNA integrity by nuclei outside the main population (NOMP) [37], by percent of haploid and cells with more than haploid DNA content [9,39], and for coefficient of variation by using model 1DAOn_DSD with Modfit LT version 3.3.11 (Verity Software House, Topsham, ME, USA).
2.4.4. Spermatogenic Staging
To assess relative numbers of haploid cells and those in earlier stages of spermatogenic development, a representative testicular cross section of approximately 0.2 g was minced for 1 min in 200 µL HBSS then fixed in 800 µL of 4% paraformaldehyde in phosphate buffered saline (Invitrogen). Days later, fixed testicular cells were diluted to 2 M/mL, filtered through 30-µm nylon mesh (Component Supply, Sparta, TN, USA), stained with PI solution, and incubated at 24 °C for 30 min. In duplicate, 10,000 events were collected by FCM at 300 cells/s or slower, with initial gating on fluorescence 2 area (FL2-A) vs. width, followed by determining the percentage of haploid nuclei out of the total events as displayed with FL2-A histograms [9,38].
2.4.5. Sperm Motility
Aliquots of milt (0.25 µL) were activated with tap water (25 µL) and motilities were measured by computer assisted sperm motion analysis assessed in a chambered slide (Leja 20 catalog number SC20-010040-B, Leja Products, Nieuw-Vennep, The Netherlands). Cells were viewed with phase microscopy (Olympus BX41, Olympus America, Inc, Center Valley, PA, USA) at 200 times total magnification. Data from approximately 500 cells per sample were electronically captured at 60 frames/s and analyzed with SpermVision, v. 3.0 (Minitube of America, Verona, WI, USA). Percent of total- and progressively motile cells were reported.
2.4.6. Sperm Morphology
One µL of milt was diluted with 99 µL HBSS, negatively stained, then viewed at 600× magnification (Olympus BX41) [37]. At least 500 cells per slide, with duplicate slides per individual, were scored for the two most observed abnormalities (macrocephaly and cytoplasmic droplets).
2.4.7. Sperm Counts
Starting with 1 µL of milt fixed in 99 µL of 4% paraformaldehyde (cat. no. FB002; ThermoFisher Scientific, Waltham, MA, USA) having been stored at 8 °C, an aliquot of 0.25 µL was diluted into 500 µL HBSS and analyzed by FCM with Bacteria Counting Kit (B-7277, Molecular Probes, Eugene, OR, USA) [9]. Duplicate counts of 20,000 events were performed on replicates and recounted if replicates varied by more than 15 percent of the standard deviation to generate counts per mL milt.
2.4.8. Adenosine Triphosphate (ATP) Content
The quantity of ATP per 1 M cells was determined by using an ATP-binding luciferase assay (ViaLight Plus Kit, cat. no. LTo7-221, Lot. No. 1309016; Lonza Rockland, Inc., Rockland, ME, USA) according to manufacturer’s instructions and data collected with a TECAN GENios plate reader (Mannedorf, Switzerland). Standard curves were generated each day samples were run. For each sample, 50 µL from a 2 µL live milt in 198 µL initial dilution (also counted later by FCM from fixed suspensions that were processed and luminescence intensities (relative light units; RLUs) were measured and used to calculate ATP concentration per number of cells [36].
2.5. Data Processing and Analyses
Gonadosomatic index (GSI) was determined according to the formula GSI = (gonad mass/body weight) × 100. All biological data were analyzed by Principal Component Analysis (PCA), a multivariate analytical procedure that reduces datasets of multiple variables to a lower number of orthogonal variables (principal components, PCs), and is useful for exploring patterns [40]. Thus, PCA was applied to fish datasets for March 2007, July 2010, and November 2010. Statistical analyses for tPCBs in whole of body carp were performed using data from Patiño et al. (2015) and log transformed to meet the assumptions of normality, with α = 0.05. One-way ANOVA was used for determining statistically significant biological variables between LVB and LVW, and OA and WB, according to seasons sampled as gonadal development and reproductive processes follow seasonal changes [16].
Except for histological measurements, all variables were transformed to normal scores prior to PCA using the Blom method in SAS Proc Rank [41], thereby achieving or improving Gaussian data distributions. Testis and liver PgCA and glycogen rank, not normally distributed, were transformed for PCA using monotonic spline (for quantitative variables) or monotonic/untie (for ordinal variables) options in SAS Proc Prinqual [41]. The prinqual procedure optimizes the properties of the correlation matrix so that PCA can be applied to qualitative, quantitative, or mixed datasets. The identity transformation was applied to the variables previously transformed to normal ranks to keep their values unchanged.
The PCA graphs were generated from the PRINQUAL output using PROC TEMPLATE & SGRENDER in SAS [41]. The number of principal components selected for interpretation was determined by Parallel Analysis which retains only those components with eigenvalues greater than the 95th percentile of random, Monte Carlo-simulated eigenvalues [42,43]. Principal component biplots were generated to graphically explore potential patterns in data distributions. Component scores were used for a rigorous assessment of differences among sampling sites within a selected component using one-way ANOVA and Tukey’s multiple comparison tests (March 2007) or t-tests (July and November 2010; two-sided, α = 0.05).
2.6. Lake Mead Water Quality Model
A full mathematical three-dimensional hydrodynamic and water quality model for Lake Mead was used to simulate probable future scenarios of lowering water levels due to climate change and regional drought. This model is implemented in Aquatic Ecosystem Model 3D (AEM3D), that approximates quantities of interest by solving the Reynolds-averaged Navier-Stokes equations with a turbulent eddy closure and solves for hydrodynamics and water quality [44]. The model has been maintained, calibrated, and updated by SNWA for over 20 years [45]. The model inflows are the Colorado River which accounts for 97% of the inflow volume, with smaller contributions from the Virgin and Muddy Rivers and LVW. Most water is released through Hoover Dam, with minor withdrawals from Southern Nevada Water Authority’s (SNWA) drinking water intake plus model-computed losses to evapotranspiration [46].
Validation of the LM3 (Lakes Mead and Mohave Model) in Lake Mead was performed by sampling sucralose (log Kow 1.67) in the treated effluent (LVW) and then comparing that concentration to the concentration at the drinking water intake in southwestern Lake Mead, Boulder Basin (Figure 1), that agreed with model results of 1.3% recycled water contribution (RWC) [14]. This approach was further validated with data in this study using methyl triclosan (log Kow 4.76), a degradant of an antimicrobial commonly used in soaps, deodorants, and other personal care products where water concentrations in LVW of 15.3 ng/L were compared to 0.15 ng/L at WB station three [26] giving an RWC of 1% that is relatively close to the LM3 model results of 1.3% RWC using the much more soluble sucralose. This gives some confidence that the LM3 model results using sucralose work reasonably well as a surrogate for more hydrophobic EOCs.
The model was run in 2023 for two sites, Hoover Dam and WB, as the two 2010 passive sampling sites in Lake Mead were above the water level. The LM3 was used to approximate dilution of a conservative tracer (sucralose, an artificial sweetener) for previous (2010) and more recent (2022) flow conditions [27]. Dilutions of highly treated WW effluent from LVW reported as RWC in Lake Mead from 2010 and 2022 [27] were compared to determine how measurements may change under current 2023 lake conditions. Hydrodynamic models are not available for LVW; however, mean flow rates were compared for the 12 years between 2010 and 2022 to determine the difference in WW loading into Lake Mead.
For predicting how water temperatures would change at WB, 4.8 °C was added to air temperature [27,28,47]. This is in accordance with RCP 8.5/late century warming projected for Clark County, NV. Wilcoxon rank sum test (MATLAB: rank sum) was used to compare RWC during historic and projected climate scenarios.
3. Results and Discussion
3.1. PCBs at Willow Beach
The sum of all PCB congeners (tPCBs) for five sites along an 8 km stretch in the Colorado River above and below WB (Figure 1) detected in water, sediment, periphyton, as well as in fish feed, fish tank caulking and gap-filler used at the WBNFH are presented in Table 1. These samples were taken based on PCA results from a 2007/08 study where tPCBs in male carp whole bodies was a variable, and WB clearly had higher concentrations than the other three sites in LMNRA [4]. The overall mean tPCBs from WB from that study was 408 ng/g ww in male carp (n = 54), being the highest of all sites. A previous study [20] also showed WB male carp contained high PCB levels (870 ng/g ww), being the second highest level among the 14 sites in the CRB. Thus, the question surfaced as to why did carp at WB have such high PCBs levels compared to the other three LMNRA sites?
Leakage from old electric transformers at Hoover Dam could be a potential source [4]. The two upstream sites above WB showed possible PCB sources as they were the only detections in sediment of five sites (Table 1). However, periphyton values indicated PCBs sources at WB with the highest tPCB values of 4.8 ng/g dry weight (dw), and then at the farthest downstream site of 5.9 ng/g dw (Table 1). These values are at least an order of magnitude lower than tPCBs levels of 40–325 ng/g dw in periphyton from Lake Mead [7]. A potential route of PCB exposure to WB carp was observed from many fish feeding on the remnants of fish feed at the WBNFH outfall that had PCB concentrations ranging from 3.4–9.1 ng/g dw (Table 1). These PCB concentrations are comparable to the mean of 6.85 ng/g dw found in fish feed samples from the U.S. [48]. Other sources of PCBs at WBNFH included caulk and gap-filler, showing an order of magnitude higher levels (Table 1) than fish food. In this study, because no PCBs were detected in water (method detection limit 460 pg/L) at any of the five WB sites, there does not appear to be a large PCB source in this stretch of the river from upstream sources.
A possible explanation of higher PCBs in WB carp despite low PCBs in water and fish feed remnants in the hatchery outfall is male carp have the highest mean age of 44 years (up to 54 years) in the CRB [20]. There are several studies that show EOCs (including PCBs) accumulate with fish age [49,50,51]. The mean age of WB male carp was 10 years older than carp at any other site in the CRB [20], and where year-round water temperatures are between 12–14 °C, being considerably lower than the other three sites in LMNRA (Figure 4). Cold water reduces metabolism and respiration, thus enhancing EOC accumulation [51]. Moreover, carp lifespan was shown to be longer in colder climates (therefore colder water temperatures) in North America [52]. The low threshold for initiation of carp growth, 12 °C [53] significantly reduced the degree days for growth at WB compared to the other three sites between 670–980% (Figure 4) and could explain the long-lived fish. In spite of WB male carp median PCBs levels of 207 ng/g ww being comparable to the median value of 228 ng/g ww from a reference site on the Hudson River, NY [54], the very long chronic exposures (up to 54 years) at WB are associated with reduced reproductive potential (Section 3.4) and testicular cancer.
3.2. Environmental Organic Contaminants in Water
All EOC results in water can be found in Alvarez and Echols (2024). Of the 130 EOCs analyzed, including metabolites and degradants, 72% were detected in passive sampling extracts in 2010: 18% (24) in OA; 38% (49) in WB; 50% (65) LVB; and 55% (72) LVW (Table 2). The site gradient for the number of EOCs detected is very clear. OA < WB < LVB < LVW (Table 2). Total EOC sums were ≈3 to 19 Xs higher in LVW compared to other sites and LVB was ≈5 Xs higher than OA and WB (Table 2). Three years after this study in 2013 and 2014 [7], results from passive sampling performed at LVB and OA showed lower values for both the number of compounds detected and the total EOC group sums detected in this 2010 study. The lower levels of EOCs in 2013/14 may be in part due to the higher populations of quagga mussels accidentally introduced to Lake Mead in 2007/08 with the population quickly expanding over time [7]. Quagga mussels are efficient filter feeders that consume plankton and organic detritus and will therefore accumulate and concentrate contaminants directly from the water column and particulate matter [55]. Hence, substantial quagga mussel populations can remove significant EOC mass from the water column through filter feeding. In 2012, the population number in Lake Mead was estimated to be 1.5 × 1012 [6], where 3.3 kg of EOCs were estimated to have been removed from the water column by quagga mussels compared with 31.3 kg of EOCs in the entire lake water column [7].
With a substantial number of EOCs detected that are relevant for water quality criteria, screening values and benchmarks were used to assess potential toxicity and effects on aquatic biota. It is interesting that none of the legacy EOCs exceeded any chronic water quality criteria and were substantially lower even at LVW, the most contaminated site (Table 3). Mirex, a legacy organochlorine pesticide that was banned in 1976, was detected at two sites, LVW (0.0013 ng/L) and at WB (0.011 ng/L). This WB site, number three (Figure 1), is where the WBNFH has an outfall, suggesting a potential Mirex source in that area. Although the Mirex concentrations at these two sites were at least two orders of magnitude under the aquatic life criteria of 1.0 ng/L (Table 3), the detections indicate historic sources.
In contrast to legacy contaminants, a newer group of EOCs, contaminants of emerging concern (CEC), include tens of thousands of very diverse compounds where many are ubiquitous in surface waters, are a source of growing concern [56]. Of the four CEC detected from POCIS (Table 4), each of the four sites showed concentrations that exceeded the ecological screening values that are used for assessing relative hazard to freshwater fish from chronic aqueous exposures in surface water [56]. At 3600 ng/L, Galaxolide (HHCB), a polycyclic musk widely used in fragrances, substantially exceeded both the low comprehensive screening value (LCSV) of 64.9 ng/L and the low population relevant screening value (LPRSV) of 910 ng/L at LVW (Table 4). Although the Galaxolide concentration indicated a low risk to aquatic biota in LVW, it is well below the high comprehensive screening value of 21,300 and high population relevant screening value (HPRSV) of 60,200 ng/L [56]. Concentrations of two other CECs, N,N-diethyltolumide (DEET), a common insect repellant, exceeded the LPRSV of 1.3 ng/L ng/L at all four sites indicating it is widely used (Table 4). The DEET concentration of 390 ng/L at LVW also exceeded the LCSV of 23.6 ng/L by more than an order of magnitude, but the values were well below the HPRSV of 7 × 105 ng/L (Table 4). This concentration was higher than 51 wastewater effluents out of 58 reported Worldwide [57]. Another CEC, triclosan is a commonly used antimicrobial in deodorants and toothpastes and it exceeded both high and low screening levels at WB and LVW (Table 4). It has a number of effects in aquatic ecosystems including reduced growth in algae and both reproduction and development in fish [7].
Of the nine pharmaceuticals analyzed within water samples, only four (44%) were detected, with all four found at LVW, three at LVB, and none at WB or OA [26]. The concentrations at LVB and LVW were generally much lower than those reported in U.S. streams [58], and the calculated risk quotient indicated they posed minimal ecological risk (Table A2). A concentration gradient was clear regarding the pharmaceuticals from their sewage treatment plant source in LVW, with the highest concentrations lowered by 11–50% in receiving waters at LVB through dilution, sorption to sediment, or degradation, and then no detections occurred further downstream at WB [26]. This gradient can be explained by the very short half-lives of pharmaceuticals when they are photodegraded in water, such as 24.9 h for azithromycin and 17.7 h for clindamycin [59].
There was a clear gradient of the 34 PAHs analyzed in water with only 1 detected in OA, 12 in WB, 15 in LVB and 17 in LVW (Table 3). The total sums in pg/L also had the same gradient as the number of detections, OA 35 pg/L, WB 1223 pg/L, LVB 2053 pg/L, and LVW 2532 pg /L (Table 3). Assessing the ecological significance of the 16 PAHs of most concern was done by using EPA benchmarks for screening expressed as Toxicity Equivalent Factor (TEF) [60] in pg/L Benzo[a]pyrene (BaP). These TEFs showed a gradient similar to the one using water concentrations for 34 PAHs, OA 0 pg/L, WB 5.25 pg/L, LVB 9.4 pg/L, and LVW 14.4 pg/L (Table 5). All TEFs were at least three orders of magnitude below the benchmark for ecological protection of 15,000 pg/L [61]. The concentrations of five known carcinogenic PAHs were similar among the three sites at which they were detected, three from LVB, and four from both LVW and WB. The most carcinogenic PAH, BaP was only detected at LVB [26].
toxics-12-00708-t003_Table 3
Table 3
Water concentration of environmental organic compounds (EOC) at four study sites in Lake Mead National Recreation Area and their chronic aquatic life criteria. Values (ng/L) were estimated by using semipermeable membrane device samplers deployed over a ~30-day period prior to sampling biota in 2010.
EOC Group
Sampling Sites
Organochlorine Pesticides
Chronic Aquatic Life Criteria 1
Overton Arm
Willow Beach 2
Las Vegas Bay 3
Las Vegas Wash
cis-chlordane
4.3
ND 4
0.0038
0.048
0.058
trans-chlordane
4.3
ND
ND
0.026
0.049
p,p’ DDT
1
ND
0.0077
ND
0.12
Dieldrin
56
0.063
0.018
0.064
0.085
Endrin
36
0.016
0.0099
0.02
ND
Lindane
950
ND
ND
0.04
0.14
Chlorpyrifos
41
0.11
0.029
0.065
0.13
Methoxychlor
30
ND
ND
ND
0.029
Mirex
1
ND
0.011
ND
0.0013
Endosulfan
56
ND
ND
0.15
1.8
Heptachlor epoxide
3.8
0.0069
0.023
0.046
ND
Polychlorinated Biphenyls
Total PCBs
14
ND
ND
0.61
0.42
Polycyclic Aromatic Hydrocarbons
Acenaphthalene
23,000
ND
ND
ND
0.32
Anthracene
1.3
ND
ND
ND
ND
Benz [a] anthracene
27
ND
0.037
0.02
0.37
Benzo [a] pyrene
27
ND
ND
0.013
ND
Fluoranthene
6160
ND
0.35
0.44
0.5
Phenanthrene
3230
ND
ND
ND
0.85
1 [58]. 2 Highest detection of five sampling sites within Willow Beach area. 3 Highest detection of surface or bottom sample. 4 ND, not detected.
toxics-12-00708-t004_Table 4
Table 4
Screening values for the contaminants of emerging concern (CEC) detected at four study sites in Lake Mead National Recreation Area for assessing relative hazard to freshwater fish from chronic exposures.
Category
CEC
Screening Value 1
Overton
Willow
Las Vegas
Las Vegas
LC 2
LPR 3
Arm
Beach 4
Bay 5
Wash
Fragrances
Galaxolide (HHCB)
64.9
910
ND
ND
140 6
3600 6
Insect repellants
N,N-diethyltolumide
23.6
1.3
2.2
2.2
19
390
Antibacterials
Triclosan
2.5
2.9
ND
5.2
2.3
8.3
Flame retardants
Tris-2(butoxyethyl)
480
1670
ND
ND
ND
160
phosphate
1 Screening Values in ng/L [56]. 2 Low Comprehensive Screening Values (LCSV) developed using all available effects information; values below LCSV predict negligible hazards to fish. 3 Low Population Relevant Screening Values (LPRSV) developed using conventional population-relevant effect information; values below LPRSV predict negligible hazards to fish. 4 Highest value of five Willow Beach sites (See inset of Figure 1). 5 Highest value at surface or bottom. 6 Bold exceeds one SV; Bold exceeds both.
toxics-12-00708-t005_Table 5
Table 5
Toxicity Equivalency Factors (TEF) 1 of Polycyclic Aromatic Hydrocarbons (PAH) detected in water sampled from sites 2 below Hoover Dam in 2010.
Willow
Las Vegas
Las Vegas
PAH
TEF
Beach
Bay
Wash
Acenaphthylene
0
ND
ND
0.32
Fluorene
0
ND
0.035
0.11
Phenanthrene
0
ND
ND
0.85
Fluoranthene
0
0
0.308
0.335
Pyrene
0
0.266
0.265
0.36
Benzo (a) anthracene 3
0.1
2.58
1.8
3.7
Chyrsene
0.01
0.67
1.1
1.9
Benzo(b)fluoranthene
0.1
0.84
ND
4.7
Benzo(k)fluoranthene
0.1
1.16
ND
2
Benzo(a)pyrene (BaP)
1
ND
6.5
ND
Benzo(a,h,i)perylene
0.01
ND
0.39
ND
Sum per site
5.82
10.94
14.44
1 Toxicity Equivalent (pg B[a]P eq/L); TEF method evaluates structurally related compounds sharing a common mechanism of action compared to a single compound [58]. 2 Only perylene was detected at Overton Arm at a level not generating a TEF. 3 Bold compounds are known carcinogens (USEPA 1993: https://rais.ornl.gov/documents/600R93089.pdf; accessed 11 September 24).
3.3. Estrogenic Activity
Results from the YES assay showed estrogenicity reported as estradiol equivalents (EEQ) at only two sites, OA (0.39 ng/L) and WB (3.1 ng/L) site three near the WBNFH (Figure 1) and none at LVB. The sample at LVW was toxic to the yeast used in the assay across multiple dilutions indicating very high toxicity and therefore no EEQs were able to be generated. Estrogenicity can also be estimated by using estrogen receptor agonist activity (ERAA) [62] for the 15 estrogenic EOCs detected. These results (Table 6) proved useful and show a clear gradient, LVW > LVB > WB > OA, with an order of magnitude difference among sites.
One interesting YES result was that the highest EEQ (3.1 ng/L) was detected at WB site three with no detections upstream or downstream. However, the ERAA value for this site of 0.0118 ng/L (Table 6) was only a fraction of the YES result, indicating other estrogenic compounds were present, but were not specifically analyzed at this site. Thus, the likely source of estrogenicity was from the WBNFH where the estrogenic effects were detected in water near the outfall. Estrogenic chemicals, mostly phytoestrogens, and estrogenic activity between 0.12–6.2 ng/L/g in fish food were found in 12 of 15 commercial fish feeds [63]. Considering the large amounts of feed observed in the WBNFH outfall, it is suspected fish feed may be the primary source of estrogenicity measured in the water at this site, which is well above the 0.1–0.4ng/L long-term EEQ environmentally safe level [64].
The estrogenic activity results determined with ERAA showed a gradient among sites. However, these are underestimates because every estrogenic chemical occurring at each site was not included, such as the sex steroid hormone 17β estradiol (E2). Because there are no YES results from LVW, the 5.36 ng/L, ERAA is helpful in assessing potential effects on fish health and reproduction at that site. Past results from LVW [65] showed E2 as high as 2.7 ng/L (E2 was not measured in this study) and when combined with the calculated ERAA (≈8.1 ng/L), this value provided a better estimate of estrogenicity at LVW. Generally, an estrogenicity of <1.0 ng/L is considered a reference value, and values of 1.0–3.0 ng/L are moderate [66]. Estrogenic values have a wide range in water with values as high as 242 ng/L in the heavily farmed Imperial Valley to non-detects (<0.15 ng/L) in Sierra Nevada foothills of California, United States [67]. Safe environmental EEQ levels for aquatic biota were developed by using a variety of in vitro assays [64]. The long-term chronic value (exposure for >60 days) of 0.1–0.3 ng/L was well exceeded in both LVW and WB.
Some studies have been performed along gradients of environmental contaminants gradients to test the hypothesis that reproduction in male fish is associated with exposure to EOCs. One such study in the lower Columbia River assessed reproductive and endocrine parameters in male resident Largescale Suckers (Catostomus macrocheilus) [37]. Sperm quality parameters were significantly lower and vitellogenin (Vtg), a hormone used in female fish for egg production, was higher in males at the site where liver contaminants in fish and EEQs in water were highest. Correlations were found among specific contaminants and reproductive or endocrine parameters: total concentration of PBDEs were negatively correlated with sperm motility, PCB-206 and BDE-154 were positively correlated with DNA fragmentation, and thyroxine (T4) [37]. In the CRB, sperm viability of Lake Mohave carp (79%), just below WB, was significantly lower than that from carp at Lake Havasu (95%) [68]. Also, gamete quality, endocrine, and reproductive data were collected among LMNRA sub-basins over 7 years (1999–2006); diminished biomarker effects were noted in 2006, and sub-basin differences were indicated by the irregular occurrences of contaminants and by several associations among chemicals (e.g., PCBs, hexachlorobenzene, galaxolide, and methyl triclosan) and biomarkers (e.g., T4, sperm motility and DNA fragmentation) [18,69].
In this study, the site with the highest estimated estrogenicity, LVW (≈8.06 ng/L), was well below the lowest estrogenic values shown to impact sperm motility in fish (600 ng/L), but was not far from the 19 ng/L associated with Vtg induction in goldfish [70,71]. The highest concentration in LMNRA of the weakly estrogenic DEHP of 0.72 µg/L at WB is close to the 1.0 µg/L that decreased sperm production, motility, and velocity in goldfish [72]. In spite of no measured individual estrogenic compounds being above concentrations that cause effects in lab studies, EEQs were well above long-term environmentally safe levels at LVW, where fish showed significantly lower sperm motility (p < 0.05), significantly more DNA fragmentation (p < 0.0001), and significantly more sperm cell forms indicative of not being reproductively mature (p < 0.001) compared to LVB (Table 6). These observations suggest that other environmental stressors, in addition to estrogenic compounds, can influence the reproductive potential of fish [18,37,68]. The high concentration of HHCB above water quality criteria in LVW could be a factor for the lower sperm quality there, compared to LVB.
3.4. Biological Variables
A summary of biological variables is shown in Table 7 for all four sites with statistical differences noted between LVW and LVB samples collected in July 2010 and OA and WB samples collected in November 2010. A multivariate analysis using PCA was performed to assess the most important biological variables separating sites within each sampling period (Figure 5). From the principal component (PC) retained in the analysis, the only PC combination that indicated a graphical pattern in the separation of multivariate fish data at OA from WB was that of PC2 and PC4; it accounted for only 27.6 percent of data variability, indicating that unknown physiological and morphometric variables (other than those included in this PCA) may be necessary to fully describe site differences among fishes.
Data for most OA fish were found to the right of PC4, suggesting a unidimensional association with higher values of PC2 (Figure 5A). Thus, based on the orientation of vector variables that met the significance criteria for interpretation (component loadings > 0.4), most OA fish showed relatively high content of liver glycogen compared to WB (Figure 5A), which was also statistically higher (p < 0.05) than WB that had higher EOC concentrations (Table 2). A common target tissue for contaminants in vertebrates is the liver, especially for ingested chemicals [73]. PCB accumulation in fish from WB was over an order of magnitude higher than fish from OA (Figure 4). Changes in hepatic glycogen are a common response to toxic exposures or diet unbalance [74]. Glycogen, for example, may be depleted in the liver under stressful conditions due to the rise of serum glucose [75,76,77]. In this study, male carp from WB had lower glycogen levels than fish from OA, supporting the hypothesis that fish from WB may be exposed to environmental stressors that were affecting overall fish health.
Conversely, data for most WB fish were found in the upper-left quadrant of the plot (Figure 5A), suggesting that they were simultaneously associated with increasing and decreasing values of PC4 and PC2, respectively—namely, most WB fish had higher incidences of abnormal sperm (Figure 5) and testicular PgCA (Figure 6). The considerably high prevalence of testicular PgCA, almost an order of magnitude higher than fish from OA (and almost double what was observed in LVB and LVW) was statistically significant (Table 6). These results are supported by another study [20], whereby carp at WB and South Cove, upstream of Lake Mead, showed the highest percentage and area of PgCA in spleen not only in the CRB, but in the nationwide USGS Large River Monitoring Network Program [20]. High incidences of PgCA in fish tissues have been used as an index of environmental exposure to contaminants in aquatic environments [78,79,80]; moreover, it has been shown that testicular PgCAs may affect steroidogenesis [81] and the regulation of spermatogonial proliferation [82].
The combination of PC1 and PC2 in the PCA for LVW and LVB yielded the clearest pattern of multivariate data separation. With a few exceptions, data from most LVB fish were in the upper-right quadrant of the plot while data from LVW fish were distributed across the other three quadrants (Figure 5B). This pattern of data distribution suggests that most LVB fish are simultaneously associated with relatively high values of length and progressive sperm motility (Figure 5B). Length in LVB fish was significantly greater (p < 0.05) than fish from LVW, most likely due to much smaller available stream habitat in LVW compared with a much larger lacustrine habitat in LVB. Progressive sperm motility was 31% higher in LVB than LVW (p < 0.05), with total EOCs being 73% lower in LVB compared to LVW (Table 2). The distribution of data from LVW fish in the PCA was best described by fish length, percentage of sperm being less reproductively mature, and large coefficient of variation (CV) in DNA indicating less DNA integrity. For example, a fish with lower length (associated with negative values on PC2 and thus lower values of fish length), higher levels of immature sperm forms and higher DNA fragmentation (CV) would be a fish from LVW (Figure 5B). PC1 and PC2 accounted for 42.5% of data variability.
Fish from LVW had significantly (p < 0.001) more reproductively immature sperm forms (57%) than LVB fish (Table 7). Spermatogenesis involves mitosis, meiosis, and cellular differentiation in the production of mature haploid sperm [37]. Genotoxic effects from contaminants can increase the number of diploid spermatids in rodents due to failure of meiotic chromosomes to separate [83]. Results from a field study in the Columbia River, WA, showed higher percentages of immature sperm stages in Largescale Suckers from sites with higher contaminants than the reference site indicating potential effects on reproduction [37]. Similar results in another study in the Imperial Valley, CA, showed a higher percent of immature sperm forms in Western Mosquitofish (Gambusia affinis) at sites contaminated with organochlorine insecticides compared to fish from a reference site [77].
Fragmentation of nuclear DNA that alters integrity in fish chromatin has been related to effects on individuals as well as populations [37], and can be caused by exposure to aromatic hydrocarbons in English Sole (Parophrys vetulus) [84]. DNA fragmentation as measured by coefficient of variation (CV), was a significant variable in the PCA analysis, separating LVW from LVB (Figure 5) and significantly higher (p = 0.0001) in LVW, 4.9% compared to LVB, 2.5% (Table 7). LVW showed both 19% higher PAH concentrations in water (Table 2) and 19% higher PAH TEFs (Table 5), and lower sperm DNA integrity at the more contaminated site.
GSIs were 36% lower (p < 0.05) at LVW compared to LVB (Table 7) as previously reported in 2003 [16]. Male carp from LVW sampled when testicular growth was complete but prior to spawning, showed lower 11-Ketotestosterone (11-Kt) compared to carp from LVB [85]. Because 11-Kt is the sex steroid hormone that controls spermatogenesis and testicular development [86], these data suggest fish in LVW, which were exposed to higher EOC concentrations, and especially those that are estrogenic (Table 6), had relatively impaired gonadal development and thus lower GSI.
Testicular fibrosis, defined as an abnormal thickening of interstitial tissue in the germinal epithelium, has been observed after exposure to environmental stressors [87,88,89], and may be a chronic tissue response to chemical exposure [88], particularly estrogenic compounds [88]. Interstitial thickness of the germinal epithelium was higher in male fish sampled in July (LVW and LVB) compared to the reference site OA sampled in November (Table 7). This was expected after fish released sperm during spawning and testicular lobules contract resulting in thicker intralobular (interstitial) spaces. In the fall, the opposite occurs, because when testes are full of sperm, interstitial spaces are reduced as the lobules expand; however, fish from WB showed a significantly (p < 0.05) higher degree of fibrosis (256%) than fish from OA, and even higher values than those from carp collected in July, when the GSI was lower (Table 7). These results indicate an increased deposition of fibrous connective tissue in the testes of WB male carp, confirming that male carp in WB were exposed to environmental stressors affecting reproductive processes [85].
3.5. Modeling Future EOC Concentrations and Water Temperatures
A water quality model was run to predict recycled water concentration (RWC) at WB below Hoover Dam as Lake Mead levels drop from changes in climate and the prolonged regional drought in the CRB. Since LVW is the primary source of EOCs into Lake Mead and it is 85% treated wastewater [10], knowing how the RWC concentration changes will reflect how EOCs change. To estimate future RWC concentrations, the LM3 water quality model was run using relevant data, including inflow and outflow volumes, water quality measurements, and meteorological parameters from 2010, 2020, and a simulation for a water depth as low as 304.8 m. Results from this LM3 Lake Mead and Lake Mohave water quality model showed mean RWC decreased substantially at WB during the passive sampling period from 1.68% in 2010 to 0.71% in 2022 despite a 10 m drop in Lake Mead (Figure A1). The model predicts values of RWC at WB would increase to 2.89% if Lake Mead dropped to 304.8 m, then decrease slightly to 2.23% at 289.6 m and not change much more, with 2.28% at 278 m being near dead-pool conditions (Figure A1). The highest RWC of 4% is at 304.8 m lake elevation in late November (Figure 7). Overall, the results of the LM3 model suggest that assuming future EOC loading is similar to the current load into Lake Mead, the concentration of any EOC at WB reported from this 2010 study could increase by as much as 135% if the lake water level drops to 304 m. While the maximum increase of EOC concentrations at WB of over 2 times is substantial, this does not raise the concentrations above the available chronic toxicity values for standard EOCs (Table 3) or raise CEC concentrations that have not exceeded screening values to values that do (Table 4). However this does not take into account EOCs or CECs that currently do not have chronic criteria or screening values, new data that may change (e.g., lower) the current criteria or screening values, or the consideration of increased toxicity from mixtures of contaminants.
While it seems counterintuitive, RWC leaving Hoover Dam was found to be higher at the higher lake elevation corresponding to 2010, versus the lower lake elevation modelled to be representative of conditions in 2022. The seemingly logical conclusion would be that more water available for dilution yields lower RWC; however, Lake Mead is a complicated system and the use of dam outlets that have different elevations drives this result. In 2010, the lake elevation was high enough that both the upper and lower Hoover Dam outlets were utilized, and the model assumes an even split between water volumes released through the two outlets when both outlets are used [46], as both outlets are open when wetted. In May of 2022, the water level of Lake Mead became low enough that the Hoover Dam upper outlet could no longer be utilized, and water was released to Lake Mohave downstream using only the lower outlet in the colder hypolimnion. During October and November, RWC tends to remain near the top of the water column in the warmer epilimnion by the time it travels to the Hoover Dam. The mixed-layer release of 2010 thus had a higher concentration of RWC than the release from 2022, which remained around 1% until the end of November (Figure 7).
Different seasonal flow patterns, lake stratification, and wind patterns can affect the water column composition of RWC. Deploying the passive samplers during different times of year may yield different results. With lake elevations used in each run of model for 2010, 2022, and the 304.8 m simulation, a strong gradient was exhibited by RWC whereby at 289.6 m and near dead-pool the RWC was mixed more thoroughly in the water column (Figure 7). The depth-averaged RWC was higher at 289.6 m and near dead-pool than it was at 304.8 m; however, due to the location of the intake, the 304.8 m simulation releases water that is high in RWC near the bottom of the gradient. Wind and wave action affect RWC movement in the lake, leading to more thorough mixing in the water column by the time the RWC travels from LVW to Hoover Dam at the lower modelled lake elevations (289.6 m and near dead-pool). Because more vertical mixing occurs in the water column at these lower elevations, the RWC released at 289.6 m and near dead-pool was less than projected at 304.8 m. The mean flow rate between 14 June and 14 July 2010, was 7.64 m3/s, and 8.99 m3/s in 2022 respectively, indicating a 17.6% increase in mean flow rate. This indicates that additional EOC loading into Lake Mead could have occurred between 2010 and 2022; however, it is difficult to quantify this as treatment technologies at the wastewater treatment plants have also improved between 2010 and 2022.
Warmer releases from Hoover Dam coupled with rising air temperatures directly affect water temperatures at Willow Beach (Table A3). Using a rank sum test, projected end of 21st century climate change causes statistically significant (<0.05) increases in water temperature in all five simulated scenarios of both Lake Mead water level and years compared (Figure 7). Air temperatures rising 4.8 °C, due to climate change, may raise water temperatures at Willow Beach between 0.7–2.1 °C. This warming will become more pronounced relating directly with the drawdown of Lake Mead water levels to 304.8 m. If the water level in Lake Mead falls, releases from Hoover Dam will become epilimnetic, and consequently, warmer and more seasonally variable. There is more mixing in the water column at lake levels of 289.6 m, and near dead pool simulations.
Climate change is raising water temperatures around the world including lakes and streams [90]. Specific effects of warmer water include holding less dissolved oxygen, increasing plankton blooms, altering thermal layering and turnover in lakes, and can increases epizootic fish disease [91]. More general concerns are the alteration of fundamental ecosystem processes and the geographical distribution of species [92]. Warmer water raises metabolism in fish that increases food consumption and respiration and exposure of fish to EOCs in food and water and therefore increases toxicity of some compounds [93]. Dissolved oxygen saturation level concentrations (DOSLC) in Indian streams were predicted to decrease by 2.3% for every 1 °C rise in water temperature so DOSLC could be lowered at WB by over 5% at the end of the Century [94]. However, the ecological response to global warming in aquatic ecosystems is complex and there is uncertainty how systems will change and respond [95].
3.1. PCBs at Willow Beach
The sum of all PCB congeners (tPCBs) for five sites along an 8 km stretch in the Colorado River above and below WB (Figure 1) detected in water, sediment, periphyton, as well as in fish feed, fish tank caulking and gap-filler used at the WBNFH are presented in Table 1. These samples were taken based on PCA results from a 2007/08 study where tPCBs in male carp whole bodies was a variable, and WB clearly had higher concentrations than the other three sites in LMNRA [4]. The overall mean tPCBs from WB from that study was 408 ng/g ww in male carp (n = 54), being the highest of all sites. A previous study [20] also showed WB male carp contained high PCB levels (870 ng/g ww), being the second highest level among the 14 sites in the CRB. Thus, the question surfaced as to why did carp at WB have such high PCBs levels compared to the other three LMNRA sites?
Leakage from old electric transformers at Hoover Dam could be a potential source [4]. The two upstream sites above WB showed possible PCB sources as they were the only detections in sediment of five sites (Table 1). However, periphyton values indicated PCBs sources at WB with the highest tPCB values of 4.8 ng/g dry weight (dw), and then at the farthest downstream site of 5.9 ng/g dw (Table 1). These values are at least an order of magnitude lower than tPCBs levels of 40–325 ng/g dw in periphyton from Lake Mead [7]. A potential route of PCB exposure to WB carp was observed from many fish feeding on the remnants of fish feed at the WBNFH outfall that had PCB concentrations ranging from 3.4–9.1 ng/g dw (Table 1). These PCB concentrations are comparable to the mean of 6.85 ng/g dw found in fish feed samples from the U.S. [48]. Other sources of PCBs at WBNFH included caulk and gap-filler, showing an order of magnitude higher levels (Table 1) than fish food. In this study, because no PCBs were detected in water (method detection limit 460 pg/L) at any of the five WB sites, there does not appear to be a large PCB source in this stretch of the river from upstream sources.
A possible explanation of higher PCBs in WB carp despite low PCBs in water and fish feed remnants in the hatchery outfall is male carp have the highest mean age of 44 years (up to 54 years) in the CRB [20]. There are several studies that show EOCs (including PCBs) accumulate with fish age [49,50,51]. The mean age of WB male carp was 10 years older than carp at any other site in the CRB [20], and where year-round water temperatures are between 12–14 °C, being considerably lower than the other three sites in LMNRA (Figure 4). Cold water reduces metabolism and respiration, thus enhancing EOC accumulation [51]. Moreover, carp lifespan was shown to be longer in colder climates (therefore colder water temperatures) in North America [52]. The low threshold for initiation of carp growth, 12 °C [53] significantly reduced the degree days for growth at WB compared to the other three sites between 670–980% (Figure 4) and could explain the long-lived fish. In spite of WB male carp median PCBs levels of 207 ng/g ww being comparable to the median value of 228 ng/g ww from a reference site on the Hudson River, NY [54], the very long chronic exposures (up to 54 years) at WB are associated with reduced reproductive potential (Section 3.4) and testicular cancer.
3.2. Environmental Organic Contaminants in Water
All EOC results in water can be found in Alvarez and Echols (2024). Of the 130 EOCs analyzed, including metabolites and degradants, 72% were detected in passive sampling extracts in 2010: 18% (24) in OA; 38% (49) in WB; 50% (65) LVB; and 55% (72) LVW (Table 2). The site gradient for the number of EOCs detected is very clear. OA < WB < LVB < LVW (Table 2). Total EOC sums were ≈3 to 19 Xs higher in LVW compared to other sites and LVB was ≈5 Xs higher than OA and WB (Table 2). Three years after this study in 2013 and 2014 [7], results from passive sampling performed at LVB and OA showed lower values for both the number of compounds detected and the total EOC group sums detected in this 2010 study. The lower levels of EOCs in 2013/14 may be in part due to the higher populations of quagga mussels accidentally introduced to Lake Mead in 2007/08 with the population quickly expanding over time [7]. Quagga mussels are efficient filter feeders that consume plankton and organic detritus and will therefore accumulate and concentrate contaminants directly from the water column and particulate matter [55]. Hence, substantial quagga mussel populations can remove significant EOC mass from the water column through filter feeding. In 2012, the population number in Lake Mead was estimated to be 1.5 × 1012 [6], where 3.3 kg of EOCs were estimated to have been removed from the water column by quagga mussels compared with 31.3 kg of EOCs in the entire lake water column [7].
With a substantial number of EOCs detected that are relevant for water quality criteria, screening values and benchmarks were used to assess potential toxicity and effects on aquatic biota. It is interesting that none of the legacy EOCs exceeded any chronic water quality criteria and were substantially lower even at LVW, the most contaminated site (Table 3). Mirex, a legacy organochlorine pesticide that was banned in 1976, was detected at two sites, LVW (0.0013 ng/L) and at WB (0.011 ng/L). This WB site, number three (Figure 1), is where the WBNFH has an outfall, suggesting a potential Mirex source in that area. Although the Mirex concentrations at these two sites were at least two orders of magnitude under the aquatic life criteria of 1.0 ng/L (Table 3), the detections indicate historic sources.
In contrast to legacy contaminants, a newer group of EOCs, contaminants of emerging concern (CEC), include tens of thousands of very diverse compounds where many are ubiquitous in surface waters, are a source of growing concern [56]. Of the four CEC detected from POCIS (Table 4), each of the four sites showed concentrations that exceeded the ecological screening values that are used for assessing relative hazard to freshwater fish from chronic aqueous exposures in surface water [56]. At 3600 ng/L, Galaxolide (HHCB), a polycyclic musk widely used in fragrances, substantially exceeded both the low comprehensive screening value (LCSV) of 64.9 ng/L and the low population relevant screening value (LPRSV) of 910 ng/L at LVW (Table 4). Although the Galaxolide concentration indicated a low risk to aquatic biota in LVW, it is well below the high comprehensive screening value of 21,300 and high population relevant screening value (HPRSV) of 60,200 ng/L [56]. Concentrations of two other CECs, N,N-diethyltolumide (DEET), a common insect repellant, exceeded the LPRSV of 1.3 ng/L ng/L at all four sites indicating it is widely used (Table 4). The DEET concentration of 390 ng/L at LVW also exceeded the LCSV of 23.6 ng/L by more than an order of magnitude, but the values were well below the HPRSV of 7 × 105 ng/L (Table 4). This concentration was higher than 51 wastewater effluents out of 58 reported Worldwide [57]. Another CEC, triclosan is a commonly used antimicrobial in deodorants and toothpastes and it exceeded both high and low screening levels at WB and LVW (Table 4). It has a number of effects in aquatic ecosystems including reduced growth in algae and both reproduction and development in fish [7].
Of the nine pharmaceuticals analyzed within water samples, only four (44%) were detected, with all four found at LVW, three at LVB, and none at WB or OA [26]. The concentrations at LVB and LVW were generally much lower than those reported in U.S. streams [58], and the calculated risk quotient indicated they posed minimal ecological risk (Table A2). A concentration gradient was clear regarding the pharmaceuticals from their sewage treatment plant source in LVW, with the highest concentrations lowered by 11–50% in receiving waters at LVB through dilution, sorption to sediment, or degradation, and then no detections occurred further downstream at WB [26]. This gradient can be explained by the very short half-lives of pharmaceuticals when they are photodegraded in water, such as 24.9 h for azithromycin and 17.7 h for clindamycin [59].
There was a clear gradient of the 34 PAHs analyzed in water with only 1 detected in OA, 12 in WB, 15 in LVB and 17 in LVW (Table 3). The total sums in pg/L also had the same gradient as the number of detections, OA 35 pg/L, WB 1223 pg/L, LVB 2053 pg/L, and LVW 2532 pg /L (Table 3). Assessing the ecological significance of the 16 PAHs of most concern was done by using EPA benchmarks for screening expressed as Toxicity Equivalent Factor (TEF) [60] in pg/L Benzo[a]pyrene (BaP). These TEFs showed a gradient similar to the one using water concentrations for 34 PAHs, OA 0 pg/L, WB 5.25 pg/L, LVB 9.4 pg/L, and LVW 14.4 pg/L (Table 5). All TEFs were at least three orders of magnitude below the benchmark for ecological protection of 15,000 pg/L [61]. The concentrations of five known carcinogenic PAHs were similar among the three sites at which they were detected, three from LVB, and four from both LVW and WB. The most carcinogenic PAH, BaP was only detected at LVB [26].
toxics-12-00708-t003_Table 3
Table 3
Water concentration of environmental organic compounds (EOC) at four study sites in Lake Mead National Recreation Area and their chronic aquatic life criteria. Values (ng/L) were estimated by using semipermeable membrane device samplers deployed over a ~30-day period prior to sampling biota in 2010.
EOC Group
Sampling Sites
Organochlorine Pesticides
Chronic Aquatic Life Criteria 1
Overton Arm
Willow Beach 2
Las Vegas Bay 3
Las Vegas Wash
cis-chlordane
4.3
ND 4
0.0038
0.048
0.058
trans-chlordane
4.3
ND
ND
0.026
0.049
p,p’ DDT
1
ND
0.0077
ND
0.12
Dieldrin
56
0.063
0.018
0.064
0.085
Endrin
36
0.016
0.0099
0.02
ND
Lindane
950
ND
ND
0.04
0.14
Chlorpyrifos
41
0.11
0.029
0.065
0.13
Methoxychlor
30
ND
ND
ND
0.029
Mirex
1
ND
0.011
ND
0.0013
Endosulfan
56
ND
ND
0.15
1.8
Heptachlor epoxide
3.8
0.0069
0.023
0.046
ND
Polychlorinated Biphenyls
Total PCBs
14
ND
ND
0.61
0.42
Polycyclic Aromatic Hydrocarbons
Acenaphthalene
23,000
ND
ND
ND
0.32
Anthracene
1.3
ND
ND
ND
ND
Benz [a] anthracene
27
ND
0.037
0.02
0.37
Benzo [a] pyrene
27
ND
ND
0.013
ND
Fluoranthene
6160
ND
0.35
0.44
0.5
Phenanthrene
3230
ND
ND
ND
0.85
1 [58]. 2 Highest detection of five sampling sites within Willow Beach area. 3 Highest detection of surface or bottom sample. 4 ND, not detected.
toxics-12-00708-t004_Table 4
Table 4
Screening values for the contaminants of emerging concern (CEC) detected at four study sites in Lake Mead National Recreation Area for assessing relative hazard to freshwater fish from chronic exposures.
Category
CEC
Screening Value 1
Overton
Willow
Las Vegas
Las Vegas
LC 2
LPR 3
Arm
Beach 4
Bay 5
Wash
Fragrances
Galaxolide (HHCB)
64.9
910
ND
ND
140 6
3600 6
Insect repellants
N,N-diethyltolumide
23.6
1.3
2.2
2.2
19
390
Antibacterials
Triclosan
2.5
2.9
ND
5.2
2.3
8.3
Flame retardants
Tris-2(butoxyethyl)
480
1670
ND
ND
ND
160
phosphate
1 Screening Values in ng/L [56]. 2 Low Comprehensive Screening Values (LCSV) developed using all available effects information; values below LCSV predict negligible hazards to fish. 3 Low Population Relevant Screening Values (LPRSV) developed using conventional population-relevant effect information; values below LPRSV predict negligible hazards to fish. 4 Highest value of five Willow Beach sites (See inset of Figure 1). 5 Highest value at surface or bottom. 6 Bold exceeds one SV; Bold exceeds both.
toxics-12-00708-t005_Table 5
Table 5
Toxicity Equivalency Factors (TEF) 1 of Polycyclic Aromatic Hydrocarbons (PAH) detected in water sampled from sites 2 below Hoover Dam in 2010.
Willow
Las Vegas
Las Vegas
PAH
TEF
Beach
Bay
Wash
Acenaphthylene
0
ND
ND
0.32
Fluorene
0
ND
0.035
0.11
Phenanthrene
0
ND
ND
0.85
Fluoranthene
0
0
0.308
0.335
Pyrene
0
0.266
0.265
0.36
Benzo (a) anthracene 3
0.1
2.58
1.8
3.7
Chyrsene
0.01
0.67
1.1
1.9
Benzo(b)fluoranthene
0.1
0.84
ND
4.7
Benzo(k)fluoranthene
0.1
1.16
ND
2
Benzo(a)pyrene (BaP)
1
ND
6.5
ND
Benzo(a,h,i)perylene
0.01
ND
0.39
ND
Sum per site
5.82
10.94
14.44
1 Toxicity Equivalent (pg B[a]P eq/L); TEF method evaluates structurally related compounds sharing a common mechanism of action compared to a single compound [58]. 2 Only perylene was detected at Overton Arm at a level not generating a TEF. 3 Bold compounds are known carcinogens (USEPA 1993: https://rais.ornl.gov/documents/600R93089.pdf; accessed 11 September 24).
3.3. Estrogenic Activity
Results from the YES assay showed estrogenicity reported as estradiol equivalents (EEQ) at only two sites, OA (0.39 ng/L) and WB (3.1 ng/L) site three near the WBNFH (Figure 1) and none at LVB. The sample at LVW was toxic to the yeast used in the assay across multiple dilutions indicating very high toxicity and therefore no EEQs were able to be generated. Estrogenicity can also be estimated by using estrogen receptor agonist activity (ERAA) [62] for the 15 estrogenic EOCs detected. These results (Table 6) proved useful and show a clear gradient, LVW > LVB > WB > OA, with an order of magnitude difference among sites.
One interesting YES result was that the highest EEQ (3.1 ng/L) was detected at WB site three with no detections upstream or downstream. However, the ERAA value for this site of 0.0118 ng/L (Table 6) was only a fraction of the YES result, indicating other estrogenic compounds were present, but were not specifically analyzed at this site. Thus, the likely source of estrogenicity was from the WBNFH where the estrogenic effects were detected in water near the outfall. Estrogenic chemicals, mostly phytoestrogens, and estrogenic activity between 0.12–6.2 ng/L/g in fish food were found in 12 of 15 commercial fish feeds [63]. Considering the large amounts of feed observed in the WBNFH outfall, it is suspected fish feed may be the primary source of estrogenicity measured in the water at this site, which is well above the 0.1–0.4ng/L long-term EEQ environmentally safe level [64].
The estrogenic activity results determined with ERAA showed a gradient among sites. However, these are underestimates because every estrogenic chemical occurring at each site was not included, such as the sex steroid hormone 17β estradiol (E2). Because there are no YES results from LVW, the 5.36 ng/L, ERAA is helpful in assessing potential effects on fish health and reproduction at that site. Past results from LVW [65] showed E2 as high as 2.7 ng/L (E2 was not measured in this study) and when combined with the calculated ERAA (≈8.1 ng/L), this value provided a better estimate of estrogenicity at LVW. Generally, an estrogenicity of <1.0 ng/L is considered a reference value, and values of 1.0–3.0 ng/L are moderate [66]. Estrogenic values have a wide range in water with values as high as 242 ng/L in the heavily farmed Imperial Valley to non-detects (<0.15 ng/L) in Sierra Nevada foothills of California, United States [67]. Safe environmental EEQ levels for aquatic biota were developed by using a variety of in vitro assays [64]. The long-term chronic value (exposure for >60 days) of 0.1–0.3 ng/L was well exceeded in both LVW and WB.
Some studies have been performed along gradients of environmental contaminants gradients to test the hypothesis that reproduction in male fish is associated with exposure to EOCs. One such study in the lower Columbia River assessed reproductive and endocrine parameters in male resident Largescale Suckers (Catostomus macrocheilus) [37]. Sperm quality parameters were significantly lower and vitellogenin (Vtg), a hormone used in female fish for egg production, was higher in males at the site where liver contaminants in fish and EEQs in water were highest. Correlations were found among specific contaminants and reproductive or endocrine parameters: total concentration of PBDEs were negatively correlated with sperm motility, PCB-206 and BDE-154 were positively correlated with DNA fragmentation, and thyroxine (T4) [37]. In the CRB, sperm viability of Lake Mohave carp (79%), just below WB, was significantly lower than that from carp at Lake Havasu (95%) [68]. Also, gamete quality, endocrine, and reproductive data were collected among LMNRA sub-basins over 7 years (1999–2006); diminished biomarker effects were noted in 2006, and sub-basin differences were indicated by the irregular occurrences of contaminants and by several associations among chemicals (e.g., PCBs, hexachlorobenzene, galaxolide, and methyl triclosan) and biomarkers (e.g., T4, sperm motility and DNA fragmentation) [18,69].
In this study, the site with the highest estimated estrogenicity, LVW (≈8.06 ng/L), was well below the lowest estrogenic values shown to impact sperm motility in fish (600 ng/L), but was not far from the 19 ng/L associated with Vtg induction in goldfish [70,71]. The highest concentration in LMNRA of the weakly estrogenic DEHP of 0.72 µg/L at WB is close to the 1.0 µg/L that decreased sperm production, motility, and velocity in goldfish [72]. In spite of no measured individual estrogenic compounds being above concentrations that cause effects in lab studies, EEQs were well above long-term environmentally safe levels at LVW, where fish showed significantly lower sperm motility (p < 0.05), significantly more DNA fragmentation (p < 0.0001), and significantly more sperm cell forms indicative of not being reproductively mature (p < 0.001) compared to LVB (Table 6). These observations suggest that other environmental stressors, in addition to estrogenic compounds, can influence the reproductive potential of fish [18,37,68]. The high concentration of HHCB above water quality criteria in LVW could be a factor for the lower sperm quality there, compared to LVB.
3.4. Biological Variables
A summary of biological variables is shown in Table 7 for all four sites with statistical differences noted between LVW and LVB samples collected in July 2010 and OA and WB samples collected in November 2010. A multivariate analysis using PCA was performed to assess the most important biological variables separating sites within each sampling period (Figure 5). From the principal component (PC) retained in the analysis, the only PC combination that indicated a graphical pattern in the separation of multivariate fish data at OA from WB was that of PC2 and PC4; it accounted for only 27.6 percent of data variability, indicating that unknown physiological and morphometric variables (other than those included in this PCA) may be necessary to fully describe site differences among fishes.
Data for most OA fish were found to the right of PC4, suggesting a unidimensional association with higher values of PC2 (Figure 5A). Thus, based on the orientation of vector variables that met the significance criteria for interpretation (component loadings > 0.4), most OA fish showed relatively high content of liver glycogen compared to WB (Figure 5A), which was also statistically higher (p < 0.05) than WB that had higher EOC concentrations (Table 2). A common target tissue for contaminants in vertebrates is the liver, especially for ingested chemicals [73]. PCB accumulation in fish from WB was over an order of magnitude higher than fish from OA (Figure 4). Changes in hepatic glycogen are a common response to toxic exposures or diet unbalance [74]. Glycogen, for example, may be depleted in the liver under stressful conditions due to the rise of serum glucose [75,76,77]. In this study, male carp from WB had lower glycogen levels than fish from OA, supporting the hypothesis that fish from WB may be exposed to environmental stressors that were affecting overall fish health.
Conversely, data for most WB fish were found in the upper-left quadrant of the plot (Figure 5A), suggesting that they were simultaneously associated with increasing and decreasing values of PC4 and PC2, respectively—namely, most WB fish had higher incidences of abnormal sperm (Figure 5) and testicular PgCA (Figure 6). The considerably high prevalence of testicular PgCA, almost an order of magnitude higher than fish from OA (and almost double what was observed in LVB and LVW) was statistically significant (Table 6). These results are supported by another study [20], whereby carp at WB and South Cove, upstream of Lake Mead, showed the highest percentage and area of PgCA in spleen not only in the CRB, but in the nationwide USGS Large River Monitoring Network Program [20]. High incidences of PgCA in fish tissues have been used as an index of environmental exposure to contaminants in aquatic environments [78,79,80]; moreover, it has been shown that testicular PgCAs may affect steroidogenesis [81] and the regulation of spermatogonial proliferation [82].
The combination of PC1 and PC2 in the PCA for LVW and LVB yielded the clearest pattern of multivariate data separation. With a few exceptions, data from most LVB fish were in the upper-right quadrant of the plot while data from LVW fish were distributed across the other three quadrants (Figure 5B). This pattern of data distribution suggests that most LVB fish are simultaneously associated with relatively high values of length and progressive sperm motility (Figure 5B). Length in LVB fish was significantly greater (p < 0.05) than fish from LVW, most likely due to much smaller available stream habitat in LVW compared with a much larger lacustrine habitat in LVB. Progressive sperm motility was 31% higher in LVB than LVW (p < 0.05), with total EOCs being 73% lower in LVB compared to LVW (Table 2). The distribution of data from LVW fish in the PCA was best described by fish length, percentage of sperm being less reproductively mature, and large coefficient of variation (CV) in DNA indicating less DNA integrity. For example, a fish with lower length (associated with negative values on PC2 and thus lower values of fish length), higher levels of immature sperm forms and higher DNA fragmentation (CV) would be a fish from LVW (Figure 5B). PC1 and PC2 accounted for 42.5% of data variability.
Fish from LVW had significantly (p < 0.001) more reproductively immature sperm forms (57%) than LVB fish (Table 7). Spermatogenesis involves mitosis, meiosis, and cellular differentiation in the production of mature haploid sperm [37]. Genotoxic effects from contaminants can increase the number of diploid spermatids in rodents due to failure of meiotic chromosomes to separate [83]. Results from a field study in the Columbia River, WA, showed higher percentages of immature sperm stages in Largescale Suckers from sites with higher contaminants than the reference site indicating potential effects on reproduction [37]. Similar results in another study in the Imperial Valley, CA, showed a higher percent of immature sperm forms in Western Mosquitofish (Gambusia affinis) at sites contaminated with organochlorine insecticides compared to fish from a reference site [77].
Fragmentation of nuclear DNA that alters integrity in fish chromatin has been related to effects on individuals as well as populations [37], and can be caused by exposure to aromatic hydrocarbons in English Sole (Parophrys vetulus) [84]. DNA fragmentation as measured by coefficient of variation (CV), was a significant variable in the PCA analysis, separating LVW from LVB (Figure 5) and significantly higher (p = 0.0001) in LVW, 4.9% compared to LVB, 2.5% (Table 7). LVW showed both 19% higher PAH concentrations in water (Table 2) and 19% higher PAH TEFs (Table 5), and lower sperm DNA integrity at the more contaminated site.
GSIs were 36% lower (p < 0.05) at LVW compared to LVB (Table 7) as previously reported in 2003 [16]. Male carp from LVW sampled when testicular growth was complete but prior to spawning, showed lower 11-Ketotestosterone (11-Kt) compared to carp from LVB [85]. Because 11-Kt is the sex steroid hormone that controls spermatogenesis and testicular development [86], these data suggest fish in LVW, which were exposed to higher EOC concentrations, and especially those that are estrogenic (Table 6), had relatively impaired gonadal development and thus lower GSI.
Testicular fibrosis, defined as an abnormal thickening of interstitial tissue in the germinal epithelium, has been observed after exposure to environmental stressors [87,88,89], and may be a chronic tissue response to chemical exposure [88], particularly estrogenic compounds [88]. Interstitial thickness of the germinal epithelium was higher in male fish sampled in July (LVW and LVB) compared to the reference site OA sampled in November (Table 7). This was expected after fish released sperm during spawning and testicular lobules contract resulting in thicker intralobular (interstitial) spaces. In the fall, the opposite occurs, because when testes are full of sperm, interstitial spaces are reduced as the lobules expand; however, fish from WB showed a significantly (p < 0.05) higher degree of fibrosis (256%) than fish from OA, and even higher values than those from carp collected in July, when the GSI was lower (Table 7). These results indicate an increased deposition of fibrous connective tissue in the testes of WB male carp, confirming that male carp in WB were exposed to environmental stressors affecting reproductive processes [85].
3.5. Modeling Future EOC Concentrations and Water Temperatures
A water quality model was run to predict recycled water concentration (RWC) at WB below Hoover Dam as Lake Mead levels drop from changes in climate and the prolonged regional drought in the CRB. Since LVW is the primary source of EOCs into Lake Mead and it is 85% treated wastewater [10], knowing how the RWC concentration changes will reflect how EOCs change. To estimate future RWC concentrations, the LM3 water quality model was run using relevant data, including inflow and outflow volumes, water quality measurements, and meteorological parameters from 2010, 2020, and a simulation for a water depth as low as 304.8 m. Results from this LM3 Lake Mead and Lake Mohave water quality model showed mean RWC decreased substantially at WB during the passive sampling period from 1.68% in 2010 to 0.71% in 2022 despite a 10 m drop in Lake Mead (Figure A1). The model predicts values of RWC at WB would increase to 2.89% if Lake Mead dropped to 304.8 m, then decrease slightly to 2.23% at 289.6 m and not change much more, with 2.28% at 278 m being near dead-pool conditions (Figure A1). The highest RWC of 4% is at 304.8 m lake elevation in late November (Figure 7). Overall, the results of the LM3 model suggest that assuming future EOC loading is similar to the current load into Lake Mead, the concentration of any EOC at WB reported from this 2010 study could increase by as much as 135% if the lake water level drops to 304 m. While the maximum increase of EOC concentrations at WB of over 2 times is substantial, this does not raise the concentrations above the available chronic toxicity values for standard EOCs (Table 3) or raise CEC concentrations that have not exceeded screening values to values that do (Table 4). However this does not take into account EOCs or CECs that currently do not have chronic criteria or screening values, new data that may change (e.g., lower) the current criteria or screening values, or the consideration of increased toxicity from mixtures of contaminants.
While it seems counterintuitive, RWC leaving Hoover Dam was found to be higher at the higher lake elevation corresponding to 2010, versus the lower lake elevation modelled to be representative of conditions in 2022. The seemingly logical conclusion would be that more water available for dilution yields lower RWC; however, Lake Mead is a complicated system and the use of dam outlets that have different elevations drives this result. In 2010, the lake elevation was high enough that both the upper and lower Hoover Dam outlets were utilized, and the model assumes an even split between water volumes released through the two outlets when both outlets are used [46], as both outlets are open when wetted. In May of 2022, the water level of Lake Mead became low enough that the Hoover Dam upper outlet could no longer be utilized, and water was released to Lake Mohave downstream using only the lower outlet in the colder hypolimnion. During October and November, RWC tends to remain near the top of the water column in the warmer epilimnion by the time it travels to the Hoover Dam. The mixed-layer release of 2010 thus had a higher concentration of RWC than the release from 2022, which remained around 1% until the end of November (Figure 7).
Different seasonal flow patterns, lake stratification, and wind patterns can affect the water column composition of RWC. Deploying the passive samplers during different times of year may yield different results. With lake elevations used in each run of model for 2010, 2022, and the 304.8 m simulation, a strong gradient was exhibited by RWC whereby at 289.6 m and near dead-pool the RWC was mixed more thoroughly in the water column (Figure 7). The depth-averaged RWC was higher at 289.6 m and near dead-pool than it was at 304.8 m; however, due to the location of the intake, the 304.8 m simulation releases water that is high in RWC near the bottom of the gradient. Wind and wave action affect RWC movement in the lake, leading to more thorough mixing in the water column by the time the RWC travels from LVW to Hoover Dam at the lower modelled lake elevations (289.6 m and near dead-pool). Because more vertical mixing occurs in the water column at these lower elevations, the RWC released at 289.6 m and near dead-pool was less than projected at 304.8 m. The mean flow rate between 14 June and 14 July 2010, was 7.64 m3/s, and 8.99 m3/s in 2022 respectively, indicating a 17.6% increase in mean flow rate. This indicates that additional EOC loading into Lake Mead could have occurred between 2010 and 2022; however, it is difficult to quantify this as treatment technologies at the wastewater treatment plants have also improved between 2010 and 2022.
Warmer releases from Hoover Dam coupled with rising air temperatures directly affect water temperatures at Willow Beach (Table A3). Using a rank sum test, projected end of 21st century climate change causes statistically significant (<0.05) increases in water temperature in all five simulated scenarios of both Lake Mead water level and years compared (Figure 7). Air temperatures rising 4.8 °C, due to climate change, may raise water temperatures at Willow Beach between 0.7–2.1 °C. This warming will become more pronounced relating directly with the drawdown of Lake Mead water levels to 304.8 m. If the water level in Lake Mead falls, releases from Hoover Dam will become epilimnetic, and consequently, warmer and more seasonally variable. There is more mixing in the water column at lake levels of 289.6 m, and near dead pool simulations.
Climate change is raising water temperatures around the world including lakes and streams [90]. Specific effects of warmer water include holding less dissolved oxygen, increasing plankton blooms, altering thermal layering and turnover in lakes, and can increases epizootic fish disease [91]. More general concerns are the alteration of fundamental ecosystem processes and the geographical distribution of species [92]. Warmer water raises metabolism in fish that increases food consumption and respiration and exposure of fish to EOCs in food and water and therefore increases toxicity of some compounds [93]. Dissolved oxygen saturation level concentrations (DOSLC) in Indian streams were predicted to decrease by 2.3% for every 1 °C rise in water temperature so DOSLC could be lowered at WB by over 5% at the end of the Century [94]. However, the ecological response to global warming in aquatic ecosystems is complex and there is uncertainty how systems will change and respond [95].
4. Conclusions
A wide range was seen in the number of detections and concentrations of EOCs in water among sites, with LVW showing the most detections at 72, and the highest total sum of EOC concentrations at 1.9 × 107 pg/L. Compared with LVW, total EOC sums were 73% lower in LVB, 93% lower in OA, and 95% lower in WB. LVB is an important area for Razorback Suckers, where this endangered species is exposed to 41 more EOCs at 4Xs higher total concentration than the reference site (OA). The significant EOC concentrations at LVW, along with reduced sperm quality in carp, suggest that over long periods (e.g., decades) of exposures to complex mixtures, well above reference concentrations, are resulting in negative reproductive effects. Although tPCBs in periphyton at WB were an order of magnitude lower than those at the other three sites, and PCBs were not detected in water, higher PCBs in fish could be explained by the much longer exposures in fish at WB. The degree days at WB were 6.7 to 9.7 times lower than at the other three sites, thus lowering the carp growth rate and increasing the lifespan up to 54 years, with a mean age of 44 years, which is ten years older than any site in the CRB. While many EOCs were detected, only four were above screening values. Concentrations of N,N diethyltolumide (DEET) in water exceeded low population relevant screen value (LPRSV) of 1.3 ng/L at all four sites and LVW exceeded the low comprehensive screening value (LCSV) of 23.6 ng/L. Triclosan water concentrations exceeded both the LCSV of 2.5 ng/L and the LPRSV of 2.9 ng/L at WB and LVW. Galaxolide (HHCB) water concentrations at LVW exceeded the LCSV (64.9 ng/L) by 1.5 orders of magnitude and LPRSV (910 ng/L) by 4 times. Individual estrogenic compounds levels were below the lowest levels demonstrated to cause effects in fish (19 ng/L), but total estrogenicity at WB, where a specific source was found in the WBNFH outfall of 3.1 ng/L, was most likely from residual fish food in the fish hatchery discharge.
Effects on biological variables were related to sites with higher environmental contaminants. At LVW, having the highest EOCs of all sites, male carp showed the most abnormal reproductive effects including higher external testicular lesions, fewer reproductively mature sperm, testicular fibrosis, DNA fragmentation and lower GSIs, sperm motility, and viability. EOCs in LVW discharged into LVB can affect other fish downstream in Boulder Basin and the Colorado River below Hoover Dam, including WB, if regional drought continues and water levels in Lake Mead lower from drier climates. Although EOCs were generally similar between OA and WB, fish from WB have been exposed much longer because of lower growth and longer lifespans (up to 54 years) resulting in higher PCB concentrations and more effects on biological variables than at OA, including lower liver glycogen, external testicular lesions, and an order of magnitude higher testicular PgCAs.
Water levels in Lake Mead have dropped 31 m, from 2010 to current levels in 2023 because of prolonged regional drought (>two decades) in the Colorado River Basin. This drop decreases the amount of water available to dilute the LVW inflow into Lake Mead and downstream through Hoover Dam into the Colorado River and into Lake Mohave. Water quality modeling predicts that a proportion of LVW (RWC) concentrations at the SNWA drinking water intake in the receiving waters of Boulder Basin have increased by 72% from 1.68% to 2.89%. Further drought influenced by continued global warming could increase RCW concentration to as much as 3.6%, which could increase EOC concentrations in Boulder Basin where Razorback Suckers are exposed. At WB, EOC concentrations could increase up to 135%, which could affect fish health and reproduction. Air temperatures rising 4.8 °C at the end of the 21st century may raise water temperatures at Willow Beach between 0.7–2.1 °C, which could increase growth rates in carp. This rise in metabolism can also increase food consumption and respiration that in turn escalate EOC exposure and accumulation particularly for slowly metabolized EOCs like PCBs. |
Title: POP1 Facilitates Proliferation in Triple-Negative Breast Cancer via m6A-Dependent Degradation of CDKN1A mRNA | Body:
Introduction
Triple-negative breast cancer (TNBC) is widely known as the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) expressions, which makes it a lack of targets for precise and effective treatment [1,2]. Because of its high proliferative capacity and high aggressiveness, TNBC is the worst prognostic subtype of breast cancer [3]. It accounts for only 15 to 20% of all cases but more than 80% of breast cancer deaths [4]. Uncontrolled proliferation is one of the most prominent features of all malignant tumors, and TNBC is no exception [5,6]. Compared to other subtypes, TNBC often exhibits a higher proliferative capacity, as indicated by the high positivity rate of the proliferative antigen Ki-67 in the tumor tissue of most patients [7–9]. This is also an important reason why it progresses faster and metastasizes earlier [10]. However, the molecular regulatory mechanism of TNBC hyperproliferation is not completely clear to date.
N6-methyladenosine (m6A) is one of the most universal posttranscriptional modifications in mammalian cells and has been proven to play key regulatory roles in most biological processes [11,12]. A range of m6A regulators, including writers, readers, and erasers precisely regulate the stability, localization, and expression of RNAs by adding, removing, and recognizing m6A modifications within the cell [13,14]. Among them, the recognition of m6A is a highly diverse regulatory process. Through different readers, RNAs with m6A modifications at specific sites are directed to different downstream elements, thus mediating a series of biological effects [15]. Therefore, elucidating these direct regulators of m6A along with complex downstream components is of great significance for enhancing the realization of malignant phenotypic regulatory signals and seeking new potential therapeutic targets.
Processing of precursors 1 (POP1), also called ribonuclease (RNase) P/MRP subunit, is an RNase that mainly localizes in the nucleus and acts in pre-RNA processing. It was first reported in yeast as a key protein component of RNase P and RNase MRP involved in posttranscriptional processing of 5.8s rRNA in the 1990s [16]. Subsequent studies were mainly carried out in lower organisms including yeast and Caenorhabditis elegans, involving spindle separation regulation and maintenance of telomerase activity [17–19]. As a typical RNA-binding protein (RBP), the biological function of POP1 in human has been poorly understood until Park et al.’s [20] study suggested that RNase P/MRP could cleavage RNAs in an m6A-dependent manner and lead to down-regulation of the corresponding gene. Previous bioinformatics analyses suggested that POP1 is up-regulated in a variety of malignancies, including TNBC, and is correlated with poor prognosis [21]. As a key protein with catalytic activity in RNase P/MRP, its role in tumors needs further investigation.
In this study, we identified that POP1 is up-regulated in TNBC and related to poor prognosis. Further analyses and experiments showed that POP1 promoted cell cycle and proliferation of TNBC by directly binding and degrading CDKN1A mRNA. Besides, this process is highly dependent on the m6A modification of CDKN1A mRNA and the recognition of the reader molecule YTH N6-methyladenosine RNA binding protein 2 (YTHDF2). More importantly, we illustrated that STM2457, an m6A inhibitor, significantly impaired the proliferation mediated by high expression of POP1 and improved the sensitivity of TNBC to the period-specific chemotherapy drug paclitaxel. Overall, our findings partially shed light on a potential regulatory mechanism for the high proliferative capacity of TNBC and suggest a new therapeutic strategy for TNBC.
Results
POP1 is up-regulated in TNBC and correlated with poor prognosis
Through a series of screenings, we identified genes that are specifically high expressing in TNBC and are associated with poor prognosis (Fig. S1A to D). Among them, 6 RBPs are worthy of attention, namely, DNMT3B, POP1, GTPBP4, RIOK1, IGF2BP1, and EXO1 (Fig. 1A). We then performed a univariate Cox regression analysis and found that POP1 had the most significant P value (Fig. 1B). Two independent datasets, GSE96058 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse96058) [22] and TCGA (https://portal.gdc.cancer.gov/) [23], indicated high expression of POP1 in TNBC (Fig. 1C and Fig. S1E). Receiver operating characteristic (ROC) curves indicated that high POP1 expression was effective in the diagnosis of TNBC [area under the curve (AUC) = 0.911, 0.798] (Fig. S1F and G). By examining mRNA and protein expression levels of POP1 in 8 breast cancer cell lines containing different subtypes and tissue samples from 26 patients, we determined that POP1 was significantly up-regulated in TNBC (Fig. 1D to F). K–m plots suggested that patients with high POP1 expression had a poorer prognosis than those with low POP1 expression (Fig. 1G and H). Further multivariate Cox regression analysis confirmed that POP1 expression along with T stage and M stage were independent predictive factors of TNBC prognosis (Fig. 1I). In addition, we also detected POP1 expression in tissues of 220 TNBC patients diagnosed and treated between 2001 and 2012 in our center by immunohistochemistry (IHC) (Fig. 1J). The results showed that the proportion of patients with high POP1 expression was significantly higher in the 55 patients who died than in the 165 patients who survived. Patients with high or low POP1 expression were grouped and analyzed for survival. Consistent with the results of above analyses, patients with high POP1 expression had worse overall survival (OS) (Fig. 1K). These results suggest that POP1 is up-regulated in TNBC and may contribute to poor clinical prognosis.
Fig. 1.
POP1 is up-regulated in TNBC and correlated with poor prognosis. (A) Venn diagram to obtain the gene intersection of differentially expressed genes, RBPs, and hazard ratio (HR) > 1 in TNBC. (B) Univariate Cox regression forest map of 6 RBPs. (C) mRNA expression levels of POP1 in different types of breast cancer tissues and normal breast tissues based on the GSE96058 dataset. (D) Quantitative reverse transcription PCR analysis and Western blot analysis to detect the mRNA and protein expression level of POP1 in different types of breast cancer cell lines and normal breast epithelial cells. The mRNA (E) and protein (F) expression levels of POP1 in different subtypes of breast cancer tissues. Analysis of mRNA levels was achieved using one-way ANOVA based on normality and homogeneity of variance. OS analysis of patients stratified by POP1 expression based on log-rank test in TNBC of TCGA (G) and GSE96058 (H). (I) Multivariate Cox regression forest map based on TNBC data from TCGA. (J) Representative microscopic IHC images of POP1 expression in the tissue sections of TNBC patients. (K) Distribution of POP1 expression in 220 patients in survival and death groups, analyzed by Yates’ correction method. **P < 0.01, ***P < 0.001.
POP1 confers proliferation by advancing the cell cycle in TNBC
To further clarify the role of POP1 in TNBC, we performed pathway enrichment analysis and gene set enrichment analysis (GSEA) analysis [24]. The results showed that POP1 was significantly enriched in proliferation-related biological processes including cell cycle, cell cycle checkpoint, and E2F pathway (Fig. 2A and Fig. S2A to C). We constructed stable POP1 overexpression and knockdown cell lines from 2 TNBC cells in which the expression of POP1 is moderate, MDA-MB-231 and SUM159PT, and verified both mRNA and protein expression of POP1 (Fig. S2D and E). Then, the Cell Counting Kit-8 (CCK-8) assay was used to assess the proliferation of cells with different POP1 expression levels. The results showed that high expression of POP1 significantly promoted the proliferation of TNBC cells, while knockdown POP1 had the opposite effect (Fig. 2B). Colony formation assay was used to compare the clonogenesis capacity of cells with different POP1 expression levels. Not surprisingly, TNBC cells with high POP1 expression had a stronger colony formation ability and vice versa (Fig. 2C and D).
Fig. 2.
POP1 confers proliferation by advancing the cell cycle in TNBC. (A) Bubble diagram showing enrichment analysis results of POP1. (B) Line diagrams of CCK-8 proliferation experiment in TNBC cells with different POP1 expression levels. Statistical analyses were performed using the 2-way repeated-measures ANOVA. Typical results (C) and a statistical graph (D) of colony formation assay. One-way ANOVA was used for statistical analysis. (E) Representative flow cytometry cell cycle profiles. (F) Photos of subcutaneous tumors of different groups. (G) Tumor growth curves of different groups in subcutaneous tumor formation experiment in nude mice. Statistical analysis was performed using 2-way repeated-measures ANOVA. (H) Statistics of subcutaneous tumors’ weight in different groups. Welch one-way ANOVA test was used to analyze the differences among different groups. (I) Representative IHC images of subcutaneous tumors targeting POP1 and PCNA. *P < 0.05, **P < 0.01, ***P < 0.001.
Cell cycle is considered to be a key process affecting cell proliferation [25]. We then performed flow cytometry to compare the cell cycle distribution of TNBC cells with different POP1 expression levels. From the results, we found that the proportion of cells with high POP1 expression in the division quiescent phase (G0-G1) was lower than that in the control group, and the proportion in the mitotic phase (S and G2-M) was higher than that in the control group (Fig. 2E and Fig. S2F and G). To demonstrate functions of POP1 in vivo, we used the constructed stable MDA-MB-231 and then injected different groups of cells into the in situ fat pad of nude mouse mammary glands. The collected tumor photograph and tumor growth curves showed that overexpression of POP1 promoted the growth of TNBC cells, while knockdown of POP1 impaired the tumorigenic ability in vivo (Fig. 2F to H). The IHC results showed that POP1 expression was highly positively correlated with the proliferation molecular marker PCNA (Fig. 2I). Taken together, these results reveal that POP1 facilitates TNBC proliferation in vitro and in vivo.
POP1 promotes decay of CDKN1A mRNA
In order to clarify the mechanism of the RNase POP1 in regulating cell cycle and proliferation of TNBC, we first obtained negative-associated genes (NAGs) of POP1. Then, we screened out key molecules involved with cell cycle regulation through a Venn diagram (Fig. 3A). Express correlation chord chart suggested a negative correlation between expression of POP1 and APBB1, APBB2, CDKN1A, CDKN1C, and TGFB1 (Fig. 3B). We then examined mRNA and protein level of these 5 genes in POP1-overexpressed or knockdown cells, respectively. The results showed that only the expression of CDKN1A changed with the change of POP1 expression (Fig. 3C and D), suggesting that CDKN1A may be a downstream target of POP1. To verify the relationship between the 2, IHC staining was implemented on continuous tissue sections of 20 TNBC patients. The results showed that the SI of POP1 was negatively correlated with CDKN1A (Spearman R = −0.833, P < 0.01) and positively correlated with PCNA (Spearman R = 0.744, P < 0.01) (Fig. 3E and F). Furthermore, by referring to the pathology reports of these patients through the medical record system, we found that there is a significant positive correlation between POP1 expression in the tissue and the Ki-67 positivity rate (Spearman R = 0.572, P = 0.008) (Fig. S2H).
Fig. 3.
POP1 promotes decay of CDKN1A mRNA. (A) Venn diagram to screening out cell cycle regulators negatively associated with POP1 expression. (B) Chord diagram showing expression correlation between POP1 and APBB1, APBB2, CDKN1A, and CDKN1C and TGFB1. (C) mRNA expression of TGFB1, CDKN1A, CDKN1C, APBB1, and APBB2 in POP1 knockdown cells and control cells. (D) Protein levels of TGFB1, CDKN1A, CDKN1C, APBB1, and APBB2 in differentially expressed POP1 cells were explored by Western blot. (E) Representative IHC images of continuous sections showing the expression of POP1, CDKN1A, and PCNA in TNBC tissues. (F) Scatterplots of POP1, CDKN1A, and PCNA SI in TNBC tissues. Spearman correlation coefficient was used to evaluate the correlation. (G) Western blot analysis of CDKN1A degradation after treated with 100 mg/ml CHX. (H) Protein degradation curves of CDKN1A (based on the amount at 0 h after treatment). (I) Degradation curves and half-life of CDKN1A mRNA in MDA-MB-231 and SUM159PT after 5 mg/ml Act-D treatment. ns, not significant; *P < 0.05; **P < 0.01.
Based on these results, it is clear that POP1 down-regulates intracellular CDKN1A expression, but how this process is achieved remains unclear. After using 100 mg/ml cycloheximide (CHX) to inhibit intracellular protein synthesis, we detected CDKN1A protein level in TNBC cells at different time points. Results showed that CHX treatment did not delay the degradation time of CDKN1A protein in POP1 knockdown cells (Fig. 3G and H). Notably, when we applied 5 μg/ml actinomycin D (Act-D) to inhibit transcription, POP1 knockdown significantly prolonged the half-life of CDKN1A (1.43 h versus 3.20 h and 2.69 h; 1.48 h versus 2.80 h and 2.99 h) (Fig. 3I and J). Collectively, POP1 down-regulates the expression of CDKN1A at the mRNA level.
POP1 degrades CDKN1A mRNA by interacting with its CDS
Given that POP1 has RNase activity, we speculated whether POP1 down-regulates CDKN1A by degrading its mRNA directly. We performed exogenous and endogenous RNA immunoprecipitation (RIP) with anti-Flag antibody and anti-POP1 antibody, respectively, and detected CDKN1A mRNA by quantitative polymerase chain reaction (qPCR). The results showed that CDKN1A was enriched in the RNAs bound to POP1 (Fig. 4A and B). To detect the specific binding region, CDKN1A mRNA was divided into 3 different parts, including 5′ untranslated region (UTR), coding sequence (CDS), and 3′UTR, and transcribed in vitro with biotin labeling (Fig. S3A). RNA pull-down experiments were used to enrich proteins that bind to different pieces of RNA. Western blot images showed that POP1 bound to the CDS of CDKN1A rather than 5′UTR or 3′UTR (Fig. 4C). Based on the MS2 pull-down system, it was further confirmed that POP1 bound to the CDS region of CDKN1A mRNA (Fig. 4D and Fig. S3B). To explore the effect of the direct binding between POP1 and CDKN1A mRNA, we performed dual-luciferase reporter assay. We found that overexpression of POP1 down-regulates the expression of luciferase downstream of CDS region of CDKN1A and vice versa, which indicated that POP1 down-regulated the mRNA of CDKN1A by binding to its CDS region (Fig. 4E).
Fig. 4.
POP1 degrades CDKN1A mRNA by interacting with its CDS. RIP-PCR results about the binding between CDKN1A mRNA and POP1 protein and in 293T cells (A), MDA-MB-231 cells, and SUM159PT cells (B). (C) Western blot analysis of POP1 after biotin-labeled CDKN1A mRNA pull down. (D) Western blot analysis of POP1 after endogenous MS2 pull-down assay. (E) Luciferase reporter assay to explore the effect of POP1 on the decay of 5′UTR, CDS, and 3′UTR of CDKN1A. (F) Design and expression verification of POP1 truncated domain. (G) RIP-PCR assay to explore the binding between CDKN1A mRNA and the N-terminal or C-terminal truncate of POP1. (H) qPCR analysis to detect the effect of POP1 truncation on CDKN1A mRNA. (I) Western blot assay to detect the influence of POP1 truncation on CDKN1A protein. (J) Western blot analysis of p-Rb to detect the effect of CDKN1A overexpression on POP1 overexpression. (K) Cell viability curves showing the effect of rescue expression of CDKN1A on cell proliferation. Representative photographs (L) and a statistical graph (M) showing the effect of overexpression of CDKN1A on the colony formation ability of cells with high POP1 expression. *P < 0.05, **P < 0.01, ***P < 0.001.
Considering that the current reports on POP1 protein domain are very limited, only the N-terminal domain (107 to 257 amino acids) and POPLD domain (617 to 708 amino acids) are known (Fig. S3C). We constructed truncated domains with the Flag tag containing the N terminus (1 to 500 amino acids) and C terminus (501 to 1,024 amino acids) of POP1 protein by querying the database. Western blot confirmed that labeled truncated POP1 was normally expressed and could be enriched by Flag magnetic beads (Fig. 4F). Further RIP-PCR experiment suggested that CDKN1A mRNA could bind to both N terminus and C terminus of POP1 protein (Fig. 4G). As for whether the N terminus or C terminus of POP1 played a key role in degrading CDKN1A mRNA, we introduced the truncated body separately in TNBC cells. Results of qPCR showed that single N terminus or C terminus did not affect intracellular CDKN1A mRNA levels (Fig. 4H). This suggests that although CDKN1A mRNA binds to both the N terminus and C terminus of POP1 protein, the truncated body does not have complete RNase activity. Not surprisingly, when we transfected the POP1 truncated body in the parent cells, the protein level of CDKN1A did not change significantly, either (Fig. 4I).
Above, we confirmed the direct regulatory effect of POP1 on CDKN1A mRNA. Then, we conducted simple rescue experiments to clarify the regulation of POP1 on the cell cycle through the degradation of CDKN1A. When we overexpressed CDKN1A in POP1-overexpressed TNBC cells, we noticed a significant reduction in the phosphorylation level of the cell cycle key checkpoint protein Rb (Fig. 4J and Fig. S3D). Moreover, overexpression of CDKN1A also impaired the proliferation ability and plate cloning ability of POP1-overexpressed cells (Fig. 4K to M). In summary, the results of this part showed that POP1 promoted TNBC proliferation by directly binding to the CDS region to degrade CDKN1A mRNA.
POP1 degrades CDKN1A mRNA in an m6A-dependent manner
Previous studies have shown that POP1 could participate in the degradation of RNA with m6A modification. In this study, we have confirmed that POP1 has the function of degrading CDKN1A mRNA. We wondered whether the degradation process depended on the m6A modification of CDKN1A. STM2457 is a proven effective inhibitor of the m6A writer complex METTL3/METTL14 [26]. First, we explored the effect of STM2457 concentration on TNBC cells and on CDKN1A expression (Fig. S4A to E). We then examined the mRNA and protein expression of CDKN1A in TNBC cells treated with METTL3 knockdown and STM2457. As expected, both knockdown and inhibition of METTL3 could up-regulate CDKN1A expression (Fig. 5A and B). RIP-PCR experiments were repeated to determine whether knockdown METTL3 and STM2457 treatment up-regulated CDKN1A by inhibiting the interaction between POP1 and CDKN1A. The results showed that inhibition of m6A significantly reduced the binding of POP1 to CDKN1A (Fig. 5C). Further detection also suggested that inhibition of m6A by knocking down METTL3 or STM2457 treatment significantly prolonged the half-life of CDKN1A mRNA (Fig. 5D). These results indicated that degradation of CDKN1A mRNA by POP1 could be regulated via m6A modification.
Fig. 5.
POP1 degrades CDKN1A mRNA in an m6A-dependent manner. qPCR and Western blot analysis of mRNA (A) and protein (B) of CDKN1A after inhibition of m6A modification by si-METTL3 or STM2457 treatment. (C) RIP-PCR used to detect the binding of POP1 and CDKN1A mRNA after inhibition of m6A modification. (D) Degradation curves and half-life of CDKN1A mRNA in MDA-MB-231 and SUM159PT after inhibition of m6A modification. (E) Potential m6A modification sites of CDKN1A mRNA predicted by SRAMP. (F) MeRIP-PCR used to detect m6A levels at 2 potential sites of CDKN1A after inhibiting m6A modification. (G) Schematic diagram of CDKN1A mutant at 2 sites. (H) RNA pull-down assay showing the binding of wild type and site mutant CDKN1A to POP1. (I) Western blot to detect the effect of STM2457 on mutant CDKN1A.
Next, we attempted to explore the details of m6A modification of CDKN1A mRNA. By consulting the online SRAMP database (http://www.cuilab.cn/sramp/) [27], we predicted the potential m6A modification sites of CDKN1A mRNA. The results suggested that the 497th AGACT near the 3′ end of the CDS region and the 1485th GGACT in the 3′UTR region had the highest confidence among all potential sites (Fig. 5E). In order to identify the m6A modification site of CDKN1A, we performed methylated RIP (MeRIP)-PCR to detect the m6A modification at 2 sites in 2 TNBC cells. The results showed that knocking down METTL3 or STM2457 could significantly reduce the enrichment of sequences near the 497th AGACT but had little effect on the enrichment level of sequences near the 1485 AGACT (Fig. 5F). To further verify the effect of m6A modification at specific sites of CDKN1A mRNA, we constructed CDKN1A with mutations in these 2 potential sites, namely, mutΔ1 and mutΔ2 (Fig. 5G). RNA pull-down assay showed that CDKN1A mRNA of wild-type and mutΔ2 could bind to POP1 while mutΔ1 no longer bound to POP1 (Fig. 5H). These results suggested that the interaction of POP1 with CDKN1A mRNA was dependent on m6A modification at position 497 of the mRNA. In addition, Western blot analysis of CDKN1A showed that STM2457 could not up-regulate the expression of mutΔ1 compared with the wild type and mutΔ2 (Fig. 5I and Fig. S4F). Taken together, POP1 degrades CDKN1A mRNA depending on its m6A modification at site 497.
YTHDF2 is the m6A reader mediating CDKN1A degradation
After it was confirmed that POP1 degraded CDKN1A mRNA in an m6A-dependent manner, the reader mediating this process became a problem that needed to be clarified. We first performed co-IP to validate widely studied YTH m6A RNA binding proteins including YTHDF1, YTHDF2, and YTHDF3. Among the proteins bound to POP1, YTHDF2 was significantly enriched (Fig. 6A). Among the proteins bound to YTHDF2, POP1 and a known adaptor protein, RIDA, were enriched (Fig. 6B). Not only that, we repeated the co-IP of the exogenous protein. By transfecting labeled POP1, YTHDF2, and RIDA, the corresponding label content was detected after enrichment. The results showed that POP1 combined directly with YTHDF2 and RIDA (Fig. 6C). High-power (60×) images of immunofluorescence show the location characteristics of these 3 molecules within the cell. Further analysis by ImageJ indicated that POP1, RIDA, and YTHDF2 had a high degree of adhesion (most Pearson R2 ≥ 0.8) (Fig. 6D). These results suggested that YTHDF2 bound to POP1, so we speculated that YTHDF2 was the reader mediating CDKN1A degradation.
Fig. 6.
YTHDF2 is the m6A reader mediating CDKN1A degradation. (A) Co-IP used to detect the binding of POP1 with YTH m6A RNA binding proteins. (B) Co-IP to detect the binding of YTHDF2 with POP1 and RIDA. (C) Exogenous co-IP to detect the binding of POP1, YTHDF2, and RIDA. (D) Immunofluorescence images showing subcellular localization and colocalization of POP1, RIDA, and YTHDF2. qPCR and Western blot analysis of mRNA (E) and protein (F) of CDKN1A after inhibition of m6A reading by si-YTHDF2 or si-RIDA. RIP-PCR (G) and RNA pull down (H) used to detect the binding of POP1 and CDKN1A mRNA after inhibition of m6A recognition. Welch one-way ANOVA test was used for difference analysis according to the data’s normality and homogeneity of variance. (I) Co-IP to detect the effect on the binding of POP1 with YTHDF2 when knocking down RIDA or treating with STM2457. (J) Degradation curves and half-life of CDKN1A mRNA in MDA-MB-231 and SUM159PT after inhibiting m6A recognition.
Subsequently, qPCR and Western blot results showed that knocking down YTHDF2 and RIDA could significantly up-regulate the mRNA and protein levels of CDKN1A without influencing its m6A modification in TNBC cells (Fig. 6E and F and Fig. S4G). These suggested that they may play an important role in mediating CDKN1A mRNA degradation. Further RIP-PCR and RNA pull-down experiments also showed that knocking down either YTHDF2 or RIDA significantly impaired the interaction between POP1 and CDKN1A mRNA (Fig. 6G and H). It was worth noting that whether knockdown of the ligation helper protein RIDA or using STM2457 inhibited m6A modification, the binding of POP1 with YTHDF2 was significantly reduced (Fig. 6I). Finally, knocking down YTHDF2 or RIDA to inhibit m6A recognition significantly extended the half-life of CDKN1A mRNA (Fig. 6J), just as inhibiting m6A modification. Collectively, we have demonstrated that YTHDF2 was the key reader mediating the degradation of CDKN1A mRNA in TNBC, which was consistent with the reported role of YTHDF2 in RNA decay in previous studies.
Promotion of TNBC proliferation by POP1 depends on m6A modification and recognition of CDKN1A
The effect of inhibiting m6A modification and recognition process on the binding of CDKN1A mRNA to POP1, YTHDF2, and RIDA was detected by MS2-based RNA pull down. The results suggested the necessity of both m6A modification and recognition in this regulatory process (Fig. 7A). So far, we have basically demonstrated that YTHDF2-RIDA-POP1 were key elements in degrading CDKN1A mRNA in an m6A-dependent manner. In order to determine whether the promotion of TNBC proliferation by POP1 depends on this function, we conducted necessary rescue experiments. First, by interfering with the m6A modification and recognition process with si-YTHDF2, si-RIDA, or STM2457 treatment, the up-regulation of CDKN1A in POP1-overexpressing cells was restored and the cell proliferation marker p-Rb was significantly reduced (Fig. 7B). Subsequent RNA half-life measurements showed clearly that both m6A modification and recognition were important for the reduction of CDKN1A half-life in POP1-overexpressing cells (Fig. 7C). Consistent with the expression of p-Rb in TNBC cells, inhibiting m6A modification or recognition of cells could also significantly reduce the colony formation ability and the proportion of proliferating cells in cells with high POP1 expression (Fig. 7D and E and Fig. S5A to C). The results of tumor formation experiment in nude mice showed that overexpression of CDKN1A or treatment with STM2457 in cells with high POP1 expression greatly impaired the tumor formation ability (Fig. 7F). It is obvious that restoring intracellular CDKN1A expression by different methods could block POP1-mediated high proliferation (Fig. 7G and Fig. S5D). These results were again confirmed by IHC staining of POP1, CDKN1A, and proliferating marker PCNA in successive subcutaneous tumor sections (Fig. 7H). In short, the expression of CDKN1A was a key indicator of proliferation, and POP1 promoted TNBC cell proliferation by down-regulating CDKN1A in an m6A-dependent manner.
Fig. 7.
Promotion of TNBC proliferation by POP1 depends on m6A modification and recognition of CDKN1A. (A) The MS2 pull-down experiment demonstrated the combination of POP1, YTHDF2, and RIDA with CDKN1A mRNA under si-YTHDF2, si-RIDA, and STM2457 treatment. (B) qPCR and Western blot analyses of the effects of interference with m6A modification and recognition on CDKN1A expression and Rb phosphorylation in POP1-overexpressed cells. (C) Degradation curves and half-life of CDKN1A mRNA in POP1-overexpressed cells after inhibiting m6A modification or recognition. (D) Statistical results of colony formation assay of POP1-overexpressed TNBC cells under different treatments. (E) Representative flow cytometry cell cycle profiles of POP1-overexpressed TNBC cells under different treatments. Subcutaneous tumor photos (F) and tumor volume growth curves (G) of TNBC cells in different treatment groups. Statistical analysis of tumor volume growth in different groups was conducted using 2-way repeated-measures ANOVA. (H) Representative IHC images of subcutaneous tumorstargeting POP1, CDKN1A, and PCNA. *P < 0.05, **P < 0.01, ***P < 0.001.
Clinical significance of POP1 and potential application of STM2457 in TNBC
Previous studies have reported that down-regulation of CDKN1A is not only related to the uncontrolled proliferation of cancer cells but also responsible for the resistance of some cell cycle-specific drugs such as CDK4/6 inhibitors and paclitaxel [28–30]. Here, we explored the influence of POP1 and CDKN1A expression on paclitaxel sensitivity and tried to find predictors of TNBC chemotherapy sensitivity and potential solutions for chemotherapy resistance. First, the colony formation experiment showed that POP1 overexpression could enhance the colony formation ability of TNBC cells treated with 20 μM paclitaxel, and STM2457 combined with it significantly enhanced the sensitivity of TNBC cells to paclitaxel (Fig. 8A and B). In vivo experiments show similar results. Treatment with 10 mg/kg of paclitaxel at 10 d after tumor formation and treatment with 50 mg/kg of STM2457 significantly improved paclitaxel sensitivity, which was reflected in significant differences in tumor growth curve, tumor size, and tumor weight (Fig. 8C to E). Annexin V-propidium iodide (PI) flow cytometry also showed that STM2457 distinctly reduced the survival rate of the cells under the treatment of paclitaxel (Fig. 8F and Fig. S6A). So far, our results have indicated that intervention of CDKN1A degradation by STM2457 had a promising role in the sensitizing effect of paclitaxel.
Fig. 8.
Clinical significance of POP1 and potential application of STM2457 in TNBC. (A) Colony formation pictures to show the effect of m6A inhibitor STM2457 on the response of TNBC cells to paclitaxel. (B) Statistical graph of the results of the colony formation assay. Subcutaneous tumor photos (C), tumor growth curves (D), and statistics of tumor weight (E) of TNBC cells showing the effect of STM2457 on sensitivity to paclitaxel in vivo. (F) Representative Annexin V-PI double dye flow cytometry results showing the effect of STM2457 on apoptosis of TNBC cells treated with 20 nM paclitaxel. (G) Expression of POP1 and CDKN1A mRNA in tissues of patients with paclitaxel sensitivity and resistance in the dataset GSE6434. (H) Representative IHC images of continuous sections showing the expression of POP1 and CDKN1A in patients with different response to neoadjuvant chemotherapy. (I) POP1 and CDKN1A expression in non-pathological complete response (pCR) patients compared with pCR patients (non-pCR N = 10, pCR N = 10). (J) Proportion of POP1 and CDKN1A expression in different OS and DFS outcomes. The statistics was conducted using the chi-square test. Survival curves of patients with different levels of POP1 and CDKN1A expression including OS (K) and DFS (L). Log-rank test was used for survival analysis of different groups. *P < 0.05, **P < 0.01, ***P < 0.001.
As to whether the expression of POP1 and CDKN1A played a certain role in predicting TNBC chemotherapy sensitivity and patient prognosis, we first referred to the online dataset GSE6434 [31]. It was clear that POP1 expression was higher and CDKN1A expression was lower in the tissues of 14 paclitaxel-resistant patients compared with 10 sensitive patients (Fig. 8G). IHC staining of tissue sections of patients receiving neoadjuvant chemotherapy based on paclitaxel in our hospital also showed similar results (Fig. 8H and I). We did IHC staining on the tissues of 220 patients in our cohort and divided them into 3 groups, namely, POP1High&CDKN1ALow, POP1Low&CDKN1AHigh, and Others, for survival analysis and comparison. The results showed that patients with high POP1 expression and low CDKN1A expression had the worst prognosis (Fig. 8J to L), and POP1 combined with CDKN1A predicted the prognosis more effectively than POP1 alone (Fig. S6B to G). Taken together, POP1 regulating CDKN1A degradation provided potential markers for prediction of TNBC chemotherapy response and prognosis and a potential strategy for sensitization chemotherapy. Finally, a model diagram summarized the content of this study (Fig. 9).
Fig. 9.
A model of this study showing that the YTHDF2–RIDA–POP1–CDKN1A axis promotes proliferation in TNBC.
POP1 is up-regulated in TNBC and correlated with poor prognosis
Through a series of screenings, we identified genes that are specifically high expressing in TNBC and are associated with poor prognosis (Fig. S1A to D). Among them, 6 RBPs are worthy of attention, namely, DNMT3B, POP1, GTPBP4, RIOK1, IGF2BP1, and EXO1 (Fig. 1A). We then performed a univariate Cox regression analysis and found that POP1 had the most significant P value (Fig. 1B). Two independent datasets, GSE96058 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse96058) [22] and TCGA (https://portal.gdc.cancer.gov/) [23], indicated high expression of POP1 in TNBC (Fig. 1C and Fig. S1E). Receiver operating characteristic (ROC) curves indicated that high POP1 expression was effective in the diagnosis of TNBC [area under the curve (AUC) = 0.911, 0.798] (Fig. S1F and G). By examining mRNA and protein expression levels of POP1 in 8 breast cancer cell lines containing different subtypes and tissue samples from 26 patients, we determined that POP1 was significantly up-regulated in TNBC (Fig. 1D to F). K–m plots suggested that patients with high POP1 expression had a poorer prognosis than those with low POP1 expression (Fig. 1G and H). Further multivariate Cox regression analysis confirmed that POP1 expression along with T stage and M stage were independent predictive factors of TNBC prognosis (Fig. 1I). In addition, we also detected POP1 expression in tissues of 220 TNBC patients diagnosed and treated between 2001 and 2012 in our center by immunohistochemistry (IHC) (Fig. 1J). The results showed that the proportion of patients with high POP1 expression was significantly higher in the 55 patients who died than in the 165 patients who survived. Patients with high or low POP1 expression were grouped and analyzed for survival. Consistent with the results of above analyses, patients with high POP1 expression had worse overall survival (OS) (Fig. 1K). These results suggest that POP1 is up-regulated in TNBC and may contribute to poor clinical prognosis.
Fig. 1.
POP1 is up-regulated in TNBC and correlated with poor prognosis. (A) Venn diagram to obtain the gene intersection of differentially expressed genes, RBPs, and hazard ratio (HR) > 1 in TNBC. (B) Univariate Cox regression forest map of 6 RBPs. (C) mRNA expression levels of POP1 in different types of breast cancer tissues and normal breast tissues based on the GSE96058 dataset. (D) Quantitative reverse transcription PCR analysis and Western blot analysis to detect the mRNA and protein expression level of POP1 in different types of breast cancer cell lines and normal breast epithelial cells. The mRNA (E) and protein (F) expression levels of POP1 in different subtypes of breast cancer tissues. Analysis of mRNA levels was achieved using one-way ANOVA based on normality and homogeneity of variance. OS analysis of patients stratified by POP1 expression based on log-rank test in TNBC of TCGA (G) and GSE96058 (H). (I) Multivariate Cox regression forest map based on TNBC data from TCGA. (J) Representative microscopic IHC images of POP1 expression in the tissue sections of TNBC patients. (K) Distribution of POP1 expression in 220 patients in survival and death groups, analyzed by Yates’ correction method. **P < 0.01, ***P < 0.001.
POP1 confers proliferation by advancing the cell cycle in TNBC
To further clarify the role of POP1 in TNBC, we performed pathway enrichment analysis and gene set enrichment analysis (GSEA) analysis [24]. The results showed that POP1 was significantly enriched in proliferation-related biological processes including cell cycle, cell cycle checkpoint, and E2F pathway (Fig. 2A and Fig. S2A to C). We constructed stable POP1 overexpression and knockdown cell lines from 2 TNBC cells in which the expression of POP1 is moderate, MDA-MB-231 and SUM159PT, and verified both mRNA and protein expression of POP1 (Fig. S2D and E). Then, the Cell Counting Kit-8 (CCK-8) assay was used to assess the proliferation of cells with different POP1 expression levels. The results showed that high expression of POP1 significantly promoted the proliferation of TNBC cells, while knockdown POP1 had the opposite effect (Fig. 2B). Colony formation assay was used to compare the clonogenesis capacity of cells with different POP1 expression levels. Not surprisingly, TNBC cells with high POP1 expression had a stronger colony formation ability and vice versa (Fig. 2C and D).
Fig. 2.
POP1 confers proliferation by advancing the cell cycle in TNBC. (A) Bubble diagram showing enrichment analysis results of POP1. (B) Line diagrams of CCK-8 proliferation experiment in TNBC cells with different POP1 expression levels. Statistical analyses were performed using the 2-way repeated-measures ANOVA. Typical results (C) and a statistical graph (D) of colony formation assay. One-way ANOVA was used for statistical analysis. (E) Representative flow cytometry cell cycle profiles. (F) Photos of subcutaneous tumors of different groups. (G) Tumor growth curves of different groups in subcutaneous tumor formation experiment in nude mice. Statistical analysis was performed using 2-way repeated-measures ANOVA. (H) Statistics of subcutaneous tumors’ weight in different groups. Welch one-way ANOVA test was used to analyze the differences among different groups. (I) Representative IHC images of subcutaneous tumors targeting POP1 and PCNA. *P < 0.05, **P < 0.01, ***P < 0.001.
Cell cycle is considered to be a key process affecting cell proliferation [25]. We then performed flow cytometry to compare the cell cycle distribution of TNBC cells with different POP1 expression levels. From the results, we found that the proportion of cells with high POP1 expression in the division quiescent phase (G0-G1) was lower than that in the control group, and the proportion in the mitotic phase (S and G2-M) was higher than that in the control group (Fig. 2E and Fig. S2F and G). To demonstrate functions of POP1 in vivo, we used the constructed stable MDA-MB-231 and then injected different groups of cells into the in situ fat pad of nude mouse mammary glands. The collected tumor photograph and tumor growth curves showed that overexpression of POP1 promoted the growth of TNBC cells, while knockdown of POP1 impaired the tumorigenic ability in vivo (Fig. 2F to H). The IHC results showed that POP1 expression was highly positively correlated with the proliferation molecular marker PCNA (Fig. 2I). Taken together, these results reveal that POP1 facilitates TNBC proliferation in vitro and in vivo.
POP1 promotes decay of CDKN1A mRNA
In order to clarify the mechanism of the RNase POP1 in regulating cell cycle and proliferation of TNBC, we first obtained negative-associated genes (NAGs) of POP1. Then, we screened out key molecules involved with cell cycle regulation through a Venn diagram (Fig. 3A). Express correlation chord chart suggested a negative correlation between expression of POP1 and APBB1, APBB2, CDKN1A, CDKN1C, and TGFB1 (Fig. 3B). We then examined mRNA and protein level of these 5 genes in POP1-overexpressed or knockdown cells, respectively. The results showed that only the expression of CDKN1A changed with the change of POP1 expression (Fig. 3C and D), suggesting that CDKN1A may be a downstream target of POP1. To verify the relationship between the 2, IHC staining was implemented on continuous tissue sections of 20 TNBC patients. The results showed that the SI of POP1 was negatively correlated with CDKN1A (Spearman R = −0.833, P < 0.01) and positively correlated with PCNA (Spearman R = 0.744, P < 0.01) (Fig. 3E and F). Furthermore, by referring to the pathology reports of these patients through the medical record system, we found that there is a significant positive correlation between POP1 expression in the tissue and the Ki-67 positivity rate (Spearman R = 0.572, P = 0.008) (Fig. S2H).
Fig. 3.
POP1 promotes decay of CDKN1A mRNA. (A) Venn diagram to screening out cell cycle regulators negatively associated with POP1 expression. (B) Chord diagram showing expression correlation between POP1 and APBB1, APBB2, CDKN1A, and CDKN1C and TGFB1. (C) mRNA expression of TGFB1, CDKN1A, CDKN1C, APBB1, and APBB2 in POP1 knockdown cells and control cells. (D) Protein levels of TGFB1, CDKN1A, CDKN1C, APBB1, and APBB2 in differentially expressed POP1 cells were explored by Western blot. (E) Representative IHC images of continuous sections showing the expression of POP1, CDKN1A, and PCNA in TNBC tissues. (F) Scatterplots of POP1, CDKN1A, and PCNA SI in TNBC tissues. Spearman correlation coefficient was used to evaluate the correlation. (G) Western blot analysis of CDKN1A degradation after treated with 100 mg/ml CHX. (H) Protein degradation curves of CDKN1A (based on the amount at 0 h after treatment). (I) Degradation curves and half-life of CDKN1A mRNA in MDA-MB-231 and SUM159PT after 5 mg/ml Act-D treatment. ns, not significant; *P < 0.05; **P < 0.01.
Based on these results, it is clear that POP1 down-regulates intracellular CDKN1A expression, but how this process is achieved remains unclear. After using 100 mg/ml cycloheximide (CHX) to inhibit intracellular protein synthesis, we detected CDKN1A protein level in TNBC cells at different time points. Results showed that CHX treatment did not delay the degradation time of CDKN1A protein in POP1 knockdown cells (Fig. 3G and H). Notably, when we applied 5 μg/ml actinomycin D (Act-D) to inhibit transcription, POP1 knockdown significantly prolonged the half-life of CDKN1A (1.43 h versus 3.20 h and 2.69 h; 1.48 h versus 2.80 h and 2.99 h) (Fig. 3I and J). Collectively, POP1 down-regulates the expression of CDKN1A at the mRNA level.
POP1 degrades CDKN1A mRNA by interacting with its CDS
Given that POP1 has RNase activity, we speculated whether POP1 down-regulates CDKN1A by degrading its mRNA directly. We performed exogenous and endogenous RNA immunoprecipitation (RIP) with anti-Flag antibody and anti-POP1 antibody, respectively, and detected CDKN1A mRNA by quantitative polymerase chain reaction (qPCR). The results showed that CDKN1A was enriched in the RNAs bound to POP1 (Fig. 4A and B). To detect the specific binding region, CDKN1A mRNA was divided into 3 different parts, including 5′ untranslated region (UTR), coding sequence (CDS), and 3′UTR, and transcribed in vitro with biotin labeling (Fig. S3A). RNA pull-down experiments were used to enrich proteins that bind to different pieces of RNA. Western blot images showed that POP1 bound to the CDS of CDKN1A rather than 5′UTR or 3′UTR (Fig. 4C). Based on the MS2 pull-down system, it was further confirmed that POP1 bound to the CDS region of CDKN1A mRNA (Fig. 4D and Fig. S3B). To explore the effect of the direct binding between POP1 and CDKN1A mRNA, we performed dual-luciferase reporter assay. We found that overexpression of POP1 down-regulates the expression of luciferase downstream of CDS region of CDKN1A and vice versa, which indicated that POP1 down-regulated the mRNA of CDKN1A by binding to its CDS region (Fig. 4E).
Fig. 4.
POP1 degrades CDKN1A mRNA by interacting with its CDS. RIP-PCR results about the binding between CDKN1A mRNA and POP1 protein and in 293T cells (A), MDA-MB-231 cells, and SUM159PT cells (B). (C) Western blot analysis of POP1 after biotin-labeled CDKN1A mRNA pull down. (D) Western blot analysis of POP1 after endogenous MS2 pull-down assay. (E) Luciferase reporter assay to explore the effect of POP1 on the decay of 5′UTR, CDS, and 3′UTR of CDKN1A. (F) Design and expression verification of POP1 truncated domain. (G) RIP-PCR assay to explore the binding between CDKN1A mRNA and the N-terminal or C-terminal truncate of POP1. (H) qPCR analysis to detect the effect of POP1 truncation on CDKN1A mRNA. (I) Western blot assay to detect the influence of POP1 truncation on CDKN1A protein. (J) Western blot analysis of p-Rb to detect the effect of CDKN1A overexpression on POP1 overexpression. (K) Cell viability curves showing the effect of rescue expression of CDKN1A on cell proliferation. Representative photographs (L) and a statistical graph (M) showing the effect of overexpression of CDKN1A on the colony formation ability of cells with high POP1 expression. *P < 0.05, **P < 0.01, ***P < 0.001.
Considering that the current reports on POP1 protein domain are very limited, only the N-terminal domain (107 to 257 amino acids) and POPLD domain (617 to 708 amino acids) are known (Fig. S3C). We constructed truncated domains with the Flag tag containing the N terminus (1 to 500 amino acids) and C terminus (501 to 1,024 amino acids) of POP1 protein by querying the database. Western blot confirmed that labeled truncated POP1 was normally expressed and could be enriched by Flag magnetic beads (Fig. 4F). Further RIP-PCR experiment suggested that CDKN1A mRNA could bind to both N terminus and C terminus of POP1 protein (Fig. 4G). As for whether the N terminus or C terminus of POP1 played a key role in degrading CDKN1A mRNA, we introduced the truncated body separately in TNBC cells. Results of qPCR showed that single N terminus or C terminus did not affect intracellular CDKN1A mRNA levels (Fig. 4H). This suggests that although CDKN1A mRNA binds to both the N terminus and C terminus of POP1 protein, the truncated body does not have complete RNase activity. Not surprisingly, when we transfected the POP1 truncated body in the parent cells, the protein level of CDKN1A did not change significantly, either (Fig. 4I).
Above, we confirmed the direct regulatory effect of POP1 on CDKN1A mRNA. Then, we conducted simple rescue experiments to clarify the regulation of POP1 on the cell cycle through the degradation of CDKN1A. When we overexpressed CDKN1A in POP1-overexpressed TNBC cells, we noticed a significant reduction in the phosphorylation level of the cell cycle key checkpoint protein Rb (Fig. 4J and Fig. S3D). Moreover, overexpression of CDKN1A also impaired the proliferation ability and plate cloning ability of POP1-overexpressed cells (Fig. 4K to M). In summary, the results of this part showed that POP1 promoted TNBC proliferation by directly binding to the CDS region to degrade CDKN1A mRNA.
POP1 degrades CDKN1A mRNA in an m6A-dependent manner
Previous studies have shown that POP1 could participate in the degradation of RNA with m6A modification. In this study, we have confirmed that POP1 has the function of degrading CDKN1A mRNA. We wondered whether the degradation process depended on the m6A modification of CDKN1A. STM2457 is a proven effective inhibitor of the m6A writer complex METTL3/METTL14 [26]. First, we explored the effect of STM2457 concentration on TNBC cells and on CDKN1A expression (Fig. S4A to E). We then examined the mRNA and protein expression of CDKN1A in TNBC cells treated with METTL3 knockdown and STM2457. As expected, both knockdown and inhibition of METTL3 could up-regulate CDKN1A expression (Fig. 5A and B). RIP-PCR experiments were repeated to determine whether knockdown METTL3 and STM2457 treatment up-regulated CDKN1A by inhibiting the interaction between POP1 and CDKN1A. The results showed that inhibition of m6A significantly reduced the binding of POP1 to CDKN1A (Fig. 5C). Further detection also suggested that inhibition of m6A by knocking down METTL3 or STM2457 treatment significantly prolonged the half-life of CDKN1A mRNA (Fig. 5D). These results indicated that degradation of CDKN1A mRNA by POP1 could be regulated via m6A modification.
Fig. 5.
POP1 degrades CDKN1A mRNA in an m6A-dependent manner. qPCR and Western blot analysis of mRNA (A) and protein (B) of CDKN1A after inhibition of m6A modification by si-METTL3 or STM2457 treatment. (C) RIP-PCR used to detect the binding of POP1 and CDKN1A mRNA after inhibition of m6A modification. (D) Degradation curves and half-life of CDKN1A mRNA in MDA-MB-231 and SUM159PT after inhibition of m6A modification. (E) Potential m6A modification sites of CDKN1A mRNA predicted by SRAMP. (F) MeRIP-PCR used to detect m6A levels at 2 potential sites of CDKN1A after inhibiting m6A modification. (G) Schematic diagram of CDKN1A mutant at 2 sites. (H) RNA pull-down assay showing the binding of wild type and site mutant CDKN1A to POP1. (I) Western blot to detect the effect of STM2457 on mutant CDKN1A.
Next, we attempted to explore the details of m6A modification of CDKN1A mRNA. By consulting the online SRAMP database (http://www.cuilab.cn/sramp/) [27], we predicted the potential m6A modification sites of CDKN1A mRNA. The results suggested that the 497th AGACT near the 3′ end of the CDS region and the 1485th GGACT in the 3′UTR region had the highest confidence among all potential sites (Fig. 5E). In order to identify the m6A modification site of CDKN1A, we performed methylated RIP (MeRIP)-PCR to detect the m6A modification at 2 sites in 2 TNBC cells. The results showed that knocking down METTL3 or STM2457 could significantly reduce the enrichment of sequences near the 497th AGACT but had little effect on the enrichment level of sequences near the 1485 AGACT (Fig. 5F). To further verify the effect of m6A modification at specific sites of CDKN1A mRNA, we constructed CDKN1A with mutations in these 2 potential sites, namely, mutΔ1 and mutΔ2 (Fig. 5G). RNA pull-down assay showed that CDKN1A mRNA of wild-type and mutΔ2 could bind to POP1 while mutΔ1 no longer bound to POP1 (Fig. 5H). These results suggested that the interaction of POP1 with CDKN1A mRNA was dependent on m6A modification at position 497 of the mRNA. In addition, Western blot analysis of CDKN1A showed that STM2457 could not up-regulate the expression of mutΔ1 compared with the wild type and mutΔ2 (Fig. 5I and Fig. S4F). Taken together, POP1 degrades CDKN1A mRNA depending on its m6A modification at site 497.
YTHDF2 is the m6A reader mediating CDKN1A degradation
After it was confirmed that POP1 degraded CDKN1A mRNA in an m6A-dependent manner, the reader mediating this process became a problem that needed to be clarified. We first performed co-IP to validate widely studied YTH m6A RNA binding proteins including YTHDF1, YTHDF2, and YTHDF3. Among the proteins bound to POP1, YTHDF2 was significantly enriched (Fig. 6A). Among the proteins bound to YTHDF2, POP1 and a known adaptor protein, RIDA, were enriched (Fig. 6B). Not only that, we repeated the co-IP of the exogenous protein. By transfecting labeled POP1, YTHDF2, and RIDA, the corresponding label content was detected after enrichment. The results showed that POP1 combined directly with YTHDF2 and RIDA (Fig. 6C). High-power (60×) images of immunofluorescence show the location characteristics of these 3 molecules within the cell. Further analysis by ImageJ indicated that POP1, RIDA, and YTHDF2 had a high degree of adhesion (most Pearson R2 ≥ 0.8) (Fig. 6D). These results suggested that YTHDF2 bound to POP1, so we speculated that YTHDF2 was the reader mediating CDKN1A degradation.
Fig. 6.
YTHDF2 is the m6A reader mediating CDKN1A degradation. (A) Co-IP used to detect the binding of POP1 with YTH m6A RNA binding proteins. (B) Co-IP to detect the binding of YTHDF2 with POP1 and RIDA. (C) Exogenous co-IP to detect the binding of POP1, YTHDF2, and RIDA. (D) Immunofluorescence images showing subcellular localization and colocalization of POP1, RIDA, and YTHDF2. qPCR and Western blot analysis of mRNA (E) and protein (F) of CDKN1A after inhibition of m6A reading by si-YTHDF2 or si-RIDA. RIP-PCR (G) and RNA pull down (H) used to detect the binding of POP1 and CDKN1A mRNA after inhibition of m6A recognition. Welch one-way ANOVA test was used for difference analysis according to the data’s normality and homogeneity of variance. (I) Co-IP to detect the effect on the binding of POP1 with YTHDF2 when knocking down RIDA or treating with STM2457. (J) Degradation curves and half-life of CDKN1A mRNA in MDA-MB-231 and SUM159PT after inhibiting m6A recognition.
Subsequently, qPCR and Western blot results showed that knocking down YTHDF2 and RIDA could significantly up-regulate the mRNA and protein levels of CDKN1A without influencing its m6A modification in TNBC cells (Fig. 6E and F and Fig. S4G). These suggested that they may play an important role in mediating CDKN1A mRNA degradation. Further RIP-PCR and RNA pull-down experiments also showed that knocking down either YTHDF2 or RIDA significantly impaired the interaction between POP1 and CDKN1A mRNA (Fig. 6G and H). It was worth noting that whether knockdown of the ligation helper protein RIDA or using STM2457 inhibited m6A modification, the binding of POP1 with YTHDF2 was significantly reduced (Fig. 6I). Finally, knocking down YTHDF2 or RIDA to inhibit m6A recognition significantly extended the half-life of CDKN1A mRNA (Fig. 6J), just as inhibiting m6A modification. Collectively, we have demonstrated that YTHDF2 was the key reader mediating the degradation of CDKN1A mRNA in TNBC, which was consistent with the reported role of YTHDF2 in RNA decay in previous studies.
Promotion of TNBC proliferation by POP1 depends on m6A modification and recognition of CDKN1A
The effect of inhibiting m6A modification and recognition process on the binding of CDKN1A mRNA to POP1, YTHDF2, and RIDA was detected by MS2-based RNA pull down. The results suggested the necessity of both m6A modification and recognition in this regulatory process (Fig. 7A). So far, we have basically demonstrated that YTHDF2-RIDA-POP1 were key elements in degrading CDKN1A mRNA in an m6A-dependent manner. In order to determine whether the promotion of TNBC proliferation by POP1 depends on this function, we conducted necessary rescue experiments. First, by interfering with the m6A modification and recognition process with si-YTHDF2, si-RIDA, or STM2457 treatment, the up-regulation of CDKN1A in POP1-overexpressing cells was restored and the cell proliferation marker p-Rb was significantly reduced (Fig. 7B). Subsequent RNA half-life measurements showed clearly that both m6A modification and recognition were important for the reduction of CDKN1A half-life in POP1-overexpressing cells (Fig. 7C). Consistent with the expression of p-Rb in TNBC cells, inhibiting m6A modification or recognition of cells could also significantly reduce the colony formation ability and the proportion of proliferating cells in cells with high POP1 expression (Fig. 7D and E and Fig. S5A to C). The results of tumor formation experiment in nude mice showed that overexpression of CDKN1A or treatment with STM2457 in cells with high POP1 expression greatly impaired the tumor formation ability (Fig. 7F). It is obvious that restoring intracellular CDKN1A expression by different methods could block POP1-mediated high proliferation (Fig. 7G and Fig. S5D). These results were again confirmed by IHC staining of POP1, CDKN1A, and proliferating marker PCNA in successive subcutaneous tumor sections (Fig. 7H). In short, the expression of CDKN1A was a key indicator of proliferation, and POP1 promoted TNBC cell proliferation by down-regulating CDKN1A in an m6A-dependent manner.
Fig. 7.
Promotion of TNBC proliferation by POP1 depends on m6A modification and recognition of CDKN1A. (A) The MS2 pull-down experiment demonstrated the combination of POP1, YTHDF2, and RIDA with CDKN1A mRNA under si-YTHDF2, si-RIDA, and STM2457 treatment. (B) qPCR and Western blot analyses of the effects of interference with m6A modification and recognition on CDKN1A expression and Rb phosphorylation in POP1-overexpressed cells. (C) Degradation curves and half-life of CDKN1A mRNA in POP1-overexpressed cells after inhibiting m6A modification or recognition. (D) Statistical results of colony formation assay of POP1-overexpressed TNBC cells under different treatments. (E) Representative flow cytometry cell cycle profiles of POP1-overexpressed TNBC cells under different treatments. Subcutaneous tumor photos (F) and tumor volume growth curves (G) of TNBC cells in different treatment groups. Statistical analysis of tumor volume growth in different groups was conducted using 2-way repeated-measures ANOVA. (H) Representative IHC images of subcutaneous tumorstargeting POP1, CDKN1A, and PCNA. *P < 0.05, **P < 0.01, ***P < 0.001.
Clinical significance of POP1 and potential application of STM2457 in TNBC
Previous studies have reported that down-regulation of CDKN1A is not only related to the uncontrolled proliferation of cancer cells but also responsible for the resistance of some cell cycle-specific drugs such as CDK4/6 inhibitors and paclitaxel [28–30]. Here, we explored the influence of POP1 and CDKN1A expression on paclitaxel sensitivity and tried to find predictors of TNBC chemotherapy sensitivity and potential solutions for chemotherapy resistance. First, the colony formation experiment showed that POP1 overexpression could enhance the colony formation ability of TNBC cells treated with 20 μM paclitaxel, and STM2457 combined with it significantly enhanced the sensitivity of TNBC cells to paclitaxel (Fig. 8A and B). In vivo experiments show similar results. Treatment with 10 mg/kg of paclitaxel at 10 d after tumor formation and treatment with 50 mg/kg of STM2457 significantly improved paclitaxel sensitivity, which was reflected in significant differences in tumor growth curve, tumor size, and tumor weight (Fig. 8C to E). Annexin V-propidium iodide (PI) flow cytometry also showed that STM2457 distinctly reduced the survival rate of the cells under the treatment of paclitaxel (Fig. 8F and Fig. S6A). So far, our results have indicated that intervention of CDKN1A degradation by STM2457 had a promising role in the sensitizing effect of paclitaxel.
Fig. 8.
Clinical significance of POP1 and potential application of STM2457 in TNBC. (A) Colony formation pictures to show the effect of m6A inhibitor STM2457 on the response of TNBC cells to paclitaxel. (B) Statistical graph of the results of the colony formation assay. Subcutaneous tumor photos (C), tumor growth curves (D), and statistics of tumor weight (E) of TNBC cells showing the effect of STM2457 on sensitivity to paclitaxel in vivo. (F) Representative Annexin V-PI double dye flow cytometry results showing the effect of STM2457 on apoptosis of TNBC cells treated with 20 nM paclitaxel. (G) Expression of POP1 and CDKN1A mRNA in tissues of patients with paclitaxel sensitivity and resistance in the dataset GSE6434. (H) Representative IHC images of continuous sections showing the expression of POP1 and CDKN1A in patients with different response to neoadjuvant chemotherapy. (I) POP1 and CDKN1A expression in non-pathological complete response (pCR) patients compared with pCR patients (non-pCR N = 10, pCR N = 10). (J) Proportion of POP1 and CDKN1A expression in different OS and DFS outcomes. The statistics was conducted using the chi-square test. Survival curves of patients with different levels of POP1 and CDKN1A expression including OS (K) and DFS (L). Log-rank test was used for survival analysis of different groups. *P < 0.05, **P < 0.01, ***P < 0.001.
As to whether the expression of POP1 and CDKN1A played a certain role in predicting TNBC chemotherapy sensitivity and patient prognosis, we first referred to the online dataset GSE6434 [31]. It was clear that POP1 expression was higher and CDKN1A expression was lower in the tissues of 14 paclitaxel-resistant patients compared with 10 sensitive patients (Fig. 8G). IHC staining of tissue sections of patients receiving neoadjuvant chemotherapy based on paclitaxel in our hospital also showed similar results (Fig. 8H and I). We did IHC staining on the tissues of 220 patients in our cohort and divided them into 3 groups, namely, POP1High&CDKN1ALow, POP1Low&CDKN1AHigh, and Others, for survival analysis and comparison. The results showed that patients with high POP1 expression and low CDKN1A expression had the worst prognosis (Fig. 8J to L), and POP1 combined with CDKN1A predicted the prognosis more effectively than POP1 alone (Fig. S6B to G). Taken together, POP1 regulating CDKN1A degradation provided potential markers for prediction of TNBC chemotherapy response and prognosis and a potential strategy for sensitization chemotherapy. Finally, a model diagram summarized the content of this study (Fig. 9).
Fig. 9.
A model of this study showing that the YTHDF2–RIDA–POP1–CDKN1A axis promotes proliferation in TNBC.
Discussion
Uncontrolled proliferation is a common feature of all malignant tumors, and it is also one of the most important causes of organ damage and patient death. Among the 4 common molecular subtypes of breast cancer, HER2 overexpression type and TNBC have significantly greater proliferation capacity than luminal type [32,33]. This is often reflected in the IHC results of patient tissue sections; the positive rate of Ki-67, a key indicator of proliferation, is often more than 50%; and some patients can even reach 80% to 90%. A higher rate of proliferation often indicates a more dangerous condition. This group of patients often face a very rapid rate of tumor progression and early distant metastasis, which are closely associated with poor prognosis. At present, the regulatory mechanism of hyperproliferation of HER2-overexpressed breast cancer has been clarified [34]. Fortunately, the monoclonal antibodies against HER2, trastuzumab and pertuzumab, as well as the small-molecule inhibitor lapatinib, have been clinically proven to have surprising efficacy and greatly improve the prognosis of patients [35–37]. However, the key genes and mechanisms driving the proliferation of TNBC remain inadequately understood, contributing to the limited availability of effective therapeutic targets and strategies beyond chemotherapy.. Therefore, in-depth research on the regulatory mechanisms and critical genes regulating cell cycle and hyperproliferation in TNBC is crucial for improving the treatment status.
It is well known that the cell cycle is regulated by a network of genes. When cells receive external signals to initiate proliferation, expression of intracellular cyclins is up-regulated, which phosphorylates the key checkpoint gene Rb and releases E2F by activating cycle-dependent kinases (CDKs) [38,39], ultimately causing cell proliferation. Correspondingly, a class of molecules called cyclin-dependent kinase inhibitor (CKI), which negatively regulates the cell cycle, also exists in the cell. It contains Ink4, which inhibits only CDK4/6, and kinase inhibition protein (Kip), which inhibits multiple CDKs simultaneously [40,41]. CDKN1A, also known as p21, is one of the most important Kip. As the downstream gene of the tumor suppressor gene p53, it has the function of potently inhibiting the cell cycle through various pathways. This includes but is not limited to inhibiting the activity of most CDKs, and binding to PCNA, a key cofactor in DNA synthesis, and directly inhibits DNA synthesis [38,42]. CDKN1A has been shown to be down-regulated in multiple malignant tumors, with its reduced expression or inactivating mutations closely linked to tumor progression. The functional status of p53-CDKN1A is also related to the efficacy of multiple chemotherapy agents and radiotherapy [43]. When CDKN1A is inactivated or down-regulated, apoptosis or senescence of cells is inhibited, resulting in drug resistance. Moreover, in breast cancer, the loss of CDKN1A expression greatly reduces the sensitivity of cell cycle-related drugs such as CDK4/6 inhibitors and paclitaxel [42]. It is worth noting that inducing CDKN1A expression or stability by a variety of methods has shown remarkable effects in inhibiting tumor progression and enhancing the therapeutic sensitivity of tumors.
In this study, we found that POP1 is specifically highly expressed in TNBC tissues and is associated with poor prognosis, which is consistent with the conclusion of a recent study [44]. However, there are limited reports concerning the function of POP1 in mammals. A recent study showed that similar to its function in yeast, POP1 also promotes tumor progression in human tumors by keeping telomeres intact [44]. As an RBP component shared by RNase P/MRP, POP1 was reported to be involved in the posttranscriptional processing and maturation of rRNA and tRNA in previous studies. The study by Park et al. [20] offers new insights into the function of RNase P/MRP, namely, one of the readers of m6A, YTHDF2, mediates the decay of some m6A-modified RNAs via RNase P/MRP in a RIDA-dependent manner. This coincides with the enrichment analysis in this study. These evidences allow us to focus on molecules that are negatively regulated by POP1 and closely related to the cell cycle, following the confirmation of POP1’s role in promoting TNBC cell cycle and proliferation. Through a sequence of experiments, we identified and validated that CDKN1A mRNA as a substrate for POP1 degrades in an m6A-dependent manner. Moreover, we confirmed that YTHDF2, rather than other readers, plays a key recognition role in this process, which is also consistent with the previous studies that YTHDF2 often mediates the down-regulation of m6A-modified RNA. However, the details of degradation of m6A substrates based on the YTHDF2/RIDA/POP1 axis are still worthy of further exploration and research. This regulatory axis was first reported roughly by Yoon et al., and the results in this study further confirm that YTHDF2-mediated down-regulation of m6A-modified mRNA is partially dependent on RIDA [20]. Considering that the RNase P/MRP where POP1 is located is a relatively large protein complex, how YTHDF2 is guided by RIDA to bind to POP1 after recognizing m6A, and how the RNA substrate overcomes steric hindrance and fully degrades the enzyme in the case of binding numerous protein molecules, is still worthy of further investigation.
When it comes to m6A, tremendous research has been reported in recent 3 years. The dysregulation or dysfunction of m6A writers, erasers, and readers can lead to changes in the m6A modification status of RNA or changes in the downstream effects of the modification, and eventually lead to abnormal expression of related genes [11]. This is involved in the regulation of almost all hallmarks of cancer, along with influencing the efficacy of many current interventions [12,45]. Therefore, many small-molecule compounds involved in the regulation of m6A have been developed to improve the treatment status of some diseases. STM2457, a compound that reduces m6A modification by suppressing the METTL3/METTL14 complex, has shown good antitumor effects in malignant tumors including leukemia, cholangiocarcinoma, and osteosarcoma and has shown potential in reversing liver and lung cancer resistance [26,46–48]. In this study, our results also suggested that STM2457 significantly inhibits CDKN1A proliferation and increases the sensitivity to paclitaxel by restoring expression of CDKN1A in TNBC. This suggests the potential of STM2457 in improving the prognosis of TNBC patients in the future.
This study has some potential limitations. First, when we were looking for the downstream POP1, it was only realized through the analysis of public databases, and it was not verified by the corresponding sequencing means. According to previous studies, in addition to RNase P/MRP, the CCR4-NOT complex can also participate in YTHDF2-mediated RNA degradation. In this study, we could not prove that POP1 is the only component of CDKN1A-dependent m6A degradation. In addition, in the process of searching for the reader mediating CDKN1A degradation, we did not verify all readers after confirming the role of YTHDF2. Finally, due to the unavailability of commercial antibodies for IHC detection of mouse POP1, we had to abandon the constructed 4T1 cell line and use human MDA-MB-231 in vivo experiments.
In conclusion, our study revealed that POP1 is up-regulated in TNBC and promotes hyperproliferation of TNBC by degrading CDKN1A in an m6A-dependent manner. Our findings suggest the important role of POP1 in regulating TNBC proliferation and provide new prognostic markers and potential therapeutic strategies for TNBC.
Materials and Methods
Patient information and tissue specimens
Tumor tissues were from surgical excision samples of patients pathologically diagnosed in Sun Yat-sen University Cancer Center (SYSUCC) from 2001 to 2012, and were obtained from the specimen bank of SYSUCC. The frozen tissues were used for qPCR and Western blot experiments, and the paraffin-embedded samples were used for IHC. Clinical information and follow-up information were obtained from the hospital’s medical record system. We kept all personal data confidential and conducted this study in accordance with the Declaration of Helsinki. All patients signed informed consent, and this study was approved by the Ethics Committee of SYSUCC to use these clinically relevant materials.
Cell lines and cell culture
All cells including the breast epithelial cell line MCF10A, breast cancer cell lines, and 293T were obtained from the State Key Laboratory of Oncology in South China (SYSUCC, Guangzhou, China). All cell lines were cultured in the American Type Culture Collection-recommended medium (Gibco) with 10% fetal bovine serum at 37 °C and 5% CO2. Before carrying out experiments, cells were examined for mycoplasma contamination by short tandem repeat analysis using the LookOut Mycoplasma PCR Detection Kit (#MP0035, Sigma-Aldrich). All cells used in the experiments were within 5 generations from thawing of cryopreserved cells.
Establishment of stable cell lines and siRNA transfection
All stable cell lines were generated from MDA-MB-231 and SUM159PT infected with retrovirus. The open reading frame sequences of POP1 and CDKN1A inserted into the pLVX-hygro vector for overexpression and short hairpin RNA (shRNA) targeting POP1 were cloned into the pSuper-retro-hygro vector. The sequence of shRNA was provided in Table S1. Target plasmid and package plasmid were cotransfected into 293T. The virus in the medium was collected and concentrated, and then the parent cells were infected for 3 d. After screening for 250 μg/ml hygromycin for 3 d, cells were amplified. The efficiency of overexpression and knockdown was verified by qPCR and Western blot.
The synthesized small interfering RNAs (siRNAs) were used at 20 nM with Lipofectamine 2000 reagents (#11668500, Invitrogen) according to user instructions. Knockdown efficiency of mRNA or protein was collected 48 h after transfection and analyzed by qPCR and Western blot. The sequence of siRNA was described in Table S1.
RNA extraction, reverse transcription, and qPCR
RNA from different cells and tissues was extracted using the TRIzol reagent (#15596018CN, Invitrogen) according to the instructive protocol. The extracted RNA was treated with RNase-free deoxyribonuclease (DNase) and reverse-transcribed according to the instructions of the kit, and cDNA was obtained (#A0010CGQ, EZBioscience). Subsequent qPCR was performed in strict accordance with the user instructions, and gene expression level was normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (#CFX96, Bio-Rad; #A0001-R1, EZBioscience). All primers used in this study were summarized in Table S1.
Western blot
The protein was extracted according to the standard experimental protocol, denatured, and analyzed by Western blot. Protein expression level was normalized to GAPDH. All the antibodies used in this study including primary and secondary antibodies were summarized in Table S2.
Cell viability assay
Different groups of cells were implanted on 96-well plates for 48 h. CCK-8 is used to detect cell proliferation according to the instructions for use (#C0037, Beyotime). After adding CCK-8 reagent, the culture was kept away from light at 37 °C for 2 h. The absorbance of each group at 450 nm was then measured using a microplate reader (#EPOCH2, BioTek).
Immunohistochemistry
We performed IHC of paraffin-embedded human breast cancer tissues and mouse mammary fat pad subcutaneous tumors. Labeled molecules, corresponding antibodies, and dilution ratios were described in Table S2. This experiment was completed with the kit in strict accordance with the steps of the instructions (#PV6000, ZSGB-BIO). IHC staining was independently scored by 2 uninformed pathologists. Tumor cell proportions were scored as follows: 0: no positive cells, 1: <10%, 2: 10% to 35%, 3: 35% to 75%, 4: >75%. Staining intensity was graded as follows: 1: no staining, 2: weak staining (light yellow), 3: moderate staining (yellow brown), 4: strong staining (brown). The staining index (SI) was calculated by multiplying the proportion of positive cells and the staining intensity. High and low expression of POP1, CDKN1A, and proliferating cell nuclear antigen (PCNA) were defined as SI ≥ 6 or SI < 6, and log-rank test was performed accordingly to statistically compare OS and disease-free survival (DFS) of these 220 patients. All antibodies used and their dilution ratios were summarized in Table S2.
Immunofluorescence
Cells were immobilized with 4% paraformaldehyde for 30 min after phosphate-buffered saline (PBS) cleaning for 3 times. They were then treated with PBST containing 0.3%Triton X-100 and 1% bovine serum albumin (BSA) at room temperature for 30 min. The primary antibody was incubated at 4 °C overnight. The labeled cells were cleaned 3 times and incubated with fluorescent secondary antibody corresponding to the reactivity at room temperature for 1 h, and then the process of sealing and antibody incubation was repeated until POP1, YTHDF2, and RIDA were all labeled. 4′,6-Diamidino-2-phenylindole (DAPI) was used to dye the nucleus for 10 min (#HY-D0814, MedChemExpress). Cells were observed and photographed using a laser confocal microscope with 60× objective lens (#CSU-W1, Nikon). Colocalization analysis was performed using ImageJ. All antibodies used and their dilution ratios were provided in Table S2.
Flow cytometry
The cell cycle was measured using a commercially available kit (#C1052, Beyotime) and in strict accordance with instructions. The cells of different groups were grown under the same culture conditions to a logarithmic stage after digestion, fixation, washing, and staining, and then were analyzed in the 488-nm channel (#CytoFLEX, BECKMAN). The cell debris removal method was applied to all groups, and the number of eligible particles in each group was ensured to exceed 20,000. After the experiment was completed, ModFit LT software (version 4.1.7) was used for further analysis. Apoptosis of cells under different treatment conditions was detected using another kit (#A211-02, Vazyme). The cells were digested and collected 48 h after paclitaxel treatment. The experiment was completed according to the protocol of the kit, and the results were analyzed and visualized using the CytoExpert software (version 2.4.0.28).
Colony formation assay
TNBC cells in the logarithmic growth phase were trypsinized, counted, and then reseeded into 6-well plates at a density of 800 cells per well. The medium was replaced every 3 to 5 d until day 14. After fixation with 4% paraformaldehyde, the cells were stained with Giemsa for 20 min, washed, air-dried, photographed, and counted under a low-power microscope (4×). In this study, colonies containing fewer than 50 cells were excluded from the count.
Luciferase reporter assay
We performed 5′UTR, CDS, and 3′UTR luciferase reporter assays of CDKN1A mRNA to assess whether POP1 induces CDKN1A mRNA decay through its binding to the CDS region. To put it simply, 20,000 cells were inoculated in a 48-well plate in triplicate and maintained for 24 h. The reporter plasmid (100 ng) along with 1 ng of pRL-TK Renilla plasmid (#E224A, Promega) was transfected into targeted cells using Lipofectamine 3000 reagent. According to the protocol provided by the manufacturer, a dual-luciferase reporting assay kit (#E1910, Promega) was applied to detect luciferase and Renilla signals 1 d after transfection. The relative activity of luciferase is calculated as the ratio of luciferase to Renilla signal.
Protein immunoprecipitation assay
After the cell samples in the medium were collected and washed with PBS, the lysis buffer (150 mM NaCl, 10 mM HEPES, 1% NP-40, pH 7.4) was used to prepare the cell lysis solution on ice. The lysates were incubated with anti-POP1 antibody (#12029-1-AP, Proteintech) and protein G-conjugated magnetic beads (#HY-K0202, MedChemExpress) or anti-Flag magnetic beads (#HY-K0207, MedChemExpress) at 4 °C overnight. The rubber beads containing the affinity antibody binding protein were then washed with washing buffer (150 mM NaCl, 10 mM HEPES, 0.1% NP-40, pH 7.4) for 6 times and then eluted with 1 M glycine solution (pH 3.0). The eluent was detected by Western blot.
RNA immunoprecipitation assay
RIP was used to detect direct interactions between CDKN1A mRNA and POP1 protein. Cells (1 × 107) were digested, collected, and then fully decomposed in lysate (20 nM tris–HCl, pH 8.0, 10 mM NaCl, 1 nM EDTA, 0.5% NP-40) with 1× protease inhibitor (#G6521, Promega) and RNasin (#N2111, Promega). Cell lysis (50 μl) was cryopreserved as input. Other lysis was then incubated with anti-POP1 antibody (#12029-1-AP, Proteintech) and protein G-conjugated magnetic beads (#HY-K0202, MedChemExpress) or anti-Flag magnetic beads (#HY-K0207, MedChemExpress) at 4 °C overnight. After washing for 5 times with precooled RIP wash buffer (1× PBS, 0.1% SDS, 0.5% Nonidet P40), the Elution Buffer [5 mM tris–HCl, pH 7.5, 1 mM EDTA, pH 8.0, 0.05% SDS, 20 mg/ml proteinase K (#HY-108717, MedChemExpress)] was used to eluate the coprecipitated RNAs. RNA was extracted by TRIzol Reagent (#15596018CN, Invitrogen) and analyzed by qPCR. All primers used in this study were summarized in Table S1.
RNA synthesis and pull down
Full-length and truncated CDKN1A were transcribed in vitro with T7 or SP6 RNA polymerase from templates amplified with T7 forward 5′-TAATACGACTCACTATAG-3′ and SP6 reverse 5′-ATTTAGGTGACACTATAG-3′ primers (#D2314, Beyotime). The obtained RNA fragments were then purified and labeled with biotin according to the Biotin RNA Labeling kit instructions (#R7061M, Beyotime). Then, the RNA pull-down experiment was performed. To put it simply, 1 × 107 cells were lysed with 1 ml of buffer [50 nM tris–HCl, pH 7.9, 10% glycerol, 100 mM KCl, 5 mM MgCl2, 10 nM β-mercaptoethanol, 0.1% NP-40, 1 mM phenylmethylsulfonyl fluoride (#ST505, Beyotime), 1× Superase-in (#AM2694, Invitrogen), 1× protase inhibitor (#G6521, Promega), 10 mM KCl]. Labeled RNA (100 pmol) was incubated with cell lysate at room temperature for 1 h. Then, each group was incubated with 100 μl of cleaned streptavidin magnetic beads (#HY-K0208, Beyotime) and the reaction products above for 1 h. The treated beads were washed 4 to 5 times, and then the eluent was used to obtain the protein and subject to Western blot analysis.
Methylated RNA immunoprecipitation
Total RNA from different samples was extracted using the TRIzol reagent (#15596018CN, Invitrogen) according to the manufacturer’s instructions. Then, the MeRIP assay was performed using Magna MeRIP m6A Kit (#17-10499, Millipore). The obtained purified RNA was decomposed into about 100-nucleotide-long oligonucleotides in the Fragmentation Buffer. After the fragment was finished, the lysate and antibodies anti-m6A (#ab208577, Abcam) were incubated overnight at 4 °C. These fragments were then reverse-transcribed before the 3′ adapters were ligated to the final cDNA. Only those fragments containing m6A modification were incorporated in the reverse-transcribed, which could be further detected by qPCR. All primers used in this study were summarized in Table S1.
MS2 pull down
MS2bp-MS2bs-based pull-down assay was carried out to detect the direct interaction between the mRNA of CDKN1A and different proteins including POP1, YTHDF2, and RIDA. In short, pcDNA3-Flag-MS2bp and pcDNA3-12× MS2bs coupled with target RNAs or pcDNA3-12× MS2bs mock were transfected together into 5 × 106 293T cells. After 48 h, the cells were digested and then lysed in the lysis buffer (20 mM tris–Cl, pH 8.0, 0.5% NP-40, 1 mM EDTA, 10 mM NaCl) added with RNasin (#N2111, Promega). The obtained lysates were incubated with anti-Flag M2 affinity gel (#A2220, Sigma-Aldrich) and washed 5 times with the lysis buffer. The obtained proteins were used for Western blotting analysis.
RNA stability assay
TNBC cells were treated with 5 μg/ml Act-D (#HY-17559, MedChemExpress) to inhibit gene transcription for all times. Then, RNA was extracted and quantified by qPCR at 0, 1, 2, and 3 h after treatment (normalized to the GAPDH expression). Subsequently, according to the formula T1/2 = ln2/kdecay adopted by Huang et al. [49], CDKN1A mRNA degradation curves of each group were fitted and the half-life of each group was calculated separately.
Xenograft tumor model
Female BALB/c nude mice (4 to 5 weeks of age) were raised in a 12-hour light–dark cycle specific pathogen-free barrier facility. They were randomly grouped according to the experimental grouping requirements and fed under the same conditions. All procedures involving animals were conducted in line with the Guide for the Care and Use of Laboratory Animals and the institutional ethical guidelines of Sun Yat-sen University for animal experiments. All animal experiments were approved by the Ethics Committee of SYSUCC. In the experiment of subcutaneous tumor formation, MDA-MB-231 cells in each group were inoculated into the subcutaneous area of the mammary gland of mice (1 × 106 each). The tumor size was measured with vernier calipers every 5 d, and the tumor volume was calculated. After 5 weeks, they were killed and tumor tissue was harvested. After the samples were photographed and weighed, they were fixed and paraffin embedded, tissue sections were prepared, and hematoxylin and eosin (H&E) and IHC staining were performed.
Statistical analysis
We used R software (version 4.2.1) and GraphPad Prism (version 9.5.0) for statistical analysis and visualization of statistical results. Statistical analysis was conducted by 2-tailed Student’s t tests, one-way analysis of variance (ANOVA), Welch one-way ANOVA test, 2-way ANOVA test, log-rank, chi-square, and other methods when appropriate. Some statistical methods were indicated in the corresponding positions in the figure legend. Cox regression model was used for univariate and multivariate statistical analysis. P < 0.05 was considered statistically significant. Significant differences were shown by *P < 0.05, **P < 0.01, ***P < 0.001, and ns, not significant.
Patient information and tissue specimens
Tumor tissues were from surgical excision samples of patients pathologically diagnosed in Sun Yat-sen University Cancer Center (SYSUCC) from 2001 to 2012, and were obtained from the specimen bank of SYSUCC. The frozen tissues were used for qPCR and Western blot experiments, and the paraffin-embedded samples were used for IHC. Clinical information and follow-up information were obtained from the hospital’s medical record system. We kept all personal data confidential and conducted this study in accordance with the Declaration of Helsinki. All patients signed informed consent, and this study was approved by the Ethics Committee of SYSUCC to use these clinically relevant materials.
Cell lines and cell culture
All cells including the breast epithelial cell line MCF10A, breast cancer cell lines, and 293T were obtained from the State Key Laboratory of Oncology in South China (SYSUCC, Guangzhou, China). All cell lines were cultured in the American Type Culture Collection-recommended medium (Gibco) with 10% fetal bovine serum at 37 °C and 5% CO2. Before carrying out experiments, cells were examined for mycoplasma contamination by short tandem repeat analysis using the LookOut Mycoplasma PCR Detection Kit (#MP0035, Sigma-Aldrich). All cells used in the experiments were within 5 generations from thawing of cryopreserved cells.
Establishment of stable cell lines and siRNA transfection
All stable cell lines were generated from MDA-MB-231 and SUM159PT infected with retrovirus. The open reading frame sequences of POP1 and CDKN1A inserted into the pLVX-hygro vector for overexpression and short hairpin RNA (shRNA) targeting POP1 were cloned into the pSuper-retro-hygro vector. The sequence of shRNA was provided in Table S1. Target plasmid and package plasmid were cotransfected into 293T. The virus in the medium was collected and concentrated, and then the parent cells were infected for 3 d. After screening for 250 μg/ml hygromycin for 3 d, cells were amplified. The efficiency of overexpression and knockdown was verified by qPCR and Western blot.
The synthesized small interfering RNAs (siRNAs) were used at 20 nM with Lipofectamine 2000 reagents (#11668500, Invitrogen) according to user instructions. Knockdown efficiency of mRNA or protein was collected 48 h after transfection and analyzed by qPCR and Western blot. The sequence of siRNA was described in Table S1.
RNA extraction, reverse transcription, and qPCR
RNA from different cells and tissues was extracted using the TRIzol reagent (#15596018CN, Invitrogen) according to the instructive protocol. The extracted RNA was treated with RNase-free deoxyribonuclease (DNase) and reverse-transcribed according to the instructions of the kit, and cDNA was obtained (#A0010CGQ, EZBioscience). Subsequent qPCR was performed in strict accordance with the user instructions, and gene expression level was normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (#CFX96, Bio-Rad; #A0001-R1, EZBioscience). All primers used in this study were summarized in Table S1.
Western blot
The protein was extracted according to the standard experimental protocol, denatured, and analyzed by Western blot. Protein expression level was normalized to GAPDH. All the antibodies used in this study including primary and secondary antibodies were summarized in Table S2.
Cell viability assay
Different groups of cells were implanted on 96-well plates for 48 h. CCK-8 is used to detect cell proliferation according to the instructions for use (#C0037, Beyotime). After adding CCK-8 reagent, the culture was kept away from light at 37 °C for 2 h. The absorbance of each group at 450 nm was then measured using a microplate reader (#EPOCH2, BioTek).
Immunohistochemistry
We performed IHC of paraffin-embedded human breast cancer tissues and mouse mammary fat pad subcutaneous tumors. Labeled molecules, corresponding antibodies, and dilution ratios were described in Table S2. This experiment was completed with the kit in strict accordance with the steps of the instructions (#PV6000, ZSGB-BIO). IHC staining was independently scored by 2 uninformed pathologists. Tumor cell proportions were scored as follows: 0: no positive cells, 1: <10%, 2: 10% to 35%, 3: 35% to 75%, 4: >75%. Staining intensity was graded as follows: 1: no staining, 2: weak staining (light yellow), 3: moderate staining (yellow brown), 4: strong staining (brown). The staining index (SI) was calculated by multiplying the proportion of positive cells and the staining intensity. High and low expression of POP1, CDKN1A, and proliferating cell nuclear antigen (PCNA) were defined as SI ≥ 6 or SI < 6, and log-rank test was performed accordingly to statistically compare OS and disease-free survival (DFS) of these 220 patients. All antibodies used and their dilution ratios were summarized in Table S2.
Immunofluorescence
Cells were immobilized with 4% paraformaldehyde for 30 min after phosphate-buffered saline (PBS) cleaning for 3 times. They were then treated with PBST containing 0.3%Triton X-100 and 1% bovine serum albumin (BSA) at room temperature for 30 min. The primary antibody was incubated at 4 °C overnight. The labeled cells were cleaned 3 times and incubated with fluorescent secondary antibody corresponding to the reactivity at room temperature for 1 h, and then the process of sealing and antibody incubation was repeated until POP1, YTHDF2, and RIDA were all labeled. 4′,6-Diamidino-2-phenylindole (DAPI) was used to dye the nucleus for 10 min (#HY-D0814, MedChemExpress). Cells were observed and photographed using a laser confocal microscope with 60× objective lens (#CSU-W1, Nikon). Colocalization analysis was performed using ImageJ. All antibodies used and their dilution ratios were provided in Table S2.
Flow cytometry
The cell cycle was measured using a commercially available kit (#C1052, Beyotime) and in strict accordance with instructions. The cells of different groups were grown under the same culture conditions to a logarithmic stage after digestion, fixation, washing, and staining, and then were analyzed in the 488-nm channel (#CytoFLEX, BECKMAN). The cell debris removal method was applied to all groups, and the number of eligible particles in each group was ensured to exceed 20,000. After the experiment was completed, ModFit LT software (version 4.1.7) was used for further analysis. Apoptosis of cells under different treatment conditions was detected using another kit (#A211-02, Vazyme). The cells were digested and collected 48 h after paclitaxel treatment. The experiment was completed according to the protocol of the kit, and the results were analyzed and visualized using the CytoExpert software (version 2.4.0.28).
Colony formation assay
TNBC cells in the logarithmic growth phase were trypsinized, counted, and then reseeded into 6-well plates at a density of 800 cells per well. The medium was replaced every 3 to 5 d until day 14. After fixation with 4% paraformaldehyde, the cells were stained with Giemsa for 20 min, washed, air-dried, photographed, and counted under a low-power microscope (4×). In this study, colonies containing fewer than 50 cells were excluded from the count.
Luciferase reporter assay
We performed 5′UTR, CDS, and 3′UTR luciferase reporter assays of CDKN1A mRNA to assess whether POP1 induces CDKN1A mRNA decay through its binding to the CDS region. To put it simply, 20,000 cells were inoculated in a 48-well plate in triplicate and maintained for 24 h. The reporter plasmid (100 ng) along with 1 ng of pRL-TK Renilla plasmid (#E224A, Promega) was transfected into targeted cells using Lipofectamine 3000 reagent. According to the protocol provided by the manufacturer, a dual-luciferase reporting assay kit (#E1910, Promega) was applied to detect luciferase and Renilla signals 1 d after transfection. The relative activity of luciferase is calculated as the ratio of luciferase to Renilla signal.
Protein immunoprecipitation assay
After the cell samples in the medium were collected and washed with PBS, the lysis buffer (150 mM NaCl, 10 mM HEPES, 1% NP-40, pH 7.4) was used to prepare the cell lysis solution on ice. The lysates were incubated with anti-POP1 antibody (#12029-1-AP, Proteintech) and protein G-conjugated magnetic beads (#HY-K0202, MedChemExpress) or anti-Flag magnetic beads (#HY-K0207, MedChemExpress) at 4 °C overnight. The rubber beads containing the affinity antibody binding protein were then washed with washing buffer (150 mM NaCl, 10 mM HEPES, 0.1% NP-40, pH 7.4) for 6 times and then eluted with 1 M glycine solution (pH 3.0). The eluent was detected by Western blot.
RNA immunoprecipitation assay
RIP was used to detect direct interactions between CDKN1A mRNA and POP1 protein. Cells (1 × 107) were digested, collected, and then fully decomposed in lysate (20 nM tris–HCl, pH 8.0, 10 mM NaCl, 1 nM EDTA, 0.5% NP-40) with 1× protease inhibitor (#G6521, Promega) and RNasin (#N2111, Promega). Cell lysis (50 μl) was cryopreserved as input. Other lysis was then incubated with anti-POP1 antibody (#12029-1-AP, Proteintech) and protein G-conjugated magnetic beads (#HY-K0202, MedChemExpress) or anti-Flag magnetic beads (#HY-K0207, MedChemExpress) at 4 °C overnight. After washing for 5 times with precooled RIP wash buffer (1× PBS, 0.1% SDS, 0.5% Nonidet P40), the Elution Buffer [5 mM tris–HCl, pH 7.5, 1 mM EDTA, pH 8.0, 0.05% SDS, 20 mg/ml proteinase K (#HY-108717, MedChemExpress)] was used to eluate the coprecipitated RNAs. RNA was extracted by TRIzol Reagent (#15596018CN, Invitrogen) and analyzed by qPCR. All primers used in this study were summarized in Table S1.
RNA synthesis and pull down
Full-length and truncated CDKN1A were transcribed in vitro with T7 or SP6 RNA polymerase from templates amplified with T7 forward 5′-TAATACGACTCACTATAG-3′ and SP6 reverse 5′-ATTTAGGTGACACTATAG-3′ primers (#D2314, Beyotime). The obtained RNA fragments were then purified and labeled with biotin according to the Biotin RNA Labeling kit instructions (#R7061M, Beyotime). Then, the RNA pull-down experiment was performed. To put it simply, 1 × 107 cells were lysed with 1 ml of buffer [50 nM tris–HCl, pH 7.9, 10% glycerol, 100 mM KCl, 5 mM MgCl2, 10 nM β-mercaptoethanol, 0.1% NP-40, 1 mM phenylmethylsulfonyl fluoride (#ST505, Beyotime), 1× Superase-in (#AM2694, Invitrogen), 1× protase inhibitor (#G6521, Promega), 10 mM KCl]. Labeled RNA (100 pmol) was incubated with cell lysate at room temperature for 1 h. Then, each group was incubated with 100 μl of cleaned streptavidin magnetic beads (#HY-K0208, Beyotime) and the reaction products above for 1 h. The treated beads were washed 4 to 5 times, and then the eluent was used to obtain the protein and subject to Western blot analysis.
Methylated RNA immunoprecipitation
Total RNA from different samples was extracted using the TRIzol reagent (#15596018CN, Invitrogen) according to the manufacturer’s instructions. Then, the MeRIP assay was performed using Magna MeRIP m6A Kit (#17-10499, Millipore). The obtained purified RNA was decomposed into about 100-nucleotide-long oligonucleotides in the Fragmentation Buffer. After the fragment was finished, the lysate and antibodies anti-m6A (#ab208577, Abcam) were incubated overnight at 4 °C. These fragments were then reverse-transcribed before the 3′ adapters were ligated to the final cDNA. Only those fragments containing m6A modification were incorporated in the reverse-transcribed, which could be further detected by qPCR. All primers used in this study were summarized in Table S1.
MS2 pull down
MS2bp-MS2bs-based pull-down assay was carried out to detect the direct interaction between the mRNA of CDKN1A and different proteins including POP1, YTHDF2, and RIDA. In short, pcDNA3-Flag-MS2bp and pcDNA3-12× MS2bs coupled with target RNAs or pcDNA3-12× MS2bs mock were transfected together into 5 × 106 293T cells. After 48 h, the cells were digested and then lysed in the lysis buffer (20 mM tris–Cl, pH 8.0, 0.5% NP-40, 1 mM EDTA, 10 mM NaCl) added with RNasin (#N2111, Promega). The obtained lysates were incubated with anti-Flag M2 affinity gel (#A2220, Sigma-Aldrich) and washed 5 times with the lysis buffer. The obtained proteins were used for Western blotting analysis.
RNA stability assay
TNBC cells were treated with 5 μg/ml Act-D (#HY-17559, MedChemExpress) to inhibit gene transcription for all times. Then, RNA was extracted and quantified by qPCR at 0, 1, 2, and 3 h after treatment (normalized to the GAPDH expression). Subsequently, according to the formula T1/2 = ln2/kdecay adopted by Huang et al. [49], CDKN1A mRNA degradation curves of each group were fitted and the half-life of each group was calculated separately.
Xenograft tumor model
Female BALB/c nude mice (4 to 5 weeks of age) were raised in a 12-hour light–dark cycle specific pathogen-free barrier facility. They were randomly grouped according to the experimental grouping requirements and fed under the same conditions. All procedures involving animals were conducted in line with the Guide for the Care and Use of Laboratory Animals and the institutional ethical guidelines of Sun Yat-sen University for animal experiments. All animal experiments were approved by the Ethics Committee of SYSUCC. In the experiment of subcutaneous tumor formation, MDA-MB-231 cells in each group were inoculated into the subcutaneous area of the mammary gland of mice (1 × 106 each). The tumor size was measured with vernier calipers every 5 d, and the tumor volume was calculated. After 5 weeks, they were killed and tumor tissue was harvested. After the samples were photographed and weighed, they were fixed and paraffin embedded, tissue sections were prepared, and hematoxylin and eosin (H&E) and IHC staining were performed.
Statistical analysis
We used R software (version 4.2.1) and GraphPad Prism (version 9.5.0) for statistical analysis and visualization of statistical results. Statistical analysis was conducted by 2-tailed Student’s t tests, one-way analysis of variance (ANOVA), Welch one-way ANOVA test, 2-way ANOVA test, log-rank, chi-square, and other methods when appropriate. Some statistical methods were indicated in the corresponding positions in the figure legend. Cox regression model was used for univariate and multivariate statistical analysis. P < 0.05 was considered statistically significant. Significant differences were shown by *P < 0.05, **P < 0.01, ***P < 0.001, and ns, not significant. |
Title: Evaluation of Turnaround Times for Antigen Testing in Hospitalized Patients With Histoplasmosis and Blastomycosis | Body:
METHODS
Patients
This was a retrospective observational study of adult patients who were hospitalized at Michigan Medicine, a 1000-bed tertiary care hospital in southeastern Michigan, and who had a positive Histoplasma or Blastomyces urine or serum antigen EIA (Miravista Diagnostics, Indianapolis, Indiana) obtained from 1 January 2015 to 31 December 2022. Patients were identified using DataDirect, a clinical data repository at Michigan Medicine [4]. This study was approved by the University of Michigan Institutional Review Board; informed consent was deferred for this retrospective study.
Only patients in whom the antigen test was the modality used to confirm the probable diagnosis of histoplasmosis or blastomycosis were included; patients whose diagnosis was obtained by other methods, who were transferred from an outside hospital after diagnosis was confirmed, or who were treated empirically were excluded. Only patients who received antifungal therapy or who died before treatment could begin and were shown to have histoplasmosis or blastomycosis at autopsy were included. Severe pulmonary infection was defined as hypoxia with need for oxygen supplementation and/or mechanical ventilation. Severe disseminated infection was defined as hemodynamic instability, with or without hypoxia or central nervous system involvement.
Data Collection
Medical records were reviewed to obtain demographic information, treatment, time from hospital admission to antigen sample collection, time from admission to antigen test result, and time from sample collection to antigen test result. Outcomes included intensive care unit (ICU) transfer, length of stay, hospital readmission within 30 days of discharge, and survival in the 60 days after diagnosis. All data were stored in a secure REDCap (Research Electronic Data Capture) database for further analysis [5].
Definitions (Figure 1)
Figure 1.
Definitions of turnaround times. Abbreviations: TAT-AC, turnaround time from admission to collection of specimen; TAT-AR, turnaround time from admission to result; TAT-CR, turnaround time from collection of specimen to result.
Turnaround time from admission to collection (TAT-AC): interval between hospital admission and collection of a specimen that yielded the earliest report of a positive antigen result supporting the diagnosis of histoplasmosis or blastomycosis.
Turnaround time from admission to result (TAT-AR): interval between hospital admission and the earliest report of a positive antigen result supporting the diagnosis of histoplasmosis or blastomycosis.
Turnaround time from collection to result (TAT-CR): interval between collection of the antigen specimen and earliest report of a positive antigen result supporting the diagnosis of histoplasmosis or blastomycosis.
Statistical Analysis
Wilcoxon rank-sum test was used to determine differences between surviving and deceased patients for TAT-AR, TAT-AC, and TAT-CR. Results are reported as median days (interquartile range [IQR]). Statistical significance was defined as P < .05; analyses were completed using SAS software, version 9.4 (SAS Institute, Cary, North Carolina) and R software, version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria).
Patients
This was a retrospective observational study of adult patients who were hospitalized at Michigan Medicine, a 1000-bed tertiary care hospital in southeastern Michigan, and who had a positive Histoplasma or Blastomyces urine or serum antigen EIA (Miravista Diagnostics, Indianapolis, Indiana) obtained from 1 January 2015 to 31 December 2022. Patients were identified using DataDirect, a clinical data repository at Michigan Medicine [4]. This study was approved by the University of Michigan Institutional Review Board; informed consent was deferred for this retrospective study.
Only patients in whom the antigen test was the modality used to confirm the probable diagnosis of histoplasmosis or blastomycosis were included; patients whose diagnosis was obtained by other methods, who were transferred from an outside hospital after diagnosis was confirmed, or who were treated empirically were excluded. Only patients who received antifungal therapy or who died before treatment could begin and were shown to have histoplasmosis or blastomycosis at autopsy were included. Severe pulmonary infection was defined as hypoxia with need for oxygen supplementation and/or mechanical ventilation. Severe disseminated infection was defined as hemodynamic instability, with or without hypoxia or central nervous system involvement.
Data Collection
Medical records were reviewed to obtain demographic information, treatment, time from hospital admission to antigen sample collection, time from admission to antigen test result, and time from sample collection to antigen test result. Outcomes included intensive care unit (ICU) transfer, length of stay, hospital readmission within 30 days of discharge, and survival in the 60 days after diagnosis. All data were stored in a secure REDCap (Research Electronic Data Capture) database for further analysis [5].
Definitions (Figure 1)
Figure 1.
Definitions of turnaround times. Abbreviations: TAT-AC, turnaround time from admission to collection of specimen; TAT-AR, turnaround time from admission to result; TAT-CR, turnaround time from collection of specimen to result.
Turnaround time from admission to collection (TAT-AC): interval between hospital admission and collection of a specimen that yielded the earliest report of a positive antigen result supporting the diagnosis of histoplasmosis or blastomycosis.
Turnaround time from admission to result (TAT-AR): interval between hospital admission and the earliest report of a positive antigen result supporting the diagnosis of histoplasmosis or blastomycosis.
Turnaround time from collection to result (TAT-CR): interval between collection of the antigen specimen and earliest report of a positive antigen result supporting the diagnosis of histoplasmosis or blastomycosis.
Statistical Analysis
Wilcoxon rank-sum test was used to determine differences between surviving and deceased patients for TAT-AR, TAT-AC, and TAT-CR. Results are reported as median days (interquartile range [IQR]). Statistical significance was defined as P < .05; analyses were completed using SAS software, version 9.4 (SAS Institute, Cary, North Carolina) and R software, version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
Demographics
A total of 39 patients met the study criteria. Mean age was 47 ± 19 years, and 26 (67%) were men; 33 (85%) were immunocompromised, including 15 who had received a solid organ transplant and 7 taking tumor necrosis factor alpha inhibitors for autoimmune diseases. Thirty-four patients had histoplasmosis; 4 of 7 with pulmonary histoplasmosis had severe disease. All 27 patients with disseminated histoplasmosis had severe disease. Five patients had blastomycosis (1 pulmonary and 4 disseminated disease); 1 patient with disseminated blastomycosis had severe disease. Nine patients (23%) died within 60 days of diagnosis, including 8 with disseminated histoplasmosis and 1 with disseminated blastomycosis.
Turnaround Times
For all 39 patients, the median days (IQR) for TAT-AC, TAT-AR, and TAT-CR were 1 (1–2.5), 6 (4–7.5), and 4 (3–5), respectively. When 30 surviving patients were compared with 9 patients who died, TAT-AC was not significantly different. Both TAT-AR and TAT-CR were significantly less for the 30 survivors compared with the 9 patients who died (P = .03 and P = .005, respectively; Table 1).
Table 1.
Association of Turnaround Times for Antigen Testing With Survival at 60 Days After the Diagnosis of Histoplasmosis or Blastomycosis in Hospitalized Patients
Cohort
Measure
Survived (n = 30)
Died (n = 9)
P Value
All patients (N = 39)
TAT-AC
1 (1–2)
1 (1–12)
.35
TAT-AR
5 (4–7)
7 (5–17)
.03
TAT-CR
4 (3–5)
5 (4–7)
.005
Patients with severe disease (n = 32)
Survived (n = 23)
Died (n = 9)
TAT-AC
1 (1–2)
1 (1–12)
.48
TAT-AR
5 (4–7)
7 (5–17)
.03
TAT-CR
4 (3–5)
5 (4–7)
.004
Data are presented as median days (interquartile range).
Abbreviations: TAT-AC, turnaround time from admission to collection of specimen; TAT-AR, turnaround time from admission to result; TAT-CR, turnaround time from collection of specimen to result.
Among 32 patients who had severe disease and who required intravenous amphotericin B (n = 29) or who died before any antifungal therapy was given (n = 3), TAT-AC for survivors and deceased patients did not differ. Both TAT-AR and TAT-CR were significantly less for survivors than for those who died (P = .03 and P = .004, respectively; Table 1).
Comparing 8 patients who had only pulmonary disease with 31 patients who had disseminated disease, no significant differences were found in TAT-AC (P = .56), TAT-AR (P = .44), or TAT-CR (P = .16). ICU transfer, length of stay, and 30-day readmission were not significantly associated with TAT-AC, TAT-AR, or TAT-CR in the overall cohort or the severe disease cohort.
Demographics
A total of 39 patients met the study criteria. Mean age was 47 ± 19 years, and 26 (67%) were men; 33 (85%) were immunocompromised, including 15 who had received a solid organ transplant and 7 taking tumor necrosis factor alpha inhibitors for autoimmune diseases. Thirty-four patients had histoplasmosis; 4 of 7 with pulmonary histoplasmosis had severe disease. All 27 patients with disseminated histoplasmosis had severe disease. Five patients had blastomycosis (1 pulmonary and 4 disseminated disease); 1 patient with disseminated blastomycosis had severe disease. Nine patients (23%) died within 60 days of diagnosis, including 8 with disseminated histoplasmosis and 1 with disseminated blastomycosis.
Turnaround Times
For all 39 patients, the median days (IQR) for TAT-AC, TAT-AR, and TAT-CR were 1 (1–2.5), 6 (4–7.5), and 4 (3–5), respectively. When 30 surviving patients were compared with 9 patients who died, TAT-AC was not significantly different. Both TAT-AR and TAT-CR were significantly less for the 30 survivors compared with the 9 patients who died (P = .03 and P = .005, respectively; Table 1).
Table 1.
Association of Turnaround Times for Antigen Testing With Survival at 60 Days After the Diagnosis of Histoplasmosis or Blastomycosis in Hospitalized Patients
Cohort
Measure
Survived (n = 30)
Died (n = 9)
P Value
All patients (N = 39)
TAT-AC
1 (1–2)
1 (1–12)
.35
TAT-AR
5 (4–7)
7 (5–17)
.03
TAT-CR
4 (3–5)
5 (4–7)
.005
Patients with severe disease (n = 32)
Survived (n = 23)
Died (n = 9)
TAT-AC
1 (1–2)
1 (1–12)
.48
TAT-AR
5 (4–7)
7 (5–17)
.03
TAT-CR
4 (3–5)
5 (4–7)
.004
Data are presented as median days (interquartile range).
Abbreviations: TAT-AC, turnaround time from admission to collection of specimen; TAT-AR, turnaround time from admission to result; TAT-CR, turnaround time from collection of specimen to result.
Among 32 patients who had severe disease and who required intravenous amphotericin B (n = 29) or who died before any antifungal therapy was given (n = 3), TAT-AC for survivors and deceased patients did not differ. Both TAT-AR and TAT-CR were significantly less for survivors than for those who died (P = .03 and P = .004, respectively; Table 1).
Comparing 8 patients who had only pulmonary disease with 31 patients who had disseminated disease, no significant differences were found in TAT-AC (P = .56), TAT-AR (P = .44), or TAT-CR (P = .16). ICU transfer, length of stay, and 30-day readmission were not significantly associated with TAT-AC, TAT-AR, or TAT-CR in the overall cohort or the severe disease cohort.
DISCUSSION
Antigen test TAT represents a single facet of the overall process of diagnosis of histoplasmosis and blastomycosis. Studies focusing on histoplasmosis have found that patients often experience multiple missed opportunities for diagnosis. Patients frequently see providers in the weeks prior to their diagnosis, but at these visits the endemic mycoses are rarely considered [6, 7]. The most frequently cited reasons for delayed diagnosis are failure to consider endemic mycoses due to their rarity and similar presentation to other diseases [3]. One study estimated that 82.9% of patients experienced at least 1 delay in diagnosis [7]. Our study demonstrates that even after the diagnosis is suspected, there remain obstacles to the timely establishment of the diagnosis, which may have an impact on patient outcomes.
Our study shows that delays in receiving results of a positive antigen test were associated with increased mortality in hospitalized patients requiring antifungal treatment for histoplasmosis or blastomycosis. These findings highlight the importance of rapid notification of test results to guide treatment for hospitalized patients with these diseases. It should be noted that this association does not necessarily imply a causal relationship between TAT and poor outcomes. Increased TAT could be associated with other logistical factors affecting the delivery of patient care, such as staffing shortages, for instance, which could themselves contribute to poor outcomes.
Considering these findings, providers should work closely with service units that process clinical specimens, as well as with designated reference laboratories to ensure that TAT is as short as possible. Potential changes to specimen processing could include ensuring different handling of antigen tests ordered in the inpatient and outpatient setting. TAT in the former can be a rate-limiting step in initiating care for ill patients, whereas outpatient testing can typically accommodate longer TAT without concern for adverse patient outcomes. Integrating electronic medical record systems directly with the notification systems of reference laboratories also could improve TAT. Reviewing the logistics of send-out tests during fringe times, such as late at night, weekends, and holidays, may help to identify potential areas for improvement. At many institutions, including ours, certain specialized tests are first sent to an outside institution before being routed to the specific laboratory performing the assay, introducing additional time before sample processing; eliminating this step would decrease TAT. In recent years, rapid point-of-care tests utilizing lateral flow techniques for the diagnosis of histoplasmosis have been developed and tested in resource-limited settings [8–10]. More studies are needed to determine their role in the diagnosis of histoplasmosis in different settings and populations. If proved to be as sensitive as the current EIA, these tests would obviate the issues we noted with TAT.
Limitations of our study are that it is from a single center, is a retrospective review, and sample size is small. Further study with larger sample sizes could investigate relationships that our study was underpowered to capture. Because logistical factors that determine how samples are processed likely differ from institution to institution, further study at other institutions could provide insights into disparate reasons for increased TAT. Many factors contribute to the mortality of patients hospitalized with histoplasmosis and blastomycosis [1]; this study was not designed to evaluate these factors, but rather focused only on comparing TAT in patients with different outcomes.
Clinicians should maintain a high index of suspicion for histoplasmosis and blastomycoses to avoid delays in diagnosis, but also must consider logistical factors at their institution that may impact their ability to provide care for their patients. Raising awareness of the importance of expeditious specimen shipping and prompt reporting of Histoplasma and Blastomyces antigen testing may help to optimize patient care. |
Title: The Successful Treatment of Multi-Resistant Colonized Burns with Large-Area Atmospheric Cold Plasma Therapy and Dermis Substitute Matrix—A Case Report | Body:
1. Introduction
In recent years, the centuries-old technology of atmospheric cold plasma (ACPT) has once again come into focus for wound therapy [1]. The technical background of ACPT involves understanding the principles of plasma generation, the properties of ACPT and its interactions with biological tissues. The basics of non-thermal cold plasma were first described by William Crookes in 1879 by experimentally ionizing gas in an electrical discharge tube by the application of high voltage, but the current term of plasma was initiated as late as 1927 by Irvin Langmuir [1,2]. Plasma is often referred to as the fourth state of matter, consisting of a partially ionized gas with equal numbers of positive ions and free electrons [3]. To generate plasma, energy is applied to a gas, causing ionization [4]. There are several methods to generate plasma, including dielectric barrier discharge, corona discharge and radio frequency discharge [5,6]. ACPT is a safe and efficient therapeutic tool in clinical practice, as shown by various preclinical and clinical studies [7]. Current applications in medicine range from the treatment of chronic wounds or skin infections to targeted cancer therapy in oncology and various applications in dentistry [7,8]. The therapeutic effects of ACPT are primarily due to the interaction of reactive oxygen and nitrogen species (RONS) with cells and tissues [9,10]. These interactions include antimicrobial action, topical cancer therapy and wound healing. However, regarding burn wound care, the ACPT data are limited to isolated case reports or series, with no study covering the topic of war burns.
In the context of current armed conflicts, burn surgery is experiencing a renaissance with a significant increase in patients [11]. As part of the broader humanitarian support, German hospitals have been treating Ukrainian soldiers wounded in the conflict with Russia, offering advanced burn care and rehabilitation [12]. War invalids from Ukraine are transferred to Germany according to a fixed distribution system, known as the cloverleaf system [13]. The treatment of severe burn injuries occurs in specialized burn centers equipped with highly advanced technology and personnel [14]. Soldiers often arrive with delays after passing through various care points, starting with emergency care in frontline field hospitals, followed by treatment in other facilities, and finally being airlifted to Germany.
Thus, we treat not fresh burns, which are typically sterile, but wounds already contaminated with bacteria. These infections often involve problematic or hospital-acquired pathogens like Acinetobacter and Pseudomonas spp., which exhibit high levels of antibiotic resistance [15]. This poses a significant challenge, as we frequently encounter cases where even reserve antibiotics fail. Our approach to managing these complex wound situations necessitates a multimodal therapy concept beyond the classical wound debridement and split-thickness skin grafting [16]. While these remain the foundation, we incorporate advanced treatments to address the specific needs of our patients. NovoSorb® Biodegradable Temporizing Matrix (BTM) (PolyNovo, Carlsbad, CA, USA) is one example, used for its effectiveness in reconstructing deep dermal burns, even in the presence of resistant bacteria [17,18]. BTM, a fully synthetic dermal skin substitute, consists of a two-layered biodegradable polyurethane foam with a temporary non-degradable sealing membrane, applied in a two-staged procedure often combined with vacuum therapy [19,20]. Additionally, phage therapy has been utilized in cases with multi-resistant pathogen colonization [18]. This approach, which involves using bacteriophages to target and destroy specific bacteria, goes far beyond classical standard therapy. The use of phages has shown promise in managing infections that are unresponsive to conventional antibiotics, providing an innovative solution to a critical problem [21].
Building on this multimodal approach, we are now implementing ACPT to further enhance our treatment arsenal [1]. ACPT generates RONS, which have potent antimicrobial effects, crucial for our patient cohort [9,10]. ACPT is a safe and efficient tool in clinical practice, demonstrating effectiveness in preclinical and clinical studies [7]. It has been used in diabetic wounds, which are often difficult to treat due to poor vascularization [22]. The non-thermal nature of ACPT ensures that it can be applied to sensitive tissue without causing local damage or significant discomfort [1]. ACPT’s antimicrobial properties are due to the interaction of RONS with cell membranes, proteins, or nucleic acids of pathogens, leading to their inactivation [10,23]. Studies have shown that ACPT can significantly reduce the microbial burden on wounds, preventing infections that complicate healing [7,8]. This is particularly beneficial for treating burn wounds colonized by multidrug-resistant pathogens [24]. Additionally, ACPT promotes wound healing by stimulating cell proliferation, migration, and differentiation. The RONS modulate signaling pathways involved in tissue regeneration and reduce inflammation, enhancing microcirculation and cellular responses [25]. Various studies have demonstrated that ACPT treatment can improve wound closure rates, reduce inflammation, and enhance tissue regeneration [23,25]. For instance, ACPT has shown an increased expression of vascular endothelial growth factor (VEGF) and other growth factors that are key molecules for neo-angiogenesis and tissue regeneration [25].
The introduction of the CPT®cube and CPT®patch by COLDPLASMATECH (Greifswald, Germany) provides a new opportunity to treating extensive wounds with an adaptable amount of cold plasma that is automatically generated to ensure optimal treatment conditions within minutes of treatment—independent of the size and depth of the area treated [26]. Additionally, this system guarantees treatment consistency and reproducibility, eliminating the effects of user variability. This application method enables the efficient treatment of large surface areas, which is crucial for our patients with extensive burns and high microbial loads. By combining ACPT with our existing therapies, we aim to ensure successful skin graft integration and prevent both local and systemic infections.
Among other successful treatments, this case report highlights the importance of ACPT using the example of a Ukrainian soldier with extensive burn wounds and defect coverage with BTM and meek micrograft transplantation against the backdrop of multi-resistant pathogen colonization. The integration of ACPT into our treatment regimen represents a significant advance in managing these challenging cases.
2. Detailed Case Description
The data of the case report at hand are based on operation reports, photo documentation and further available information from the electronic patient file of the hospital information system during the course of treatment in the burn intensive care unit (ICU) of the Department of Plastic, Aesthetic, Hand and Reconstructive Surgery at Hannover Medical School. The patient gave his informed written consent to publish this paper.
A 40-year-old male soldier from the Ukrainian armed forces sustained severe injuries while engaged in combat during the Ukraine–Russia conflict. In February 2024, he was struck by rocket fire. A direct hit near his position resulted in the traumatic amputation of his left lower leg and the ignition of his pants, leading to significant burns. Following stabilization at a front-line primary care hospital, the patient underwent epifascial debridement and amputation of the left lower leg in Kyiv. He was then transferred to our clinic via the national cloverleaf system as described above.
The patient was admitted to our specialized burn ICU 15 days after the initial injury. This delay in the transfer to our specialized burn unit was critical, as we had anticipated the presence of highly resistant pathogens. On admission to the burn ICU, the patient was awake and cardiopulmonary stable. The patient stated that he was 180 cm tall and weighed 80 kg. Figure 1 shows the wound condition on admission. Under analgesia sedation, adhered wound dressings were removed, revealing extensive epifascial abrasions on both legs (Figure 1a–c) and a mid-tibia level amputation with a high fibula fracture on the left leg (Figure 1a). Second-degree burns affected the right hand, and the distal phalanx of the little right finger was necrotic (Figure 1d). Untreated third-degree burns were noted on the buttocks (Figure 1e). Overall, approximately 23% total body surface area (TBSA) was affected, with an abbreviated burn severity index (ABSI) of 6 points. No previous illnesses were known. Preliminary medical documentation was incomplete and only available in Ukrainian.
Initial microbiological results from the Ukrainian hospital indicated wound colonization with Bacillus cereus and Enterococcus faecalis. The patient received an intraoperative single-shot calculated antibiotic treatment with piperacillin/tazobactam and vancomycin without postoperative continuation as suggested by our antibiotic stewardship team as an empirical anti-infective therapy based on the previous Ukrainian results. Given the microbiologically and macroscopically contaminated wounds, hydrotherapeutic wound debridement and wound care with aseptic dressings was initially performed. The patient was admitted to the burn ICU spontaneously breathing and with stable circulation. Microbiological samples were taken as part of the initial treatment (day 0). Preventive isolation of the patient was carried out in the burn ICU. To achieve clean wound conditions, operative wound debridement was performed the following day (day 1) using sharp surgical techniques in combination with the ultrasonic-assisted hydro-surgical debridement system Sonoca 300 (Söring GmbH, Quickborn, Germany). Intraoperatively, microbiological samples were again collected to determine the microbial burden. For temporary wound coverage, the application of EpiGARD® (Biovision GmbH, Ilmenau, Germany) as a temporary skin substitute on both legs and polyhexanide gel and fatty gauze on the right hand were performed. After intraoperative temporary wound covering with EpiGARD®, daily dressing changes were performed with topical wound disinfection using iodine (Braunol®, B. Braun SE, Melsungen, Germany) and octenidine dihydrochloride (octenisept®, Schülke & Mayr, Norderstedt, Germany) solutions.
In congruence with our experience in the treatment of other Ukrainian patients, the microbiological findings from day 0 and day 1 showed a mixed flora of multi-resistant pathogens. Table 1 gives an overview of the microbial burden detected on admission and after the first surgery on day 1, as well as the development over the further course of treatment. The microbiological results indicated the colonization with Acinetobacter baumannii of all wounds, the nose, throat, and rectal areas, which was of particular concern (Table 1). Blood cultures and urine were found to be sterile on admission. Our experience with Ukrainian soldiers has shown that they were always colonized or infected with these problematic pathogens. Due to operative care, there is a risk of pathogen translocation leading to sepsis, causing previously stable patients to decompensate. Therefore, our goal was to reduce the local bacterial load as much as possible. Only reserve antibiotics like cefiderocol and colistin presented as effective against these germs but these are known to have considerable drawbacks like a significant cost and undesirable drug effects. Thus, we decided to restrict them to inevitable administration, e.g., perioperative treatment and sepsis. Despite considering phage therapy to combat Acinetobacter baumannii, we could not proceed due to the unavailability of suitable phages from the National Center for Phage Therapy at Hannover Medical School. Phage therapy has shown promise in managing infections that are unresponsive to conventional antibiotics, providing an innovative solution to a critical problem. However, in this case, the lack of appropriate phages necessitated alternative approaches.
Consequently, we rapidly initiated ACPT to reduce the microbial load and mitigate the risk of systemic infection. As ACPT has shown promising results in treating chronic wounds with microbial colonization but the treatment area is limited in most devices, we incorporated the cold plasma device from COLDPLASMATECH (COLDPLASMATECH GmbH, Greifswald, Germany) into our treatment regimen (Figure 2a). The treatment was continued daily, with the CPT®patch (Figure 2b) used inside an airtight bag to enclose the patient’s entire body, leaving only his head uncovered (Figure 2c). This allowed for extensive application of RONS to all wound areas, spaces, cavities and dressings, which is crucial for comprehensive microbial reduction. To reduce the bacterial load in the nasopharyngeal region, the patient was also completely covered intermittently with drapes and ACPT was applied to the entire patient (Figure 2d).
On day 6, the surgical epifascial debridement of all remaining necrotic areas was conducted, followed by hydro-surgical VERSAJET (Smith & Nephew, London, UK) debridement and temporary coverage with BTM. The BTM was slit or fenestrated in several places to allow fluid drainage and the safe passage of cold plasma. Antibiotics were discontinued after prolonged admission postoperatively, as recommended by our antibiotic stewardship team, while ACPT continued. The patient remained stable and BTM integration proceeded despite the formation of pus draining through the breaches containing massive amounts of Acinetobacter baumannii. This allowed for the split-thickness skin grafting of the right leg using the meek micrograft technique on day 29. The patient was positioned on an air-fluidized bed postoperatively to reduce shear forces and pressure on the grafted areas.
On day 33, the patient developed sudden delirium, a rise in serum inflammation parameters, opaque and yellowish sputum as well as infiltrates in the thoracic X ray which were interpreted as sepsis from pneumonia caused by Acinetobacter baumannii, requiring the renewed administration of cefiderocol and meropenem. Figure 3 shows the course of the infection parameters over the entire course of treatment.
Following the total regression of sepsis at day 36, further split-thickness skin grafting and debridement procedures were successfully performed on day 38, extending the administration of cefiderocol and meropenem for two days postoperatively. On day 53, a re-transplantation of the gluteal region using meek micrografting was necessary. Hereafter, an approximately 90% coverage of the burned areas was achieved. The wound healing process was not yet fully completed at the time of this case report, but it had progressed significantly, as shown in Figure 4. The smaller residual defects were subjected to secondary wound healing.
This multimodal and complex therapeutic approach, integrating advanced techniques such as BTM and ACPT, aims to ensure successful wound healing and graft integration while preventing systemic infections. Daily treatment with the cold plasma device helped to manage the microbial burden effectively, reducing the risk of pathogen translocation and subsequent sepsis.
3. Discussion
The presented case illustrates the complex management of severe burn injuries complicated by multi-drug resistant infections. The integration of novel therapies such as ACPT, alongside traditional wound care and selective antibiotic use, highlights the potential for innovative approaches in managing challenging cases in the post-antibiotic era.
The transfer of Ukrainian patients to Germany can now be considered a standardized procedure [13]. However, there are challenges that need to be overcome in their future treatment. Once the acute treatment has been completed, multidisciplinary rehabilitation treatment takes place, beginning in the acute hospital and continuing in outpatient and inpatient rehabilitation areas [27]. A significant problem remains in the uncovered costs for rehabilitation and nursing home accommodation for patients who are seriously injured, cannot be rehabilitated and cannot return to Ukraine. Furthermore, there is a lack of comprehensive outpatient care structures and interpreters for Ukrainian patients, whose costs have to be borne and whose payer is often unclear [28].
To our knowledge, this paper mentioning ACPT is among the first for burn wound treatment, especially in combination with BTM. Numerous studies with BTM have been conducted to investigate the safety and ability for permanent wound closure in combination with autologous split-thickness skin grafts in a two-stage surgical procedure in sheep, pigs and humans [20]. However, a disadvantage was found to be the period of up to 3 weeks for integration and vascularization of BTM, resulting in prolonged hospitalization times [17]. As shown by the present case report, BTM has once again proven its versatile properties and application in microbial-colonized wounds as described elsewhere [17,18]. Regarding the combination of BTM with meek micrografting, the results of the presented case report are in line with the only available case series in the literature, showing reliable aesthetic and functional results [19].
Despite medical efforts, wound infections still pose a major challenge when treating burn patients in a burn ICU, especially with pathogens like Pseudomonas aeruginosa, Acinetobacter baumannii and others [29,30]. The major problem leading to increased mortality lies in the emergence of drug-resistant strains [29,30]. Ziuzina et al. explored the ability of cold plasma to inactivate bacterial biofilms and reduce quorum sensing-regulated virulence factors [31]. ACPT has the ability to reduce microbial load, making it a good option to replace antibiotics and combat bacterial strains with increasing antibiotic resistance [32]. Successful case reports using ACPT demonstrated reduced the microbial burden and improved the healing capacities of wounds colonized with Pseudomonas aeruginosa and multidrug-resistant Staphylococcus aureus (MRSA) [33,34]. Through the application of 180 s of ACPT to MRSA, the authors observed significantly decreased biomass and susceptible biofilms after 72 h [34]. Other studies described the successful decolonization of MRSA, Staphylococcus aureus and Escherichia coli using ACPT in a porcine skin model in vitro [35], as well as a reduction in the need for antibiotics in the treatment arm compared to standard wound therapy, from 23% to 4% [26]. Bayliss et al. even found the restoration of antibiotic sensitivity in MRSA following treatment with non-thermal atmospheric gas plasma [36].
The first randomized controlled trials showed a reduced wound surface and a shortened time to wound closure, independent of background disinfection in the treatment of chronic diabetic ulcers [22]. Souza et al. investigated the effect of argon atmospheric plasma on burn wound healing. They found that cold plasma promotes wound healing by stimulating the inflammatory response and controlling the redox state. The study shows that plasma influences the release of cytokines and growth factors, leading to accelerated healing [37], whereas Duchesne et al. focused on the modulation of endothelial nitric oxide synthase (eNOS) by cold atmospheric plasma and its impact on burn wound neovascularization. They demonstrated that cold plasma enhances eNOS signaling and promotes the formation of new blood vessels, supporting the healing of burn wounds [38].
Other studies showed the potential of ACPT mediated by RONS as therapeutic mediators in inactivating viruses such as SARS-CoV-2 [39]. Furthermore, an animal study in burned mice showed that wounds treated with ACPT healed faster than the controls based on the enhanced expression of the dermal–epidermal junctions’ components and increased collagen type I expression [40].
However, little is known about the effect of ACPT on Acinetobacter baumannii. One study reported that increased Acinetobacter baumannii biofilm reduced the efficacy of antimicrobial properties of ACPT, resulting in greater tolerance to plasma exposure [41]. Therefore, the early and consistent application of ACPT appears to be promising in cases of wound colonization with Acinetobacter baumannii, as demonstrated in this case report. ACPT has shown promising results in the treatment of burn wounds by leveraging its antimicrobial properties and ability to promote wound healing [24], but the published literature is still limited to preclinical studies or isolated case series. For example, ACPT has been described as a promising alternative in the case of split skin graft failure in a 42-year-old female patient with extensive wound healing disorders [42]. Taking the findings of all published studies together, ACPT is a versatile option to reduce microbial burden in wound therapy and improve wound healing.
In our multimodal therapy approach, the effect of cold plasma cannot be viewed in isolation. We did not conduct a quantitative analysis of microbial load, and therefore, the plasma effect cannot be directly inferred from our data. This case report merely describes an instance where, compared to similar cases where we faced extreme difficulties with multi-resistant pathogens, the use of ACPT appeared to have mitigated these issues. This can only serve as a suggestion for further investigation, and no causality can be derived from this single case.
Some studies reported that the effect of plasma application can vary widely even within a one-centimeter difference in human tissue, and hypothetically minimal negative effects at the molecular level could not be excluded when using handheld devices like plasma jets [43]. All these findings and possibilities are subject to ongoing studies, and current results suggest that the unproven adverse effects are outweighed by ACPT’s many benefits. Furthermore, these adverse results could not be reproduced in this case report with the automatic device used. Rather, an extensive application to the entire body of the patient appears to develop a comprehensive effect of the ACPT, as can be seen from the increasing wound consolidation and decreasing bacterial load. Compared to other plasma devices certified for clinical practice, such as the kINPen® MED plasma-pen (INP Greifswald/neoplas tools GmbH, Greifswald, Germany), the PlasmaDerm® device (CINOGY Technologies GmbH, Duderstadt, Germany) and the SteriPlas plasma torch device (Adtec Ltd., London, UK), the plasma device used in the presented study (COLDPLASMATECH, Greifswald, Germany) showed comparable and reliable results regarding antimicrobial effects and improved wound healing [44]. Research into ACPT is an ongoing process with studies focusing on optimizing plasma sources, understanding the mechanism of action and expanding clinical applications [45].
In modern burn treatment approaches, pathogen-specific phages are now used for targeted topical treatment as part of isolated therapeutic trials [18]. However, as can be seen in this case, phages are not available or clinically applicable in all cases. Modern treatment approaches are therefore needed to apply typical and systematic anti-infective therapeutic approaches, particularly in the case of complex multi-resistant bacterial colonization. Regarding the microbial burden of the burn wounds in the current case report, the results are in line with the published literature. Of great concern are carbapenemase-producing Gram-negative bacteria (CPGN), such as Acinetobacter baumannii and Klebsiella pneumoniae, as seen in epidemiological studies among refugees and war-wounded Ukrainians [15,29]. The rising prevalence has a direct influence on the patients’ treatment and the German health system, such as infection prevention measures, a higher number of isolations, additional microbiological testing and overall organization within the hospital [15].
The present case report can be seen as a valuable complement to the published literature. Overall, ACPT leverages the unique properties of non-thermal plasma that lead to beneficial therapeutic effects such as antimicrobial action, wound healing and cancer treatment, making it a versatile and future-oriented therapeutic tool in modern medicine. As there is a lack of randomized controlled trials as well as sufficient meta-analysis, further studies are needed in the near future to evaluate its potential in clinical application. Expertise should be bundled in comprehensive centers that use ACPT as part of the clinical routine.
4. Conclusions
Automated large-scale ACPT represents a novel and effective approach to managing burn wounds, combining potent antimicrobial action with enhanced healing capabilities. This dual-action makes it a valuable addition to the current therapeutic arsenal for burn care. The present case report was able to illustrate the importance in the treatment of multi-resistant colonized complex burn wounds and prove a safe, patient individual, time-effective application. Further research and clinical trials will help optimize its application and establish standardized treatment protocols. Bundling expertise in specialized centers seems promising for future applications. |
Title: An Innovative Inhibitor with a New Chemical Moiety Aimed at Biliverdin IXβ Reductase for Thrombocytopenia and Resilient against Cellular Degradation | Body:
1. Introduction
Platelets, also known as thrombocytes, originate from megakaryocytes (MK), which differentiate from multipotent hematopoietic stem cells [1,2]. In addition to their roles in blood hemostasis and pathological thrombosis, platelets are also involved in inflammation, neoangiogenesis, innate immunity, adaptive immune response, and tumor metastasis [3,4,5]. Consequently, modulating platelet responses and regulating their population represent significant therapeutic strategies against platelet disorders [6]. Dysregulation of blood platelets may cause bleeding disorders [7,8]. One such disorder is thrombocytopenia, a genetic bleeding disorder caused by reduced platelet counts [9]. Typical interventions for thrombocytopenia involve increasing platelet counts through platelet generation [10], induction, or transfusion [11]. Conversely, thrombocytosis involves excessive platelets in the blood, contrasting with thrombocytopenia symptoms [12]. A large-scale transcriptomic analysis on essential and reactive thrombocytosis cohorts identified biliverdin IXβ reductase (BLVRB) as a regulator of platelet production through MK differentiation by controlling reactive oxygen species (ROS) [13,14].
In the pathway of heme degradation, BLVRB utilizes NAD(P)H downstream of heme oxygenase(s)-1 (inducible HMOX1) and -2 (constitutive HMOX2) to reduce biliverdin (BV)-IXβ to bilirubin (BR)-IXβ. BR, the product of this process, acts as a potent antioxidant [15,16] and exhibits apparent cytoprotective effects [17], although high concentrations can lead to toxicity (hyperbilirubinemia). Consequently, the BV/BR redox cycle, under the control of BLVRB, plays a crucial role in regulating reactive oxygen species (ROS) [18]. Studies involving induced pluripotent stem cells (iPSCs) expressing a loss-of-function mutant of BLVRB (BLVRBS111L) have demonstrated ROS accumulation and significantly increased proliferation, as measured by MK-colony-forming (CFU-MK), in modified CD34+/BLVRBS111L hematopoietic stem cells (HSCs). In contrast, wild-type BLVRB does not exhibit these effects [14]. This loss-of-function mutant (BLVRBS111L) induces MK differentiation and consequently promotes platelet production through ROS accumulation [13,19]. Leveraging the role of BLVRB in this pathway is a novel strategy to increase platelet count though the removal of antioxidant BR by inhibition of BLVRB reductase activity [14].
Several compounds, notably xanthene dyes and acridine-based molecules, which are structurally similar to BLVRB substrate flavin mononucleotide (FMN), have been shown to inhibit BLVRB [20]. Erythrosin B and phloxine B, two of the most potent BLVRB inhibitors, localize in the BLVRB active site [21,22]. Furthermore, structural studies have revealed active site hydrogen bond networks and elucidated the critical role of S111 in catalysis [23,24,25]. Despite binding to the active site, enzyme kinetics studies have shown that these inhibitors act in a noncompetitive manner [22,25,26]. Both erythrosin B and phloxine B have been utilized as food colorants, with phloxine B also exhibiting antimicrobial activity against various Gram-positive bacteria [27]. There are some limitations to the potential use of both inhibitors in the clinical setting. Chronic administration of erythrosin B in rats results in thyroid stimulation, which may promote thyroid tumor growth [28]. Additionally, through meticulous investigation using NMR and dynamic light scattering (DLS) experiments, it has been confirmed that both erythrosin B and phloxine B induce the multimerization of BLVRB [29]. Hence, there is a need for new drug candidates targeting BLVRB to treat platelet disorders.
In pursuit of this goal, a drug repurposing approach has been employed to screen for new candidates, resulting in the identification of 20 potential inhibitors [30]. However, this method may be less attractive to pharmaceutical companies, as generic products with the same active pharmaceutical ingredient (API) can still be used off-label in clinical practice [31]. Thus, there is a desire for novel compounds. Among the inhibitors derived from the repositioning of FDA-approved drugs, olsalazine exhibits the strongest affinity for inhibition [30]. However, reports from recent studies further substantiate this susceptibility by demonstrating olsalazine’s degradation in various human gastrointestinal environments [32,33]. Olsalazine contains a diazenyl bond susceptible to cleavage by abundant Azoreductase (AzoR) [34]. It has also been reported that olsalazine has a plasma elimination half-life of approximately 1 h [35].
Consequently, our focus shifted towards developing a new compound that is not subject to cleavage by AzoR while retaining similar inhibitory, biochemical, and biophysical properties. To this end, we devised a new chemical scheme to replace the diazenyl bond of olsalazine with an alkene bond, resulting in the creation of olsalkene (OSK). Through NMR chemical shift perturbation (CSP) analysis, we elucidated the binding mode of OSK to the active site of BLVRB. Detailed biophysical characteristics of BLVRB-binding were assessed using isothermal titration calorimetry (ITC). Furthermore, we determined the structure of the BLVRB-OSK complex, which corroborates the binding mode identified through NMR studies. These discoveries elucidate the mechanism of action of these potential drug candidates and provide novel modalities that can be tested in vitro and in preclinical studies.
2. Materials and Methods
2.1. Chemicals
All chemicals, including olsalazine, phloxine B, FMN, NADH, NAD+, NADPH, NADP+, and methylhydroquinone (2-MHQ), were purchased from Sigma Aldrich (St. Louis, MO, USA), unless otherwise specified. For synthesis of olsalkene, high-purity (>99.9%) organic solvents, reagents, and starting materials from Sigma-Aldrich, TCI were used as is. 15N-ammonium chloride, 13C-glucose, and 2H-dimethylsulfoxide (d6-DMSO) were purchased from Cambridge Isotope Laboratories. All compounds in the manuscript are >95% pure by NMR analysis.
2.2. Chemical Synthesis of Olsalkene (OSK)
Merck aluminum sheets with silica gel 60 F254 were used for thin-layer chromatographic analyses. Visualization was performed by UV light and staining with phosphomolybdic acid and potassium permanganate. Merck silica gel 60 (230–400 mesh) was used for compound purification by column chromatography. All proton NMR spectra were recorded using a Bruker 400 MHz NMR. Chemical shifts are reported in parts per million (ppm) relative to an internal standard. Product mass was determined by Shimadzu (Kyoto, Japan) (MALDI-TOF) mass spectrometer.
We have synthesized the olsalkene using the following method (Scheme 1). To a stirred solution of 5-formyl-2-hydroxybenzoic acid (1 g, 0.0060 mole), methyl iodide (1.49 mL, 0.0240 mole) and dried K2CO3 (2.91 g, 0.0210 mole) were added in dry DMF (10 mL) solvent contained in a 100 mL two-necked round-bottomed flask kept under nitrogen atmosphere at overnight in a magnetic stirrer; the progress of the reaction was monitored through TLC analysis. After completion, the crude mass was quenched with water (3 × 25 mL) and extracted with dichloromethane, and purification on column chromatographic method yielded methyl 5-formyl-2-methoxybenzoate (950 mg, 95%). Apart, Zn powder (1.01 g, 0.0224 mole) was taken in THF at 0 °C, and TiCl4 (2.12 g, 0.0112 mole) was added for 30 min successively. The solution was warmed from room temperature to 60 °C for 2 h, the reaction mixture was again cooled, and methyl 5-formyl-2-methoxybenzoate (500 mg, 0.0028 mole) in THF was added dropwise, which refluxed for 4 h, was further quenched with K2CO3, and extracted with DCM, providing (E)-dimethyl 5,5′-(ethene-1,2-diyl)bis(2-methoxybenzoate) (150 mg, 15%). To a solution of (E)-dimethyl 5,5′-(ethene-1,2-diyl)bis(2-methoxybenzoate) (100 mg, 0.0003 mole) in DCM at −78 °C, BBr3 (240 µL, 0.0014) was added in a 10 mL round-bottomed flask and allowed to stir in room temperature for 5 h. Then, 1 mL of 1 M HCl was added for quenching and was stirred well for 15 min. Then, the solvent was removed under reduced pressure, and the crude mass was dissolved in ethyl acetate and washed twice with water and brine solution. The resulting mixture was dissolved in 95% ethanol, followed by potassium hydroxide solution addition, wherein it was allowed to stir for 3 h under reflux condition. After precipitation occurred, the reaction mass was again acidified with 1 M HCl and brine solution, extracted, and underwent recrystallization from ethyl acetate solvent. Finally, the colorless crystalline solid of the (E)-5,5′-(ethene-1,2-diyl)bis(2-hydroxybenzoic acid) (60 mg, 72%) was achieved and further confirmed by 1H NMR and mass spectra analyses (purity > 99%). 1H NMR (CDCl3, 400 MHz) δ 6.96 (d, 2H, J = 8.64 Hz), 7.12 (s, 2H), 7.79 (d, 2H, J = 8.56 Hz), 7.96 (s, 2H); MALDI-TOF m/z calcd for C16H12O6: 300.27, found 322.5044 (M+Na), respectively.
2.3. Expression and Partial Purification of AzoR
E. coli DH5α cells were cultured in LB medium to obtain AzoR protein at 37 °C. Since various electrophilic quinones increase the mRNA level of the acpD gene-encoding AzoR protein [36], 0.5 mM methylhydroquinone (2-MHQ) was added to the culture when the optical density of the cells at 600 nm (OD600) reached 0.8. The cells were harvested by centrifugation after ~3–4 h. To enrich the AzoR protein, anion-exchange column chromatography using the Hitrap-Q HP column (Cytiva, Marlborough, MA, USA) was performed, in which the column was pre-equilibrated with buffer (pH 8.0, 25 mM Tris-HCl), and then 1.0 M NaCl gradient was applied to elute proteins. The AzoR activity in the elution fractions was measured by the enzyme activity assay using 0.1 mM menadione and 0.1 mM NADH, in which decreasing absorption at 340 nm was measured following the previously reported method [37]. A 0.1 M stock solution of menadione was prepared in 100% DMSO. The elution fractions were diluted 100-fold before the assay, in which 1% DMSO was additionally added to dissolve the 0.1 mM menadione. Finally, the elution fraction of the highest activity was used for the cleavage assay of olsalazine after 1000-fold dilution in phosphate-based saline (PBS) buffer. The fraction containing AzoR was used without further purification because the sole purpose of AzoR is to check the cleavage of the diazenyl bond in olsalazine.
2.4. Expression and Purification of BLVRB
The human BLVRB protein was prepared following the previously reported method [30]. Briefly, the N-terminal His6-tagged BLVRB was expressed in the E. coli BL21(DE3) strain using the pET-21b protein expression vector. The transformed E. coli cells were grown in LB and M9 media at 37 °C to express the nonlabeled and isotope-labeled (15N or 13C/15N) BLVRB proteins, respectively. To induce protein expression, 0.5 mM IPTG was added to the cultures when the OD600 reached 0.8, and then the cells were harvested after ~3–4 h. Cells were lysed by sonication in buffer (pH 8.0, 25 mM Tris-HCl, 500 mM NaCl, and 10 mM β-mercaptoethanol). The BLVRB protein was first purified by His-tag affinity column chromatography using the HisTrap HP column (Cytiva). To remove the His-tag, the protein elution was dialyzed in buffer (pH 8.0, 50 mM Tris-HCl, and 1 mM dithiothreitol) overnight at 4 °C, after adding thrombin (~5 units per mg protein). Since thrombin bound to HiTrap-Q HP column at pH 8.0, ion-exchange column chromatography was performed with buffer (pH 8.0, 25 mM Tris-HCl, and 1 mM dithiothreitol), and then the elution was performed with 1.0 M NaCl gradient; BLVRB and thrombin eluted at ~150 and ~300 mM NaCl, respectively. To separate the His6-tagged BLVRB from the completely cleaved BLVRB, the HisTrap HP column was tandemly attached to the next of the HiTrap-Q column. The BLVRB protein was further purified by size exclusion chromatography (SEC) using the HiLoad 16/600 Superdex 75 (Cytiva) with nonbuffered solution (50 mM NaCl and 1 mM dithiothreitol). The protein fractions were concentrated using an ultracentrifugal filter with 10 kDa MWCO (Millipore, Burlington, MA, USA) and then were stored at −70 °C before use. Apo-BLVRB was prepared using the previously reported refolding method [38], with additional column chromatography steps. Briefly, the purified BLVRB was denatured in buffer (pH 7.5, 25 mM Tris-HCl, 10 mM β-mercaptoethanol, and 6.0 M urea) and then applied to the HisTrap HP column. After washing with buffer (pH 5.0, 25 mM Na-acetate, 10 mM β-mercaptoethanol, and 6.0 M urea), they were eluted with an additional 1.0 M NaCl gradient. After 2-fold dilution with buffer (pH 5.0, 20 mM Na-acetate, 10 mM β-mercaptoethanol, and 6.0 M urea), apo-BLVRB was further purified by cation-exchange column chromatography using the HiTrap-SP column (Cytiva). Protein elution was performed by 1.0 M NaCl gradient in the presence of 6.0 M urea. The protein was refolded by dialysis in buffer (pH 6.5, 50 mM Bis-tris, 150 mM NaCl, and 1 mM DTT). After removing the aggregate (~20%) by centrifuge, it was purified by SEC using the HiLoad 16/600 Superdex 75 with buffer (pH 6.5, 10 mM Bis-tris, 50 mM NaCl, and 1 mM DTT). The apo-BLVRB solution was concentrated and then stored at −70 °C. Although thawing the frozen sample caused ~10% precipitation of apo-BLVRB, the maintenance of the structure was confirmed by 1H-15N HSQC spectrum. The concentration was also determined using UV absorption with an extinction coefficient (A280) of 14,440 M−1⋅cm−1. The purity was confirmed by SDS-PAGE gel analysis.
2.5. Crystallization and Structure Determination of the BLVRB:OSK Complex
Overall, protocols to crystallize BLVRB protein and soak the crystals with the OSK molecule were very similar to the previously reported method [30]. Briefly, 1 mM NADPH was added to the solution of BLVRB protein and then was further purified by SEC in buffer (pH 8.0, 20 mM Tris-HCl, 150 mM NaCl, and 1 mM DTT) using the HiLoad 16/600 Superdex 75 (Cytiva, Amersham, UK). The protein fractions were concentrated using the ultracentrifugal filter of 10 kDa MWCO. The protein stock solution (14.8 mg/mL) was aliquoted and then stored at −70 °C before usage. The BLVRB crystals were grown in buffer (pH 6.5, 0.1 M Bis-Tris, and 1.9~2.0 M ammonium sulfate) using the hanging-drop vapor-diffusion method at 18 °C. For soaking of the OSK molecule, the crystals were incubated in the same crystallization buffer, including 1 mM OSK and 0.2 mM NADP+ for 1 day at 18 °C. A total of 1% DMSO was additionally present in the soaking solution, since the OSK stock solution (100 mM) was prepared in 100% DMSO. The crystals of the BLVRB:OSK complex were looped and flash-frozen in liquid nitrogen, in which 20~25% glycerol was used as a cryo-protectant. The X-ray diffraction data were collected on the beamline 5C at the Pohang accelerator laboratory. The diffraction data were processed using the HKL-2000 program [39], and then the structure was solved by molecular replacement (MR) using the previously reported coordinate of BLVRB (PDB code, 7ERA) [30]. The structure model was improved by alternating cycles of model building using the Coot 0.9.8.92 program [40] with refinement using the PHENIX 1.21.2-5419 software package [41]. The structural visualizations were performed using the Chimera program [42]. Schematic diagrams of the interaction networks about OSK and OSA in complex with BLVRB were generated using LIGPLOT+ v.2.2 [43].
2.6. Enzyme Activity Assays
Enzyme kinetics were monitored using the absorption of NADPH at 340 nm with the SpectraMax Gemini EM Microplate Reader (Molecular Devices, San Jose, CA, USA). The enzyme reactions were conducted with 0.5 µM BLVRB, 100 µM FMN, and 100 µM NADPH in PBS buffer containing 0.5% DMSO in the presence of various concentrations of inhibitors (OSK and OSA). The initial velocities of the reactions were estimated from data collected over 5 min and were fitted to the Michaelis–Menten equation. The inhibition model and inhibition constants were determined using the in-house Python script of nonlinear least squares regression fitting.
2.7. Isothermal Titration Calorimetry (ITC)
The ITC experiment was conducted in buffer (pH 6.5, 50 mM Bis-tris, 50 mM NaCl, and 0.1 mM TCEP) using a Microcal Auto-iTC200 (Malvern Instruments, Malvern, UK) at 25 °C. The calorimetric cell (200 μL) and syringe (40 μL) were loaded with 0.09 mM BLVRB and 1.1 mM drug (OSK or OSA), respectively. The ITC data were analyzed after subtracting the heat from blank injections that were measured under the same conditions but in the absence of BLVRB. Data processing was carried out using the Origin software provided by the manufacturer, where the fittings were performed with a one-site binding model.
2.8. NMR Experiments
All NMR experiments were performed with the Bruker 800 MHz spectrometer at 25 °C. A total of 0.2 mM 15N-labeled BLVRB or apo-BLVRB was prepared in buffer (pH 6.5, 50 mM Bis-tris, 50 mM NaCl, and 5% D2O), and their 1H-15N heteronuclear single-quantum coherence (HSQC) spectra were recorded in the presence and absence of drug molecules. The 1-dimensional (1D) 1H spectra to monitor the enzyme reactions were conducted in PBS buffer. The chemical shift perturbation (CSP) data were presented using the square root of (ΔH)2 + [(ΔN)/6]2 to normalize the relative effects from the CSPs of 1H and 15N [44]. The diffusion-ordered spectroscopy (DOSY) spectra were measured using the ledbpgppr2s, with 80 ms diffusion delay, 2 s relaxation delay, and 32 gradient strength increments. The backbone assignments were referenced from BMRB Entry ID: 27463 for holo BLVRB and Entry ID: 27462 for apo BLVRB to assign the spectra in the presence and absence of the drug.
2.1. Chemicals
All chemicals, including olsalazine, phloxine B, FMN, NADH, NAD+, NADPH, NADP+, and methylhydroquinone (2-MHQ), were purchased from Sigma Aldrich (St. Louis, MO, USA), unless otherwise specified. For synthesis of olsalkene, high-purity (>99.9%) organic solvents, reagents, and starting materials from Sigma-Aldrich, TCI were used as is. 15N-ammonium chloride, 13C-glucose, and 2H-dimethylsulfoxide (d6-DMSO) were purchased from Cambridge Isotope Laboratories. All compounds in the manuscript are >95% pure by NMR analysis.
2.2. Chemical Synthesis of Olsalkene (OSK)
Merck aluminum sheets with silica gel 60 F254 were used for thin-layer chromatographic analyses. Visualization was performed by UV light and staining with phosphomolybdic acid and potassium permanganate. Merck silica gel 60 (230–400 mesh) was used for compound purification by column chromatography. All proton NMR spectra were recorded using a Bruker 400 MHz NMR. Chemical shifts are reported in parts per million (ppm) relative to an internal standard. Product mass was determined by Shimadzu (Kyoto, Japan) (MALDI-TOF) mass spectrometer.
We have synthesized the olsalkene using the following method (Scheme 1). To a stirred solution of 5-formyl-2-hydroxybenzoic acid (1 g, 0.0060 mole), methyl iodide (1.49 mL, 0.0240 mole) and dried K2CO3 (2.91 g, 0.0210 mole) were added in dry DMF (10 mL) solvent contained in a 100 mL two-necked round-bottomed flask kept under nitrogen atmosphere at overnight in a magnetic stirrer; the progress of the reaction was monitored through TLC analysis. After completion, the crude mass was quenched with water (3 × 25 mL) and extracted with dichloromethane, and purification on column chromatographic method yielded methyl 5-formyl-2-methoxybenzoate (950 mg, 95%). Apart, Zn powder (1.01 g, 0.0224 mole) was taken in THF at 0 °C, and TiCl4 (2.12 g, 0.0112 mole) was added for 30 min successively. The solution was warmed from room temperature to 60 °C for 2 h, the reaction mixture was again cooled, and methyl 5-formyl-2-methoxybenzoate (500 mg, 0.0028 mole) in THF was added dropwise, which refluxed for 4 h, was further quenched with K2CO3, and extracted with DCM, providing (E)-dimethyl 5,5′-(ethene-1,2-diyl)bis(2-methoxybenzoate) (150 mg, 15%). To a solution of (E)-dimethyl 5,5′-(ethene-1,2-diyl)bis(2-methoxybenzoate) (100 mg, 0.0003 mole) in DCM at −78 °C, BBr3 (240 µL, 0.0014) was added in a 10 mL round-bottomed flask and allowed to stir in room temperature for 5 h. Then, 1 mL of 1 M HCl was added for quenching and was stirred well for 15 min. Then, the solvent was removed under reduced pressure, and the crude mass was dissolved in ethyl acetate and washed twice with water and brine solution. The resulting mixture was dissolved in 95% ethanol, followed by potassium hydroxide solution addition, wherein it was allowed to stir for 3 h under reflux condition. After precipitation occurred, the reaction mass was again acidified with 1 M HCl and brine solution, extracted, and underwent recrystallization from ethyl acetate solvent. Finally, the colorless crystalline solid of the (E)-5,5′-(ethene-1,2-diyl)bis(2-hydroxybenzoic acid) (60 mg, 72%) was achieved and further confirmed by 1H NMR and mass spectra analyses (purity > 99%). 1H NMR (CDCl3, 400 MHz) δ 6.96 (d, 2H, J = 8.64 Hz), 7.12 (s, 2H), 7.79 (d, 2H, J = 8.56 Hz), 7.96 (s, 2H); MALDI-TOF m/z calcd for C16H12O6: 300.27, found 322.5044 (M+Na), respectively.
2.3. Expression and Partial Purification of AzoR
E. coli DH5α cells were cultured in LB medium to obtain AzoR protein at 37 °C. Since various electrophilic quinones increase the mRNA level of the acpD gene-encoding AzoR protein [36], 0.5 mM methylhydroquinone (2-MHQ) was added to the culture when the optical density of the cells at 600 nm (OD600) reached 0.8. The cells were harvested by centrifugation after ~3–4 h. To enrich the AzoR protein, anion-exchange column chromatography using the Hitrap-Q HP column (Cytiva, Marlborough, MA, USA) was performed, in which the column was pre-equilibrated with buffer (pH 8.0, 25 mM Tris-HCl), and then 1.0 M NaCl gradient was applied to elute proteins. The AzoR activity in the elution fractions was measured by the enzyme activity assay using 0.1 mM menadione and 0.1 mM NADH, in which decreasing absorption at 340 nm was measured following the previously reported method [37]. A 0.1 M stock solution of menadione was prepared in 100% DMSO. The elution fractions were diluted 100-fold before the assay, in which 1% DMSO was additionally added to dissolve the 0.1 mM menadione. Finally, the elution fraction of the highest activity was used for the cleavage assay of olsalazine after 1000-fold dilution in phosphate-based saline (PBS) buffer. The fraction containing AzoR was used without further purification because the sole purpose of AzoR is to check the cleavage of the diazenyl bond in olsalazine.
2.4. Expression and Purification of BLVRB
The human BLVRB protein was prepared following the previously reported method [30]. Briefly, the N-terminal His6-tagged BLVRB was expressed in the E. coli BL21(DE3) strain using the pET-21b protein expression vector. The transformed E. coli cells were grown in LB and M9 media at 37 °C to express the nonlabeled and isotope-labeled (15N or 13C/15N) BLVRB proteins, respectively. To induce protein expression, 0.5 mM IPTG was added to the cultures when the OD600 reached 0.8, and then the cells were harvested after ~3–4 h. Cells were lysed by sonication in buffer (pH 8.0, 25 mM Tris-HCl, 500 mM NaCl, and 10 mM β-mercaptoethanol). The BLVRB protein was first purified by His-tag affinity column chromatography using the HisTrap HP column (Cytiva). To remove the His-tag, the protein elution was dialyzed in buffer (pH 8.0, 50 mM Tris-HCl, and 1 mM dithiothreitol) overnight at 4 °C, after adding thrombin (~5 units per mg protein). Since thrombin bound to HiTrap-Q HP column at pH 8.0, ion-exchange column chromatography was performed with buffer (pH 8.0, 25 mM Tris-HCl, and 1 mM dithiothreitol), and then the elution was performed with 1.0 M NaCl gradient; BLVRB and thrombin eluted at ~150 and ~300 mM NaCl, respectively. To separate the His6-tagged BLVRB from the completely cleaved BLVRB, the HisTrap HP column was tandemly attached to the next of the HiTrap-Q column. The BLVRB protein was further purified by size exclusion chromatography (SEC) using the HiLoad 16/600 Superdex 75 (Cytiva) with nonbuffered solution (50 mM NaCl and 1 mM dithiothreitol). The protein fractions were concentrated using an ultracentrifugal filter with 10 kDa MWCO (Millipore, Burlington, MA, USA) and then were stored at −70 °C before use. Apo-BLVRB was prepared using the previously reported refolding method [38], with additional column chromatography steps. Briefly, the purified BLVRB was denatured in buffer (pH 7.5, 25 mM Tris-HCl, 10 mM β-mercaptoethanol, and 6.0 M urea) and then applied to the HisTrap HP column. After washing with buffer (pH 5.0, 25 mM Na-acetate, 10 mM β-mercaptoethanol, and 6.0 M urea), they were eluted with an additional 1.0 M NaCl gradient. After 2-fold dilution with buffer (pH 5.0, 20 mM Na-acetate, 10 mM β-mercaptoethanol, and 6.0 M urea), apo-BLVRB was further purified by cation-exchange column chromatography using the HiTrap-SP column (Cytiva). Protein elution was performed by 1.0 M NaCl gradient in the presence of 6.0 M urea. The protein was refolded by dialysis in buffer (pH 6.5, 50 mM Bis-tris, 150 mM NaCl, and 1 mM DTT). After removing the aggregate (~20%) by centrifuge, it was purified by SEC using the HiLoad 16/600 Superdex 75 with buffer (pH 6.5, 10 mM Bis-tris, 50 mM NaCl, and 1 mM DTT). The apo-BLVRB solution was concentrated and then stored at −70 °C. Although thawing the frozen sample caused ~10% precipitation of apo-BLVRB, the maintenance of the structure was confirmed by 1H-15N HSQC spectrum. The concentration was also determined using UV absorption with an extinction coefficient (A280) of 14,440 M−1⋅cm−1. The purity was confirmed by SDS-PAGE gel analysis.
2.5. Crystallization and Structure Determination of the BLVRB:OSK Complex
Overall, protocols to crystallize BLVRB protein and soak the crystals with the OSK molecule were very similar to the previously reported method [30]. Briefly, 1 mM NADPH was added to the solution of BLVRB protein and then was further purified by SEC in buffer (pH 8.0, 20 mM Tris-HCl, 150 mM NaCl, and 1 mM DTT) using the HiLoad 16/600 Superdex 75 (Cytiva, Amersham, UK). The protein fractions were concentrated using the ultracentrifugal filter of 10 kDa MWCO. The protein stock solution (14.8 mg/mL) was aliquoted and then stored at −70 °C before usage. The BLVRB crystals were grown in buffer (pH 6.5, 0.1 M Bis-Tris, and 1.9~2.0 M ammonium sulfate) using the hanging-drop vapor-diffusion method at 18 °C. For soaking of the OSK molecule, the crystals were incubated in the same crystallization buffer, including 1 mM OSK and 0.2 mM NADP+ for 1 day at 18 °C. A total of 1% DMSO was additionally present in the soaking solution, since the OSK stock solution (100 mM) was prepared in 100% DMSO. The crystals of the BLVRB:OSK complex were looped and flash-frozen in liquid nitrogen, in which 20~25% glycerol was used as a cryo-protectant. The X-ray diffraction data were collected on the beamline 5C at the Pohang accelerator laboratory. The diffraction data were processed using the HKL-2000 program [39], and then the structure was solved by molecular replacement (MR) using the previously reported coordinate of BLVRB (PDB code, 7ERA) [30]. The structure model was improved by alternating cycles of model building using the Coot 0.9.8.92 program [40] with refinement using the PHENIX 1.21.2-5419 software package [41]. The structural visualizations were performed using the Chimera program [42]. Schematic diagrams of the interaction networks about OSK and OSA in complex with BLVRB were generated using LIGPLOT+ v.2.2 [43].
2.6. Enzyme Activity Assays
Enzyme kinetics were monitored using the absorption of NADPH at 340 nm with the SpectraMax Gemini EM Microplate Reader (Molecular Devices, San Jose, CA, USA). The enzyme reactions were conducted with 0.5 µM BLVRB, 100 µM FMN, and 100 µM NADPH in PBS buffer containing 0.5% DMSO in the presence of various concentrations of inhibitors (OSK and OSA). The initial velocities of the reactions were estimated from data collected over 5 min and were fitted to the Michaelis–Menten equation. The inhibition model and inhibition constants were determined using the in-house Python script of nonlinear least squares regression fitting.
2.7. Isothermal Titration Calorimetry (ITC)
The ITC experiment was conducted in buffer (pH 6.5, 50 mM Bis-tris, 50 mM NaCl, and 0.1 mM TCEP) using a Microcal Auto-iTC200 (Malvern Instruments, Malvern, UK) at 25 °C. The calorimetric cell (200 μL) and syringe (40 μL) were loaded with 0.09 mM BLVRB and 1.1 mM drug (OSK or OSA), respectively. The ITC data were analyzed after subtracting the heat from blank injections that were measured under the same conditions but in the absence of BLVRB. Data processing was carried out using the Origin software provided by the manufacturer, where the fittings were performed with a one-site binding model.
2.8. NMR Experiments
All NMR experiments were performed with the Bruker 800 MHz spectrometer at 25 °C. A total of 0.2 mM 15N-labeled BLVRB or apo-BLVRB was prepared in buffer (pH 6.5, 50 mM Bis-tris, 50 mM NaCl, and 5% D2O), and their 1H-15N heteronuclear single-quantum coherence (HSQC) spectra were recorded in the presence and absence of drug molecules. The 1-dimensional (1D) 1H spectra to monitor the enzyme reactions were conducted in PBS buffer. The chemical shift perturbation (CSP) data were presented using the square root of (ΔH)2 + [(ΔN)/6]2 to normalize the relative effects from the CSPs of 1H and 15N [44]. The diffusion-ordered spectroscopy (DOSY) spectra were measured using the ledbpgppr2s, with 80 ms diffusion delay, 2 s relaxation delay, and 32 gradient strength increments. The backbone assignments were referenced from BMRB Entry ID: 27463 for holo BLVRB and Entry ID: 27462 for apo BLVRB to assign the spectra in the presence and absence of the drug.
3. Results and Discussion
3.1. Enhancing Stability: Substituting Diazenyl Bond with Alkene Bond to Prevent Azoreductase Cleavage
The revelation of biliverdin IXβ reductase (BLVRB) as a promising target for thrombocytopenia treatment has sparked endeavors to create inhibitors. Recently, olsalazine (OSA) has emerged as a top contender through repurposing FDA-approved drugs for this purpose. Since initially prescribed for ulcerative colitis as an anti-inflammatory agent, OSA is metabolized by Azoreductase (AzoR) [34], and the cleavage of OSA to 5-aminosalicylic acid (5-ASA) was tested by using the partially purified Escherichia coli (E. coli) AzoR (Figure S1). The expression of AzoR was enhanced by adding methylhydroquinone (2-MHQ) to the cell culture [36]. Indeed, the incubation of OSA with the column fraction containing AzoR activity and NADPH lead to the cleavage of its diazenyl bond and the formation of 5-ASA (Figure 1 and Figure S2), the active compound responsible for its therapeutic effects. Thus, employing OSA as a BLVRB inhibitor in vivo poses uncertainties due to this bond cleavage. Furthermore, the binding of 5-ASA, the products of AzoR catalysis, to BLVRB is reduced significantly (Figure S3). Therefore, it is crucial to create a noncleavable compound. To achieve this, we developed a new synthetic scheme (Supplementary Materials) that replaces the diazenyl bond with an alkene bond while preserving the trans configuration (Supplementary Materials). This yields olsalkene (OSK), which remains intact and is not cleaved by AzoR (Figure 2).
3.2. Thermodynamic Analysis of OSK Binding to BLVRB
Isothermal titration calorimetry (ITC) is employed to directly measure the heat generated during BLVRB-OSK complex formation at constant temperature, enabling the characterization of thermodynamic quantities such as ΔH and ΔS. The dissociation constant (KD) of OSK is found to be 140 nM (Table 1; Figure 3), indicating that OSK serves as a potent and effective binder to BLVRB. Comparative analysis with previously identified xanthene-based inhibitor, phloxine B, reveals that OSK exhibits tighter binding affinity. Furthermore, OSK exhibits more specific interactions with BLVRB compared with xanthene-based inhibitors, as evidenced by the enthalpic contribution observed in the ITC data (Table 1). The binding of OSK is primarily driven by enthalpic changes, reflecting specific interactions between BLVRB and the compound. The disparity in thermodynamic values between OSA and OSK can be elucidated by the substitution of the diazenyl bond with an alkene bond. In summary, the ITC findings support the presence of specific interactions between BLVRB and OSK.
3.3. Investigating the Binding Site of Olsalkene (OSK) in BLVRB Using NMR Spectroscopy
Protein-based NMR spectroscopy stands out as a highly effective method for identifying intermolecular interactions, including those involving small molecules and proteins. Additionally, protein-based NMR methods furnish valuable insights into binding sites, crucial for optimizing the positioning of small molecules within complexes. Typically, this involves monitoring chemical shift changes in 1H-15N HSQC spectra. Assigning amide resonances is imperative for interpreting HSQC spectra, a task facilitated by HNCA and HN(CO)CA spectra (Supplementary Materials), which provide 95% of backbone resonance assignments. Consequently, we conducted HSQC experiments utilizing 15N-labeled BLVRB, titrating OSK to monitor chemical shift changes upon binding. Notably, the FMN binding site in BLVRB was utilized for OSA binding, with Ser111 previously identified as a key residue in BLVRB’s catalytic mechanism. Thus, we monitored the amide chemical shift changes in these residues in HSQC spectra and observed that OSK binding to BLVRB induced shifts in the amide resonances of the FMN binding site (Figure 4A). Additionally, verification of the drug’s binding effect on the BLVRB surface was successfully accomplished by analyzing chemical shift perturbations (CSPs) and mapping them onto the BLVRB surface structure (Figure 4D). This analysis clearly identified the FMN binding site, particularly the xanthene ring of FMN, as the binding site of OSK.
3.4. X-ray Crystal Structure of BLVRB-OSK Complex
The crystal structures of BLVRB bound to NADP+ and OSK were resolved at a resolution of 1.7 Å (Supplementary Materials). The crystals belong to space group P21212, with each crystal asymmetric unit containing two BLVRB molecules (designated as Mol-A and -B). The heavy atom root mean square deviation (RMSD) between Mol-A and Mol-B varies by 1.91 Å. Notably, a specific region of BLVRB, situated within the vicinity of crystal contacts, exhibits considerable disparity between Mol-A and Mol-B. Particularly, the structures of drug-binding pockets display distinctions between Mol-A and Mol-B. While the OSK-binding pocket structure in Mol-B is influenced by the presence of the second BLVRB molecule due to crystal contacts, the structure of the OSK-binding pocket in Mol-A remains intact in a monomeric state of BLVRB. Furthermore, overlaying the Mol-A complexed with OSK onto Mol-B reveals a spatial collision between its R174 residue and the D130 of the original Mol-A. Additionally, Mol-B, including bound NADP+ and drug molecules, has higher crystallographic B factors. Consequently, detailed structural analyses of the complex were focused on Mol-A.
The BLVRB-OSK complex structure resembles previously determined structures with RMSDs of 1.06 and 0.17 Å compared with the BLVRB-NADP+-FMN (PDB ID: 1HE4) and BLVRB-NADP+-OSA (PDB ID: 7ERA) complexes, respectively. This indicates a high degree of structural similarity between BLVRB in complex with OSK and BLVRB complexed with its native substrate and OSA. The binding site of OSK closely associates with the nicotinamide moiety of the NADP+ cofactor, where the xanthene ring of FMN adopts a similar orientation to xanthene-based inhibitors like phloxine B and erythrosine, as evidenced by NMR titration experiments (Figure 4). A primary driving force for the binding of OSK is the ring-stacking interaction between the nicotinamide moiety of NADP+ and OSK, along with hydrophobic interactions involving residues S111, F113, W116, L125, V128, P152, and H153 (Figure 5B). Additionally, favorable interactions are facilitated by positively charged residues R78, K120, R124, R170, and K178. Notably, OSK displaces the sidechain of W116 (as observed in the FMN complex) and shifts it toward the binding pocket by approximately 2.5 Å. These observations align well with the binding affinity measured via ITC (Figure 5 and Table 1).
3.5. BLVRB Enzymatic Kinetics with Inhibitors
In examining the enzymatic kinetics of BLVRB in the presence of NADPH with inhibitors OSK and OSA, the Lineweaver–Burk plot reveals a mixed inhibition pattern characterized by competitive (Kic) and uncompetitive (Kiu) inhibition constants (Figure 6). Starting with OSK, the Lineweaver–Burk plot illustrates a maximum velocity (Vm) of 4.80 ± 0.16 µM/min and a Michaelis constant (Km) of 61.73 ± 5.28 µM. The competitive inhibition constant (Kic) of 0.56 ± 0.10 µM suggests that OSK competes with the substrate (FMN) for binding to the active site of BLVRB. Meanwhile, the uncompetitive inhibition constant (Kiu) of 0.61 ± 0.06 µM indicates that OSK binds to both NADP+ and NADPH-complexed BLVRB (Figure 6A), altering its conformation and inhibiting enzymatic activity. Moving to OSA, similar kinetics are observed with a Vm of 4.71 ± 0.09 µM/min and a Km of 61.57 ± 2.75 µM. The competitive (Kic = 0.55 ± 0.04 µM) and uncompetitive (Kiu = 0.79 ± 0.06 µM) inhibition constants suggest that OSA also competes with the substrate for binding to the active site and interacts with both NADP+ and NADPH-complexed BLVRB. Overall, the mixed inhibition pattern observed for OSK and OSA suggests that these inhibitors can bind to both the NADP+ and NADPH-complexed BLVRB, affecting BLVRB activity. Furthermore, both OSK and OSA are able to bind to the apo-BLVRB distinct from the xanthene-based inhibitor, phloxine B, implying better inhibitory activity of OSK and OSA (Figure S4). Taken together, these results show the kinetic mechanism of BLVRB inhibition with OSK and OSA (Figure 6A). Understanding the detailed kinetics of these inhibitors is essential for unraveling the regulatory mechanisms of BLVRB and may have implications for drug development targeting this enzyme.
3.1. Enhancing Stability: Substituting Diazenyl Bond with Alkene Bond to Prevent Azoreductase Cleavage
The revelation of biliverdin IXβ reductase (BLVRB) as a promising target for thrombocytopenia treatment has sparked endeavors to create inhibitors. Recently, olsalazine (OSA) has emerged as a top contender through repurposing FDA-approved drugs for this purpose. Since initially prescribed for ulcerative colitis as an anti-inflammatory agent, OSA is metabolized by Azoreductase (AzoR) [34], and the cleavage of OSA to 5-aminosalicylic acid (5-ASA) was tested by using the partially purified Escherichia coli (E. coli) AzoR (Figure S1). The expression of AzoR was enhanced by adding methylhydroquinone (2-MHQ) to the cell culture [36]. Indeed, the incubation of OSA with the column fraction containing AzoR activity and NADPH lead to the cleavage of its diazenyl bond and the formation of 5-ASA (Figure 1 and Figure S2), the active compound responsible for its therapeutic effects. Thus, employing OSA as a BLVRB inhibitor in vivo poses uncertainties due to this bond cleavage. Furthermore, the binding of 5-ASA, the products of AzoR catalysis, to BLVRB is reduced significantly (Figure S3). Therefore, it is crucial to create a noncleavable compound. To achieve this, we developed a new synthetic scheme (Supplementary Materials) that replaces the diazenyl bond with an alkene bond while preserving the trans configuration (Supplementary Materials). This yields olsalkene (OSK), which remains intact and is not cleaved by AzoR (Figure 2).
3.2. Thermodynamic Analysis of OSK Binding to BLVRB
Isothermal titration calorimetry (ITC) is employed to directly measure the heat generated during BLVRB-OSK complex formation at constant temperature, enabling the characterization of thermodynamic quantities such as ΔH and ΔS. The dissociation constant (KD) of OSK is found to be 140 nM (Table 1; Figure 3), indicating that OSK serves as a potent and effective binder to BLVRB. Comparative analysis with previously identified xanthene-based inhibitor, phloxine B, reveals that OSK exhibits tighter binding affinity. Furthermore, OSK exhibits more specific interactions with BLVRB compared with xanthene-based inhibitors, as evidenced by the enthalpic contribution observed in the ITC data (Table 1). The binding of OSK is primarily driven by enthalpic changes, reflecting specific interactions between BLVRB and the compound. The disparity in thermodynamic values between OSA and OSK can be elucidated by the substitution of the diazenyl bond with an alkene bond. In summary, the ITC findings support the presence of specific interactions between BLVRB and OSK.
3.3. Investigating the Binding Site of Olsalkene (OSK) in BLVRB Using NMR Spectroscopy
Protein-based NMR spectroscopy stands out as a highly effective method for identifying intermolecular interactions, including those involving small molecules and proteins. Additionally, protein-based NMR methods furnish valuable insights into binding sites, crucial for optimizing the positioning of small molecules within complexes. Typically, this involves monitoring chemical shift changes in 1H-15N HSQC spectra. Assigning amide resonances is imperative for interpreting HSQC spectra, a task facilitated by HNCA and HN(CO)CA spectra (Supplementary Materials), which provide 95% of backbone resonance assignments. Consequently, we conducted HSQC experiments utilizing 15N-labeled BLVRB, titrating OSK to monitor chemical shift changes upon binding. Notably, the FMN binding site in BLVRB was utilized for OSA binding, with Ser111 previously identified as a key residue in BLVRB’s catalytic mechanism. Thus, we monitored the amide chemical shift changes in these residues in HSQC spectra and observed that OSK binding to BLVRB induced shifts in the amide resonances of the FMN binding site (Figure 4A). Additionally, verification of the drug’s binding effect on the BLVRB surface was successfully accomplished by analyzing chemical shift perturbations (CSPs) and mapping them onto the BLVRB surface structure (Figure 4D). This analysis clearly identified the FMN binding site, particularly the xanthene ring of FMN, as the binding site of OSK.
3.4. X-ray Crystal Structure of BLVRB-OSK Complex
The crystal structures of BLVRB bound to NADP+ and OSK were resolved at a resolution of 1.7 Å (Supplementary Materials). The crystals belong to space group P21212, with each crystal asymmetric unit containing two BLVRB molecules (designated as Mol-A and -B). The heavy atom root mean square deviation (RMSD) between Mol-A and Mol-B varies by 1.91 Å. Notably, a specific region of BLVRB, situated within the vicinity of crystal contacts, exhibits considerable disparity between Mol-A and Mol-B. Particularly, the structures of drug-binding pockets display distinctions between Mol-A and Mol-B. While the OSK-binding pocket structure in Mol-B is influenced by the presence of the second BLVRB molecule due to crystal contacts, the structure of the OSK-binding pocket in Mol-A remains intact in a monomeric state of BLVRB. Furthermore, overlaying the Mol-A complexed with OSK onto Mol-B reveals a spatial collision between its R174 residue and the D130 of the original Mol-A. Additionally, Mol-B, including bound NADP+ and drug molecules, has higher crystallographic B factors. Consequently, detailed structural analyses of the complex were focused on Mol-A.
The BLVRB-OSK complex structure resembles previously determined structures with RMSDs of 1.06 and 0.17 Å compared with the BLVRB-NADP+-FMN (PDB ID: 1HE4) and BLVRB-NADP+-OSA (PDB ID: 7ERA) complexes, respectively. This indicates a high degree of structural similarity between BLVRB in complex with OSK and BLVRB complexed with its native substrate and OSA. The binding site of OSK closely associates with the nicotinamide moiety of the NADP+ cofactor, where the xanthene ring of FMN adopts a similar orientation to xanthene-based inhibitors like phloxine B and erythrosine, as evidenced by NMR titration experiments (Figure 4). A primary driving force for the binding of OSK is the ring-stacking interaction between the nicotinamide moiety of NADP+ and OSK, along with hydrophobic interactions involving residues S111, F113, W116, L125, V128, P152, and H153 (Figure 5B). Additionally, favorable interactions are facilitated by positively charged residues R78, K120, R124, R170, and K178. Notably, OSK displaces the sidechain of W116 (as observed in the FMN complex) and shifts it toward the binding pocket by approximately 2.5 Å. These observations align well with the binding affinity measured via ITC (Figure 5 and Table 1).
3.5. BLVRB Enzymatic Kinetics with Inhibitors
In examining the enzymatic kinetics of BLVRB in the presence of NADPH with inhibitors OSK and OSA, the Lineweaver–Burk plot reveals a mixed inhibition pattern characterized by competitive (Kic) and uncompetitive (Kiu) inhibition constants (Figure 6). Starting with OSK, the Lineweaver–Burk plot illustrates a maximum velocity (Vm) of 4.80 ± 0.16 µM/min and a Michaelis constant (Km) of 61.73 ± 5.28 µM. The competitive inhibition constant (Kic) of 0.56 ± 0.10 µM suggests that OSK competes with the substrate (FMN) for binding to the active site of BLVRB. Meanwhile, the uncompetitive inhibition constant (Kiu) of 0.61 ± 0.06 µM indicates that OSK binds to both NADP+ and NADPH-complexed BLVRB (Figure 6A), altering its conformation and inhibiting enzymatic activity. Moving to OSA, similar kinetics are observed with a Vm of 4.71 ± 0.09 µM/min and a Km of 61.57 ± 2.75 µM. The competitive (Kic = 0.55 ± 0.04 µM) and uncompetitive (Kiu = 0.79 ± 0.06 µM) inhibition constants suggest that OSA also competes with the substrate for binding to the active site and interacts with both NADP+ and NADPH-complexed BLVRB. Overall, the mixed inhibition pattern observed for OSK and OSA suggests that these inhibitors can bind to both the NADP+ and NADPH-complexed BLVRB, affecting BLVRB activity. Furthermore, both OSK and OSA are able to bind to the apo-BLVRB distinct from the xanthene-based inhibitor, phloxine B, implying better inhibitory activity of OSK and OSA (Figure S4). Taken together, these results show the kinetic mechanism of BLVRB inhibition with OSK and OSA (Figure 6A). Understanding the detailed kinetics of these inhibitors is essential for unraveling the regulatory mechanisms of BLVRB and may have implications for drug development targeting this enzyme.
4. Conclusions
In brief, our study successfully developed a novel inhibitor of BLVRB, substituting the diazenyl bond of OSA with an alkene bond to avoid cleavage by AzoR in vivo. We characterized the binding properties of OSK using a combination of biochemical and biophysical techniques, including NMR spectroscopy, isothermal titration calorimetry, and X-ray crystallography. OSK exhibited high affinity and specific binding to the substrate binding pocket of BLVRB with a KD of 0.140 µM, akin to the previously studied OSA. Through NMR analysis, we confirmed that the binding pocket of OSK closely resembles that of BLVRB’s natural partner, FMN, and involves an important residue for BLVRB enzymatic activity, Ser111.
Unlike certain xanthene-based inhibitors, which may induce BLVRB multimerization, potentially leading to treatment complications, OSK demonstrated selective binding to BLVRB without such adverse effects. Through a comprehensive analysis involving NMR, ITC, and crystallography, we elucidated the mechanism of action of OSK. Specifically, we identified hydrophobic interactions and ring stacking between NADP+ and the inhibitor, as well as favorable electrostatic interactions with the sidechain of R78 in BLVRB, contributing to increased binding affinity.
Moving OSK to clinical trials requires extensive preclinical studies to evaluate its pharmacokinetics, toxicity, and efficacy. Given its specificity and high binding affinity to BLVRB, OSK shows promise as a therapeutic agent, particularly for diseases involving oxidative stress and impaired heme metabolism. The next steps would involve optimizing its formulation, determining appropriate dosing, and conducting safety assessments in animal models before initiating Phase I clinical trials. We also anticipate that this novel inhibitor, OSK, which is resistant to cleavage by AzoR, will provide a valuable platform for further investigations into the physiological functions of BLVRB, such as its role in ROS accumulation and megakaryocyte differentiation. |
Title: Integrated Ultrasound Characterization of the Diet-Induced Obesity (DIO) Model in Young Adult c57bl/6j Mice: Assessment of Cardiovascular, Renal and Hepatic Changes | Body:
1. Introduction
Overweight and obesity, which can lead to age-associated disorders such as insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), and cardiovascular diseases, have become a global health concern for the young and adult population, with gender-related differences. Consequently, preclinical models of obesity and metabolic syndrome are of great interest, particularly those that more closely resemble the gradual weight gain that occurs in Western humans due to excessive calorie consumption and unbalanced diets, leading to subtle obesity phenotypes. Although sexual dimorphism has been demonstrated in mouse models of obesity, little data are available on female mice [1].
Diet-induced obesity (DIO) in the C57BL/6J mouse substrain is widely used since it shows a high genetic predisposition to develop the metabolic disorders observed in obesity [1]. Furthermore, C57BL/6J mice are widely used to generate genetically modified animals, which could greatly help in understanding gene–disease interactions. In particular, the C57BL/6J strain from supplier Charles Rivers appears remarkably prone to obesity induced by both a high-fat diet (>60% energy from fat) and a “Western-type” diet (WD) (~45% energy from fat associated with higher levels of sucrose as an increased carbohydrate component) [2].
Some commercial rodent diets more closely resemble fat concentrations typically consumed by humans [3] and have been enriched with various levels of simple sugars (sucrose, fructose) and cholesterol to accelerate the progression of liver disease and induce atherosclerosis [4]. Overall, WD is able to induce obesity, hepatic steatosis and insulin resistance, fat deposition in peripheral tissues, and dysregulated lipid metabolism in rodent models [5], providing more relevant physiologic insight to study human metabolic syndrome [3,6].
Common study endpoints in DIO models include the following: (i) body weight and composition, (ii) food intake and feeding efficiency, (iii) blood chemistry parameters for the evaluation of liver and kidney function, (iv) lipid and glucose metabolism, (v) insulin resistance, (vi) hepatomegaly (measured as percentual liver to body weight ratio) and, (vii) histopathological alterations. In addition, high-frequency ultrasound imaging (HFUS) can be a useful complementary tool for studying the morphofunctional changes in the heart, liver and kidney in such animal models in vivo and non-invasively. Lessa and Resende et al. [7,8] first highlighted the feasibility of HFUS to study the progression of hepatic steatosis and/or fibrosis in rodent models of steatosis in female rats after 4, 8, and 15 weeks of liver injury induction and compared ultrasound findings to histological results [7]. In agreement with the literature, the authors found that an increased ratio of liver to right renal cortex echogenicity had a sensitivity of 90% and a specificity of 100% for the detection of fatty liver disease and was highly correlated with intermediate or severe histopathological scores. Furthermore, increased portal vein diameter was correlated with histological findings of hepatic steatosis and fibrosis and was indicative of portal hypertension [7]. Di Lascio and colleagues performed a detailed ultrasound evaluation of the cardiovascular system, liver, and kidneys in male ob/ob mice on SD and C57BL6 at 8 and 25 weeks of age, highlighting early cardiac dysfunction and vascular changes over time in ob/ob mice [9]. The same research group performed a wide phenotypic evaluation in male C57BL6 and db/db mice under SD at 14 weeks of age, including an extensive description of cardiovascular, renal, and hepatic ultrasound parameters [10]. They highlighted not only higher body weight (BW), blood triglycerides, and aspartate aminotransferase (AST) values in db/db mice, but also differences in structural and functional features of the left ventricle, as well as of the abdominal aorta, carotid artery, and renal vessels [10]. The ratio between liver and renal echogenicity showed a higher trend in db/db mice without reaching statistical significance. The authors emphasized that the overall results of their study could be a useful reference for future intervention experiments using these specific mouse strains and that their imaging approach could be useful to longitudinally characterize different mouse models in the field of obesity research [9,10]. Similarly, Cui and colleagues explored the utility of combining ultrasound imaging and serum biomarkers to detect steatohepatitis and fibrosis in male C57L/J mice after treatment with diethylnitrosamine and a high-fat diet. Animals were serially monitored by ultrasound from 1 to 10 months of age, and imaging results were related to anatomical and histopathological findings. Overall, the authors highlighted the translational utility of a complementary approach that combines the conventional assessment of serum parameters and liver echostructure to improve the accuracy of liver pathology information [11]. More recently, Pantaleão and coworkers focused on the ability of ultrasound to detect early alterations of NAFLD in WD-fed male rats [12]. They analyzed the relationship between liver and renal cortex echogenicity at different time points over 16 weeks, integrating body and liver weight measurements and anatomo-histological findings. WD-fed rats had a significantly higher liver weight and liver-to-renal brightness ratio than controls, with histology supporting the ultrasound findings. Overall, they highlighted the translational value of the WD-induced NAFLD and the proposed HFUS approach in assessing the early stages of liver disease in rats, providing baseline knowledge for further investigations in similar animal models. The present study had two purposes. One was to extensively phenotype the C57BL/6J mouse strain as a DIO model paradigm in order to collect baseline data for further comparison with mice with similar genetic backgrounds and engineered for specific genes of interest. The other was to evaluate the utility of HFUS as a complementary tool to detect early and monitor over time subtle morphofunctional changes in the liver, heart, and kidney. The novelty of the present work, compared to previous studies using HFUS in similar contexts, concerns the following: the DIO animal model (C57BL/6J strain from Charles Rivers, WD); the investigation of both genders; the timeline of metabolic syndrome monitoring (early stages of the disease in young and adult mice); the state-of-the-art ultrasound methods used to analyze liver changes; broad-spectrum parameters monitored over time in the same subject, useful to better clarify the complex physiological effects of dietary interventions according to the 3Rs.
2. Materials and Methods
2.1. Compliance with Ethical Standards
The experimental procedures complied with the European Communities Council directive (2010/63/EU) national regulations (D.L. 26/2014). The present study was approved by the Animal Welfare Board of Fondazione Toscana Life Sciences and by the Italian Ministry of Health (authorization number 175/2021-03-03 and 175/2023-02-17). The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines and National Institutes of Health (NIH) recommendations were followed. All efforts were made to minimize the number of animals used and their suffering. All the study data are maintained and available upon request from the corresponding author.
2.2. Study Design and Animals
C57BL/6J mice were purchased from Jackson Laboratories (Bar Harbor, ME) via Charles River (Calco, LC, Italy) at 7 weeks of age. Mice were housed in groups of up to 4 mice per cage (individual housing could produce bias in metabolic murine models) under standard conditions (12 h light cycle, room temperature of 20–23 °C), and free access to food and water [13]. After 7 days of acclimation, the animals were marked for identification. For practical reasons, we divided the 32 experimental animals into two cohorts subsequently tested as diet groups (WD; SD), each containing 16 mice and divided into two sets based on sex (eight males and eight females), examined simultaneously. Experimental sets of mixed sexes are not recommended for studies of energy balance due to sex differences in adipose tissue depots, inflammatory cytokines, blood lipid profile, and insulin activity [14,15].
Mice in the DIO group were switched from the standard diet (SD: 3% fat, 4RF21, Mucedola®, Milan, Italy; Kcal 18.5% from protein; 3% from fat; 53.5% carbohydrates, 3% sucrose; kcal/g 3.150) to a lipid-rich diet (WD: 0.2% cholesterol and 21% butter, Western U8958 version 35, SAFE®, Paris, France; Kcal 14.4% from protein; 38.1% from fat; 47% carbohydrates, 33% sucrose; kcal/g 4.2594) starting from the beginning of 8 weeks of age until the end of 24 weeks of age. The WD was stored at 4 °C and replaced once per week to avoid lipid peroxidation. The global feeding period was selected based on the literature considering findings in mice fed a high-fat diet [16,17]. All WD-fed mice appeared healthy and active throughout the diet intervention period, and no mouse had to be euthanized before 24 weeks of age.
Mice in the control group were fed SD from weaning until 24 weeks of age. One male mouse was excluded from the study for dental malocclusion. Body weight and food consumption were monitored twice per week using a precision balance reading to the nearest 0.1 g. Mice were weighted immediately after food replenishment [18]. The amount of food remaining in the cages was subtracted from the amount initially recorded, including any large (>5 mm) pieces of uneaten food found on the cage floor [18]. For each mouse, average daily food intake (g) per week was calculated. According to NIH-MMPCs guidelines, we calculated the body weight (BW) change (g) from the initial body weight measurements to analyze the overall effect of diet on BW [15]. Furthermore, the body condition score (BCS), a non-invasive method for assessing health and nutritional status in laboratory rodents, was monitored at relevant time points [19,20]. Food intake of mice was analyzed as the daily amount of food (g) consumed by a single mouse for each experimental week. To evaluate changes in metabolism according to food intake, the food efficiency ratio (FER) was monitored weekly as the (total weight gain/total food intake) × 100. As recommended, data were analyzed separately by sex, due to metabolic differences related to hormonal influence, and stress was minimized by assigning responsibility for animal handling to a single, experienced Doctor of Veterinary Medicine (DVM) throughout the study [15]. Experimental blinding was not achieved because the sole experimenter and sonographer inevitably knew the housing condition and the dietary regimen of each mouse.
2.3. Biochemical Analysis
Sample collection time of day, feeding state, route, and vessels for blood collection were standardized throughout the study. At 8 and 16 weeks of age, whole blood was collected in K3-EDTA vials (100 µL/mouse, at 8:00–9:00 a.m.) by facial vein puncture (40–60 s), under 3% isoflurane + 2Lt/min oxygen anesthesia consistent with current veterinary recommendations [21,22]. Non-fasting values of glucose, cholesterol, and triglycerides were measured (Multicare IN, Biochemical System International), and hyperglycemia was monitored as non-fasting blood glucose ≥ 250 mg/dL [23,24].
At 24 weeks of age, mice were fasted for 3 h at 8:00–11:00 a.m. and blood was collected prior to euthanasia under 3% isoflurane + 2Lt/min oxygen anesthesia by cardiac puncture (40–60 s) [25]. Blood samples were centrifuged (3500 rpm, 15 min) and sera were immediately frozen (−20 °C). Serum alanine aminotransferase (ALT), aspartate transaminase (AST), cholesterol, triglycerides, glucose, and blood urea nitrogen (BUN) results were provided by the laboratories of a research organization (IZSLT, Rome, Italy; Galileo Research, Pisa, Italy) accredited by the Italian Ministry of Health to carry out studies on rodents for research purposes. In addition, fasted serum insulin was determined using a Mercodia ultrasensitive mouse insulin ELISA kit. The insulin ELISA protocol is adopted from the manufacturer’s instructions. Insulin units were converted from ng/mL to pmol/L by multiplying by 172.18, and subsequently to µIU/mL by dividing by a conversion factor of 6. Measured blood glucose was converted from mg/dL to mmol/L by multiplying by 0.0555. HOMA-IR was calculated by multiplying insulin (µIU/mL) by glucose (mmol/L) and dividing by 22.5. The translational calculation of HOMA-IR adjusted to murine species was adopted from Fraulob et al. (2010) [26], and the conversion factor of insulin was derived from Knopp et al. (2019) [27]. Alternatively, insulin sensitivity was assessed by using the homeostasis model assessment-2 (HOMA2) index using an online-based calculator version 2.2.3 on the Diabetes Trials Unit of the University of Oxford website [28], providing values for insulin resistance (HOMA-IR), steady-state β-cell function (HOMA-%B), and insulin sensitivity (HOMA-%S) [29].
The metabolic changes in the blood chemistry parameters in C57BL/6J have been evaluated against gender- and age-dependent reference intervals [30,31,32].
2.4. Ultrasound Imaging
Ultrasound images at 8, 16 and 24 weeks of age were acquired and analyzed by an experienced veterinary operator, using a high-frequency ultrasound unit (VisualSonics 2100, Toronto, ON, Canada) equipped with an MS550 Blue Transducer probe (central frequency 40 MHz; focal length 6 mm; depth of penetration 5–15 mm; 30–40 µm axial and 70–90 µm lateral resolution), and the Vevo Lab software (version 3.0.0). Mice were not fasted before imaging to minimize discomfort and avoid fasting-induced metabolic changes [33]. Body temperature was monitored using a rectal probe and kept in a physiological interval by an infrared lamp. During imaging sessions, mice were kept under inhalant anesthesia (induction chamber: 4% isoflurane plus 2 Lt/min oxygen; maintenance with nose cone: 1.5–1.8%) on a heated platform. These concentrations of isoflurane are recommended for cardiac function studies because they produce stable body temperature, mean arterial pressure (MAP), and heart rate (hr) values in C57BL/6 mice comparable to those observed in awake animals [34]. The induction of anesthesia and stabilization of heart rate lasted approximately 10 min before echocardiography [35]. Approximately 5 min of this time interval was useful for performing trichotomy of the thorax and abdomen of mice by depilatory cream. In that way, the experimental procedure was refined to prevent handling stress and health risks resulting from the ingestion of depilatory creams during normal grooming behavior [36]. Next, mice were positioned in dorsal recumbency, a coupling gel was applied to the depilated skin, and the animal’s limbs were coated with conductive paste and taped to four electrocardiogram (ECG) electrodes incorporated in the platform for the measurement of heart rate, ECG and respiratory rate. Echocardiography took no more than 10 min per mouse. To be consistent between animals and images, we stored optimized acquisition settings on the instrument. All B-mode ultrasound images were acquired under the same data capture settings (frequency = 40 MHz, frame rate = 20 images/s, gain = 30 dB, depth = 11 mm, width = 13.00 mm, dynamic range = 60 dB, line density = high, sensitivity = high) for each mouse. The time gain compensation was set to adjust the tissue echogenicity to be as constant as possible regardless of the depth, and the transmit power was set at 100%. Two-dimensional echocardiography B-mode loops were acquired through the chest wall in single-plane parasternal long-axis (LAX) and short axis (SAX), and subsequently analyzed offline. The following measurements of the left ventricle (LV) were performed in LAX: the thickness of the interventricular septum (IVS) or the LV anterior wall (LVAW); the LV interior diameter (LVID); and the LV posterior wall (LVPW). End-diastolic (d) and end-systolic (s) measurements were obtained at the time of maximal and minimal internal chamber dimensions, respectively, and were measured for at least three heartbeats to calculate an average value [37,38,39].
These measurements were used to calculate:Ejection fraction (EF, %): 100 ∗ ((LV Vol;d − LV Vol;s)/LV Vol;d) as a measurement of how much blood the left ventricle pumps out with each contraction;Fractional shortening (FS, %): 100 ∗ ((LVID;d − LVID;s)/LVID;d) as a measurement of the reduction in the length of the end-diastolic diameter that occurs by the end of systole;LV mass (g) corrected (corr): 1.053 ∗ ((LVID;d + LVPW;d + IVS;d)3 − LVID;d3) ∗ 0.8 considering myocardial density approximately 1.053 g/mL and multiplying the LV mass value by 0.8 to “correct” for an overestimation of LV mass, according to the manufacturer’s instructions [40].LV volume (vol); d (µL): ((7.0/(2.4 + LVID;d)) ∗ LVID;d3LV vol; s (µL) ((7.0/(2.4 + LVID;s)) ∗ LVID;s3Stroke volume (SV) (µL): (LV Vol, d − LV Vol, s) as the volume of blood pumped out of the left ventricle of the heart during each systolic cardiac contraction;Cardiac output (CO) (mL/minute): (SV × hr)/1000 as the amount of blood the heart pumps through the circulatory system in a minute.Relative wall thickness (RWT): as the index of LV geometric remodeling less vulnerable to variability related to body morphometry, calculated as septal wall thickness + posterior wall thickness divided by LV diastolic diameter ((SWTd + PWTd)/LVEDD)) [39,40,41,42,43]
Based on the recommendation about cardiac measures indexing [39,40,41,42], 1D measures (diameter) were normalized by BW1/3, and 3D measures (volume/mass) were normalized by BW to avoid biases related to BW changes [43].
Next, liver images were acquired by two-dimensional B-mode imaging through the ventral body wall and subsequently analyzed offline. Four to six B-mode cine-loops imaging the left and right lobes of the liver and at the caudate lobe–right kidney interface were acquired in sagittal and axial imaging planes. To be consistent between animals and images, all B-mode ultrasound images were acquired under the same data capture settings (frequency = 40 MHz, frame rate = 16 images/s, gain = 30 dB, depth = 11 mm, width = 13.00 mm, dynamic range = 60 dB, sensitivity = high) for each mouse. The time gain compensation was set to adjust the tissue echogenicity as constant as possible regardless of the depth, and the transmit power was set at 100%. The liver parenchyma was examined for echogenicity, echostructure, presence or absence of nodules, and border definition. B-mode images (256 grayscale values) were selected at the tele-expiratory phase to reduce artifacts related to respiratory motion to a minimum, as well as shadowing artifacts related to bone or intestinal gas.
The detection and grading of hepatic steatosis by visual inspection and parametric evaluation of the liver were performed according to previously validated protocols in humans, already applied to mouse models:HFUS visual grading [9,44,45]: the left lobe, anterior and posterior portions of the right lobe, left and right portions of the middle lobe, and the caudate lobe of the liver were imaged, and the following parameters were assessed:Echostructure—score 1: homogenous liver parenchyma and regular hepatic surface; score 2 (mild steatosis): diffuse parenchymal mild heterogeneity, reduced visualization of the diaphragm and small peripheral vessels with no change on liver surface; score 3 (moderate steatosis): discrete coarse and heterogeneous parenchymal echogenicity, dotted or slightly irregular liver surface; score 4 (severe steatosis): extensive coarse and heterogeneous parenchymal echostructure, marked echogenicity, irregular or nodular hepatic surface with underlying regenerative nodules, obscured diaphragm and reduced visibility of kidney.Echogenicity (relative to the renal cortex)—score 0: liver less echogenic than the renal cortex; score 1: hepatic echogenicity equal to the renal cortex; score 2: liver more echogenic than the renal cortex.Presence of ascites—score 0: absent; score 1: present.Parametric analysis: Overall, normal hepatic parenchyma is less echogenic than the right renal cortex in rodents [7]. The hepatic echogenicity increases due to the presence of fatty infiltration and/or fibrosis, changing the relation between the liver and the right renal cortex [12].Hepatic-renal ratio (HR): A longitudinal view was acquired in order to have both the liver (caudate lobe) and the right kidney clearly visualized. Liver echogenicity was compared with that of the renal parenchyma, to normalize differences in the overall ultrasound gain value used for the acquisitions. A region of interest (ROI, (0.1 ± 0.02 mm2) was manually drawn and placed in the liver parenchyma, avoiding focal hypo- and hyperechogenicity. A second ROI was positioned in correspondence with a portion of the renal cortex devoid of large vessels along the focusing area of the image at the same distance from the probe to avoid distorting effects in ultrasonic wave patterns. HR values were obtained by dividing the mean grey level of the hepatic ROI for that obtained for the renal one (pixel intensity = average intensity/mm2, arbitrary units, a.u.) [9,44].Hepatic-portal vein ratio (HPV): Similarly, liver echogenicity was normalized to that corresponding to blood within the portal vein. Axial plane ultrasound images were acquired to visualize a portion of the portal vein in the center of the liver. One ROI (0.1 ± 0.02 mm2) was manually drawn and positioned within the lumen of the portal vein, while a second one was positioned in the liver parenchyma avoiding focal hypo- and hyperechogenicity, at the same depth and as close as possible to the center of the image, to maintain comparable ultrasound attenuation and avoid effects related to borderline echo distortion [9,44].Gray-level histogram analysis of echogenicity (GLH): Liver images at different scanning planes (left lateral lobe, longitudinal; caudate lobe, longitudinal; right median lobe, axial) were analyzed using a gray-level histogram to obtain the quantitative mean and standard deviation values of echogenicity of each spatial region. Anatomical landmarks (greater curvature of stomach; cranial pole of the right kidney; porta hepatis, at the level which aorta, portal vein, and caudal vena cava are visible in cross-section) were chosen to scan reproducible imaging planes. ROIs (1 ± 0.02 mm2) were manually drawn in the liver parenchyma, avoiding focal hypo- and hyperechogenicity and as close as possible to the center of the image. This approach includes more representative parts of the liver parenchyma and avoids distortion of image artifacts, with good intra-observer reproducibility [46]. Changes in brightness and variance of the liver parenchyma were reported as follows: mean echogenicity of different lobes; standard deviation of brightness within ROI encompassing right median lobe as measure of tissue heterogeneity; standard deviation of brightness among ROIs in all planes imaged as measures of anisotropy [46,47].Portal vein (PV) diameter: The evaluation of portal hypertension, through the measurement of the portal vein widening, is a recognized marker to non-invasively assess the severity of liver diseases [7,45].
Furthermore, renal alterations as a consequence of the WD have been investigated by HFUS. Since a good correlation of some ultrasonographic features with renal function has been demonstrated, cortical echogenicity and cortical thickness normalized for BW (CRT/BW) were calculated [48,49]. The renal cortical thickness was measured in the sagittal plane at the level of the mid-kidney over a medullary pyramid, perpendicular to the capsule, as was the distance from the corticomedullary junction to the renal capsule [48]. In addition, the renal pulsatility index (PI) and resistive index (RI) were measured. These parameters have been used in both clinical and animal research to assess pathological changes in renal hemodynamics related to the early stages of diabetic nephropathy or to systemic hemodynamic changes, such as hr, blood pressure, and cardiac output [50,51,52,53,54].
In particular, RI is considered an unbiased parameter for flow analysis, since it is the ratio of velocities. Normal RI value in adults would be in the range of 0.47–0.70, with differences of less than 5–8% between the two kidneys [55,56,57]. The RI value for 24-week-old C57Bl/6 male mice fed standard chow was approximately around 0.5 ± 0.15, while the PI value for the same mouse model at 21 weeks of age was 1.18 ± 0.19 (ultrasound examination performed under anesthetic regimen comparable to that used in our experiments as assumed hemodynamically neutral) [50,58]. In all 24-week-old mice, the right kidney was identified in the B-mode longitudinal plane with the same acquisition setting used for the liver, and the maximum longitudinal plane was evaluated, focusing on the renal papilla in the center of the organ. Color Doppler images of the renal vascular tree were used to identify an interlobar artery and guide the positioning of sample volume in correspondence of an intra-renal segmental artery. Blood flow was measured by pulse wave (PW) Doppler mode with a PRF (pulse repetition frequency) of 9–10 kHz. On the PW spectrum, the peak systolic velocity (PSV), the end diastolic velocity (EDV) and the velocity time integral (VTI) were calculated over an average of three to four cardiac cycles. Mean velocity (MV) was obtained by semi-automatically tracing the envelope of the flow signal corresponding to a single cardiac cycle. Based on these measurements, the RI was calculated as follows: RI = (PSV − EDV)/PSV [51,59], while the PI was assessed as (PSV − EDV)/MV [58]. After the imaging sessions, mice were allowed to recover completely in a heated cage, and monitored for signs of pain or discomfort.
2.5. Histological Examination
Liver was weighted, and samples of the organs of interest were formalin-fixed and embedded in paraffin as previously described [59]. Briefly, 7 μm sections were cut from each paraffin block and stained with hematoxylin and eosin (H&E) for morphological evaluation. Periodic acid–Schiff (PAS) and Masson’s trichrome staining were used to assess glycogen and collagen content, respectively. Two unblinded examiners, including an anatomical pathologist and a veterinarian with competencies in animal pathology (VB and SG) reviewed histological sections and performed the histopathological evaluation using a Nikon Eclipse E600 light microscope equipped with a digital camera. The morphometric analysis was performed using Nis element AR software version 3 (Nikon Instruments, Melville, NY, USA). Steatosis and inflammation were assessed on H&E-stained slides. At least five microscopic fields per section (400× magnification) and three section per animal were examined, including at least two different hepatic lobes; microscopic fields including hepatic venules and portal tracts were excluded. The pathological grades of fatty liver disease in each mouse were determined by the NAFLD preclinical scoring system [60], a validated histological score adapted in rodents from the human NASH–Cancer Research Network scoring system [61]. This simplified score comprises four histological features which are evaluated semi-quantitatively: steatosis: macrovescicular (score 0–3), microvescicular (score 0–3), hypertrophy (score 0–3); and inflammation (score 0–3). A global NAFLD activity score (NAS) was defined as the unweighted sum of the scores for steatosis, hypertrophy and inflammation, thus ranging from 0 to 9. In addition, the presence and extent of fibrosis was evaluated for NAFLD staging [61]. Changes from normal hepatocyte morphology were classified as borderline lesions according to steatosis, activity, fibrosis (SAF) scoring system [62], and differential diagnoses were made from glycogen accumulation [63]. Histological analysis of renal structural changes was performed on H&E- and PAS-stained sections on 20 glomeruli per mouse throughout the cortex, using a simplified scoring system according to that previously described in laboratory rodents [64]. In particular, the presence of morphological alterations of the glomerular areas was examined by two unblinded anatomists, with a semi-quantitative score as follows: score 0: none or <30% of glomeruli altered; score 1: >30–<70% of glomeruli altered; score 2: >70% of glomeruli altered.
2.6. Statistical Analysis
In this experimental protocol, we examined the effects of SD and WD in C57Bl/6J WT mice for future comparison with genetically modified mice, C57Bl/6J genetic background. The experimental group included 7–8 mice (n = 7–8). The sample size was calculated a priori, comparing means by extrapolating the expected data from relevant scientific literature [65] in order to obtain statistically significant results for differences of at least 30% (reference mean = 100; test mean = 70) between the averages of the measurements carried out for the various parameters examined (http://www.biomath.info/power/ttest.htm, accessed on 20 March 2021) [66]. A power of 80%, an expected variability of 20% (standard deviation = 20 March 2021) and a significance level of 5% (p ≤ 0.05) were considered. In evaluating the size of the experimental groups, consideration was given to the need to minimize the use of animals while maintaining sufficient power to detect significant effects. To account for expected attrition of animals throughout the duration of the study, the calculated sample size was adjusted by 10%. Further statistical analyses were performed using GraphPad Prism software version 8.0 (GraphPad Software, Inc., San Diego, CA, USA). Since the distribution of the data was unknown, variables were analyzed by the use of nonparametric tests where appropriate. The t test/Mann–Whitney test was used to compare differences in experimental outcomes between sex-and age- matched control and DIO groups. Repeated measures—a one-way ANOVA/Wilcoxon test was used to detect differences in experimental outcomes over time (8, 16, 24 weeks of age) in the same group. Repeated measures—a two-way ANOVA mixed effect model followed by Sidak’s multiple comparison post hoc test was used where appropriate, to compare group differences within time points of interest. Statistical significance was set at p < 0.05. Extreme values of the biochemical analysis of a 24-week-old male mouse in the SD group were excluded from the statistical calculations, and insulin sensitivity data were lost for one male mouse in WD group for insufficient sample quantity. Pearson correlations (one-tailed) were performed between the semi-quantitative and ordinal data of the NAS scoring system and the HFUS visual grading, as well as the numerical values of the parametric ultrasound analyses.
2.1. Compliance with Ethical Standards
The experimental procedures complied with the European Communities Council directive (2010/63/EU) national regulations (D.L. 26/2014). The present study was approved by the Animal Welfare Board of Fondazione Toscana Life Sciences and by the Italian Ministry of Health (authorization number 175/2021-03-03 and 175/2023-02-17). The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines and National Institutes of Health (NIH) recommendations were followed. All efforts were made to minimize the number of animals used and their suffering. All the study data are maintained and available upon request from the corresponding author.
2.2. Study Design and Animals
C57BL/6J mice were purchased from Jackson Laboratories (Bar Harbor, ME) via Charles River (Calco, LC, Italy) at 7 weeks of age. Mice were housed in groups of up to 4 mice per cage (individual housing could produce bias in metabolic murine models) under standard conditions (12 h light cycle, room temperature of 20–23 °C), and free access to food and water [13]. After 7 days of acclimation, the animals were marked for identification. For practical reasons, we divided the 32 experimental animals into two cohorts subsequently tested as diet groups (WD; SD), each containing 16 mice and divided into two sets based on sex (eight males and eight females), examined simultaneously. Experimental sets of mixed sexes are not recommended for studies of energy balance due to sex differences in adipose tissue depots, inflammatory cytokines, blood lipid profile, and insulin activity [14,15].
Mice in the DIO group were switched from the standard diet (SD: 3% fat, 4RF21, Mucedola®, Milan, Italy; Kcal 18.5% from protein; 3% from fat; 53.5% carbohydrates, 3% sucrose; kcal/g 3.150) to a lipid-rich diet (WD: 0.2% cholesterol and 21% butter, Western U8958 version 35, SAFE®, Paris, France; Kcal 14.4% from protein; 38.1% from fat; 47% carbohydrates, 33% sucrose; kcal/g 4.2594) starting from the beginning of 8 weeks of age until the end of 24 weeks of age. The WD was stored at 4 °C and replaced once per week to avoid lipid peroxidation. The global feeding period was selected based on the literature considering findings in mice fed a high-fat diet [16,17]. All WD-fed mice appeared healthy and active throughout the diet intervention period, and no mouse had to be euthanized before 24 weeks of age.
Mice in the control group were fed SD from weaning until 24 weeks of age. One male mouse was excluded from the study for dental malocclusion. Body weight and food consumption were monitored twice per week using a precision balance reading to the nearest 0.1 g. Mice were weighted immediately after food replenishment [18]. The amount of food remaining in the cages was subtracted from the amount initially recorded, including any large (>5 mm) pieces of uneaten food found on the cage floor [18]. For each mouse, average daily food intake (g) per week was calculated. According to NIH-MMPCs guidelines, we calculated the body weight (BW) change (g) from the initial body weight measurements to analyze the overall effect of diet on BW [15]. Furthermore, the body condition score (BCS), a non-invasive method for assessing health and nutritional status in laboratory rodents, was monitored at relevant time points [19,20]. Food intake of mice was analyzed as the daily amount of food (g) consumed by a single mouse for each experimental week. To evaluate changes in metabolism according to food intake, the food efficiency ratio (FER) was monitored weekly as the (total weight gain/total food intake) × 100. As recommended, data were analyzed separately by sex, due to metabolic differences related to hormonal influence, and stress was minimized by assigning responsibility for animal handling to a single, experienced Doctor of Veterinary Medicine (DVM) throughout the study [15]. Experimental blinding was not achieved because the sole experimenter and sonographer inevitably knew the housing condition and the dietary regimen of each mouse.
2.3. Biochemical Analysis
Sample collection time of day, feeding state, route, and vessels for blood collection were standardized throughout the study. At 8 and 16 weeks of age, whole blood was collected in K3-EDTA vials (100 µL/mouse, at 8:00–9:00 a.m.) by facial vein puncture (40–60 s), under 3% isoflurane + 2Lt/min oxygen anesthesia consistent with current veterinary recommendations [21,22]. Non-fasting values of glucose, cholesterol, and triglycerides were measured (Multicare IN, Biochemical System International), and hyperglycemia was monitored as non-fasting blood glucose ≥ 250 mg/dL [23,24].
At 24 weeks of age, mice were fasted for 3 h at 8:00–11:00 a.m. and blood was collected prior to euthanasia under 3% isoflurane + 2Lt/min oxygen anesthesia by cardiac puncture (40–60 s) [25]. Blood samples were centrifuged (3500 rpm, 15 min) and sera were immediately frozen (−20 °C). Serum alanine aminotransferase (ALT), aspartate transaminase (AST), cholesterol, triglycerides, glucose, and blood urea nitrogen (BUN) results were provided by the laboratories of a research organization (IZSLT, Rome, Italy; Galileo Research, Pisa, Italy) accredited by the Italian Ministry of Health to carry out studies on rodents for research purposes. In addition, fasted serum insulin was determined using a Mercodia ultrasensitive mouse insulin ELISA kit. The insulin ELISA protocol is adopted from the manufacturer’s instructions. Insulin units were converted from ng/mL to pmol/L by multiplying by 172.18, and subsequently to µIU/mL by dividing by a conversion factor of 6. Measured blood glucose was converted from mg/dL to mmol/L by multiplying by 0.0555. HOMA-IR was calculated by multiplying insulin (µIU/mL) by glucose (mmol/L) and dividing by 22.5. The translational calculation of HOMA-IR adjusted to murine species was adopted from Fraulob et al. (2010) [26], and the conversion factor of insulin was derived from Knopp et al. (2019) [27]. Alternatively, insulin sensitivity was assessed by using the homeostasis model assessment-2 (HOMA2) index using an online-based calculator version 2.2.3 on the Diabetes Trials Unit of the University of Oxford website [28], providing values for insulin resistance (HOMA-IR), steady-state β-cell function (HOMA-%B), and insulin sensitivity (HOMA-%S) [29].
The metabolic changes in the blood chemistry parameters in C57BL/6J have been evaluated against gender- and age-dependent reference intervals [30,31,32].
2.4. Ultrasound Imaging
Ultrasound images at 8, 16 and 24 weeks of age were acquired and analyzed by an experienced veterinary operator, using a high-frequency ultrasound unit (VisualSonics 2100, Toronto, ON, Canada) equipped with an MS550 Blue Transducer probe (central frequency 40 MHz; focal length 6 mm; depth of penetration 5–15 mm; 30–40 µm axial and 70–90 µm lateral resolution), and the Vevo Lab software (version 3.0.0). Mice were not fasted before imaging to minimize discomfort and avoid fasting-induced metabolic changes [33]. Body temperature was monitored using a rectal probe and kept in a physiological interval by an infrared lamp. During imaging sessions, mice were kept under inhalant anesthesia (induction chamber: 4% isoflurane plus 2 Lt/min oxygen; maintenance with nose cone: 1.5–1.8%) on a heated platform. These concentrations of isoflurane are recommended for cardiac function studies because they produce stable body temperature, mean arterial pressure (MAP), and heart rate (hr) values in C57BL/6 mice comparable to those observed in awake animals [34]. The induction of anesthesia and stabilization of heart rate lasted approximately 10 min before echocardiography [35]. Approximately 5 min of this time interval was useful for performing trichotomy of the thorax and abdomen of mice by depilatory cream. In that way, the experimental procedure was refined to prevent handling stress and health risks resulting from the ingestion of depilatory creams during normal grooming behavior [36]. Next, mice were positioned in dorsal recumbency, a coupling gel was applied to the depilated skin, and the animal’s limbs were coated with conductive paste and taped to four electrocardiogram (ECG) electrodes incorporated in the platform for the measurement of heart rate, ECG and respiratory rate. Echocardiography took no more than 10 min per mouse. To be consistent between animals and images, we stored optimized acquisition settings on the instrument. All B-mode ultrasound images were acquired under the same data capture settings (frequency = 40 MHz, frame rate = 20 images/s, gain = 30 dB, depth = 11 mm, width = 13.00 mm, dynamic range = 60 dB, line density = high, sensitivity = high) for each mouse. The time gain compensation was set to adjust the tissue echogenicity to be as constant as possible regardless of the depth, and the transmit power was set at 100%. Two-dimensional echocardiography B-mode loops were acquired through the chest wall in single-plane parasternal long-axis (LAX) and short axis (SAX), and subsequently analyzed offline. The following measurements of the left ventricle (LV) were performed in LAX: the thickness of the interventricular septum (IVS) or the LV anterior wall (LVAW); the LV interior diameter (LVID); and the LV posterior wall (LVPW). End-diastolic (d) and end-systolic (s) measurements were obtained at the time of maximal and minimal internal chamber dimensions, respectively, and were measured for at least three heartbeats to calculate an average value [37,38,39].
These measurements were used to calculate:Ejection fraction (EF, %): 100 ∗ ((LV Vol;d − LV Vol;s)/LV Vol;d) as a measurement of how much blood the left ventricle pumps out with each contraction;Fractional shortening (FS, %): 100 ∗ ((LVID;d − LVID;s)/LVID;d) as a measurement of the reduction in the length of the end-diastolic diameter that occurs by the end of systole;LV mass (g) corrected (corr): 1.053 ∗ ((LVID;d + LVPW;d + IVS;d)3 − LVID;d3) ∗ 0.8 considering myocardial density approximately 1.053 g/mL and multiplying the LV mass value by 0.8 to “correct” for an overestimation of LV mass, according to the manufacturer’s instructions [40].LV volume (vol); d (µL): ((7.0/(2.4 + LVID;d)) ∗ LVID;d3LV vol; s (µL) ((7.0/(2.4 + LVID;s)) ∗ LVID;s3Stroke volume (SV) (µL): (LV Vol, d − LV Vol, s) as the volume of blood pumped out of the left ventricle of the heart during each systolic cardiac contraction;Cardiac output (CO) (mL/minute): (SV × hr)/1000 as the amount of blood the heart pumps through the circulatory system in a minute.Relative wall thickness (RWT): as the index of LV geometric remodeling less vulnerable to variability related to body morphometry, calculated as septal wall thickness + posterior wall thickness divided by LV diastolic diameter ((SWTd + PWTd)/LVEDD)) [39,40,41,42,43]
Based on the recommendation about cardiac measures indexing [39,40,41,42], 1D measures (diameter) were normalized by BW1/3, and 3D measures (volume/mass) were normalized by BW to avoid biases related to BW changes [43].
Next, liver images were acquired by two-dimensional B-mode imaging through the ventral body wall and subsequently analyzed offline. Four to six B-mode cine-loops imaging the left and right lobes of the liver and at the caudate lobe–right kidney interface were acquired in sagittal and axial imaging planes. To be consistent between animals and images, all B-mode ultrasound images were acquired under the same data capture settings (frequency = 40 MHz, frame rate = 16 images/s, gain = 30 dB, depth = 11 mm, width = 13.00 mm, dynamic range = 60 dB, sensitivity = high) for each mouse. The time gain compensation was set to adjust the tissue echogenicity as constant as possible regardless of the depth, and the transmit power was set at 100%. The liver parenchyma was examined for echogenicity, echostructure, presence or absence of nodules, and border definition. B-mode images (256 grayscale values) were selected at the tele-expiratory phase to reduce artifacts related to respiratory motion to a minimum, as well as shadowing artifacts related to bone or intestinal gas.
The detection and grading of hepatic steatosis by visual inspection and parametric evaluation of the liver were performed according to previously validated protocols in humans, already applied to mouse models:HFUS visual grading [9,44,45]: the left lobe, anterior and posterior portions of the right lobe, left and right portions of the middle lobe, and the caudate lobe of the liver were imaged, and the following parameters were assessed:Echostructure—score 1: homogenous liver parenchyma and regular hepatic surface; score 2 (mild steatosis): diffuse parenchymal mild heterogeneity, reduced visualization of the diaphragm and small peripheral vessels with no change on liver surface; score 3 (moderate steatosis): discrete coarse and heterogeneous parenchymal echogenicity, dotted or slightly irregular liver surface; score 4 (severe steatosis): extensive coarse and heterogeneous parenchymal echostructure, marked echogenicity, irregular or nodular hepatic surface with underlying regenerative nodules, obscured diaphragm and reduced visibility of kidney.Echogenicity (relative to the renal cortex)—score 0: liver less echogenic than the renal cortex; score 1: hepatic echogenicity equal to the renal cortex; score 2: liver more echogenic than the renal cortex.Presence of ascites—score 0: absent; score 1: present.Parametric analysis: Overall, normal hepatic parenchyma is less echogenic than the right renal cortex in rodents [7]. The hepatic echogenicity increases due to the presence of fatty infiltration and/or fibrosis, changing the relation between the liver and the right renal cortex [12].Hepatic-renal ratio (HR): A longitudinal view was acquired in order to have both the liver (caudate lobe) and the right kidney clearly visualized. Liver echogenicity was compared with that of the renal parenchyma, to normalize differences in the overall ultrasound gain value used for the acquisitions. A region of interest (ROI, (0.1 ± 0.02 mm2) was manually drawn and placed in the liver parenchyma, avoiding focal hypo- and hyperechogenicity. A second ROI was positioned in correspondence with a portion of the renal cortex devoid of large vessels along the focusing area of the image at the same distance from the probe to avoid distorting effects in ultrasonic wave patterns. HR values were obtained by dividing the mean grey level of the hepatic ROI for that obtained for the renal one (pixel intensity = average intensity/mm2, arbitrary units, a.u.) [9,44].Hepatic-portal vein ratio (HPV): Similarly, liver echogenicity was normalized to that corresponding to blood within the portal vein. Axial plane ultrasound images were acquired to visualize a portion of the portal vein in the center of the liver. One ROI (0.1 ± 0.02 mm2) was manually drawn and positioned within the lumen of the portal vein, while a second one was positioned in the liver parenchyma avoiding focal hypo- and hyperechogenicity, at the same depth and as close as possible to the center of the image, to maintain comparable ultrasound attenuation and avoid effects related to borderline echo distortion [9,44].Gray-level histogram analysis of echogenicity (GLH): Liver images at different scanning planes (left lateral lobe, longitudinal; caudate lobe, longitudinal; right median lobe, axial) were analyzed using a gray-level histogram to obtain the quantitative mean and standard deviation values of echogenicity of each spatial region. Anatomical landmarks (greater curvature of stomach; cranial pole of the right kidney; porta hepatis, at the level which aorta, portal vein, and caudal vena cava are visible in cross-section) were chosen to scan reproducible imaging planes. ROIs (1 ± 0.02 mm2) were manually drawn in the liver parenchyma, avoiding focal hypo- and hyperechogenicity and as close as possible to the center of the image. This approach includes more representative parts of the liver parenchyma and avoids distortion of image artifacts, with good intra-observer reproducibility [46]. Changes in brightness and variance of the liver parenchyma were reported as follows: mean echogenicity of different lobes; standard deviation of brightness within ROI encompassing right median lobe as measure of tissue heterogeneity; standard deviation of brightness among ROIs in all planes imaged as measures of anisotropy [46,47].Portal vein (PV) diameter: The evaluation of portal hypertension, through the measurement of the portal vein widening, is a recognized marker to non-invasively assess the severity of liver diseases [7,45].
Furthermore, renal alterations as a consequence of the WD have been investigated by HFUS. Since a good correlation of some ultrasonographic features with renal function has been demonstrated, cortical echogenicity and cortical thickness normalized for BW (CRT/BW) were calculated [48,49]. The renal cortical thickness was measured in the sagittal plane at the level of the mid-kidney over a medullary pyramid, perpendicular to the capsule, as was the distance from the corticomedullary junction to the renal capsule [48]. In addition, the renal pulsatility index (PI) and resistive index (RI) were measured. These parameters have been used in both clinical and animal research to assess pathological changes in renal hemodynamics related to the early stages of diabetic nephropathy or to systemic hemodynamic changes, such as hr, blood pressure, and cardiac output [50,51,52,53,54].
In particular, RI is considered an unbiased parameter for flow analysis, since it is the ratio of velocities. Normal RI value in adults would be in the range of 0.47–0.70, with differences of less than 5–8% between the two kidneys [55,56,57]. The RI value for 24-week-old C57Bl/6 male mice fed standard chow was approximately around 0.5 ± 0.15, while the PI value for the same mouse model at 21 weeks of age was 1.18 ± 0.19 (ultrasound examination performed under anesthetic regimen comparable to that used in our experiments as assumed hemodynamically neutral) [50,58]. In all 24-week-old mice, the right kidney was identified in the B-mode longitudinal plane with the same acquisition setting used for the liver, and the maximum longitudinal plane was evaluated, focusing on the renal papilla in the center of the organ. Color Doppler images of the renal vascular tree were used to identify an interlobar artery and guide the positioning of sample volume in correspondence of an intra-renal segmental artery. Blood flow was measured by pulse wave (PW) Doppler mode with a PRF (pulse repetition frequency) of 9–10 kHz. On the PW spectrum, the peak systolic velocity (PSV), the end diastolic velocity (EDV) and the velocity time integral (VTI) were calculated over an average of three to four cardiac cycles. Mean velocity (MV) was obtained by semi-automatically tracing the envelope of the flow signal corresponding to a single cardiac cycle. Based on these measurements, the RI was calculated as follows: RI = (PSV − EDV)/PSV [51,59], while the PI was assessed as (PSV − EDV)/MV [58]. After the imaging sessions, mice were allowed to recover completely in a heated cage, and monitored for signs of pain or discomfort.
2.5. Histological Examination
Liver was weighted, and samples of the organs of interest were formalin-fixed and embedded in paraffin as previously described [59]. Briefly, 7 μm sections were cut from each paraffin block and stained with hematoxylin and eosin (H&E) for morphological evaluation. Periodic acid–Schiff (PAS) and Masson’s trichrome staining were used to assess glycogen and collagen content, respectively. Two unblinded examiners, including an anatomical pathologist and a veterinarian with competencies in animal pathology (VB and SG) reviewed histological sections and performed the histopathological evaluation using a Nikon Eclipse E600 light microscope equipped with a digital camera. The morphometric analysis was performed using Nis element AR software version 3 (Nikon Instruments, Melville, NY, USA). Steatosis and inflammation were assessed on H&E-stained slides. At least five microscopic fields per section (400× magnification) and three section per animal were examined, including at least two different hepatic lobes; microscopic fields including hepatic venules and portal tracts were excluded. The pathological grades of fatty liver disease in each mouse were determined by the NAFLD preclinical scoring system [60], a validated histological score adapted in rodents from the human NASH–Cancer Research Network scoring system [61]. This simplified score comprises four histological features which are evaluated semi-quantitatively: steatosis: macrovescicular (score 0–3), microvescicular (score 0–3), hypertrophy (score 0–3); and inflammation (score 0–3). A global NAFLD activity score (NAS) was defined as the unweighted sum of the scores for steatosis, hypertrophy and inflammation, thus ranging from 0 to 9. In addition, the presence and extent of fibrosis was evaluated for NAFLD staging [61]. Changes from normal hepatocyte morphology were classified as borderline lesions according to steatosis, activity, fibrosis (SAF) scoring system [62], and differential diagnoses were made from glycogen accumulation [63]. Histological analysis of renal structural changes was performed on H&E- and PAS-stained sections on 20 glomeruli per mouse throughout the cortex, using a simplified scoring system according to that previously described in laboratory rodents [64]. In particular, the presence of morphological alterations of the glomerular areas was examined by two unblinded anatomists, with a semi-quantitative score as follows: score 0: none or <30% of glomeruli altered; score 1: >30–<70% of glomeruli altered; score 2: >70% of glomeruli altered.
2.6. Statistical Analysis
In this experimental protocol, we examined the effects of SD and WD in C57Bl/6J WT mice for future comparison with genetically modified mice, C57Bl/6J genetic background. The experimental group included 7–8 mice (n = 7–8). The sample size was calculated a priori, comparing means by extrapolating the expected data from relevant scientific literature [65] in order to obtain statistically significant results for differences of at least 30% (reference mean = 100; test mean = 70) between the averages of the measurements carried out for the various parameters examined (http://www.biomath.info/power/ttest.htm, accessed on 20 March 2021) [66]. A power of 80%, an expected variability of 20% (standard deviation = 20 March 2021) and a significance level of 5% (p ≤ 0.05) were considered. In evaluating the size of the experimental groups, consideration was given to the need to minimize the use of animals while maintaining sufficient power to detect significant effects. To account for expected attrition of animals throughout the duration of the study, the calculated sample size was adjusted by 10%. Further statistical analyses were performed using GraphPad Prism software version 8.0 (GraphPad Software, Inc., San Diego, CA, USA). Since the distribution of the data was unknown, variables were analyzed by the use of nonparametric tests where appropriate. The t test/Mann–Whitney test was used to compare differences in experimental outcomes between sex-and age- matched control and DIO groups. Repeated measures—a one-way ANOVA/Wilcoxon test was used to detect differences in experimental outcomes over time (8, 16, 24 weeks of age) in the same group. Repeated measures—a two-way ANOVA mixed effect model followed by Sidak’s multiple comparison post hoc test was used where appropriate, to compare group differences within time points of interest. Statistical significance was set at p < 0.05. Extreme values of the biochemical analysis of a 24-week-old male mouse in the SD group were excluded from the statistical calculations, and insulin sensitivity data were lost for one male mouse in WD group for insufficient sample quantity. Pearson correlations (one-tailed) were performed between the semi-quantitative and ordinal data of the NAS scoring system and the HFUS visual grading, as well as the numerical values of the parametric ultrasound analyses.
3. Results
3.1. WD Affects Body Weight and Nutritional Phenotype of C57Bl/6J Mice
As expected, BW and nutritional status of C57Bl/6J mice were significantly affected by WD. Baseline BW (g) was not different between male SD and WD groups (Mann–Whitney test, p = 0.7587), while it was statistically higher in the WD group between 13 and 15 weeks of age [ANOVA, diet: F (16, 208) = 5.978; p < 0.0001; Sidak’s test p < 0.05] (Figure 1A). Conversely, the female SD group was significantly heavier than the WD group at 8 weeks of age [Mann–Whitney test, p = 0.0005; ANOVA, diet: F (1, 14) = 0.08653; p < 0.7730; Sidak’s test p < 0.05] (Figure 1B), although all records over time were within the strain and age range provided by the supplier [32,67] and derived from the literature [68]. For that reason, BW gain (g) from baseline was correctly used for comparisons [69]. Consistently, WD increased rates of BW gain in both male and female mice (Figure 1C,D). In male mice, body weight increased significantly from 12 to 15 weeks of age [ANOVA, diet: F (1, 13) = 15.03, p < 0.0001; age: F (2.188, 28.45) = 121.2, p < 0.0001; age × diet: F (16, 208) = 7.182, p < 0.0001; Sidak’s test p < 0.01], while in female mice from 9 to 10 weeks of age and at the endpoint [ANOVA, diet: F (1, 14) = 11.43, p < 0.0001; age: F (3.235, 45.29) = 92.79, p < 0.0001; age × diet: F (16, 224) = 5.217 p < 0.0001; Sidak’s test p < 0.05].
Differently, the WD intervention produced a significant increase in BCS only in C57BL/6J males at 16 and 24 weeks of age [ANOVA, diet: F (1, 14) = 289.0; age: F (1.000, 14.00) = 73.00; age × diet: F (2, 28) = 73.00; p = 0.0001; Sidak’s test p < 0.05] (Figure 2A,B). This finding was consistent with visual evidence of increased subcutaneous, abdominal, and gonadal adipose tissue depots, as well as “fatty liver” appearance at necropsy of male mice [20], suggesting sex-dependent differences in fat distribution patterns (Figure 2C,D).
At 24 weeks of age, the BW (g) differed significantly only between male groups (Mann–Whitney test, p = 0.0342) (Figure 3A,B). Overall, liver weight (g) was not significantly different between SD- and WD-fed groups (Figure 3C,D), while the ratio of liver weight to body weight (%) (Figure 3E,F) was significantly higher in the WD-fed males and females compared to the control groups (Mann–Whitney test, p = 0.0289 and p = 0.0379, respectively).
3.2. WD Influences Feeding Behavior in C57Bl/6J Substrain
Overall, WD influenced food intake in an age- and sex-specific manner (Figure 4). The male WD group had significantly higher food intake at 8 and 12 weeks, while eating significantly less from 16 to 24 weeks of age than SD males (ANOVA, diet: F (1, 13) = 132.5, p < 0.0001; age: F (1.826, 23.73) = 15.27, p < 0.0001; age × diet: F (16, 208) = 35.31, p < 0.0001; Sidak’s test p < 0.0001). Female WD mice ate significantly more than the sex-matched control group from 8 to 12 weeks of age, while eating significantly less at 16 and 18 weeks of age [ANOVA, diet: F (1, 14) = 1.754; p < 0.2066; age F (1.348, 18.87) = 24.31; p < 0.0001; age × diet F (16, 224) = 24.31, p < 0.0001; Sidak’s test p < 0.0001], with a pattern similar to what was described in males (Figure 4A,B).
Assuming that WD had a higher caloric density than SD, male WD group showed higher calorie intake than SD group from 8 to 17 weeks of age, while it was lower thereafter, and nearly isocaloric from 21 to 24 weeks of age, reaching statistical significance at different time points [ANOVA, diet: F (1, 13) = 279.6, p < 0.0001; age: F (2.047, 26.62) = 22.18, p < 0.0001; age × diet: F (16, 208) = 46.17, p < 0.0001; Sidak’s test p < 0.05–p < 0.0001]. The female WD group showed a significantly higher caloric intake than the female SD group from 8 to 16 weeks of age but, unlike males, the trend never reversed and the isocaloric phase was reached at 22 weeks of age [ANOVA, diet: F (1, 14) = 70.44, p < 0.0001; age × diet: F (16, 224) = 26.79, p < 0.0001; Sidak’s test p < 0.05–p < 0.0001] (Figure 4C,D).
The male WD group displayed a greater FER than the male SD group from 13 weeks of age, reaching significance at 14 and from 18 to 20 weeks of age [ANOVA, diet: F (1, 13) = 10.27, p < 0.0001; age: F (2.013, 26.16) = 96.11, p < 0.0001; age × diets: F (16, 208) = 10.67, p < 0.0001; Sidak’s test p < 0.05]. Conversely, FER was not significantly different between female WD mice and the sex-matched control group at any time point [ANOVA, age: F (2.276, 31.86) = 60.16; p < 0.0001; age × diet: F (2.276, 31.86) = 60.16; p < 0.0001] (Figure 4E,F). Taken together, these results suggest that the trend for greater BW gain in WD-fed C57Bl/6J might be explained by a temporary hyperphagic phase observed within the first 10 week of diet, followed by a normalization phase. However, FER was significantly different only between male WD- and SD-fed mice in the last experimental weeks, when WD-fed mice developed an overweight phenotype, even though they ate less and consumed an equivalent amount of calories to SD-fed mice.
3.3. WD Induces Changes in Lipid Metabolism of C57Bl/6J Substrain
Non-fasting basal total cholesterol levels were not significantly different between male groups, while, reasonably, WD-fed mice developed hypercholesterolemia at 16 weeks of age [ANOVA, age: F (1, 13) = 11.28; p = 0.0051; age × diet: F (1, 13) = 10.67, p = 0.0061; Sidak’s test p > 0.05] (Figure 5A). Conversely, male WD group had significantly higher non-fasting triglycerides at 8 weeks of age, whereas the trend was reversed at 16 weeks compared to the SD group [ANOVA, age × diet: F (1, 13) = 10.67, p = 0.0061; Sidak’s test p > 0.01] (Figure 5C).
For that reason, changes in plasma triglycerides from baseline were used for comparisons, and the significance of triglycerides decrease at 16 weeks of age was confirmed in the WD group (Mann–Whitney test p = 0.0003) (Figure 5E).
Superimposable results were observed between female groups for non-fasting total cholesterol [ANOVA, age: F (1, 14) = 15.03; p = 0.0017; age × diet: F (1, 14) = 25.66, p = 0.0002; Sidak’s test p > 0.001] (Figure 5B). Furthermore, the female SD group also showed significantly higher values of non-fasting triglycerides at 8 weeks, as well as at 16 weeks of age [ANOVA, diet: F (1, 14) = 29.23; p < 0.0001; age: F (1, 14) = 23.89; p = 0.0002; Sidak’s test p < 0.05] (Figure 5D), but these results were not confirmed comparing changes in plasma triglycerides from baseline (Figure 5F).
Consistently, fasting total cholesterol was also significantly higher in the WD group of both sexes compared to SD-fed counterparts at 24 weeks of age (Mann–Whitney test p = 0.0143 and p = 0.0002, respectively) (Figure 6A,B). Conversely, at the same age, fasting triglycerides were significantly greater only in male WD group compared to SD-fed controls (Mann–Whitney test p = 0.0370) (Figure 6C,D).
Considering the above results, the diet-induced BW gain and changes in macroscopic liver appearance were overall associated with progressive hypercholesterolemia in both female and male WD-fed mice. Unexpectedly, non-fasting triglycerides showed a significant reduction at 16 weeks of age only in WD-fed males. This finding has been described in other studies on C57Bl/6J mice fed a high-fat, high-cholesterol diet [11,70]. The hypothesis was that this specific diet might reduce synthesis and/or increase clearance of triglycerides or decrease hepatic VLDL secretion and/or increase absorption of triglycerides from adipose tissue, due to the acute increase in insulin [11]. Furthermore, the expression of lipoprotein lipase might be induced in the liver of adult mice with high dietary cholesterol, lowering plasma triglycerides levels [71]. Finally, these results could be interpreted considering the known sex- and age-related differences in serum biochemical parameters of C57BL/6J mice [30] and also based on the vending source of mouse substrain [72,73].
3.4. WD Impairs Glucose Homeostasis in C57Bl/6J Substrain
No significant changes over time in non-fasting blood glucose (mg/dL) were observed between sex-matched SD- and WD-fed groups (Figure 7A,B). However, two males in the WD-fed group showed mild hyperglycemia at 16 weeks of age. At 24 weeks of age, fasting blood glucose was significantly higher in the WD group of both sexes compared to the SD-fed counterparts (Mann–Whitney test, males: p = 0.0002 and females: p = 0.0030) (Figure 7C,D). Furthermore, the male WD group showed a significantly higher fasting insulin and lower HOMA-%S (Mann–Whitney test p = 0.0012 and p = 0.0043, respectively) (Figure 7E,G). In the male WD group, a trend towards impaired β-cell function (HOMA-%B) and insulin resistance (HOMA-IR) was observed, although it was not statistically significant (Figure 7I,M), similarly to the female WD group (Figure 7F,H,L,N).
These findings are consistent with the sexual dimorphism in glucose homeostasis described in the C57BL/6J mouse strain, although few studies have evaluated the metabolic effects of WD in female mice [1]. Furthermore, the higher insulin levels and lower insulin sensitivity found in WD-fed male mice are concordant with the obvious overweight phenotype and fatty liver appearance, as well as clear dyslipidemia, at 24 weeks of age.
3.5. WD Induces Changes in Hepatic and Renal Biochemistry of C57Bl/6J Mice
The male WD group showed higher ALT (U/L) and AST (U/L) values compared to SD-fed counterpart, reaching statistical significance for AST (Mann–Whitney test p = 0.002) (Figure 8A,C). A similar trend was observed between SD-fed and WD-fed female groups for AST. Conversely, female WD group showed significantly lower ALT values compared to sex-matched controls (Mann–Whitney test p = 0.0134) (Figure 8B,D). However, liver enzymes were in the normal range reported in the literature for both SD-fed and WD-fed mice [73,74]. Interestingly, BUN (mg/dL) was significantly higher in the SD groups of both sexes (Mann–Whitney test, males: p = 0.0003 and females: p = 0.0001) (Figure 8E,F), although it was in the reference range for healthy mice. Taken together, the changes observed in transaminases and azotemia would suggest a mild effect of WD on the metabolism of various organs, such as liver, heart, muscles, pancreas and kidneys [75], and related to decreased conversion of ammonia to urea in hepatic diseases and/or to low-protein diet [76]. Furthermore, increased visceral fat and decreased muscle mass in metabolic syndrome are gaining attention as risk factors for fatty liver disease and have been found to be related to decreased BUN value [77].
3.6. WD Causes Progressive Structural and Functional Changes in the Heart, Liver, and Kidney of C57Bl/6J Mice That Can Be Detected Early and Monitored In Vivo by HFUS
All animals showed normal sinus rhythm on ECG, and based on the visual wall motion assessed, no evident myocardial contraction abnormalities of the left ventricle were captured on the B-mode movie frames acquired in the long and short axis views.
Descriptive statistics (mean ± SD) for echocardiographic morphometric and functional parameters over time are reported in Table 1. ANOVA for repeated measures was used to evaluate the differences between the sex-matched diet groups concerning the echocardiographic parameters. Since post hoc test revealed that some normalized echocardiographic measurements at 8 weeks of age were statistically different between the sex-matched groups, the magnitude of changes from baseline was used in these cases for comparisons. In male mice, IVS/LVAW; s variations over time showed a significative interaction between age and diets [ANOVA, F (1, 13) = 8.393, p = 0.0125]. LVPW; d values were significantly lower in WD-fed males at 16 and 24 weeks compared to SD-fed counterpart [ANOVA, diet: F (1, 13) = 15.61, p = 0.0017; age: F (1.585, 20.61) = 4.283, p = 0.0354; Sidak’s test p < 0.05], as well as LVPW; s values [ANOVA, diet: F (1, 13) = 6.011, p = 0.0291; age: F (1.946, 25.30) = 5.476, p = 0.0111]. Global systolic function parameters EF and FS showed no statistical differences between SD- and WD-fed male groups, as well as LV mass corrected, diastolic and systolic LV vol; d, LV vol; s, hr, SV and CO. Although the post hoc test showed no statistical differences between SD- and WD-fed male mice at any time point, RWT was significantly affected by age and diet, with a higher tendency for SD-male mice [ANOVA, diet: F (1, 13) = 10.35, p = 0.0067; age: F (1.833, 23.83) = 11,38, p = 0.0005]. In female mice, EF, FS, LV mass-corrected and hr showed no statistical differences between SD- and WD-fed female groups. In contrast to males, SV and CO showed significant changes with age as well as a significant interaction between age and diets, showing a lower trend in female WD-fed mice [ANOVA, diet: CO: F (1, 14) = 6.523, p < 0.0229; age, SV, F (1.422, 19.91) = 12,17, p = 0.0010—CO, F (1.405, 19.67) = 7.852, p = 0.0063; age × diets, SV: F (2, 28) = 9.257, p = 0.0008—CO, F (2, 28) = 5.614, p = 0.0089; Sidak test CO 16 weeks of age, p = 0.0074]. Finally, the magnitude of RWT changes in female mice was significantly affected by age and the interaction between age and diets, with a higher trend for WD-fed mice [ANOVA, age: F (1, 14) = 6.749, p = 0.0211; age × diets: F (1, 14) = 4.618, p = 0.0496; Sidak test at 16 weeks of age, p = 0.0175].
Qualitative ultrasound examination of liver at baseline showed no altered echogenicity and echostructure in all mice. Visual inspection highlighted that at 24 weeks of age, 14% of SD-fed male mice showed a slightly diffuse increase in liver echogenicity/heterogeneity of echotexture and, in all mice, liver was less echogenic than the renal cortex. Conversely, WD-fed male mice showed a diffusely increased echogenicity (12.5%), a discretely (16.6%) or extensively coarsened and heterogeneous parenchyma (12.5%), whose brightness was higher to the right renal cortex in some mice (25%). At the same age, all SD-fed female mice showed no obvious ultrasonographic alterations of the liver, while WD-fed female mice showed a diffusely increased parenchymal echogenicity (62.5%) or discretely coarsened and heterogeneous parenchyma (37.5%), as well as liver brightness nearly equal to the right renal cortex in some mice (25%). The results of the qualitative ultrasound examination of liver were in agreement with histological findings (see Table 2, Figure 9 and Figure 10, and Section 3.7 for details).
In multiparametric analysis, all ultrasound measures analyzed at baseline were not statistically different between the sex-matched groups.
WD-fed female group showed a progressive increase in HR index from 16 to 24 weeks of age compared to SD-fed female group [ANOVA, diet: F (1, 14) = 29.76, p < 0.0001; age: F (1.649, 23.09) = 6.939, p = 0.0065; age × diet: F (2, 28) = 0.3446, p = 0.0459; Sidak’s test, females: 16 weeks, p = 0.0005; 24 weeks, p = 0.0033] (Figure 11).
A similar trend was observed in the WD-fed male group compared to the SD-fed counterpart at the same time points, although no significance was reached.
Comparable results were obtained between female groups regarding HPV ratio [ANOVA, diet: F (1, 13) = 12.50, p < 0.0037; age: F (1.576, 20.48) = 9767, p = 0.0019)].
As a representation of the local variation in tissue echogenicity, heterogeneity of right median liver lobe showed significant effects of interaction between diet intervention and age in females at 24 weeks of age [ANOVA, age × diet F (2, 28) = 4.434, p = 0.0212; 24 weeks Sidak’s test p = 0.0258]. Anisotropy, as a measure of regional variation in tissue brightness across different lobes, showed significant changes across different diets in mice of both sexes, with WD-fed mice showing a trend toward higher values than age- and sex-matched SD-fed mice [ANOVA, diet: males, F (1, 13) = 6.430, p = 0.0249—females, F (1, 14) = 7.899, p = 0.0139] (Figure 12).
Finally, the pattern of temporal changes in PV diameter differed between SD- and WD-fed male mice, although this value was not statistically different at any time point in both sex-matched comparisons [ANOVA, age: males, F (1.965, 25.54) = 8.409, p = 0.0016—females, F (1.542, 21,58) = 3.617, p = 0.0544; age × diets: males, F (2, 26) = 3.894, p = 0.0332] (Figure 13).
Over time, all animals showed a normal ultrasound appearance of the right kidney, characterized by the thin, bright line of the renal capsule, a regular and defined renal cortical margin, the medulla in the medial portion of the kidney darker than the surrounding cortex, and the pelvis almost entirely occupied by the papilla [53]. Although WD-fed mice showed slightly lower normalized cortical thickness values, no significant differences were found in CRT/BW between sex-matched groups fed different diets. However, unlike males, ANOVA showed a significant increase in renal cortex echogenicity from baseline in WD-fed female mice compared to SD-fed ones [ANOVA, diet: F (1, 14) = 8.796, p = 0.0102]. At 24 weeks of age, PI and RI were statistically different between SD- and WD-fed female mice (Mann–Whitney test, p ≤ 0.01) and compared to the reference range. This finding could be likely related to the lower trend for SV and CO found in WD-fed females compared to SD-fed ones [51] (Figure 14).
3.7. WD Induces Histological Changes in the Liver and Kidney of C57Bl/6J Mice
The pathological grades of fatty liver disease in each mouse were determined by the NAFLD preclinical scoring system as reported in the Material and Methods section. Briefly, in microvesicular steatosis, small, clearly defined lipid droplets are widespread in the cytoplasm of hepatocytes, whereas large lipid droplets that occupy almost the entire cytoplasm of hepatocytes characterize macrovesicular steatosis. Lipid droplets appear optically empty in H&E- and PAS-stained sections and progressively displace cell nuclei marginally. Hypertrophic hepatocytes have the same cytoplasmic characteristics but appear much larger than surrounding ones. In Table 3, the total score assigned for the most relevant histological features in murine liver samples is listed by grading category and the number of subjects per score is shown.
In H&E-stained liver sections from 24-week-old mice fed SD of either sex, hepatocytes showed a normal appearance with abundant cytoplasm and centrally located round nuclei. The PAS staining revealed glycogen-rich areas, allowing the differential diagnosis of physiological glycogen accumulation in mouse liver from borderline pathological foam cells [78]. In WD-fed female mice, 37.5% of subjects showed the lowest grade of macrovesicular hepatic steatosis, while 75% displayed the lowest grade of microvesicular hepatic steatosis and/or hypertrophic hepatocytes encompassing the entire grading window. Mild liver inflammation was observed in one (12.5%) and mild fibrosis in two (25%) WD-fed female mice. Three WD-fed male mice (37.5%) showed histological features of borderline “ballooning” lesions, characterized by “patches” of hepatocytes with round shape and pale, reticulated cytoplasm, with normal or enlarged size, as confirmed by a comparison of serial sections stained with H&E and PAS. Macrovesicular hepatic steatosis at the lowest grade was observed in 62.5% of WD-fed male mice, in almost all cases (50%) associated with microvesicular hepatic steatosis in a mixed pattern. No hypertrophic hepatocytes and inflammation were observed, while mild fibrosis was found in the liver of 50% of WD-fed males. A moderate positive linear correlation was found in male WD mice between US visual grading and NAFLD histological score (Pearson r = 0.6379; p = 0.0444), HR index (Pearson r = 0.6604; p = 0.0373) and mean liver echogenicity (Pearson r = 0.7214; p = 0.0217). Examination of renal histological specimens revealed no tubulointerstitial lesions in mice of either sex fed different diets. However, we observed Bowman’s space narrowing in glomeruli from WD-fed mice compared to age- and sex-matched SD-fed mice. Table 4 displays the qualitative, semi-quantitative grading of structural renal changes with respect to the proportion of glomeruli affected. Notably, according to the renal score grading used, a different pattern of frequency distribution between sexes was observed under WD: score 0: males, 14.28%, females, 0%; score 1: males, 42.85%, females, 85.71%; score 2: males, 48.85%, females, 14.28%.
Comparable histological features, with progressive collapse of Bowman’s space, have been described early in patients with diabetic nephropathy, leading to glomerulosclerosis in advanced stages of disease [79], and in rodent models fed a high-fat diet [80,81,82].
3.1. WD Affects Body Weight and Nutritional Phenotype of C57Bl/6J Mice
As expected, BW and nutritional status of C57Bl/6J mice were significantly affected by WD. Baseline BW (g) was not different between male SD and WD groups (Mann–Whitney test, p = 0.7587), while it was statistically higher in the WD group between 13 and 15 weeks of age [ANOVA, diet: F (16, 208) = 5.978; p < 0.0001; Sidak’s test p < 0.05] (Figure 1A). Conversely, the female SD group was significantly heavier than the WD group at 8 weeks of age [Mann–Whitney test, p = 0.0005; ANOVA, diet: F (1, 14) = 0.08653; p < 0.7730; Sidak’s test p < 0.05] (Figure 1B), although all records over time were within the strain and age range provided by the supplier [32,67] and derived from the literature [68]. For that reason, BW gain (g) from baseline was correctly used for comparisons [69]. Consistently, WD increased rates of BW gain in both male and female mice (Figure 1C,D). In male mice, body weight increased significantly from 12 to 15 weeks of age [ANOVA, diet: F (1, 13) = 15.03, p < 0.0001; age: F (2.188, 28.45) = 121.2, p < 0.0001; age × diet: F (16, 208) = 7.182, p < 0.0001; Sidak’s test p < 0.01], while in female mice from 9 to 10 weeks of age and at the endpoint [ANOVA, diet: F (1, 14) = 11.43, p < 0.0001; age: F (3.235, 45.29) = 92.79, p < 0.0001; age × diet: F (16, 224) = 5.217 p < 0.0001; Sidak’s test p < 0.05].
Differently, the WD intervention produced a significant increase in BCS only in C57BL/6J males at 16 and 24 weeks of age [ANOVA, diet: F (1, 14) = 289.0; age: F (1.000, 14.00) = 73.00; age × diet: F (2, 28) = 73.00; p = 0.0001; Sidak’s test p < 0.05] (Figure 2A,B). This finding was consistent with visual evidence of increased subcutaneous, abdominal, and gonadal adipose tissue depots, as well as “fatty liver” appearance at necropsy of male mice [20], suggesting sex-dependent differences in fat distribution patterns (Figure 2C,D).
At 24 weeks of age, the BW (g) differed significantly only between male groups (Mann–Whitney test, p = 0.0342) (Figure 3A,B). Overall, liver weight (g) was not significantly different between SD- and WD-fed groups (Figure 3C,D), while the ratio of liver weight to body weight (%) (Figure 3E,F) was significantly higher in the WD-fed males and females compared to the control groups (Mann–Whitney test, p = 0.0289 and p = 0.0379, respectively).
3.2. WD Influences Feeding Behavior in C57Bl/6J Substrain
Overall, WD influenced food intake in an age- and sex-specific manner (Figure 4). The male WD group had significantly higher food intake at 8 and 12 weeks, while eating significantly less from 16 to 24 weeks of age than SD males (ANOVA, diet: F (1, 13) = 132.5, p < 0.0001; age: F (1.826, 23.73) = 15.27, p < 0.0001; age × diet: F (16, 208) = 35.31, p < 0.0001; Sidak’s test p < 0.0001). Female WD mice ate significantly more than the sex-matched control group from 8 to 12 weeks of age, while eating significantly less at 16 and 18 weeks of age [ANOVA, diet: F (1, 14) = 1.754; p < 0.2066; age F (1.348, 18.87) = 24.31; p < 0.0001; age × diet F (16, 224) = 24.31, p < 0.0001; Sidak’s test p < 0.0001], with a pattern similar to what was described in males (Figure 4A,B).
Assuming that WD had a higher caloric density than SD, male WD group showed higher calorie intake than SD group from 8 to 17 weeks of age, while it was lower thereafter, and nearly isocaloric from 21 to 24 weeks of age, reaching statistical significance at different time points [ANOVA, diet: F (1, 13) = 279.6, p < 0.0001; age: F (2.047, 26.62) = 22.18, p < 0.0001; age × diet: F (16, 208) = 46.17, p < 0.0001; Sidak’s test p < 0.05–p < 0.0001]. The female WD group showed a significantly higher caloric intake than the female SD group from 8 to 16 weeks of age but, unlike males, the trend never reversed and the isocaloric phase was reached at 22 weeks of age [ANOVA, diet: F (1, 14) = 70.44, p < 0.0001; age × diet: F (16, 224) = 26.79, p < 0.0001; Sidak’s test p < 0.05–p < 0.0001] (Figure 4C,D).
The male WD group displayed a greater FER than the male SD group from 13 weeks of age, reaching significance at 14 and from 18 to 20 weeks of age [ANOVA, diet: F (1, 13) = 10.27, p < 0.0001; age: F (2.013, 26.16) = 96.11, p < 0.0001; age × diets: F (16, 208) = 10.67, p < 0.0001; Sidak’s test p < 0.05]. Conversely, FER was not significantly different between female WD mice and the sex-matched control group at any time point [ANOVA, age: F (2.276, 31.86) = 60.16; p < 0.0001; age × diet: F (2.276, 31.86) = 60.16; p < 0.0001] (Figure 4E,F). Taken together, these results suggest that the trend for greater BW gain in WD-fed C57Bl/6J might be explained by a temporary hyperphagic phase observed within the first 10 week of diet, followed by a normalization phase. However, FER was significantly different only between male WD- and SD-fed mice in the last experimental weeks, when WD-fed mice developed an overweight phenotype, even though they ate less and consumed an equivalent amount of calories to SD-fed mice.
3.3. WD Induces Changes in Lipid Metabolism of C57Bl/6J Substrain
Non-fasting basal total cholesterol levels were not significantly different between male groups, while, reasonably, WD-fed mice developed hypercholesterolemia at 16 weeks of age [ANOVA, age: F (1, 13) = 11.28; p = 0.0051; age × diet: F (1, 13) = 10.67, p = 0.0061; Sidak’s test p > 0.05] (Figure 5A). Conversely, male WD group had significantly higher non-fasting triglycerides at 8 weeks of age, whereas the trend was reversed at 16 weeks compared to the SD group [ANOVA, age × diet: F (1, 13) = 10.67, p = 0.0061; Sidak’s test p > 0.01] (Figure 5C).
For that reason, changes in plasma triglycerides from baseline were used for comparisons, and the significance of triglycerides decrease at 16 weeks of age was confirmed in the WD group (Mann–Whitney test p = 0.0003) (Figure 5E).
Superimposable results were observed between female groups for non-fasting total cholesterol [ANOVA, age: F (1, 14) = 15.03; p = 0.0017; age × diet: F (1, 14) = 25.66, p = 0.0002; Sidak’s test p > 0.001] (Figure 5B). Furthermore, the female SD group also showed significantly higher values of non-fasting triglycerides at 8 weeks, as well as at 16 weeks of age [ANOVA, diet: F (1, 14) = 29.23; p < 0.0001; age: F (1, 14) = 23.89; p = 0.0002; Sidak’s test p < 0.05] (Figure 5D), but these results were not confirmed comparing changes in plasma triglycerides from baseline (Figure 5F).
Consistently, fasting total cholesterol was also significantly higher in the WD group of both sexes compared to SD-fed counterparts at 24 weeks of age (Mann–Whitney test p = 0.0143 and p = 0.0002, respectively) (Figure 6A,B). Conversely, at the same age, fasting triglycerides were significantly greater only in male WD group compared to SD-fed controls (Mann–Whitney test p = 0.0370) (Figure 6C,D).
Considering the above results, the diet-induced BW gain and changes in macroscopic liver appearance were overall associated with progressive hypercholesterolemia in both female and male WD-fed mice. Unexpectedly, non-fasting triglycerides showed a significant reduction at 16 weeks of age only in WD-fed males. This finding has been described in other studies on C57Bl/6J mice fed a high-fat, high-cholesterol diet [11,70]. The hypothesis was that this specific diet might reduce synthesis and/or increase clearance of triglycerides or decrease hepatic VLDL secretion and/or increase absorption of triglycerides from adipose tissue, due to the acute increase in insulin [11]. Furthermore, the expression of lipoprotein lipase might be induced in the liver of adult mice with high dietary cholesterol, lowering plasma triglycerides levels [71]. Finally, these results could be interpreted considering the known sex- and age-related differences in serum biochemical parameters of C57BL/6J mice [30] and also based on the vending source of mouse substrain [72,73].
3.4. WD Impairs Glucose Homeostasis in C57Bl/6J Substrain
No significant changes over time in non-fasting blood glucose (mg/dL) were observed between sex-matched SD- and WD-fed groups (Figure 7A,B). However, two males in the WD-fed group showed mild hyperglycemia at 16 weeks of age. At 24 weeks of age, fasting blood glucose was significantly higher in the WD group of both sexes compared to the SD-fed counterparts (Mann–Whitney test, males: p = 0.0002 and females: p = 0.0030) (Figure 7C,D). Furthermore, the male WD group showed a significantly higher fasting insulin and lower HOMA-%S (Mann–Whitney test p = 0.0012 and p = 0.0043, respectively) (Figure 7E,G). In the male WD group, a trend towards impaired β-cell function (HOMA-%B) and insulin resistance (HOMA-IR) was observed, although it was not statistically significant (Figure 7I,M), similarly to the female WD group (Figure 7F,H,L,N).
These findings are consistent with the sexual dimorphism in glucose homeostasis described in the C57BL/6J mouse strain, although few studies have evaluated the metabolic effects of WD in female mice [1]. Furthermore, the higher insulin levels and lower insulin sensitivity found in WD-fed male mice are concordant with the obvious overweight phenotype and fatty liver appearance, as well as clear dyslipidemia, at 24 weeks of age.
3.5. WD Induces Changes in Hepatic and Renal Biochemistry of C57Bl/6J Mice
The male WD group showed higher ALT (U/L) and AST (U/L) values compared to SD-fed counterpart, reaching statistical significance for AST (Mann–Whitney test p = 0.002) (Figure 8A,C). A similar trend was observed between SD-fed and WD-fed female groups for AST. Conversely, female WD group showed significantly lower ALT values compared to sex-matched controls (Mann–Whitney test p = 0.0134) (Figure 8B,D). However, liver enzymes were in the normal range reported in the literature for both SD-fed and WD-fed mice [73,74]. Interestingly, BUN (mg/dL) was significantly higher in the SD groups of both sexes (Mann–Whitney test, males: p = 0.0003 and females: p = 0.0001) (Figure 8E,F), although it was in the reference range for healthy mice. Taken together, the changes observed in transaminases and azotemia would suggest a mild effect of WD on the metabolism of various organs, such as liver, heart, muscles, pancreas and kidneys [75], and related to decreased conversion of ammonia to urea in hepatic diseases and/or to low-protein diet [76]. Furthermore, increased visceral fat and decreased muscle mass in metabolic syndrome are gaining attention as risk factors for fatty liver disease and have been found to be related to decreased BUN value [77].
3.6. WD Causes Progressive Structural and Functional Changes in the Heart, Liver, and Kidney of C57Bl/6J Mice That Can Be Detected Early and Monitored In Vivo by HFUS
All animals showed normal sinus rhythm on ECG, and based on the visual wall motion assessed, no evident myocardial contraction abnormalities of the left ventricle were captured on the B-mode movie frames acquired in the long and short axis views.
Descriptive statistics (mean ± SD) for echocardiographic morphometric and functional parameters over time are reported in Table 1. ANOVA for repeated measures was used to evaluate the differences between the sex-matched diet groups concerning the echocardiographic parameters. Since post hoc test revealed that some normalized echocardiographic measurements at 8 weeks of age were statistically different between the sex-matched groups, the magnitude of changes from baseline was used in these cases for comparisons. In male mice, IVS/LVAW; s variations over time showed a significative interaction between age and diets [ANOVA, F (1, 13) = 8.393, p = 0.0125]. LVPW; d values were significantly lower in WD-fed males at 16 and 24 weeks compared to SD-fed counterpart [ANOVA, diet: F (1, 13) = 15.61, p = 0.0017; age: F (1.585, 20.61) = 4.283, p = 0.0354; Sidak’s test p < 0.05], as well as LVPW; s values [ANOVA, diet: F (1, 13) = 6.011, p = 0.0291; age: F (1.946, 25.30) = 5.476, p = 0.0111]. Global systolic function parameters EF and FS showed no statistical differences between SD- and WD-fed male groups, as well as LV mass corrected, diastolic and systolic LV vol; d, LV vol; s, hr, SV and CO. Although the post hoc test showed no statistical differences between SD- and WD-fed male mice at any time point, RWT was significantly affected by age and diet, with a higher tendency for SD-male mice [ANOVA, diet: F (1, 13) = 10.35, p = 0.0067; age: F (1.833, 23.83) = 11,38, p = 0.0005]. In female mice, EF, FS, LV mass-corrected and hr showed no statistical differences between SD- and WD-fed female groups. In contrast to males, SV and CO showed significant changes with age as well as a significant interaction between age and diets, showing a lower trend in female WD-fed mice [ANOVA, diet: CO: F (1, 14) = 6.523, p < 0.0229; age, SV, F (1.422, 19.91) = 12,17, p = 0.0010—CO, F (1.405, 19.67) = 7.852, p = 0.0063; age × diets, SV: F (2, 28) = 9.257, p = 0.0008—CO, F (2, 28) = 5.614, p = 0.0089; Sidak test CO 16 weeks of age, p = 0.0074]. Finally, the magnitude of RWT changes in female mice was significantly affected by age and the interaction between age and diets, with a higher trend for WD-fed mice [ANOVA, age: F (1, 14) = 6.749, p = 0.0211; age × diets: F (1, 14) = 4.618, p = 0.0496; Sidak test at 16 weeks of age, p = 0.0175].
Qualitative ultrasound examination of liver at baseline showed no altered echogenicity and echostructure in all mice. Visual inspection highlighted that at 24 weeks of age, 14% of SD-fed male mice showed a slightly diffuse increase in liver echogenicity/heterogeneity of echotexture and, in all mice, liver was less echogenic than the renal cortex. Conversely, WD-fed male mice showed a diffusely increased echogenicity (12.5%), a discretely (16.6%) or extensively coarsened and heterogeneous parenchyma (12.5%), whose brightness was higher to the right renal cortex in some mice (25%). At the same age, all SD-fed female mice showed no obvious ultrasonographic alterations of the liver, while WD-fed female mice showed a diffusely increased parenchymal echogenicity (62.5%) or discretely coarsened and heterogeneous parenchyma (37.5%), as well as liver brightness nearly equal to the right renal cortex in some mice (25%). The results of the qualitative ultrasound examination of liver were in agreement with histological findings (see Table 2, Figure 9 and Figure 10, and Section 3.7 for details).
In multiparametric analysis, all ultrasound measures analyzed at baseline were not statistically different between the sex-matched groups.
WD-fed female group showed a progressive increase in HR index from 16 to 24 weeks of age compared to SD-fed female group [ANOVA, diet: F (1, 14) = 29.76, p < 0.0001; age: F (1.649, 23.09) = 6.939, p = 0.0065; age × diet: F (2, 28) = 0.3446, p = 0.0459; Sidak’s test, females: 16 weeks, p = 0.0005; 24 weeks, p = 0.0033] (Figure 11).
A similar trend was observed in the WD-fed male group compared to the SD-fed counterpart at the same time points, although no significance was reached.
Comparable results were obtained between female groups regarding HPV ratio [ANOVA, diet: F (1, 13) = 12.50, p < 0.0037; age: F (1.576, 20.48) = 9767, p = 0.0019)].
As a representation of the local variation in tissue echogenicity, heterogeneity of right median liver lobe showed significant effects of interaction between diet intervention and age in females at 24 weeks of age [ANOVA, age × diet F (2, 28) = 4.434, p = 0.0212; 24 weeks Sidak’s test p = 0.0258]. Anisotropy, as a measure of regional variation in tissue brightness across different lobes, showed significant changes across different diets in mice of both sexes, with WD-fed mice showing a trend toward higher values than age- and sex-matched SD-fed mice [ANOVA, diet: males, F (1, 13) = 6.430, p = 0.0249—females, F (1, 14) = 7.899, p = 0.0139] (Figure 12).
Finally, the pattern of temporal changes in PV diameter differed between SD- and WD-fed male mice, although this value was not statistically different at any time point in both sex-matched comparisons [ANOVA, age: males, F (1.965, 25.54) = 8.409, p = 0.0016—females, F (1.542, 21,58) = 3.617, p = 0.0544; age × diets: males, F (2, 26) = 3.894, p = 0.0332] (Figure 13).
Over time, all animals showed a normal ultrasound appearance of the right kidney, characterized by the thin, bright line of the renal capsule, a regular and defined renal cortical margin, the medulla in the medial portion of the kidney darker than the surrounding cortex, and the pelvis almost entirely occupied by the papilla [53]. Although WD-fed mice showed slightly lower normalized cortical thickness values, no significant differences were found in CRT/BW between sex-matched groups fed different diets. However, unlike males, ANOVA showed a significant increase in renal cortex echogenicity from baseline in WD-fed female mice compared to SD-fed ones [ANOVA, diet: F (1, 14) = 8.796, p = 0.0102]. At 24 weeks of age, PI and RI were statistically different between SD- and WD-fed female mice (Mann–Whitney test, p ≤ 0.01) and compared to the reference range. This finding could be likely related to the lower trend for SV and CO found in WD-fed females compared to SD-fed ones [51] (Figure 14).
3.7. WD Induces Histological Changes in the Liver and Kidney of C57Bl/6J Mice
The pathological grades of fatty liver disease in each mouse were determined by the NAFLD preclinical scoring system as reported in the Material and Methods section. Briefly, in microvesicular steatosis, small, clearly defined lipid droplets are widespread in the cytoplasm of hepatocytes, whereas large lipid droplets that occupy almost the entire cytoplasm of hepatocytes characterize macrovesicular steatosis. Lipid droplets appear optically empty in H&E- and PAS-stained sections and progressively displace cell nuclei marginally. Hypertrophic hepatocytes have the same cytoplasmic characteristics but appear much larger than surrounding ones. In Table 3, the total score assigned for the most relevant histological features in murine liver samples is listed by grading category and the number of subjects per score is shown.
In H&E-stained liver sections from 24-week-old mice fed SD of either sex, hepatocytes showed a normal appearance with abundant cytoplasm and centrally located round nuclei. The PAS staining revealed glycogen-rich areas, allowing the differential diagnosis of physiological glycogen accumulation in mouse liver from borderline pathological foam cells [78]. In WD-fed female mice, 37.5% of subjects showed the lowest grade of macrovesicular hepatic steatosis, while 75% displayed the lowest grade of microvesicular hepatic steatosis and/or hypertrophic hepatocytes encompassing the entire grading window. Mild liver inflammation was observed in one (12.5%) and mild fibrosis in two (25%) WD-fed female mice. Three WD-fed male mice (37.5%) showed histological features of borderline “ballooning” lesions, characterized by “patches” of hepatocytes with round shape and pale, reticulated cytoplasm, with normal or enlarged size, as confirmed by a comparison of serial sections stained with H&E and PAS. Macrovesicular hepatic steatosis at the lowest grade was observed in 62.5% of WD-fed male mice, in almost all cases (50%) associated with microvesicular hepatic steatosis in a mixed pattern. No hypertrophic hepatocytes and inflammation were observed, while mild fibrosis was found in the liver of 50% of WD-fed males. A moderate positive linear correlation was found in male WD mice between US visual grading and NAFLD histological score (Pearson r = 0.6379; p = 0.0444), HR index (Pearson r = 0.6604; p = 0.0373) and mean liver echogenicity (Pearson r = 0.7214; p = 0.0217). Examination of renal histological specimens revealed no tubulointerstitial lesions in mice of either sex fed different diets. However, we observed Bowman’s space narrowing in glomeruli from WD-fed mice compared to age- and sex-matched SD-fed mice. Table 4 displays the qualitative, semi-quantitative grading of structural renal changes with respect to the proportion of glomeruli affected. Notably, according to the renal score grading used, a different pattern of frequency distribution between sexes was observed under WD: score 0: males, 14.28%, females, 0%; score 1: males, 42.85%, females, 85.71%; score 2: males, 48.85%, females, 14.28%.
Comparable histological features, with progressive collapse of Bowman’s space, have been described early in patients with diabetic nephropathy, leading to glomerulosclerosis in advanced stages of disease [79], and in rodent models fed a high-fat diet [80,81,82].
4. Discussion
Among available models of metabolic dysregulation, WD-fed C57Bl/6J mice are useful for studying the onset of diet-induced metabolic abnormalities by sex and age, contributing to the development of preventive measures and management strategies in biomedical research. In addition to overweight status, the proposed DIO animal model displays major metabolic alterations, such as dyslipidemia and insulin resistance, which are known to be associated with hepatic steatosis and an increased risk of cardiovascular and renal comorbidities. This multiorgan involvement has been characterized in a few studies on murine models of metabolic diseases [9,10], and HFUS appears an ideal tool to collect in vivo morphofunctional information on organs of interest in combination with conventional biochemical and histological techniques. In this study, we demonstrated that HFUS represents a useful complementary tool to monitor in vivo cardiovascular, hepatic, and renal changes in such animal models over time. Furthermore, our multimodality approach highlighted sex-specific differences in the C57Bl/6J DIO murine model fed with WD, mainly explained by the influence of sexual hormones, as in humans [83].
As expected, WD-fed mice of both sexes showed greater changes in BW gain than SD-fed mice as a result of higher caloric intake and FER. WD-fed males and females showed differences in fat distribution, as evidenced in vivo by higher BCS score, and at necropsy by a greater amount of subcutaneous and visceral adipose tissue, as well as a paler liver in males. Furthermore, both WD-fed males and females showed hepatomegaly, as evidenced by liver/body weight ratio results. In our study, non-fasting total cholesterol increased significantly in earlier experimental phases in both sexes as a response to WD. Furthermore, WD-fed mice demonstrated changes in glucose homeostasis and insulin sensitivity, as well as higher values of transaminases and lower BUN values compared to SD-fed mice of the same sex, which could likely reflect the involvement of diverse organs in diet-related metabolic changes, including heart, liver, and kidney, as detailed above [73,74,75,76,77,84]. In the present study, the effects of lipid overload in these organs were assessed in vivo and over time by HFUS, detecting early changes at 16 weeks of age for some parameters. Our results are indicative of early stages of cardiac remodeling, likely toward concentric hypertrophy in females and eccentric hypertrophy in males, with preserved ejection fraction associated with significantly different LVPW values in WD-fed male mice, as well as CO and RWT parameters in WD-fed female mice, from the age of 16 weeks [85,86]. Nevertheless, WD-fed female mice showed reduced SV and CO compared to their SD-fed counterparts, suggesting incipient progression to deterioration of systolic function [87]. Our findings appear consistent with those of previous studies, showing that large dietary amounts of fat and refined sugars contribute to early metabolic alterations in the myocardium, quite distinct from the chronic conditions of diabetic cardiomyopathy or cardiac hypertrophy induced mainly by fat overload [83]. Such alterations could be very interesting from a translational point of view, perhaps more closely resembling those that occur in humans and more easily reversible with therapies [85]. A previous study showed that in C57Bl/6J male mice feeding a WD for 24 weeks, diastolic dysfunction was detectable by echocardiography after 16 weeks of diet duration, followed by signs of systolic dysfunction starting from 20 and 24 weeks of diet [88]. Another recent report describes, in the same animal model fed a WD for 7 months, a distinct cardiac remodeling phenotype characterized by a reduction in EF and SV associated with alterations in cardiac metabolism due to an anomalous accumulation of lipids both in the endothelium and in the myocardium [89]. Considering this evidence, a strength of our study is that it included both sexes in the echocardiographic analysis, but a limitation was that diastolic function and strain analysis of the left ventricle were not performed. Similarly, HFUS allowed us to detect an increase in liver echogenicity and parenchymal heterogeneity at 16 and/or 24 weeks of age and that the brightness of liver parenchyma became comparable up to exceeding that of the right renal cortex at the intermediate or final experimental time point. The HR ratio showed a trend toward greater values in WD-fed male mice, reaching significance in WD-fed female mice compared to SD-fed counterparts. Moreover, the tissue heterogeneity was significantly higher in WD-fed mice compared to SD-fed mice of the same age and sex. Overall, we found concordance between qualitative ultrasound evaluation of liver changes and histopathological analysis of NAFLD at 24 weeks of age, as well as a significant correlation in WD-fed male mice between semi-quantitative HFUS visual grading, HR index, mean liver echogenicity, and NAFLD histological score. The absence of significant differences for some hepatic measurements obtained with HFUS between the matched groups could be related to technical limits of specificity and sensitivity in the case of fat accumulation in the liver was quite mild [11]. Furthermore, the heterogeneity of phenotypes induced in C57Bl/6J mouse substrain feeding high-fat dietary regimens, related to variability in individual response, might result in statistically non-significant results [90,91]. Furthermore, the quantitative and qualitative parameters used in our study were derived from various preclinical and clinical studies, but an ultrasonographic multiparametric analysis system for hepatic steatosis has not yet been standardized in mice [8,9,44,45,46,47]. Therefore, the absence of significance for some quantitative ultrasound characteristics could also be related to interspecies differences, suggesting the need for further investigations to implement more specific diagnostic approaches dedicated to murine species. Finally, HFUS provided morphological and functional information on the kidney in vivo, including information on the renal circulation, complementary to that obtained by traditional histology.
In our study, PI and RI were significantly reduced in WD-fed female mice compared to SD-fed ones. In the literature, PI and RI were found to be significantly correlated to glomerular filtration rate and serum creatinine and very sensitive to identify early stages of chronic kidney disease in case of obesity and diabetes [57]. Usually, increased RI suggests impaired renal function, although it could also indicate extra-renal pathologies [86,92]. The development of renal alterations after the consumption of a WD has been observed particularly in women, but the pathogenetic mechanisms involved are poorly understood [93]. The WD-induced hepato-cardio-renal syndrome has been investigated in rodent models, suggesting impaired activation of the renin-angiotensin aldosterone system, especially in female mice [93]. The complex interplay of different factors (WD, overweight, impaired glucose tolerance, dyslipidemia, fatty liver disease, and aging) could contribute to metabolic cardiomyopathy characterized by impaired diastolic relaxation with preserved ejection fraction, which in turn could induce a reduction in the LV early diastolic filling [86]. In this regard, due to the limitations of the available equipment/technical issues, the evaluation of diastolic function was not carried out by echocardiography, and serum creatinine was not determined in WD-fed mice, even if this serum parameter resulted within normal ranges in mice of both sexes fed SD, encouraging future research that addresses these gaps.
In conclusion, the reference values for the C57BL/6J mice of both sexes reported here could contribute to support researchers for interpreting the biological data of mouse mutants and corresponding wild-type controls based on the substrain examined. Overall, our findings showed that HFUS might be a useful tool to complement conventional methods of studying DIO research models for more comprehensive evaluation. HFUS offers several possibilities for refinement in preclinical research; for example, it does not use ionizing radiation and allows the examination of different organs with minimal discomfort for mice. In addition, HFUS provides longitudinal information on disease progression in the same subject, avoiding the sacrifice of cohorts of animals at different times, and improves statistical power compared to cross-sectional studies, allowing each animal to be its own control. Finally, ultrasound imaging is a relevant translatable imaging modality, which could improve the “bench-to-bedside” process, rapidly exploiting preclinical research findings into clinical research applications. Although the relatively low signal-to-noise ratio in ultrasound images represents a potential challenge in calculating image features compared to other imaging modalities, this is a rapidly evolving technology, and the development of advanced computerized image analysis methods will be crucial to improve diagnostic accuracy. |
Title: Primary Lymphoma of the Lacrimal Gland on PET/CT Imaging | Body:
Ethics
Informed Consent: Informed consent was obtained from the patient. |
Title: A comparison of male and female renal pelvis urobiome of unilateral stone formers using 2bRAD-M | Body:
Introduction
Urolithiasis, a highly prevalent urological disease, exhibits varying prevalence rates across different regions, with Asia at 1–5%, Europe at 5–9%, and North America at 7–13%, while its global incidence continues to rise [1]. The recurrence rate of urolithiasis within five years after the initial stone episode is estimated to be 67% [2]. Repeated occurrences of calculi can result in chronic kidney disease and renal failure, imposing significant social and economic burdens [3]. Although surgical treatment advancements have significantly enhanced stone removal efficacy, medical treatment has remained largely unchanged for several decades due to limited understanding of the underlying pathogenesis of urolithiasis.
Urolithiasis exhibits a higher prevalence in males compared to females, with a twofold increase in incidence rates among men. However, recent reports suggest a reduction in this gender disparity [4]. Given its multifactorial nature, numerous intrinsic and extrinsic factors contribute to the formation of urinary stones. The precise mechanisms underlying the observed sex differences in urolithiasis remain incompletely explored. Speculations have arisen regarding the potential influence of lifestyle and dietary choices, while genetic backgrounds, sex hormones, and urine chemistries are also believed to play contributory roles in these disparities [5, 6].
Recent advancements in sequencing and culturomics have unveiled the presence of extensive and diverse microbial communities in the urinary tract [7, 8]. These findings have shed light on the distinct variations in the bladder urinary microbiome (urobiome) between individuals with urolithiasis and those who are healthy, offering a fresh perspective on urolithiasis [9, 10]. Notably, significant disparities in the bladder urobiome have been observed between healthy males and females. Fouts et al. discovered that the genus Corynebacterium prevailed in males, while the genus Lactobacillus dominated in females [11].
It is possible to speculate that the disparity in urolithiasis risk between sexes may be partially attributed to variations in urobiome community composition between males and females. While previous studies have primarily focused on profiling the bladder urobiome, it is important to note that the microbiota colonizing the kidney can be better assessed through analysis of renal pelvis urine. In this study, we collected renal pelvis urine samples from unilateral stone formers on both sides and utilized 2bRAD sequencing for Microbiome (2bRAD-M), a novel sequencing technique capable of providing precise species-resolution profiles, to characterize the renal pelvis urobiome. The study involved the division of samples into four groups, namely stone side of males (SM), stone side of females (SF), non-stone side of males (NSM), and non-stone side of females (NSF). The study aimed to investigate potential differences in diversity, microbial community composition, and predicted functions of the renal pelvis urobiome among four distinct groups.
Materials and methods
Patient recruitment
A total of 30 unilateral stone formers experiencing their initial episode were recruited in the study. All patients were diagnosed by computed tomography and received percutaneous nephrolithotomy. To mitigate the potential influence of confounding variables on the urobiome, stringent exclusion criteria were established: obstructed stones, urinary tract infections (UTIs), other urologic diseases, a history of major urological surgeries (e.g., nephrectomy, cystectomy, prostatectomy), antibiotic treatment within four weeks, and urinary catheterization within four weeks. The removed stones obtained during surgery were subjected to chemical composition analysis. Relevant clinical characteristics of patients were documented.
Patient recruitment
A total of 30 unilateral stone formers experiencing their initial episode were recruited in the study. All patients were diagnosed by computed tomography and received percutaneous nephrolithotomy. To mitigate the potential influence of confounding variables on the urobiome, stringent exclusion criteria were established: obstructed stones, urinary tract infections (UTIs), other urologic diseases, a history of major urological surgeries (e.g., nephrectomy, cystectomy, prostatectomy), antibiotic treatment within four weeks, and urinary catheterization within four weeks. The removed stones obtained during surgery were subjected to chemical composition analysis. Relevant clinical characteristics of patients were documented.
Sample collection and processing
Renal pelvis urine collection was approved by the Ethical Review Board of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology under the number 2021S130 in 2021. Informed consent was obtained from all participants. Prior to the collection of urine from the renal pelvis, the bladder was emptied using a urethral catheter to prevent the contamination of bladder urine. Subsequently, a ureteroscope was gently inserted into the renal pelvis on the side with the stone, and a ureteral catheter was introduced through the ureteroscope to obtain 5 ml of urine from the renal pelvis. This procedure was then repeated using a new ureteroscope and ureteral catheter to collect urine from the renal pelvis on the non-stone side. The entire process was conducted under sterile conditions. Following collection, the samples were promptly stored at − 80 °C until further analysis, within a time frame of 1 h.
DNA extraction, library preparation and sequencing
Genomic DNA was extracted using the TIANamp Micro DNA Kit (Tiangen). The preparation of 2bRAD libraries was conducted following established methodologies [12]. Initially, 4 U of the BcgI restriction enzyme (NEB) was employed to digest the genomic DNA at 37 °C for a duration of 3 h. Subsequently, a ligation reaction was carried out at 4 °C for 16 h within a 20 µl reaction volume. This reaction comprised 10 µl of the digested product, 0.2 µM of each of the library-specific adaptors (Ada1 and Ada2), 1 mM ATP (NEB), 1 × T4 DNA Ligase Buffer, and 800 U of T4 DNA ligase (NEB). The heat inactivation of BcgI was performed at 65 °C for 20 min. Following this step, the ligation products underwent PCR amplification within a 40 µl reaction volume. This amplification reaction contained 7 µl of ligated DNA, 0.1 µM of each primer (Primer1 and Primer2 for Illumina), 0.3 mM dNTPs, 1 × Phusion HF buffer, and 0.4 U of Phusion high-fidelity DNA polymerase (NEB). Each PCR reaction consisted of 16–28 cycles of denaturation at 98 °C for 5 s, annealing at 60 °C for 20 s, and extension at 72 °C for 10 s, followed by a final extension of 10 min at 72 °C. The resulting library products were purified using the QIAquick PCR purification kit (Qiagen) and subsequently subjected to sequencing on the Illumina HiSeq X™ Ten platform. Library construction and Illumina sequencing procedures were performed by OE BioTech Co., Ltd. in Qingdao.
Sequencing processing and quantitative analysis
The fragments resulting from the digestion, referred to as enzyme reads, were generated from raw reads using the recognition site of the BcgI restriction enzyme. Clean reads were extracted from these enzyme reads by eliminating reads containing over 8% unknown bases or more than 20% low-quality bases. At the core of 2bRAD-M is the distinctive 2bRAD tag database (2b-Tag-DB), encompassing BcgI-derived tags unique to different taxa. These tags were identified from a vast collection of 173,165 microbial genomes, encompassing bacteria, fungi, and archaea. To conduct taxonomic profiling of the urobiome, the 2bRAD-M computational pipeline (accessible at https://github.com/shihuang047/2bRAD-M) was employed. Initially, the clean reads were aligned against the pre-established 2b-Tag-DB, identifying the microbial taxa present within the sample. To ensure the accuracy of species identification, a G score was computed for each identified species using the formula: G score species i =\documentclass[12pt]{minimal}
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\begin{document}$$\:\sqrt{{S}_{i}\times\:{t}_{i}\:}$$\end{document} (where S represents the number of reads assigned to all 2bRAD tags of species i in the sample, and t represents the number of all sequenced 2bRAD tags of species i in the sample). Only candidate taxa surpassing a minimum G score threshold of 10 were considered. Subsequently, a secondary 2b-Tag-DB was constructed, housing more specific 2bRAD tags for each candidate taxon compared to the default 2b-Tag-DB. All reads were remapped against this sample-specific 2b-Tag-DB to accurately quantify the abundance of candidate taxa. The relative abundance of a particular species was determined using the formula: Relative abundance \documentclass[12pt]{minimal}
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\begin{document}$$\text{Relative}\:\text{abundance}_{\text{species}\:\text{i}}=\frac{S_\text{i}/T_\text{i}}{\sum_\text{i=1}^\text{n}S_\text{i}/T_\text{i}}$$\end{document} (where S denotes the number of reads assigned to all 2bRAD tags of species i in the sample, and T represents the total number of theoretical 2bRAD tags of species i). A taxonomic abundance profile was ultimately created.
Bioinformatic analysis
For the assessment of alpha diversity, the Chao1, Shannon, and Simpson indices were computed using the “vegan” package, and their distributions were visually depicted through boxplots [13]. To explore beta diversity, metrics including Bray-Curtis, Binary Jaccard, and Euclidean distance were calculated via the “vegan” package. These dissimilarity measurements were then represented through a principal coordinate analysis (PCoA) scatter plot [13]. To uncover distinct taxa between groups, the Linear Discriminant Analysis Effect Size (LEfSe) technique was employed, focusing on taxa with an LDA score of ≥ 2.0 [14]. Furthermore, predictive functional insights into the urobiome were obtained through Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses [15, 16].
Statistical analysis
Statistical analysis was conducted using SPSS (version 26) and R software (version 4.1.1). In the context of clinical parameters, continuous variables were represented as mean ± SD and subjected to comparison using an unpaired t-test. For categorical variables, percentages were utilized, and a Fisher’s exact test was employed for comparisons. Group comparisons in terms of alpha diversity were evaluated using the Wilcoxon test, while for beta diversity, permutational multivariate analysis of variance (PERMANOVA) was performed. Within each gender, a paired Wilcoxon test was utilized to compare microbial communities between the stone side and the non-stone side. The differences between groups in predicted functions (COG and KEGG) were analyzed by the Wilcoxon test. A p-value less than 0.05 was considered statistically significant.
Statistical analysis
Statistical analysis was conducted using SPSS (version 26) and R software (version 4.1.1). In the context of clinical parameters, continuous variables were represented as mean ± SD and subjected to comparison using an unpaired t-test. For categorical variables, percentages were utilized, and a Fisher’s exact test was employed for comparisons. Group comparisons in terms of alpha diversity were evaluated using the Wilcoxon test, while for beta diversity, permutational multivariate analysis of variance (PERMANOVA) was performed. Within each gender, a paired Wilcoxon test was utilized to compare microbial communities between the stone side and the non-stone side. The differences between groups in predicted functions (COG and KEGG) were analyzed by the Wilcoxon test. A p-value less than 0.05 was considered statistically significant.
Results
Comparison of clinical characteristics between male and female stone formers
In the study, a total of 21 male and 9 female patients were included. The clinical characteristics of these enrolled patients are detailed in Table 1. Notably, no significant disparities were observed between genders concerning age, BMI, stone side, stone count, and stone composition. This finding suggests the absence of significant confounding factors within our analysis.
Table 1Comparison of clinical characteristics between male and female stone formersParameterMale (n = 21)Female (n = 9)P-valueAge49.00 ± 17.2049.89 ± 11.690.889Body mass index (kg/m2)24.03 ± 2.7723.29 ± 2.320.486Stone side0.999 Left13 (61.90%)6 (66.67%) Right8 (38.10%)3 (33.33%)Stone count0.694 Single12 (57.14%)4 (44.44%) Multiple9 (42,86%)5 (55.56%)Stone composition0.474 CaOx4 (19.05%)4 (44.44%) CaOx + CaP6 (28.57%)1 (11.11%) Uric acid2 (9.52%)1 (11.11%) NA9 (42.86%)3 (33.33%)
Microbial profiling of the urobiome of the stone side between genders
We initially directed our focus toward the urobiome within the renal pelvis of the stone side. Specifically, we denoted the stone side of male patients as “SM” and that of female patients as “SF”. Notably, our analysis revealed no discernible distinctions in terms of alpha and beta diversity between SM and SF [17]. Within SM, a total of 9 phyla were identified, whereas SF exhibited 8 phyla. The primary phyla in SM were Proteobacteria (88.04%), Firmicutes (7.82%), and Actinobacteria (3.70%). On the other hand, the dominant phyla in SF consisted of Proteobacteria (92.70%), Actinobacteria (3.55%), and Bacteroidetes (1.89%) (Fig. 1A). 84 and 56 genera were identified in SM and SF, respectively. In SM, the five most prevalent genera were Acinetobacter (35.28%), Cupriavidus (22.26%), Sphingomonas (8.93%), Staphylococcus (7.67%), and Pseudomonas (6.90%). Major genera within SF encompassed Acinetobacter (38.19%), Cupriavidus (27.30%), Pseudomonas (8.72%), Sphingomonas (7.94%), and Moraxella (4.63%) (Fig. 1B). Furthermore, at the species level, we identified 217 species in SM and 107 species in SF. Cupriavidus pauculus (21.15%), Acinetobacter junii (16.24%), Acinetobacter sp_CIP_110321 (9.43%), Sphingomonas paucimobilis (7.78%), and Staphylococcus aureus (7.67%) were dominant in SM, while C. pauculus (26.19%), A. junii (15.27%), A. sp_CIP_110321 (12.66%), S. paucimobilis (7.88%), and Acinetobacter ursingii (6.38%) were dominant in SF (Fig. 1C).
Fig. 1Microbial profiling of the urobiome between SM and SF. (A) The distribution of major microbial phyla between SM and SF. (B) The distribution of the top 30 microbial genera between SM and SF. (C) The distribution of the top 30 microbial species between SM and SF. The cladogram (D) and the histogram of LDAscore (E) showed 14 biomarkers with significant differences between SM and SF using LefSe
Subsequently, we conducted LEfSe to pinpoint distinct taxa between SM and SF. This analysis highlighted 13 discriminative features exhibiting noteworthy variations in relative abundance between the two groups. Intriguingly, all of these features displayed significantly higher levels in SF. Notable among them were species such as Lactobacillus iners, Atopobium deltae, Lawsonella clevelandensis, and Meyerozyma guilliermondii (Fig. 1D, E).
Microbial profiling of the urobiome of the non-stone side between genders
We proceeded to profile the urobiome within the renal pelvis of the non-stone side. This exploration involved designating the non-stone side of male patients as “NSM” and that of female patients as “NSF”. While alpha diversity analysis did not reveal any noteworthy differences between NSM and NSF, beta diversity analysis did identify a divergence in the Binary Jaccard distance between the two (p = 0.027), as illustrated in Fig. S1. In terms of phyla distribution, we found a total of 10 phyla in NSM and 11 in NSF. Remarkably, the most dominant phyla in both groups were Proteobacteria (85.23% and 78.72%), Firmicutes (8.59% and 9.26%), and Actinobacteria (3.11% and 6.29%) (Fig. 2A). Exploring genera revealed 66 in NSM and 114 in NSF. Among the dominant genera in NSM were Acinetobacter (38.71%), Cupriavidus (17.91%), Pseudomonas (9.66%), Sphingomonas (6.62%), and Stenotrophomonas (4.89%). Major genera within NSF encompassed Acinetobacter (29.61%), Cupriavidus (22.68%), Sphingomonas (9.58%), Pseudomonas (7.96%), and Lactobacillus (7.92%) (Fig. 2B). Further examination at the species level uncovered 163 species in NSM and 316 in NSF. Dominant species in NSM included C. pauculus (17.33%), A. junii (15.82%), A. sp_CIP_110321 (12.06%), S. paucimobilis (6.30%), and A. ursingii (5.35%). Key species within NSF comprised C. pauculus (21.48%), A. junii (15.21%), S. paucimobilis (9.11%), A. sp_CIP_110321 (7.05%) and L. iners (5.87%) (Fig. 2C).
Fig. 2Microbial profiling of the urobiome between NSM and NSF. (A) The distribution of major microbial phyla between NSM and NSF. (B) The distribution of the top 30 microbial genera between NSM and NSF. (C) The distribution of the top 30 microbial species between NSM and NSF. The cladogram ((D) and the histogram of LDAscore (E) showed 70 biomarkers with significant differences between NSM and NSF using LEfSe
A total of 70 distinct taxa showing significant differences were identified through the LEfSe analysis. Specifically, we observed an elevated abundance of A. sp_CIP_110321 and M. guilliermondii in NSM. In contrast, NSF exhibited overrepresentation of 27 species, with the top 5 species exhibiting the most prominent increase being Gardnerella vaginalis, L. iners, Atopobium vaginae, Jeongeupia sp_USM3, and Lautropia sp_KCOM_2505 (Fig. 2D, E).
Differences of predicted function between SM and SF/ NSM and NSF
We further predicted the function of the urobiome among these groups and compared them using the Wilcoxon test. We found 57 COGs differed between SM and SF. The top 5 COGs were COG0031 (Cysteine synthase), COG0591 (Na+/proline symporter), COG0614 (ABC-type Fe3+-hydroxamate transport system, periplasmic component), COG0605 (Superoxide dismutase), and COG1009 (NADH: ubiquinone oxidoreductase subunit 5 (chain L)/Multisubunit Na+/H + antiporter, MnhA subunit) (Fig. 3A). KEGG analysis showed that MAPK signaling pathway was significantly enriched in SM (Fig. 3B).
There were 145 differential COGs between NSM and NSF. The top 5 COGs were COG0503 (Adenine/guanine phosphoribosyltransferase or related PRPP-binding protein), COG2265 (tRNA/tmRNA/rRNA uracil-C5-methylase, TrmA/RlmC/RlmD family), COG0462 (Phosphoribosylpyrophosphate synthetase), COG1077 (Actin-like ATPase involved in cell morphogenesis), and COG0282 (Acetate kinase) (Fig. 3C). KEGG analysis indicated that hematopoietic cell lineage and phosphotransferase system were significantly higher in NSF (Fig. 3D).
Fig. 3Differences of predicted function between SM and SF/ NSM and NSF. (A) The top 10 differential COGs between SM and SF. (B) Differential KEGG pathways between SM and SF. (C) The top 10 differential COGs between NSM and NSF. (D) Differential KEGG pathways between NSM and NSF
Differential taxa between SM and NSM
We subsequently delved into a comparative analysis of the taxonomic profiles of male patients between the stone side and the non-stone side. Strikingly, no significant distinctions emerged between SM and NSM in relation to alpha and beta diversity [17]. Our investigation spotlighted 18 discriminative features with considerably different relative abundances between SM and NSM (Fig. 4A, B). At the genus level, the urobiome of SM displayed an enrichment in Vulcaniibacterium, while the urobiome of NSM exhibited an enrichment in Meyerozyma and Prevotella. At the species level, the urobiome of SM exhibited enrichments in Vulcaniibacterium thermophilum, Sphingomonas sp_NIC1, and Sphingomonas aquatilis. In contrast, the urobiome of NSM displayed enrichments in M. guilliermondii and Corynebacterium aurimucosum. Furthermore, we applied the paired Wilcoxon test to ascertain the relative abundance of taxa between SM and NSM. This analysis indicated an increased abundance of M. guilliermondii, Moraxella osloensis, and Pseudomonas sp_FW104_15G4B 222 within the urobiome of NSM (Fig. 4C).
Fig. 4Differential taxa between SM and NSM. The cladogram (A) and the histogram of LDAscore (B) showed showed 19 biomarkers with significant differences between SM and NSM using LEfSe. (C) The boxplot showed 3 species were increased in NSM through paired Wilcoxon test
Differential taxa between SF and NSF
The urobiome of female patients between the stone side and the non-stone side was also compared. SF exhibited a similarity to NSF with respect to alpha and beta diversity [17]. Applying the LEfSe analysis, we identified 9 distinctive features demonstrating significant differences between SF and NSF (Fig. 5A, B). Specifically, the species M. guilliermondii exhibited enrichment in SF. By employing the paired Wilcoxon test, we further determined that the abundance of A. sp_CIP_110321, M. guilliermondii, A. ursingii, and M. osloensis was heightened in SF (Fig. 5C).
Fig. 5Differential taxa between SF and NSF. The cladogram (A) and the histogram of LDAscore (B) showed showed 9 biomarkers with significant differences between SF and NSF using LEfSe. (C) The boxplot showed 4 species were increased in SF through paired Wilcoxon test
Differences of predicted function between SM and NSM/ SF and NSF
The COG functional annotation showed that there were 141 differential COGs between SM and NSM. The top 5 COGs were COG2814 (predicted arabinose efflux permease, MFS family), COG1309 (DNA-binding transcriptional regulator, AcrR family), COG0583 (DNA-binding transcriptional regulator, LysR family), COG0626 (Cystathionine beta-lyase/cystathionine gamma-synthase), and COG1187 (16 S rRNA U516 pseudouridylate synthase RsuA and related 23 S rRNA U2605, pseudouridylate synthases) (Fig. 6A). Based on the KEGG database, we found 17 pathways that were significantly different between SM and NSM. The top 5 KEGG pathways were sulfur metabolism, folate biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, arginine biosynthesis, and protein export (Fig. 6B).
Fig. 6Differences of predicted function between SM and NSM/ SF and NSF. (A) The top 10 differential COGs between SM and NSM. (B) Differential KEGG pathways between SM and NSM. (C) The top 10 differential COGs between SF and NSF. (D) Differential KEGG pathways between SF and NSF
SF and NSF had functional differences in terms of 71 COGs. The top 5 COGs were COG1132 (ABC-type multidrug transport system, ATPase and permease component), COG2265 (tRNA/tmRNA/rRNA uracil-C5-methylase, TrmA/RlmC/RlmD family), COG0503 (Adenine/guanine phosphoribosyltransferase or related PRPP-binding protein), COG0462 (Phosphoribosylpyrophosphate synthetase), and COG1077 (Actin-like ATPase involved in cell morphogenesis) (Fig. 6C). KEGG analysis indicated that phosphotransferase system and bile secretion were enriched in NSF (Fig. 6D).
Comparison of clinical characteristics between male and female stone formers
In the study, a total of 21 male and 9 female patients were included. The clinical characteristics of these enrolled patients are detailed in Table 1. Notably, no significant disparities were observed between genders concerning age, BMI, stone side, stone count, and stone composition. This finding suggests the absence of significant confounding factors within our analysis.
Table 1Comparison of clinical characteristics between male and female stone formersParameterMale (n = 21)Female (n = 9)P-valueAge49.00 ± 17.2049.89 ± 11.690.889Body mass index (kg/m2)24.03 ± 2.7723.29 ± 2.320.486Stone side0.999 Left13 (61.90%)6 (66.67%) Right8 (38.10%)3 (33.33%)Stone count0.694 Single12 (57.14%)4 (44.44%) Multiple9 (42,86%)5 (55.56%)Stone composition0.474 CaOx4 (19.05%)4 (44.44%) CaOx + CaP6 (28.57%)1 (11.11%) Uric acid2 (9.52%)1 (11.11%) NA9 (42.86%)3 (33.33%)
Microbial profiling of the urobiome of the stone side between genders
We initially directed our focus toward the urobiome within the renal pelvis of the stone side. Specifically, we denoted the stone side of male patients as “SM” and that of female patients as “SF”. Notably, our analysis revealed no discernible distinctions in terms of alpha and beta diversity between SM and SF [17]. Within SM, a total of 9 phyla were identified, whereas SF exhibited 8 phyla. The primary phyla in SM were Proteobacteria (88.04%), Firmicutes (7.82%), and Actinobacteria (3.70%). On the other hand, the dominant phyla in SF consisted of Proteobacteria (92.70%), Actinobacteria (3.55%), and Bacteroidetes (1.89%) (Fig. 1A). 84 and 56 genera were identified in SM and SF, respectively. In SM, the five most prevalent genera were Acinetobacter (35.28%), Cupriavidus (22.26%), Sphingomonas (8.93%), Staphylococcus (7.67%), and Pseudomonas (6.90%). Major genera within SF encompassed Acinetobacter (38.19%), Cupriavidus (27.30%), Pseudomonas (8.72%), Sphingomonas (7.94%), and Moraxella (4.63%) (Fig. 1B). Furthermore, at the species level, we identified 217 species in SM and 107 species in SF. Cupriavidus pauculus (21.15%), Acinetobacter junii (16.24%), Acinetobacter sp_CIP_110321 (9.43%), Sphingomonas paucimobilis (7.78%), and Staphylococcus aureus (7.67%) were dominant in SM, while C. pauculus (26.19%), A. junii (15.27%), A. sp_CIP_110321 (12.66%), S. paucimobilis (7.88%), and Acinetobacter ursingii (6.38%) were dominant in SF (Fig. 1C).
Fig. 1Microbial profiling of the urobiome between SM and SF. (A) The distribution of major microbial phyla between SM and SF. (B) The distribution of the top 30 microbial genera between SM and SF. (C) The distribution of the top 30 microbial species between SM and SF. The cladogram (D) and the histogram of LDAscore (E) showed 14 biomarkers with significant differences between SM and SF using LefSe
Subsequently, we conducted LEfSe to pinpoint distinct taxa between SM and SF. This analysis highlighted 13 discriminative features exhibiting noteworthy variations in relative abundance between the two groups. Intriguingly, all of these features displayed significantly higher levels in SF. Notable among them were species such as Lactobacillus iners, Atopobium deltae, Lawsonella clevelandensis, and Meyerozyma guilliermondii (Fig. 1D, E).
Microbial profiling of the urobiome of the non-stone side between genders
We proceeded to profile the urobiome within the renal pelvis of the non-stone side. This exploration involved designating the non-stone side of male patients as “NSM” and that of female patients as “NSF”. While alpha diversity analysis did not reveal any noteworthy differences between NSM and NSF, beta diversity analysis did identify a divergence in the Binary Jaccard distance between the two (p = 0.027), as illustrated in Fig. S1. In terms of phyla distribution, we found a total of 10 phyla in NSM and 11 in NSF. Remarkably, the most dominant phyla in both groups were Proteobacteria (85.23% and 78.72%), Firmicutes (8.59% and 9.26%), and Actinobacteria (3.11% and 6.29%) (Fig. 2A). Exploring genera revealed 66 in NSM and 114 in NSF. Among the dominant genera in NSM were Acinetobacter (38.71%), Cupriavidus (17.91%), Pseudomonas (9.66%), Sphingomonas (6.62%), and Stenotrophomonas (4.89%). Major genera within NSF encompassed Acinetobacter (29.61%), Cupriavidus (22.68%), Sphingomonas (9.58%), Pseudomonas (7.96%), and Lactobacillus (7.92%) (Fig. 2B). Further examination at the species level uncovered 163 species in NSM and 316 in NSF. Dominant species in NSM included C. pauculus (17.33%), A. junii (15.82%), A. sp_CIP_110321 (12.06%), S. paucimobilis (6.30%), and A. ursingii (5.35%). Key species within NSF comprised C. pauculus (21.48%), A. junii (15.21%), S. paucimobilis (9.11%), A. sp_CIP_110321 (7.05%) and L. iners (5.87%) (Fig. 2C).
Fig. 2Microbial profiling of the urobiome between NSM and NSF. (A) The distribution of major microbial phyla between NSM and NSF. (B) The distribution of the top 30 microbial genera between NSM and NSF. (C) The distribution of the top 30 microbial species between NSM and NSF. The cladogram ((D) and the histogram of LDAscore (E) showed 70 biomarkers with significant differences between NSM and NSF using LEfSe
A total of 70 distinct taxa showing significant differences were identified through the LEfSe analysis. Specifically, we observed an elevated abundance of A. sp_CIP_110321 and M. guilliermondii in NSM. In contrast, NSF exhibited overrepresentation of 27 species, with the top 5 species exhibiting the most prominent increase being Gardnerella vaginalis, L. iners, Atopobium vaginae, Jeongeupia sp_USM3, and Lautropia sp_KCOM_2505 (Fig. 2D, E).
Differences of predicted function between SM and SF/ NSM and NSF
We further predicted the function of the urobiome among these groups and compared them using the Wilcoxon test. We found 57 COGs differed between SM and SF. The top 5 COGs were COG0031 (Cysteine synthase), COG0591 (Na+/proline symporter), COG0614 (ABC-type Fe3+-hydroxamate transport system, periplasmic component), COG0605 (Superoxide dismutase), and COG1009 (NADH: ubiquinone oxidoreductase subunit 5 (chain L)/Multisubunit Na+/H + antiporter, MnhA subunit) (Fig. 3A). KEGG analysis showed that MAPK signaling pathway was significantly enriched in SM (Fig. 3B).
There were 145 differential COGs between NSM and NSF. The top 5 COGs were COG0503 (Adenine/guanine phosphoribosyltransferase or related PRPP-binding protein), COG2265 (tRNA/tmRNA/rRNA uracil-C5-methylase, TrmA/RlmC/RlmD family), COG0462 (Phosphoribosylpyrophosphate synthetase), COG1077 (Actin-like ATPase involved in cell morphogenesis), and COG0282 (Acetate kinase) (Fig. 3C). KEGG analysis indicated that hematopoietic cell lineage and phosphotransferase system were significantly higher in NSF (Fig. 3D).
Fig. 3Differences of predicted function between SM and SF/ NSM and NSF. (A) The top 10 differential COGs between SM and SF. (B) Differential KEGG pathways between SM and SF. (C) The top 10 differential COGs between NSM and NSF. (D) Differential KEGG pathways between NSM and NSF
Differential taxa between SM and NSM
We subsequently delved into a comparative analysis of the taxonomic profiles of male patients between the stone side and the non-stone side. Strikingly, no significant distinctions emerged between SM and NSM in relation to alpha and beta diversity [17]. Our investigation spotlighted 18 discriminative features with considerably different relative abundances between SM and NSM (Fig. 4A, B). At the genus level, the urobiome of SM displayed an enrichment in Vulcaniibacterium, while the urobiome of NSM exhibited an enrichment in Meyerozyma and Prevotella. At the species level, the urobiome of SM exhibited enrichments in Vulcaniibacterium thermophilum, Sphingomonas sp_NIC1, and Sphingomonas aquatilis. In contrast, the urobiome of NSM displayed enrichments in M. guilliermondii and Corynebacterium aurimucosum. Furthermore, we applied the paired Wilcoxon test to ascertain the relative abundance of taxa between SM and NSM. This analysis indicated an increased abundance of M. guilliermondii, Moraxella osloensis, and Pseudomonas sp_FW104_15G4B 222 within the urobiome of NSM (Fig. 4C).
Fig. 4Differential taxa between SM and NSM. The cladogram (A) and the histogram of LDAscore (B) showed showed 19 biomarkers with significant differences between SM and NSM using LEfSe. (C) The boxplot showed 3 species were increased in NSM through paired Wilcoxon test
Differential taxa between SF and NSF
The urobiome of female patients between the stone side and the non-stone side was also compared. SF exhibited a similarity to NSF with respect to alpha and beta diversity [17]. Applying the LEfSe analysis, we identified 9 distinctive features demonstrating significant differences between SF and NSF (Fig. 5A, B). Specifically, the species M. guilliermondii exhibited enrichment in SF. By employing the paired Wilcoxon test, we further determined that the abundance of A. sp_CIP_110321, M. guilliermondii, A. ursingii, and M. osloensis was heightened in SF (Fig. 5C).
Fig. 5Differential taxa between SF and NSF. The cladogram (A) and the histogram of LDAscore (B) showed showed 9 biomarkers with significant differences between SF and NSF using LEfSe. (C) The boxplot showed 4 species were increased in SF through paired Wilcoxon test
Differences of predicted function between SM and NSM/ SF and NSF
The COG functional annotation showed that there were 141 differential COGs between SM and NSM. The top 5 COGs were COG2814 (predicted arabinose efflux permease, MFS family), COG1309 (DNA-binding transcriptional regulator, AcrR family), COG0583 (DNA-binding transcriptional regulator, LysR family), COG0626 (Cystathionine beta-lyase/cystathionine gamma-synthase), and COG1187 (16 S rRNA U516 pseudouridylate synthase RsuA and related 23 S rRNA U2605, pseudouridylate synthases) (Fig. 6A). Based on the KEGG database, we found 17 pathways that were significantly different between SM and NSM. The top 5 KEGG pathways were sulfur metabolism, folate biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, arginine biosynthesis, and protein export (Fig. 6B).
Fig. 6Differences of predicted function between SM and NSM/ SF and NSF. (A) The top 10 differential COGs between SM and NSM. (B) Differential KEGG pathways between SM and NSM. (C) The top 10 differential COGs between SF and NSF. (D) Differential KEGG pathways between SF and NSF
SF and NSF had functional differences in terms of 71 COGs. The top 5 COGs were COG1132 (ABC-type multidrug transport system, ATPase and permease component), COG2265 (tRNA/tmRNA/rRNA uracil-C5-methylase, TrmA/RlmC/RlmD family), COG0503 (Adenine/guanine phosphoribosyltransferase or related PRPP-binding protein), COG0462 (Phosphoribosylpyrophosphate synthetase), and COG1077 (Actin-like ATPase involved in cell morphogenesis) (Fig. 6C). KEGG analysis indicated that phosphotransferase system and bile secretion were enriched in NSF (Fig. 6D).
Discussion
Urolithiasis exhibits a higher prevalence among men compared to women, with men experiencing up to twice the incidence rate. However, recent trends have indicated a notable rise in urolithiasis among women, leading to a reduction in the gender gap [18]. The evolving landscape of urolithiasis incidence underscores the need for personalized medical interventions, potentially tailored to a patient’s gender. Yet, the precise mechanisms that contribute to these gender-based differences remain elusive. Researchers have pointed toward potential involvement of sex hormones in shaping the sex-specific variance. In male stone formers, the serum testosterone levels were significantly higher compared to healthy men [19]. Conversely, estrogen appears to harbor antilithogenic effects. A substantial cohort study revealed that postmenopausal women faced an increased risk of kidney stones [20]. However, a recent comprehensive cross-sectional study found no substantial link between sex hormones and the history of kidney stones [21].
Indeed, the complexities of sex differences in urolithiasis extend beyond hormonal dynamics, suggesting involvement of other contributing factors. Notably, advancements in sequencing and culturomics methodologies have unveiled the intricate and diverse microbial population residing within the urinary tract [7, 8]. This microbial ecosystem, referred to as the urobiome, has emerged as a key player in the context of urolithiasis [22]. Numerous studies have highlighted distinctive features in the urobiome of stone formers compared to healthy controls. Utilizing 16 S rRNA gene sequencing, Zampini et al. identified an elevated presence of the Enterobacteriaceae family and a reduction in Lactobacillus genus among stone formers [23]. Xie et al. revealed Acinetobacter as the dominant enriched genus in stone formers, contrasting with Prevotella, which prevailed in healthy controls [10]. By enhanced quantitative urine culture, Dornbier et al. isolated live bacteria from stone samples which were consistent with enriched species identified by sequencing, including Staphylococcus epidermidis, Enterobacter cloacae, Escherichia coli, and Lactobacillus gasseri [24]. Collectively, these investigations underscore the potential engagement of the urobiome in the intricate mechanisms underpinning urolithiasis.
Fouts et al. conducted a pioneering study that scrutinized the bladder urobiome, making a comparative assessment between healthy males and females [11]. Their findings were insightful, with Lactobacillus emerging as the predominant genus in females, while Corynebacterium dominated in males. The prevalence of Lactobacillus is often linked with a healthy state. This genus contributes to the creation of an acidic environment through the production of lactic and other acids. This low-pH setting can disadvantage other potentially harmful pathogens [25]. Some studies have revealed a close link between Corynebacterium and urolithiasis. Liu et al. observed an increased abundance of Corynebacterium in the renal pelvis urobiome of the stone sides compared to that of the non-stone sides [26]. Dornbier et al. reported that Corynebacterium was the dominant taxa in calcium phosphate stones via 16 S rRNA sequencing, and was cultured and isolated from the stone homogenate [24]. Corynebacterium urealyticum possesses the capacity to generate urease, an enzyme that hydrolyzes urea to yield ammonia. This enzymatic action can elevate urine pH and foster the development of struvite stones [27]. However, there is no direct evidence that Corynebacterium can promote calcium-containing stones or uric acid stones formation. Lewis et al. found that healthy females harbored a greater diversity of bacterial genera compared to males, particularly within the phyla Actinobacteria and Bacteroidetes [28]. Urinary microbial communities can be categorized into distinct urotypes, each marked by the predominance of a specific genus. Among females, diverse urotypes were identified, encompassing Prevotella, Sneathia, Gardnerella, Atopobium, Lactobacillus, Shigella, Escherichia, Enterococcus, Streptococcus, and Citrobacter. Several of these urotypes, such as those dominated by Lactobacillus crispatus, G. vaginalis, and A. vaginae, were exclusively prevalent in healthy women [29]. However, the same study revealed the differences of the urobiome between healthy females and males were slight. The Prevotella, Shigella, Enterococcus, Streptococcus and Citrobacter urotypes which were dominant in the female urobiome were also found in the male urobiome [29].
Xu et al. found a progressive increase in the risk of developing urinary calculi among females with advancing age [30]. Notably, the female urobiome exhibits alterations in tandem with age. Research by Price et al. highlighted that the prevalence of the Gardnerella urotype was higher in pre-menopausal women, while the Escherichia urotype was more predominant in post-menopausal women [31]. Additionally, the impact of estrogen therapy on the urobiome has been substantiated. Studies have demonstrated that estrogen therapy can potentially modulate the urobiome by augmenting the abundance of Lactobacillus while concurrently reducing the prevalence of pathogenic bacteria within the bladder [32–34]. Such observations prompt a tantalizing speculation that the urobiome is susceptible to influences stemming from age, sex hormones, and urine chemistries. The intricate interplay among these factors further contributes to the overall risk of stone formation.
Our diversity analysis has unveiled intriguing insights into the urobiome across genders and its connection to stone formation. The urobiome of SM exhibited a remarkable similarity to that of SF, emphasizing potential shared microbial characteristics among male and female stone formers. However, a rather unexpected finding emerged as we explored the non-stone side: NSM and NSF displayed a pronounced dissimilarity in beta diversity, as evidenced by the Binary Jaccard distance. This observation implies distinct microbial community compositions between genders on the non-stone side. Moreover, NSF exhibited a heightened presence of microbial genera and species compared to NSM. An important discovery in our study was that L. iners was enriched in the female urobiome of both sides compared to the male urobiome. Lactobacillus species are widely recognized as hallmarks of health, with their decline potentially contributing to urinary dysbiosis. Such dysbiosis could pave the way for uropathogen colonization. Notably, L. iners in the bladder was proven to share 99.99% similarity with that in the vagina [35]. This microbe’s exceptional adaptability to varying environments, coupled with its relatively stable abundance under changing conditions, positions it as a key player [36]. Additionally, our study highlighted elevated levels of G. vaginalis and A. vaginae on the non-stone side of females, reinforcing findings by Gottschick et al. and further indicating the potential significance of these urotypes in gender-specific microbial disparities [29].
Our findings suggest distinct microbial community structures between NSM and NSF, whereas the other comparisons did not show as marked microbial community differences. We assumed that the non-stone side may preserve more sex-specific microbial communities, which remain less affected by the pathophysiological conditions associated with stone formation. As found in our study, NSF exhibited an enrichment in Lactobacillus iners, Gardnerella vaginalis, and Atopobium vaginae, taxa commonly linked to the female urobiome. Thus, the female non-stone side might be more representative of a typical female urobiome, while the male non-stone side likely reflects a typical male urobiome, accounting for the significant differences observed between NSM and NSF. In contrast, the microenvironment on the stone side may override or disrupt inherent microbial differences by promoting the colonization of specific taxa, leading to more homogenous microbial profiles across sexes on the stone side. This could explain the fewer differences between stone male (SM) and stone female (SF) groups, as the microbial communities on the stone side may be more influenced by stones than by sex.
We then delved into the comparison of the urobiomes between genders, considering both the stone and non-stone sides. Remarkably, we identified several distinct species between these sides, unveiling potentially pivotal insights. Notably, two Sphingomonas species exhibited enrichment in SM when contrasted with NSM. Sphingomonas has been previously associated with inducing morbidity, particularly linked to urinary-related conditions like bladder and prostate cancer [37, 38]. Prevotella demonstrated higher prevalence in NSM compared to SM. This is particularly intriguing considering that certain Prevotella species are capable of producing short-chain fatty acids that contribute to the maintenance of intestinal barrier integrity, suggesting Prevotella might protect the urothelium via short-chain fatty acids to prevent against stone formation [39]. Intriguingly, we also noted the enrichment of opportunistic urinary pathogens, Acinetobacter and Moraxella, in SF in comparison to NSF. This highlights the possibility that the urobiome of distinct genders might host a diverse array of pathogenic and protective species that could potentially contribute to the intricate mechanisms underlying urolithiasis.
Furthermore, our investigation extended to predicting COG annotations and KEGG pathways, followed by a comprehensive comparison across the various groups. The outcomes of this analysis underscored substantial dissimilarities in urobiome function within the different groups. These findings resonate with the perspective that the intricate composition of the urinary microbial community holds the capacity to influence urobiome function, thereby exerting a subsequent impact on the intricate interplay between the microbiome and the host. However, the role of the urobiome function in stone formation remains to be explored.
It is important to acknowledge the limitations in this study. Firstly, it should be noted that the sample has a skewed distribution in gender. Patient recruitment was performed in strict accordance with the inclusion and exclusion criteria. It turned out that the number of males was greater than that of females, but we can’t determine the specific reasons for the sample size bias. Given that infective stones are more prevalent in females, the exclusion of patients with UTIs may have disproportionately impacted the inclusion of female participants. Secondly, the sample size employed in this study may not be sufficient to extrapolate the findings to a broader context. The validation of the current results necessitates a more extensive dataset with a larger cohort. Thirdly, the stone type in our study is heterogenous. Different types of stones may have distinct formation mechanisms, and it is plausible that the urobiome varies depending on the type of stone. The inability to control for or stratify by stone type might obscure specific microbial patterns linked to specific stone types. Further research with a more homogenous stone cohort or stratification by stone type is needed. Fourthly, the absence of healthy control participants is another limitation. Including healthy individuals would have provided a baseline for understanding microbial differences attributable to urolithiasis and the natural variations in the urobiome between genders. However, recruiting healthy controls poses ethical challenges, particularly due to the invasive nature of renal pelvis urine collection. Fifthly, the descriptive nature of our study precludes us from establishing a definitive causal relationship between the microbiota and urolithiasis. It’s imperative to conduct further experiments to ascertain whether the observed microbial communities indeed play a causal role or are simply incidental findings. Lastly, the intricate alterations in microbial functionality across the different groups underscore the complexity of the situation. Deeper insights into the mechanisms underpinning these changes necessitate additional research efforts.
Conclusion
Our study marks a pioneering effort in the domain of renal pelvis urobiome research by introducing a gender-based comparison, facilitated through the utilization of the 2bRAD-M technique. We identified distinct microbial taxa and predicted functions that underscore the nuanced interplay between gender and urobiome composition. Our findings suggest the potential for exploring personalized treatment strategies for urolithiasis based on gender-specific microbial characteristics. However, further research is needed to establish a causal relationship between the urobiome and stone formation before such approaches can be fully realized.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1: Fig S1. Microbial biodiversity between NSM and NSF. (A) Comparison of alpha diversity (Chao1, Shannon index, and Simpson index) between NSM and NSF. (B) Comparison of beta diversity between NSM and NSF based on Binary Jaccard distance, Bray-Curtis distance, and Euclidean distance. |
Title: iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia | Body:
Introduction
Over the past few decades, advances in sequencing technologies have led to a rapid increase in the availability of public transcriptomic datasets [1,2]. Integrative analyses of these public expression datasets has resulted in a comprehensive view of organism transcriptomic states [3,4], the generation of new biological hypotheses [5,6], and inference of co-expression networks and transcriptional regulation [7,8].
Independent Component Analysis (ICA) has proven to be a powerful method to extract knowledge from large transcriptomics compendia [9–16]. ICA is a machine learning algorithm designed to separate mixed signals into their original source components, based on the equation X = MA, where X is the data matrix, M is the components matrix (sometimes called S in the literature for ‘sources’), and A is the activities matrix [17].
In the context of the transcriptome, ICA can be applied to transcriptomics datasets to extract gene modules whose gene membership is statistically independent to other modules. The components M calculated by ICA are independently modulated groups of genes, and thus have been termed iModulons. Many iModulons are consistent with regulons, or groups of genes regulated by the same transcriptional regulator, in model bacteria [9,10]. iModulons can be genetically observed through binding sites at gene promoters in many cases [18], and can be used to discover new regulons or gene functions in less-characterized organisms [11,19]. The activities matrix A contains the condition-specific activation levels of each iModulon. For regulator-associated iModulons, they represent the activity states of the corresponding transcriptional regulator. iModulon activities have intuitive interpretations, and together with the components M comprises a data-drive approximation of the structure and activity of an organism’s transcriptional regulatory network (TRN) [20–22].
iModulons have many properties that lend themselves to knowledge generation from large datasets. ICA outcompeted 42 other regulatory module detection algorithms, including WGCNA and biclustering algorithms, in detecting known regulons across E. coli, yeast, and human transcriptomics data [23]. Independent components have been shown to be conserved across different datasets [24,25], batches [26] and dimensionalities within the same dataset [27,28]. ICA has been applied now to a large number of microbial organisms [10,11,21,29–37], demonstrating iModulon analysis as a powerful tool to interpret the ocean of publicly available transcriptomic data to advance our understanding of transcriptome organization.
We have outlined a five-step workflow, called iModulonMiner (https://github.com/SBRG/iModulonMiner), that enables researchers to build and characterize the iModulon structure for any organism or cell type with sufficient public data (Fig 1A). The first two steps are to download and process all publicly available RNA-seq data for a given organism. Third, the data must be inspected to ensure quality, and curated to include all appropriate metadata. Next, ICA can be applied to the high-quality compendium to produce independent components. Finally, the independent components are processed into iModulons and can subsequently be characterized. To facilitate iModulon characterization, interpretation, and visualization, we also present PyModulon, a Python library for downstream iModulon analysis (https://pymodulon.readthedocs.io/en/latest/).
10.1371/journal.pcbi.1012546.g001
Fig 1
Introduction to the iModulonMiner using public B. subtilis RNA-seq data as a case study.
a) Graphical representation of the five step workflow. b) Pie chart illustrating the quality control process. Numbers at the beginning of arrows represent the number of datasets before the quality control step, and numbers at the end represent the number of passed datasets after the step. c) Number of high-quality RNA-seq datasets for B. subtilis in NCBI SRA over time. d) Scatter plot of the top two principal components of the B. subtilis expression compendium. Points are colored based on the growth phase parsed from the literature. e) Bar chart showing the expression of dnaA across four projects. Points show individual replicates, while bars show the average expression for a given condition. Bars with a red star serve as the reference condition for the project.
Design and implementation
Step 1: Compiling all public transcriptomics data for an organism
The NCBI Sequence Read Archive (SRA) is a public repository for sequencing data that is partnered with the EMBL European Nucleotide Archive (ENA) and the DNA Databank of Japan (DDBJ) [38]. We provide a script (1_download_metadata/download_metadata.sh) that uses Entrez Direct [39] to search for all public RNA-seq datasets on SRA and compile annotated metadata into a single tab-separated file. Missing metadata is manually extracted from corresponding literature (Supplementary Methods in S1 Text). Each row in the file corresponds to a single experiment, and users may manually add private datasets.
Although iModulons can be computed from other expression data types, including microarray, RNA-seq, and proteomics, microarray datasets tend to produce more uncharacterized iModulons and induce stronger batch effects through platform heterogeneity [24], and proteomics typically has reduced coverage. For these reasons, we have designed the first two steps specifically for compiling and processing RNA-seq data. We recommend having RNA-seq data for at least 50 unique conditions for an organism before proceeding with the remaining pipeline.
Step 2: Processing RNA-seq data
Users have the flexibility to select their preferred RNA-seq processing pipeline. Alternatively, they can follow the pipelines listed in https://github.com/SBRG/iModulonMiner/tree/main/2_process_data. For prokaryotic data, the tab-separated metadata file can be directly piped into the prokaryotic RNA-seq processing pipeline implemented using Nextflow v22.10.0 [40] for reproducibility and scalability. The first step in the pipeline is to download the raw FASTQ files from NCBI using fasterq-dump (https://github.com/ncbi/sra-tools/wiki/HowTo:-fasterq-dump). Next, read trimming is performed using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) with the default options, followed by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) on the trimmed reads. Next, reads are aligned to the genome using Bowtie [41]. The read direction is inferred using RSEQC [42] before generating read counts using featureCounts [43]. Finally, all quality control metrics are compiled using MultiQC [44] and the final expression dataset is reported in units of log-transformed Transcripts per Million (log-TPM). Raw counts are also saved and the median of ratios could be used for following steps as well. In our experience for E. coli, the use of normalized counts and TPM yield very similar results. It is expected that in eukaryotes, the differences will be more significant (Note A in S1 Text). The Nextflow pipeline can be run locally or on high-performance computing such as Amazon Web Services (AWS).
Step 3: Quality control and data normalization
To guarantee a high quality expression dataset, data that failed any of the following FASTQC metrics are discarded: per base sequence quality, per sequence quality scores, per base n content, and adapter content. Samples that contain under 500,000 reads mapped to coding sequences are also discarded. Hierarchical clustering is used to identify samples that do not conform to a typical expression profile, which is a criteria for exclusion [3].
Manual curation of metadata is also performed to include experimental details of the samples. These include the strain of the sample, culture media, temperature, growth phase, and relevant experimental information that can facilitate downstream iModulon characterization. The recommended curations can be found in the step3 instructions (https://github.com/SBRG/iModulonMiner/blob/main/3_quality_control/expression_QC_part1.ipynb).
To obviate any batch effects resulting from combining different expression datasets, reference conditions are selected within each project to normalize each dataset. This ensures that nearly all independent components are due to biological variation, rather than technical variation. When choosing this type of normalization as opposed to a single global reference condition, gene expression and iModulon activities can only be compared within a project to a reference condition, rather than across projects.
Step 4: Computing the optimal number of robust independent components
To compute the optimal independent components, an extension of ICA is performed on the RNA-seq dataset as described in McConn et al. [48].
Briefly, FastICA [49] (scikit-learn v1.0.2 [50]) is executed repeatedly with varying random seeds. A timeout mechanism is implemented to prevent indefinite waiting from potential convergence issues within the algorithm. The resulting independent components are clustered using DBSCAN [51] (scikit-learn v1.0.2 [50]) to identify robust components, using a maximum distance threshold of 0.1 and a minimum cluster size of half the number of FastICA executions. To account for identical components with opposite signs, the following distance metric is used for computing the distance matrix:
dx,y=1−||ρx,y||
where ρx,y is the Pearson correlation between components x and y. The final robust components are defined as the centroids of the clusters.
Since the number of dimensions in ICA can alter the results, we apply the above procedure to the compendia multiple times, ranging the number of dimensions from 10 to the nearest multiple of 10 below the sample count, in steps of 10. To identify the optimal dimensionality, we compare the number of independent components (ICs) with single genes to the number of ICs that are correlated (Pearson correlation > 0.7) with the ICs in the largest dimension (called “final components”). We select the number of dimensions where the number of non-single gene ICs is equal to the number of final components in that dimension (Fig B in S1 Text).
The iModulons from different subsets of the dataset converge as the number of unique conditions increases (Fig C in S1 Text). Once an iModulon structure has been defined for an organism, this structure can be inverted to infer iModulon activities for new transcriptional dataset without re-running ICA, using the function infer_activities (Supplementary Results in S1 Text).
Step 5: Characterizing, annotating and visualizing iModulon results with PyModulon
To facilitate the analysis and understanding of iModulons, we have developed the PyModulon Python package to streamline the process of downstream iModulon analysis (https://github.com/SBRG/pymodulon). PyModulon offers a suite of tools that enable researchers to explore, visualize, and gain insights into the complex relationships and patterns within iModulons.
At the core of the PyModulon package is the IcaData object. The object contains all data related to iModulons for a given dataset, including the M and A matrices (Note B in S1 Text), the expression matrix, a draft TRN mined from literature, and thresholds used to define iModulons gene membership. Through PyModulon, users can delve into various aspects of the IcaData object for iModulon mining. This includes exploring iModulons through gene annotations and functional enrichments, visualizing the iModulons and their activities using a variety of plotting functions, performing motif search, clustering iModulon activities, and creating interactive dashboards for the organism of interest on iModulonDB.org. Furthermore, PyModulon offers functionalities that allow researchers to compare iModulon structures across organisms and estimate iModulon activities for external datasets. The comprehensive list of PyModulon’s functionalities can be found at https://pymodulon.readthedocs.io/en/latest/. For more detailed information on the implementation of these functionalities, please refer to the Supplementary Methods in S1 Text.
Workflow alternatives
Several steps of the workflow can be replaced by alternative methods. For example, an alternative processing workflow for eukaryotic RNA-seq data is available in nf-core (https://nf-co.re/rnaseq) [45]. Public or private data can be collected and aligned using the nf-core workflow. Alignment and quantification options include tools such as STAR [46], however pseudo alignment using Salmon [47] is viable for the generation of count matrices and TPM values, as they are necessary for running ICA while BAM alignment files are not. Standard parameters for nf-core alignment for read trimming and feature counts can be used. Suggested alternatives for processing and quality control of the data have been included in the workflow documentation. Most functions for analysis of data in PyModulon are effective regardless of organism type; however several functions specific to data processing and analysis of eukaryotic organisms have been added to PyModulon, and the usage of these functions are demonstrated in the iModulonMiner workflow. For an example of analysis and results for a S. cerevisiae dataset, please refer to the Supplementary Results in S1 Text. Additionally, possible alternatives to the fastICA algorithm used in Step 4 are suggested in the README file on the GitHub repository.
Workflow computational performance
Data processing times
Downloading experimental metadata using esearch and efetch typically takes only a few minutes (< 10 minutes for 15,000 E. coli samples). fasterq-dump with Nextflow is used to download and stage the RNA-Seq data in parallel with other tasks. The Nextflow RNA-Seq processing pipeline typically takes a few hours for several hundred samples.
ICA computational requirements
We evaluated the computational requirements for this analysis on the E. coli PRECISE-1K RNA-Seq compendium consisting of 4257 genes and 1035 samples, with an input gene expression log2(TPM) matrix of 80.2 MB. The machine used had the following specifications: CPU: AMD Ryzen Threadripper PRO 5995WX (256 MB cache, 64 cores, 128 threads), RAM: 256GB, DDR4, 3200 MT/s, 64 threads were used for the following evaluation. Time at 200 dimensionality (typical for the E. coli dataset) was 7.96 minutes to complete 100 FastICA runs, 1.10 minutes for distance matrix and clustering calculation, and 16.02 seconds for processing the final matrices. The resulting file sizes were 2.2 GB of temporary files, 13.3 MB for the final M matrix, and 2.9 MB for the final A matrix. Results for other dimensionalities are shown in Note C in S1 Text.
Step 1: Compiling all public transcriptomics data for an organism
The NCBI Sequence Read Archive (SRA) is a public repository for sequencing data that is partnered with the EMBL European Nucleotide Archive (ENA) and the DNA Databank of Japan (DDBJ) [38]. We provide a script (1_download_metadata/download_metadata.sh) that uses Entrez Direct [39] to search for all public RNA-seq datasets on SRA and compile annotated metadata into a single tab-separated file. Missing metadata is manually extracted from corresponding literature (Supplementary Methods in S1 Text). Each row in the file corresponds to a single experiment, and users may manually add private datasets.
Although iModulons can be computed from other expression data types, including microarray, RNA-seq, and proteomics, microarray datasets tend to produce more uncharacterized iModulons and induce stronger batch effects through platform heterogeneity [24], and proteomics typically has reduced coverage. For these reasons, we have designed the first two steps specifically for compiling and processing RNA-seq data. We recommend having RNA-seq data for at least 50 unique conditions for an organism before proceeding with the remaining pipeline.
Step 2: Processing RNA-seq data
Users have the flexibility to select their preferred RNA-seq processing pipeline. Alternatively, they can follow the pipelines listed in https://github.com/SBRG/iModulonMiner/tree/main/2_process_data. For prokaryotic data, the tab-separated metadata file can be directly piped into the prokaryotic RNA-seq processing pipeline implemented using Nextflow v22.10.0 [40] for reproducibility and scalability. The first step in the pipeline is to download the raw FASTQ files from NCBI using fasterq-dump (https://github.com/ncbi/sra-tools/wiki/HowTo:-fasterq-dump). Next, read trimming is performed using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) with the default options, followed by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) on the trimmed reads. Next, reads are aligned to the genome using Bowtie [41]. The read direction is inferred using RSEQC [42] before generating read counts using featureCounts [43]. Finally, all quality control metrics are compiled using MultiQC [44] and the final expression dataset is reported in units of log-transformed Transcripts per Million (log-TPM). Raw counts are also saved and the median of ratios could be used for following steps as well. In our experience for E. coli, the use of normalized counts and TPM yield very similar results. It is expected that in eukaryotes, the differences will be more significant (Note A in S1 Text). The Nextflow pipeline can be run locally or on high-performance computing such as Amazon Web Services (AWS).
Step 3: Quality control and data normalization
To guarantee a high quality expression dataset, data that failed any of the following FASTQC metrics are discarded: per base sequence quality, per sequence quality scores, per base n content, and adapter content. Samples that contain under 500,000 reads mapped to coding sequences are also discarded. Hierarchical clustering is used to identify samples that do not conform to a typical expression profile, which is a criteria for exclusion [3].
Manual curation of metadata is also performed to include experimental details of the samples. These include the strain of the sample, culture media, temperature, growth phase, and relevant experimental information that can facilitate downstream iModulon characterization. The recommended curations can be found in the step3 instructions (https://github.com/SBRG/iModulonMiner/blob/main/3_quality_control/expression_QC_part1.ipynb).
To obviate any batch effects resulting from combining different expression datasets, reference conditions are selected within each project to normalize each dataset. This ensures that nearly all independent components are due to biological variation, rather than technical variation. When choosing this type of normalization as opposed to a single global reference condition, gene expression and iModulon activities can only be compared within a project to a reference condition, rather than across projects.
Step 4: Computing the optimal number of robust independent components
To compute the optimal independent components, an extension of ICA is performed on the RNA-seq dataset as described in McConn et al. [48].
Briefly, FastICA [49] (scikit-learn v1.0.2 [50]) is executed repeatedly with varying random seeds. A timeout mechanism is implemented to prevent indefinite waiting from potential convergence issues within the algorithm. The resulting independent components are clustered using DBSCAN [51] (scikit-learn v1.0.2 [50]) to identify robust components, using a maximum distance threshold of 0.1 and a minimum cluster size of half the number of FastICA executions. To account for identical components with opposite signs, the following distance metric is used for computing the distance matrix:
dx,y=1−||ρx,y||
where ρx,y is the Pearson correlation between components x and y. The final robust components are defined as the centroids of the clusters.
Since the number of dimensions in ICA can alter the results, we apply the above procedure to the compendia multiple times, ranging the number of dimensions from 10 to the nearest multiple of 10 below the sample count, in steps of 10. To identify the optimal dimensionality, we compare the number of independent components (ICs) with single genes to the number of ICs that are correlated (Pearson correlation > 0.7) with the ICs in the largest dimension (called “final components”). We select the number of dimensions where the number of non-single gene ICs is equal to the number of final components in that dimension (Fig B in S1 Text).
The iModulons from different subsets of the dataset converge as the number of unique conditions increases (Fig C in S1 Text). Once an iModulon structure has been defined for an organism, this structure can be inverted to infer iModulon activities for new transcriptional dataset without re-running ICA, using the function infer_activities (Supplementary Results in S1 Text).
Step 5: Characterizing, annotating and visualizing iModulon results with PyModulon
To facilitate the analysis and understanding of iModulons, we have developed the PyModulon Python package to streamline the process of downstream iModulon analysis (https://github.com/SBRG/pymodulon). PyModulon offers a suite of tools that enable researchers to explore, visualize, and gain insights into the complex relationships and patterns within iModulons.
At the core of the PyModulon package is the IcaData object. The object contains all data related to iModulons for a given dataset, including the M and A matrices (Note B in S1 Text), the expression matrix, a draft TRN mined from literature, and thresholds used to define iModulons gene membership. Through PyModulon, users can delve into various aspects of the IcaData object for iModulon mining. This includes exploring iModulons through gene annotations and functional enrichments, visualizing the iModulons and their activities using a variety of plotting functions, performing motif search, clustering iModulon activities, and creating interactive dashboards for the organism of interest on iModulonDB.org. Furthermore, PyModulon offers functionalities that allow researchers to compare iModulon structures across organisms and estimate iModulon activities for external datasets. The comprehensive list of PyModulon’s functionalities can be found at https://pymodulon.readthedocs.io/en/latest/. For more detailed information on the implementation of these functionalities, please refer to the Supplementary Methods in S1 Text.
Workflow alternatives
Several steps of the workflow can be replaced by alternative methods. For example, an alternative processing workflow for eukaryotic RNA-seq data is available in nf-core (https://nf-co.re/rnaseq) [45]. Public or private data can be collected and aligned using the nf-core workflow. Alignment and quantification options include tools such as STAR [46], however pseudo alignment using Salmon [47] is viable for the generation of count matrices and TPM values, as they are necessary for running ICA while BAM alignment files are not. Standard parameters for nf-core alignment for read trimming and feature counts can be used. Suggested alternatives for processing and quality control of the data have been included in the workflow documentation. Most functions for analysis of data in PyModulon are effective regardless of organism type; however several functions specific to data processing and analysis of eukaryotic organisms have been added to PyModulon, and the usage of these functions are demonstrated in the iModulonMiner workflow. For an example of analysis and results for a S. cerevisiae dataset, please refer to the Supplementary Results in S1 Text. Additionally, possible alternatives to the fastICA algorithm used in Step 4 are suggested in the README file on the GitHub repository.
Workflow computational performance
Data processing times
Downloading experimental metadata using esearch and efetch typically takes only a few minutes (< 10 minutes for 15,000 E. coli samples). fasterq-dump with Nextflow is used to download and stage the RNA-Seq data in parallel with other tasks. The Nextflow RNA-Seq processing pipeline typically takes a few hours for several hundred samples.
ICA computational requirements
We evaluated the computational requirements for this analysis on the E. coli PRECISE-1K RNA-Seq compendium consisting of 4257 genes and 1035 samples, with an input gene expression log2(TPM) matrix of 80.2 MB. The machine used had the following specifications: CPU: AMD Ryzen Threadripper PRO 5995WX (256 MB cache, 64 cores, 128 threads), RAM: 256GB, DDR4, 3200 MT/s, 64 threads were used for the following evaluation. Time at 200 dimensionality (typical for the E. coli dataset) was 7.96 minutes to complete 100 FastICA runs, 1.10 minutes for distance matrix and clustering calculation, and 16.02 seconds for processing the final matrices. The resulting file sizes were 2.2 GB of temporary files, 13.3 MB for the final M matrix, and 2.9 MB for the final A matrix. Results for other dimensionalities are shown in Note C in S1 Text.
Data processing times
Downloading experimental metadata using esearch and efetch typically takes only a few minutes (< 10 minutes for 15,000 E. coli samples). fasterq-dump with Nextflow is used to download and stage the RNA-Seq data in parallel with other tasks. The Nextflow RNA-Seq processing pipeline typically takes a few hours for several hundred samples.
ICA computational requirements
We evaluated the computational requirements for this analysis on the E. coli PRECISE-1K RNA-Seq compendium consisting of 4257 genes and 1035 samples, with an input gene expression log2(TPM) matrix of 80.2 MB. The machine used had the following specifications: CPU: AMD Ryzen Threadripper PRO 5995WX (256 MB cache, 64 cores, 128 threads), RAM: 256GB, DDR4, 3200 MT/s, 64 threads were used for the following evaluation. Time at 200 dimensionality (typical for the E. coli dataset) was 7.96 minutes to complete 100 FastICA runs, 1.10 minutes for distance matrix and clustering calculation, and 16.02 seconds for processing the final matrices. The resulting file sizes were 2.2 GB of temporary files, 13.3 MB for the final M matrix, and 2.9 MB for the final A matrix. Results for other dimensionalities are shown in Note C in S1 Text.
Results
Here, we demonstrate how to build the iModulon structure of Bacillus subtilis from publicly available RNA-seq datasets using the workflow (Fig 1A) and characterize the iModulons with Pymodulon. All code to reproduce these results is available at: https://github.com/SBRG/iModulonMiner.
Results from Steps 1 and 2: Compilation and processing of all publicly available RNA-seq datasets for B. subtilis
We compile the metadata for all publicly available RNA-seq data for B. subtilis in NCBI SRA (https://github.com/sbrg/iModulonMiner/tree/main/1_download_metadata). Here we utilize a dataset of 718 samples labeled as Bacillus subtilis RNA-seq data.
The B. subtilis dataset was subsequently processed using the RNA-seq pipeline available at https://github.com/sbrg/iModulonMiner/tree/main/2_process_data (Fig D in S1 Text). Ten samples failed to complete the processing pipeline, resulting in expression counts for 708 datasets.
Results from Step 3: Quality control, metadata curation, and normalization
The B. subtilis compendium was subjected to five quality control criteria (Fig 1B). During manual curation, we removed some non-traditional RNA-seq datasets, such as TermSeq or RiboSeq. The final high-quality B. subtilis compendium contained 265 RNA-seq datasets (Fig 1C). Although manual curation is the most time-consuming part of the workflow, it facilitates deep characterization of patterns in the gene expression compendium. For example, application of Principal Component Analysis (PCA) to the B. subtilis expression compendium revealed that a large portion of the expression variation could be explained by the growth stage (Fig 1D). Finally, the log-TPM data within each project was centered to a project-specific reference condition (Fig 1E).
Results from Step 4: Running independent component analysis
The optICA script (https://github.com/sbrg/iModulonMiner/tree/main/4_optICA) computes the optimal set of independent components and their activities (Note D in S1 Text). We apply a threshold to each independent component (Design and Implementation), resulting in gene sets called iModulons. This process resulted in 72 iModulons for the B. subtilis compendium that explained 67% of the expression variance in the compendium (Fig E in S1 Text).
Results from Step 5: Characterizing iModulons
Here, we describe how the contents of the PyModulon package contributes to understanding information encoded in iModulons.
iModulons are defined and grouped into categories based on annotation
The IcaData object, which houses all the relevant information about the identified iModulons, is generated (Fig 2A). Each independent component from ICA contains a gene weight for every gene in the genome. Only genes with weights above a specific threshold are considered to be in an iModulon. (Fig 2B). All thresholds are computed during initialization of the IcaData object (Supplementary Methods in S1 Text). Individual thresholds can be adjusted using the change_threshold function.
The compute_trn_enrichments function automatically identifies iModulons that significantly overlap with regulons found in the literature. The method can be used to search for simple regulons (i.e., groups of genes regulated by a single regulator) or complex regulons (i.e., groups of genes regulated by a combination of regulators). This method is built on top of the compute_annotation_enrichment method, which can be used for gene set enrichment analysis against any gene set, such as gene ontology terms, KEGG pathways, plasmids, or phages. Annotating iModulons typically results in their categorization into one of four classes: regulatory, functional, single-gene, or uncharacterized.
10.1371/journal.pcbi.1012546.g002
Fig 2
Overview of the B. subtilis iModulon structure.
(a) Graphical representation of the IcaData object from PyModulon, illustrating the data, attributes, and methods stored in the object. (b) Example of an iModulon. Each point represents a gene. The x-axis shows the location of the gene in the genome, and the y-axis measures the weight of the gene in the Zur iModulon. Genes with prior evidence of Zur regulation are highlighted in orange. Genes outside the dashed black line are members of the Zur iModulon, whereas the genes inside the dashed black lines are not in the Zur iModulon. (c) Treemap of the 72 B. subtilis iModulons. The size of each box represents the fraction of expression variance that is explained by the iModulon. (d) Scatter plot comparing the overlap of each iModulon and its associated regulon(s). The circle size scales with the number of genes in the iModulon, and the color indicates the general category of the iModulon. (e) Venn diagram between the three SigB iModulons and the SigB regulon. (f) Motif identified upstream of all 58 genes in the ResD iModulon.
Of the 72 B. subtilis iModulons, 52 iModulons represented the effects of known transcriptional regulators. Together, these Regulatory iModulons explain 57% of the variance in the dataset (Fig 2C). The iModulon recall and regulon recall can be used to assess the accuracy of regulator enrichments (Fig 2D). The iModulon recall is the fraction of the iModulon that is part of the pre-defined regulon from the literature, whereas the regulon recall is the fraction of the regulon that is captured by the iModulon. iModulons in the top left-quadrant often represent subsets of known regulons. For example, there are three iModulons that each capture different subsets of the SigB regulon (Fig 2E). Even though only 28 of the 58 genes (48%) in the ResD iModulon have published ResD binding sites, we identified a conserved 16 base pair motif upstream of all 58 genes in the iModulon (Fig 2F).
Five additional iModulons were dominated by a single, high-coefficient gene, and are automatically identified by the method find_single_gene_imodulons. These Single Gene (SG) iModulons may arise from over-decomposition of the dataset [27,48] or artificial knock-out or overexpression of single genes. Together, these iModulons contribute to 1% of the variance.
The remaining 15 iModulons that could not be mapped to regulons present likely targets for the discovery of new regulons. Of those, the strongest candidates are the nine Functional iModulons, or iModulons that could be assigned a putative function. For example, one iModulon contains five genes in the same operon: yvaC, yvaD, yvaE, yvaF, and azoRB. Since YvaF is a putative transcription factor, we hypothesize that this iModulon is controlled by YvaF. Six Uncharacterized iModulons primarily contained either uncharacterized or unrelated genes, and contributed to 2% of the variance in the dataset.
Altogether, these 72 iModulons provide a quantitative framework for understanding the TRN of B. subtilis. This framework can be used to both re-interpret previously published studies in the context of the full compendium, and to rapidly analyze new data.
iModulon visualization empowers data exploration
PyModulon contains a suite of functions to create informative visualization, as described here: https://pymodulon.readthedocs.io/en/latest/tutorials/plotting_functions.html. One such function computes a clustered heatmap of iModulon activities to identify correlated groups of iModulons (Fig F in S1 Text). These iModulons often respond to a common stimulus, and represent a computational method to define stimulons [52,53]. Here, we present two case studies that develop hypotheses of regulatory mechanisms based on iModulon visualizations.
First, we identified an uncharacterized iModulon that contains genes responsible for capsular polyglutamate synthesis, biofilm components, and synthesis of the peptide/polyketide antibiotic bacillaene [54] (Fig 3A). This iModulon is activated in early biofilm production and stationary phase (Fig 3B). As no single regulator is known to control all of these processes, this iModulon presents a hypothesis of the existence of a novel global regulator of biofilm formation.
10.1371/journal.pcbi.1012546.g003
Fig 3
Examples of insights derived from iModulons.
(a) Scatter plot of the gene weights in the newly discovered early-biofilm iModulon, created using the plot_gene_weights function. Genes outside the horizontal dashed black lines are in the iModulon, and genes are colored by their Cluster of Orthologous Gene (COG) category. (b) Bar plot of the iModulon activities for the early-biofilm iModulon, created from the plot_activities function. Individual points show iModulon activities for replicates, whereas the bars show the average activity for an experimental condition. Asterisks indicate the reference condition for each project. (c) Venn diagram comparing the SPbeta-1, SPbeta-2 and YonO-1 iModulons against the genes in the SPβ prophage. The asterisk indicates that one gene (yozZ) was in all three iModulons, but not in the prophage. (d) Scatter plot comparing the SPbeta-1 and SPbeta-2 iModulon activities, created from the compare_activities function. Each point represents a gene expression dataset under a specific condition. The center diagonal line is the 45-degree line of equal activities. (e) Scatter plot comparing the SPbeta-2 and YonO-1 iModulon activities. Each point represents a gene expression dataset under a specific condition.
Second, we examine three iModulons that contain three distinct sections of the B. subtilis prophage SPβ (Fig 3C), one of which coincided with nearly all genes known to be transcribed by YonO, a recently discovered single subunit phage RNA polymerase [55]. The SPbeta-1 and SPbeta-2 iModulons diverge in a single experiment, where B. subtilis was infected with either the phage Phi3T or SPβ [56] (Fig 3D). The activities of the YonO-1 iModulon are nearly identical to the SPbeta-2 iModulon (Fig 3E). However, the two major differences include YonO mutant strains [55] and a dataset where B. subtilis was exposed to heat shock at 53C [57]. The abnormally low YonO-1 iModulon activities are expected from the YonO mutant strain; however, the low activity during heat shock may indicate that the phage RNA polymerase YonO may be more sensitive to heat shock than the main B. subtilis RNA polymerase.
Comparing iModulon structures across datasets and organisms reveals robustness
The differences in dataset and experimental conditions can create different iModulons within the same organism (see Note E in S1 Text), but previous studies have shown that similar iModulons can be found across disparate datasets [24,25]. To demonstrate this property, we use the compare_ica method to map the similarities between the iModulon structure presented here and an iModulon structure computed from a single microarray dataset [10,58]. The similarities are defined by the Pearson R correlation between the independent component gene weights, and are represented by the thickness of the arrows in Fig G in S1 Text. Of the 72 iModulons extracted from the RNA-seq compendium, 47 iModulons (65%) were highly similar to the microarray iModulons (Fig 4A). For example, nearly every gene in the Zur iModulon has nearly identical gene weights in both datasets (Fig 4B).
10.1371/journal.pcbi.1012546.g004
Fig 4
Comparison of iModulon structures between datasets.
(a) Bar chart comparing the iModulons found in this RNA-seq dataset compared to a previous microarray dataset, colored by the type of iModulon. iModulons that are conserved between the two datasets are shown in a darker color. (b-d) Scatter plots comparing gene weights of iModulons found in different datasets, created using the compare_gene_weights function. Horizontal and vertical dashed lines indicate iModulon thresholds. Diagonal dashed line indicates the 45-degree line of equal gene weights. Genes in red are members of both iModulons. (b) Comparison of the Zur iModulon gene weights computed from the RNA-seq and microarray datasets. (c) Comparison of an uncharacterized iModulon found in both the RNA-seq and microarray datasets. (d) Comparison of the B. subtilis GlpP iModulon to the E. coli GlpR iModulon.
Presence of iModulons in two disparate datasets lends confidence of the biological significance of the component [24]. For example, an iModulon containing many uncharacterized genes was found in both datasets (Fig 4C). This is the same uncharacterized iModulon (uncharacterized-5) that was activated in the first few days of biofilm development (Fig H in S1 Text). In the microarray dataset, this uncharacterized iModulon was downregulated in late sporulation. This observation supports that the genes in this iModulon are likely co-regulated by a transcriptional regulator related to biofilm development.
In addition, iModulons can be compared between organisms using gene orthology. We compared the B. subtilis iModulon structure to a previously published E. coli iModulon structure [9], and found many orthologous iModulons (defined as iModulons containing orthologous genes with similar gene coefficients). We identified 22 iModulons in the B. subtilis dataset that were orthologous to E. coli iModulons (Fig I in S1 Text). For example, the weights of the genes in the B. subtilis GlpP iModulon were nearly identical to their orthologs in the E. coli GlpR iModulon, indicating that these genes are modulated in similar ratios across the two organisms (Fig 4D).
The iModulonDB web page hosts iModulon analysis results
The results for the Bacillus subtilis dataset discussed here are available at https://iModulonDB.org/dataset.html?organism=b_subtilis&dataset=modulome [59].
Results from Steps 1 and 2: Compilation and processing of all publicly available RNA-seq datasets for B. subtilis
We compile the metadata for all publicly available RNA-seq data for B. subtilis in NCBI SRA (https://github.com/sbrg/iModulonMiner/tree/main/1_download_metadata). Here we utilize a dataset of 718 samples labeled as Bacillus subtilis RNA-seq data.
The B. subtilis dataset was subsequently processed using the RNA-seq pipeline available at https://github.com/sbrg/iModulonMiner/tree/main/2_process_data (Fig D in S1 Text). Ten samples failed to complete the processing pipeline, resulting in expression counts for 708 datasets.
Results from Step 3: Quality control, metadata curation, and normalization
The B. subtilis compendium was subjected to five quality control criteria (Fig 1B). During manual curation, we removed some non-traditional RNA-seq datasets, such as TermSeq or RiboSeq. The final high-quality B. subtilis compendium contained 265 RNA-seq datasets (Fig 1C). Although manual curation is the most time-consuming part of the workflow, it facilitates deep characterization of patterns in the gene expression compendium. For example, application of Principal Component Analysis (PCA) to the B. subtilis expression compendium revealed that a large portion of the expression variation could be explained by the growth stage (Fig 1D). Finally, the log-TPM data within each project was centered to a project-specific reference condition (Fig 1E).
Results from Step 4: Running independent component analysis
The optICA script (https://github.com/sbrg/iModulonMiner/tree/main/4_optICA) computes the optimal set of independent components and their activities (Note D in S1 Text). We apply a threshold to each independent component (Design and Implementation), resulting in gene sets called iModulons. This process resulted in 72 iModulons for the B. subtilis compendium that explained 67% of the expression variance in the compendium (Fig E in S1 Text).
Results from Step 5: Characterizing iModulons
Here, we describe how the contents of the PyModulon package contributes to understanding information encoded in iModulons.
iModulons are defined and grouped into categories based on annotation
The IcaData object, which houses all the relevant information about the identified iModulons, is generated (Fig 2A). Each independent component from ICA contains a gene weight for every gene in the genome. Only genes with weights above a specific threshold are considered to be in an iModulon. (Fig 2B). All thresholds are computed during initialization of the IcaData object (Supplementary Methods in S1 Text). Individual thresholds can be adjusted using the change_threshold function.
The compute_trn_enrichments function automatically identifies iModulons that significantly overlap with regulons found in the literature. The method can be used to search for simple regulons (i.e., groups of genes regulated by a single regulator) or complex regulons (i.e., groups of genes regulated by a combination of regulators). This method is built on top of the compute_annotation_enrichment method, which can be used for gene set enrichment analysis against any gene set, such as gene ontology terms, KEGG pathways, plasmids, or phages. Annotating iModulons typically results in their categorization into one of four classes: regulatory, functional, single-gene, or uncharacterized.
10.1371/journal.pcbi.1012546.g002
Fig 2
Overview of the B. subtilis iModulon structure.
(a) Graphical representation of the IcaData object from PyModulon, illustrating the data, attributes, and methods stored in the object. (b) Example of an iModulon. Each point represents a gene. The x-axis shows the location of the gene in the genome, and the y-axis measures the weight of the gene in the Zur iModulon. Genes with prior evidence of Zur regulation are highlighted in orange. Genes outside the dashed black line are members of the Zur iModulon, whereas the genes inside the dashed black lines are not in the Zur iModulon. (c) Treemap of the 72 B. subtilis iModulons. The size of each box represents the fraction of expression variance that is explained by the iModulon. (d) Scatter plot comparing the overlap of each iModulon and its associated regulon(s). The circle size scales with the number of genes in the iModulon, and the color indicates the general category of the iModulon. (e) Venn diagram between the three SigB iModulons and the SigB regulon. (f) Motif identified upstream of all 58 genes in the ResD iModulon.
Of the 72 B. subtilis iModulons, 52 iModulons represented the effects of known transcriptional regulators. Together, these Regulatory iModulons explain 57% of the variance in the dataset (Fig 2C). The iModulon recall and regulon recall can be used to assess the accuracy of regulator enrichments (Fig 2D). The iModulon recall is the fraction of the iModulon that is part of the pre-defined regulon from the literature, whereas the regulon recall is the fraction of the regulon that is captured by the iModulon. iModulons in the top left-quadrant often represent subsets of known regulons. For example, there are three iModulons that each capture different subsets of the SigB regulon (Fig 2E). Even though only 28 of the 58 genes (48%) in the ResD iModulon have published ResD binding sites, we identified a conserved 16 base pair motif upstream of all 58 genes in the iModulon (Fig 2F).
Five additional iModulons were dominated by a single, high-coefficient gene, and are automatically identified by the method find_single_gene_imodulons. These Single Gene (SG) iModulons may arise from over-decomposition of the dataset [27,48] or artificial knock-out or overexpression of single genes. Together, these iModulons contribute to 1% of the variance.
The remaining 15 iModulons that could not be mapped to regulons present likely targets for the discovery of new regulons. Of those, the strongest candidates are the nine Functional iModulons, or iModulons that could be assigned a putative function. For example, one iModulon contains five genes in the same operon: yvaC, yvaD, yvaE, yvaF, and azoRB. Since YvaF is a putative transcription factor, we hypothesize that this iModulon is controlled by YvaF. Six Uncharacterized iModulons primarily contained either uncharacterized or unrelated genes, and contributed to 2% of the variance in the dataset.
Altogether, these 72 iModulons provide a quantitative framework for understanding the TRN of B. subtilis. This framework can be used to both re-interpret previously published studies in the context of the full compendium, and to rapidly analyze new data.
iModulon visualization empowers data exploration
PyModulon contains a suite of functions to create informative visualization, as described here: https://pymodulon.readthedocs.io/en/latest/tutorials/plotting_functions.html. One such function computes a clustered heatmap of iModulon activities to identify correlated groups of iModulons (Fig F in S1 Text). These iModulons often respond to a common stimulus, and represent a computational method to define stimulons [52,53]. Here, we present two case studies that develop hypotheses of regulatory mechanisms based on iModulon visualizations.
First, we identified an uncharacterized iModulon that contains genes responsible for capsular polyglutamate synthesis, biofilm components, and synthesis of the peptide/polyketide antibiotic bacillaene [54] (Fig 3A). This iModulon is activated in early biofilm production and stationary phase (Fig 3B). As no single regulator is known to control all of these processes, this iModulon presents a hypothesis of the existence of a novel global regulator of biofilm formation.
10.1371/journal.pcbi.1012546.g003
Fig 3
Examples of insights derived from iModulons.
(a) Scatter plot of the gene weights in the newly discovered early-biofilm iModulon, created using the plot_gene_weights function. Genes outside the horizontal dashed black lines are in the iModulon, and genes are colored by their Cluster of Orthologous Gene (COG) category. (b) Bar plot of the iModulon activities for the early-biofilm iModulon, created from the plot_activities function. Individual points show iModulon activities for replicates, whereas the bars show the average activity for an experimental condition. Asterisks indicate the reference condition for each project. (c) Venn diagram comparing the SPbeta-1, SPbeta-2 and YonO-1 iModulons against the genes in the SPβ prophage. The asterisk indicates that one gene (yozZ) was in all three iModulons, but not in the prophage. (d) Scatter plot comparing the SPbeta-1 and SPbeta-2 iModulon activities, created from the compare_activities function. Each point represents a gene expression dataset under a specific condition. The center diagonal line is the 45-degree line of equal activities. (e) Scatter plot comparing the SPbeta-2 and YonO-1 iModulon activities. Each point represents a gene expression dataset under a specific condition.
Second, we examine three iModulons that contain three distinct sections of the B. subtilis prophage SPβ (Fig 3C), one of which coincided with nearly all genes known to be transcribed by YonO, a recently discovered single subunit phage RNA polymerase [55]. The SPbeta-1 and SPbeta-2 iModulons diverge in a single experiment, where B. subtilis was infected with either the phage Phi3T or SPβ [56] (Fig 3D). The activities of the YonO-1 iModulon are nearly identical to the SPbeta-2 iModulon (Fig 3E). However, the two major differences include YonO mutant strains [55] and a dataset where B. subtilis was exposed to heat shock at 53C [57]. The abnormally low YonO-1 iModulon activities are expected from the YonO mutant strain; however, the low activity during heat shock may indicate that the phage RNA polymerase YonO may be more sensitive to heat shock than the main B. subtilis RNA polymerase.
Comparing iModulon structures across datasets and organisms reveals robustness
The differences in dataset and experimental conditions can create different iModulons within the same organism (see Note E in S1 Text), but previous studies have shown that similar iModulons can be found across disparate datasets [24,25]. To demonstrate this property, we use the compare_ica method to map the similarities between the iModulon structure presented here and an iModulon structure computed from a single microarray dataset [10,58]. The similarities are defined by the Pearson R correlation between the independent component gene weights, and are represented by the thickness of the arrows in Fig G in S1 Text. Of the 72 iModulons extracted from the RNA-seq compendium, 47 iModulons (65%) were highly similar to the microarray iModulons (Fig 4A). For example, nearly every gene in the Zur iModulon has nearly identical gene weights in both datasets (Fig 4B).
10.1371/journal.pcbi.1012546.g004
Fig 4
Comparison of iModulon structures between datasets.
(a) Bar chart comparing the iModulons found in this RNA-seq dataset compared to a previous microarray dataset, colored by the type of iModulon. iModulons that are conserved between the two datasets are shown in a darker color. (b-d) Scatter plots comparing gene weights of iModulons found in different datasets, created using the compare_gene_weights function. Horizontal and vertical dashed lines indicate iModulon thresholds. Diagonal dashed line indicates the 45-degree line of equal gene weights. Genes in red are members of both iModulons. (b) Comparison of the Zur iModulon gene weights computed from the RNA-seq and microarray datasets. (c) Comparison of an uncharacterized iModulon found in both the RNA-seq and microarray datasets. (d) Comparison of the B. subtilis GlpP iModulon to the E. coli GlpR iModulon.
Presence of iModulons in two disparate datasets lends confidence of the biological significance of the component [24]. For example, an iModulon containing many uncharacterized genes was found in both datasets (Fig 4C). This is the same uncharacterized iModulon (uncharacterized-5) that was activated in the first few days of biofilm development (Fig H in S1 Text). In the microarray dataset, this uncharacterized iModulon was downregulated in late sporulation. This observation supports that the genes in this iModulon are likely co-regulated by a transcriptional regulator related to biofilm development.
In addition, iModulons can be compared between organisms using gene orthology. We compared the B. subtilis iModulon structure to a previously published E. coli iModulon structure [9], and found many orthologous iModulons (defined as iModulons containing orthologous genes with similar gene coefficients). We identified 22 iModulons in the B. subtilis dataset that were orthologous to E. coli iModulons (Fig I in S1 Text). For example, the weights of the genes in the B. subtilis GlpP iModulon were nearly identical to their orthologs in the E. coli GlpR iModulon, indicating that these genes are modulated in similar ratios across the two organisms (Fig 4D).
The iModulonDB web page hosts iModulon analysis results
The results for the Bacillus subtilis dataset discussed here are available at https://iModulonDB.org/dataset.html?organism=b_subtilis&dataset=modulome [59].
Availability and future directions
We have described two complementary tools to compile and explore iModulons. First, we present a GitHub repository that walks through each analysis in this manuscript (https://github.com/sbrg/iModulonMiner). The pipeline is modular, as any step can be replaced with an alternative process, and the code in the repository can be modified for any new organism of interest. Second, we present PyModulon, a Python package for exploring iModulon properties, enrichments, and activities (https://pymodulon.readthedocs.io/en/latest/). We foresee that this workflow will be broadly applied to all publicly available datasets, resulting in a database of iModulons for every organism with sufficient data.
Supporting information
S1 Text
Supplementary Information file that contains Supplementary Methods, Results, Notes A-E, Figs A-K and References.
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Title: 12552 Hypoglycemia As Initial Manifestation Of Hepatocellular Carcinoma In A Patient With Diabetes | Body: |
Title: Strategic Advances in Combination Therapy for Metastatic Castration-Sensitive Prostate Cancer: Current Insights and Future Perspectives | Body:
1. Introduction
The treatment landscape of metastatic prostate cancer has evolved tremendously over the past two decades, driven by advancements in the understanding of tumor biology, as well as the development and approval of various new agents [1]. This evolution began with the FDA approval of docetaxel for metastatic castration-resistant prostate cancer (mCRPC) in 2004. Since then, the efficacy of several other drug classes has been established, improving survival rates and setting new standards of care for patients with mCRPC [2].
A paradigm shift toward treatment intensification in the early disease stages has recently occurred. Numerous novel strategies for metastatic castration-sensitive prostate cancer (mCSPC) have arisen from therapies proven successful in mCRPC [3] (Figure 1). This shift involves using several androgen receptor pathway inhibitors (ARPIs) in addition to androgen deprivation therapy (ADT). The shift is supported by evidence demonstrating the superiority of combining ADT with novel hormonal agents over ADT monotherapy [3]. Similarly, chemotherapy, once reserved for mCRPC, has prolonged survival in selected patients with mCSPC who have high-volume (HV) metastases and have received upfront chemotherapy [4]. This evidence supports the concept of early combination therapy in patients with mCSPC.
mCSPC incidence is increasing, as evidenced by US-based studies showing the shift in the stage of prostate cancer diagnosis, which is likely influenced by changes in prostate-specific antigen (PSA) screening recommendations by the US Preventative Services Task Force [5]. While not implying direct causality, the rising incidence of metastatic prostate cancer is a high-priority issue due to the incurable nature of advanced disease, which is associated with inevitable therapy resistance and poorer survival outcomes [6]. Over the past decade, treatment for mCSPC has evolved significantly, driven by large, randomized, phase 3 clinical trials demonstrating improvements in overall survival (OS) and quality of life (QoL) with combination therapy over the historical standard of ADT alone [7].
mCSPC treatment aims to prolong survival through long-term tumor suppression by targeting tumor proliferation, particularly the androgen receptor (AR) pathway. Treatments directed at the AR pathway include ADT and ARPIs [8]. ADT reduces the amount of androgen available for AR binding, diminishes AR-mediated cell signaling, and increases cell cycle arrest and apoptosis [9]. ARPIs, such as abiraterone acetate, apalutamide, darolutamide, and enzalutamide, inhibit androgen synthesis or compete to bind to the AR, thereby disrupting the cell cycle [10]. Taxanes, such as docetaxel, stabilize microtubules, prevent mitosis, and inhibit AR translocation from the cytoplasm to the nucleus [11]. Combining treatments with different mechanisms of action often achieves the highest tumor regression [12].
A large body of evidence supports upfront combination treatment of ADT with an ARPI and/or docetaxel in men with mCSPC. However, real-world data show a significantly low rate of adoption of this combination regimen in clinical practice. This strategy is currently used in less than half of patients eligible for treatment intensification in many countries [13].
This review aims to present the scientific rationale and the most recent evidence for mCSPC treatment strategies, focusing on treatment intensification using ADT combined with ARPI and/or docetaxel. We aim to provide guidance on applying this evidence to ensure the appropriate use of all available treatment options in clinical practice. Furthermore, we discuss the contemporary treatment landscape for mCSPC, analyze the clinical evidence supporting the combined treatment approach, and identify key issues requiring further investigation. Thus, we seek to offer insights that will optimize treatment strategies and improve patient outcomes in mCSPC.
2. Theoretical Background
2.1. Targeting the Androgen Signaling Axis
Androgens play a pivotal role in maintaining male physiology and prostate function. AR activation is central to the pathogenesis of prostate cancer, driving tumor growth and progression [14]. The seminal discovery by Charles Huggins and Clarence Hodges in the 1940s, demonstrating tumor regression after androgen deprivation, laid the foundation for targeting AR in prostate cancer therapy [15]. However, despite initial success, resistance to AR-directed therapies remains a significant challenge, particularly in metastatic prostate cancer, which remains largely incurable [16].
The AR is a ligand-dependent transcription factor belonging to the steroid receptor family. Under normal physiological conditions, testosterone and dihydrotestosterone (DHT) are its principal ligands. Ligand binding induces conformational changes in the AR, leading to its nuclear translocation and activation of target gene transcription, representing essential processes for prostate cancer progression [17].
ADT, achieved via surgical or medical castration, has been a cornerstone in prostate cancer treatment [18]. The development of next-generation ARPIs, such as abiraterone and enzalutamide, which provided survival benefits in CRPC, marked significant milestones [19]. Despite these advancements, tumor resistance has evolved, necessitating further exploration [20].
Prostate cancer can synthesize androgens intratumorally, maintaining AR signaling despite castrate serum testosterone levels [21]. Enzymes involved in androgen biosynthesis, such as CYP17A1 and AKR1C3, are upregulated in CRPC. Inhibitors targeting these pathways, such as abiraterone and other ARPIs, aim to disrupt androgen biosynthesis but face limitations due to the complexity and redundancy of steroidogenic pathways [22].
Targeting the downstream effects of AR activation, such as specific gene transcription and oncogenic pathways, offers another therapeutic avenue. AR signaling intersects with other pathways, such as PI3K/AKT and DNA damage repair mechanisms; thus, combination therapies might be able to overcome tumor resistance [23].
Figure 1 illustrates the mechanisms of androgen signaling and the therapeutic targets in the systemic treatment of advanced PC.
Figure 1
Mechanisms of androgen signaling and therapeutic targets in systemic treatment of advanced prostate cancer. Androgen signaling and the therapeutic targets of systemic therapies for advanced prostate cancer encompass a range of receptor-mediated and pre/post-receptor mechanisms, each serving as a focal point for distinct therapeutic strategies. The synthesis of androgens is precisely controlled by the hypothalamic–pituitary–gonadal and hypothalamic–pituitary–adrenal axes, which regulate the production of gonadal and adrenal androgens, precursors to DHT, the main ligand for the androgen receptor (pre-receptor activity). Upon ligand binding, the androgen receptor moves from the cytoplasm to the nucleus, where it binds to DNA as a homodimer, facilitating the transactivation of target genes and signaling pathways (post-receptor activity). The figure also highlights various clinically approved and experimental inhibitors. Abbreviations: ACTH, adrenocorticotropic hormone; AKT, AKR thymoma; AR, androgen receptor; ARE, androgen response element; CYP17A1, cytochrome P450 17A1; DHEA, dehydroepiandrosterone; DHT, dihydrotestosterone; DNA, deoxyribonucleic acid; FSH, follicle-stimulating hormone; GnRH, gonadotropin-releasing hormone; HRR, homologous recombinational repair; LH, luteinizing hormone; PARP, poly (ADP-ribose) polymerase; PI3K, phosphoinositide 3-kinase; PSA, prostate-specific antigen; PSMA, prostate-specific membrane antigen; PTEN, phosphatase and tensin homolog. Created with BioRender.com.
2.2. Combination Therapies
Combination therapy leverages multiple treatment modalities to enhance therapeutic efficacy and counteract resistance, which is particularly crucial in prostate cancer due to its heterogeneity and capacity to develop resistance [24]. By integrating surgery, radiation, hormone therapy, chemotherapy, and targeted therapies, combination therapy targets prostate cancer using multiple strategies, thereby increasing the likelihood of successful treatment outcomes [25]. The slow-growing nature of prostate cancer and its dependence on various growth factors make it particularly suitable for such a multimodal approach [26].
The heterogeneity within prostate tumors means that different tumor areas may respond variably to treatment [27]. Therefore, combination therapies can more effectively target all cancerous regions and reduce the risk of resistance [28]. Strategic combinations in cancer therapy address tumor complexity and heterogeneity by developing regimens targeting independent molecular pathways, thus overcoming resistance and enhancing patient outcomes [29]. Additionally, exploring epigenetic regulators and alternative androgen biosynthesis pathways offers new treatment opportunities [30].
The therapeutic landscape for prostate cancer continues to evolve with deeper insights into AR signaling and resistance mechanisms. While next-generation AR-directed therapies have improved outcomes, resistance remains a significant challenge [31,32]. Combination therapy presents a promising approach to enhancing treatment efficacy and improving patient outcomes by addressing the complexity and heterogeneity of prostate cancer [33,34]. Ongoing research on the molecular underpinnings of AR function and resistance is crucial for developing more effective treatments, ultimately aiming to transform prostate cancer into a manageable chronic condition [35,36].
2.1. Targeting the Androgen Signaling Axis
Androgens play a pivotal role in maintaining male physiology and prostate function. AR activation is central to the pathogenesis of prostate cancer, driving tumor growth and progression [14]. The seminal discovery by Charles Huggins and Clarence Hodges in the 1940s, demonstrating tumor regression after androgen deprivation, laid the foundation for targeting AR in prostate cancer therapy [15]. However, despite initial success, resistance to AR-directed therapies remains a significant challenge, particularly in metastatic prostate cancer, which remains largely incurable [16].
The AR is a ligand-dependent transcription factor belonging to the steroid receptor family. Under normal physiological conditions, testosterone and dihydrotestosterone (DHT) are its principal ligands. Ligand binding induces conformational changes in the AR, leading to its nuclear translocation and activation of target gene transcription, representing essential processes for prostate cancer progression [17].
ADT, achieved via surgical or medical castration, has been a cornerstone in prostate cancer treatment [18]. The development of next-generation ARPIs, such as abiraterone and enzalutamide, which provided survival benefits in CRPC, marked significant milestones [19]. Despite these advancements, tumor resistance has evolved, necessitating further exploration [20].
Prostate cancer can synthesize androgens intratumorally, maintaining AR signaling despite castrate serum testosterone levels [21]. Enzymes involved in androgen biosynthesis, such as CYP17A1 and AKR1C3, are upregulated in CRPC. Inhibitors targeting these pathways, such as abiraterone and other ARPIs, aim to disrupt androgen biosynthesis but face limitations due to the complexity and redundancy of steroidogenic pathways [22].
Targeting the downstream effects of AR activation, such as specific gene transcription and oncogenic pathways, offers another therapeutic avenue. AR signaling intersects with other pathways, such as PI3K/AKT and DNA damage repair mechanisms; thus, combination therapies might be able to overcome tumor resistance [23].
Figure 1 illustrates the mechanisms of androgen signaling and the therapeutic targets in the systemic treatment of advanced PC.
Figure 1
Mechanisms of androgen signaling and therapeutic targets in systemic treatment of advanced prostate cancer. Androgen signaling and the therapeutic targets of systemic therapies for advanced prostate cancer encompass a range of receptor-mediated and pre/post-receptor mechanisms, each serving as a focal point for distinct therapeutic strategies. The synthesis of androgens is precisely controlled by the hypothalamic–pituitary–gonadal and hypothalamic–pituitary–adrenal axes, which regulate the production of gonadal and adrenal androgens, precursors to DHT, the main ligand for the androgen receptor (pre-receptor activity). Upon ligand binding, the androgen receptor moves from the cytoplasm to the nucleus, where it binds to DNA as a homodimer, facilitating the transactivation of target genes and signaling pathways (post-receptor activity). The figure also highlights various clinically approved and experimental inhibitors. Abbreviations: ACTH, adrenocorticotropic hormone; AKT, AKR thymoma; AR, androgen receptor; ARE, androgen response element; CYP17A1, cytochrome P450 17A1; DHEA, dehydroepiandrosterone; DHT, dihydrotestosterone; DNA, deoxyribonucleic acid; FSH, follicle-stimulating hormone; GnRH, gonadotropin-releasing hormone; HRR, homologous recombinational repair; LH, luteinizing hormone; PARP, poly (ADP-ribose) polymerase; PI3K, phosphoinositide 3-kinase; PSA, prostate-specific antigen; PSMA, prostate-specific membrane antigen; PTEN, phosphatase and tensin homolog. Created with BioRender.com.
2.2. Combination Therapies
Combination therapy leverages multiple treatment modalities to enhance therapeutic efficacy and counteract resistance, which is particularly crucial in prostate cancer due to its heterogeneity and capacity to develop resistance [24]. By integrating surgery, radiation, hormone therapy, chemotherapy, and targeted therapies, combination therapy targets prostate cancer using multiple strategies, thereby increasing the likelihood of successful treatment outcomes [25]. The slow-growing nature of prostate cancer and its dependence on various growth factors make it particularly suitable for such a multimodal approach [26].
The heterogeneity within prostate tumors means that different tumor areas may respond variably to treatment [27]. Therefore, combination therapies can more effectively target all cancerous regions and reduce the risk of resistance [28]. Strategic combinations in cancer therapy address tumor complexity and heterogeneity by developing regimens targeting independent molecular pathways, thus overcoming resistance and enhancing patient outcomes [29]. Additionally, exploring epigenetic regulators and alternative androgen biosynthesis pathways offers new treatment opportunities [30].
The therapeutic landscape for prostate cancer continues to evolve with deeper insights into AR signaling and resistance mechanisms. While next-generation AR-directed therapies have improved outcomes, resistance remains a significant challenge [31,32]. Combination therapy presents a promising approach to enhancing treatment efficacy and improving patient outcomes by addressing the complexity and heterogeneity of prostate cancer [33,34]. Ongoing research on the molecular underpinnings of AR function and resistance is crucial for developing more effective treatments, ultimately aiming to transform prostate cancer into a manageable chronic condition [35,36].
3. Clinical Development for Combination Therapy
3.1. Systemic Treatment in mCSPC
3.1.1. Androgen-Deprivation Therapy
AR inhibition remains the mainstay treatment for metastatic prostate cancer, a practice grounded in seminal experiments from 1941, which demonstrated the androgen-driven and androgen-dependent nature of prostate cancer [37]. Androgen signaling is crucial for prostate cancer growth and survival, even in treatment-resistant cases [36].
Testosterone suppression (TS) therapy initially involved surgical castration (bilateral orchiectomy) and diethylstilbestrol, eventually evolving to include luteinizing hormone-releasing hormone (LHRH) agonists and antagonists based on the understanding of hypothalamic–pituitary control of gonadal testosterone production [38]. Combining antiandrogens with ADT, known as complete androgen blockade, would counteract both testicular and adrenal androgens. Early-generation AR inhibitors, such as flutamide, bicalutamide, nilutamide, and cyproterone acetate, are typically not used alone but are combined with TS to prevent initial flare responses from LHRH agonists [39].
A meta-analysis of 8275 men from 27 trials comparing TS alone to combined ADT showed improved 5-year OS with nonsteroidal antiandrogens (absolute benefit 3%; two-sided p = 0.005) and potentially worse outcomes with cyproterone acetate (absolute reduction 3%; two-sided p = 0.04). These findings support the use of ADT combination with early-generation AR inhibitors as a control in mCSPC clinical trials, although real-world practices vary [7].
LHRH agonists or antagonists reduce serum testosterone to castration levels (≤50 ng/dL or ≤1.74 nmol/L) [40]. However, phase 3 trials in mCSPC revealed that patients on ADT alone often develop castration resistance within a year, with a median OS of 36–54 months [41]. Therefore, guidelines recommend ADT monotherapy only when combination therapy is not feasible [42]. Furthermore, ADT with treatment intensification is advised for mCSPC unless contraindications exist [13].
Adverse events (AEs) related to ADT use include hot flushes, gynecomastia, decreased libido, sexual dysfunction, bone loss, increased fracture risk, sarcopenic obesity linked to metabolic syndrome, diabetes, cardiovascular disease, cognitive and mood disturbances, anemia, and reduced testis and penis volume, as well as testosterone flares with LHRH agonists. Short-term nonsteroidal AR inhibitors with LHRH agonists to control testosterone flare, patient education on heart-healthy lifestyle changes, optimizing control of cardiovascular risk factors, and regular bone health assessments and interventions such as physical activity, calcium, vitamin D, and bone-supportive therapy are used to manage these AEs [43].
3.1.2. Treatment Intensification—Doublet Therapies
Abiraterone Acetate
Abiraterone acetate was evaluated in several clinical trials for mCSPC to determine its effectiveness in improving OS and other clinical outcomes when combined with ADT. In the STAMPEDE trial, around 1000 mCSPC patients were randomized to receive either ADT alone or ADT combined with abiraterone. After a 40-month follow-up, the combination of abiraterone + ADT reduced the risk of death by approximately 40% (hazard ratio (HR): 0.61; 95% confidence interval (CI): 0.49–0.75), with significant improvements in other secondary endpoints [44]. A post hoc analysis of 1003 metastatic patients, stratified by LATITUDE criteria, showed that median survival was higher in the abiraterone +ADT arm (79 months vs. 46 months) after a median follow-up of 73 months. Both low-risk (HR: 0.54; 95% CI: 0.40–0.74) and high-risk (HR: 0.54; 95% CI: 0.43–0.69) groups benefited from the treatment [45].
The LATITUDE trial included 1199 patients with high-risk mCSPC, defined as having at least two of the following: Gleason score ≥ 8, ≥3 bone lesions, or visceral metastases. Patients were randomized to receive abiraterone with prednisone or placebo with ADT. The trial demonstrated a similar approximately 40% reduction in the risk of death with abiraterone + ADT (HR: 0.66; 95% CI: 0.56–0.78; p < 0.001) after a median follow-up of 51.8 months. The median OS was 53.3 months in the abiraterone + ADT group compared to 36.5 months in the placebo group. Secondary endpoints, such as radiographic progression-free survival (rPFS), PSA progression, and time to chemotherapy initiation, also showed significant improvements [46].
The STAMPEDE trial demonstrated a high incidence of severe and life-threatening grade 3–5 AEs, particularly in combination therapy arms. Adding abiraterone significantly increased the frequency of these AEs. This finding underscores the necessity for a careful risk-benefit analysis when incorporating abiraterone into treatment plans for mCSPC, especially in patients aged ≥ 70 years who experienced higher incidences of grade 3–5 AEs (47% vs. 33%) and a higher rate of treatment-related deaths (9 vs. 3). Additionally, severe hypertension or cardiac disorders were observed in 10% of patients, and grade 3–5 liver toxicity was noted in 7% of patients, highlighting the importance of vigilant monitoring of blood pressure, as well as renal and hepatic function, to improve patient outcomes when using abiraterone in combination therapy [44].
These findings align with those reported in the LATITUDE trial, also demonstrating a higher frequency of AEs in the abiraterone group. However, these events were generally mild and primarily related to mineralocorticoid excess (hypertension, hypokalemia, and edema), hormonal effects (fatigue and hot flushes), and liver toxicity [44]. Despite these AEs, the overall discontinuation rate due to AEs in the LATITUDE trial was 12%. Importantly, patient-reported outcomes in LATITUDE indicated improvements in pain intensity, fatigue, functional decline, and overall health-related QoL, further emphasizing the complex balance between the therapeutic benefits and the risks of AEs when using abiraterone in mCSPC [47].
In February 2018, the FDA approved abiraterone in combination with prednisone for mCSPC based on the results of the STAMPEDE and LATITUDE trials. The National Comprehensive Cancer Network (NCCN) Panel recommends abiraterone with 5 mg once-daily prednisone as a treatment option with ADT for newly diagnosed mCSPC. Alternatively, the fine-particle formulation of abiraterone can be used to reduce financial toxicity and improve adherence [48].
Apalutamide
The double-blind phase 3 TITAN trial evaluated the efficacy and safety of apalutamide in combination with ADT compared to ADT alone in patients with mCSPC [49]. This trial was designed to address the ongoing need for more effective treatment strategies in such patients. The TITAN trial randomized 1052 patients with mCSPC to receive either ADT with apalutamide (240 mg/day) or a placebo. The participants were carefully stratified based on Gleason score at diagnosis, geographic region, and previous docetaxel treatment to ensure balanced comparison groups. The median follow-up duration was 22.7 months, allowing for a robust assessment of long-term outcomes. Importantly, both primary endpoints were achieved, demonstrating the significant benefit of apalutamide in this setting. At 24 months, the rPFS was notably higher in the apalutamide group (68.2%) compared to the placebo group (47.5%), with an HR for radiographic progression or death of 0.48 (95% CI: 0.39–0.60; p < 0.001). Furthermore, OS was also significantly improved in the apalutamide group (82.4%) compared to the placebo group (73.5%), with an HR for death of 0.67 (95% CI: 0.51–0.89; p = 0.005). These findings underscore the potential of adding apalutamide to standard ADT to enhance outcomes in patients with mCSPC.
In the final analysis, the median OS was not reached in the apalutamide group compared to 52.2 months in the ADT alone group (HR: 0.65; 95% CI: 0.53–0.79; p < 0.001) after a median follow-up of 44 months. After adjusting for crossover, the risk of death was reduced by 48%, indicating a consistent benefit across both HV and low-volume (LV) disease. A post hoc subgroup analysis of the TITAN trial re-evaluated outcomes based on the volume and timing of metastases, showing synchronous HV disease in 54% of patients. OS benefits were statistically significant in patients with synchronous HV disease (HR: 0.68; 95% CI: 0.53–0.87; p = 0.002) and metachronous LV disease (HR: 0.22; 95% CI: 0.09–0.55; p = 0.001). However, no significant OS benefits were observed in patients with synchronous LV or metachronous HV disease, likely due to limited patient numbers [50].
More frequently observed AEs in the apalutamide group included rash, hypothyroidism, and ischemic heart disease. Nevertheless, health-related QoL was maintained during treatment with apalutamide [51].
The TITAN trial demonstrated significant improvements in both rPFS and OS by adding apalutamide to ADT in patients with mCSPC. These findings support apalutamide as a category 1 treatment option for mCSPC, which received FDA approval in September 2019 [52]. The consistent survival benefits across different subgroups and the maintained QoL highlight the robust efficacy and tolerability of apalutamide in this patient population [53].
Enzalutamide
The management of mCSPC has evolved with the introduction of enzalutamide, an ARPI, as an addition to standard ADT. Recent clinical trials, namely ENZAMET and ARCHES, have provided significant evidence of the efficacy and safety of this combination [8].
The ENZAMET trial was an open-label, randomized phase 3 study involving 1125 patients with mCSPC. The patients were randomized to receive either enzalutamide (160 mg/day) + ADT (LHRH analog or surgical castration) or a first-generation antiandrogen (bicalutamide, nilutamide, or flutamide) + ADT [54]. Stratification was based on disease volume, planned use of early docetaxel, planned use of bone antiresorptive therapy, comorbidity score, and trial site. At the first interim analysis with a median follow-up of 34 months, the enzalutamide group showed a 33% reduction in the risk of death compared to the control group (HR: 0.67; 95% CI: 0.52–0.86; p = 0.002). An analysis after a median follow-up of 68 months revealed that the 5-year OS rate was 67% in the enzalutamide group versus 57% in the control group (HR: 0.70; 95% CI: 0.58–0.84; p < 0.001). Secondary endpoints also showed improvement with enzalutamide, including PFS based on PSA levels and clinical PFS. A post hoc analysis indicated a significant OS benefit in patients with synchronous mCSPC (HR: 0.73; 95% CI: 0.55–0.99), but not in metachronous patients [55].
The ARCHES trial, a double-blind, randomized phase 3 study, included 1150 patients with mCSPC. The participants were randomized to receive either enzalutamide (160 mg/day) + ADT or placebo + ADT, with stratification based on disease volume and prior docetaxel use. After a median follow-up of 14.4 months, the enzalutamide group demonstrated significantly improved rPFS (19.0 months vs. not reached; HR: 0.39; 95% CI: 0.30–0.50; p < 0.001) [56]. At the final OS analysis, although 32% of patients in the placebo group crossed over to enzalutamide after unblinding, enzalutamide still reduced the risk of death by 34% compared to placebo (HR: 0.66; 95% CI: 0.53–0.81; p < 0.001) [57].
The safety of enzalutamide in these trials was consistent with previous studies on CRPC, with common AEs including fatigue, seizures, and hypertension. The safety profiles observed in both ENZAMET and ARCHES trials were similar, confirming the manageable safety of enzalutamide in mCSPC [54,56].
Overall, enzalutamide, when combined with ADT, significantly improves survival in patients with mCSPC. The ENZAMET and ARCHES trials demonstrated substantial improvements in OS and PFS with enzalutamide + ADT compared to standard ADT regimens. The FDA’s approval of enzalutamide for mCSPC in December 2019 reflects its robust efficacy and manageable safety profile, supporting its use as a category 1 treatment option for patients with mCSPC [58].
Docetaxel
Docetaxel has been studied as an initial treatment option for patients with mCSPC based on several key phase 3 trials: ECOG 3805/CHAARTED, STAMPEDE, and GETUG-AFU-15 [41,59,60]. These studies provided significant insights into the efficacy of combining docetaxel with ADT for improving OS in patients with mCSPC.
The ECOG 3805/CHAARTED trial randomized 790 patients with mCSPC to receive either docetaxel (75 mg/m2 intravenously every 3 weeks for 6 doses) + ADT or ADT alone [41]. After a median follow-up of 53.7 months, the combination therapy arm showed longer OS than the ADT-alone arm (57.6 months vs. 47.2 months; HR: 0.72; 95% CI: 0.59–0.89; p = 0.002) [61]. Subgroup analysis revealed that the survival benefit was more pronounced in patients with HV disease (65% of participants) (HR: 0.63; 95% CI: 0.50–0.79; p < 0.001). However, patients with LV did not obtain a significant survival benefit by adding docetaxel (HR: 1.04; 95% CI: 0.70–1.55; p = 0.860).
The STAMPEDE trial, a multi-arm, multi-stage phase 3 study, included patients with both non-metastatic (M0) and metastatic (M1) CSPC [59]. The trial confirmed the survival advantage of adding docetaxel to ADT, as seen in the CHAARTED trial. In the STAMPEDE trial, the extent of disease was not evaluated in 1087 patients with metastatic disease, but the median OS for all M1 patients was 5.4 years in the ADT + docetaxel arm versus 3.6 years in the ADT-only arm (a difference of 1.8 years compared to a 1.1-year difference in the CHAARTED).
The GETUG-AFU-15 trial evaluated ADT combined with docetaxel [62]. After 83.9 months of follow-up, this combination did not significantly improve OS compared to ADT alone. However, post hoc analysis considering metastatic disease volume indicated a trend toward benefit in the HV group (HR: 0.78; 95% CI: 0.56–1.09; p = 0.140) [60].
A meta-analysis of three trials involving 2,261 patients with mHSPC revealed that the combination of ADT with docetaxel did not improve OS in men with metachronous LV disease, as indicated by an HR of 0.98 (95% CI: 0.67–1.45). However, the most significant OS benefit and reduction in the risk of death were observed in patients with synchronous HV disease (HR: 0.60; 95% CI: 0.52–0.69). Thus, while ADT combined with docetaxel markedly improves OS in patients with HV mCSPC, particularly in those with synchronous HV disease, a different systemic therapy approach may be necessary for patients with metachronous LV disease [63].
Additionally, triplet therapy options including ADT, docetaxel, and ARPI have improved OS compared to ADT + docetaxel alone [64]. Consequently, ADT and docetaxel are not recommended for patients with LV mCSPC, whereas triplet therapy is advised for medically fit patients with HV disease.
The key clinical trials discussed in this section, along with their outcomes and adverse events, are summarized in Table 1 for a comprehensive comparison.
3.1.3. Treatment Intensification—Triplet Therapies
Rationale
Numerous trials demonstrated the significant OS benefit of beginning systemic therapy earlier at the mCSPC stage, not waiting until the tumor becomes castration-resistant [8,65,66]. This benefit is likely due to several reasons: mCSPC might have more favorable disease biology, less acquired treatment resistance, and therefore demonstrates a more durable treatment response [8]. Using docetaxel in mCSPC is likely more efficacious at targeting AR-independent cancer cells early compared to its use in mCRPC since these cancer cells might have had opportunities to develop resistance [8,67]. Treatment in the mCSPC setting is also often better tolerated given fewer cumulative toxicities from prior systemic therapy, as well as fewer symptoms and a lower disease burden before disease progression. Real-world studies showed that only approximately 50% of patients receive second-line therapies upon disease progression, with many possibly becoming too frail to receive docetaxel in the mCRPC setting. Therefore, earlier treatment intensification may also increase the number of patients receiving life-prolonging systemic therapy [68].
The effectiveness of chemohormonal triplet therapy may stem from the synergistic and complementary effects of the three agents [69]. ADT, the cornerstone of hormonal therapy for prostate cancer, inhibits tumor growth by lowering androgen levels produced in the testicles through medication or surgery. ARPIs, including androgen synthesis inhibitors (abiraterone) and antiandrogens (enzalutamide, apalutamide, and darolutamide), suppress other androgen sources to achieve the maximum suppression of the androgen axis, further improving the anti-tumor effect [28]. Docetaxel, a semisynthetic taxane, exhibits significant antitumor activity by inhibiting microtubular depolymerization and attenuating the effects of BCL-2 and BCL-XL expression. The taxane-induced microtubule stabilization arrests cells in the G2/M phase and induces bcl-2 phosphorylation, promoting a cascade of events that ultimately cause apoptotic cell death [70].
ADT lowers androgen levels, inhibiting the growth of androgen-sensitive CSPC cells that respond well to this treatment [9]. The combination of docetaxel and ADT can create a synergistic effect by simultaneously inhibiting androgen signaling pathways and cell division [71]. Clinical trials, such as CHAARTED and STAMPEDE, showed that combining docetaxel with ADT significantly improves survival in patients with mCSPC, providing clinical evidence of the effectiveness of docetaxel against mCSPC [41,72].
Docetaxel Plus Abiraterone
The PEACE-1 trial was an international, open-label, randomized phase 3 study conducted across seven European countries, aiming to assess the efficacy of adding abiraterone acetate with prednisone to the standard of care (SOC) in patients with de novo mCSPC [73]. The study utilized a 2 × 2 factorial design and included 1173 patients randomized into four groups: SOC ADT alone or with docetaxel, SOC with radiotherapy (RT), SOC with abiraterone, and SOC with RT and abiraterone. The primary endpoints were rPFS and OS. The results showed that patients receiving abiraterone had significantly longer rPFS compared to those who did not. Specifically, the overall rPFS in the abiraterone group had an HR of 0.54 (99.9% CI, 0.41–0.71; p < 0.001). For those receiving abiraterone with docetaxel, the rPFS had an HR of 0.50 (99.9% CI, 0.34–0.71; p < 0.001). Furthermore, adding abiraterone to SOC significantly improved OS, with the overall OS in the abiraterone group showing an HR of 0.82 (95.1% CI, 0.69–0.98; p = 0.030). In patients receiving abiraterone with docetaxel, the OS had an HR of 0.75 (95.1% CI, 0.59–0.95; p = 0.017).
Subgroup analysis provides significant insights into the efficacy of different treatment combinations for mCSPC based on disease burden. For patients with HV disease, adding abiraterone acetate + prednisone to the SOC (consisting of ADT and docetaxel) markedly improved OS and rPFS. Specifically, the median OS for patients receiving the triplet therapy was 5.1 years compared to 3.5 years for those receiving only ADT plus docetaxel, with an HR of 0.72 (95% CI: 0.55–0.95; p = 0.019). Additionally, the median rPFS was 4.1 years for those receiving the triplet therapy versus 1.6 years for those receiving the SOC alone (HR: 0.47; 95% CI: 0.30–0.72; p < 0.001). In contrast, the OS data remain immature for patients with LV disease, and median OS has not yet been reached in either treatment group. The rPFS also showed significant improvement with the triplet therapy: the median rPFS was not reached versus 2.7 years for the SOC alone (HR: 0.58; 95% CI: 0.29–1.15; p = 0.006).
The study also examined treatment-related AEs, finding that rates of neutropenia, febrile neutropenia, fatigue, and neuropathy were similar between triplet and doublet therapy groups. However, the incidence of grade ≥ 3 AEs was higher in the triplet therapy group (63% vs. 52%). Specifically, grade 3 hypertension and grade 3 transaminase increase were more common in the triplet group (22% vs. 13% and 6% vs. 1%, respectively). Other AEs, such as febrile neutropenia, fatigue, and neuropathy, did not show significant differences between the groups.
The PEACE-1 trial demonstrated that adding abiraterone to ADT and docetaxel significantly improves both rPFS and OS in patients with de novo mCSPC. These results emphasize the substantial benefit of triplet therapy for HV mCSPC. Thus, it should be considered a standard treatment option for this subgroup. For LV disease, while the survival benefit is still being evaluated, triplet therapy shows promise, particularly in improving PFS.
Docetaxel Plus Darolutamide
The ARASENS trial, an international phase 3 study, evaluated the efficacy and safety of adding darolutamide to ADT and docetaxel in patients with mCSPC. This trial randomized 1306 patients to receive either the combination of ADT, docetaxel, and darolutamide or ADT, docetaxel, and a matching placebo [74]. The primary endpoint of the study was OS, with time to CRPC, skeletal event-free survival, and time to initiation of subsequent systemic antineoplastic therapy as secondary endpoints. The study did not stratify patients by disease volume, and 86% of participants had synchronous metastatic disease. The results demonstrated that adding darolutamide significantly improved OS compared to the placebo group. At 4 years, the OS rate was 62.7% (95% CI: 58.7–66.7) in the darolutamide group versus 50.4% (95% CI: 46.3–54.6) in the placebo group, reflecting a 32% reduction in the risk of death (HR: 0.68; 95% CI: 0.57–0.80; p < 0.001). Additionally, darolutamide showed significant benefits across several secondary endpoints. The time to CRPC was markedly prolonged (HR: 0.36; 95% CI: 0.30–0.42; p < 0.001), skeletal event-free survival improved (HR: 0.61; 95% CI: 0.52–0.72; p < 0.001), and the time to initiation of subsequent systemic antineoplastic therapy was extended (HR: 0.39; 95% CI: 0.33–0.46; p < 0.001).
Post hoc analyses demonstrated the OS benefit for patients with HV disease (HR: 0.69; 95% CI: 0.57–0.82), while the benefit was less clear in patients with LV disease (HR: 0.68; 95% CI: 0.41–1.13), as defined by the CHAARTED criteria. Additionally, the trial provided clear evidence of survival benefits for both high-risk patients (HR: 0.71; 95% CI: 0.58–0.86) and low-risk patients (HR: 0.62; 95% CI: 0.42–0.90) according to the LATITUDE criteria.
AEs were comparable between darolutamide and placebo groups, with most AEs being known effects of docetaxel. The most common AEs included alopecia, neutropenia, fatigue, and anemia. Darolutamide was associated with a higher incidence of rash (16.6% vs. 13.5%) and hypertension (13.7% vs. 9.2%), consistent with the known effects of ARPIs.
The findings of the ARASENS trial support the addition of darolutamide to ADT and docetaxel for patients with mCSPC, demonstrating significant improvements in OS and secondary endpoints. The consistency of the OS benefit across most subgroups underscores the robustness of these findings. However, the less pronounced benefit in patients with LV disease warrants further research with larger sample sizes and longer follow-ups. The ARASENS trial established darolutamide in combination with ADT and docetaxel as a superior treatment regimen for mCSPC, offering a substantial survival advantage and delayed disease progression. Based on these results, the FDA approved this triplet therapy in August 2022, marking a significant advancement in mCSPC management [75].
Docetaxel Plus Enzalutamide
The ENZAMET (Enzalutamide for Metastatic Prostate Cancer) study investigated the concurrent use of docetaxel with enzalutamide, with 45% of patients receiving planned docetaxel at the investigator’s discretion. Additionally, 85% of patients in the control arm received subsequent therapy, including 76% who received either abiraterone or enzalutamide upon progression [55]. A prespecified analysis demonstrated a significant difference in OS, favoring the enzalutamide arm among the subset of 362 men with synchronous metastatic disease planned for docetaxel, with a 5-year OS of 60% compared to 52% (HR: 0.73; 95% CI: 0.55–0.99). This survival benefit was not observed in patients with metachronous disease planned for docetaxel (HR: 1.10; 95% CI: 0.65–1.86). Within the synchronous population planned for docetaxel, OS estimates favored enzalutamide in both HV and LV subgroups. Survival curves indicated higher OS rates in the first 30 months for participants receiving enzalutamide + docetaxel + testosterone suppression (TS) versus those contemporaneously accrued to enzalutamide + TS in the highest-risk subgroup (synchronous, HV), highlighting the potential necessity of early chemotherapy in rapidly lethal disease. The AEs of adding enzalutamide to the SOC were overall similar to those of enzalutamide in previous clinical trials.
These findings provide robust evidence supporting the use of enzalutamide in combination with ADT and docetaxel, particularly for patients with synchronous metastatic disease, indicating a significant improvement in OS and underscoring the importance of early intervention in high-risk populations.
Table 2 provides a summary of key clinical trials evaluating triplet therapies, highlighting their efficacy, patient populations, and key findings.
3.1.4. Network Meta-Analysis
Until recently, several network meta-analyses (NMA) have been conducted to update the existing evidence on the comparative efficacy of systemic therapy in mCSPC by prognostic subgroups, aiming to support clinical practice guidelines. Among these studies, one conducted by Hoeh et al. focused on evaluating the efficacy of triplet versus doublet therapies in mCSPC, specifically stratified by disease volume (low vs. high) [76]. Their analysis, which incorporated data from 10 randomized controlled trials (RCTs), centered on OS outcomes for various treatment regimens, including doublet therapy (ARPI + ADT or docetaxel + ADT) and triplet therapy (ARPI + docetaxel + ADT). The NMA was conducted separately for patients with LV and HV mCSPC based on the CHAARTED criteria. Their findings revealed that combination therapies other than ARPI + ADT did not show substantial benefits compared to ADT alone for LV mCSPC. Moreover, no significant OS differences were observed between triplet therapies and the ARAPI + ADT doublet therapy. In contrast, all combination therapies improved OS compared to ADT alone for HV mCSPC. Notably, the triplet regimen of darolutamide + docetaxel + ADT ranked the highest regarding the OS benefit (p = 0.920), followed closely by abiraterone + docetaxel + ADT (p = 0.850). Specifically, darolutamide + docetaxel + ADT showed a significant advantage in terms of OS over ARAT + ADT (HR: 0.76; 95% CI: 0.59–0.97). This study underscores the necessity of stratifying patients by disease volume when making treatment decisions for mCSPC. Therefore, while triplet therapy may not provide significant OS benefits for patients with LV mCSPC, it offers superior outcomes for those with HV disease, thereby emphasizing the importance of personalized treatment strategies based on disease burden.
Building on this, Jian et al. conducted a systematic review and NMA encompassing 18 publications from 12 clinical trials to further compare the efficacy of currently available combination therapies in patients with mCSPC [69]. The overall findings corroborated those of Hoeh et al., with triplet therapy ranking first in terms of OS (HR: 0.57; 95% credible interval (CrI): 0.48–0.67) and rPFS (HR: 0.33; 95% CrI: 0.26–0.41) benefits in the general mCSPC population. For HV mCSPC, triplet therapy was also ranked first in OS (HR: 0.57; 95% CrI: 0.44–0.75) and rPFS (HR: 0.29; 95% CrI: 0.23–0.37) benefits, followed by the doublet therapy of ADT + rezvilutamide (OS: HR: 0.58; 95% CrI: 0.44–0.77; rPFS: HR: 0.44; 95% CrI: 0.33–0.58) and ADT + docetaxel (OS: HR: 0.75; 95% CrI: 0.62–0.91; rPFS: HR: 0.63; 95% CrI: 0.52–0.77). In LV mCSPC, the combination of ADT with ARAT ranked first in OS (HR: 0.68; 95% CrI: 0.58–0.80) and rPFS (HR: 0.50; 95% CrI: 0.42–0.60) benefits, with ADT + apalutamide being the top therapy for OS (HR: 0.53; 95% CrI: 0.35–0.79) and ADT + enzalutamide showing significant improvement in OS and rPFS (OS: HR: 0.56; 95% CrI: 0.40–0.77; rPFS: HR: 0.29; 95% CrI: 0.22–0.39). However, triplet therapies did not demonstrate improvements in OS or rPFS in LV disease (OS: HR: 0.81; 95% CrI: 0.60–1.08; rPFS: HR: 0.67; 95% CrI: 0.50–0.91) and were associated with a higher risk of AEs (any AE: odds ratio (OR): 2.50; 95% CrI: 1.80–3.50) and grade ≥ 3 AEs (OR: 3.20; 95% CrI: 2.40–4.30) compared to other therapies, further suggesting the need for careful consideration of disease volume when selecting treatment modalities.
Complementing these findings, Dr. Riaz et al. comprehensively evaluated contemporary systemic treatment options for patients with mCSPC [77]. This study synthesized data from ten phase 3 RCTs involving 11,043 patients, assessing key outcomes, such as OS, PFS, grade ≥ 3 AEs, and health-related QoL. The results reinforced the benefits of triplet therapies, such as darolutamide combined with docetaxel and ADT and abiraterone with prednisone combined with docetaxel and ADT, in improving OS compared to docetaxel combined with ADT but did not show significant benefits over ARPI doublets, such as abiraterone with prednisone combined with ADT, enzalutamide combined with ADT, and apalutamide combined with ADT. Specifically, the HR for the darolutamide triplet and the abiraterone with prednisone triplet was 0.68 (95% CI: 0.57–0.81) and 0.75 (95% CI: 0.59–0.95), respectively, compared to docetaxel combined with ADT. Subgroup analyses further highlighted that abiraterone with prednisone combined with docetaxel and ADT provided an OS advantage for patients with HV disease compared to docetaxel combined with ADT (HR: 0.72; 95% CI: 0.55–0.95) but not ARPI doublets. Conversely, triplet therapies did not significantly outperform ARPI doublets or docetaxel combined with ADT for LV disease. Moreover, the increased risk of grade ≥ 3 AEs associated with triplet therapies underscores the importance of balancing efficacy with safety when considering treatment options. Overall, these studies collectively emphasize the critical role of disease volume stratification in guiding treatment decisions for mCSPC.
Table 3 summarizes the key findings from network meta-analyses evaluating the efficacy of combination therapies in mCSPC, stratified by disease volume.
3.2. Radiotherapy in mCSPC
The treatment for the primary tumor in metastatic disease is an evolving strategy aimed at eliminating significant sources of lethal metastatic seeding [79,80]. Multiple clinical trials have investigated the efficacy of prostate RT in patients with mCSPC, particularly focusing on outcomes related to failure-free survival (FFS), OS, and QoL.
The STAMPEDE trial randomly assigned 2061 men to receive SOC (ADT with concurrent docetaxel permitted from late 2015) or SOC + prostate RT [25]. The primary outcomes demonstrated that prostate RT significantly improved FFS but did not improve OS in the overall cohort (HR: 0.92; 95% CI: 0.80–1.06; p = 0.226). However, a pre-planned analysis revealed a pronounced OS benefit in patients with a low metastatic burden (HR: 0.68; 95% CI: 0.52–0.90; p = 0.007), which was not evident in high-burden disease (interaction p = 0.010). This benefit was consistent in long-term follow-up, with no evidence of deterioration in global QoL or long-term high-grade urinary toxicity [81].
The HORRAD trial randomized de novo mCSPC patients to either ADT alone or ADT + RT using two RT schedules: 70 Gy in 35 fractions over 7 weeks or 57.76 Gy in 19 fractions over 6 weeks [82]. The trial showed a modest improvement in time to PSA progression favoring the RT arm (median, 15 vs. 12 months, HR: 0.78; 95% CI: 0.63–0.97; p = 0.020) but no OS benefit.
The PEACE-1 phase 3 trial assessed the survival benefit of adding prostate RT in men with low-burden disease receiving intensified systemic treatment (docetaxel and/or abiraterone). The trial found that combining prostate RT with intensified systemic treatment improved rPFS (HR: 0.50; 95% CI: 0.28–0.88; p < 0.001) and CRPC-free survival (HR: 0.32; 95% CI: 0.23–0.44; p < 0.001) in men with low-burden mCSPC, although no OS improvement was detected. Additionally, early prostate RT prevented severe urologic morbidity, irrespective of metastatic burden.
Meta-analyses combining data from trials such as HORRAD and STAMPEDE, as well as secondary analyses of PEACE-1, reinforced the potential benefits of prostate RT in patients with low-burden disease. Specifically, these analyses highlighted improved survival outcomes by adding RT to the SOC, particularly in patients with fewer bone metastases [83]. Ongoing trials, such as PEACE-6 Oligo (PRESTO) and PLATON, are evaluating the role of stereotactic radiotherapy (SBRT) in oligometastatic CSPC patients. These studies aim to further define the benefits of targeted RT to metastatic sites in combination with the SOC [84,85].
The evidence from these trials supports the proactive treatment of the primary tumor with RT in patients with low-burden mCSPC. The differential impact of metastatic burden on treatment outcomes underscores the need for personalized treatment strategies. While prostate RT improves FFS and reduces CRPC incidence in low-burden disease, its role in combination with systemic therapies, such as abiraterone and docetaxel, warrants further exploration. Prostate RT is an established standard for synchronous, low-burden/volume mCSPC, offering significant benefits in terms of FFS and QoL without compromising OS in the overall population [81].
3.1. Systemic Treatment in mCSPC
3.1.1. Androgen-Deprivation Therapy
AR inhibition remains the mainstay treatment for metastatic prostate cancer, a practice grounded in seminal experiments from 1941, which demonstrated the androgen-driven and androgen-dependent nature of prostate cancer [37]. Androgen signaling is crucial for prostate cancer growth and survival, even in treatment-resistant cases [36].
Testosterone suppression (TS) therapy initially involved surgical castration (bilateral orchiectomy) and diethylstilbestrol, eventually evolving to include luteinizing hormone-releasing hormone (LHRH) agonists and antagonists based on the understanding of hypothalamic–pituitary control of gonadal testosterone production [38]. Combining antiandrogens with ADT, known as complete androgen blockade, would counteract both testicular and adrenal androgens. Early-generation AR inhibitors, such as flutamide, bicalutamide, nilutamide, and cyproterone acetate, are typically not used alone but are combined with TS to prevent initial flare responses from LHRH agonists [39].
A meta-analysis of 8275 men from 27 trials comparing TS alone to combined ADT showed improved 5-year OS with nonsteroidal antiandrogens (absolute benefit 3%; two-sided p = 0.005) and potentially worse outcomes with cyproterone acetate (absolute reduction 3%; two-sided p = 0.04). These findings support the use of ADT combination with early-generation AR inhibitors as a control in mCSPC clinical trials, although real-world practices vary [7].
LHRH agonists or antagonists reduce serum testosterone to castration levels (≤50 ng/dL or ≤1.74 nmol/L) [40]. However, phase 3 trials in mCSPC revealed that patients on ADT alone often develop castration resistance within a year, with a median OS of 36–54 months [41]. Therefore, guidelines recommend ADT monotherapy only when combination therapy is not feasible [42]. Furthermore, ADT with treatment intensification is advised for mCSPC unless contraindications exist [13].
Adverse events (AEs) related to ADT use include hot flushes, gynecomastia, decreased libido, sexual dysfunction, bone loss, increased fracture risk, sarcopenic obesity linked to metabolic syndrome, diabetes, cardiovascular disease, cognitive and mood disturbances, anemia, and reduced testis and penis volume, as well as testosterone flares with LHRH agonists. Short-term nonsteroidal AR inhibitors with LHRH agonists to control testosterone flare, patient education on heart-healthy lifestyle changes, optimizing control of cardiovascular risk factors, and regular bone health assessments and interventions such as physical activity, calcium, vitamin D, and bone-supportive therapy are used to manage these AEs [43].
3.1.2. Treatment Intensification—Doublet Therapies
Abiraterone Acetate
Abiraterone acetate was evaluated in several clinical trials for mCSPC to determine its effectiveness in improving OS and other clinical outcomes when combined with ADT. In the STAMPEDE trial, around 1000 mCSPC patients were randomized to receive either ADT alone or ADT combined with abiraterone. After a 40-month follow-up, the combination of abiraterone + ADT reduced the risk of death by approximately 40% (hazard ratio (HR): 0.61; 95% confidence interval (CI): 0.49–0.75), with significant improvements in other secondary endpoints [44]. A post hoc analysis of 1003 metastatic patients, stratified by LATITUDE criteria, showed that median survival was higher in the abiraterone +ADT arm (79 months vs. 46 months) after a median follow-up of 73 months. Both low-risk (HR: 0.54; 95% CI: 0.40–0.74) and high-risk (HR: 0.54; 95% CI: 0.43–0.69) groups benefited from the treatment [45].
The LATITUDE trial included 1199 patients with high-risk mCSPC, defined as having at least two of the following: Gleason score ≥ 8, ≥3 bone lesions, or visceral metastases. Patients were randomized to receive abiraterone with prednisone or placebo with ADT. The trial demonstrated a similar approximately 40% reduction in the risk of death with abiraterone + ADT (HR: 0.66; 95% CI: 0.56–0.78; p < 0.001) after a median follow-up of 51.8 months. The median OS was 53.3 months in the abiraterone + ADT group compared to 36.5 months in the placebo group. Secondary endpoints, such as radiographic progression-free survival (rPFS), PSA progression, and time to chemotherapy initiation, also showed significant improvements [46].
The STAMPEDE trial demonstrated a high incidence of severe and life-threatening grade 3–5 AEs, particularly in combination therapy arms. Adding abiraterone significantly increased the frequency of these AEs. This finding underscores the necessity for a careful risk-benefit analysis when incorporating abiraterone into treatment plans for mCSPC, especially in patients aged ≥ 70 years who experienced higher incidences of grade 3–5 AEs (47% vs. 33%) and a higher rate of treatment-related deaths (9 vs. 3). Additionally, severe hypertension or cardiac disorders were observed in 10% of patients, and grade 3–5 liver toxicity was noted in 7% of patients, highlighting the importance of vigilant monitoring of blood pressure, as well as renal and hepatic function, to improve patient outcomes when using abiraterone in combination therapy [44].
These findings align with those reported in the LATITUDE trial, also demonstrating a higher frequency of AEs in the abiraterone group. However, these events were generally mild and primarily related to mineralocorticoid excess (hypertension, hypokalemia, and edema), hormonal effects (fatigue and hot flushes), and liver toxicity [44]. Despite these AEs, the overall discontinuation rate due to AEs in the LATITUDE trial was 12%. Importantly, patient-reported outcomes in LATITUDE indicated improvements in pain intensity, fatigue, functional decline, and overall health-related QoL, further emphasizing the complex balance between the therapeutic benefits and the risks of AEs when using abiraterone in mCSPC [47].
In February 2018, the FDA approved abiraterone in combination with prednisone for mCSPC based on the results of the STAMPEDE and LATITUDE trials. The National Comprehensive Cancer Network (NCCN) Panel recommends abiraterone with 5 mg once-daily prednisone as a treatment option with ADT for newly diagnosed mCSPC. Alternatively, the fine-particle formulation of abiraterone can be used to reduce financial toxicity and improve adherence [48].
Apalutamide
The double-blind phase 3 TITAN trial evaluated the efficacy and safety of apalutamide in combination with ADT compared to ADT alone in patients with mCSPC [49]. This trial was designed to address the ongoing need for more effective treatment strategies in such patients. The TITAN trial randomized 1052 patients with mCSPC to receive either ADT with apalutamide (240 mg/day) or a placebo. The participants were carefully stratified based on Gleason score at diagnosis, geographic region, and previous docetaxel treatment to ensure balanced comparison groups. The median follow-up duration was 22.7 months, allowing for a robust assessment of long-term outcomes. Importantly, both primary endpoints were achieved, demonstrating the significant benefit of apalutamide in this setting. At 24 months, the rPFS was notably higher in the apalutamide group (68.2%) compared to the placebo group (47.5%), with an HR for radiographic progression or death of 0.48 (95% CI: 0.39–0.60; p < 0.001). Furthermore, OS was also significantly improved in the apalutamide group (82.4%) compared to the placebo group (73.5%), with an HR for death of 0.67 (95% CI: 0.51–0.89; p = 0.005). These findings underscore the potential of adding apalutamide to standard ADT to enhance outcomes in patients with mCSPC.
In the final analysis, the median OS was not reached in the apalutamide group compared to 52.2 months in the ADT alone group (HR: 0.65; 95% CI: 0.53–0.79; p < 0.001) after a median follow-up of 44 months. After adjusting for crossover, the risk of death was reduced by 48%, indicating a consistent benefit across both HV and low-volume (LV) disease. A post hoc subgroup analysis of the TITAN trial re-evaluated outcomes based on the volume and timing of metastases, showing synchronous HV disease in 54% of patients. OS benefits were statistically significant in patients with synchronous HV disease (HR: 0.68; 95% CI: 0.53–0.87; p = 0.002) and metachronous LV disease (HR: 0.22; 95% CI: 0.09–0.55; p = 0.001). However, no significant OS benefits were observed in patients with synchronous LV or metachronous HV disease, likely due to limited patient numbers [50].
More frequently observed AEs in the apalutamide group included rash, hypothyroidism, and ischemic heart disease. Nevertheless, health-related QoL was maintained during treatment with apalutamide [51].
The TITAN trial demonstrated significant improvements in both rPFS and OS by adding apalutamide to ADT in patients with mCSPC. These findings support apalutamide as a category 1 treatment option for mCSPC, which received FDA approval in September 2019 [52]. The consistent survival benefits across different subgroups and the maintained QoL highlight the robust efficacy and tolerability of apalutamide in this patient population [53].
Enzalutamide
The management of mCSPC has evolved with the introduction of enzalutamide, an ARPI, as an addition to standard ADT. Recent clinical trials, namely ENZAMET and ARCHES, have provided significant evidence of the efficacy and safety of this combination [8].
The ENZAMET trial was an open-label, randomized phase 3 study involving 1125 patients with mCSPC. The patients were randomized to receive either enzalutamide (160 mg/day) + ADT (LHRH analog or surgical castration) or a first-generation antiandrogen (bicalutamide, nilutamide, or flutamide) + ADT [54]. Stratification was based on disease volume, planned use of early docetaxel, planned use of bone antiresorptive therapy, comorbidity score, and trial site. At the first interim analysis with a median follow-up of 34 months, the enzalutamide group showed a 33% reduction in the risk of death compared to the control group (HR: 0.67; 95% CI: 0.52–0.86; p = 0.002). An analysis after a median follow-up of 68 months revealed that the 5-year OS rate was 67% in the enzalutamide group versus 57% in the control group (HR: 0.70; 95% CI: 0.58–0.84; p < 0.001). Secondary endpoints also showed improvement with enzalutamide, including PFS based on PSA levels and clinical PFS. A post hoc analysis indicated a significant OS benefit in patients with synchronous mCSPC (HR: 0.73; 95% CI: 0.55–0.99), but not in metachronous patients [55].
The ARCHES trial, a double-blind, randomized phase 3 study, included 1150 patients with mCSPC. The participants were randomized to receive either enzalutamide (160 mg/day) + ADT or placebo + ADT, with stratification based on disease volume and prior docetaxel use. After a median follow-up of 14.4 months, the enzalutamide group demonstrated significantly improved rPFS (19.0 months vs. not reached; HR: 0.39; 95% CI: 0.30–0.50; p < 0.001) [56]. At the final OS analysis, although 32% of patients in the placebo group crossed over to enzalutamide after unblinding, enzalutamide still reduced the risk of death by 34% compared to placebo (HR: 0.66; 95% CI: 0.53–0.81; p < 0.001) [57].
The safety of enzalutamide in these trials was consistent with previous studies on CRPC, with common AEs including fatigue, seizures, and hypertension. The safety profiles observed in both ENZAMET and ARCHES trials were similar, confirming the manageable safety of enzalutamide in mCSPC [54,56].
Overall, enzalutamide, when combined with ADT, significantly improves survival in patients with mCSPC. The ENZAMET and ARCHES trials demonstrated substantial improvements in OS and PFS with enzalutamide + ADT compared to standard ADT regimens. The FDA’s approval of enzalutamide for mCSPC in December 2019 reflects its robust efficacy and manageable safety profile, supporting its use as a category 1 treatment option for patients with mCSPC [58].
Docetaxel
Docetaxel has been studied as an initial treatment option for patients with mCSPC based on several key phase 3 trials: ECOG 3805/CHAARTED, STAMPEDE, and GETUG-AFU-15 [41,59,60]. These studies provided significant insights into the efficacy of combining docetaxel with ADT for improving OS in patients with mCSPC.
The ECOG 3805/CHAARTED trial randomized 790 patients with mCSPC to receive either docetaxel (75 mg/m2 intravenously every 3 weeks for 6 doses) + ADT or ADT alone [41]. After a median follow-up of 53.7 months, the combination therapy arm showed longer OS than the ADT-alone arm (57.6 months vs. 47.2 months; HR: 0.72; 95% CI: 0.59–0.89; p = 0.002) [61]. Subgroup analysis revealed that the survival benefit was more pronounced in patients with HV disease (65% of participants) (HR: 0.63; 95% CI: 0.50–0.79; p < 0.001). However, patients with LV did not obtain a significant survival benefit by adding docetaxel (HR: 1.04; 95% CI: 0.70–1.55; p = 0.860).
The STAMPEDE trial, a multi-arm, multi-stage phase 3 study, included patients with both non-metastatic (M0) and metastatic (M1) CSPC [59]. The trial confirmed the survival advantage of adding docetaxel to ADT, as seen in the CHAARTED trial. In the STAMPEDE trial, the extent of disease was not evaluated in 1087 patients with metastatic disease, but the median OS for all M1 patients was 5.4 years in the ADT + docetaxel arm versus 3.6 years in the ADT-only arm (a difference of 1.8 years compared to a 1.1-year difference in the CHAARTED).
The GETUG-AFU-15 trial evaluated ADT combined with docetaxel [62]. After 83.9 months of follow-up, this combination did not significantly improve OS compared to ADT alone. However, post hoc analysis considering metastatic disease volume indicated a trend toward benefit in the HV group (HR: 0.78; 95% CI: 0.56–1.09; p = 0.140) [60].
A meta-analysis of three trials involving 2,261 patients with mHSPC revealed that the combination of ADT with docetaxel did not improve OS in men with metachronous LV disease, as indicated by an HR of 0.98 (95% CI: 0.67–1.45). However, the most significant OS benefit and reduction in the risk of death were observed in patients with synchronous HV disease (HR: 0.60; 95% CI: 0.52–0.69). Thus, while ADT combined with docetaxel markedly improves OS in patients with HV mCSPC, particularly in those with synchronous HV disease, a different systemic therapy approach may be necessary for patients with metachronous LV disease [63].
Additionally, triplet therapy options including ADT, docetaxel, and ARPI have improved OS compared to ADT + docetaxel alone [64]. Consequently, ADT and docetaxel are not recommended for patients with LV mCSPC, whereas triplet therapy is advised for medically fit patients with HV disease.
The key clinical trials discussed in this section, along with their outcomes and adverse events, are summarized in Table 1 for a comprehensive comparison.
3.1.3. Treatment Intensification—Triplet Therapies
Rationale
Numerous trials demonstrated the significant OS benefit of beginning systemic therapy earlier at the mCSPC stage, not waiting until the tumor becomes castration-resistant [8,65,66]. This benefit is likely due to several reasons: mCSPC might have more favorable disease biology, less acquired treatment resistance, and therefore demonstrates a more durable treatment response [8]. Using docetaxel in mCSPC is likely more efficacious at targeting AR-independent cancer cells early compared to its use in mCRPC since these cancer cells might have had opportunities to develop resistance [8,67]. Treatment in the mCSPC setting is also often better tolerated given fewer cumulative toxicities from prior systemic therapy, as well as fewer symptoms and a lower disease burden before disease progression. Real-world studies showed that only approximately 50% of patients receive second-line therapies upon disease progression, with many possibly becoming too frail to receive docetaxel in the mCRPC setting. Therefore, earlier treatment intensification may also increase the number of patients receiving life-prolonging systemic therapy [68].
The effectiveness of chemohormonal triplet therapy may stem from the synergistic and complementary effects of the three agents [69]. ADT, the cornerstone of hormonal therapy for prostate cancer, inhibits tumor growth by lowering androgen levels produced in the testicles through medication or surgery. ARPIs, including androgen synthesis inhibitors (abiraterone) and antiandrogens (enzalutamide, apalutamide, and darolutamide), suppress other androgen sources to achieve the maximum suppression of the androgen axis, further improving the anti-tumor effect [28]. Docetaxel, a semisynthetic taxane, exhibits significant antitumor activity by inhibiting microtubular depolymerization and attenuating the effects of BCL-2 and BCL-XL expression. The taxane-induced microtubule stabilization arrests cells in the G2/M phase and induces bcl-2 phosphorylation, promoting a cascade of events that ultimately cause apoptotic cell death [70].
ADT lowers androgen levels, inhibiting the growth of androgen-sensitive CSPC cells that respond well to this treatment [9]. The combination of docetaxel and ADT can create a synergistic effect by simultaneously inhibiting androgen signaling pathways and cell division [71]. Clinical trials, such as CHAARTED and STAMPEDE, showed that combining docetaxel with ADT significantly improves survival in patients with mCSPC, providing clinical evidence of the effectiveness of docetaxel against mCSPC [41,72].
Docetaxel Plus Abiraterone
The PEACE-1 trial was an international, open-label, randomized phase 3 study conducted across seven European countries, aiming to assess the efficacy of adding abiraterone acetate with prednisone to the standard of care (SOC) in patients with de novo mCSPC [73]. The study utilized a 2 × 2 factorial design and included 1173 patients randomized into four groups: SOC ADT alone or with docetaxel, SOC with radiotherapy (RT), SOC with abiraterone, and SOC with RT and abiraterone. The primary endpoints were rPFS and OS. The results showed that patients receiving abiraterone had significantly longer rPFS compared to those who did not. Specifically, the overall rPFS in the abiraterone group had an HR of 0.54 (99.9% CI, 0.41–0.71; p < 0.001). For those receiving abiraterone with docetaxel, the rPFS had an HR of 0.50 (99.9% CI, 0.34–0.71; p < 0.001). Furthermore, adding abiraterone to SOC significantly improved OS, with the overall OS in the abiraterone group showing an HR of 0.82 (95.1% CI, 0.69–0.98; p = 0.030). In patients receiving abiraterone with docetaxel, the OS had an HR of 0.75 (95.1% CI, 0.59–0.95; p = 0.017).
Subgroup analysis provides significant insights into the efficacy of different treatment combinations for mCSPC based on disease burden. For patients with HV disease, adding abiraterone acetate + prednisone to the SOC (consisting of ADT and docetaxel) markedly improved OS and rPFS. Specifically, the median OS for patients receiving the triplet therapy was 5.1 years compared to 3.5 years for those receiving only ADT plus docetaxel, with an HR of 0.72 (95% CI: 0.55–0.95; p = 0.019). Additionally, the median rPFS was 4.1 years for those receiving the triplet therapy versus 1.6 years for those receiving the SOC alone (HR: 0.47; 95% CI: 0.30–0.72; p < 0.001). In contrast, the OS data remain immature for patients with LV disease, and median OS has not yet been reached in either treatment group. The rPFS also showed significant improvement with the triplet therapy: the median rPFS was not reached versus 2.7 years for the SOC alone (HR: 0.58; 95% CI: 0.29–1.15; p = 0.006).
The study also examined treatment-related AEs, finding that rates of neutropenia, febrile neutropenia, fatigue, and neuropathy were similar between triplet and doublet therapy groups. However, the incidence of grade ≥ 3 AEs was higher in the triplet therapy group (63% vs. 52%). Specifically, grade 3 hypertension and grade 3 transaminase increase were more common in the triplet group (22% vs. 13% and 6% vs. 1%, respectively). Other AEs, such as febrile neutropenia, fatigue, and neuropathy, did not show significant differences between the groups.
The PEACE-1 trial demonstrated that adding abiraterone to ADT and docetaxel significantly improves both rPFS and OS in patients with de novo mCSPC. These results emphasize the substantial benefit of triplet therapy for HV mCSPC. Thus, it should be considered a standard treatment option for this subgroup. For LV disease, while the survival benefit is still being evaluated, triplet therapy shows promise, particularly in improving PFS.
Docetaxel Plus Darolutamide
The ARASENS trial, an international phase 3 study, evaluated the efficacy and safety of adding darolutamide to ADT and docetaxel in patients with mCSPC. This trial randomized 1306 patients to receive either the combination of ADT, docetaxel, and darolutamide or ADT, docetaxel, and a matching placebo [74]. The primary endpoint of the study was OS, with time to CRPC, skeletal event-free survival, and time to initiation of subsequent systemic antineoplastic therapy as secondary endpoints. The study did not stratify patients by disease volume, and 86% of participants had synchronous metastatic disease. The results demonstrated that adding darolutamide significantly improved OS compared to the placebo group. At 4 years, the OS rate was 62.7% (95% CI: 58.7–66.7) in the darolutamide group versus 50.4% (95% CI: 46.3–54.6) in the placebo group, reflecting a 32% reduction in the risk of death (HR: 0.68; 95% CI: 0.57–0.80; p < 0.001). Additionally, darolutamide showed significant benefits across several secondary endpoints. The time to CRPC was markedly prolonged (HR: 0.36; 95% CI: 0.30–0.42; p < 0.001), skeletal event-free survival improved (HR: 0.61; 95% CI: 0.52–0.72; p < 0.001), and the time to initiation of subsequent systemic antineoplastic therapy was extended (HR: 0.39; 95% CI: 0.33–0.46; p < 0.001).
Post hoc analyses demonstrated the OS benefit for patients with HV disease (HR: 0.69; 95% CI: 0.57–0.82), while the benefit was less clear in patients with LV disease (HR: 0.68; 95% CI: 0.41–1.13), as defined by the CHAARTED criteria. Additionally, the trial provided clear evidence of survival benefits for both high-risk patients (HR: 0.71; 95% CI: 0.58–0.86) and low-risk patients (HR: 0.62; 95% CI: 0.42–0.90) according to the LATITUDE criteria.
AEs were comparable between darolutamide and placebo groups, with most AEs being known effects of docetaxel. The most common AEs included alopecia, neutropenia, fatigue, and anemia. Darolutamide was associated with a higher incidence of rash (16.6% vs. 13.5%) and hypertension (13.7% vs. 9.2%), consistent with the known effects of ARPIs.
The findings of the ARASENS trial support the addition of darolutamide to ADT and docetaxel for patients with mCSPC, demonstrating significant improvements in OS and secondary endpoints. The consistency of the OS benefit across most subgroups underscores the robustness of these findings. However, the less pronounced benefit in patients with LV disease warrants further research with larger sample sizes and longer follow-ups. The ARASENS trial established darolutamide in combination with ADT and docetaxel as a superior treatment regimen for mCSPC, offering a substantial survival advantage and delayed disease progression. Based on these results, the FDA approved this triplet therapy in August 2022, marking a significant advancement in mCSPC management [75].
Docetaxel Plus Enzalutamide
The ENZAMET (Enzalutamide for Metastatic Prostate Cancer) study investigated the concurrent use of docetaxel with enzalutamide, with 45% of patients receiving planned docetaxel at the investigator’s discretion. Additionally, 85% of patients in the control arm received subsequent therapy, including 76% who received either abiraterone or enzalutamide upon progression [55]. A prespecified analysis demonstrated a significant difference in OS, favoring the enzalutamide arm among the subset of 362 men with synchronous metastatic disease planned for docetaxel, with a 5-year OS of 60% compared to 52% (HR: 0.73; 95% CI: 0.55–0.99). This survival benefit was not observed in patients with metachronous disease planned for docetaxel (HR: 1.10; 95% CI: 0.65–1.86). Within the synchronous population planned for docetaxel, OS estimates favored enzalutamide in both HV and LV subgroups. Survival curves indicated higher OS rates in the first 30 months for participants receiving enzalutamide + docetaxel + testosterone suppression (TS) versus those contemporaneously accrued to enzalutamide + TS in the highest-risk subgroup (synchronous, HV), highlighting the potential necessity of early chemotherapy in rapidly lethal disease. The AEs of adding enzalutamide to the SOC were overall similar to those of enzalutamide in previous clinical trials.
These findings provide robust evidence supporting the use of enzalutamide in combination with ADT and docetaxel, particularly for patients with synchronous metastatic disease, indicating a significant improvement in OS and underscoring the importance of early intervention in high-risk populations.
Table 2 provides a summary of key clinical trials evaluating triplet therapies, highlighting their efficacy, patient populations, and key findings.
3.1.4. Network Meta-Analysis
Until recently, several network meta-analyses (NMA) have been conducted to update the existing evidence on the comparative efficacy of systemic therapy in mCSPC by prognostic subgroups, aiming to support clinical practice guidelines. Among these studies, one conducted by Hoeh et al. focused on evaluating the efficacy of triplet versus doublet therapies in mCSPC, specifically stratified by disease volume (low vs. high) [76]. Their analysis, which incorporated data from 10 randomized controlled trials (RCTs), centered on OS outcomes for various treatment regimens, including doublet therapy (ARPI + ADT or docetaxel + ADT) and triplet therapy (ARPI + docetaxel + ADT). The NMA was conducted separately for patients with LV and HV mCSPC based on the CHAARTED criteria. Their findings revealed that combination therapies other than ARPI + ADT did not show substantial benefits compared to ADT alone for LV mCSPC. Moreover, no significant OS differences were observed between triplet therapies and the ARAPI + ADT doublet therapy. In contrast, all combination therapies improved OS compared to ADT alone for HV mCSPC. Notably, the triplet regimen of darolutamide + docetaxel + ADT ranked the highest regarding the OS benefit (p = 0.920), followed closely by abiraterone + docetaxel + ADT (p = 0.850). Specifically, darolutamide + docetaxel + ADT showed a significant advantage in terms of OS over ARAT + ADT (HR: 0.76; 95% CI: 0.59–0.97). This study underscores the necessity of stratifying patients by disease volume when making treatment decisions for mCSPC. Therefore, while triplet therapy may not provide significant OS benefits for patients with LV mCSPC, it offers superior outcomes for those with HV disease, thereby emphasizing the importance of personalized treatment strategies based on disease burden.
Building on this, Jian et al. conducted a systematic review and NMA encompassing 18 publications from 12 clinical trials to further compare the efficacy of currently available combination therapies in patients with mCSPC [69]. The overall findings corroborated those of Hoeh et al., with triplet therapy ranking first in terms of OS (HR: 0.57; 95% credible interval (CrI): 0.48–0.67) and rPFS (HR: 0.33; 95% CrI: 0.26–0.41) benefits in the general mCSPC population. For HV mCSPC, triplet therapy was also ranked first in OS (HR: 0.57; 95% CrI: 0.44–0.75) and rPFS (HR: 0.29; 95% CrI: 0.23–0.37) benefits, followed by the doublet therapy of ADT + rezvilutamide (OS: HR: 0.58; 95% CrI: 0.44–0.77; rPFS: HR: 0.44; 95% CrI: 0.33–0.58) and ADT + docetaxel (OS: HR: 0.75; 95% CrI: 0.62–0.91; rPFS: HR: 0.63; 95% CrI: 0.52–0.77). In LV mCSPC, the combination of ADT with ARAT ranked first in OS (HR: 0.68; 95% CrI: 0.58–0.80) and rPFS (HR: 0.50; 95% CrI: 0.42–0.60) benefits, with ADT + apalutamide being the top therapy for OS (HR: 0.53; 95% CrI: 0.35–0.79) and ADT + enzalutamide showing significant improvement in OS and rPFS (OS: HR: 0.56; 95% CrI: 0.40–0.77; rPFS: HR: 0.29; 95% CrI: 0.22–0.39). However, triplet therapies did not demonstrate improvements in OS or rPFS in LV disease (OS: HR: 0.81; 95% CrI: 0.60–1.08; rPFS: HR: 0.67; 95% CrI: 0.50–0.91) and were associated with a higher risk of AEs (any AE: odds ratio (OR): 2.50; 95% CrI: 1.80–3.50) and grade ≥ 3 AEs (OR: 3.20; 95% CrI: 2.40–4.30) compared to other therapies, further suggesting the need for careful consideration of disease volume when selecting treatment modalities.
Complementing these findings, Dr. Riaz et al. comprehensively evaluated contemporary systemic treatment options for patients with mCSPC [77]. This study synthesized data from ten phase 3 RCTs involving 11,043 patients, assessing key outcomes, such as OS, PFS, grade ≥ 3 AEs, and health-related QoL. The results reinforced the benefits of triplet therapies, such as darolutamide combined with docetaxel and ADT and abiraterone with prednisone combined with docetaxel and ADT, in improving OS compared to docetaxel combined with ADT but did not show significant benefits over ARPI doublets, such as abiraterone with prednisone combined with ADT, enzalutamide combined with ADT, and apalutamide combined with ADT. Specifically, the HR for the darolutamide triplet and the abiraterone with prednisone triplet was 0.68 (95% CI: 0.57–0.81) and 0.75 (95% CI: 0.59–0.95), respectively, compared to docetaxel combined with ADT. Subgroup analyses further highlighted that abiraterone with prednisone combined with docetaxel and ADT provided an OS advantage for patients with HV disease compared to docetaxel combined with ADT (HR: 0.72; 95% CI: 0.55–0.95) but not ARPI doublets. Conversely, triplet therapies did not significantly outperform ARPI doublets or docetaxel combined with ADT for LV disease. Moreover, the increased risk of grade ≥ 3 AEs associated with triplet therapies underscores the importance of balancing efficacy with safety when considering treatment options. Overall, these studies collectively emphasize the critical role of disease volume stratification in guiding treatment decisions for mCSPC.
Table 3 summarizes the key findings from network meta-analyses evaluating the efficacy of combination therapies in mCSPC, stratified by disease volume.
3.1.1. Androgen-Deprivation Therapy
AR inhibition remains the mainstay treatment for metastatic prostate cancer, a practice grounded in seminal experiments from 1941, which demonstrated the androgen-driven and androgen-dependent nature of prostate cancer [37]. Androgen signaling is crucial for prostate cancer growth and survival, even in treatment-resistant cases [36].
Testosterone suppression (TS) therapy initially involved surgical castration (bilateral orchiectomy) and diethylstilbestrol, eventually evolving to include luteinizing hormone-releasing hormone (LHRH) agonists and antagonists based on the understanding of hypothalamic–pituitary control of gonadal testosterone production [38]. Combining antiandrogens with ADT, known as complete androgen blockade, would counteract both testicular and adrenal androgens. Early-generation AR inhibitors, such as flutamide, bicalutamide, nilutamide, and cyproterone acetate, are typically not used alone but are combined with TS to prevent initial flare responses from LHRH agonists [39].
A meta-analysis of 8275 men from 27 trials comparing TS alone to combined ADT showed improved 5-year OS with nonsteroidal antiandrogens (absolute benefit 3%; two-sided p = 0.005) and potentially worse outcomes with cyproterone acetate (absolute reduction 3%; two-sided p = 0.04). These findings support the use of ADT combination with early-generation AR inhibitors as a control in mCSPC clinical trials, although real-world practices vary [7].
LHRH agonists or antagonists reduce serum testosterone to castration levels (≤50 ng/dL or ≤1.74 nmol/L) [40]. However, phase 3 trials in mCSPC revealed that patients on ADT alone often develop castration resistance within a year, with a median OS of 36–54 months [41]. Therefore, guidelines recommend ADT monotherapy only when combination therapy is not feasible [42]. Furthermore, ADT with treatment intensification is advised for mCSPC unless contraindications exist [13].
Adverse events (AEs) related to ADT use include hot flushes, gynecomastia, decreased libido, sexual dysfunction, bone loss, increased fracture risk, sarcopenic obesity linked to metabolic syndrome, diabetes, cardiovascular disease, cognitive and mood disturbances, anemia, and reduced testis and penis volume, as well as testosterone flares with LHRH agonists. Short-term nonsteroidal AR inhibitors with LHRH agonists to control testosterone flare, patient education on heart-healthy lifestyle changes, optimizing control of cardiovascular risk factors, and regular bone health assessments and interventions such as physical activity, calcium, vitamin D, and bone-supportive therapy are used to manage these AEs [43].
3.1.2. Treatment Intensification—Doublet Therapies
Abiraterone Acetate
Abiraterone acetate was evaluated in several clinical trials for mCSPC to determine its effectiveness in improving OS and other clinical outcomes when combined with ADT. In the STAMPEDE trial, around 1000 mCSPC patients were randomized to receive either ADT alone or ADT combined with abiraterone. After a 40-month follow-up, the combination of abiraterone + ADT reduced the risk of death by approximately 40% (hazard ratio (HR): 0.61; 95% confidence interval (CI): 0.49–0.75), with significant improvements in other secondary endpoints [44]. A post hoc analysis of 1003 metastatic patients, stratified by LATITUDE criteria, showed that median survival was higher in the abiraterone +ADT arm (79 months vs. 46 months) after a median follow-up of 73 months. Both low-risk (HR: 0.54; 95% CI: 0.40–0.74) and high-risk (HR: 0.54; 95% CI: 0.43–0.69) groups benefited from the treatment [45].
The LATITUDE trial included 1199 patients with high-risk mCSPC, defined as having at least two of the following: Gleason score ≥ 8, ≥3 bone lesions, or visceral metastases. Patients were randomized to receive abiraterone with prednisone or placebo with ADT. The trial demonstrated a similar approximately 40% reduction in the risk of death with abiraterone + ADT (HR: 0.66; 95% CI: 0.56–0.78; p < 0.001) after a median follow-up of 51.8 months. The median OS was 53.3 months in the abiraterone + ADT group compared to 36.5 months in the placebo group. Secondary endpoints, such as radiographic progression-free survival (rPFS), PSA progression, and time to chemotherapy initiation, also showed significant improvements [46].
The STAMPEDE trial demonstrated a high incidence of severe and life-threatening grade 3–5 AEs, particularly in combination therapy arms. Adding abiraterone significantly increased the frequency of these AEs. This finding underscores the necessity for a careful risk-benefit analysis when incorporating abiraterone into treatment plans for mCSPC, especially in patients aged ≥ 70 years who experienced higher incidences of grade 3–5 AEs (47% vs. 33%) and a higher rate of treatment-related deaths (9 vs. 3). Additionally, severe hypertension or cardiac disorders were observed in 10% of patients, and grade 3–5 liver toxicity was noted in 7% of patients, highlighting the importance of vigilant monitoring of blood pressure, as well as renal and hepatic function, to improve patient outcomes when using abiraterone in combination therapy [44].
These findings align with those reported in the LATITUDE trial, also demonstrating a higher frequency of AEs in the abiraterone group. However, these events were generally mild and primarily related to mineralocorticoid excess (hypertension, hypokalemia, and edema), hormonal effects (fatigue and hot flushes), and liver toxicity [44]. Despite these AEs, the overall discontinuation rate due to AEs in the LATITUDE trial was 12%. Importantly, patient-reported outcomes in LATITUDE indicated improvements in pain intensity, fatigue, functional decline, and overall health-related QoL, further emphasizing the complex balance between the therapeutic benefits and the risks of AEs when using abiraterone in mCSPC [47].
In February 2018, the FDA approved abiraterone in combination with prednisone for mCSPC based on the results of the STAMPEDE and LATITUDE trials. The National Comprehensive Cancer Network (NCCN) Panel recommends abiraterone with 5 mg once-daily prednisone as a treatment option with ADT for newly diagnosed mCSPC. Alternatively, the fine-particle formulation of abiraterone can be used to reduce financial toxicity and improve adherence [48].
Apalutamide
The double-blind phase 3 TITAN trial evaluated the efficacy and safety of apalutamide in combination with ADT compared to ADT alone in patients with mCSPC [49]. This trial was designed to address the ongoing need for more effective treatment strategies in such patients. The TITAN trial randomized 1052 patients with mCSPC to receive either ADT with apalutamide (240 mg/day) or a placebo. The participants were carefully stratified based on Gleason score at diagnosis, geographic region, and previous docetaxel treatment to ensure balanced comparison groups. The median follow-up duration was 22.7 months, allowing for a robust assessment of long-term outcomes. Importantly, both primary endpoints were achieved, demonstrating the significant benefit of apalutamide in this setting. At 24 months, the rPFS was notably higher in the apalutamide group (68.2%) compared to the placebo group (47.5%), with an HR for radiographic progression or death of 0.48 (95% CI: 0.39–0.60; p < 0.001). Furthermore, OS was also significantly improved in the apalutamide group (82.4%) compared to the placebo group (73.5%), with an HR for death of 0.67 (95% CI: 0.51–0.89; p = 0.005). These findings underscore the potential of adding apalutamide to standard ADT to enhance outcomes in patients with mCSPC.
In the final analysis, the median OS was not reached in the apalutamide group compared to 52.2 months in the ADT alone group (HR: 0.65; 95% CI: 0.53–0.79; p < 0.001) after a median follow-up of 44 months. After adjusting for crossover, the risk of death was reduced by 48%, indicating a consistent benefit across both HV and low-volume (LV) disease. A post hoc subgroup analysis of the TITAN trial re-evaluated outcomes based on the volume and timing of metastases, showing synchronous HV disease in 54% of patients. OS benefits were statistically significant in patients with synchronous HV disease (HR: 0.68; 95% CI: 0.53–0.87; p = 0.002) and metachronous LV disease (HR: 0.22; 95% CI: 0.09–0.55; p = 0.001). However, no significant OS benefits were observed in patients with synchronous LV or metachronous HV disease, likely due to limited patient numbers [50].
More frequently observed AEs in the apalutamide group included rash, hypothyroidism, and ischemic heart disease. Nevertheless, health-related QoL was maintained during treatment with apalutamide [51].
The TITAN trial demonstrated significant improvements in both rPFS and OS by adding apalutamide to ADT in patients with mCSPC. These findings support apalutamide as a category 1 treatment option for mCSPC, which received FDA approval in September 2019 [52]. The consistent survival benefits across different subgroups and the maintained QoL highlight the robust efficacy and tolerability of apalutamide in this patient population [53].
Enzalutamide
The management of mCSPC has evolved with the introduction of enzalutamide, an ARPI, as an addition to standard ADT. Recent clinical trials, namely ENZAMET and ARCHES, have provided significant evidence of the efficacy and safety of this combination [8].
The ENZAMET trial was an open-label, randomized phase 3 study involving 1125 patients with mCSPC. The patients were randomized to receive either enzalutamide (160 mg/day) + ADT (LHRH analog or surgical castration) or a first-generation antiandrogen (bicalutamide, nilutamide, or flutamide) + ADT [54]. Stratification was based on disease volume, planned use of early docetaxel, planned use of bone antiresorptive therapy, comorbidity score, and trial site. At the first interim analysis with a median follow-up of 34 months, the enzalutamide group showed a 33% reduction in the risk of death compared to the control group (HR: 0.67; 95% CI: 0.52–0.86; p = 0.002). An analysis after a median follow-up of 68 months revealed that the 5-year OS rate was 67% in the enzalutamide group versus 57% in the control group (HR: 0.70; 95% CI: 0.58–0.84; p < 0.001). Secondary endpoints also showed improvement with enzalutamide, including PFS based on PSA levels and clinical PFS. A post hoc analysis indicated a significant OS benefit in patients with synchronous mCSPC (HR: 0.73; 95% CI: 0.55–0.99), but not in metachronous patients [55].
The ARCHES trial, a double-blind, randomized phase 3 study, included 1150 patients with mCSPC. The participants were randomized to receive either enzalutamide (160 mg/day) + ADT or placebo + ADT, with stratification based on disease volume and prior docetaxel use. After a median follow-up of 14.4 months, the enzalutamide group demonstrated significantly improved rPFS (19.0 months vs. not reached; HR: 0.39; 95% CI: 0.30–0.50; p < 0.001) [56]. At the final OS analysis, although 32% of patients in the placebo group crossed over to enzalutamide after unblinding, enzalutamide still reduced the risk of death by 34% compared to placebo (HR: 0.66; 95% CI: 0.53–0.81; p < 0.001) [57].
The safety of enzalutamide in these trials was consistent with previous studies on CRPC, with common AEs including fatigue, seizures, and hypertension. The safety profiles observed in both ENZAMET and ARCHES trials were similar, confirming the manageable safety of enzalutamide in mCSPC [54,56].
Overall, enzalutamide, when combined with ADT, significantly improves survival in patients with mCSPC. The ENZAMET and ARCHES trials demonstrated substantial improvements in OS and PFS with enzalutamide + ADT compared to standard ADT regimens. The FDA’s approval of enzalutamide for mCSPC in December 2019 reflects its robust efficacy and manageable safety profile, supporting its use as a category 1 treatment option for patients with mCSPC [58].
Docetaxel
Docetaxel has been studied as an initial treatment option for patients with mCSPC based on several key phase 3 trials: ECOG 3805/CHAARTED, STAMPEDE, and GETUG-AFU-15 [41,59,60]. These studies provided significant insights into the efficacy of combining docetaxel with ADT for improving OS in patients with mCSPC.
The ECOG 3805/CHAARTED trial randomized 790 patients with mCSPC to receive either docetaxel (75 mg/m2 intravenously every 3 weeks for 6 doses) + ADT or ADT alone [41]. After a median follow-up of 53.7 months, the combination therapy arm showed longer OS than the ADT-alone arm (57.6 months vs. 47.2 months; HR: 0.72; 95% CI: 0.59–0.89; p = 0.002) [61]. Subgroup analysis revealed that the survival benefit was more pronounced in patients with HV disease (65% of participants) (HR: 0.63; 95% CI: 0.50–0.79; p < 0.001). However, patients with LV did not obtain a significant survival benefit by adding docetaxel (HR: 1.04; 95% CI: 0.70–1.55; p = 0.860).
The STAMPEDE trial, a multi-arm, multi-stage phase 3 study, included patients with both non-metastatic (M0) and metastatic (M1) CSPC [59]. The trial confirmed the survival advantage of adding docetaxel to ADT, as seen in the CHAARTED trial. In the STAMPEDE trial, the extent of disease was not evaluated in 1087 patients with metastatic disease, but the median OS for all M1 patients was 5.4 years in the ADT + docetaxel arm versus 3.6 years in the ADT-only arm (a difference of 1.8 years compared to a 1.1-year difference in the CHAARTED).
The GETUG-AFU-15 trial evaluated ADT combined with docetaxel [62]. After 83.9 months of follow-up, this combination did not significantly improve OS compared to ADT alone. However, post hoc analysis considering metastatic disease volume indicated a trend toward benefit in the HV group (HR: 0.78; 95% CI: 0.56–1.09; p = 0.140) [60].
A meta-analysis of three trials involving 2,261 patients with mHSPC revealed that the combination of ADT with docetaxel did not improve OS in men with metachronous LV disease, as indicated by an HR of 0.98 (95% CI: 0.67–1.45). However, the most significant OS benefit and reduction in the risk of death were observed in patients with synchronous HV disease (HR: 0.60; 95% CI: 0.52–0.69). Thus, while ADT combined with docetaxel markedly improves OS in patients with HV mCSPC, particularly in those with synchronous HV disease, a different systemic therapy approach may be necessary for patients with metachronous LV disease [63].
Additionally, triplet therapy options including ADT, docetaxel, and ARPI have improved OS compared to ADT + docetaxel alone [64]. Consequently, ADT and docetaxel are not recommended for patients with LV mCSPC, whereas triplet therapy is advised for medically fit patients with HV disease.
The key clinical trials discussed in this section, along with their outcomes and adverse events, are summarized in Table 1 for a comprehensive comparison.
Abiraterone Acetate
Abiraterone acetate was evaluated in several clinical trials for mCSPC to determine its effectiveness in improving OS and other clinical outcomes when combined with ADT. In the STAMPEDE trial, around 1000 mCSPC patients were randomized to receive either ADT alone or ADT combined with abiraterone. After a 40-month follow-up, the combination of abiraterone + ADT reduced the risk of death by approximately 40% (hazard ratio (HR): 0.61; 95% confidence interval (CI): 0.49–0.75), with significant improvements in other secondary endpoints [44]. A post hoc analysis of 1003 metastatic patients, stratified by LATITUDE criteria, showed that median survival was higher in the abiraterone +ADT arm (79 months vs. 46 months) after a median follow-up of 73 months. Both low-risk (HR: 0.54; 95% CI: 0.40–0.74) and high-risk (HR: 0.54; 95% CI: 0.43–0.69) groups benefited from the treatment [45].
The LATITUDE trial included 1199 patients with high-risk mCSPC, defined as having at least two of the following: Gleason score ≥ 8, ≥3 bone lesions, or visceral metastases. Patients were randomized to receive abiraterone with prednisone or placebo with ADT. The trial demonstrated a similar approximately 40% reduction in the risk of death with abiraterone + ADT (HR: 0.66; 95% CI: 0.56–0.78; p < 0.001) after a median follow-up of 51.8 months. The median OS was 53.3 months in the abiraterone + ADT group compared to 36.5 months in the placebo group. Secondary endpoints, such as radiographic progression-free survival (rPFS), PSA progression, and time to chemotherapy initiation, also showed significant improvements [46].
The STAMPEDE trial demonstrated a high incidence of severe and life-threatening grade 3–5 AEs, particularly in combination therapy arms. Adding abiraterone significantly increased the frequency of these AEs. This finding underscores the necessity for a careful risk-benefit analysis when incorporating abiraterone into treatment plans for mCSPC, especially in patients aged ≥ 70 years who experienced higher incidences of grade 3–5 AEs (47% vs. 33%) and a higher rate of treatment-related deaths (9 vs. 3). Additionally, severe hypertension or cardiac disorders were observed in 10% of patients, and grade 3–5 liver toxicity was noted in 7% of patients, highlighting the importance of vigilant monitoring of blood pressure, as well as renal and hepatic function, to improve patient outcomes when using abiraterone in combination therapy [44].
These findings align with those reported in the LATITUDE trial, also demonstrating a higher frequency of AEs in the abiraterone group. However, these events were generally mild and primarily related to mineralocorticoid excess (hypertension, hypokalemia, and edema), hormonal effects (fatigue and hot flushes), and liver toxicity [44]. Despite these AEs, the overall discontinuation rate due to AEs in the LATITUDE trial was 12%. Importantly, patient-reported outcomes in LATITUDE indicated improvements in pain intensity, fatigue, functional decline, and overall health-related QoL, further emphasizing the complex balance between the therapeutic benefits and the risks of AEs when using abiraterone in mCSPC [47].
In February 2018, the FDA approved abiraterone in combination with prednisone for mCSPC based on the results of the STAMPEDE and LATITUDE trials. The National Comprehensive Cancer Network (NCCN) Panel recommends abiraterone with 5 mg once-daily prednisone as a treatment option with ADT for newly diagnosed mCSPC. Alternatively, the fine-particle formulation of abiraterone can be used to reduce financial toxicity and improve adherence [48].
Apalutamide
The double-blind phase 3 TITAN trial evaluated the efficacy and safety of apalutamide in combination with ADT compared to ADT alone in patients with mCSPC [49]. This trial was designed to address the ongoing need for more effective treatment strategies in such patients. The TITAN trial randomized 1052 patients with mCSPC to receive either ADT with apalutamide (240 mg/day) or a placebo. The participants were carefully stratified based on Gleason score at diagnosis, geographic region, and previous docetaxel treatment to ensure balanced comparison groups. The median follow-up duration was 22.7 months, allowing for a robust assessment of long-term outcomes. Importantly, both primary endpoints were achieved, demonstrating the significant benefit of apalutamide in this setting. At 24 months, the rPFS was notably higher in the apalutamide group (68.2%) compared to the placebo group (47.5%), with an HR for radiographic progression or death of 0.48 (95% CI: 0.39–0.60; p < 0.001). Furthermore, OS was also significantly improved in the apalutamide group (82.4%) compared to the placebo group (73.5%), with an HR for death of 0.67 (95% CI: 0.51–0.89; p = 0.005). These findings underscore the potential of adding apalutamide to standard ADT to enhance outcomes in patients with mCSPC.
In the final analysis, the median OS was not reached in the apalutamide group compared to 52.2 months in the ADT alone group (HR: 0.65; 95% CI: 0.53–0.79; p < 0.001) after a median follow-up of 44 months. After adjusting for crossover, the risk of death was reduced by 48%, indicating a consistent benefit across both HV and low-volume (LV) disease. A post hoc subgroup analysis of the TITAN trial re-evaluated outcomes based on the volume and timing of metastases, showing synchronous HV disease in 54% of patients. OS benefits were statistically significant in patients with synchronous HV disease (HR: 0.68; 95% CI: 0.53–0.87; p = 0.002) and metachronous LV disease (HR: 0.22; 95% CI: 0.09–0.55; p = 0.001). However, no significant OS benefits were observed in patients with synchronous LV or metachronous HV disease, likely due to limited patient numbers [50].
More frequently observed AEs in the apalutamide group included rash, hypothyroidism, and ischemic heart disease. Nevertheless, health-related QoL was maintained during treatment with apalutamide [51].
The TITAN trial demonstrated significant improvements in both rPFS and OS by adding apalutamide to ADT in patients with mCSPC. These findings support apalutamide as a category 1 treatment option for mCSPC, which received FDA approval in September 2019 [52]. The consistent survival benefits across different subgroups and the maintained QoL highlight the robust efficacy and tolerability of apalutamide in this patient population [53].
Enzalutamide
The management of mCSPC has evolved with the introduction of enzalutamide, an ARPI, as an addition to standard ADT. Recent clinical trials, namely ENZAMET and ARCHES, have provided significant evidence of the efficacy and safety of this combination [8].
The ENZAMET trial was an open-label, randomized phase 3 study involving 1125 patients with mCSPC. The patients were randomized to receive either enzalutamide (160 mg/day) + ADT (LHRH analog or surgical castration) or a first-generation antiandrogen (bicalutamide, nilutamide, or flutamide) + ADT [54]. Stratification was based on disease volume, planned use of early docetaxel, planned use of bone antiresorptive therapy, comorbidity score, and trial site. At the first interim analysis with a median follow-up of 34 months, the enzalutamide group showed a 33% reduction in the risk of death compared to the control group (HR: 0.67; 95% CI: 0.52–0.86; p = 0.002). An analysis after a median follow-up of 68 months revealed that the 5-year OS rate was 67% in the enzalutamide group versus 57% in the control group (HR: 0.70; 95% CI: 0.58–0.84; p < 0.001). Secondary endpoints also showed improvement with enzalutamide, including PFS based on PSA levels and clinical PFS. A post hoc analysis indicated a significant OS benefit in patients with synchronous mCSPC (HR: 0.73; 95% CI: 0.55–0.99), but not in metachronous patients [55].
The ARCHES trial, a double-blind, randomized phase 3 study, included 1150 patients with mCSPC. The participants were randomized to receive either enzalutamide (160 mg/day) + ADT or placebo + ADT, with stratification based on disease volume and prior docetaxel use. After a median follow-up of 14.4 months, the enzalutamide group demonstrated significantly improved rPFS (19.0 months vs. not reached; HR: 0.39; 95% CI: 0.30–0.50; p < 0.001) [56]. At the final OS analysis, although 32% of patients in the placebo group crossed over to enzalutamide after unblinding, enzalutamide still reduced the risk of death by 34% compared to placebo (HR: 0.66; 95% CI: 0.53–0.81; p < 0.001) [57].
The safety of enzalutamide in these trials was consistent with previous studies on CRPC, with common AEs including fatigue, seizures, and hypertension. The safety profiles observed in both ENZAMET and ARCHES trials were similar, confirming the manageable safety of enzalutamide in mCSPC [54,56].
Overall, enzalutamide, when combined with ADT, significantly improves survival in patients with mCSPC. The ENZAMET and ARCHES trials demonstrated substantial improvements in OS and PFS with enzalutamide + ADT compared to standard ADT regimens. The FDA’s approval of enzalutamide for mCSPC in December 2019 reflects its robust efficacy and manageable safety profile, supporting its use as a category 1 treatment option for patients with mCSPC [58].
Docetaxel
Docetaxel has been studied as an initial treatment option for patients with mCSPC based on several key phase 3 trials: ECOG 3805/CHAARTED, STAMPEDE, and GETUG-AFU-15 [41,59,60]. These studies provided significant insights into the efficacy of combining docetaxel with ADT for improving OS in patients with mCSPC.
The ECOG 3805/CHAARTED trial randomized 790 patients with mCSPC to receive either docetaxel (75 mg/m2 intravenously every 3 weeks for 6 doses) + ADT or ADT alone [41]. After a median follow-up of 53.7 months, the combination therapy arm showed longer OS than the ADT-alone arm (57.6 months vs. 47.2 months; HR: 0.72; 95% CI: 0.59–0.89; p = 0.002) [61]. Subgroup analysis revealed that the survival benefit was more pronounced in patients with HV disease (65% of participants) (HR: 0.63; 95% CI: 0.50–0.79; p < 0.001). However, patients with LV did not obtain a significant survival benefit by adding docetaxel (HR: 1.04; 95% CI: 0.70–1.55; p = 0.860).
The STAMPEDE trial, a multi-arm, multi-stage phase 3 study, included patients with both non-metastatic (M0) and metastatic (M1) CSPC [59]. The trial confirmed the survival advantage of adding docetaxel to ADT, as seen in the CHAARTED trial. In the STAMPEDE trial, the extent of disease was not evaluated in 1087 patients with metastatic disease, but the median OS for all M1 patients was 5.4 years in the ADT + docetaxel arm versus 3.6 years in the ADT-only arm (a difference of 1.8 years compared to a 1.1-year difference in the CHAARTED).
The GETUG-AFU-15 trial evaluated ADT combined with docetaxel [62]. After 83.9 months of follow-up, this combination did not significantly improve OS compared to ADT alone. However, post hoc analysis considering metastatic disease volume indicated a trend toward benefit in the HV group (HR: 0.78; 95% CI: 0.56–1.09; p = 0.140) [60].
A meta-analysis of three trials involving 2,261 patients with mHSPC revealed that the combination of ADT with docetaxel did not improve OS in men with metachronous LV disease, as indicated by an HR of 0.98 (95% CI: 0.67–1.45). However, the most significant OS benefit and reduction in the risk of death were observed in patients with synchronous HV disease (HR: 0.60; 95% CI: 0.52–0.69). Thus, while ADT combined with docetaxel markedly improves OS in patients with HV mCSPC, particularly in those with synchronous HV disease, a different systemic therapy approach may be necessary for patients with metachronous LV disease [63].
Additionally, triplet therapy options including ADT, docetaxel, and ARPI have improved OS compared to ADT + docetaxel alone [64]. Consequently, ADT and docetaxel are not recommended for patients with LV mCSPC, whereas triplet therapy is advised for medically fit patients with HV disease.
The key clinical trials discussed in this section, along with their outcomes and adverse events, are summarized in Table 1 for a comprehensive comparison.
3.1.3. Treatment Intensification—Triplet Therapies
Rationale
Numerous trials demonstrated the significant OS benefit of beginning systemic therapy earlier at the mCSPC stage, not waiting until the tumor becomes castration-resistant [8,65,66]. This benefit is likely due to several reasons: mCSPC might have more favorable disease biology, less acquired treatment resistance, and therefore demonstrates a more durable treatment response [8]. Using docetaxel in mCSPC is likely more efficacious at targeting AR-independent cancer cells early compared to its use in mCRPC since these cancer cells might have had opportunities to develop resistance [8,67]. Treatment in the mCSPC setting is also often better tolerated given fewer cumulative toxicities from prior systemic therapy, as well as fewer symptoms and a lower disease burden before disease progression. Real-world studies showed that only approximately 50% of patients receive second-line therapies upon disease progression, with many possibly becoming too frail to receive docetaxel in the mCRPC setting. Therefore, earlier treatment intensification may also increase the number of patients receiving life-prolonging systemic therapy [68].
The effectiveness of chemohormonal triplet therapy may stem from the synergistic and complementary effects of the three agents [69]. ADT, the cornerstone of hormonal therapy for prostate cancer, inhibits tumor growth by lowering androgen levels produced in the testicles through medication or surgery. ARPIs, including androgen synthesis inhibitors (abiraterone) and antiandrogens (enzalutamide, apalutamide, and darolutamide), suppress other androgen sources to achieve the maximum suppression of the androgen axis, further improving the anti-tumor effect [28]. Docetaxel, a semisynthetic taxane, exhibits significant antitumor activity by inhibiting microtubular depolymerization and attenuating the effects of BCL-2 and BCL-XL expression. The taxane-induced microtubule stabilization arrests cells in the G2/M phase and induces bcl-2 phosphorylation, promoting a cascade of events that ultimately cause apoptotic cell death [70].
ADT lowers androgen levels, inhibiting the growth of androgen-sensitive CSPC cells that respond well to this treatment [9]. The combination of docetaxel and ADT can create a synergistic effect by simultaneously inhibiting androgen signaling pathways and cell division [71]. Clinical trials, such as CHAARTED and STAMPEDE, showed that combining docetaxel with ADT significantly improves survival in patients with mCSPC, providing clinical evidence of the effectiveness of docetaxel against mCSPC [41,72].
Docetaxel Plus Abiraterone
The PEACE-1 trial was an international, open-label, randomized phase 3 study conducted across seven European countries, aiming to assess the efficacy of adding abiraterone acetate with prednisone to the standard of care (SOC) in patients with de novo mCSPC [73]. The study utilized a 2 × 2 factorial design and included 1173 patients randomized into four groups: SOC ADT alone or with docetaxel, SOC with radiotherapy (RT), SOC with abiraterone, and SOC with RT and abiraterone. The primary endpoints were rPFS and OS. The results showed that patients receiving abiraterone had significantly longer rPFS compared to those who did not. Specifically, the overall rPFS in the abiraterone group had an HR of 0.54 (99.9% CI, 0.41–0.71; p < 0.001). For those receiving abiraterone with docetaxel, the rPFS had an HR of 0.50 (99.9% CI, 0.34–0.71; p < 0.001). Furthermore, adding abiraterone to SOC significantly improved OS, with the overall OS in the abiraterone group showing an HR of 0.82 (95.1% CI, 0.69–0.98; p = 0.030). In patients receiving abiraterone with docetaxel, the OS had an HR of 0.75 (95.1% CI, 0.59–0.95; p = 0.017).
Subgroup analysis provides significant insights into the efficacy of different treatment combinations for mCSPC based on disease burden. For patients with HV disease, adding abiraterone acetate + prednisone to the SOC (consisting of ADT and docetaxel) markedly improved OS and rPFS. Specifically, the median OS for patients receiving the triplet therapy was 5.1 years compared to 3.5 years for those receiving only ADT plus docetaxel, with an HR of 0.72 (95% CI: 0.55–0.95; p = 0.019). Additionally, the median rPFS was 4.1 years for those receiving the triplet therapy versus 1.6 years for those receiving the SOC alone (HR: 0.47; 95% CI: 0.30–0.72; p < 0.001). In contrast, the OS data remain immature for patients with LV disease, and median OS has not yet been reached in either treatment group. The rPFS also showed significant improvement with the triplet therapy: the median rPFS was not reached versus 2.7 years for the SOC alone (HR: 0.58; 95% CI: 0.29–1.15; p = 0.006).
The study also examined treatment-related AEs, finding that rates of neutropenia, febrile neutropenia, fatigue, and neuropathy were similar between triplet and doublet therapy groups. However, the incidence of grade ≥ 3 AEs was higher in the triplet therapy group (63% vs. 52%). Specifically, grade 3 hypertension and grade 3 transaminase increase were more common in the triplet group (22% vs. 13% and 6% vs. 1%, respectively). Other AEs, such as febrile neutropenia, fatigue, and neuropathy, did not show significant differences between the groups.
The PEACE-1 trial demonstrated that adding abiraterone to ADT and docetaxel significantly improves both rPFS and OS in patients with de novo mCSPC. These results emphasize the substantial benefit of triplet therapy for HV mCSPC. Thus, it should be considered a standard treatment option for this subgroup. For LV disease, while the survival benefit is still being evaluated, triplet therapy shows promise, particularly in improving PFS.
Docetaxel Plus Darolutamide
The ARASENS trial, an international phase 3 study, evaluated the efficacy and safety of adding darolutamide to ADT and docetaxel in patients with mCSPC. This trial randomized 1306 patients to receive either the combination of ADT, docetaxel, and darolutamide or ADT, docetaxel, and a matching placebo [74]. The primary endpoint of the study was OS, with time to CRPC, skeletal event-free survival, and time to initiation of subsequent systemic antineoplastic therapy as secondary endpoints. The study did not stratify patients by disease volume, and 86% of participants had synchronous metastatic disease. The results demonstrated that adding darolutamide significantly improved OS compared to the placebo group. At 4 years, the OS rate was 62.7% (95% CI: 58.7–66.7) in the darolutamide group versus 50.4% (95% CI: 46.3–54.6) in the placebo group, reflecting a 32% reduction in the risk of death (HR: 0.68; 95% CI: 0.57–0.80; p < 0.001). Additionally, darolutamide showed significant benefits across several secondary endpoints. The time to CRPC was markedly prolonged (HR: 0.36; 95% CI: 0.30–0.42; p < 0.001), skeletal event-free survival improved (HR: 0.61; 95% CI: 0.52–0.72; p < 0.001), and the time to initiation of subsequent systemic antineoplastic therapy was extended (HR: 0.39; 95% CI: 0.33–0.46; p < 0.001).
Post hoc analyses demonstrated the OS benefit for patients with HV disease (HR: 0.69; 95% CI: 0.57–0.82), while the benefit was less clear in patients with LV disease (HR: 0.68; 95% CI: 0.41–1.13), as defined by the CHAARTED criteria. Additionally, the trial provided clear evidence of survival benefits for both high-risk patients (HR: 0.71; 95% CI: 0.58–0.86) and low-risk patients (HR: 0.62; 95% CI: 0.42–0.90) according to the LATITUDE criteria.
AEs were comparable between darolutamide and placebo groups, with most AEs being known effects of docetaxel. The most common AEs included alopecia, neutropenia, fatigue, and anemia. Darolutamide was associated with a higher incidence of rash (16.6% vs. 13.5%) and hypertension (13.7% vs. 9.2%), consistent with the known effects of ARPIs.
The findings of the ARASENS trial support the addition of darolutamide to ADT and docetaxel for patients with mCSPC, demonstrating significant improvements in OS and secondary endpoints. The consistency of the OS benefit across most subgroups underscores the robustness of these findings. However, the less pronounced benefit in patients with LV disease warrants further research with larger sample sizes and longer follow-ups. The ARASENS trial established darolutamide in combination with ADT and docetaxel as a superior treatment regimen for mCSPC, offering a substantial survival advantage and delayed disease progression. Based on these results, the FDA approved this triplet therapy in August 2022, marking a significant advancement in mCSPC management [75].
Docetaxel Plus Enzalutamide
The ENZAMET (Enzalutamide for Metastatic Prostate Cancer) study investigated the concurrent use of docetaxel with enzalutamide, with 45% of patients receiving planned docetaxel at the investigator’s discretion. Additionally, 85% of patients in the control arm received subsequent therapy, including 76% who received either abiraterone or enzalutamide upon progression [55]. A prespecified analysis demonstrated a significant difference in OS, favoring the enzalutamide arm among the subset of 362 men with synchronous metastatic disease planned for docetaxel, with a 5-year OS of 60% compared to 52% (HR: 0.73; 95% CI: 0.55–0.99). This survival benefit was not observed in patients with metachronous disease planned for docetaxel (HR: 1.10; 95% CI: 0.65–1.86). Within the synchronous population planned for docetaxel, OS estimates favored enzalutamide in both HV and LV subgroups. Survival curves indicated higher OS rates in the first 30 months for participants receiving enzalutamide + docetaxel + testosterone suppression (TS) versus those contemporaneously accrued to enzalutamide + TS in the highest-risk subgroup (synchronous, HV), highlighting the potential necessity of early chemotherapy in rapidly lethal disease. The AEs of adding enzalutamide to the SOC were overall similar to those of enzalutamide in previous clinical trials.
These findings provide robust evidence supporting the use of enzalutamide in combination with ADT and docetaxel, particularly for patients with synchronous metastatic disease, indicating a significant improvement in OS and underscoring the importance of early intervention in high-risk populations.
Table 2 provides a summary of key clinical trials evaluating triplet therapies, highlighting their efficacy, patient populations, and key findings.
Rationale
Numerous trials demonstrated the significant OS benefit of beginning systemic therapy earlier at the mCSPC stage, not waiting until the tumor becomes castration-resistant [8,65,66]. This benefit is likely due to several reasons: mCSPC might have more favorable disease biology, less acquired treatment resistance, and therefore demonstrates a more durable treatment response [8]. Using docetaxel in mCSPC is likely more efficacious at targeting AR-independent cancer cells early compared to its use in mCRPC since these cancer cells might have had opportunities to develop resistance [8,67]. Treatment in the mCSPC setting is also often better tolerated given fewer cumulative toxicities from prior systemic therapy, as well as fewer symptoms and a lower disease burden before disease progression. Real-world studies showed that only approximately 50% of patients receive second-line therapies upon disease progression, with many possibly becoming too frail to receive docetaxel in the mCRPC setting. Therefore, earlier treatment intensification may also increase the number of patients receiving life-prolonging systemic therapy [68].
The effectiveness of chemohormonal triplet therapy may stem from the synergistic and complementary effects of the three agents [69]. ADT, the cornerstone of hormonal therapy for prostate cancer, inhibits tumor growth by lowering androgen levels produced in the testicles through medication or surgery. ARPIs, including androgen synthesis inhibitors (abiraterone) and antiandrogens (enzalutamide, apalutamide, and darolutamide), suppress other androgen sources to achieve the maximum suppression of the androgen axis, further improving the anti-tumor effect [28]. Docetaxel, a semisynthetic taxane, exhibits significant antitumor activity by inhibiting microtubular depolymerization and attenuating the effects of BCL-2 and BCL-XL expression. The taxane-induced microtubule stabilization arrests cells in the G2/M phase and induces bcl-2 phosphorylation, promoting a cascade of events that ultimately cause apoptotic cell death [70].
ADT lowers androgen levels, inhibiting the growth of androgen-sensitive CSPC cells that respond well to this treatment [9]. The combination of docetaxel and ADT can create a synergistic effect by simultaneously inhibiting androgen signaling pathways and cell division [71]. Clinical trials, such as CHAARTED and STAMPEDE, showed that combining docetaxel with ADT significantly improves survival in patients with mCSPC, providing clinical evidence of the effectiveness of docetaxel against mCSPC [41,72].
Docetaxel Plus Abiraterone
The PEACE-1 trial was an international, open-label, randomized phase 3 study conducted across seven European countries, aiming to assess the efficacy of adding abiraterone acetate with prednisone to the standard of care (SOC) in patients with de novo mCSPC [73]. The study utilized a 2 × 2 factorial design and included 1173 patients randomized into four groups: SOC ADT alone or with docetaxel, SOC with radiotherapy (RT), SOC with abiraterone, and SOC with RT and abiraterone. The primary endpoints were rPFS and OS. The results showed that patients receiving abiraterone had significantly longer rPFS compared to those who did not. Specifically, the overall rPFS in the abiraterone group had an HR of 0.54 (99.9% CI, 0.41–0.71; p < 0.001). For those receiving abiraterone with docetaxel, the rPFS had an HR of 0.50 (99.9% CI, 0.34–0.71; p < 0.001). Furthermore, adding abiraterone to SOC significantly improved OS, with the overall OS in the abiraterone group showing an HR of 0.82 (95.1% CI, 0.69–0.98; p = 0.030). In patients receiving abiraterone with docetaxel, the OS had an HR of 0.75 (95.1% CI, 0.59–0.95; p = 0.017).
Subgroup analysis provides significant insights into the efficacy of different treatment combinations for mCSPC based on disease burden. For patients with HV disease, adding abiraterone acetate + prednisone to the SOC (consisting of ADT and docetaxel) markedly improved OS and rPFS. Specifically, the median OS for patients receiving the triplet therapy was 5.1 years compared to 3.5 years for those receiving only ADT plus docetaxel, with an HR of 0.72 (95% CI: 0.55–0.95; p = 0.019). Additionally, the median rPFS was 4.1 years for those receiving the triplet therapy versus 1.6 years for those receiving the SOC alone (HR: 0.47; 95% CI: 0.30–0.72; p < 0.001). In contrast, the OS data remain immature for patients with LV disease, and median OS has not yet been reached in either treatment group. The rPFS also showed significant improvement with the triplet therapy: the median rPFS was not reached versus 2.7 years for the SOC alone (HR: 0.58; 95% CI: 0.29–1.15; p = 0.006).
The study also examined treatment-related AEs, finding that rates of neutropenia, febrile neutropenia, fatigue, and neuropathy were similar between triplet and doublet therapy groups. However, the incidence of grade ≥ 3 AEs was higher in the triplet therapy group (63% vs. 52%). Specifically, grade 3 hypertension and grade 3 transaminase increase were more common in the triplet group (22% vs. 13% and 6% vs. 1%, respectively). Other AEs, such as febrile neutropenia, fatigue, and neuropathy, did not show significant differences between the groups.
The PEACE-1 trial demonstrated that adding abiraterone to ADT and docetaxel significantly improves both rPFS and OS in patients with de novo mCSPC. These results emphasize the substantial benefit of triplet therapy for HV mCSPC. Thus, it should be considered a standard treatment option for this subgroup. For LV disease, while the survival benefit is still being evaluated, triplet therapy shows promise, particularly in improving PFS.
Docetaxel Plus Darolutamide
The ARASENS trial, an international phase 3 study, evaluated the efficacy and safety of adding darolutamide to ADT and docetaxel in patients with mCSPC. This trial randomized 1306 patients to receive either the combination of ADT, docetaxel, and darolutamide or ADT, docetaxel, and a matching placebo [74]. The primary endpoint of the study was OS, with time to CRPC, skeletal event-free survival, and time to initiation of subsequent systemic antineoplastic therapy as secondary endpoints. The study did not stratify patients by disease volume, and 86% of participants had synchronous metastatic disease. The results demonstrated that adding darolutamide significantly improved OS compared to the placebo group. At 4 years, the OS rate was 62.7% (95% CI: 58.7–66.7) in the darolutamide group versus 50.4% (95% CI: 46.3–54.6) in the placebo group, reflecting a 32% reduction in the risk of death (HR: 0.68; 95% CI: 0.57–0.80; p < 0.001). Additionally, darolutamide showed significant benefits across several secondary endpoints. The time to CRPC was markedly prolonged (HR: 0.36; 95% CI: 0.30–0.42; p < 0.001), skeletal event-free survival improved (HR: 0.61; 95% CI: 0.52–0.72; p < 0.001), and the time to initiation of subsequent systemic antineoplastic therapy was extended (HR: 0.39; 95% CI: 0.33–0.46; p < 0.001).
Post hoc analyses demonstrated the OS benefit for patients with HV disease (HR: 0.69; 95% CI: 0.57–0.82), while the benefit was less clear in patients with LV disease (HR: 0.68; 95% CI: 0.41–1.13), as defined by the CHAARTED criteria. Additionally, the trial provided clear evidence of survival benefits for both high-risk patients (HR: 0.71; 95% CI: 0.58–0.86) and low-risk patients (HR: 0.62; 95% CI: 0.42–0.90) according to the LATITUDE criteria.
AEs were comparable between darolutamide and placebo groups, with most AEs being known effects of docetaxel. The most common AEs included alopecia, neutropenia, fatigue, and anemia. Darolutamide was associated with a higher incidence of rash (16.6% vs. 13.5%) and hypertension (13.7% vs. 9.2%), consistent with the known effects of ARPIs.
The findings of the ARASENS trial support the addition of darolutamide to ADT and docetaxel for patients with mCSPC, demonstrating significant improvements in OS and secondary endpoints. The consistency of the OS benefit across most subgroups underscores the robustness of these findings. However, the less pronounced benefit in patients with LV disease warrants further research with larger sample sizes and longer follow-ups. The ARASENS trial established darolutamide in combination with ADT and docetaxel as a superior treatment regimen for mCSPC, offering a substantial survival advantage and delayed disease progression. Based on these results, the FDA approved this triplet therapy in August 2022, marking a significant advancement in mCSPC management [75].
Docetaxel Plus Enzalutamide
The ENZAMET (Enzalutamide for Metastatic Prostate Cancer) study investigated the concurrent use of docetaxel with enzalutamide, with 45% of patients receiving planned docetaxel at the investigator’s discretion. Additionally, 85% of patients in the control arm received subsequent therapy, including 76% who received either abiraterone or enzalutamide upon progression [55]. A prespecified analysis demonstrated a significant difference in OS, favoring the enzalutamide arm among the subset of 362 men with synchronous metastatic disease planned for docetaxel, with a 5-year OS of 60% compared to 52% (HR: 0.73; 95% CI: 0.55–0.99). This survival benefit was not observed in patients with metachronous disease planned for docetaxel (HR: 1.10; 95% CI: 0.65–1.86). Within the synchronous population planned for docetaxel, OS estimates favored enzalutamide in both HV and LV subgroups. Survival curves indicated higher OS rates in the first 30 months for participants receiving enzalutamide + docetaxel + testosterone suppression (TS) versus those contemporaneously accrued to enzalutamide + TS in the highest-risk subgroup (synchronous, HV), highlighting the potential necessity of early chemotherapy in rapidly lethal disease. The AEs of adding enzalutamide to the SOC were overall similar to those of enzalutamide in previous clinical trials.
These findings provide robust evidence supporting the use of enzalutamide in combination with ADT and docetaxel, particularly for patients with synchronous metastatic disease, indicating a significant improvement in OS and underscoring the importance of early intervention in high-risk populations.
Table 2 provides a summary of key clinical trials evaluating triplet therapies, highlighting their efficacy, patient populations, and key findings.
3.1.4. Network Meta-Analysis
Until recently, several network meta-analyses (NMA) have been conducted to update the existing evidence on the comparative efficacy of systemic therapy in mCSPC by prognostic subgroups, aiming to support clinical practice guidelines. Among these studies, one conducted by Hoeh et al. focused on evaluating the efficacy of triplet versus doublet therapies in mCSPC, specifically stratified by disease volume (low vs. high) [76]. Their analysis, which incorporated data from 10 randomized controlled trials (RCTs), centered on OS outcomes for various treatment regimens, including doublet therapy (ARPI + ADT or docetaxel + ADT) and triplet therapy (ARPI + docetaxel + ADT). The NMA was conducted separately for patients with LV and HV mCSPC based on the CHAARTED criteria. Their findings revealed that combination therapies other than ARPI + ADT did not show substantial benefits compared to ADT alone for LV mCSPC. Moreover, no significant OS differences were observed between triplet therapies and the ARAPI + ADT doublet therapy. In contrast, all combination therapies improved OS compared to ADT alone for HV mCSPC. Notably, the triplet regimen of darolutamide + docetaxel + ADT ranked the highest regarding the OS benefit (p = 0.920), followed closely by abiraterone + docetaxel + ADT (p = 0.850). Specifically, darolutamide + docetaxel + ADT showed a significant advantage in terms of OS over ARAT + ADT (HR: 0.76; 95% CI: 0.59–0.97). This study underscores the necessity of stratifying patients by disease volume when making treatment decisions for mCSPC. Therefore, while triplet therapy may not provide significant OS benefits for patients with LV mCSPC, it offers superior outcomes for those with HV disease, thereby emphasizing the importance of personalized treatment strategies based on disease burden.
Building on this, Jian et al. conducted a systematic review and NMA encompassing 18 publications from 12 clinical trials to further compare the efficacy of currently available combination therapies in patients with mCSPC [69]. The overall findings corroborated those of Hoeh et al., with triplet therapy ranking first in terms of OS (HR: 0.57; 95% credible interval (CrI): 0.48–0.67) and rPFS (HR: 0.33; 95% CrI: 0.26–0.41) benefits in the general mCSPC population. For HV mCSPC, triplet therapy was also ranked first in OS (HR: 0.57; 95% CrI: 0.44–0.75) and rPFS (HR: 0.29; 95% CrI: 0.23–0.37) benefits, followed by the doublet therapy of ADT + rezvilutamide (OS: HR: 0.58; 95% CrI: 0.44–0.77; rPFS: HR: 0.44; 95% CrI: 0.33–0.58) and ADT + docetaxel (OS: HR: 0.75; 95% CrI: 0.62–0.91; rPFS: HR: 0.63; 95% CrI: 0.52–0.77). In LV mCSPC, the combination of ADT with ARAT ranked first in OS (HR: 0.68; 95% CrI: 0.58–0.80) and rPFS (HR: 0.50; 95% CrI: 0.42–0.60) benefits, with ADT + apalutamide being the top therapy for OS (HR: 0.53; 95% CrI: 0.35–0.79) and ADT + enzalutamide showing significant improvement in OS and rPFS (OS: HR: 0.56; 95% CrI: 0.40–0.77; rPFS: HR: 0.29; 95% CrI: 0.22–0.39). However, triplet therapies did not demonstrate improvements in OS or rPFS in LV disease (OS: HR: 0.81; 95% CrI: 0.60–1.08; rPFS: HR: 0.67; 95% CrI: 0.50–0.91) and were associated with a higher risk of AEs (any AE: odds ratio (OR): 2.50; 95% CrI: 1.80–3.50) and grade ≥ 3 AEs (OR: 3.20; 95% CrI: 2.40–4.30) compared to other therapies, further suggesting the need for careful consideration of disease volume when selecting treatment modalities.
Complementing these findings, Dr. Riaz et al. comprehensively evaluated contemporary systemic treatment options for patients with mCSPC [77]. This study synthesized data from ten phase 3 RCTs involving 11,043 patients, assessing key outcomes, such as OS, PFS, grade ≥ 3 AEs, and health-related QoL. The results reinforced the benefits of triplet therapies, such as darolutamide combined with docetaxel and ADT and abiraterone with prednisone combined with docetaxel and ADT, in improving OS compared to docetaxel combined with ADT but did not show significant benefits over ARPI doublets, such as abiraterone with prednisone combined with ADT, enzalutamide combined with ADT, and apalutamide combined with ADT. Specifically, the HR for the darolutamide triplet and the abiraterone with prednisone triplet was 0.68 (95% CI: 0.57–0.81) and 0.75 (95% CI: 0.59–0.95), respectively, compared to docetaxel combined with ADT. Subgroup analyses further highlighted that abiraterone with prednisone combined with docetaxel and ADT provided an OS advantage for patients with HV disease compared to docetaxel combined with ADT (HR: 0.72; 95% CI: 0.55–0.95) but not ARPI doublets. Conversely, triplet therapies did not significantly outperform ARPI doublets or docetaxel combined with ADT for LV disease. Moreover, the increased risk of grade ≥ 3 AEs associated with triplet therapies underscores the importance of balancing efficacy with safety when considering treatment options. Overall, these studies collectively emphasize the critical role of disease volume stratification in guiding treatment decisions for mCSPC.
Table 3 summarizes the key findings from network meta-analyses evaluating the efficacy of combination therapies in mCSPC, stratified by disease volume.
3.2. Radiotherapy in mCSPC
The treatment for the primary tumor in metastatic disease is an evolving strategy aimed at eliminating significant sources of lethal metastatic seeding [79,80]. Multiple clinical trials have investigated the efficacy of prostate RT in patients with mCSPC, particularly focusing on outcomes related to failure-free survival (FFS), OS, and QoL.
The STAMPEDE trial randomly assigned 2061 men to receive SOC (ADT with concurrent docetaxel permitted from late 2015) or SOC + prostate RT [25]. The primary outcomes demonstrated that prostate RT significantly improved FFS but did not improve OS in the overall cohort (HR: 0.92; 95% CI: 0.80–1.06; p = 0.226). However, a pre-planned analysis revealed a pronounced OS benefit in patients with a low metastatic burden (HR: 0.68; 95% CI: 0.52–0.90; p = 0.007), which was not evident in high-burden disease (interaction p = 0.010). This benefit was consistent in long-term follow-up, with no evidence of deterioration in global QoL or long-term high-grade urinary toxicity [81].
The HORRAD trial randomized de novo mCSPC patients to either ADT alone or ADT + RT using two RT schedules: 70 Gy in 35 fractions over 7 weeks or 57.76 Gy in 19 fractions over 6 weeks [82]. The trial showed a modest improvement in time to PSA progression favoring the RT arm (median, 15 vs. 12 months, HR: 0.78; 95% CI: 0.63–0.97; p = 0.020) but no OS benefit.
The PEACE-1 phase 3 trial assessed the survival benefit of adding prostate RT in men with low-burden disease receiving intensified systemic treatment (docetaxel and/or abiraterone). The trial found that combining prostate RT with intensified systemic treatment improved rPFS (HR: 0.50; 95% CI: 0.28–0.88; p < 0.001) and CRPC-free survival (HR: 0.32; 95% CI: 0.23–0.44; p < 0.001) in men with low-burden mCSPC, although no OS improvement was detected. Additionally, early prostate RT prevented severe urologic morbidity, irrespective of metastatic burden.
Meta-analyses combining data from trials such as HORRAD and STAMPEDE, as well as secondary analyses of PEACE-1, reinforced the potential benefits of prostate RT in patients with low-burden disease. Specifically, these analyses highlighted improved survival outcomes by adding RT to the SOC, particularly in patients with fewer bone metastases [83]. Ongoing trials, such as PEACE-6 Oligo (PRESTO) and PLATON, are evaluating the role of stereotactic radiotherapy (SBRT) in oligometastatic CSPC patients. These studies aim to further define the benefits of targeted RT to metastatic sites in combination with the SOC [84,85].
The evidence from these trials supports the proactive treatment of the primary tumor with RT in patients with low-burden mCSPC. The differential impact of metastatic burden on treatment outcomes underscores the need for personalized treatment strategies. While prostate RT improves FFS and reduces CRPC incidence in low-burden disease, its role in combination with systemic therapies, such as abiraterone and docetaxel, warrants further exploration. Prostate RT is an established standard for synchronous, low-burden/volume mCSPC, offering significant benefits in terms of FFS and QoL without compromising OS in the overall population [81].
4. Guidelines
Various professional organizations and associations have modified their guidelines for the optimal management of patients with mCSPC based on compelling evidence demonstrating the effectiveness of early combination therapy involving ADT with ARPIs or docetaxel [48,86]. Considering the robust evidence from the above-mentioned trials, the current NCCN and EAU recommendations advocate for (1) ADT in conjunction with abiraterone, apalutamide, or enzalutamide; (2) ADT in combination with docetaxel along with abiraterone or darolutamide; or (3) ADT with external beam RT to the primary tumor for low metastatic burden as the first-line treatment for mCSPC.
5. Considerations
5.1. Real-World Evidence and Patterns
A recent Austrian multicenter study evaluated the effectiveness and tolerability of triplet therapy (ADT, ARPI, and chemotherapy) in treating mCSPC in a real-world setting [87]. This study involved 97 patients from 16 Austrian medical centers, with the majority receiving abiraterone (79.4%) and darolutamide (17.5%). Simultaneous administration of chemotherapy and ARPI significantly improved treatment outcomes, with a 99% decline in PSA levels in all patients and imaging responses in 88% of patients receiving abiraterone and 75% of patients receiving darolutamide. AEs were observed in 61.9% of patients, with 15% experiencing grade 3–5 events, including fatigue, dermatologic issues, infections, and polyneuropathy. Notably, starting ARPI before chemotherapy was associated with progression to more advanced disease. The study underscores the high effectiveness and tolerability of triplet therapy, especially for HV synchronous mCSPC, and highlights the importance of concurrent administration for achieving optimal outcomes.
Similarly, a recent study presented at the 2024 ASCO conference examined the outcomes of first-line triplet therapy (ADT, docetaxel, and abiraterone) in 58 Indian patients with mCSPC treated between May 2022 and December 2023 [88]. The patient cohort primarily comprised individuals with de novo metastatic disease (93.1%), an average age of 64 years, HV disease (94.8%), and a Gleason score ≥ 8 (69%). Comorbidities were prevalent in 89.7% of the patients, and 90% had an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1. The median number of triplet therapy cycles administered was six. Grade ≤ 3 AEs were reported in 22.4% of patients, including hypertension (6.9%), febrile neutropenia (5.2%), hyperglycemia (5.2%), and urinary tract infection (3.4%), with no treatment-related deaths. Dose reductions were necessary in 24.1% of cases. Notably, 62% of patients achieved an undetectable PSA level (<0.2 ng/mL), with a 12-month OS rate of 98.3% and a biochemical PFS rate of 96.6%. Thus, triplet therapy is effective and well tolerated in Indian mCSPC patients, with low rates of serious AEs and high survival rates, supporting its real-world applicability.
Furthermore, another study presented at the 2024 ASCO conference aimed to assess the real-world application of guidelines recommending ARPIs and/or docetaxel with ADT for mCSPC in the US from 2017 to 2023 [89]. Using data from the Komodo Research Dataset (from January 2017 to September 2023), the retrospective cohort study included 10,717 men with a median age of 65 years. Findings indicated that 28% of patients received ARPIs, 9% of patients received docetaxel, and 2.5% of patients received both. From 2017 to 2023, ARPI use rose from 13% to 47%, and ARPI + docetaxel use increased from 0.8% to 15%, while the use of ADT + docetaxel and ADT alone declined. Factors such as younger age, de novo mCSPC, bone-only metastases, and opioid use were associated with higher treatment intensification. Despite increased adoption of ARPI and/or docetaxel, over a third of patients still received ADT alone, highlighting areas for improvement in clinical practice.
These three studies collectively underscore the effectiveness and tolerability of triplet therapy in treating mCSPC, particularly highlighting its critical success and the importance of concurrent administration in real-world clinical settings. Additionally, they show the increasing adoption of these therapies in clinical practice, while indicating the need for further improvement to ensure broader adherence to treatment guidelines. This body of evidence supports the critical role of triplet therapy in achieving optimal outcomes for mCSPC patients and underscores the potential for enhancing clinical practice based on these findings.
A systematic review explored the real-world application of clinical trial findings in the treatment of mCSPC, highlighting significant advances since 2015, including docetaxel chemotherapy and ARPI alongside ADT [90]. Analyzing 13 studies encompassing 166,876 patients, the review aimed to determine treatment utilization rates and identify influencing factors. The utilization rates for treatment intensification with docetaxel or neoadjuvant hormonal therapy (NHT) ranged from 9.3% to 38.1%. Younger, white, urban-dwelling patients and those treated in private or academic settings were more likely to receive these treatments, with oncologists more inclined to prescribe NHT than urologists. Temporal trends showed increasing NHT utilization but a decline in docetaxel use after 2015–2016, revealing a significant gap between clinical trial evidence and real-world practice. This underscores the need for improved strategies to facilitate the adoption of evidence-based treatments in routine clinical care.
5.2. Factors Influencing the Low Uptake of Combination Therapy
Despite high-level evidence and explicit guideline recommendations for combination therapy in mCSPC, the uptake in real-world settings remains significantly lacking [13,91,92]. Several factors contribute to this discrepancy, including cost and reimbursement models, availability and geographical variation, age and treatment setting, regulatory and reimbursement approvals, physician decision-making and practice patterns, and resource limitations in developing countries. The financial burden and variability in reimbursement models significantly influence patient access to therapy [13]. For instance, although being a cost-effective option, docetaxel use in the US remains low, likely due to cost-related issues [65,92]. Furthermore, the availability of therapeutic agents and health economics vary greatly by geographical location, affecting access to combination therapy [93]. While younger patients and those treated in private hospitals are more likely to receive combination therapy, overall uptake remains low across different age groups and settings. Delays and differences in the approval and reimbursement of new drugs across countries further contribute to varied uptake rates. Additionally, clinicians’ decisions, often influenced by local practice patterns and resource availability, impact the low adoption rates. Even in high-income countries, adherence to guidelines is inconsistent, indicating a gap between evidence-based recommendations and clinical practice [94,95]. Resource constraints significantly impact the ability to implement combination therapies in developing countries, dictating the preference for more affordable options such as ADT alone or ADT combined with docetaxel [96].
Real-world data underscore the complexities of implementing new treatment standards. The adoption of combination therapy for mCSPC follows a pattern described by the diffusion of innovation theory, with early adopters leading the way (Figure 2). However, widespread adoption is hindered by several factors. The adoption process starts with innovators and early adopters, followed by a gradual uptake by the majority. Currently, the uptake of combination therapy is in the early adoption phase, despite substantial evidence supporting its benefits. Although the efficacy of combination therapies has been well documented for over half a decade, broad adoption has not yet been observed. The uro-oncology community might require more time to fully integrate these treatments into standard practice. Clinicians must maintain high standards of care, ensuring that evidence-based practices are upheld and not compromised due to slower adoption rates. These considerations highlight the need for ongoing efforts to narrow the gap between clinical evidence and practice, ensuring that patients receive optimal, evidence-based care [13].
5.1. Real-World Evidence and Patterns
A recent Austrian multicenter study evaluated the effectiveness and tolerability of triplet therapy (ADT, ARPI, and chemotherapy) in treating mCSPC in a real-world setting [87]. This study involved 97 patients from 16 Austrian medical centers, with the majority receiving abiraterone (79.4%) and darolutamide (17.5%). Simultaneous administration of chemotherapy and ARPI significantly improved treatment outcomes, with a 99% decline in PSA levels in all patients and imaging responses in 88% of patients receiving abiraterone and 75% of patients receiving darolutamide. AEs were observed in 61.9% of patients, with 15% experiencing grade 3–5 events, including fatigue, dermatologic issues, infections, and polyneuropathy. Notably, starting ARPI before chemotherapy was associated with progression to more advanced disease. The study underscores the high effectiveness and tolerability of triplet therapy, especially for HV synchronous mCSPC, and highlights the importance of concurrent administration for achieving optimal outcomes.
Similarly, a recent study presented at the 2024 ASCO conference examined the outcomes of first-line triplet therapy (ADT, docetaxel, and abiraterone) in 58 Indian patients with mCSPC treated between May 2022 and December 2023 [88]. The patient cohort primarily comprised individuals with de novo metastatic disease (93.1%), an average age of 64 years, HV disease (94.8%), and a Gleason score ≥ 8 (69%). Comorbidities were prevalent in 89.7% of the patients, and 90% had an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1. The median number of triplet therapy cycles administered was six. Grade ≤ 3 AEs were reported in 22.4% of patients, including hypertension (6.9%), febrile neutropenia (5.2%), hyperglycemia (5.2%), and urinary tract infection (3.4%), with no treatment-related deaths. Dose reductions were necessary in 24.1% of cases. Notably, 62% of patients achieved an undetectable PSA level (<0.2 ng/mL), with a 12-month OS rate of 98.3% and a biochemical PFS rate of 96.6%. Thus, triplet therapy is effective and well tolerated in Indian mCSPC patients, with low rates of serious AEs and high survival rates, supporting its real-world applicability.
Furthermore, another study presented at the 2024 ASCO conference aimed to assess the real-world application of guidelines recommending ARPIs and/or docetaxel with ADT for mCSPC in the US from 2017 to 2023 [89]. Using data from the Komodo Research Dataset (from January 2017 to September 2023), the retrospective cohort study included 10,717 men with a median age of 65 years. Findings indicated that 28% of patients received ARPIs, 9% of patients received docetaxel, and 2.5% of patients received both. From 2017 to 2023, ARPI use rose from 13% to 47%, and ARPI + docetaxel use increased from 0.8% to 15%, while the use of ADT + docetaxel and ADT alone declined. Factors such as younger age, de novo mCSPC, bone-only metastases, and opioid use were associated with higher treatment intensification. Despite increased adoption of ARPI and/or docetaxel, over a third of patients still received ADT alone, highlighting areas for improvement in clinical practice.
These three studies collectively underscore the effectiveness and tolerability of triplet therapy in treating mCSPC, particularly highlighting its critical success and the importance of concurrent administration in real-world clinical settings. Additionally, they show the increasing adoption of these therapies in clinical practice, while indicating the need for further improvement to ensure broader adherence to treatment guidelines. This body of evidence supports the critical role of triplet therapy in achieving optimal outcomes for mCSPC patients and underscores the potential for enhancing clinical practice based on these findings.
A systematic review explored the real-world application of clinical trial findings in the treatment of mCSPC, highlighting significant advances since 2015, including docetaxel chemotherapy and ARPI alongside ADT [90]. Analyzing 13 studies encompassing 166,876 patients, the review aimed to determine treatment utilization rates and identify influencing factors. The utilization rates for treatment intensification with docetaxel or neoadjuvant hormonal therapy (NHT) ranged from 9.3% to 38.1%. Younger, white, urban-dwelling patients and those treated in private or academic settings were more likely to receive these treatments, with oncologists more inclined to prescribe NHT than urologists. Temporal trends showed increasing NHT utilization but a decline in docetaxel use after 2015–2016, revealing a significant gap between clinical trial evidence and real-world practice. This underscores the need for improved strategies to facilitate the adoption of evidence-based treatments in routine clinical care.
5.2. Factors Influencing the Low Uptake of Combination Therapy
Despite high-level evidence and explicit guideline recommendations for combination therapy in mCSPC, the uptake in real-world settings remains significantly lacking [13,91,92]. Several factors contribute to this discrepancy, including cost and reimbursement models, availability and geographical variation, age and treatment setting, regulatory and reimbursement approvals, physician decision-making and practice patterns, and resource limitations in developing countries. The financial burden and variability in reimbursement models significantly influence patient access to therapy [13]. For instance, although being a cost-effective option, docetaxel use in the US remains low, likely due to cost-related issues [65,92]. Furthermore, the availability of therapeutic agents and health economics vary greatly by geographical location, affecting access to combination therapy [93]. While younger patients and those treated in private hospitals are more likely to receive combination therapy, overall uptake remains low across different age groups and settings. Delays and differences in the approval and reimbursement of new drugs across countries further contribute to varied uptake rates. Additionally, clinicians’ decisions, often influenced by local practice patterns and resource availability, impact the low adoption rates. Even in high-income countries, adherence to guidelines is inconsistent, indicating a gap between evidence-based recommendations and clinical practice [94,95]. Resource constraints significantly impact the ability to implement combination therapies in developing countries, dictating the preference for more affordable options such as ADT alone or ADT combined with docetaxel [96].
Real-world data underscore the complexities of implementing new treatment standards. The adoption of combination therapy for mCSPC follows a pattern described by the diffusion of innovation theory, with early adopters leading the way (Figure 2). However, widespread adoption is hindered by several factors. The adoption process starts with innovators and early adopters, followed by a gradual uptake by the majority. Currently, the uptake of combination therapy is in the early adoption phase, despite substantial evidence supporting its benefits. Although the efficacy of combination therapies has been well documented for over half a decade, broad adoption has not yet been observed. The uro-oncology community might require more time to fully integrate these treatments into standard practice. Clinicians must maintain high standards of care, ensuring that evidence-based practices are upheld and not compromised due to slower adoption rates. These considerations highlight the need for ongoing efforts to narrow the gap between clinical evidence and practice, ensuring that patients receive optimal, evidence-based care [13].
6. Ongoing Issues and Trials in mCSPC Treatment
6.1. What Is the Optimal Agent to Use Following Triplet Therapy
The treatment landscape for mCSPC is rapidly evolving, particularly in the context of post-triplet therapy options. The advent of novel agents, such as cabazitaxel, Lutetium-177 PSMA-617 (LuPSMA), and PARP inhibitors (PARPis), for patients with homologous recombination repair mutations (HRRm) underscores the necessity for identifying the most effective and safe post-triplet therapy agents [65].
Cabazitaxel, a next-generation taxane, demonstrated substantial efficacy in mCRPC post-docetaxel settings [97]. Its ability to overcome resistance mechanisms to other taxanes and its manageable toxicity profile make it a strong candidate for further exploration. Notably, its application to overcome taxane resistance is particularly relevant for patients who have progressed on prior therapies [98]. Targeted radioligand therapy with LuPSMA showed promise in mCRPC, especially in heavily pre-treated patients [99]. The TheraP trial highlighted its potential, demonstrating favorable responses in patients who have progressed after chemotherapy [100]. The capacity of this therapy to target PSMA-positive disease presents a significant advancement in personalized treatment [101]. For patients with HRRm, PARPis represent a targeted therapeutic approach exploiting the concept of synthetic lethality of cancer cells deficient in HRR. Ongoing trials, such as TALAPRO-3 and AMPLITUDE, are currently evaluating the efficacy of PARPis in the mCSPC setting. These agents hold promise for significantly impacting the treatment paradigm in HRRm patients [102].
To summarize, while there is no universal answer to the optimal agent following triplet therapy in mCSPC, the emerging evidence suggests that cabazitaxel, LuPSMA, and PARPis each offer distinct advantages depending on the patient’s specific disease characteristics and prior treatment history. The ability of cabazitaxel to overcome resistance, the targeted approach of LuPSMA to PSMA-positive disease, and the potential of PARPis in HRRm patients highlight the importance of personalized treatment. Further research and ongoing clinical trials will be crucial in refining these options and identifying the most effective post-triplet therapy agent for individual patients.
In line with the evolving treatment landscape for mCSPC, several ongoing clinical trials are exploring novel therapeutic combinations and approaches that build upon the current SOC. Noteworthy ongoing trials include CASCARA, which is investigating the efficacy of a quadruplet therapy regimen comprising ADT combined with cabazitaxel, carboplatin, and abiraterone in patients with HV mCSPC [103]. The CASCARA trial is designed to evaluate whether this intensified approach can provide superior outcomes in patients with more aggressive disease profiles. Another important trial, UpFront PSMA, focuses on the potential benefits of incorporating LuPSMA in mCSPC treatment [104]. By targeting PSMA-positive disease, LuPSMA may offer a more precise and effective treatment option in this setting. The UpFront PSMA trial will provide critical insights into the role of PSMA-targeted therapies early in the treatment course of mCSPC. Additionally, the AMPLITUDE trial assesses the efficacy of PARP inhibitors in mCSPC patients with HRRm [105]. Given the promising results of PARPis in other settings, the AMPLITUDE trial seeks to determine whether these agents can offer significant benefits when introduced earlier in treatment, particularly for patients harboring HRRm.
6.2. Post-Triplet Therapy Strategy for Patients with HRRm
For patients with mCRPC and HRRm post-triplet therapy, the debate between PARPi monotherapy and the combination of PARPis with ARPIs is ongoing [106,107]. PARPi monotherapy leverages synthetic lethality in HRR-deficient cells, with initial studies indicating significant efficacy and manageable safety profiles. The combination of PARPis with ARPIs may provide synergistic effects, enhancing antitumor activity, although the risk of compounded toxicities necessitates careful patient selection and management [108]. Early data from trials, such as TALAPRO-3, suggest potential benefits [109].
6.3. Comparative Efficacy of Triplet Therapy vs. PARPis in the HRR-Mutated Population
The comparative efficacy of triplet therapy versus PARPis in the HRR-mutated population is an area of active research. Ongoing trials, such as TALAPRO-3 and AMPLITUDE, are expected to provide critical insights [66,109]. Preliminary data suggest that PARPis could offer a significant advantage in patients harboring HRRm, potentially altering the SOC in this subset. Research showed that suppressing HRR expression by androgen receptor inhibition increases cell vulnerability to PARPis, leading to PARP-dependent DNA damage-induced cell death [110].
6.4. The Need for Head-to-Head Trials
The combination of ADT with ARPIs versus the triplet combination of ADT, docetaxel, and ARPIs remains a critical area of investigation [111]. Currently, direct comparisons between these therapeutic strategies are limited. Hence, future research should focus on head-to-head trials to elucidate the benefits and drawbacks of these approaches.
6.5. Novel Emerging Therapeutic Agents
Lutetium-177 PSMA-617 treatment yielded encouraging results in the VISION and TheraP trials, involving mCRPC patients previously treated with ARPIs and taxane regimens who had PSMA-positive disease [99,100]. Patients receiving radioligand therapy demonstrated improved rPFS and OS in the VISION trial, along with significant PSA response in the TheraP trial. Building on these results, the efficacy of targeted radionuclide therapy as part of triplet therapy in mCSPC patients is now under investigation in ongoing trials, such as the UpFront PSMA and PSMAddition [112]. Another critical aspect of prostate cancer treatment focuses on the phosphatidylinositol 3-kinase (PI3K)-AKT pathway, which plays a key role in tumorigenesis, progression, and treatment resistance [113]. In this area, the ProCAID trial assessed the combination of capivasertib, a selective inhibitor of all three AKT isoforms, with docetaxel chemotherapy [114]. The trial revealed a median OS benefit in the capivasertib + docetaxel arm, especially among patients who had previously been treated with abiraterone or enzalutamide. The ongoing CAPItello-281 study seeks to further investigate the potential of targeting the PI3K-AKT pathway, exploring the triplet combination of capivasertib, abiraterone, and ADT in PTEN-deficient mCSPC patients [115]. Meanwhile, advances in the understanding of immune checkpoint receptors and ligands have led to transformative breakthroughs in cancer treatment. Pembrolizumab, a PD-1 inhibitor, showed significant promise across various tumor types [116]. In the context of prostate cancer, the KEYNOTE-991 trial is currently evaluating the efficacy and safety of pembrolizumab in combination with enzalutamide and ADT in mCSPC patients compared to the standard regimen of enzalutamide + ADT [117]. This trial aims to determine whether the integration of immunotherapy can enhance treatment outcomes for these patients.
The trend toward low adoption of combination treatment in real-world settings impedes the generation of efficacy data outside clinical trials. Additional therapeutic agents currently indicated for mCRPC are anticipated to be approved for earlier-stage hormone-sensitive disease as part of combination treatments with ADT [65,118,119]. This highlights the need for further investigation into the optimal application of combination therapies.
6.6. Treatment Intensification and De-Escalation
Intensifying treatment by combining multiple agents raises questions about therapy duration. Optimal application may involve determining how to intensify and de-escalate treatment [85]. Considering no established superiority of continuous over intermittent ADT, intermittent regimens should be considered after initial treatment intensification in selected patients [120]. PSA kinetics may serve as biomarkers to guide treatment de-escalation in mCSPC patients [7], although further trials are needed to define optimal strategies for timing and choice of agents.
Table 4 provides an overview of ongoing clinical trials and emerging therapeutic strategies in mCSPC.
6.1. What Is the Optimal Agent to Use Following Triplet Therapy
The treatment landscape for mCSPC is rapidly evolving, particularly in the context of post-triplet therapy options. The advent of novel agents, such as cabazitaxel, Lutetium-177 PSMA-617 (LuPSMA), and PARP inhibitors (PARPis), for patients with homologous recombination repair mutations (HRRm) underscores the necessity for identifying the most effective and safe post-triplet therapy agents [65].
Cabazitaxel, a next-generation taxane, demonstrated substantial efficacy in mCRPC post-docetaxel settings [97]. Its ability to overcome resistance mechanisms to other taxanes and its manageable toxicity profile make it a strong candidate for further exploration. Notably, its application to overcome taxane resistance is particularly relevant for patients who have progressed on prior therapies [98]. Targeted radioligand therapy with LuPSMA showed promise in mCRPC, especially in heavily pre-treated patients [99]. The TheraP trial highlighted its potential, demonstrating favorable responses in patients who have progressed after chemotherapy [100]. The capacity of this therapy to target PSMA-positive disease presents a significant advancement in personalized treatment [101]. For patients with HRRm, PARPis represent a targeted therapeutic approach exploiting the concept of synthetic lethality of cancer cells deficient in HRR. Ongoing trials, such as TALAPRO-3 and AMPLITUDE, are currently evaluating the efficacy of PARPis in the mCSPC setting. These agents hold promise for significantly impacting the treatment paradigm in HRRm patients [102].
To summarize, while there is no universal answer to the optimal agent following triplet therapy in mCSPC, the emerging evidence suggests that cabazitaxel, LuPSMA, and PARPis each offer distinct advantages depending on the patient’s specific disease characteristics and prior treatment history. The ability of cabazitaxel to overcome resistance, the targeted approach of LuPSMA to PSMA-positive disease, and the potential of PARPis in HRRm patients highlight the importance of personalized treatment. Further research and ongoing clinical trials will be crucial in refining these options and identifying the most effective post-triplet therapy agent for individual patients.
In line with the evolving treatment landscape for mCSPC, several ongoing clinical trials are exploring novel therapeutic combinations and approaches that build upon the current SOC. Noteworthy ongoing trials include CASCARA, which is investigating the efficacy of a quadruplet therapy regimen comprising ADT combined with cabazitaxel, carboplatin, and abiraterone in patients with HV mCSPC [103]. The CASCARA trial is designed to evaluate whether this intensified approach can provide superior outcomes in patients with more aggressive disease profiles. Another important trial, UpFront PSMA, focuses on the potential benefits of incorporating LuPSMA in mCSPC treatment [104]. By targeting PSMA-positive disease, LuPSMA may offer a more precise and effective treatment option in this setting. The UpFront PSMA trial will provide critical insights into the role of PSMA-targeted therapies early in the treatment course of mCSPC. Additionally, the AMPLITUDE trial assesses the efficacy of PARP inhibitors in mCSPC patients with HRRm [105]. Given the promising results of PARPis in other settings, the AMPLITUDE trial seeks to determine whether these agents can offer significant benefits when introduced earlier in treatment, particularly for patients harboring HRRm.
6.2. Post-Triplet Therapy Strategy for Patients with HRRm
For patients with mCRPC and HRRm post-triplet therapy, the debate between PARPi monotherapy and the combination of PARPis with ARPIs is ongoing [106,107]. PARPi monotherapy leverages synthetic lethality in HRR-deficient cells, with initial studies indicating significant efficacy and manageable safety profiles. The combination of PARPis with ARPIs may provide synergistic effects, enhancing antitumor activity, although the risk of compounded toxicities necessitates careful patient selection and management [108]. Early data from trials, such as TALAPRO-3, suggest potential benefits [109].
6.3. Comparative Efficacy of Triplet Therapy vs. PARPis in the HRR-Mutated Population
The comparative efficacy of triplet therapy versus PARPis in the HRR-mutated population is an area of active research. Ongoing trials, such as TALAPRO-3 and AMPLITUDE, are expected to provide critical insights [66,109]. Preliminary data suggest that PARPis could offer a significant advantage in patients harboring HRRm, potentially altering the SOC in this subset. Research showed that suppressing HRR expression by androgen receptor inhibition increases cell vulnerability to PARPis, leading to PARP-dependent DNA damage-induced cell death [110].
6.4. The Need for Head-to-Head Trials
The combination of ADT with ARPIs versus the triplet combination of ADT, docetaxel, and ARPIs remains a critical area of investigation [111]. Currently, direct comparisons between these therapeutic strategies are limited. Hence, future research should focus on head-to-head trials to elucidate the benefits and drawbacks of these approaches.
6.5. Novel Emerging Therapeutic Agents
Lutetium-177 PSMA-617 treatment yielded encouraging results in the VISION and TheraP trials, involving mCRPC patients previously treated with ARPIs and taxane regimens who had PSMA-positive disease [99,100]. Patients receiving radioligand therapy demonstrated improved rPFS and OS in the VISION trial, along with significant PSA response in the TheraP trial. Building on these results, the efficacy of targeted radionuclide therapy as part of triplet therapy in mCSPC patients is now under investigation in ongoing trials, such as the UpFront PSMA and PSMAddition [112]. Another critical aspect of prostate cancer treatment focuses on the phosphatidylinositol 3-kinase (PI3K)-AKT pathway, which plays a key role in tumorigenesis, progression, and treatment resistance [113]. In this area, the ProCAID trial assessed the combination of capivasertib, a selective inhibitor of all three AKT isoforms, with docetaxel chemotherapy [114]. The trial revealed a median OS benefit in the capivasertib + docetaxel arm, especially among patients who had previously been treated with abiraterone or enzalutamide. The ongoing CAPItello-281 study seeks to further investigate the potential of targeting the PI3K-AKT pathway, exploring the triplet combination of capivasertib, abiraterone, and ADT in PTEN-deficient mCSPC patients [115]. Meanwhile, advances in the understanding of immune checkpoint receptors and ligands have led to transformative breakthroughs in cancer treatment. Pembrolizumab, a PD-1 inhibitor, showed significant promise across various tumor types [116]. In the context of prostate cancer, the KEYNOTE-991 trial is currently evaluating the efficacy and safety of pembrolizumab in combination with enzalutamide and ADT in mCSPC patients compared to the standard regimen of enzalutamide + ADT [117]. This trial aims to determine whether the integration of immunotherapy can enhance treatment outcomes for these patients.
The trend toward low adoption of combination treatment in real-world settings impedes the generation of efficacy data outside clinical trials. Additional therapeutic agents currently indicated for mCRPC are anticipated to be approved for earlier-stage hormone-sensitive disease as part of combination treatments with ADT [65,118,119]. This highlights the need for further investigation into the optimal application of combination therapies.
6.6. Treatment Intensification and De-Escalation
Intensifying treatment by combining multiple agents raises questions about therapy duration. Optimal application may involve determining how to intensify and de-escalate treatment [85]. Considering no established superiority of continuous over intermittent ADT, intermittent regimens should be considered after initial treatment intensification in selected patients [120]. PSA kinetics may serve as biomarkers to guide treatment de-escalation in mCSPC patients [7], although further trials are needed to define optimal strategies for timing and choice of agents.
Table 4 provides an overview of ongoing clinical trials and emerging therapeutic strategies in mCSPC.
7. Conclusions
The treatment landscape for mCSPC has evolved significantly, with studies showing that early treatment intensification using a combination of ADT, ARPIs, and chemotherapy improves survival. Despite strong evidence and guideline recommendations, real-world data indicate low adoption rates of these intensified treatments due to factors such as regulatory and reimbursement differences, resource limitations, and a lack of biomarkers for treatment selection. Increased awareness and efforts are needed to ensure evidence-based treatments are delivered to patients.
Combination therapy, including ADT with ARPIs and docetaxel, offers the best chance for long-term survival in eligible patients. Research continues to explore the molecular biology of mCSPC, treatment resistance mechanisms, and optimized schedules for targeted therapies. Clinical trials, such as PEACE-1 and ARASENS, demonstrated that adding ARPIs to ADT and docetaxel significantly improves survival, especially in patients with synchronous, HV mCSPC. However, a one-size-fits-all approach may lead to overtreatment. Furthermore, personalized treatment strategies must be based on factors such as disease volume, timing of metastases, and patient fitness (Figure 3).
Real-world data reveal that many mCSPC patients are still undertreated, often receiving only ADT without the recommended doublet or triplet therapies. This highlights a gap between clinical trial findings and their implementation in practice. Future clinical trials should continue to explore the optimal use of triplet therapy versus doublet combinations, particularly for different patient subgroups. Personalized treatment approaches that take into account disease characteristics and patient comorbidities are essential to improve outcomes and reduce the burden of overtreatment. As new evidence emerges, guidelines and practices must be updated to reflect the most effective treatment strategies. |
Title: Hemoglobin scavenger receptor CD163 as a potential biomarker of hemolysis-induced hepatobiliary injury in sickle cell disease | Body:
INTRODUCTION
Sickle cell disease (SCD) is caused by a homozygous mutation in the β-globin gene, which leads to erythrocyte sickling, vasoocclusion, and intense hemolysis (1). SCD-induced hemolysis and accumulation of hemoglobin (2), heme, and iron (3) promotes both acute and chronic liver injury (1, 4–9) that affect up to 10–40% of SCD patients (10–15). However, therapeutic approaches to prevent liver injury in SCD are limited, and the mechanism promoting progressive liver injury in SCD remains poorly understood. Unfortunately, the reported incidence of liver complications has increased with the growing life expectancy of patients with SCD (16).
Previously, we have shown that SCD mice manifest hemolysis-induced iron-heme-hemoglobin accumulation (2, 3) and hepatobiliary injury under baseline conditions (17–19). Cell-free hemoglobin (Hb/HbS) released after intravascular red blood cell (RBC) hemolysis is scavenged by plasma haptoglobin, which chaperones it to the liver for hemoglobin scavenger receptor CD163 (20, 21)-dependent clearance by macrophages (hepatic Kupffer cells) (22, 23). However, chronic hemolysis in SCD results in depletion of plasma haptoglobin, leading to HbS clearance in the liver through a relatively less efficient process involving direct binding of Hb to CD163 on macrophages (22, 24–26). Thus, CD163 plays a pivotal role in scavenging hemoglobin-haptoglobin as well as cell-free Hb. A limited number of studies in the past have shown a positive correlation of CD163 expression with poor disease outcomes in patients with SCD (27–29). However, the role and regulation of CD163 in specifically regulating hepatobiliary injury in SCD have not yet been fully elucidated.
The transmembrane scavenger receptor CD163 is expressed exclusively on macrophages and monocytes as a membrane-bound form (30–34), which can be shed from the macrophage surface by inflammation-inducible TACE/ADAM17 enzyme, releasing the soluble CD163 in the blood (35). Whereas soluble CD163 has been previously associated with various disease pathophysiologies (28–31), the regulation of membrane-bound CD163 is not completely understood. This study aimed to investigate the regulation of membrane-bound CD163 in SCD liver using both a humanized mouse model and liver biopsy samples from patients. Here, we show that chronic liver injury in SCD patients is associated with elevated levels of membrane-bound hepatic CD163 in Kupffer cells and monocytes. SCD-induced chronic hemolysis (with the attendant increases in heme, hemoglobin, and haptoglobin) elevates CD163 expression in the liver of SCD mice. Mechanistically we show that heme oxygenase 1 (HO-1) positively regulates hepatic CD163 expression independent of nuclear factor erythroid 2-related factor 2 (NRF2) signaling. We further demonstrate that the interaction between CD163 and HO-1 is not dependent on hemoglobin binding of CD163. This study identifies CD163 as a potential biomarker of hemolysis-induced hepatobiliary injury in SCD. Thorough understanding of the HO-1-mediated signaling pathways that control membrane-bound CD163 expression will be useful in developing novel therapeutic approaches to treat hemolysis-induced tissue injury in SCD and other hemolytic diseases.
METHODS
Animals and Treatment
Townes SCD mice [SS, homozygous for Hbatm1(HBA)Tow, homozygous for Hbbtm2(HBG1,HBB*)Tow] and nonsickle control mice [AS, homozygous for Hbatm1(HBA)Tow, compound heterozygous for Hbbtm2(HBG1,HBB*)Tow/Hbbtm3(HBG1,HBB)Tow] (36) were obtained from the Jackson Laboratory (Bar Harbor, ME) and housed in a specific pathogen-free animal facility at the Medical College of Wisconsin and University of Pittsburgh animal facilities. The Institutional Animal Care and Use Committees at the Medical College of Wisconsin and University of Pittsburgh approved all animal experiments. Five or more mice were assessed at all given time points. Three or more male and female mice were used in all the experiments. Equal numbers of males and females were always used in these experiments, and unless otherwise stated all the mice used were 3 mo old. Although the majority of the mice used for this study were bred at the University of Pittsburgh, a portion of our mice were bred at the Medical College of Wisconsin to ensure the study’s completion. We recognize this circumstantial limitation linked to the study.
All animal experiments were approved by the Institutional Animal Care Committee at the University of Pittsburgh and the Medical College of Wisconsin under AUA-20087570 and AUA00008293, respectively. All the mice used were 25.0–28.0 g and were housed under controlled conditions of temperature (21–23°C), humidity (55%), and light (14:10-h light-dark regimen). The animals had free access to food (Lab diet 5LOD irradiated) and tap water ad libitum. Animals were housed in polypropylene cages (not more than 5 per cage) containing sterile paddy husk (PJ Murphy) as bedding and domes (Bio-Serv) and were regularly checked for pain or any discomfort. Mice were regularly checked for the symptoms of pain and distress like lethargic, shaking, hunched posture, droopy eyelids, red tears, loss of appetite, abdominal swelling, and retreating to a corner. Mice exhibiting these symptoms were excluded from the study. After reaching the end point of the experiment, ketamine hydrochloride (100 mg/kg) + xylazine (50 mg/kg) was administered intraperitoneally as anesthesia, and liver and blood were collected; after that, mice were euthanized by cervical dislocation under anesthesia.
Iron dextran treatment.
Control and SCD mice were administered 10 doses (1 dose every second day) of 100 mg/kg body wt iron dextran (Sigma D8517-25ML) intraperitoneally. Mice were euthanized after 3 wk, and liver and blood samples were collected for further analysis.
CD163 blocking.
CD163 activity was blocked with Antigenic Blocking Peptide CD163 N-epitope (FabGennix), which was administered subcutaneously at the dose of 1 mg/kg mouse body weight or as mentioned in the text. The peptide was dissolved in a warmed physiological saline solution and injected with insulin syringes with the ultrafine needle (BD, United States) into an immobilized mouse. The mouse was monitored for 60 min after the injection. The mice were euthanized 72 h post injection, and blood and liver were collected.
For competitive binding assay, the mice were injected with Antigenic Blocking Peptide CD163 N-epitope (FabGennix) via tail vein (iv) with three different doses of the blocker: 0.1 mg/kg, 1 mg/kg, and 3 mg/kg. After 60 min, all mice were administered 10 µM oxyhemoglobin (oxyHb) by tail vein route of injection. After 3 h mice were euthanized, and blood was collected from vena cava on sodium citrate as an anticoagulant. Blood was split into two tubes, and one tube was centrifuged (3,500 g, 15 min) to obtain platelet-poor plasma. Then, heme assay was performed as per the manufacturer’s instructions (heme assay kit; Abcam ab272534) on the whole blood and plasma.
Oxyhemoglobin treatment.
OxyHb (10 μm/kg) was injected intravenously by the tail vein route. Mice were euthanized after 6 h, and liver and blood samples were collected for further analysis.
LPS treatment.
Mice were administered 0.1 μg/kg LPS intravenously as described previously (37). Mice were euthanized after 3 h, and liver and blood samples were collected for further analysis.
Hypoxia-reperfusion treatment.
Control (AS) and SCD (SS) mice housed in their standard cages were transferred into a hypoxia chamber (BioSpherix) fitted with a ProOx 110 gas controller (BioSpherix) in line with a high-pressure double-gauge mixed gas primary nitrogen regulator fitted onto a vaporized nitrogen source. Mice were exposed to 7% oxygen for 2 h (hypoxia) and returned to room air (reoxygenation) for 1 h. Mice were euthanized, and liver and blood samples were collected for further analysis.
Clodronate-liposome treatment.
A high-dose clodronate-liposome treatment (100–150 μL/mouse) was used to deplete hepatic Kupffer cells. Mice were euthanized 24–72 h post injection.
Proximity Ligation Assay
The method of tissue fixation and permeabilization is described in Tissue Immunofluorescence. Proximity ligation assay (PLA) assays were performed according to the manufacturer’s instructions (Sigma). For the visualization, Duo link in situ detection reagent red was used (Sigma) and nuclei were visualized with DAPI. Images of confocal slices were acquired with a Nikon A1 spectral confocal microscope. Control experiments were performed with just one primary antibody in the incubation. The signal was quantified with ImageJ.
Western Blot
Immunoblotting was performed as described elsewhere (38). The primary antibodies used in this study are available in Supplemental Table S1. Membranes were washed five times for 5 min each in Tris-buffered saline-Tween 20 (TBST) before being probed with horseradish peroxidase (HRP)-conjugated secondary antibodies (1:5,000 diluted in 5% milk; Santa Cruz Biotechnology) for 1.5 h at room temperature. Membranes were washed three times for 10 min each in TBST and visualized with the Enhanced Chemiluminescence System (GE Healthcare). Supplemental Table S1 lists all the antibodies used for Western blotting.
Heme Assay
Heme assay was performed as per the manufacturer’s instructions (heme assay kit; Abcam ab272534). Briefly, liver tissue was homogenized as directed. The homogenates were centrifuged, and heme levels were measured for each sample as per the manufacturer’s instructions. A similar method was used for plasma heme assay.
Scanning Electron Microscopy
Whole liver was collected from control (AS), SCD, and SCD + clodronate mice for scanning electron microscopy (SE) imaging. Slices of whole liver were fixed in 2.5% glutaraldehyde in PBS (pH 7.4) for 10 min. Tissue samples were washed thoroughly in PBS for 15 min. Tissues were then fixed in 1% osmium tetroxide (OsO4) in PBS for 60 min. Samples were dehydrated with different concentrations of ethanol (30%, 50%, 70%, 90%) for 15 min and then critical point dried. Samples were visualized with a Field Emission Scanning Electron Microscope (JEOL JSM6335F) at a magnification of ×10,000–30,000.
Serum Biochemistry
Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were measured in serum samples taken before euthanasia. Serum biochemistry was measured by automated testing in the Clinical Chemistry Division, University of Pittsburgh School of Medicine.
Tissue Immunofluorescence
Tissue samples were frozen in OCT compound (Sakura 4583) on dry ice and stored at −80°C. Cryopreserved samples were cut into 5-µm sections, washed in PBS, and then fixed in 2% paraformaldehyde for 30 min. After washing, slides were washed with PBS and permeabilized with 0.1% Triton X-100 in PBS for 20 min at room temperature. Samples were washed three times with PBS and then blocked with 2% goat serum in 0.1% Tween 20 in PBS (PBST) for 30 min at room temperature. Antibodies were diluted in 2% goat serum-PBST and incubated at 4°C overnight. Primary and secondary antibodies used are available in the Supplemental Material. Images were taken on a Nikon A1 spectral confocal microscope. Supplemental Table S2 lists the antibodies used for immunohistochemical (IHC) assays.
Coimmunoprecipitation Assay
Coimmunoprecipitation of TLR9 with HO-1 and CD163 was performed with the Pierce Direct IP kit (ThermoFisher Scientific, Illinois) per the manufacturer’s instructions. Briefly, anti-TLR9 and normal mouse IgG were each cross-linked with AminoLink Plus Coupling Resin in a rotator that goes from end to end for 120 min at room temperature. The compounds were centrifuged in a spin column to fix the antibody-resin mix. After washing with quenching buffer, sodium cyanoborohydride solution, 1× coupling buffer, and wash solution, the immobilized antibodies were ready for coimmunoprecipitation. Liver tissue was lysed with immunoprecipitation (IP) lysis buffer. Protein lysates were precleared with Control Agarose Resin to remove nonspecific combinations. For immunoprecipitation, the antibody-cross-linked resin was washed with IP wash buffer three times and added to protein lysate. The immunoprecipitation reaction was performed in a rotator at 4°C overnight. After that, immunoprecipitation products were eluted with elution buffer and analyzed with immunoblotting technique.
mRNA Isolation and Real-Time Polymerase Chain Reaction
mRNA was isolated and purified from livers of AS and SCD mice (n = 3/group). mRNA was isolated with TRIzol (Invitrogen). RT-PCR was performed as described elsewhere (17, 39). Changes in target mRNA were normalized to GAPDH and 18S mRNA for each sample and presented as fold change over the average of the respective control group. Each sample was run in triplicate. Supplemental Table S3 lists all the primer sequences used for qRT-PCR analysis.
siRNA Treatment in Hep3B Cells
The Hep3B human hepatoma cell line, which was obtained from ATCC (Manassas, VA), was transfected according to the manufacturers’ protocol. Briefly, Hep3Bs grown in Eagle’s minimal essential medium (ATCC) with 10% fetal bovine serum (Atlanta Biologicals, Lawrenceville, GA) were seeded onto six-well plates and transiently transfected with validated human HO-1 siRNA or negative control siRNA 1 (Ambion, Inc., Austin, TX) at a final concentration of 25 nmol/L in the presence of Lipofectamine MAX reagent (Invitrogen, Carlsbad, CA), as per the manufacturer’s instructions. The cells were harvested 72 h after transfection for RNA or protein extraction.
Chromatin Immunoprecipitation Assay
The chromatin immunoprecipitation (ChIP) assay was conducted with the SimpleChIP Plus Sonication Chromatin IP Kit (no. 56383, Cell Signaling Technology), following manufacturer’s instructions. Briefly, 25 mg of tissue was chopped with a sterile razor blade and subsequently incubated with a 1% formaldehyde + protease inhibitor cocktail (PIC) mixture for 10 min at room temperature. After removal of the formaldehyde, tissue was suspended in 1 mL of 1× ChIP Sonication Cell Lysis Buffer + PIC for chromatin preparation. Nuclei were prepared, and the chromatin was sonicated with a Bioruptor Pico sonication device (Diagnode, Denville, NJ) with a 30 s on/30 s off cycle for 8 min. The collected supernatant was immunoprecipitated by incubates at 4°C for 12–16 h with 2 µg of anti-NRF2 antibody. The immunocomplexes were spun for 2 h at 4°C with 30 µL ChIP-Grade Protein G Magnetic Beads, followed by three 5-min washes with low-salt wash buffer and one with high-salt wash buffer. ChIP elution buffer was used to elute the chromatin for 30 min at 65°C with moderate vortex mixing (1,200 rpm). Cross-links were broken with 5 M NaCl and proteinase K for 2 h at 65°C. The samples were then treated with RNase A for 1 h at 37°C. Finally, ChIP DNA was purified and measured by qPCR.
Quantification of CD163 Level by ELISA
The Mouse CD163 SimpleStep ELISA Kit (Abcam ab272204) was employed to assess the level of CD163, following the manufacturer’s protocol. Initially, 50 µL of all samples (liver lysate) or standards was dispensed into appropriate wells, followed by the addition of 50 µL of CD163 antibody cocktail to each well. The plate was then sealed and incubated for 1 h at room temperature with shaking. After the incubation period, the wells underwent three washes with a wash solution. Subsequently, the TMB substrate solution was added to the plate, and after a 10-min incubation with the substrate the enzymatic reaction was terminated. Finally, the absorbance of the resultant color was measured at 450 nm with a Tecan Infinite M200Pro plate reader.
Binding Affinity of CD163 with Hba and HO-1 by Sandwich ELISA
In this study, we examined the binding affinity of CD163 with Hba and HO-1 post administration of CD163 blocker or oxyHb. Liver lysate obtained from SCD mice was treated with 3 mg/mL of CD163 blocker or 10 µM oxyHb. After a 30-min incubation period, the samples were applied to a plate coated with CD163-specific antibodies. Additionally, capture antibodies targeting Hba and HO-1, or control IgG, along with an HRP-conjugated secondary antibody, were introduced to the plate. After a 1-h incubation, the wells were washed three times with a wash solution. Subsequently, the TMB substrate solution was added to the plate, and after a 10-min incubation with the substrate the enzymatic reaction was halted. Finally, the absorbance of the resulting color was measured at 450 nm with a Tecan Infinite M200Pro plate reader.
Statistical Analysis
All comparisons between two groups were deemed statistically significant by unpaired two-tailed Student’s t test if P < 0.05 or P < 0.01. When more than two groups were compared, statistical analysis was done by one-way and two-way ANOVA with Bonferroni correction. Calculations were performed with Prism version 7.0a (GraphPad Software). All in vitro studies are either a compilation of three independent experiments or representative of at least three independent experiments. Error bars represent standard deviation.
Human Subjects
This study was approved by the local Institutional Review Board (IRB-U.Pitt) and conducted in accordance with the Declaration of Helsinki and NIH guidelines for using human specimens. Deidentified needle biopsy specimens of liver from SCD patients and age-matched healthy control human subjects were retrospectively reviewed for associated pathology and liver disease. Samples were obtained for light microscopy by standard procedures (17).
Animals and Treatment
Townes SCD mice [SS, homozygous for Hbatm1(HBA)Tow, homozygous for Hbbtm2(HBG1,HBB*)Tow] and nonsickle control mice [AS, homozygous for Hbatm1(HBA)Tow, compound heterozygous for Hbbtm2(HBG1,HBB*)Tow/Hbbtm3(HBG1,HBB)Tow] (36) were obtained from the Jackson Laboratory (Bar Harbor, ME) and housed in a specific pathogen-free animal facility at the Medical College of Wisconsin and University of Pittsburgh animal facilities. The Institutional Animal Care and Use Committees at the Medical College of Wisconsin and University of Pittsburgh approved all animal experiments. Five or more mice were assessed at all given time points. Three or more male and female mice were used in all the experiments. Equal numbers of males and females were always used in these experiments, and unless otherwise stated all the mice used were 3 mo old. Although the majority of the mice used for this study were bred at the University of Pittsburgh, a portion of our mice were bred at the Medical College of Wisconsin to ensure the study’s completion. We recognize this circumstantial limitation linked to the study.
All animal experiments were approved by the Institutional Animal Care Committee at the University of Pittsburgh and the Medical College of Wisconsin under AUA-20087570 and AUA00008293, respectively. All the mice used were 25.0–28.0 g and were housed under controlled conditions of temperature (21–23°C), humidity (55%), and light (14:10-h light-dark regimen). The animals had free access to food (Lab diet 5LOD irradiated) and tap water ad libitum. Animals were housed in polypropylene cages (not more than 5 per cage) containing sterile paddy husk (PJ Murphy) as bedding and domes (Bio-Serv) and were regularly checked for pain or any discomfort. Mice were regularly checked for the symptoms of pain and distress like lethargic, shaking, hunched posture, droopy eyelids, red tears, loss of appetite, abdominal swelling, and retreating to a corner. Mice exhibiting these symptoms were excluded from the study. After reaching the end point of the experiment, ketamine hydrochloride (100 mg/kg) + xylazine (50 mg/kg) was administered intraperitoneally as anesthesia, and liver and blood were collected; after that, mice were euthanized by cervical dislocation under anesthesia.
Iron dextran treatment.
Control and SCD mice were administered 10 doses (1 dose every second day) of 100 mg/kg body wt iron dextran (Sigma D8517-25ML) intraperitoneally. Mice were euthanized after 3 wk, and liver and blood samples were collected for further analysis.
CD163 blocking.
CD163 activity was blocked with Antigenic Blocking Peptide CD163 N-epitope (FabGennix), which was administered subcutaneously at the dose of 1 mg/kg mouse body weight or as mentioned in the text. The peptide was dissolved in a warmed physiological saline solution and injected with insulin syringes with the ultrafine needle (BD, United States) into an immobilized mouse. The mouse was monitored for 60 min after the injection. The mice were euthanized 72 h post injection, and blood and liver were collected.
For competitive binding assay, the mice were injected with Antigenic Blocking Peptide CD163 N-epitope (FabGennix) via tail vein (iv) with three different doses of the blocker: 0.1 mg/kg, 1 mg/kg, and 3 mg/kg. After 60 min, all mice were administered 10 µM oxyhemoglobin (oxyHb) by tail vein route of injection. After 3 h mice were euthanized, and blood was collected from vena cava on sodium citrate as an anticoagulant. Blood was split into two tubes, and one tube was centrifuged (3,500 g, 15 min) to obtain platelet-poor plasma. Then, heme assay was performed as per the manufacturer’s instructions (heme assay kit; Abcam ab272534) on the whole blood and plasma.
Oxyhemoglobin treatment.
OxyHb (10 μm/kg) was injected intravenously by the tail vein route. Mice were euthanized after 6 h, and liver and blood samples were collected for further analysis.
LPS treatment.
Mice were administered 0.1 μg/kg LPS intravenously as described previously (37). Mice were euthanized after 3 h, and liver and blood samples were collected for further analysis.
Hypoxia-reperfusion treatment.
Control (AS) and SCD (SS) mice housed in their standard cages were transferred into a hypoxia chamber (BioSpherix) fitted with a ProOx 110 gas controller (BioSpherix) in line with a high-pressure double-gauge mixed gas primary nitrogen regulator fitted onto a vaporized nitrogen source. Mice were exposed to 7% oxygen for 2 h (hypoxia) and returned to room air (reoxygenation) for 1 h. Mice were euthanized, and liver and blood samples were collected for further analysis.
Clodronate-liposome treatment.
A high-dose clodronate-liposome treatment (100–150 μL/mouse) was used to deplete hepatic Kupffer cells. Mice were euthanized 24–72 h post injection.
Iron dextran treatment.
Control and SCD mice were administered 10 doses (1 dose every second day) of 100 mg/kg body wt iron dextran (Sigma D8517-25ML) intraperitoneally. Mice were euthanized after 3 wk, and liver and blood samples were collected for further analysis.
CD163 blocking.
CD163 activity was blocked with Antigenic Blocking Peptide CD163 N-epitope (FabGennix), which was administered subcutaneously at the dose of 1 mg/kg mouse body weight or as mentioned in the text. The peptide was dissolved in a warmed physiological saline solution and injected with insulin syringes with the ultrafine needle (BD, United States) into an immobilized mouse. The mouse was monitored for 60 min after the injection. The mice were euthanized 72 h post injection, and blood and liver were collected.
For competitive binding assay, the mice were injected with Antigenic Blocking Peptide CD163 N-epitope (FabGennix) via tail vein (iv) with three different doses of the blocker: 0.1 mg/kg, 1 mg/kg, and 3 mg/kg. After 60 min, all mice were administered 10 µM oxyhemoglobin (oxyHb) by tail vein route of injection. After 3 h mice were euthanized, and blood was collected from vena cava on sodium citrate as an anticoagulant. Blood was split into two tubes, and one tube was centrifuged (3,500 g, 15 min) to obtain platelet-poor plasma. Then, heme assay was performed as per the manufacturer’s instructions (heme assay kit; Abcam ab272534) on the whole blood and plasma.
Oxyhemoglobin treatment.
OxyHb (10 μm/kg) was injected intravenously by the tail vein route. Mice were euthanized after 6 h, and liver and blood samples were collected for further analysis.
LPS treatment.
Mice were administered 0.1 μg/kg LPS intravenously as described previously (37). Mice were euthanized after 3 h, and liver and blood samples were collected for further analysis.
Hypoxia-reperfusion treatment.
Control (AS) and SCD (SS) mice housed in their standard cages were transferred into a hypoxia chamber (BioSpherix) fitted with a ProOx 110 gas controller (BioSpherix) in line with a high-pressure double-gauge mixed gas primary nitrogen regulator fitted onto a vaporized nitrogen source. Mice were exposed to 7% oxygen for 2 h (hypoxia) and returned to room air (reoxygenation) for 1 h. Mice were euthanized, and liver and blood samples were collected for further analysis.
Clodronate-liposome treatment.
A high-dose clodronate-liposome treatment (100–150 μL/mouse) was used to deplete hepatic Kupffer cells. Mice were euthanized 24–72 h post injection.
Proximity Ligation Assay
The method of tissue fixation and permeabilization is described in Tissue Immunofluorescence. Proximity ligation assay (PLA) assays were performed according to the manufacturer’s instructions (Sigma). For the visualization, Duo link in situ detection reagent red was used (Sigma) and nuclei were visualized with DAPI. Images of confocal slices were acquired with a Nikon A1 spectral confocal microscope. Control experiments were performed with just one primary antibody in the incubation. The signal was quantified with ImageJ.
Western Blot
Immunoblotting was performed as described elsewhere (38). The primary antibodies used in this study are available in Supplemental Table S1. Membranes were washed five times for 5 min each in Tris-buffered saline-Tween 20 (TBST) before being probed with horseradish peroxidase (HRP)-conjugated secondary antibodies (1:5,000 diluted in 5% milk; Santa Cruz Biotechnology) for 1.5 h at room temperature. Membranes were washed three times for 10 min each in TBST and visualized with the Enhanced Chemiluminescence System (GE Healthcare). Supplemental Table S1 lists all the antibodies used for Western blotting.
Heme Assay
Heme assay was performed as per the manufacturer’s instructions (heme assay kit; Abcam ab272534). Briefly, liver tissue was homogenized as directed. The homogenates were centrifuged, and heme levels were measured for each sample as per the manufacturer’s instructions. A similar method was used for plasma heme assay.
Scanning Electron Microscopy
Whole liver was collected from control (AS), SCD, and SCD + clodronate mice for scanning electron microscopy (SE) imaging. Slices of whole liver were fixed in 2.5% glutaraldehyde in PBS (pH 7.4) for 10 min. Tissue samples were washed thoroughly in PBS for 15 min. Tissues were then fixed in 1% osmium tetroxide (OsO4) in PBS for 60 min. Samples were dehydrated with different concentrations of ethanol (30%, 50%, 70%, 90%) for 15 min and then critical point dried. Samples were visualized with a Field Emission Scanning Electron Microscope (JEOL JSM6335F) at a magnification of ×10,000–30,000.
Serum Biochemistry
Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were measured in serum samples taken before euthanasia. Serum biochemistry was measured by automated testing in the Clinical Chemistry Division, University of Pittsburgh School of Medicine.
Tissue Immunofluorescence
Tissue samples were frozen in OCT compound (Sakura 4583) on dry ice and stored at −80°C. Cryopreserved samples were cut into 5-µm sections, washed in PBS, and then fixed in 2% paraformaldehyde for 30 min. After washing, slides were washed with PBS and permeabilized with 0.1% Triton X-100 in PBS for 20 min at room temperature. Samples were washed three times with PBS and then blocked with 2% goat serum in 0.1% Tween 20 in PBS (PBST) for 30 min at room temperature. Antibodies were diluted in 2% goat serum-PBST and incubated at 4°C overnight. Primary and secondary antibodies used are available in the Supplemental Material. Images were taken on a Nikon A1 spectral confocal microscope. Supplemental Table S2 lists the antibodies used for immunohistochemical (IHC) assays.
Coimmunoprecipitation Assay
Coimmunoprecipitation of TLR9 with HO-1 and CD163 was performed with the Pierce Direct IP kit (ThermoFisher Scientific, Illinois) per the manufacturer’s instructions. Briefly, anti-TLR9 and normal mouse IgG were each cross-linked with AminoLink Plus Coupling Resin in a rotator that goes from end to end for 120 min at room temperature. The compounds were centrifuged in a spin column to fix the antibody-resin mix. After washing with quenching buffer, sodium cyanoborohydride solution, 1× coupling buffer, and wash solution, the immobilized antibodies were ready for coimmunoprecipitation. Liver tissue was lysed with immunoprecipitation (IP) lysis buffer. Protein lysates were precleared with Control Agarose Resin to remove nonspecific combinations. For immunoprecipitation, the antibody-cross-linked resin was washed with IP wash buffer three times and added to protein lysate. The immunoprecipitation reaction was performed in a rotator at 4°C overnight. After that, immunoprecipitation products were eluted with elution buffer and analyzed with immunoblotting technique.
mRNA Isolation and Real-Time Polymerase Chain Reaction
mRNA was isolated and purified from livers of AS and SCD mice (n = 3/group). mRNA was isolated with TRIzol (Invitrogen). RT-PCR was performed as described elsewhere (17, 39). Changes in target mRNA were normalized to GAPDH and 18S mRNA for each sample and presented as fold change over the average of the respective control group. Each sample was run in triplicate. Supplemental Table S3 lists all the primer sequences used for qRT-PCR analysis.
siRNA Treatment in Hep3B Cells
The Hep3B human hepatoma cell line, which was obtained from ATCC (Manassas, VA), was transfected according to the manufacturers’ protocol. Briefly, Hep3Bs grown in Eagle’s minimal essential medium (ATCC) with 10% fetal bovine serum (Atlanta Biologicals, Lawrenceville, GA) were seeded onto six-well plates and transiently transfected with validated human HO-1 siRNA or negative control siRNA 1 (Ambion, Inc., Austin, TX) at a final concentration of 25 nmol/L in the presence of Lipofectamine MAX reagent (Invitrogen, Carlsbad, CA), as per the manufacturer’s instructions. The cells were harvested 72 h after transfection for RNA or protein extraction.
Chromatin Immunoprecipitation Assay
The chromatin immunoprecipitation (ChIP) assay was conducted with the SimpleChIP Plus Sonication Chromatin IP Kit (no. 56383, Cell Signaling Technology), following manufacturer’s instructions. Briefly, 25 mg of tissue was chopped with a sterile razor blade and subsequently incubated with a 1% formaldehyde + protease inhibitor cocktail (PIC) mixture for 10 min at room temperature. After removal of the formaldehyde, tissue was suspended in 1 mL of 1× ChIP Sonication Cell Lysis Buffer + PIC for chromatin preparation. Nuclei were prepared, and the chromatin was sonicated with a Bioruptor Pico sonication device (Diagnode, Denville, NJ) with a 30 s on/30 s off cycle for 8 min. The collected supernatant was immunoprecipitated by incubates at 4°C for 12–16 h with 2 µg of anti-NRF2 antibody. The immunocomplexes were spun for 2 h at 4°C with 30 µL ChIP-Grade Protein G Magnetic Beads, followed by three 5-min washes with low-salt wash buffer and one with high-salt wash buffer. ChIP elution buffer was used to elute the chromatin for 30 min at 65°C with moderate vortex mixing (1,200 rpm). Cross-links were broken with 5 M NaCl and proteinase K for 2 h at 65°C. The samples were then treated with RNase A for 1 h at 37°C. Finally, ChIP DNA was purified and measured by qPCR.
Quantification of CD163 Level by ELISA
The Mouse CD163 SimpleStep ELISA Kit (Abcam ab272204) was employed to assess the level of CD163, following the manufacturer’s protocol. Initially, 50 µL of all samples (liver lysate) or standards was dispensed into appropriate wells, followed by the addition of 50 µL of CD163 antibody cocktail to each well. The plate was then sealed and incubated for 1 h at room temperature with shaking. After the incubation period, the wells underwent three washes with a wash solution. Subsequently, the TMB substrate solution was added to the plate, and after a 10-min incubation with the substrate the enzymatic reaction was terminated. Finally, the absorbance of the resultant color was measured at 450 nm with a Tecan Infinite M200Pro plate reader.
Binding Affinity of CD163 with Hba and HO-1 by Sandwich ELISA
In this study, we examined the binding affinity of CD163 with Hba and HO-1 post administration of CD163 blocker or oxyHb. Liver lysate obtained from SCD mice was treated with 3 mg/mL of CD163 blocker or 10 µM oxyHb. After a 30-min incubation period, the samples were applied to a plate coated with CD163-specific antibodies. Additionally, capture antibodies targeting Hba and HO-1, or control IgG, along with an HRP-conjugated secondary antibody, were introduced to the plate. After a 1-h incubation, the wells were washed three times with a wash solution. Subsequently, the TMB substrate solution was added to the plate, and after a 10-min incubation with the substrate the enzymatic reaction was halted. Finally, the absorbance of the resulting color was measured at 450 nm with a Tecan Infinite M200Pro plate reader.
Statistical Analysis
All comparisons between two groups were deemed statistically significant by unpaired two-tailed Student’s t test if P < 0.05 or P < 0.01. When more than two groups were compared, statistical analysis was done by one-way and two-way ANOVA with Bonferroni correction. Calculations were performed with Prism version 7.0a (GraphPad Software). All in vitro studies are either a compilation of three independent experiments or representative of at least three independent experiments. Error bars represent standard deviation.
Human Subjects
This study was approved by the local Institutional Review Board (IRB-U.Pitt) and conducted in accordance with the Declaration of Helsinki and NIH guidelines for using human specimens. Deidentified needle biopsy specimens of liver from SCD patients and age-matched healthy control human subjects were retrospectively reviewed for associated pathology and liver disease. Samples were obtained for light microscopy by standard procedures (17).
RESULTS
Hemolysis-Induced Accumulation of Hemoglobin-Heme-Iron Promotes the Expression of Membrane-Bound CD163 in SCD
To assess the role of CD163 in hemolysis-induced hepatobiliary injury in SCD, we first analyzed the hepatic expression of CD163 (membrane-bound CD163) in SCD patient biopsied liver samples. Immunofluorescence (IF) of CD163 showed significant upregulation of CD163 in hepatic Kupffer cell-like structures in SCD patients compared to healthy control human liver samples (Fig. 1A).
Figure 1.
Hemoglobin scavenger receptor CD163 is associated with hepatobiliary injury in sickle cell disease (SCD). A: representative images of immunofluorescence (IF) analysis of SCD patient biopsied liver samples show abundant CD163-positive cells. B, left: representative IF images (merged and single channels) of colocalization assay show higher percentage of CD163 colocalized with positive F4/80 staining of Kupffer cells in SCD mouse liver. Right: bar graph depicts the Pearson’s coefficient of colocalization in control (CON) and SCD mouse liver. C, left: representative IF images (merged and single channels) of colocalization assay show higher percentage of CD163 colocalized with positive CD11b staining of monocytes in SCD mouse liver. Right: bar graph depicts the Pearson’s coefficient of colocalization in control and SCD mouse liver. The error bars represent SD. Arrowheads indicate liver sinusoidal endothelial cells. *P < 0.05, **P < 0.01. Scale bars, 50 µm.
To further analyze the effect of SCD-induced hemolysis in SCD, we used the Townes SCD mouse model. IF revealed that CD163 is predominantly expressed in hepatic monocytes and Kupffer cells in both control and SCD mice (Fig. 1, B and C). Quantification of colocalization revealed significant upregulation of CD163 in the hepatic monocytes and Kupffer cells in SCD mouse liver (Fig. 1, B and C). Next, we investigated the factors that might influence CD163 expression in the SCD mouse liver. Interestingly, whereas administration of LPS (0.1 μg/kg; Fig. 2A; bar graph shows densitometric analysis) or hypoxia (Fig. 2B; bar graph shows densitometric analysis) did not influence CD163 expression, increase in hepatic oxyhemoglobin (Fig. 2C; bar graph shows densitometric analysis), and iron content (Fig. 2D; bar graph shows densitometric analysis) caused significant enrichment of CD163 in the liver of SCD mice compared to littermate control mice. Taken together, our data suggest that CD163 increases in SCD mouse and patient liver because of hemolysis-induced hepatic accumulation of hemoglobin-heme and iron.
Figure 2.
CD163 expression is regulated by hemolysis associated with sickle cell disease (SCD). A and B, top: the schemes of LPS (A) and hypoxia (B) [in SCD and littermate control (CON) mice]. Bottom: representative Western blot micrographs show unchanged levels of CD163 in the liver of SCD and control mice after LPS or hypoxia induction. Densitometric analysis of CD163 protein in each condition: the error bars represent SD. C, top: scheme of treatment SCD and control mice with intravenous injection of 10 µM oxyhemoglobin (oxyHb) for hemolysis induction. Bottom: representative Western blot micrograph and densitometric analysis show increased levels of CD163 in the liver of SCD mice after oxyHb treatment. D, top: scheme of SCD and control mouse treatment with 100 mg/kg body wt of iron dextran (ID). Bottom: representative Western blot micrograph and densitometric analysis reveal higher levels of CD163 in the liver of SCD mice after ID treatment compared to other treatments. The error bars represent SD. *P < 0.05. Figure created with BioRender.com.
Loss of CD163 Promotes Hemolysis-Induced Hepatobiliary Injury in SCD Mouse Liver
As CD163 expression positively correlated with hemolysis, which is a main pathological factor in SCD, we hypothesized that the increased expression of CD163 in SCD liver would protect against hemolysis-induced hepatobiliary injury. To establish the protective role of CD163 in SCD-induced hepatobiliary injury, we depleted CD163 in the SCD liver by two different approaches.
As the liver resident macrophages (Kupffer cells) are the primary cells known to express CD163 (21), we first depleted hepatic Kupffer cells with clodronate-liposome as previously described (40). We observed a strong reduction of Kupffer cell marker CLEC4F in the liver post clodronate-liposome treatment as expected (Fig. 3A). Kupffer cell depletion also led to an increase in total number of erythrocytes sequestered in hepatic sinusoids (as shown by SE in Fig. 3B; bar graph shows quantification). Interestingly, we found that the depletion of hepatic Kupffer cells in SCD mice by clodronate liposomes led to strong loss of CD163 (Fig. 3C), Hb, as well as heme oxygenase 1 (HO-1). Hematoxylin and eosin (H&E) staining showed increased entrapment of RBCs in SCD mice post clodronate treatment (Fig. 3D). We also confirmed exacerbated liver injury in this model as evidenced by the enhanced expression of serum markers of liver injury (ALT and AST) (Fig. 3E).
Figure 3.
Loss of CD163 promotes hemolysis-induced hepatobiliary injury in the sickle cell disease (SCD) mouse liver. A: representative immunofluorescence staining shows strong reduction of Kupffer cell marker CLEC4F in the SCD mice liver post clodronate-liposome treatment (right) compared to nontreated SCD mice (left). Scale bars, 50 µm. B, left: representative scanning electron microscopy (SE) images show increased presence of hepatic hemoglobin and total number of erythrocytes sequestered in hepatic sinusoids in SCD mice liver after Kupffer cell depletion. Right: statistical analysis reveals increased numbers of red blood cells (RBCs) sequestered in the hepatic sinusoids per field of view (FOV). C: Western blot analysis of CD163 shows reduced expression post macrophage depletion in SCD mouse liver. Kupffer cell marker CLEC4F expression is reduced post clodronate treatment. Kupffer cell depletion also caused hepatic hemoglobin (Hb) accumulation and reduced expression of heme oxygenase-1 (HO-1) in SCD mice post clodronate treatment compared to untreated SCD mice. D: representative hematoxylin and eosin (H&E) staining shows increased hepatic vasoocclusion and several entrapped RBCs in the liver of SCD mice post clodronate treatment. Magnification: ×100. E: biochemical analysis of the liver injury markers shows that clodronate-liposome treatment leads to significant (∼10-fold) increase in concentrations of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) 24 h post treatment compared to vehicle-treated mice. This increase attenuated after 48 h post treatment, but the levels of markers remained elevated compared to control mice. F: ELISA shows that blockage of CD163 activity by an antigenic blocking peptide resulted in increased levels of heme in plasma with also a slight elevation in the liver of SCD mice. G: representative IF images showing hemoglobin accumulation in the liver of control mouse, SCD mouse and SCD mouse treated with CD163 blocker. Arrow indicates hemoglobin positive cells. Scale bars, 50 µm. H: liver injury biomarkers ALT and AST were elevated in SCD mouse serum after CD163 blocking compared to untreated mice. The error bars represent SD. Scale bars, 50 μm. *P < 0.05. Figure created with BioRender.com.
Additionally, we used a CD163 peptide blocker (previously uncharacterized) to examine the specific effects of blocking CD163 activity in the liver of SCD mice. Blocking CD163 activity led to a significant increase in serum and hepatic heme levels, confirming the loss of function of CD163 (Fig. 3F). As in the case of Kupffer cell depletion in the SCD mouse liver, we found enhanced accumulation of Hb in the SCD liver post CD163 depletion by IF (Fig. 3G). Serum markers of liver injury were also significantly upregulated (Fig. 3H) in SCD mice after blocking CD163, suggesting its role in protection against hemolysis-induced hepatobiliary injury in SCD.
To further confirm the specificity of the CD163 blocker as well as to understand its mode of action, we performed the following set of experiments. We delivered three different concentrations (from lowest to highest) of CD163 blocker to SCD mice and then measured the amount of heme in their blood. Our hypothesis was that if CD163 blocker inhibited CD163 function, it would be unable to bind with Hb, resulting in an increase in total heme levels in the blood (Fig. 4, A and B). We also hypothesized that the increase in heme level would be positively correlated with the increasing concentration of the CD163 blocker utilized. As shown in Fig. 4B, we found that the amount of heme in the blood increased significantly after CD163 blocking, and it correlated positively with the increasing concentration of the blocker.
Figure 4.
CD163 blocker competes with hemoglobin (Hb) for binding. A: schematic showing the experimental plan to analyze the effect of CD163 blocker in sickle cell disease (SCD) mouse liver. B: bar graph depicting the results of heme ELISA assay post administration of CD163 blocker in the plasma of SCD mice. C: schematic showing the experimental plan to examine the mode of action of the CD163 blocker used in blocking CD163-Hb binding. HO-1, heme oxygenase-1. D: bar graphs depicting the results of heme ELISA assay post CD163 blocker administration in the plasma of SCD mice. oxHb, oxyhemoglobin; Veh, vehicle. The error bars represent SD. *P < 0.05. Figure created with BioRender.com.
Next, to decipher the competition between CD163 and its natural substrate (Hb) and the therapeutic blocker, we used the following approach. We administered three different concentrations (from lowest to highest) of CD163 blocker to SCD mice, followed by a second administration of oxyhemoglobin 0.5 h later in the same mice, and then measured heme levels in the blood and liver within 1 h (Fig. 4C). Our hypothesis was that if the blocker and the natural substrate (Hb) compete for the same binding site, then adding the blocker first will not let Hb bind with CD163 anymore, resulting in significant increase in plasma heme level. As indicated in Fig. 4D, heme levels were found to be dramatically raised in both the blood and plasma of SCD mice, and it positively correlated with the concentration of the CD163 blocker utilized. Notably, the heme level post administration of blocker and oxyhemoglobin treatment was significantly higher than oxyhemoglobin treatment alone (Fig. 4D). Thus, we speculate that the CD163 blocker inhibits Hb-CD163 binding by competing for the same binding site. Collectively, our data also suggest that loss of CD163 activity further exacerbates hemoglobin-heme-iron-induced hepatobiliary injury in SCD.
HO-1 Positively Regulates Hepatic CD163 Expression Independent of NRF2 Signaling
To understand the significance of membrane-bound CD163 in protecting against hemolysis-induced liver injury in SCD and to define the signaling pathway linked to membrane-bound CD163-mediated injury resolution, we analyzed the molecular mechanism behind the elevated CD163 expression in the SCD liver. Once internalized through the CD163 receptor, the hemoglobin/hemoglobin-haptoglobin complex is degraded by heme oxygenase 1 (HO-1) to release heme, carbon monoxide, and biliverdin/bilirubin (22, 41). As CD163 and HO-1 are intricately linked in hemoglobin trafficking and degradation, and Kupffer cell depletion led to reduced expression of both CD163 and HO-1, we examined whether HO-1 can regulate CD163 levels. We found colocalization of CD163 (Fig. 2B) and HO-1 (Fig. 5A, quantified in Fig. 5B) by IHC in both control and SCD mouse liver that was significantly increased in SCD mice. As hepatic Kupffer cells are the primary nonhepatocyte cell type in the liver parenchyma, we analyzed the mRNA expression of HO-1 and CD163 in hepatocytes and nonhepatocytes isolated from the liver of SCD mice. As shown in Fig. 5C, nonhepatocytes had significantly higher expression of both HO-1 and CD163.
Figure 5.
Heme oxygenase-1 (HO-1) positively regulates CD163 level in sickle cell disease (SCD) mouse liver. A and B: representative immunohistochemistry image (A) and colocalization analysis (B) show increased colocalization of CD163 and HO-1 in hepatic Kupffer cells of SCD mouse liver. CON, control. C: qRT-PCR analysis of hepatocytes and nonhepatocytes isolated from control mouse liver exhibits increased expression of both CD163 and HO-1 in the nonhepatocyte cell population. D: the representative images acquired during proximity ligation assay (PLA) show increased interactions between CD163 and HO-1 in SCD mice compared to littermate control mice. Scale bars, 50 µm. E: fluorescence intensity quantification of PLA demonstrating significantly higher intensity in the SCD group compared to control mice. FOV, field of view. F and G: series of Western blot micrographs (for the treatment pattern see Fig. 2) showing the levels of HO-1 in liver lysates of control mice, untreated SCD mice, and SCD mice after treatment with iron dextran (F) and treatments with clodronate and hemoglobin as well as undergoing hypoxic conditions (G). H, left: representative Western blot micrographs showing decreased levels of CD163 protein expression after HO-1 knockdown in Hep3B human hepatoma cell line compared with untreated cells. Right: densitometric analysis of micrographs shows significantly reduced levels of CD163 protein expression after HO-1 knockdown and confirms the efficiency of performed knockdown in control and SCD mouse liver tissue. *P < 0.05; #P < 0.06–0.08.
To identify potential interactions between CD163 and HO-1, we first performed a proximity ligation assay in control and SCD mouse liver. Both control and SCD mouse liver showed a positive signal, which was increased significantly in SCD mice (Fig. 5D, quantified in Fig. 5E). Moreover, Western blot analysis showed that HO-1 displays a similar expression pattern post hemoglobin-heme-iron accumulation (Fig. 5, F and G). Next, we explored whether HO-1 can regulate CD163 expression in the liver. Remarkably, knocking down HO-1 in the Hep3B liver cell line caused significant reduction of CD163 protein expression (Fig. 5H, quantified).
The transcription factor nuclear factor erythroid-derived-2-like 2 (NFE2L2), also known as NRF2, regulates a subset of downstream target genes including HO-1 (42–44). We hypothesized that HO-1 also regulates CD163 via NRF2 (45). To confirm the effect of NRF2 on CD163 expression, we performed a CHIP-seq analysis. Interestingly, CHIP-seq confirmed no binding/association between NRF2 and CD163 (Fig. 6A). NRF2-HO-1 binding was used as a positive control, and as shown in Fig. 6A the result confirmed that NRF2 can bind to HO-1. In addition, we found a higher relative enrichment of NRF-2 binding to HO-1 in SCD mice compared to littermate control mice. Altogether, these findings suggest that HO-1 regulates CD163 expression independent of NRF2 in the SCD mouse liver.
Figure 6.
Heme oxygenase-1 (HO-1) positively regulates CD163 level independent of nuclear factor erythroid 2-related factor 2 (NRF2) signaling. A, left: schematic showing the experimental flow of chromatin immunoprecipitation (ChIP) assay to determine the binding between CD163-NRF2 as well as NRF2 and HO-1. Right: bar graph depicts that compared to control mice sickle cell disease (SCD) mice exhibit higher levels of NRF2 binding to the HO-1 gene in the liver. The error bars represent SD. IP, immunoprecipitation. B: bioinformatic predictive tool analysis suggesting the presence of a molecular basis for the binding of CD163 and HO-1 with TLR9. C: representative Western blot micrograph showing coimmunoprecipitation confirming complex formation between CD163 and HO-1 in SCD mouse liver. D: representative Western blot micrograph showing the effect of hypoxia, LPS, and aging in CD163 and HO-1 interaction in SCD mouse liver. E, top: schematic diagram depicting the steps of sandwich ELISA assay. Bottom: comparative analysis of absorbance suggests stronger binding affinity of CD163-hemoglobin (Hb) compared to CD163-HO-1. F: comparative analysis of absorbance values suggests that whereas CD163 blocker blocks CD163-Hb binding, no significant change was seen in CD163-HO-1 binding. Similarly, administration of oxyhemoglobin did not significantly change CD163-HO-1 binding. *P < 0.05; **P < 0.001; ***P < 0.005; #P < 0.06–0.08. ns, Not significant. Figure created with BioRender.com.
Membrane-Bound CD163 Can Form a Complex with HO-1 Independent of Its Hemoglobin Binding Capacity
Since HO-1 regulates CD163 independently of NRF2, we hypothesized that HO-1 may regulate CD163 posttranslationally, independent of its Hb binding capacity. To confirm the possibility of an interaction between HO-1-and CD163, we used two different complementary approaches. First, to understand the interaction of HO-1 and CD163 we performed a bioinformatic analysis. The crystal structures of HO-1 (1N3U) and CD163 (6K0O) were retrieved from the Protein Data Bank (www.rcsb.org) and Uniprot. The potential interactions with HO-1 with CD163 were carried out with the pyDockWEB platform (https://life.bsc.es/servlet/pydock). Docked complex was visualized by the UCSF Chimera molecular visualization program. As shown in Fig. 6B, the algorithm predicted strong binding affinity of HO-1 with CD163. Second, to confirm the interaction between CD163 and HO-1 we performed a coimmunoprecipitation assay. HO-1 and CD163 formed a complex as shown in Fig. 6C.
Next, we examined the effect of hypoxia, inflammation, and aging, a common set of pathophysiology frequently linked to SCD, in regulating CD163-HO-1 interaction. We administered LPS in SCD mice to induce increased inflammation and injury. To induce oxidative stress in SCD mice we utilized hypoxia chambers (4 h of hypoxia followed by 2 h of normoxia). To compare the effect of aging we used 7-mo-old SCD mouse liver. We performed a coimmunoprecipitation assay between HO-1 and CD163 under each of these conditions. As shown in Fig. 6D, compared to control (SCD mouse liver at baseline) liver (Fig. 6C), oxidative stress caused an increase in CD163-HO-1 binding. However, aging as well as increased injury and inflammation led to a decrease in this binding.
Finally, as hemoglobin is the known natural substrate for CD163, we examined whether HO-1 competes with Hb for its binding or whether the CD163-HO-1 binding is independent of the Hb binding capacity of CD163. We performed a sandwich ELISA assay to further determine the binding dynamics of CD163 with Hb and HO-1, respectively. As shown in the schematic diagram in Fig. 6E, we added SCD mouse liver lysate in CD163-coated ELISA plates, followed by addition of Hb and HO-1 antibodies to the wells. Hb and CD163 binding appeared strongest with highest absorbance values. However, HO-1 showed comparatively weaker binding dynamics and absorbance values than those of Hb. Moreover, adding CD163 blocker to the wells before antibody administration blocked Hb binding to CD163 (Fig. 6F) but did not affect the CD163-HO-1 binding significantly. Similarly, adding oxyhemoglobin to the wells resulted in no significant changes in binding affinity between HO-1 and CD163 (Fig. 6F). Collectively, our data suggest that membrane-bound CD163 interacts with Hb as well as HO-1. Whereas binding with Hb is of strongest affinity, and promotes Hb clearance, HO-1 binding could lead to cytoprotective functions of the membrane-bound form of CD163 and is not dependent on Hb-CD163 complex formation.
Hemolysis-Induced Accumulation of Hemoglobin-Heme-Iron Promotes the Expression of Membrane-Bound CD163 in SCD
To assess the role of CD163 in hemolysis-induced hepatobiliary injury in SCD, we first analyzed the hepatic expression of CD163 (membrane-bound CD163) in SCD patient biopsied liver samples. Immunofluorescence (IF) of CD163 showed significant upregulation of CD163 in hepatic Kupffer cell-like structures in SCD patients compared to healthy control human liver samples (Fig. 1A).
Figure 1.
Hemoglobin scavenger receptor CD163 is associated with hepatobiliary injury in sickle cell disease (SCD). A: representative images of immunofluorescence (IF) analysis of SCD patient biopsied liver samples show abundant CD163-positive cells. B, left: representative IF images (merged and single channels) of colocalization assay show higher percentage of CD163 colocalized with positive F4/80 staining of Kupffer cells in SCD mouse liver. Right: bar graph depicts the Pearson’s coefficient of colocalization in control (CON) and SCD mouse liver. C, left: representative IF images (merged and single channels) of colocalization assay show higher percentage of CD163 colocalized with positive CD11b staining of monocytes in SCD mouse liver. Right: bar graph depicts the Pearson’s coefficient of colocalization in control and SCD mouse liver. The error bars represent SD. Arrowheads indicate liver sinusoidal endothelial cells. *P < 0.05, **P < 0.01. Scale bars, 50 µm.
To further analyze the effect of SCD-induced hemolysis in SCD, we used the Townes SCD mouse model. IF revealed that CD163 is predominantly expressed in hepatic monocytes and Kupffer cells in both control and SCD mice (Fig. 1, B and C). Quantification of colocalization revealed significant upregulation of CD163 in the hepatic monocytes and Kupffer cells in SCD mouse liver (Fig. 1, B and C). Next, we investigated the factors that might influence CD163 expression in the SCD mouse liver. Interestingly, whereas administration of LPS (0.1 μg/kg; Fig. 2A; bar graph shows densitometric analysis) or hypoxia (Fig. 2B; bar graph shows densitometric analysis) did not influence CD163 expression, increase in hepatic oxyhemoglobin (Fig. 2C; bar graph shows densitometric analysis), and iron content (Fig. 2D; bar graph shows densitometric analysis) caused significant enrichment of CD163 in the liver of SCD mice compared to littermate control mice. Taken together, our data suggest that CD163 increases in SCD mouse and patient liver because of hemolysis-induced hepatic accumulation of hemoglobin-heme and iron.
Figure 2.
CD163 expression is regulated by hemolysis associated with sickle cell disease (SCD). A and B, top: the schemes of LPS (A) and hypoxia (B) [in SCD and littermate control (CON) mice]. Bottom: representative Western blot micrographs show unchanged levels of CD163 in the liver of SCD and control mice after LPS or hypoxia induction. Densitometric analysis of CD163 protein in each condition: the error bars represent SD. C, top: scheme of treatment SCD and control mice with intravenous injection of 10 µM oxyhemoglobin (oxyHb) for hemolysis induction. Bottom: representative Western blot micrograph and densitometric analysis show increased levels of CD163 in the liver of SCD mice after oxyHb treatment. D, top: scheme of SCD and control mouse treatment with 100 mg/kg body wt of iron dextran (ID). Bottom: representative Western blot micrograph and densitometric analysis reveal higher levels of CD163 in the liver of SCD mice after ID treatment compared to other treatments. The error bars represent SD. *P < 0.05. Figure created with BioRender.com.
Loss of CD163 Promotes Hemolysis-Induced Hepatobiliary Injury in SCD Mouse Liver
As CD163 expression positively correlated with hemolysis, which is a main pathological factor in SCD, we hypothesized that the increased expression of CD163 in SCD liver would protect against hemolysis-induced hepatobiliary injury. To establish the protective role of CD163 in SCD-induced hepatobiliary injury, we depleted CD163 in the SCD liver by two different approaches.
As the liver resident macrophages (Kupffer cells) are the primary cells known to express CD163 (21), we first depleted hepatic Kupffer cells with clodronate-liposome as previously described (40). We observed a strong reduction of Kupffer cell marker CLEC4F in the liver post clodronate-liposome treatment as expected (Fig. 3A). Kupffer cell depletion also led to an increase in total number of erythrocytes sequestered in hepatic sinusoids (as shown by SE in Fig. 3B; bar graph shows quantification). Interestingly, we found that the depletion of hepatic Kupffer cells in SCD mice by clodronate liposomes led to strong loss of CD163 (Fig. 3C), Hb, as well as heme oxygenase 1 (HO-1). Hematoxylin and eosin (H&E) staining showed increased entrapment of RBCs in SCD mice post clodronate treatment (Fig. 3D). We also confirmed exacerbated liver injury in this model as evidenced by the enhanced expression of serum markers of liver injury (ALT and AST) (Fig. 3E).
Figure 3.
Loss of CD163 promotes hemolysis-induced hepatobiliary injury in the sickle cell disease (SCD) mouse liver. A: representative immunofluorescence staining shows strong reduction of Kupffer cell marker CLEC4F in the SCD mice liver post clodronate-liposome treatment (right) compared to nontreated SCD mice (left). Scale bars, 50 µm. B, left: representative scanning electron microscopy (SE) images show increased presence of hepatic hemoglobin and total number of erythrocytes sequestered in hepatic sinusoids in SCD mice liver after Kupffer cell depletion. Right: statistical analysis reveals increased numbers of red blood cells (RBCs) sequestered in the hepatic sinusoids per field of view (FOV). C: Western blot analysis of CD163 shows reduced expression post macrophage depletion in SCD mouse liver. Kupffer cell marker CLEC4F expression is reduced post clodronate treatment. Kupffer cell depletion also caused hepatic hemoglobin (Hb) accumulation and reduced expression of heme oxygenase-1 (HO-1) in SCD mice post clodronate treatment compared to untreated SCD mice. D: representative hematoxylin and eosin (H&E) staining shows increased hepatic vasoocclusion and several entrapped RBCs in the liver of SCD mice post clodronate treatment. Magnification: ×100. E: biochemical analysis of the liver injury markers shows that clodronate-liposome treatment leads to significant (∼10-fold) increase in concentrations of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) 24 h post treatment compared to vehicle-treated mice. This increase attenuated after 48 h post treatment, but the levels of markers remained elevated compared to control mice. F: ELISA shows that blockage of CD163 activity by an antigenic blocking peptide resulted in increased levels of heme in plasma with also a slight elevation in the liver of SCD mice. G: representative IF images showing hemoglobin accumulation in the liver of control mouse, SCD mouse and SCD mouse treated with CD163 blocker. Arrow indicates hemoglobin positive cells. Scale bars, 50 µm. H: liver injury biomarkers ALT and AST were elevated in SCD mouse serum after CD163 blocking compared to untreated mice. The error bars represent SD. Scale bars, 50 μm. *P < 0.05. Figure created with BioRender.com.
Additionally, we used a CD163 peptide blocker (previously uncharacterized) to examine the specific effects of blocking CD163 activity in the liver of SCD mice. Blocking CD163 activity led to a significant increase in serum and hepatic heme levels, confirming the loss of function of CD163 (Fig. 3F). As in the case of Kupffer cell depletion in the SCD mouse liver, we found enhanced accumulation of Hb in the SCD liver post CD163 depletion by IF (Fig. 3G). Serum markers of liver injury were also significantly upregulated (Fig. 3H) in SCD mice after blocking CD163, suggesting its role in protection against hemolysis-induced hepatobiliary injury in SCD.
To further confirm the specificity of the CD163 blocker as well as to understand its mode of action, we performed the following set of experiments. We delivered three different concentrations (from lowest to highest) of CD163 blocker to SCD mice and then measured the amount of heme in their blood. Our hypothesis was that if CD163 blocker inhibited CD163 function, it would be unable to bind with Hb, resulting in an increase in total heme levels in the blood (Fig. 4, A and B). We also hypothesized that the increase in heme level would be positively correlated with the increasing concentration of the CD163 blocker utilized. As shown in Fig. 4B, we found that the amount of heme in the blood increased significantly after CD163 blocking, and it correlated positively with the increasing concentration of the blocker.
Figure 4.
CD163 blocker competes with hemoglobin (Hb) for binding. A: schematic showing the experimental plan to analyze the effect of CD163 blocker in sickle cell disease (SCD) mouse liver. B: bar graph depicting the results of heme ELISA assay post administration of CD163 blocker in the plasma of SCD mice. C: schematic showing the experimental plan to examine the mode of action of the CD163 blocker used in blocking CD163-Hb binding. HO-1, heme oxygenase-1. D: bar graphs depicting the results of heme ELISA assay post CD163 blocker administration in the plasma of SCD mice. oxHb, oxyhemoglobin; Veh, vehicle. The error bars represent SD. *P < 0.05. Figure created with BioRender.com.
Next, to decipher the competition between CD163 and its natural substrate (Hb) and the therapeutic blocker, we used the following approach. We administered three different concentrations (from lowest to highest) of CD163 blocker to SCD mice, followed by a second administration of oxyhemoglobin 0.5 h later in the same mice, and then measured heme levels in the blood and liver within 1 h (Fig. 4C). Our hypothesis was that if the blocker and the natural substrate (Hb) compete for the same binding site, then adding the blocker first will not let Hb bind with CD163 anymore, resulting in significant increase in plasma heme level. As indicated in Fig. 4D, heme levels were found to be dramatically raised in both the blood and plasma of SCD mice, and it positively correlated with the concentration of the CD163 blocker utilized. Notably, the heme level post administration of blocker and oxyhemoglobin treatment was significantly higher than oxyhemoglobin treatment alone (Fig. 4D). Thus, we speculate that the CD163 blocker inhibits Hb-CD163 binding by competing for the same binding site. Collectively, our data also suggest that loss of CD163 activity further exacerbates hemoglobin-heme-iron-induced hepatobiliary injury in SCD.
HO-1 Positively Regulates Hepatic CD163 Expression Independent of NRF2 Signaling
To understand the significance of membrane-bound CD163 in protecting against hemolysis-induced liver injury in SCD and to define the signaling pathway linked to membrane-bound CD163-mediated injury resolution, we analyzed the molecular mechanism behind the elevated CD163 expression in the SCD liver. Once internalized through the CD163 receptor, the hemoglobin/hemoglobin-haptoglobin complex is degraded by heme oxygenase 1 (HO-1) to release heme, carbon monoxide, and biliverdin/bilirubin (22, 41). As CD163 and HO-1 are intricately linked in hemoglobin trafficking and degradation, and Kupffer cell depletion led to reduced expression of both CD163 and HO-1, we examined whether HO-1 can regulate CD163 levels. We found colocalization of CD163 (Fig. 2B) and HO-1 (Fig. 5A, quantified in Fig. 5B) by IHC in both control and SCD mouse liver that was significantly increased in SCD mice. As hepatic Kupffer cells are the primary nonhepatocyte cell type in the liver parenchyma, we analyzed the mRNA expression of HO-1 and CD163 in hepatocytes and nonhepatocytes isolated from the liver of SCD mice. As shown in Fig. 5C, nonhepatocytes had significantly higher expression of both HO-1 and CD163.
Figure 5.
Heme oxygenase-1 (HO-1) positively regulates CD163 level in sickle cell disease (SCD) mouse liver. A and B: representative immunohistochemistry image (A) and colocalization analysis (B) show increased colocalization of CD163 and HO-1 in hepatic Kupffer cells of SCD mouse liver. CON, control. C: qRT-PCR analysis of hepatocytes and nonhepatocytes isolated from control mouse liver exhibits increased expression of both CD163 and HO-1 in the nonhepatocyte cell population. D: the representative images acquired during proximity ligation assay (PLA) show increased interactions between CD163 and HO-1 in SCD mice compared to littermate control mice. Scale bars, 50 µm. E: fluorescence intensity quantification of PLA demonstrating significantly higher intensity in the SCD group compared to control mice. FOV, field of view. F and G: series of Western blot micrographs (for the treatment pattern see Fig. 2) showing the levels of HO-1 in liver lysates of control mice, untreated SCD mice, and SCD mice after treatment with iron dextran (F) and treatments with clodronate and hemoglobin as well as undergoing hypoxic conditions (G). H, left: representative Western blot micrographs showing decreased levels of CD163 protein expression after HO-1 knockdown in Hep3B human hepatoma cell line compared with untreated cells. Right: densitometric analysis of micrographs shows significantly reduced levels of CD163 protein expression after HO-1 knockdown and confirms the efficiency of performed knockdown in control and SCD mouse liver tissue. *P < 0.05; #P < 0.06–0.08.
To identify potential interactions between CD163 and HO-1, we first performed a proximity ligation assay in control and SCD mouse liver. Both control and SCD mouse liver showed a positive signal, which was increased significantly in SCD mice (Fig. 5D, quantified in Fig. 5E). Moreover, Western blot analysis showed that HO-1 displays a similar expression pattern post hemoglobin-heme-iron accumulation (Fig. 5, F and G). Next, we explored whether HO-1 can regulate CD163 expression in the liver. Remarkably, knocking down HO-1 in the Hep3B liver cell line caused significant reduction of CD163 protein expression (Fig. 5H, quantified).
The transcription factor nuclear factor erythroid-derived-2-like 2 (NFE2L2), also known as NRF2, regulates a subset of downstream target genes including HO-1 (42–44). We hypothesized that HO-1 also regulates CD163 via NRF2 (45). To confirm the effect of NRF2 on CD163 expression, we performed a CHIP-seq analysis. Interestingly, CHIP-seq confirmed no binding/association between NRF2 and CD163 (Fig. 6A). NRF2-HO-1 binding was used as a positive control, and as shown in Fig. 6A the result confirmed that NRF2 can bind to HO-1. In addition, we found a higher relative enrichment of NRF-2 binding to HO-1 in SCD mice compared to littermate control mice. Altogether, these findings suggest that HO-1 regulates CD163 expression independent of NRF2 in the SCD mouse liver.
Figure 6.
Heme oxygenase-1 (HO-1) positively regulates CD163 level independent of nuclear factor erythroid 2-related factor 2 (NRF2) signaling. A, left: schematic showing the experimental flow of chromatin immunoprecipitation (ChIP) assay to determine the binding between CD163-NRF2 as well as NRF2 and HO-1. Right: bar graph depicts that compared to control mice sickle cell disease (SCD) mice exhibit higher levels of NRF2 binding to the HO-1 gene in the liver. The error bars represent SD. IP, immunoprecipitation. B: bioinformatic predictive tool analysis suggesting the presence of a molecular basis for the binding of CD163 and HO-1 with TLR9. C: representative Western blot micrograph showing coimmunoprecipitation confirming complex formation between CD163 and HO-1 in SCD mouse liver. D: representative Western blot micrograph showing the effect of hypoxia, LPS, and aging in CD163 and HO-1 interaction in SCD mouse liver. E, top: schematic diagram depicting the steps of sandwich ELISA assay. Bottom: comparative analysis of absorbance suggests stronger binding affinity of CD163-hemoglobin (Hb) compared to CD163-HO-1. F: comparative analysis of absorbance values suggests that whereas CD163 blocker blocks CD163-Hb binding, no significant change was seen in CD163-HO-1 binding. Similarly, administration of oxyhemoglobin did not significantly change CD163-HO-1 binding. *P < 0.05; **P < 0.001; ***P < 0.005; #P < 0.06–0.08. ns, Not significant. Figure created with BioRender.com.
Membrane-Bound CD163 Can Form a Complex with HO-1 Independent of Its Hemoglobin Binding Capacity
Since HO-1 regulates CD163 independently of NRF2, we hypothesized that HO-1 may regulate CD163 posttranslationally, independent of its Hb binding capacity. To confirm the possibility of an interaction between HO-1-and CD163, we used two different complementary approaches. First, to understand the interaction of HO-1 and CD163 we performed a bioinformatic analysis. The crystal structures of HO-1 (1N3U) and CD163 (6K0O) were retrieved from the Protein Data Bank (www.rcsb.org) and Uniprot. The potential interactions with HO-1 with CD163 were carried out with the pyDockWEB platform (https://life.bsc.es/servlet/pydock). Docked complex was visualized by the UCSF Chimera molecular visualization program. As shown in Fig. 6B, the algorithm predicted strong binding affinity of HO-1 with CD163. Second, to confirm the interaction between CD163 and HO-1 we performed a coimmunoprecipitation assay. HO-1 and CD163 formed a complex as shown in Fig. 6C.
Next, we examined the effect of hypoxia, inflammation, and aging, a common set of pathophysiology frequently linked to SCD, in regulating CD163-HO-1 interaction. We administered LPS in SCD mice to induce increased inflammation and injury. To induce oxidative stress in SCD mice we utilized hypoxia chambers (4 h of hypoxia followed by 2 h of normoxia). To compare the effect of aging we used 7-mo-old SCD mouse liver. We performed a coimmunoprecipitation assay between HO-1 and CD163 under each of these conditions. As shown in Fig. 6D, compared to control (SCD mouse liver at baseline) liver (Fig. 6C), oxidative stress caused an increase in CD163-HO-1 binding. However, aging as well as increased injury and inflammation led to a decrease in this binding.
Finally, as hemoglobin is the known natural substrate for CD163, we examined whether HO-1 competes with Hb for its binding or whether the CD163-HO-1 binding is independent of the Hb binding capacity of CD163. We performed a sandwich ELISA assay to further determine the binding dynamics of CD163 with Hb and HO-1, respectively. As shown in the schematic diagram in Fig. 6E, we added SCD mouse liver lysate in CD163-coated ELISA plates, followed by addition of Hb and HO-1 antibodies to the wells. Hb and CD163 binding appeared strongest with highest absorbance values. However, HO-1 showed comparatively weaker binding dynamics and absorbance values than those of Hb. Moreover, adding CD163 blocker to the wells before antibody administration blocked Hb binding to CD163 (Fig. 6F) but did not affect the CD163-HO-1 binding significantly. Similarly, adding oxyhemoglobin to the wells resulted in no significant changes in binding affinity between HO-1 and CD163 (Fig. 6F). Collectively, our data suggest that membrane-bound CD163 interacts with Hb as well as HO-1. Whereas binding with Hb is of strongest affinity, and promotes Hb clearance, HO-1 binding could lead to cytoprotective functions of the membrane-bound form of CD163 and is not dependent on Hb-CD163 complex formation.
DISCUSSION
Over the last few decades, research on the multifaceted pathophysiology associated with HbS polymerization and hemolysis in SCD has led to the identification of many new therapeutic molecules (46–48). Previous studies in children and adults with SCD have shown increased soluble CD163 levels in the plasma that correlated with pulmonary hypertension and vasoocclusion. Along that line, we here show that membrane-bound CD163 can potentially be used as a novel therapeutic molecule/biomarker of SCD-related hepatobiliary injury. We confirm that elevated hemoglobin, heme, and iron levels stimulate CD163 expression in the SCD liver. Loss of CD163 expression or activity is associated with exacerbated liver injury in SCD patients due to accumulation of hemoglobin in the liver. Although previous studies have suggested LPS-induced inhibition of CD163 (31), we did not see any change in CD163 expression upon LPS treatment in the hepatic Kupffer cells. The differences can be attributed to differences in dosage, tissue-specific effects, or other unknown effects associated with increased hepatic hemoglobin level and need to be addressed in future.
HO-1 is a crucial enzyme in the process of heme detoxification as it catalyzes the decomposition of heme into biliverdin, iron, and carbon monoxide (49, 50). Along with heme detoxification, HO-1 has cytoprotective, anti-inflammatory, and antioxidant properties (51, 52). Thus, the activation of HO-1 is a critical factor in safeguarding tissues and endothelium from oxidative stress and hemolysis. Previous studies have confirmed that HO-1 is upregulated in the SCD mouse liver to protect against hemolysis- and oxidative stress-induced tissue damage (3, 44, 53, 54). HO-1-mediated regulation of membrane-bound CD163 hints at an additional protective role of HO-1 in hemoglobin scavenging. Our findings are consistent with the previously recognized role of CD163 in HO-1/HMOX1 expression in macrophage cell lines (55, 56). Both CD163 and HO-1 have been linked to anti-inflammatory and wound healing properties (33, 57–60). Their expression is also dependent on endocytic trafficking (55) and IL10 (61, 62). Thus, although the effect of HO-1 on membrane-bound CD163 expression could be due to a direct interaction, other indirect effects through changes in iron/ferritin levels, HO-1-mediated endocytic trafficking, or IL10 regulation cannot be ruled out. We hypothesize that the regulation of CD163 by HO-1 might occur through the following two mechanisms (Fig. 7): 1) Loss of HO-1 promotes accumulation of heme and thereby increases the proinflammatory status of the cells. Previous studies have shown that CD163 is negatively regulated by the proinflammatory status of the cells (63, 64). Thus, HO-1 can indirectly upregulate the three proteins via its proinflammatory action. 2) Loss of HO-1 can change the polarity of hepatic Kupffer cells, which might affect the localization and stability of CD163.
Figure 7.
The CD163 and heme oxygenase-1 (HO-1) signaling pathway in sickle cell disease (SCD)-related liver complications. Schematic diagram depicting the HO-1-CD163 interaction in SCD mouse liver. Cell-free hemoglobin (Hb) released after intravascular hemolysis is scavenged by plasma haptoglobin (HP), which chaperones it to the liver for hemoglobin scavenger receptor CD163-dependent clearance by macrophages. Cell-free Hb on its own can also bind to CD163, albeit less efficiently. In both cases, it leads to endocytosis and degradation of Hb and the release of heme. HO-1 metabolizes heme to produce carbon monoxide, iron, and biliverdin. Along with its role in heme degradation, HO-1 also regulates CD163 expression in the hepatic Kupffer cells. Loss of HO-1 in the liver reduces CD163 expression, which can further impact the Hb-heme metabolism in SCD liver. RBC, red blood cell. Figure created with BioRender.com.
A key finding of our study is NRF2-independent regulation of CD163 by HO-1. We found that NRF2 cannot transcriptionally regulate CD163 in a CHIP-seq assay. The Keap1-NRF2 signaling pathway controls the expression of many cytoprotective and antioxidant genes, including HO-1 (45). NRF2-independent roles of HO-1 are not very well understood. Our study highlights one such previously undescribed function of HO-1 in the SCD liver. As there are several other transcription factors needed for NRF2 dimerization and downstream signaling (65), future work will be needed to confirm whether there is any potential association of those transcriptional factors with HO-1 in the regulation of CD163. It will be very interesting to characterize the downstream effect of oxidative stress, inflammation, as well as aging on CD163-HO-1 binding.
Our study demonstrates that although membrane-bound CD163 has a higher binding affinity for Hb compared to HO-1, blocking or increasing Hb binding does not impact CD163-HO-1 interaction. This conclusion is supported by a comparative analysis of the binding affinities of CD163 with Hb and HO-1 with or without the blocker. Similarly, administration of oxyhemoglobin increased CD163-HO-1 interaction. Thus, we speculate that membrane-bound CD163 binds with HO-1 post Hb binding and internalization and binding with HO-1 might promote CD163 recycling. We also speculate that, whereas the main role of CD163 in SCD appears to be binding to and removal of cell-free Hb, the interaction with HO-1 also holds importance (particularly in SCD) and could enhance the cytoprotective effects of CD163 and its stability/endocytic recycling. Subsequent investigations should verify whether soluble CD163 exhibits comparable binding affinity as well as identifying the binding sites.
The study has a few limitations. First, inflammation in humans and mice activates different subsets of signaling pathways. Future work is needed to confirm the interaction of HO-1 and CD163 in hepatobiliary injury in patients with SCD. Second, although we see loss of expression of HO-1 and CD163 upon Kupffer cell depletion, the exact role of Kupffer cells in regulating the expression of these proteins needs to be determined. Third, the effect of HO-1 on soluble CD163 was not examined in this study. Finally, given the expanding interest in altering macrophage activity toward cancer, the potential of our findings might be beneficial to liver cancer treatment in patients with hemoglobinopathies (35). In summary, our finding of a protective role of CD163 in hemolysis-induced organ damage may inspire future investigations exploring the molecular mechanism underlying CD163-mediated hepatic Hb clearance not only in SCD but also in other hemolytic diseases.
DATA AVAILABILITY
Data will be made available upon reasonable request.
SUPPLEMENTAL MATERIAL
10.6084/m9.figshare.25533679
Supplemental Material: https://doi.org/10.6084/m9.figshare.25533679.
GRANTS
This work was supported by National Institutes of Health (NIH), National Institute of Diabetes and Digestive and Kidney Diseases 1K01DK125617-01 (T.P.-S.), American Society of Hematology junior faculty scholar award (T.P.-S.), and Versiti start-up fund (T.P.-S.); National Heart, Lung, and Blood Institute Grants R01HL128297 (P.S.), R01HL141080 (P.S.), and R01HL166345; and American Heart Association 18TPA34170588 (P.S.) and 23TPA1074022. T.W.K. was supported by American Heart Association postdoctoral fellowship AHA828786.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the authors.
AUTHOR CONTRIBUTIONS
T.P.-S. conceived and designed research; T.W.K., Ay.S., A.M., A.S., R.U., R.K.D., B.S., C.H., O.K., J.T., and T.P.-S. performed experiments; T.W.K., Ay.S., A.M., R.U. and T.P.-S. analyzed data; Ay.S., R.U., G.J.K., and T.P.-S. interpreted results of experiments; R.U., Ay.S., T.W.K., A.S. and T.P.-S. prepared figures; T.P.-S. drafted manuscript; E.M.N. and T.P.-S. edited and revised manuscript; All the authors approved final version of manuscript. |
Title: 苯达莫司汀联合托法替布治疗T幼淋巴细胞白血病1例 | Body: |
Title: Association of healthy eating index (HEI), alternative healthy eating index (AHEI) with antioxidant capacity of maternal breast milk and infant’s urine: a cross-sectional study | Body:
Introduction
The optimal food for infants is human breast milk (BM), which contains all the essential nutrients, fluid, and energy required for infants that is necessary for their physical and psychological growth1. While formula milk has a standard content of nutrients, the composition of BM varies with the mothers’ dietary habits, ethnicity, and the time of the day that she breastfeeds her infant2. BM contains unique antioxidants that can improve an infant’s immune function and protect against some diseases3,4.
Infants are at high risk for generating excess reactive oxygen species (ROS) because of the rapid transfer from the womb to an environment with higher oxygen concentrations5. Infants who receive BM containing high levels of antioxidants have lower oxidative stress than infants fed by formula6. Oxidative injury in infants may be implicated in causing many disorders, including respiratory diseases, necrotizing enterocolitis, and other chronic diseases7,8. Prematurity is also associated with oxidative stress because of the deficiency in antioxidant systems, and BM provides a complete antioxidant profile advantageous for premature neonates9.
A mother’s diet can affect the levels of vitamins in BM, including A, C, B6, B12, and other nutrients such as fatty acids10. A comparison of dietary patterns among mothers who breastfed their infants showed that different dietary patterns resulted in different levels of antioxidants in BM11. A healthy diet, rich in fruits, vegetables, grains, and nuts during lactation can increase the antioxidants in BM. Higher levels of some antioxidants such as 2, 2′-diphenyl-1-picrylhydrazyl (DPPH) and thiol were found in the milk of mothers who adhere to a healthy dietary pattern. In comparison, adherence to an unhealthy dietary pattern led to lower levels of thiol in the BM12.
With the increasing interest in evaluating diet quality in diverse populations, several methods have been developed to assess health outcomes. The Healthy eating index (HEI) is a measure of the quality of the diet in terms of compliance with the Dietary Guidelines for Americans, which is the basis of nutrition decisions for the United States government13. The HEI-2010 consists of twelve components including nine adequacy (total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein, seafood and plant protein, and fatty acids) as well as three moderation (refined grains, sodium, and empty calories) and uses minimum restriction standards to score each component14. The Alternative Healthy Eating Index (AHEI) was created in 2002 as a revision of the HEI, and is based on foods that can predict the risk of chronic diseases15. The AHEI-2010 contains 11 different food groups. Six dietary elements are recommended for highest intake including vegetables, fruit, whole grains, nuts and legumes, long-chain omega-3 fatty acids and polyunsaturated fatty acids (PUFA); one component for which moderate intake is ideally advocated (alcohol); and ascertain four dietary elements for to avoid or limit including sugar-sweetened beverages and fruit juices, red and processed meat, trans fatty acids and sodium16.
HEI is a standard nutritional index that can be used for lactating mothers17. Freitas et al.. found that the total HEI score was inversely related with the margaric and linoleic acids of mother’s BM17. Moreover, a higher intake of food groups such as vegetables, fruits and meats was directly correlated with specific BM oligosaccharides and adiponectin18,19. Previous studies have shown that a high maternal AHEI scores during pregnancy significantly improves anthropometric outcomes in newborn infants20–22. Few studies have focused on food intake during human milk feeding and its effect on the antioxidant content of BM and infant’s urine. We have previously investigated the relationship of quality indicators of BM and infant urine with different dietary plans such as Mediterranean diet (MedDiet), dietary approaches to stop hypertension (DASH), low carbohydrate diet (LCD), and food quality score (FQS)23–26. In this study, we aimed to determine the HEI and AHEI score of a lactating mother’s diet and then evaluate the association of these nutritional indicators with the antioxidant panel of BM and the infant’s urine.
Methods
Study population
In this cross-sectional study, 350 healthy mother-infant pairs were recruited from 4 health centers in Birjand, South Khorasan, Iran, in February 2021. Inclusion criteria were healthy breatfeeding mothers aged 20–35 years who had given birth 1–6-months previously. Mothers with acute or chronic diseases, incomplete data as well as low/high enery intake (< 800 kcal and > 4200 kcal, respectively) were excluded. Before the study started, all of the participants were asked to provide written informed consent. The Birjand University of Medical Sciences Medical Ethics Committee approved the study (IR.BUMS.REC.1400.379), which was carried out following the Helsinki Declaration on human subjects’ studies.
Initially, 350 breastfeeding women were recruited in the first stage. After the interview, we excluded participants with more than 10% incomplete food frequency questionnaire (FFQ) items (n = 19), an energy intake of < 800 kcal (n = 18) or > 4200 kcal (n = 11), and missing data on other variables (n = 2) from data analysis. As a result, our final statistical analysis was conducted on 300 breastfeeding women.
Demographic and anthropometric assessment
Demographic, anthropometric, and socio-economic data of the study participants was gathered by a trained nurse. A standard questionnaire was used to collect information on demographic status including the mother’s age, type of delivery (natural/cesarean), history of chronic diseases (Yes/No), infant age, as well as infant sex. In terms of socio-economic variables, the educational attainment of mothers was evaluated separately, categorized into three distinct response options: ‘elementary (9 years),’ ‘intermediate (10–12 years),’ and ‘university (13 years or more)’. Mothers were asked to report their perception of their economic status by selecting one of three descriptive choices: less than enough, enough, or more than enough. Each subject’s height and weight were measured using standard method and then, BMI [weight (kg)/height (m2)] was calculated. Furthermore, the infant’s height and head circumference were recorded using precise tape measurements to the nearest millimeter. Weight assessments were made using electronic scales, ensuring precision to the nearest 0.1 kg. For maternal blood pressure assessment, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured multiple times using a mercury sphygmomanometer after the patient had a period of sitting and resting. This method enhances the accuracy of blood pressure readings, and the average of these measurements over a 25-minute period was documented.
Milk and urine sampling
BM samples were manually collected from mothers 1 to 6 months postpartum in the early morning between 7 and 10 am. Each mother provided 20 ml samples, that were collected into sterile tubes, and transferred to the laboratory on dry ice. On the same day, a urine specimen of 10 ml was gathered from each baby in a urine bag. The samples were freeze-dried and stored at -80 °C until analysis.
Oxidant-antioxidant status assessment
The test panel including four analytical tests to measure the total antioxidant status of each BM and urine sample, which is briefly described as follows:
Ferric reducing, antioxidant power (FRAP) assay
The FRAP assay was carried out using the methodology established by Benzie and Strain27, as described previously12. This assay reduces a Fe3 + tripyridyltriazine complex to its ferrous form in the presence of antioxidants within the samples. By combining the test sample with the FRAP reagent and measuring the absorbance at 593 nm, the antioxidant capacity of the samples could be quantified in terms of µmol/L.
DPPH assay
The DPPH method was employed to quantify the free radical scavenging activity of the samples, as described formerly12. Based on the Brand-Williams protocol with modifications28, this method involved incubating the samples with DPPH solution and measuring the absorbance at 517 nm. The percentage of antiradical efficiency was calculated to determine the scavenging capacity of the milk samples, expressed in µmol Trolox equivalent/L.
Thiobarbituric acid reactive substances (TBARs) assay
The TBARs assay was used to assess the level of malonyldialdehyde (MDA), a byproduct of lipid peroxidation29. Mixing the samples with TBARs reagent and measuring the fluorescence spectrum at specific wavelengths, the concentration of TBARs adducts was determined and compared to a standard curve for quantification in µmol TBARs/L26.
Ellman’s assay
The Ellman assay measures the total thiol concentration or sulfhydryl groups (T-SH) within the samples26. Following the technique initially described by Ellman and modified by Hu30, this assay relies on the interaction of TS-H with DTNB to produce a colored anion, with absorbance measured at 412 nm. The concentration of sulfhydryl groups in the human milk samples could be accurately determined by comparing the results to a T-SH group standard and presenting the data in µmol/L.
Nutritional assessment
The study participants’ food intake was assessed using a semi-quantitative FFQ, which comprised 65 food items and demonstrated good reliability and validity for an Iranian population31. The intake of each food group was determined by the portion size and frequency of use (per day, week, month, rarely, and never). Finally, the amount of each food item intake was calculated (based on grams) and inverted to daily intake using household scales. We used Nutritionist IV software (version 7.0; N-Squared Computing, Salem, OR) rectified for Iranian food ingredients for nutrient and energy intake assessment. Based on this software, the intake of energy and all macronutrients including carbohydrate, non-starch polysaccharide, protein, fat, saturated fat, monounsaturated fat, PUFA, trans fat, cholesterol, starch, total sugar (glucose, fructose, sucrose, maltose, and lactose), and fiber, and also micronutrients (vitamins and minerals) including sodium, potassium, calcium, magnesium, phosphorus, iron, copper, zinc, chloride, manganese, selenium, iodine, retinol, carotene, vitamin D, vitamin E, thiamin, riboflavin, niacin, vitamin B6, vitamin B12, folate, pantothenate, biotin, and vitamin C were determined for all participants.
Each participant was given a specific score for each component of HEI-2010 and AHEI-2010 according to their daily food group intake14,16. Each nutritional index has several components, reflecting an essential aspect of diet quality. The HEI and AHEI components and scoring methods are shown in the Tables 1 and 2. For calculating HEI score, 12 components were used for all study population (Table 1). Participants with higher intake of healthy items of this nutritional index including whole fruit, total fruit, dark green vegetables, total vegetables, whole grains, dairy products, total protein foods, seafood and plant protein and lower intake of unhealthy items including fatty acids, refined grains, sodium and empty calories (including snacks and sweets) got the maximum score (score = 5) for each item and those with low intake of healthy items and high intake of unhealthy items got the minimum score (score = 0). In order to calculate the AHEI score, 11 components were used for all study population (Table 2). Participants with a higher intake of healthy items of this nutritional index including vegetables, fruit, whole grains, nuts and beans, n-3 fatty acids, and PUFA and low intake of unhealthy items including sugar sweetened beverages, red meat/processed meat, trans fatty acids, sodium and alcohol got the maximum score (score = 10) for each item and those with low intake of healthy items and high intake of unhealthy items got the minimum score (score = 0). Finally, the scores of all components were added together and recorded as the final score. The total score is 100 for HEI and 110 for AHEI; higher scores indicate better diet quality.
Table 1Distribution of healthy eating index-2010 (HEI-2010) components and standards for scoring.ComponentMaximum scoreStandard for maximum scoreStandard for zero scoreWhole fruit5≥ 0.8 serving / 1000 kcalWithout whole fruitTotal fruit5≥ 0.4 serving / 1000 kcalWithout total fruitDark green vegetables5≥ 0.2 serving / 1000 kcalWithout dark green vegetablesTotal vegetables5≥ 1.1 serving / 1000 kcalWithout total vegetablesWhole grains10≥ 1.5 ounce / 1000 kcalWithout whole grainsDairy products10≥ 1.3 cups / 1000 kcalWithout dairy productTotal protein foods5≥ 2.5 ounce / 1000 kcalWithout total protein foodsSeafood and plant protein5≥ 0.8 ounce / 1000 kcalWithout seafood and plant proteinsFatty acids10(PUFA + MUFA) / SFA ≥ 2.5(PUFA + MUFA) / SFA ≤ 1.2Refined grains10≤ 1.8 ounce / 1000 kcal≥ 4.3 ounce / 1000 kcalSodium10≤ 1.1 g / 1000 kcal≥ 2 g / 1000 kcalEmpty calories20≤ 19% of total energy intake≥ 50% of total energy intake
Table 2Distribution of alternative healthy eating index-2010 (AHEI-2010) components and standards for scoring.ComponentMaximum scoreStandard for maximum scoreStandard for zero scoreVegetables10≥ 5 serving/ day0 serving/ dayFruit10≥ 4 serving/day0 serving/ dayWhole grainsMales1090 gram/ day0 gram/ dayFemales75 gram/ daySugar sweetened beverages10≤ 1 serving / day≥ 1 serving/ dayNuts and beans10≥ 1 serving/ day0 serving/ dayRed meat/processed meat10≤ 1 serving / day≥ 1.5 serving/ dayTrans fatty acids10≤ 0.5% of energy/day≥ 4% of energy/dayn-3 fatty acids10≥ 250 mg/ day0 mg/ dayPUFA10≥ 10% of energy intake/ day≤ 2% of energy intake/daySodiumThe lowest decimal/ mg a dayThe highest decimal/ mg a dayAlcoholMales100.5–2 liquor / day≥ 3.5 liquor/ dayFemales0.5–1.5 liquor/ day≥ 2.5 liquor/ day
Statistical analysis
SPSS software version 16 was used to analyze the study data. A Kolmogorov–Smirnov test was applied to judge whether the data were normally distributed. Data presented as Mean ± SD/median (interquartile range) or percent (number) for continuous or categorical variables, respectively. Normally-distributed continuous compared between four quartiles by using one-way ANOVA. We used linear regression to calculate adjusted β estimates for assessment of the association between HEI and AHEI components with the antioxidant concentration of breast milk (BM) and the infant’s urine. Multivariate binary logistic regression was used to examine the odds of low antioxidant concentration of BM and infant’s urine across quartiles of HEI and AHEI. The lowest HEI and AHEI quartile was considered the reference category for analysis of the odds ratios (ORs) and their 95% confidence intervals (CIs) for the other quartiles. The first model was adjusted for the mother’s age and energy intake for BM samples and the infant’s age and sex for the infant’s urine samples. The second model was adjusted for the related samples’ mother’s BMI, infant weight, and head circumference. p value < 0.05 was considered statistically significant.
Study population
In this cross-sectional study, 350 healthy mother-infant pairs were recruited from 4 health centers in Birjand, South Khorasan, Iran, in February 2021. Inclusion criteria were healthy breatfeeding mothers aged 20–35 years who had given birth 1–6-months previously. Mothers with acute or chronic diseases, incomplete data as well as low/high enery intake (< 800 kcal and > 4200 kcal, respectively) were excluded. Before the study started, all of the participants were asked to provide written informed consent. The Birjand University of Medical Sciences Medical Ethics Committee approved the study (IR.BUMS.REC.1400.379), which was carried out following the Helsinki Declaration on human subjects’ studies.
Initially, 350 breastfeeding women were recruited in the first stage. After the interview, we excluded participants with more than 10% incomplete food frequency questionnaire (FFQ) items (n = 19), an energy intake of < 800 kcal (n = 18) or > 4200 kcal (n = 11), and missing data on other variables (n = 2) from data analysis. As a result, our final statistical analysis was conducted on 300 breastfeeding women.
Demographic and anthropometric assessment
Demographic, anthropometric, and socio-economic data of the study participants was gathered by a trained nurse. A standard questionnaire was used to collect information on demographic status including the mother’s age, type of delivery (natural/cesarean), history of chronic diseases (Yes/No), infant age, as well as infant sex. In terms of socio-economic variables, the educational attainment of mothers was evaluated separately, categorized into three distinct response options: ‘elementary (9 years),’ ‘intermediate (10–12 years),’ and ‘university (13 years or more)’. Mothers were asked to report their perception of their economic status by selecting one of three descriptive choices: less than enough, enough, or more than enough. Each subject’s height and weight were measured using standard method and then, BMI [weight (kg)/height (m2)] was calculated. Furthermore, the infant’s height and head circumference were recorded using precise tape measurements to the nearest millimeter. Weight assessments were made using electronic scales, ensuring precision to the nearest 0.1 kg. For maternal blood pressure assessment, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured multiple times using a mercury sphygmomanometer after the patient had a period of sitting and resting. This method enhances the accuracy of blood pressure readings, and the average of these measurements over a 25-minute period was documented.
Milk and urine sampling
BM samples were manually collected from mothers 1 to 6 months postpartum in the early morning between 7 and 10 am. Each mother provided 20 ml samples, that were collected into sterile tubes, and transferred to the laboratory on dry ice. On the same day, a urine specimen of 10 ml was gathered from each baby in a urine bag. The samples were freeze-dried and stored at -80 °C until analysis.
Oxidant-antioxidant status assessment
The test panel including four analytical tests to measure the total antioxidant status of each BM and urine sample, which is briefly described as follows:
Ferric reducing, antioxidant power (FRAP) assay
The FRAP assay was carried out using the methodology established by Benzie and Strain27, as described previously12. This assay reduces a Fe3 + tripyridyltriazine complex to its ferrous form in the presence of antioxidants within the samples. By combining the test sample with the FRAP reagent and measuring the absorbance at 593 nm, the antioxidant capacity of the samples could be quantified in terms of µmol/L.
DPPH assay
The DPPH method was employed to quantify the free radical scavenging activity of the samples, as described formerly12. Based on the Brand-Williams protocol with modifications28, this method involved incubating the samples with DPPH solution and measuring the absorbance at 517 nm. The percentage of antiradical efficiency was calculated to determine the scavenging capacity of the milk samples, expressed in µmol Trolox equivalent/L.
Thiobarbituric acid reactive substances (TBARs) assay
The TBARs assay was used to assess the level of malonyldialdehyde (MDA), a byproduct of lipid peroxidation29. Mixing the samples with TBARs reagent and measuring the fluorescence spectrum at specific wavelengths, the concentration of TBARs adducts was determined and compared to a standard curve for quantification in µmol TBARs/L26.
Ellman’s assay
The Ellman assay measures the total thiol concentration or sulfhydryl groups (T-SH) within the samples26. Following the technique initially described by Ellman and modified by Hu30, this assay relies on the interaction of TS-H with DTNB to produce a colored anion, with absorbance measured at 412 nm. The concentration of sulfhydryl groups in the human milk samples could be accurately determined by comparing the results to a T-SH group standard and presenting the data in µmol/L.
Ferric reducing, antioxidant power (FRAP) assay
The FRAP assay was carried out using the methodology established by Benzie and Strain27, as described previously12. This assay reduces a Fe3 + tripyridyltriazine complex to its ferrous form in the presence of antioxidants within the samples. By combining the test sample with the FRAP reagent and measuring the absorbance at 593 nm, the antioxidant capacity of the samples could be quantified in terms of µmol/L.
DPPH assay
The DPPH method was employed to quantify the free radical scavenging activity of the samples, as described formerly12. Based on the Brand-Williams protocol with modifications28, this method involved incubating the samples with DPPH solution and measuring the absorbance at 517 nm. The percentage of antiradical efficiency was calculated to determine the scavenging capacity of the milk samples, expressed in µmol Trolox equivalent/L.
Thiobarbituric acid reactive substances (TBARs) assay
The TBARs assay was used to assess the level of malonyldialdehyde (MDA), a byproduct of lipid peroxidation29. Mixing the samples with TBARs reagent and measuring the fluorescence spectrum at specific wavelengths, the concentration of TBARs adducts was determined and compared to a standard curve for quantification in µmol TBARs/L26.
Ellman’s assay
The Ellman assay measures the total thiol concentration or sulfhydryl groups (T-SH) within the samples26. Following the technique initially described by Ellman and modified by Hu30, this assay relies on the interaction of TS-H with DTNB to produce a colored anion, with absorbance measured at 412 nm. The concentration of sulfhydryl groups in the human milk samples could be accurately determined by comparing the results to a T-SH group standard and presenting the data in µmol/L.
Nutritional assessment
The study participants’ food intake was assessed using a semi-quantitative FFQ, which comprised 65 food items and demonstrated good reliability and validity for an Iranian population31. The intake of each food group was determined by the portion size and frequency of use (per day, week, month, rarely, and never). Finally, the amount of each food item intake was calculated (based on grams) and inverted to daily intake using household scales. We used Nutritionist IV software (version 7.0; N-Squared Computing, Salem, OR) rectified for Iranian food ingredients for nutrient and energy intake assessment. Based on this software, the intake of energy and all macronutrients including carbohydrate, non-starch polysaccharide, protein, fat, saturated fat, monounsaturated fat, PUFA, trans fat, cholesterol, starch, total sugar (glucose, fructose, sucrose, maltose, and lactose), and fiber, and also micronutrients (vitamins and minerals) including sodium, potassium, calcium, magnesium, phosphorus, iron, copper, zinc, chloride, manganese, selenium, iodine, retinol, carotene, vitamin D, vitamin E, thiamin, riboflavin, niacin, vitamin B6, vitamin B12, folate, pantothenate, biotin, and vitamin C were determined for all participants.
Each participant was given a specific score for each component of HEI-2010 and AHEI-2010 according to their daily food group intake14,16. Each nutritional index has several components, reflecting an essential aspect of diet quality. The HEI and AHEI components and scoring methods are shown in the Tables 1 and 2. For calculating HEI score, 12 components were used for all study population (Table 1). Participants with higher intake of healthy items of this nutritional index including whole fruit, total fruit, dark green vegetables, total vegetables, whole grains, dairy products, total protein foods, seafood and plant protein and lower intake of unhealthy items including fatty acids, refined grains, sodium and empty calories (including snacks and sweets) got the maximum score (score = 5) for each item and those with low intake of healthy items and high intake of unhealthy items got the minimum score (score = 0). In order to calculate the AHEI score, 11 components were used for all study population (Table 2). Participants with a higher intake of healthy items of this nutritional index including vegetables, fruit, whole grains, nuts and beans, n-3 fatty acids, and PUFA and low intake of unhealthy items including sugar sweetened beverages, red meat/processed meat, trans fatty acids, sodium and alcohol got the maximum score (score = 10) for each item and those with low intake of healthy items and high intake of unhealthy items got the minimum score (score = 0). Finally, the scores of all components were added together and recorded as the final score. The total score is 100 for HEI and 110 for AHEI; higher scores indicate better diet quality.
Table 1Distribution of healthy eating index-2010 (HEI-2010) components and standards for scoring.ComponentMaximum scoreStandard for maximum scoreStandard for zero scoreWhole fruit5≥ 0.8 serving / 1000 kcalWithout whole fruitTotal fruit5≥ 0.4 serving / 1000 kcalWithout total fruitDark green vegetables5≥ 0.2 serving / 1000 kcalWithout dark green vegetablesTotal vegetables5≥ 1.1 serving / 1000 kcalWithout total vegetablesWhole grains10≥ 1.5 ounce / 1000 kcalWithout whole grainsDairy products10≥ 1.3 cups / 1000 kcalWithout dairy productTotal protein foods5≥ 2.5 ounce / 1000 kcalWithout total protein foodsSeafood and plant protein5≥ 0.8 ounce / 1000 kcalWithout seafood and plant proteinsFatty acids10(PUFA + MUFA) / SFA ≥ 2.5(PUFA + MUFA) / SFA ≤ 1.2Refined grains10≤ 1.8 ounce / 1000 kcal≥ 4.3 ounce / 1000 kcalSodium10≤ 1.1 g / 1000 kcal≥ 2 g / 1000 kcalEmpty calories20≤ 19% of total energy intake≥ 50% of total energy intake
Table 2Distribution of alternative healthy eating index-2010 (AHEI-2010) components and standards for scoring.ComponentMaximum scoreStandard for maximum scoreStandard for zero scoreVegetables10≥ 5 serving/ day0 serving/ dayFruit10≥ 4 serving/day0 serving/ dayWhole grainsMales1090 gram/ day0 gram/ dayFemales75 gram/ daySugar sweetened beverages10≤ 1 serving / day≥ 1 serving/ dayNuts and beans10≥ 1 serving/ day0 serving/ dayRed meat/processed meat10≤ 1 serving / day≥ 1.5 serving/ dayTrans fatty acids10≤ 0.5% of energy/day≥ 4% of energy/dayn-3 fatty acids10≥ 250 mg/ day0 mg/ dayPUFA10≥ 10% of energy intake/ day≤ 2% of energy intake/daySodiumThe lowest decimal/ mg a dayThe highest decimal/ mg a dayAlcoholMales100.5–2 liquor / day≥ 3.5 liquor/ dayFemales0.5–1.5 liquor/ day≥ 2.5 liquor/ day
Statistical analysis
SPSS software version 16 was used to analyze the study data. A Kolmogorov–Smirnov test was applied to judge whether the data were normally distributed. Data presented as Mean ± SD/median (interquartile range) or percent (number) for continuous or categorical variables, respectively. Normally-distributed continuous compared between four quartiles by using one-way ANOVA. We used linear regression to calculate adjusted β estimates for assessment of the association between HEI and AHEI components with the antioxidant concentration of breast milk (BM) and the infant’s urine. Multivariate binary logistic regression was used to examine the odds of low antioxidant concentration of BM and infant’s urine across quartiles of HEI and AHEI. The lowest HEI and AHEI quartile was considered the reference category for analysis of the odds ratios (ORs) and their 95% confidence intervals (CIs) for the other quartiles. The first model was adjusted for the mother’s age and energy intake for BM samples and the infant’s age and sex for the infant’s urine samples. The second model was adjusted for the related samples’ mother’s BMI, infant weight, and head circumference. p value < 0.05 was considered statistically significant.
Results
The baseline characteristics of the study participants (n = 300) are shown in Table 3. The average age of breastfeeding mothers was 29.5 ± 5.9 years, and the mean BMI was 24.8 ± 4.3 kg/m2. The average HEI and AHEI for the participants were 70.1 ± 11.1 and 55.8 ± 7.3, respectively. The mean total energy intake of mothers was 2102 ± 796.5 Kcal/day, and carbohydrate, protein, fat and fiber intake were 184.7 ± 41.0, 45.7 ± 11.2, 117.3 ± 19.9, and 22.2 ± 5.4 gr/day, respectively.
Table 3Characteristics of the study participants.VariablesMother Mother’s age (year)29.5 ± 5.9 Mother Height (cm)159.6 ± 11.7 Mother Weight (kg)63.7 ± 12.05 Mother BMI (kg/m2)24.8 ± 4.3 Mother SBP (mmHg)104.2 ± 10.5 Mother DBP (mmHg)72.1 ± 1.4 Mother’s education, n(%) 10–12 y71 (23.5%) 0–9 y102 (34%) > 13y127 (42.5%) Income status, n (%) Less than enough84 (28%) Enough207 (69%) To the extent of savings9 (3%) Type of delivery (natural)183 (61.0%) Mother’s chronic disease history, n (%) Yes33 (11.0%) No267 (89.0%) Mother HEI score70.1 ± 11.11 Mother AHEI score55.8 ± 7.3 Total energy intake (kcal/day)2102 ± 796.5 Carbohydrate (g/day)184.7 ± 41.0 Protein (g/day)45.7 ± 11.2 Fat (g/day)117.32 ± 19.90 Fiber (g/day)22.2 ± 5.4 Fruit (g/day)194.4(120.8, 299.0) Vegetables (g/day)160.4 (107.3, 221.0) Legumes (g/day)21.1(14.9, 27.4) Nuts (g/day)9.7 (3.04, 20.7) Whole grains (g/day)111.1 (81.4, 149.9) Dairy products (g/day)139.62 (70.2, 239.5) Sea foods (g/day)5.88 (2.4, 12.5) n-3 fatty acids (mg/day)18.97 (1.9, 54.1)Infant Infant age (day)106.4 ± 57.4 Infant sex, % (n) Boy162 (54.0%) Girl138 (46.0%) Infant weight (kg)5.9 ± 1.5 Infant height (cm)58.7 ± 8.1 Infant head circumference (cm)39.6 ± 4.6Data presented as Mean ± SD /median (interquartile range) or number (%) as appropriate.BMI Body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, HEI healthy eating index, AHEI alternative healthy eating index.
The average age of the infants was 106.38 ± 57.36 days, and their average weight and head circumference were 5.9 ± 1.5 kg and 39.6 ± 4.6 cm, respectively.
The mean concentration of the BM and infant’s urine antioxidants across different quartiles of HEI is shown in Table 4. The mean concentration of infant’s urine DPPH was significantly different between HEI quartiles and it was highest in the last quartile compared to the first quartile (12.6 ± 8.6 vs. 9.2 ± 7.1, p = 0.028). However, we found no significant difference in the BM or infant’s urine concentration of other antioxidants between HEI quartiles.
Table 4The comparison of anti-oxidant concentration of breast milk and the infant’s urine between quartiles of HEI.VariablesHEI quartilesp valueQ1Q2Q3Q4Milk DPPH (µmol Trolox equivalent /L)308.2 ± 74.2307.1 ± 77.6315.7 ± 82.4334.3 ± 121.40.25Milk FRAP (µmol /L)536.6 ± 127.6540.1 ± 153.5543.4 ± 137.7551.1 ± 175.60.95Milk MDA (µmol TBARs/L)0.12 ± 0.060.11 ± 0.060.11 ± 0.090.11 ± 0.080.86Milk thiol (µmol /L)81.0 ± 23.676.0 ± 19.582.3 ± 21.479.5 ± 18.50.32Infant’s urine DPPH (µmol Trolox equivalent /L)9.2 ± 7.19.3 ± 8.29.7 ± 7.412.6 ± 8.6
0.028
Infant’s urine FRAP (µmol /L)19.1 ± 13.120.8 ± 15.822.1 ± 17.225.8 ± 17.80.07Infant’s urine MDA (µmol Trolox equivalent /L)1.8 ± 1.71.8 ± 1.71.8 ± 1.41.9 ± 1.80.97Significant value are in bold.DPPH Diphenylpicrylhydrazyl, FRAP Ferric reducing ability of plasma, MDA Malondialdehyde, HEI healthy eating index.p value obtained from one-way ANOVA.
The mean concentration of the BM and infant’s urine antioxidants were not associated with AHEI quartiles (Table 5).
Table 5The comparison of anti-oxidant concentration of breast milk and the infant’s urine between quartiles of AHEI.VariablesAHEI quartilesp valueQ1Q2Q3Q4Milk DPPH (µmol Trolox equivalent /L)310.0 ± 79.3320.9 ± 90.8311.5 ± 80.8323.2 ± 98.80.60Milk FRAP (µmol /L)515.4 ± 117.7547.2 ± 104.3549.2 ± 132.2552.5 ± 114.30.43Milk MDA (µmol TBARs/L)0.18 ± 0.100.17 ± 0.120.17 ± 0.070.16 ± 0.070.82Milk thiol (µmol /L)77.6 ± 17.279.0 ± 16.078.4 ± 12.577.9 ± 16.30.95Infant’s urine DPPH (µmol Trolox equivalent /L)9.5 ± 7.610.1± 7.39.8 ± 6.711.41 ± 9.620.45Infant’s urine FRAP (µmol /L)20.5 ± 15.724.5 ± 16.820.9 ± 14.023.4 ± 18.730.36Infant’s urine MDA (µmol Trolox equivalent /L)1.8 ± 1.61.7 ± 1.21.7 ± 1.32.05 ± 1.910.57DPPH Diphenylpicrylhydrazyl, FRAP Ferric reducing ability of plasma, MDA Malondialdehyde, AHEI alternative healthy eating index.p value obtained from one-way ANOVA.
Linear regression analysis of HEI and AHEI components and antioxidant concentration of BM and infant’s urine is indicated in Table 6. There was a direct association between whole fruit score and the DPPH level of BM (p < 0.05). Also, there was a direct relationship between the dark green vegetable score and the level of the infant’s urine DPPH, FRAP, and MDA (p < 0.01). However, there was an inverse association between this component and the thiol level of BM (p < 0.01). The total vegetable score indicated a direct association with the FRAP of BM, the infant’s urine FRAP, and DPPH (p < 0.05). There was an inverse association between seafood and plant protein and also beans and nuts with the MDA of BM (p < 0.05). HEI was directly associated with the DPPH of BM, the infant’s urine DPPH, and FRAP (p < 0.05). However, AHEI was not significantly associated with any BM or infant urine antioxidant concentration.
Table 6Multivariate linear regression between each component of the HEI and AHEI and anti-oxidant concentration of breast milk and infant’s urine.HEI componentBreast milkInfant’s urineDPPHFRAPMDAThiolDPPHFRAPMDAβ
p
β
p
β
p
β
p
β
p
β
p
β
p
Whole fruit7.97
0.022
-0.830.86-0.0040.36-1.110.08-0.160.590.230.700.0310.62Total fruit4.220.36-3.600.58-0.0080.10-1.81
0.034
0.150.700.980.230.0260.76Dark green vegetables3.600.243.630.3700.91-1.42
0.009
0.98
< 0.001
1.77
0.001
0.17
0.002
Total vegetables2.280.164.25
0.046
-0.0010.50-0.260.370.35
0.011
0.69
0.015
0.040.16Whole grains-2.180.30-5.040.07-0.0020.410.530.160.240.180.160.66-0.040.34Dairy products-0.740.75-1.710.5800.860.140.730.370.060.770.060.0060.88Total protein foods0.400.93-6.510.32-0.0070.19-0.210.810.450.291.100.200.0180.84Seafood and plant protein1.420.73-3.280.55-0.011
0.018
-0.340.640.560.120.580.430.0900.23Nuts and beans1.690.34-1.560.51-0.005
0.010
-0.120.690.0550.68-0.090.750.0140.61Fatty acids (MUFA + PUFA/SFA)1.770.31-2.070.380.00010.890.220.480.0820.580.0750.81-0.0160.61Trans fatty acid2.930.711.900.86-0.0110.192.010.160.600.380.720.610.0840.56n-3 fatty acid1.180.60-0.260.93-0.0020.41-0.730.070.090.620.070.850.0120.76PUFA1.720.401.120.68-0.0010.74-0.0730.840.230.200.360.390.0230.54Refined grains0.590.78-0.630.830.0010.790.520.170.0030.990.270.470.0350.37Sodium0.890.88212.890.090.0020.80-0.360.73-0.540.26-0.570.56-0.0250.81Empty calories1.180.16-0.030.98-0.0010.42-0.0050.97-0.0210.75-0.0440.74-0.020.14Sugar-sweetened beverages -0.740.81-0.590.90-0.0020.540.640.29-0.060.830.0740.89-0.0530.36Red meat and processed meat-0.210.8790.1010.960.0020.230.390.110.070.510.0540.79-0.0210.33HEI0.97
0.049
-0.140.83-0.0010.15-0.0330.710.09
0.031
0.17
0.035
0.0010.93AHEI0.860.25-0.080.940.00010.580.0940.4830.110.110.130.300.0100.43Breast milk adjusted for mother’s age and energy intake and mother’s BMIInfant’s urine adjusted for infant age and sex, weight, and head circumferenceSignificance of bold values are p < 0.05.
Odds ratios for the low antioxidant concentration of BM and infant’s urine across quartiles of HEI are assessed using crude and adjusted models (Table 7).
Table 7Multivariate-adjusted odds ratios β (95% CIs) for low anti-oxidant concentration of breast milk and infant’s urine across quartiles of HEI.CrudeModel 1Model 2Breast milk DPPH Q1111 Q21.32 (0.68–2.56)1.34 (0.69–2.63)1.37 (0.69–2.70) Q30.74 (0.39–1.41)0.75 (0.39–1.45)0.74 (0.38–1.45) Q40.83 (0.43–1.58)0.92 (0.47–1.80)0.97 (0.49–1.94) FRAP Q1111 Q20.94 (0.48–1.82)0.94 (0.48–1.83)0.97 (0.49–1.93) Q31.24 (0.65–2.38)1.21 (0.63–2.35)1.22 (0.62–2.39) Q41.25 (0.65–2.40)1.26 (0.64–2.50)1.39 (0.69–2.79) MDA Q1111 Q21.23 (0.63–2.39)1.28 (0.65–2.50)1.30 (0.66–2.56) Q31.78 (0.92–3.42)1.93 (0.990–3.77)2.03 (1.02–4.01)* Q41.35 (0.70–2.60)1.44 (0.73–2.85)1.37 (0.68–2.74) Thiol Q1111 Q21.61 (0.82–3.14)1.59 (0.81–3.14)1.54 (0.77–3.07) Q30.95 (0.50–1.82)0.94 (0.48–1.82)0.98 (0.50–1.93) Q40.89 (0.46–1.72)0.97 (0.49–1.92)0.99 (0.49–1.99)Infant’s urine DPPH Q1111 Q20.85 (0.43–1.69)0.84 (0.43–1.67)0.84 (0.42–1.68) Q30.58 (0.30–1.14)0.55 (0.29–1.08)0.59 (0.30–1.16) Q40.32 (0.16–0.64)**0.27 (0.13–0.54)**0.33 (0.17–0.66)** FRAP Q1111 Q20.60 (0.30–1.22)0.61 (0.31–1.20)0.58 (0.29–1.19) Q30.51 (0.26–1.02)0.51 (0.26–0.98)*0.51 (0.25–1.02) Q40.43 (0.21–0.85)0.42 (0.21–0.83)*0.41 (0.20–0.83)* MDA Q1111 Q20.87 (0.43–1.73)0.90 (0.46–1.75)0.86 (0.43–1.73) Q30.65 (0.33–1.27)0.65 (0.33–1.25)0.66 (0.33–1.32) Q40.83 (0.43–1.63)0.70 (0.36–1.37)0.83 (0.42–1.63)DPPH Diphenylpicrylhydrazyl, FRAP Ferric reducing ability of plasma, MDA Malondialdehyde.Odds ratios with 95% CI obtained from binary logistic regression tests.Breast milk model 1 adjusted for the mother’s age and energy intake; and model 2 additionally adjusted for the mother’s BMI.Infant’s urine model 1 adjusted for infant’s age and sex; and model 2 additionally adjusted for infant’s weight and head circumference.*p < 0.05.**p < 0.01.**p < 0.001.
The odds of low levels of BM MDA increased in the last quartile of HEI compared to the first quartile (OR = 2.03, 95% CI: 1.02–4.01; p < 0.05). However, the odds of low levels of infant’s urine DPPH (OR = 0.33, 95% CI: 0.17–0.66; p < 0.01) and FRAP (OR = 0.41, 95% CI = 0.20–0.83; p < 0.05) decreased in the last quartile compared to the first quartile.
Odds ratios for the low antioxidant concentration of BM and infant’s urine across quartiles of AHEI are assessed using crude and adjusted models (Table 8). In all models, we found no significant association between AHEI quartiles and antioxidant concentration of BM and infant’s urine.
Table 8Multivariate-adjusted odds ratios (95% CIs) for low anti-oxidant concentration of breast milk and infant’s urine across quartiles of AHEI.CrudeModel 1Model 2Breast milk DPPH Q1111 Q20.60 (0.31–1.14)0.61 (0.32–1.17)0.70 (0.36–1.35) Q31.21 (0.63–2.32)1.25 (0.65–2.41)1.29 (0.65–2.53) Q40.85 (0.45–1.62)0.89 (0.46–1.71)1.02 (0.52–1.98) FRAP Q1111 Q20.50 (0.26–0.97)0.51 (0.26–0.99)0.57 (0.29–1.12) Q30.54 (0.28–1.05)0.55 (0.28–1.07)0.54 (0.27–1.09) Q40.57 (0.30–1.11)0.58 (0.30–1.13)0.65 (0.33–1.27) MDA Q1111 Q21.18 (0.62–2.25)1.16 (0.60–2.22)1.16 (0.60–2.25) Q31.11 (0.58–2.13)1.12 (0.58–2.17)1.20 (0.61–2.36) Q41.42 (0.74–2.73)1.44 (0.75–2.77)1.38 (0.71–2.68) Thiol Q1111 Q20.83 (0.43–1.58)0.60 (0.44–1.61)0.82 (0.42–1.59) Q30.68 (0.53–1.31)0.66 (0.34–1.28)0.62 (0.31–1.23) Q41.12 (0.58–2.14)1.11 (0.58–2.16)1.03 (0.53–2.02)Infant’s urine DPPH Q1111 Q21.03 (0.54–1.96)0.98 (0.51–1.91)1.02 (0.52–2) Q30.97 (0.51–1.84)0.98 (0.51–1.90)1 (0.51–1.93) Q40.79 (0.41–1.49)0.75 (0.39–1.46)0.72 (0.37–1.41) FRAP Q1111 Q20.71 (0.37–1.35)0.54 (0.27–1.07)0.58 (0.29–1.18) Q30.67 (0.35–1.27)0.60 (0.30–1.18)0.63 (0.32–1.26) Q40.71 (0.37–1.34)0.62 (0.31–1.23)0.62 (0.31–1.24) MDA Q1111 Q21.03 (0.54–1.95)0.95 (0.48–1.87)1.03 (0.52–2.04) Q31.03 (0.54–1.94)1.04 (0.53–2.03)1.11 (0.56–2.18) Q40.97 (0.51–1.84)0.92 (0.47–1.80)0.92 (0.47–1.82)DPPH Diphenylpicrylhydrazyl, FRAP Ferric reducing ability of plasma, MDA Malondialdehyde.Odds ratios with 95% CI obtained from binary logistic regression tests.Breast milk model 1 adjusted for the mother’s age and energy intake, and model 2 additionally adjusted for the mother’s BMI.Infant’s urine model 1 adjusted for infant’s age and sex, model 2 additionally adjusted for infant’s weight and head circumference.
Discussion
In this cross-sectional study, we found lower breast milk MDA and higher infant urine DDPH and FRAP levels among mothers with higher HEI scores. However, there was no significant association between AHEI with the antioxidant content of BM, and the infant’s urine.
The health of infants is directly associated not only with the nutritional elements and quality of the BM but also with its antioxidant content. Our findings suggest that breastfeeding mothers who adhere to a healthier diet pattern are more likely to provide BM rich in antioxidants, which may protect cells from oxidative stress and maintain overall health. Oxidative stress occurs when an imbalance exists between oxidants, such as free radicals, and antioxidants in the body. Free radicals are unstable molecules potentially damage cells and promote susceptibility to different health conditions, including inflammation, cardiovascular disease, and cancer. Antioxidants are compounds that combat free radicals and help prevent oxidative damage. BM contains a broad spectrum of antioxidants, including vitamins C and E, β-carotene, and selenium, essential for infant growth and development32. Our research shows that mothers with a higher HEI score tend to have BM with lower concentrations of MDA, a marker of oxidative stress. So, mothers with lower HEI may likely produce higher ROS levels through mammary gland lactocytes. Maternal diet quality directly impacts the oxidant-antioxidant balance in BM, which may, in turn, influence the health and development of breastfed infants. Because of these effects, we found that infants of mothers with higher HEI scores had higher levels of DDPH and FRAP in their urine, further supporting the link between maternal diet quality and infant antioxidants.
Also, some components of HEI showed a relationship with the antioxidant content of BM and the infant’s urine. High whole and total fruit intake indicated a higher DPPH and thiol content in BM, respectively. Dark green vegetable intake directly affected all antioxidant concentrations in the infant’s urine, including DPPH, thiol, and MDA, but had an inverse effect on the amount of thiol in the BM. It has been shown that non-heme iron dietary nutrients such as fruits and vegetables could beneficially affect oxidative stress. In contrast, heme-iron dietary nutrients, including meat groups, especially red meat, worsen this situation throughout the body33.
Several studies have investigated the relationship between breastfeeding mothers’ diet quality and the BM’s antioxidant content and infant’s urine12,23,26. Higher levels of DPPH and thiol were reported in the BM of lactating mothers who adhered to a healthy dietary pattern characterized by high consumption of fruits, vegetables, grains, and nuts. Debski et al.. reported that the activity of glutathione peroxidase was considerably higher in milk specimens of vegetarian versus non-vegetarian mothers34.
It has been reported that following a four-week MedDiet style significantly promoted HEI scores (+ 27 units) among breastfeeding obese women35. In another study, the commitment of mothers to Polish-adapted MedDiet meal was a remarkable predictor of BM mineral composition in the 1st month of lactation36.
We have previously demonstrated that adherence to healthy dietary patterns such as MedDiet or DASH styles was associated with higher BM and infant urine DDPH and FRAP levels23,26. However, following an unhealthy dietary pattern resulted in a decrease in thiol levels12. A high score for MedDiet indicated a higher consumption of fruit, vegetables, legumes, grains, nuts, and olive oil; a moderate intake of poultry, eggs, seafood, and dairy products; and a low intake of red and processed meats37. Similarly, a high HEI score correlates with a high consumption of fruits, vegetables, grains, dairy products, seafood, and plant protein13. Tsopmo et al.. have reported that a high intake of fruits, vegetables, and grains during lactation can improve the antioxidant phytochemicals of the BM38. Fruits and vegetables are rich food sources of phytonutrients, which can improve our body’s antioxidant defense38,39. The mother’s nutritional habit also affects the antioxidant capacity of BM. No similar investigation was found to compare our results about the HEI/AHEI and total antioxidant capacity (TAC) of BM. However, an elevated intake of dairy products, fruits/vegetables, cereals, and nuts enhanced the TAC of human milk40. Antioxidant compounds of fruits and vegetables such as α-Tocopherol, ascorbic acid, and β-Carotene can enhance TAC of blood plasma41. TAC is representative of all body antioxidants that can be used to evaluate the quality of one’s diet42. Moreover, dietary TAC is another potential indicator for assessing diet quality, and it reveals a positive correlation with several nutritional indexes such as HEI43. Based on this correlation and direct relation between dietary TAC and plasma TAC status44, HEI score can be positively associated with dietary TAC and serum TAC levels.
To the best of our knowledge, this is the first study reporting a comprehensive analysis of the oxidant-antioxidant status through specialized assays such as the FRAP, DPPH, TBARs, and Ellman assays, exemplified a thorough and systematic approach to understanding the nutritional composition and antioxidant properties of human milk. Regarding limitations, notably, our study is observational, and further research is needed to establish causality. Additionally, oxidative status was measured in the infant only by indices in the urine; we cannot necessarily extrapolate this to their serum status. BMI is not an appropriate indicator for assessing the nutritional status of women after childbirth. However, our findings provide valuable insights into the potential benefits of a healthy diet for breastfeeding mothers and their infants.
Conclusion
Our study demonstrates that a higher quality of diet in breastfeeding mothers, as measured by the HEI, can positively impact the oxidant-antioxidant balance of BM and the infant’s urine. A healthier diet pattern is associated with lower oxidative stress markers in BM and infant urine levels, indicating greater antioxidant activity. The advantages of BM against oxidative stress were shown through the lower levels of urine markers in BM-feeding infants versus those fed with formula due to the higher and more diverse presence of antioxidants in BM45. These findings suggested the critical role of maternal nutrition in supporting the health and development of breastfed infants and emphasized the need for further investigation in this area. By promoting healthy eating habits among breastfeeding mothers, health authorities can help ensure future generations’ optimal growth and development. |
Title: Viral infection induces inflammatory signals that coordinate YAP regulation of dysplastic cells in lung alveoli | Body:
Introduction
Pathogen invasion and environmental insults are common causes of lung injury. The regeneration of injured lung epithelium is mediated by lung progenitor cells. Although several types of progenitor cells can contribute to lung regeneration, the activation and contribution of progenitor cell types vary depending on the nature and severity of the injury (1–4). Identifying the injury-induced signal that activates certain types of lung progenitor cells will provide insights into understanding how the activation of progenitor cell types and regenerative pathways is regulated in different lung-injury settings, especially the activation of the dysplastic remodeling pathway, which leads to chronic impact on lung function.
The gas-exchanging epithelium in the lung alveoli is composed of alveolar type 1 cells (AT1s), which are responsible for gas exchange, and alveolar type 2 cells (AT2s), which make surfactant protein to prevent alveoli collapse and function as progenitor cells to maintain alveolar epithelium integrity (3, 5). It has long been shown that AT2 cells, bronchoalveolar stem cells (BASCs), and airway club cells are facultative progenitor cells that can repair injured alveoli via generating AT1 and AT2 cells (1, 2, 4, 6). However, recent studies showed that following respiratory viral infections, such as influenza and Sendai virus, a group of rare distal airway progenitor cells are activated and repair the injured alveoli with KRT5+ dysplastic cells (7–10). Though the integrity of alveolar epithelial barrier is restored through this dysplastic repair pathway, these KRT5+ cells were thought pathological, as they barely differentiate into alveolar epithelial cells, but generate ectopic goblet cells and tuft cells (8, 11–13). In human patients with severe influenza or COVID-19 infection, extensive expansion of dysplastic KRT5+ cells correlates with long-term compromised lung function after viral clearance (14–16). Although ectopic expansion of dysplastic KRT5+ cells in the alveoli is frequently induced by severe respiratory viral infection, the mechanism that activates this dysplastic response and how these dysplastic cells maintain their identity within injured alveoli are still unclear.
We studied the potential mechanism that controls the development of dysplastic remodeling during viral infection. We identified an immune-epithelial module mediated by IFN-γ that promoted dysplastic cell expansion in viral-infected lungs. IFN-γ coordinated dysplastic cell expansion via the focal adhesion kinase (FAK)/Yes-associated protein 1 (YAP) signaling axis. We further demonstrated that YAP is required for both the expansion and maintenance of dysplastic cells, which provides a potential therapeutic target that not only prevents dysplastic cell expansion, but also reduces the persistent dysplastic cells in the alveoli.
Results
Robust expansion of dysplastic KRT5+ cells occurs following strong antiviral immune response.
To investigate the mechanism that regulates dysplastic KRT5+ cell expansion following influenza A virus (IAV) (H1N1 PR8 strain) infection, we first compared the lung histology in mice subjected to IAV to that of those challenged by bleomycin, a chemotherapy reagent that can also induce severe alveolar damage, but that has a limited extent of alveolar KRT5+ cell formation (8). We confirmed that both injury models led to comparable levels of lung histological change and similar levels of alveolar epithelial cell ablation (Supplemental Figure 1, A–D; supplemental material available online with this article; https://doi.org/10.1172/JCI176828DS1). Intriguingly, the dysplastic KRT5+ cells from IAV-infected lungs tend to form the typical KRT5+ “pod” structure, whereas most of the KRT5+ dysplastic cells in bleomycin-injured lungs formed a luminal-like structure and were frequently colocalized with SCGB1A1+ club cells (Figure 1, A–D). IAV-infected mice also showed much larger KRT5+ lung areas and higher Krt5 mRNA levels compared with those challenged by bleomycin (Figure 1, E–H). Additionally, IAV-infected lungs showed goblet cell hyperplasia in both the airway and alveoli, whereas bleomycin-injured lungs exhibited virtually no goblet cells (Figure 1, I and J). Thus, these results suggest that additional factors in IAV-infected lungs stimulate KRT5+ cell expansion.
Inflammatory signals have recently been identified as drivers for tissue regeneration (17). We therefore compared the mRNA expression of immune factors in mice injured by these 2 models. We found that IAV-infected lungs showed much higher levels of Ifnb1 and Ifng compared with those challenged by bleomycin (Figure 1K). Interestingly, the expression of Ifng reached its peak around day 7, when dysplastic KRT5+ cells first appeared in the distal airway, and remained at a high level until at least day 10, when dysplastic KRT5+ cells started to form large pod-like structures (18). In contrast, the expression of other cytokines we tested was either moderately induced, such as Il17, Il22, Il13, Il5, and Tnfa, or returned to baseline levels before the appearance of KRT5+ cells in the alveoli, such as Il1b (Figure 1K). These observations were also supported by the data from measuring inflammatory cytokine levels in bronchoalveolar lavage fluid (BALF) and lung homogenate (Supplemental Figure 1, E and F). Consistent with the high expression of inflammatory cytokines, IAV-infected lungs showed more infiltrated lymphocytes, NK cells, CD4+ T cells, monocyte-derived alveolar macrophages, and CD8+ T cells than bleomycin-injured lungs (Figure 1L and Supplemental Figure 1G). Costaining for markers of immune cells with dysplastic KRT5+ cells revealed that CD8+ T cells, the major source of IFN-γ during viral infection (19), were recruited to injured alveoli areas, including KRT5+ pods. Intriguingly, at day 28 after injury, we still observed abundant CD8+ T cells in the injured alveoli area (Supplemental Figure 1H). We also detected abundant CCR2+ monocyte–derived alveolar macrophages in the injured lung area, consistent with their role in tissue remodeling (20). However, NK cells, alveolar macrophages, and CD4+ T cells were less frequently detected in KRT5+ areas (Figure 1M). In contrast, very few of these immune cells were found in KRT5+ lung areas of bleomycin-injured lungs (Figure 1M). Thus, these observations suggest that the expansion of dysplastic KRT5+ cells in viral-infected lungs coincides with the robust antiviral inflammatory response, suggesting a role of inflammatory signals in controlling this process.
Infection-induced IFN-γ is required for dysplastic remodeling in IAV-infected lungs.
Interferon signaling is critical to the host antiviral response and can directly signal to lung epithelial cells (19). Considering the potential link among infiltration of CD8+ T cells, expression kinetics of IFN-γ, and dysplastic KRT5+ cell formation after IAV infection, we hypothesized that IFN-γ may contribute to the formation of dysplastic KRT5+ cells. To test this, we inactivated interferon receptor 1 (Ifngr1) via Shhcre (hereafter Ifngr1EKO), in which Cre recombinase is expressed in all the lung epithelial cells (21). Ifngr1EKO mice showed normal lung epithelial cell composition and alveolar morphology compared with control mice, as indicated by immunofluorescent staining of epithelial cell-type–specific markers and histological staining (Supplemental Figure 2, A–E). Remarkably, following IAV infection, Ifngr1EKO mutant lungs showed a reduction in the KRT5+ lung area and decreased Krt5 mRNA expression levels compared with control lungs (Figure 2, A–C). The numbers of alveolar DCLK1+ tuft cells and alveolar goblet cells, which are both derived from dysplastic KRT5+ cells, were also decreased (Figure 2, D–G) (12, 13, 22). In contrast, the goblet cell hyperplasia response in the airway occurred normally in Ifngr1EKO mutant lungs, suggesting that these cells are not derived from KRT5+ cells (Supplemental Figure 2, F and G). Further analysis revealed that the reduced formation of KRT5+ cells in Ifngr1EKO mice was not due to changes in viral clearance efficiency, as the Ifngr1EKO mutant mice and control mice showed comparable levels of viral nucleoprotein (NP) and nonstructural protein 1 (NS1) during lung recovery (Supplemental Figure 2H).
To determine whether Ifngr1 is also required for the formation of dysplastic KRT5+ cells in bleomycin-injured lungs, we exposed control and Ifngr1EKO mutant mice to bleomycin challenge and found that the formation of dysplastic KRT5+ cells was not affected, indicating a dispensable role of IFN-γ signaling in the formation of dysplastic KRT5+ cells in bleomycin-injured lungs (Supplemental Figure 2, I–K). Thus, these data demonstrate that IFN-γ specifically regulates the formation of dysplastic cells induced by IAV, but not bleomycin.
Inhibition of IFN-γ signaling promotes lung recovery following IAV infection.
In addition to intrapulmonary p63+ cells, the SOX2+ airway epithelial cells harbor multiple progenitor cell populations, such as club cells and BASCs, which can generate fresh AT2 cells to repair lung alveoli (10, 23). To determine whether inhibition of epithelial IFN-γ signaling affects lung regeneration, we generated Sox2creERT2;Ifngr1fl/fl;R26-tdTomato mice (hereafter Ifngr1Sox2–KO) to trace the fate of SOX2+ progenitor cells while inactivating Ifngr1 (Figure 2H). Consistent with the phenotype of Ifngr1EKO mice, the expansion of KRT5+ dysplastic cells was largely suppressed in lungs from Ifngr1Sox2–KO mice after IAV infection (Figure 2, I and J). Notably, the mutant lungs showed an increase in lineage-traced AT2 cells in the injured alveoli (Figure 2, K and L). Although the weight loss at the early stage and animal survival were not different between control and mutant mice, the mutant exhibited improved body weight recovery after day 10 (Figure 2, M and N). Consistently, the mutant mice showed improved functional lung recovery, including maximal voluntary ventilation (MVV), forced vital capacity (FVC), and lung compliance, and ameliorated fibrosis at 2 months after infection (Figure 2, O–U). These findings demonstrate that disruption of epithelial IFN-γ signaling promotes lung recovery following IAV infection.
IFN-γ regulates lung dysplastic remodeling in a Stat1-independent manner.
The canonical IFN-γ signaling acts through the JAK/STAT signaling axis to relay IFN-γ signals to the nucleus and activate downstream gene expression (24). To determine whether IFN-γ regulates dysplastic KRT5+ cell expansion through the JAK/STAT pathway, we used JAK1/JAK2 antagonist or ShhCre;Stat1fl/fl (here after Stat1EKO) mutant mice to test the role of JAK/STAT signaling in this process. Using an established organoid culture system (10), we purified and cultured primary intrapulmonary p63+ progenitor enriched cells (EpCAM+Integrin β4+). Treatment of cultured intrapulmonary p63+ cells with IFN-γ promotes their transdifferentiation into KRT5+ cells, and this effect was diminished when baricitinib or fedratinib, specific antagonists for JAK1 and JAK2 (25), was added to the culture medium (Supplemental Figure 3, A and B). Consistently, administration of baricitinib into IAV-infected mice from day 7 to day 11 after infection resulted in reduced KRT5+ alveolar area (Supplemental Figure 3, C–E). Interestingly, the formation of dysplastic KRT5+ cells was not affected in Stat1EKO mutant mice as compared with controls after IAV infection (Supplemental Figure 3, F–H), suggesting a dispensable role of Stat1 in this process.
Dysplastic KRT5+ cells exhibit elevated focal adhesion and YAP activity during their migration.
To further investigate the intracellular signaling pathway by which IFN-γ regulates the formation of dysplastic KRT5+ cells, we performed single-cell RNA-sequencing (scRNA-Seq) assay on lung epithelial cells isolated from IAV-infected mice at day 14 after infection. The lung epithelial cells were classified into 7 cell clusters. These clusters consisted of secretory cells, ciliated cells, dysplastic KRT5+ cells, AT2 cells, AT1 cells, CLDN4+ intermediate cells, which are also named damage-associated transient progenitors (DATPs) or prealveolar type-1 transitional cell state (PATS) (26–28), and a cell population expressing high levels of cell-cycle–related genes, such as Ki67 and Ccnb2 (Figure 3, A and B, and Supplemental Figure 4A). We found that dysplastic KRT5+ cells, proliferating cells, and CLDN4+ intermediate cells were enriched in the expression of genes related to the type I cytokine signaling pathway, such as Ifngr1, Ifngr2, and Stat1, compared with genes related to type II and type III cytokines (Figure 3C). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that dysplastic KRT5+ cells were enriched in the expression of genes associated with regulation of actin cytoskeleton, focal adhesion, and Hippo pathway (Figure 3D). In line with this, dysplastic KRT5+ cells expressed high levels of integrin and cell junction–related genes, such as Itgb1, Itgb4, Itgb5, Itgb6, Cldn1, and Cldn4 (Supplemental Figure 4B). Because of these observations, we speculated that the actin cytoskeleton pathway and focal adhesion pathway, both of which can induce nuclear YAP activity via the FAK/c-Src (herein termed Src, the founding member of the Src family of nonreceptor protein tyrosine kinases) signaling axis (29), might play a role in regulating the expansion of dysplastic KRT5+ cells. Using an antibody specific to the active form of Src, phosphorylated at Tyr416 (30), we demonstrated that Src was widely activated in dysplastic KRT5+ cells at day 12 after infection, when these cells had just migrated into the alveoli (8, 18). As the KRT5+ cells expanded, the Src signal gradually decreased in dysplastic KRT5+ cells closer to the bronchoalveolar duct, while the dysplastic KRT5+ cells at the periphery of pods retained Src expression, consistent with the migration route of dysplastic KRT5+ cells that move from the distal airway to the alveoli (7, 18). By day 18 after infection, only a limited number of dysplastic KRT5+ cells were found expressing activated Src (Figure 3, E and F). By using a published dataset of YAP-regulated genes (31), we further demonstrated that canonical YAP target genes such as Ccn1, Axl, Arfgeh17, and Snai2 were highly expressed in dysplastic KRT5+ cells (Figure 3G). Consistently, the nuclear YAP and Ki67 were expressed in a pattern resembling phosphorylated Src during dysplastic KRT5+ cell expansion, suggesting a causal link of YAP activation and dysplastic KRT5+ cell expansion (Figure 3, H and I). Notably, we also found that most of the SRC+ dysplastic KRT5+ cells expressed nuclear YAP (Supplemental Figure 4, C and D). Thus, these results demonstrate that the FAK/Src-YAP axis is highly activated during the expansion of dysplastic KRT5+ cells, prompting us to investigate their role in controlling this process.
FAK/Src-YAP signaling axis is required for IFN-γ–mediated dysplastic remodeling.
Since IFN-γ is required for the formation of dysplastic KRT5+ cells and these cells exhibit high focal adhesion pathway activity during their migration, we hypothesized that IFN-γ may act through JAK to activate the FAK/Src-YAP pathway to mediate this dysplastic response. To evaluate the role of JAK in activating the FAK/Src-YAP pathway, we examined p-SRC and YAP expression in cultured primary intrapulmonary p63+ progenitor enriched cells and found increased p-SRC+ cells and YAP+ cells following IFN-γ treatment. Inhibition of JAK activity using baricitinib or fedratinib diminished this effect (Supplemental Figure 5, A–D). We also examined the total abundance of YAP using Western blot and found that IFN-γ treatment increased the total abundance of YAP, and this effect was diminished by adding baricitinib or fedratinib (Supplemental Figure 5E). Additionally, Ifngr1 mutant mice exhibited a reduced number of nuclear YAP-expressing cells following IAV infection (Supplemental Figure 5, F–I). We next investigated the role of FAK/Src in this process. We found that in the presence of the FAK antagonist (PF-573228) or the Src antagonist (dasatinib), IFN-γ–mediated nuclear YAP translocation was also diminished (Figure 4, A and B). Similarly, IFN-γ treatment promoted cultured p63+ progenitor cells to transdifferentiate into KRT5+ cells, and this effect was inhibited by PF-573228 or dasatinib (Figure 4, C and D). Consistently, oral delivery of FAK antagonist (PND-1186) or Src antagonist (saracatinib) into IAV-infected mice from day 7 to day 11 after infection resulted in reduced KRT5+ lung areas compared with control mice receiving PBS (Figure 4, E–G).
We further tested the role of YAP in this process by crossing Sox2creERT2 mice with Yapfl mice and generating Yap heterozygous (here after YapSox2–hKO) and homozygous mutant mice (here after YapSox2–KO). Tamoxifen was injected 14 days prior to IAV infection. The mutant mice showed normal ratios of airway and alveolar epithelial cells (Supplemental Figure 5, J–M). However, following IAV infection, the expansion of dysplastic KRT5+ cells was severely suppressed in both YapSox2–hKO and YapSox2–KO mutant lungs (Figure 4, H–J). To determine whether forced activation of YAP in SOX2+ progenitor cells promotes KRT5+ cell expansion in IAV-infected mice, we generated Sox2creERT2;Mst1fl/fl;Mst2–/– mutant mice (hereafter Mst1/2Sox2–KO), in which YAP is constitutively activated in SOX2 progenitor cells after tamoxifen induction (Supplemental Figure 6, A and B). After IAV infection, we observed a trend suggesting that constitutively activated YAP in SOX2+ progenitors promotes dysplastic KRT5+ cell formation, although it was not statistically significant (Supplemental Figure 6, C–E). We then assessed the requirement of YAP in expanding KRT5+ cells by generating Krt5creERT2;Yapfl/+ (hereafter YapKrt5–hKO) and Krt5creERT2;Yapfl/fl (hereafter YapKrt5–KO) mice. Tamoxifen was injected from day 7 to day 11 after infection to induce CRE activity (Figure 4K). Similarly to what occurred with the phenotype of YapSox2–hKO mutant mice, the expansion of dysplastic KRT5+ cells was severely suppressed in both YapKrt5–hKO and YapKrt5–KO mutant lungs (Figure 4, L and M). Thus, in response to infection-induced IFN-γ signal, dysplastic KRT5+ cells activate FAK/Src-YAP signaling to proliferate and migrate to the injured lung alveoli.
YAP is required for the long-term maintenance of dysplastic KRT5+ cells, and inhibition of YAP leads to their differentiation into club cells.
The persistence of alveolar KRT5+ cells impedes functional lung regeneration and chronically affects lung function (11). To determine whether YAP is also required for the long-term maintenance of dysplastic KRT5+ cells in the alveoli, we introduced R26-tdtomato reporter into a YapKrt5–hKO and YapKrt5–KO mutant mouse background. This allowed us to trace dysplastic KRT5+ cells while assessing the possible cell-fate conversion that may be induced by Yap inactivation. Tamoxifen was administered at day 21 after infection, when most of KRT5+ cells had migrated into the alveoli and no longer expressed Ki67, and the mice were analyzed at 14 days after tamoxifen administration (Figure 5A). Immunofluorescent analysis showed that most of the lineage-traced cells in control animals continued to express KRT5, with only a few cells losing KRT5 expression and instead expressing SCGB1A1 (Figure 5B). In contrast, in Yap mutant mice, we observed an increase in the proportion of lineage-traced cells expressing club cell markers, including SCGB1A1, ITGb4, and SCGB3A2 (Figure 5B and Supplemental Figure 6, F and G), while the percentages of cells expressing KRT5 decreased. Interestingly, this phenotype was also shown to be YAP dose dependent (Figure 5, C and D). We did not observe any lineage-traced cells expressing surfactant-associated protein C (SFTPC) in either control or Yap mutant mice (Figure 5E). The percentage of DCLK1+ tuft cells in total lineage-traced cells was not affected upon Yap inactivation (Figure 5, F and G). Intriguingly, though Yap deletion affects the maintenance of dysplastic KRT5+ cells, the lung fibrosis was not ameliorated (Supplemental Figure 6H). Thus, we provide a strategy for converting persistent KRT5+ cells into distal club-like cells.
IFN-γ promotes human AT2 cells to transdifferentiate into dysplastic cells.
Unlike in mice, in which the dysplastic KRT5+ cells are derived from intrapulmonary p63+ cells, in humans, AT2 cells can transdifferentiate into dysplastic KRT5+ cells during lung injury or disease (32). To investigate whether IFN-γ signaling also mediates dysplastic cell formation in human lungs, we first analyzed published scRNA-Seq data of COVID-19 lungs (33). We included AT2 cells, AT1 cells, KRT8+ intermediate cells, and basal cells in our analysis. Monocle 3 analysis suggested that human AT2 cells differentiate into AT1 cells and KRT8+ intermediate cells through different trajectories and that KRT8+ intermediate cells may further differentiate into basal cells (Supplemental Figure 7, A and B). This observation is consistent with a recent report that in human lungs, KRT8+ intermediate cells can serve as precursors for dysplastic KRT5+ cells (32). Gene Set Enrichment Analysis (GSEA) revealed that these KRT8+ intermediate cells exhibited high activity in response to IFN-γ, focal adhesion, and the actin cytoskeleton pathway compared with AT2 cells (Supplemental Figure 7, C–E). Using lung sections from COVID-19 patients, we observed CD8+ T cells appearing in the KRT5+ lung area and p-SRC and nuclear YAP expression in KRT5+ cells (Figure 6A). To further investigate whether IFN-γ can promote the transdifferentiation of human AT2 cells into dysplastic KRT5+ cells through the FAK/Src-YAP pathway, we isolated primary AT2 cells from human lung tissues using magnetic bead–based selection with HTII-280 antibody (Supplemental Figure 7, F and G). The sorted cells were cocultured with MRC5 cells, a human fetal lung fibroblast cell line, as feeders to promote the organoid formation. Recombinant human IFN-γ was added to the culture medium on day 14 when single AT2 cells had grown into sphere-like organoids, and organoids were collected at day 21 for analysis (Figure 6B). Immunofluorescent analysis showed that following IFN-γ treatment, there was a decrease of organoids expressing the AT2 cell marker, including SFTPC and HTII-280, whereas the organoids expressing KRT8, KRT17, or KRT5 increased (Figure 6, C and D). Interestingly, some KRT8+ cells also expressed HTII-280 (Supplemental Figure 7, H–K). Supplementation of IFN-γ–treated human AT2 organoids with Src antagonist (dasatinib) or FAK antagonist (PF-573228) prevented their transdifferentiation into KRT8+, KRT17+, or KRT5+ dysplastic cells and partially reversed the expression of HTII-280, but the expression of SFTPC was not reversed by either of these 2 inhibitors (Figure 6, E and F). We also confirmed the expression of p-SRC and YAP in IFN-γ–treated human lung organoids (Supplemental Figure 7, L–O). These observations were supported by detailed quantification using cell-type–specific markers (Figure 6, G–L). Collectively, these findings in humans are consistent with our findings in mice and are suggestive of a potential role of IFN-γ and FAK/Src-YAP signaling in controlling viral pneumonia–induced dysplastic remodeling in human lungs.
Robust expansion of dysplastic KRT5+ cells occurs following strong antiviral immune response.
To investigate the mechanism that regulates dysplastic KRT5+ cell expansion following influenza A virus (IAV) (H1N1 PR8 strain) infection, we first compared the lung histology in mice subjected to IAV to that of those challenged by bleomycin, a chemotherapy reagent that can also induce severe alveolar damage, but that has a limited extent of alveolar KRT5+ cell formation (8). We confirmed that both injury models led to comparable levels of lung histological change and similar levels of alveolar epithelial cell ablation (Supplemental Figure 1, A–D; supplemental material available online with this article; https://doi.org/10.1172/JCI176828DS1). Intriguingly, the dysplastic KRT5+ cells from IAV-infected lungs tend to form the typical KRT5+ “pod” structure, whereas most of the KRT5+ dysplastic cells in bleomycin-injured lungs formed a luminal-like structure and were frequently colocalized with SCGB1A1+ club cells (Figure 1, A–D). IAV-infected mice also showed much larger KRT5+ lung areas and higher Krt5 mRNA levels compared with those challenged by bleomycin (Figure 1, E–H). Additionally, IAV-infected lungs showed goblet cell hyperplasia in both the airway and alveoli, whereas bleomycin-injured lungs exhibited virtually no goblet cells (Figure 1, I and J). Thus, these results suggest that additional factors in IAV-infected lungs stimulate KRT5+ cell expansion.
Inflammatory signals have recently been identified as drivers for tissue regeneration (17). We therefore compared the mRNA expression of immune factors in mice injured by these 2 models. We found that IAV-infected lungs showed much higher levels of Ifnb1 and Ifng compared with those challenged by bleomycin (Figure 1K). Interestingly, the expression of Ifng reached its peak around day 7, when dysplastic KRT5+ cells first appeared in the distal airway, and remained at a high level until at least day 10, when dysplastic KRT5+ cells started to form large pod-like structures (18). In contrast, the expression of other cytokines we tested was either moderately induced, such as Il17, Il22, Il13, Il5, and Tnfa, or returned to baseline levels before the appearance of KRT5+ cells in the alveoli, such as Il1b (Figure 1K). These observations were also supported by the data from measuring inflammatory cytokine levels in bronchoalveolar lavage fluid (BALF) and lung homogenate (Supplemental Figure 1, E and F). Consistent with the high expression of inflammatory cytokines, IAV-infected lungs showed more infiltrated lymphocytes, NK cells, CD4+ T cells, monocyte-derived alveolar macrophages, and CD8+ T cells than bleomycin-injured lungs (Figure 1L and Supplemental Figure 1G). Costaining for markers of immune cells with dysplastic KRT5+ cells revealed that CD8+ T cells, the major source of IFN-γ during viral infection (19), were recruited to injured alveoli areas, including KRT5+ pods. Intriguingly, at day 28 after injury, we still observed abundant CD8+ T cells in the injured alveoli area (Supplemental Figure 1H). We also detected abundant CCR2+ monocyte–derived alveolar macrophages in the injured lung area, consistent with their role in tissue remodeling (20). However, NK cells, alveolar macrophages, and CD4+ T cells were less frequently detected in KRT5+ areas (Figure 1M). In contrast, very few of these immune cells were found in KRT5+ lung areas of bleomycin-injured lungs (Figure 1M). Thus, these observations suggest that the expansion of dysplastic KRT5+ cells in viral-infected lungs coincides with the robust antiviral inflammatory response, suggesting a role of inflammatory signals in controlling this process.
Infection-induced IFN-γ is required for dysplastic remodeling in IAV-infected lungs.
Interferon signaling is critical to the host antiviral response and can directly signal to lung epithelial cells (19). Considering the potential link among infiltration of CD8+ T cells, expression kinetics of IFN-γ, and dysplastic KRT5+ cell formation after IAV infection, we hypothesized that IFN-γ may contribute to the formation of dysplastic KRT5+ cells. To test this, we inactivated interferon receptor 1 (Ifngr1) via Shhcre (hereafter Ifngr1EKO), in which Cre recombinase is expressed in all the lung epithelial cells (21). Ifngr1EKO mice showed normal lung epithelial cell composition and alveolar morphology compared with control mice, as indicated by immunofluorescent staining of epithelial cell-type–specific markers and histological staining (Supplemental Figure 2, A–E). Remarkably, following IAV infection, Ifngr1EKO mutant lungs showed a reduction in the KRT5+ lung area and decreased Krt5 mRNA expression levels compared with control lungs (Figure 2, A–C). The numbers of alveolar DCLK1+ tuft cells and alveolar goblet cells, which are both derived from dysplastic KRT5+ cells, were also decreased (Figure 2, D–G) (12, 13, 22). In contrast, the goblet cell hyperplasia response in the airway occurred normally in Ifngr1EKO mutant lungs, suggesting that these cells are not derived from KRT5+ cells (Supplemental Figure 2, F and G). Further analysis revealed that the reduced formation of KRT5+ cells in Ifngr1EKO mice was not due to changes in viral clearance efficiency, as the Ifngr1EKO mutant mice and control mice showed comparable levels of viral nucleoprotein (NP) and nonstructural protein 1 (NS1) during lung recovery (Supplemental Figure 2H).
To determine whether Ifngr1 is also required for the formation of dysplastic KRT5+ cells in bleomycin-injured lungs, we exposed control and Ifngr1EKO mutant mice to bleomycin challenge and found that the formation of dysplastic KRT5+ cells was not affected, indicating a dispensable role of IFN-γ signaling in the formation of dysplastic KRT5+ cells in bleomycin-injured lungs (Supplemental Figure 2, I–K). Thus, these data demonstrate that IFN-γ specifically regulates the formation of dysplastic cells induced by IAV, but not bleomycin.
Inhibition of IFN-γ signaling promotes lung recovery following IAV infection.
In addition to intrapulmonary p63+ cells, the SOX2+ airway epithelial cells harbor multiple progenitor cell populations, such as club cells and BASCs, which can generate fresh AT2 cells to repair lung alveoli (10, 23). To determine whether inhibition of epithelial IFN-γ signaling affects lung regeneration, we generated Sox2creERT2;Ifngr1fl/fl;R26-tdTomato mice (hereafter Ifngr1Sox2–KO) to trace the fate of SOX2+ progenitor cells while inactivating Ifngr1 (Figure 2H). Consistent with the phenotype of Ifngr1EKO mice, the expansion of KRT5+ dysplastic cells was largely suppressed in lungs from Ifngr1Sox2–KO mice after IAV infection (Figure 2, I and J). Notably, the mutant lungs showed an increase in lineage-traced AT2 cells in the injured alveoli (Figure 2, K and L). Although the weight loss at the early stage and animal survival were not different between control and mutant mice, the mutant exhibited improved body weight recovery after day 10 (Figure 2, M and N). Consistently, the mutant mice showed improved functional lung recovery, including maximal voluntary ventilation (MVV), forced vital capacity (FVC), and lung compliance, and ameliorated fibrosis at 2 months after infection (Figure 2, O–U). These findings demonstrate that disruption of epithelial IFN-γ signaling promotes lung recovery following IAV infection.
IFN-γ regulates lung dysplastic remodeling in a Stat1-independent manner.
The canonical IFN-γ signaling acts through the JAK/STAT signaling axis to relay IFN-γ signals to the nucleus and activate downstream gene expression (24). To determine whether IFN-γ regulates dysplastic KRT5+ cell expansion through the JAK/STAT pathway, we used JAK1/JAK2 antagonist or ShhCre;Stat1fl/fl (here after Stat1EKO) mutant mice to test the role of JAK/STAT signaling in this process. Using an established organoid culture system (10), we purified and cultured primary intrapulmonary p63+ progenitor enriched cells (EpCAM+Integrin β4+). Treatment of cultured intrapulmonary p63+ cells with IFN-γ promotes their transdifferentiation into KRT5+ cells, and this effect was diminished when baricitinib or fedratinib, specific antagonists for JAK1 and JAK2 (25), was added to the culture medium (Supplemental Figure 3, A and B). Consistently, administration of baricitinib into IAV-infected mice from day 7 to day 11 after infection resulted in reduced KRT5+ alveolar area (Supplemental Figure 3, C–E). Interestingly, the formation of dysplastic KRT5+ cells was not affected in Stat1EKO mutant mice as compared with controls after IAV infection (Supplemental Figure 3, F–H), suggesting a dispensable role of Stat1 in this process.
Dysplastic KRT5+ cells exhibit elevated focal adhesion and YAP activity during their migration.
To further investigate the intracellular signaling pathway by which IFN-γ regulates the formation of dysplastic KRT5+ cells, we performed single-cell RNA-sequencing (scRNA-Seq) assay on lung epithelial cells isolated from IAV-infected mice at day 14 after infection. The lung epithelial cells were classified into 7 cell clusters. These clusters consisted of secretory cells, ciliated cells, dysplastic KRT5+ cells, AT2 cells, AT1 cells, CLDN4+ intermediate cells, which are also named damage-associated transient progenitors (DATPs) or prealveolar type-1 transitional cell state (PATS) (26–28), and a cell population expressing high levels of cell-cycle–related genes, such as Ki67 and Ccnb2 (Figure 3, A and B, and Supplemental Figure 4A). We found that dysplastic KRT5+ cells, proliferating cells, and CLDN4+ intermediate cells were enriched in the expression of genes related to the type I cytokine signaling pathway, such as Ifngr1, Ifngr2, and Stat1, compared with genes related to type II and type III cytokines (Figure 3C). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that dysplastic KRT5+ cells were enriched in the expression of genes associated with regulation of actin cytoskeleton, focal adhesion, and Hippo pathway (Figure 3D). In line with this, dysplastic KRT5+ cells expressed high levels of integrin and cell junction–related genes, such as Itgb1, Itgb4, Itgb5, Itgb6, Cldn1, and Cldn4 (Supplemental Figure 4B). Because of these observations, we speculated that the actin cytoskeleton pathway and focal adhesion pathway, both of which can induce nuclear YAP activity via the FAK/c-Src (herein termed Src, the founding member of the Src family of nonreceptor protein tyrosine kinases) signaling axis (29), might play a role in regulating the expansion of dysplastic KRT5+ cells. Using an antibody specific to the active form of Src, phosphorylated at Tyr416 (30), we demonstrated that Src was widely activated in dysplastic KRT5+ cells at day 12 after infection, when these cells had just migrated into the alveoli (8, 18). As the KRT5+ cells expanded, the Src signal gradually decreased in dysplastic KRT5+ cells closer to the bronchoalveolar duct, while the dysplastic KRT5+ cells at the periphery of pods retained Src expression, consistent with the migration route of dysplastic KRT5+ cells that move from the distal airway to the alveoli (7, 18). By day 18 after infection, only a limited number of dysplastic KRT5+ cells were found expressing activated Src (Figure 3, E and F). By using a published dataset of YAP-regulated genes (31), we further demonstrated that canonical YAP target genes such as Ccn1, Axl, Arfgeh17, and Snai2 were highly expressed in dysplastic KRT5+ cells (Figure 3G). Consistently, the nuclear YAP and Ki67 were expressed in a pattern resembling phosphorylated Src during dysplastic KRT5+ cell expansion, suggesting a causal link of YAP activation and dysplastic KRT5+ cell expansion (Figure 3, H and I). Notably, we also found that most of the SRC+ dysplastic KRT5+ cells expressed nuclear YAP (Supplemental Figure 4, C and D). Thus, these results demonstrate that the FAK/Src-YAP axis is highly activated during the expansion of dysplastic KRT5+ cells, prompting us to investigate their role in controlling this process.
FAK/Src-YAP signaling axis is required for IFN-γ–mediated dysplastic remodeling.
Since IFN-γ is required for the formation of dysplastic KRT5+ cells and these cells exhibit high focal adhesion pathway activity during their migration, we hypothesized that IFN-γ may act through JAK to activate the FAK/Src-YAP pathway to mediate this dysplastic response. To evaluate the role of JAK in activating the FAK/Src-YAP pathway, we examined p-SRC and YAP expression in cultured primary intrapulmonary p63+ progenitor enriched cells and found increased p-SRC+ cells and YAP+ cells following IFN-γ treatment. Inhibition of JAK activity using baricitinib or fedratinib diminished this effect (Supplemental Figure 5, A–D). We also examined the total abundance of YAP using Western blot and found that IFN-γ treatment increased the total abundance of YAP, and this effect was diminished by adding baricitinib or fedratinib (Supplemental Figure 5E). Additionally, Ifngr1 mutant mice exhibited a reduced number of nuclear YAP-expressing cells following IAV infection (Supplemental Figure 5, F–I). We next investigated the role of FAK/Src in this process. We found that in the presence of the FAK antagonist (PF-573228) or the Src antagonist (dasatinib), IFN-γ–mediated nuclear YAP translocation was also diminished (Figure 4, A and B). Similarly, IFN-γ treatment promoted cultured p63+ progenitor cells to transdifferentiate into KRT5+ cells, and this effect was inhibited by PF-573228 or dasatinib (Figure 4, C and D). Consistently, oral delivery of FAK antagonist (PND-1186) or Src antagonist (saracatinib) into IAV-infected mice from day 7 to day 11 after infection resulted in reduced KRT5+ lung areas compared with control mice receiving PBS (Figure 4, E–G).
We further tested the role of YAP in this process by crossing Sox2creERT2 mice with Yapfl mice and generating Yap heterozygous (here after YapSox2–hKO) and homozygous mutant mice (here after YapSox2–KO). Tamoxifen was injected 14 days prior to IAV infection. The mutant mice showed normal ratios of airway and alveolar epithelial cells (Supplemental Figure 5, J–M). However, following IAV infection, the expansion of dysplastic KRT5+ cells was severely suppressed in both YapSox2–hKO and YapSox2–KO mutant lungs (Figure 4, H–J). To determine whether forced activation of YAP in SOX2+ progenitor cells promotes KRT5+ cell expansion in IAV-infected mice, we generated Sox2creERT2;Mst1fl/fl;Mst2–/– mutant mice (hereafter Mst1/2Sox2–KO), in which YAP is constitutively activated in SOX2 progenitor cells after tamoxifen induction (Supplemental Figure 6, A and B). After IAV infection, we observed a trend suggesting that constitutively activated YAP in SOX2+ progenitors promotes dysplastic KRT5+ cell formation, although it was not statistically significant (Supplemental Figure 6, C–E). We then assessed the requirement of YAP in expanding KRT5+ cells by generating Krt5creERT2;Yapfl/+ (hereafter YapKrt5–hKO) and Krt5creERT2;Yapfl/fl (hereafter YapKrt5–KO) mice. Tamoxifen was injected from day 7 to day 11 after infection to induce CRE activity (Figure 4K). Similarly to what occurred with the phenotype of YapSox2–hKO mutant mice, the expansion of dysplastic KRT5+ cells was severely suppressed in both YapKrt5–hKO and YapKrt5–KO mutant lungs (Figure 4, L and M). Thus, in response to infection-induced IFN-γ signal, dysplastic KRT5+ cells activate FAK/Src-YAP signaling to proliferate and migrate to the injured lung alveoli.
YAP is required for the long-term maintenance of dysplastic KRT5+ cells, and inhibition of YAP leads to their differentiation into club cells.
The persistence of alveolar KRT5+ cells impedes functional lung regeneration and chronically affects lung function (11). To determine whether YAP is also required for the long-term maintenance of dysplastic KRT5+ cells in the alveoli, we introduced R26-tdtomato reporter into a YapKrt5–hKO and YapKrt5–KO mutant mouse background. This allowed us to trace dysplastic KRT5+ cells while assessing the possible cell-fate conversion that may be induced by Yap inactivation. Tamoxifen was administered at day 21 after infection, when most of KRT5+ cells had migrated into the alveoli and no longer expressed Ki67, and the mice were analyzed at 14 days after tamoxifen administration (Figure 5A). Immunofluorescent analysis showed that most of the lineage-traced cells in control animals continued to express KRT5, with only a few cells losing KRT5 expression and instead expressing SCGB1A1 (Figure 5B). In contrast, in Yap mutant mice, we observed an increase in the proportion of lineage-traced cells expressing club cell markers, including SCGB1A1, ITGb4, and SCGB3A2 (Figure 5B and Supplemental Figure 6, F and G), while the percentages of cells expressing KRT5 decreased. Interestingly, this phenotype was also shown to be YAP dose dependent (Figure 5, C and D). We did not observe any lineage-traced cells expressing surfactant-associated protein C (SFTPC) in either control or Yap mutant mice (Figure 5E). The percentage of DCLK1+ tuft cells in total lineage-traced cells was not affected upon Yap inactivation (Figure 5, F and G). Intriguingly, though Yap deletion affects the maintenance of dysplastic KRT5+ cells, the lung fibrosis was not ameliorated (Supplemental Figure 6H). Thus, we provide a strategy for converting persistent KRT5+ cells into distal club-like cells.
IFN-γ promotes human AT2 cells to transdifferentiate into dysplastic cells.
Unlike in mice, in which the dysplastic KRT5+ cells are derived from intrapulmonary p63+ cells, in humans, AT2 cells can transdifferentiate into dysplastic KRT5+ cells during lung injury or disease (32). To investigate whether IFN-γ signaling also mediates dysplastic cell formation in human lungs, we first analyzed published scRNA-Seq data of COVID-19 lungs (33). We included AT2 cells, AT1 cells, KRT8+ intermediate cells, and basal cells in our analysis. Monocle 3 analysis suggested that human AT2 cells differentiate into AT1 cells and KRT8+ intermediate cells through different trajectories and that KRT8+ intermediate cells may further differentiate into basal cells (Supplemental Figure 7, A and B). This observation is consistent with a recent report that in human lungs, KRT8+ intermediate cells can serve as precursors for dysplastic KRT5+ cells (32). Gene Set Enrichment Analysis (GSEA) revealed that these KRT8+ intermediate cells exhibited high activity in response to IFN-γ, focal adhesion, and the actin cytoskeleton pathway compared with AT2 cells (Supplemental Figure 7, C–E). Using lung sections from COVID-19 patients, we observed CD8+ T cells appearing in the KRT5+ lung area and p-SRC and nuclear YAP expression in KRT5+ cells (Figure 6A). To further investigate whether IFN-γ can promote the transdifferentiation of human AT2 cells into dysplastic KRT5+ cells through the FAK/Src-YAP pathway, we isolated primary AT2 cells from human lung tissues using magnetic bead–based selection with HTII-280 antibody (Supplemental Figure 7, F and G). The sorted cells were cocultured with MRC5 cells, a human fetal lung fibroblast cell line, as feeders to promote the organoid formation. Recombinant human IFN-γ was added to the culture medium on day 14 when single AT2 cells had grown into sphere-like organoids, and organoids were collected at day 21 for analysis (Figure 6B). Immunofluorescent analysis showed that following IFN-γ treatment, there was a decrease of organoids expressing the AT2 cell marker, including SFTPC and HTII-280, whereas the organoids expressing KRT8, KRT17, or KRT5 increased (Figure 6, C and D). Interestingly, some KRT8+ cells also expressed HTII-280 (Supplemental Figure 7, H–K). Supplementation of IFN-γ–treated human AT2 organoids with Src antagonist (dasatinib) or FAK antagonist (PF-573228) prevented their transdifferentiation into KRT8+, KRT17+, or KRT5+ dysplastic cells and partially reversed the expression of HTII-280, but the expression of SFTPC was not reversed by either of these 2 inhibitors (Figure 6, E and F). We also confirmed the expression of p-SRC and YAP in IFN-γ–treated human lung organoids (Supplemental Figure 7, L–O). These observations were supported by detailed quantification using cell-type–specific markers (Figure 6, G–L). Collectively, these findings in humans are consistent with our findings in mice and are suggestive of a potential role of IFN-γ and FAK/Src-YAP signaling in controlling viral pneumonia–induced dysplastic remodeling in human lungs.
Discussion
Several mechanisms have been proposed for understanding the activation of dysplastic remodeling in viral-infected lungs (8, 10, 23). However, why viral infection, as opposed to other types of injury, is particularly effective in triggering this dysplastic response remains unexplained. Our findings reveal that a viral infection–induced inflammatory niche is critical for the robust expansion of KRT5+ dysplastic cells. Inactivation of IFN-γ signaling reduced the formation of dysplastic cells in IAV- but not bleomycin-injured lungs, suggesting that the dysplastic cell formation is differentially regulated between these 2 injury models. This is consistent with recent studies that show intrapulmonary p63+ cells are barely activated upon bleomycin challenge (34, 35). Previous studies showed that blocking IFN-γ signaling during respiratory viral infection limits tissue injury and reduces lung pathology (36–39). The results showing that epithelial Ifngr1 mutant mice show improved lung recovery after IAV infection, indicated by increased AT2 cell formation and improved lung capacity, suggest that dysplastic cell formation contributes to IFN-γ–induced lung pathology. It is worth noting that, in addition to T cells, we also observed more monocyte-derived alveolar macrophages in IAV- than bleomycin-injured lungs; these macrophages are extensively involved in tissue injury repair (20, 40). A recent study suggests that IL-22 can also act on lung epithelial cells and promote dysplastic cell formation (41), indicating that infection-induced inflammatory factors may work in a cooperative manner to control lung dysplastic remodeling. These complementary results highlight the role of an injury-induced inflammatory signal in promoting aberrant tissue regeneration and provide a perspective for understanding the frequent occurrence of dysplastic remodeling following severe viral pneumonia.
Cell migration requires dynamic rearrangement of the actin cytoskeleton. Our findings demonstrate that dysplastic KRT5+ cells exhibit high activity of the actin cytoskeleton and focal adhesion pathways during their migration. Similarly to the transition from AT2s to AT1s (42), intrapulmonary p63+ cells change their small, wedged cell shape into an extended and flattened cell shape when transformed into dysplastic KRT5+ cells, indicating extensive cytoskeletal remodeling. As a critical downstream effector of the actin cytoskeleton and focal adhesion signaling pathways, the nuclear YAP signal is highly activated during this process. Interestingly, the KRT8+ intermediate cells in IAV-infected lungs also express high levels of genes related to IFN-γ and YAP signaling. Using multiple transgenic mouse lines, we demonstrate that high YAP activity is required for dysplastic KRT5+ cell initiation, migration, and maintenance. Heterozygous deletion of YAP in intrapulmonary p63+ progenitor cells or migrating dysplastic cells prevents their expansion, while in persistent dysplastic cells, it converts them into distal club–like cells. As distal club cells can function as progenitor cells to generate AT2s in various conditions (1, 5, 28), this finding suggests a promising strategy for converting dysplastic KRT5+ cells into distal lung progenitor cells. It is worth noting that in addition to regulating KRT5+ cells, hippo signaling has also been shown to regulate AT2 cells to AT1 cell differentiation (43–45). During lung injury, AT2 cells are important lung progenitor cells for repairing of an alveolar area with mild to moderate injury (46). Future studies to uncover the specific mechanism that regulates hippo signaling activation across different cell types will provide new therapeutic strategies for targeting hippo signaling for promoting lung regeneration.
The ectopic expansion of dysplastic KRT5+ cells in lung alveoli has been reported in patients after influenza or COVID-19 infection. Unlike mouse AT2 cells, which can only differentiate into AT1 cells, human AT2 cells can also differentiate into dysplastic cells upon injury. The finding that dysplastic cells in COVID-19 patient lungs express p-SRC and nuclear YAP signal and that IFN-γ promotes human AT2 cell transdifferentiate into dysplastic cells in a FAK/Src-dependent manner suggests that IFN-γ–mediated activation of the FAK/Src-YAP signaling axis may also regulate dysplastic alveolar remodeling in viral-infected human lungs. Overall, our findings highlight the role of injury-induced inflammatory signals in the activation of alveolar remodeling and uncover a critical role of YAP in controlling dysplastic cell expansion and maintenance.
Methods
Sex as a biological variable.
Our study examined male and female animals, and similar findings are reported for both sexes. Human samples were obtained from both male and female subjects.
Mice.
Shhcre, Ifngr1fl/fl, Sox2creERT, Krt5creERT, R26-tdTomato(R26-tdT), Yapfl/fl, and Mst1fl/fl;Mst2–/– mice have all been described previously (10, 17, 20, 34, 47–49). Stat1fl/fl mice (T051946) were purchased from GemPharmatech. All mice were bred on the C57BL/6 background and kept in specific pathogen–free conditions with a 12-hour light/12-hour dark cycle.
Influenza- and bleomycin-injury models.
Influenza A/H1N1/Puerto Rico/8/34 (PR8) was propagated in embryonated chicken eggs. Male (8–10 weeks) and female (10–12 weeks) mice were anesthetized by intraperitoneal injection of 200 mg/kg sodium pentobarbital and then intratracheally administered with 100 PFU PR8 in 40 μL sterile PBS (pH = 7.4). For bleomycin treatment, male (6–8 weeks) and female (8–10 weeks) mice were anesthetized by isoflurane and intranasally administered with 1 mg/kg bleomycin (Hushi, XW90419342) in 40 μL sterile PBS once a week for 4 weeks.
Tamoxifen treatment.
Tamoxifen was completely dissolved in anhydrous ethanol at a concentration of 100 mg/mL (need vortex and instantaneous heating), then diluted in corn oil to 10 mg/mL. Mice were treated with 100 mg/kg tamoxifen (ABCONE, T56488) by intraperitoneal injection at indicated time points.
Inhibitor treatment.
Src inhibitor saracatinib (20 mg/kg/time, Selleck, S1006-100 mg), FAK inhibitor PND-1186 (150 mg/kg/time, Selleck, S7653-200 mg), or JAK1/JAK2 inhibitor baricitinib phosphate (10 mg/kg/time, MCE, HY-15315A-50 mg) was delivered via oral gavage from day 7 to day 11 after infection. Mice were sacrificed at day 14 after infection.
MVV detection.
Mice were anesthetized by intraperitoneal injection of 200 mg/kg sodium pentobarbital and then MVV was detected using the RoVent Advanced Small Animal Ventilator (Kent Scientific).
Pulmonary function test.
Lung-function parameters were measured using the EMMS eSpira Forced Manoeuvers System. Mice were anesthetized before an endotracheal cannula was inserted into their trachea. The dynamic compliance results were obtained from the resistance and compliance test. FVC results were obtained from the pressure volume test.
Histology staining.
Mice were sacrificed with CO2, and lungs were inflated with 4% paraformaldehyde (PFA) and fixed overnight, followed by 3 washes with PBS. Human lung tissues were fixed in 4% PFA overnight, followed by 3 washes with PBS. Organoids were collected and fixed with 4%PFA for 30 minutes at 4°C. Lungs tissues and organoids were then prepared for paraffin (8 μm) or cryo (10 μm) sectioning. Goblet cells were stained using a Periodic Acid–Schiff (PAS) Staining Kit (MilliporeSigma). Collagen fibers were stained using the Picro Sirius Red Stain Kit (Phygene). To quantify PAS-positive area and fibrosis area, the left lung lobes were imaged using Olympus BX51 and an image mosaic was created using ImageJ software (NIH). The PAS-positive areas and fibrosis areas were measured using ImageJ.
Immunostaining.
The following primary antibodies were used at the indicated concentrations for immunofluorescence staining: goat anti-SCGB3A2 polyclonal antibody (5 μg/mL) (R&D Systems, AF3465), rat anti-ITGB4 monoclonal antibody (5 μg/mL) (BioLegend, 123615), rat anti-F4/80 monoclonal antibody (5 μg/mL) (BioLegend, 111603), rabbit anti-CCR2 monoclonal antibody (5 μg/mL) (Abcam, ab216863), rabbit anti-KRT5 polyclonal antibody (5 μg/mL) (BioLegend, 905504), chicken anti-KRT5 polyclonal antibody (5 μg/mL) (BioLegend, 905903), mouse anti-SCGB1A1 monoclonal antibody (5 μg/mL) (Santa Cruz Biotechnology Inc., sc-365992), rabbit anti-CCSP polyclonal antibody (5 μg/mL) (Seven Hills, WRAB-3950), mouse anti-FOXJ1 monoclonal antibody (5 μg/mL) (Invitrogen, 14-9965-82), hamster anti-PDPN monoclonal antibody (5 μg/mL) (Invitrogen, 14-5381-82), rat anti-CD4 monoclonal antibody (5 μg/mL) (BioLegend, 100427), rat anti-CD8a monoclonal antibody (5 μg/mL) (BioLegend, 100707), mouse anti-NK1.1 monoclonal antibody (5 μg/mL) (BioLegend, 108729), rabbit anti-DCAMKL1 polyclonal antibody (5 μg/mL) (Abcam, ab31704), rabbit anti-proSPC polyclonal antibody (5 μg/mL) (MilliporeSigma, AB3786), rabbit anti-phospho-Src(Tyr416) polyclonal antibody (5 μg/mL) (Cell Signaling Technology, 2101S), rabbit anti-YAP polyclonal antibody (5 μg/mL) (Cell Signaling Technology, 4912S), mouse anti-KI67 monoclonal antibody(5 μg/mL) (BioLegend, 151204), rabbit anti-KI67 monoclonal antibody (5 μg/mL) (Cell Signaling Technology, 9129S), mouse anti-P63 monoclonal antibody (5 μg/mL) (Abcam, ab735), mouse anti-KRT17 monoclonal antibody (5 μg/mL) (Santa Cruz Biotechnology Inc., sc-393002), rat anti-KRT8 polyclonal antibody (5 μg/mL) (DSHB, Troma-1), mouse anti-human HTII-280 (5 μg/mL) (Terrace Biotech, TB-27AHT2-280), and mouse anti-human CD8a monoclonal antibody (5 μg/mL) (Invitrogen, 12-0088-42). The following secondary antibodies were from Jackson Immunoresearch: Cy3-conjugated goat anti-rabbit IgG (5 μg/mL) (code 111-165-003), FITC-conjugated goat anti-rabbit IgG (5 μg/mL) (code 111-095-003), Cy3-conjugated goat anti-mouse IgG (5 μg/mL) (code 115-165-003), FITC-conjugated goat anti-mouse IgG (5 μg/mL) (code 115-095-003), Cy3-conjugated goat anti-rat IgG (5 μg/mL) (code 112-125-003), and FITC-conjugated goat anti-rat IgG (5 μg/mL) (code 112-545-003). The following secondary antibodies were from Abcam: Alexa Fluor 555–conjugated donkey anti-goat IgG H&L (5 μg/mL) (ab150130), Alexa Fluor 647–conjugated goat anti-chicken IgY (5 μg/mL) (ab150171), Alexa Fluor 488–conjugated goat anti-chicken IgY (5 μg/mL) (ab150169), and Alexa Fluor 405–conjugated goat anti-chicken IgY (5 μg/mL) (ab175674). The following secondary antibodies were from Invitrogen: Alexa Fluor 488–conjugated goat anti-Syrian hamster IgG (5 μg/mL) (A-21110) and Alexa Fluor 546–conjugated goat anti-Syrian hamster IgG (5 μg/mL) (A-21111). DAPI solution (1 mg/mL) (Invitrogen) was used to counterstain nuclei. Images were acquired by Olympus BX51. Data were analyzed with ImageJ (NIH).
YAP immunofluorescent staining was performed using TSA fluorescein kits. The following antibodies were used: rabbit anti-YAP polyclonal antibody (5 μg/mL) (Cell Signaling Technology, 4912S) and goat anti rabbit IgG (H+L) HRP (5 μg/mL) (Invitrogen, 31430). Alexa Fluor 488 tyramide reagent (Invitrogen, B40953) was used to amplify the signal.
Quantification of KRT5+ lung area.
To quantify KRT5+ or PDPN– area, the left lung lobes were sectioned and stained for KRT5 and PDPN antibodies, then were imaged by Olympus BX51, and an image mosaic was created using ImageJ software. The KRT5+ or PDPN– areas were measured using outline spline in the measure menu of Axiovision 4.8.
Quantification of pods with different expression levels of YAP, SRC, and KI67.
To quantify number of different types of pods in lung, the left lung lobes were sectioned and stained for KRT5 and p-SRC antibodies or KRT5 and KI67 and YAP antibodies, then were imaged using Olympus BX51, and an image mosaic was created using ImageJ software. We defined more than 85% of cells as KRT5+p-SRC+ cells or KRT5+KI67+YAP+ cells as high pods, 30% to 85% of cells as KRT5+p-SRC+ cells or KRT5+KI67+YAP+ cells as medium pods, and less than 30% of cells as KRT5+p-SRC+ cells or KRT5+KI67+YAP+ cells as low pods.
Western blot.
Organoids were collected and washed in PBS to remove Matrigel and then lysed in RIPA (50 mM tris-HCl [pH 8.0], 150 mM NaCl, 0.1% SDS, 0.15% Na-Deoxycholate, 1% NP-40, and 2 mM EDTA [pH 8.0]) containing protease inhibitors (MCE, 317717) and phosphatase inhibitor PhosSTOP (Roche, 04906845001). Protein content was quantified by standard bicinchoninic acid (BCA) assay using the Omni-Easy BCA Protein Assay Kit (epizyme, ZJ102). Equal protein amounts (10 μg/sample) were run on 4.5%–10% bis-tris protein gels (epizyme, PG112) and transferred to PVDF Transfer Membranes (Thermo Scientific, 88518). Membranes were blocked in 5% milk solution for 1 hour at room temperature and probed overnight at 4°C with YAP antibodies (Cell Signaling Technology, 4912S) in 1:1,000 dilution and β-actin antibody (Cell Signaling Technology, 4967) in 1:5,000. After 3 washes in 1× Tris-buffered saline with 0.1% Tween 20 detergent (TBST) buffer (10 minutes each), membranes were incubated with peroxidase-labeled anti-rabbit (Invitrogen, 31460) secondary antibody (1:5,000) and peroxidase-labeled anti-mouse (Invitrogen, 31430) secondary antibody (1:5,000) for 1 hour at room temperature. After subsequent washes in 1× TBST buffer, the Enhanced Chemiluminescence Substrate Kit (Thermo Fisher Scientific, 32132) was used for band detection according to the manufacturer’s instructions. Membranes were imaged on the ChemiDoc MP Imaging System (Bio-Rad).
Sircol collagen assay.
Collagen quantification was performed using the Sirius Red Total Collagen Detection Kit following the manufacturer’s protocol (Chondrex, 9062). Briefly, the left lung was homogenized and digested overnight at 4°C in 2 mL 0.05 M acetic acid with 0.1% v/m pepsin and centrifuged at 18,400g for 30 minutes. The supernatant was collected, and 0.5 mL Sircol dye was incubated with 100 μL of the supernatant for 20 minutes. This sample was centrifuged at 9,600g for 3 minutes, and the resulting pellet was washed in the acid salt wash buffer and resuspended in 250 μL extraction buffer. Absorbance was measured at a wavelength of 530 nm in a microplate reader. The collagen content was determined by comparing it to a standard curve, represented as mg/mL of left lung homogenate.
BALF and lung homogenate cytokine panel.
Mice were euthanized by intraperitoneal injection of a lethal dose of sodium pentobarbital, after which a catheter was introduced into the trachea. A 1 mL syringe was loaded with 1 mL of sterile balanced salt solution with protease inhibitors, and then BALF was collected. This was repeated 3 times. Lavage fluid was centrifuged for 7 minutes at 400g and 4°C. Supernatant was collected and stored at –80°C. Mice were euthanized and whole lung was collected in PBS with protease inhibitors, then homogenate, and centrifuged at 18,400g for 30 minutes. The supernatant was collected and stored at –80°C. We detected cytokines using the Mouse Inflammation Panel (13-plex) with V-bottom Plate Flow Cytometry Kit following the manufacturer’s protocol (BioLegend, 740446). Briefly, samples were incubated with beads and shaken at 800 rpm on a plate shaker for 2 hours at room temperature. Samples were washed before being incubated with detection antibodies and shaken at 800 rpm on a plate shaker for 1 hour at room temperature. SA-PE was added directly, and samples were shaken at 800 rpm on a plate shaker for 0.5 hours at room temperature. Beads were washed and resuspended by pipetting and samples were read on flow cytometer BD Fortessa (BD Biosciences). The assay FCS files were analyzed using BioLegend’s LEGENDplex data analysis software, version 8.0.
Flow cytometry.
Mice were sacrificed with CO2 and perfused with 5 mL of cold PBS to eliminate blood. Whole lungs were digested by gentle shaking in lysis buffer containing DMEM, 225 U/mL collagenase type II, 25 U/mL DNase I, 5% FBS, 2 mM MgCl2, 2 mM CaCl2, and 20 mM HEPES for 60 minutes at 37°C, and then mechanically dissociated using GentleMACS C tubes (Miltenyi Biotec), followed by straining through a 70 μm filter resuspended in 40% Percoll, underlining with 66% Percoll. We centrifuged at 700g for 20 minutes and set deceleration (DEC) to 0” in this step. Hematopoietic cells were isolated from the interphase for analysis. The single-cell suspensions from above were pelleted, suspended (PBS), aliquoted at approximately 1 × 106 cells per tube, and stained with Fc blocking antibody (5 mg/mL, BD) and the Live/Dead Fixable Dead Cell Stain Kit (Invitrogen) at room temperature for 10 minutes. The cells were washed and then incubated with antibody cocktail for 45 minutes at 4°C. The following antibodies were used: Pacific blue–conjugated rat anti-CD4 monoclonal antibody (5 μg/mL) (BioLegend, 100427), PE-conjugated rat anti-CD8a monoclonal antibody (5 μg/mL) (BioLegend, 100707), Alexa Fluor 700–conjugated mouse anti-NK1.1 monoclonal antibody (5 μg/mL) (BioLegend, 108729), Apc-cy7–conjugated rat anti-CD90.2 monoclonal antibody (5 μg/mL) (BioLegend, 105327), FITC-conjugated rat anti-CD45 monoclonal antibody (5 μg/mL) (BioLegend, 103122), Alexa Fluor 700–conjugated mouse anti-CD11b monoclonal antibody (5 μg/mL) (BioLegend, 111222), PE-conjugated rat anti-F4/80 monoclonal antibody (5 μg/mL) (BioLegend, 111603), and APC-conjugated rat anti-Ly6c monoclonal antibody (5 μg/mL) (BioLegend, 128015). Absolute counting beads were used to count cell numbers (Invitrogen, C36950). Lymphocytes were sorted as AmCyan–FITC+Apc-cy7+ cells. NK cells were sorted as AmCyan–FITC+Alexa Fluor 700+ cells, CD4+ T cells were sorted as AmCyan–FITC+Apc-cy7+Pacific blue+ cells, and CD8+ T cells were sorted as AmCyan–FITC+Apc-cy7+PE+ cells. Alveolar macrophages were sorted as AmCyan–FITC+Apc-cy7+Alexa Fluor 700+PE+ cells, and monocyte-derived inflammatory macrophages were sorted as AmCyan–FITC+Apc-cy7+Alexa Fluor 700+APC+ cells. Samples were analyzed using BD Fortessa (BD Biosciences). Data were analyzed with FlowJo software, version 10.4
Mouse tissue digestion and cell sorting.
Mice were sacrificed using CO2 at 14 days post infection (dpi) after IAV challenge and transcardially perfused with 5 mL of cold PBS. Whole lungs were digested by gentle shaking in lysis buffer containing DMEM, 2.5 U/mL dispase II, 225 U/mL collagenase type I, 25 U/mL DNase I, 5% FBS, 2 mM MgCl2, 2 mM CaCl2, and 20 mM HEPES for 30 minutes at 37°C. Cell suspension was filtered through a 70 μm strainer, and red blood cells were removed using RBC lysis buffer (BioLegend, 420301). The single-cell suspensions from above were pelleted and stained for indicated antibodies.
For scRNA-Seq, the following antibodies were used: FITC-conjugated rat anti-CD45 (5 μg/mL) (BioLegend, 103107), FITC- conjugated rat anti-CD31 (5 μg/mL) (BioLegend, 102405), and APC-conjugated rat anti-EPCAM (5 μg/mL) (BioLegend, 118214). DAPI solution (1 mg/mL) (Invitrogen) was used to counterstain nuclei. Anti-FITC microbeads (Miltenyi Biotec) were used to clear endothelial cells and immune cells by AutoMACS Pro. Target cells were sorted as DAPI–FITC–APC+ using BD FACSAria III.
For sorting LNEP cells and fibroblast, the following antibodies were used: FITC-conjugated rat anti-CD45 (5 μg/mL) (BioLegend, 103107), FITC-conjugated rat anti-CD31 (5 μg/mL) (BioLegend, 102405), APC-cy7–conjugated rat anti-EPCAM (5 μg/mL) (BioLegend, 118217), APC-conjugated rat anti-CD200 (5 μg/mL) (BioLegend, 123809), and PE-cy7–conjugated rat anti-integrin β4 (5 μg/mL) (BioLegend, 123615). DAPI solution (1 mg/mL) (Invitrogen) was used to counterstain nuclei. Anti-FITC microbeads (Miltenyi Biotec) were used to clear endothelial cells and immune cells by AutoMACS Pro. LNEP cells were sorted as DAPI–FITC–APC-cy7+APC+PE-cy7+ cells, and fibroblasts were sorted as DAPI–FITC–APC-cy7– cells.
Mouse organoid culture.
LNEP cells and fibroblasts (20,000:50,000) were cocultured in Pneumacult-ALI Medium (STEMCELL Technologies, 5001) diluted 1:1 in growth factor–reduced Matrigel (Corning-Biocoat, 356231) and seeded in trans wells (TCS002024). Pneumacult-ALI Medium with 5% FBS was added in the first 7 days to form spheres; 10 μM Y27632 (MCE, HY-10583) was added at the first 2 days, and culture medium was replenished every other day. For IFN-γ treatment, 100 ng/mL mouse recombinant IFN-γ (novoprotein, C746) or PBS was added at day 8, and organoids were collected at day 11 for analysis. For inhibitor treatment, cells were pretreated with indicated inhibitors at day 7. Twenty-four hours later, fresh medium containing IFN-γ and indicated inhibitors were replenished and organoids were maintained for another 72 hours. The following molecules were used for LNEP organoid treatment: 10 μM JAK1/JAK2 inhibitor fedratinib (MCE, HY-10409), 10 μM JAK1/JAK2 inhibitor baricitinib (MCE, HY-15315), 10 μM FAK inhibitor PF-573228 (MCE, HY-10461), and 10 μM Src inhibitor dasatinib (MCE, HY-10181).
scRNA-Seq and analysis.
Sorted epithelial cells were used to perform scRNA-Seq with the 10x Genomics Chromium Platform at 14 dpi. FASTQ files were processed using the Cell Ranger pipeline. For further downstream analysis, R package Seurat (version 4.2.0) was used. Low-quality cells (with fewer than 200 or more than 7,500 genes and with more than 10% mitochondrial transcripts) were removed. ScaleData(), RunPCA(), FindNeighbors(), FindClusters() and RunTSNE() functions were used to cluster and visualize cells. Cell annotation was achieved according to the marker gene expression. Immune receptor expression was plotted using the Dotplot() function. The upregulated expression gene set was used to perform KEGG enrichment analysis using the clusterProfiler R package after removal of ribosomal genes. Visualization of YAP downstream gene expression was shown using Nebulosa (version 1.8.0) R package. The trajectory analysis was inferred using the R package Monocle 3 (version 1.0.0) by importing the counts from the Seurat object. For reanalysis of human RNA-Seq data, only data from COVID-19–infected lungs were used.
Human AEC2 sorting.
Human lung tissue was dissected into 1 cm3 pieces and washed with 50 mL cold PBS, then dissected into smaller pieces (1–2 mm3). Human lung pieces were digested by gentle shaking in lysis buffer containing DMEM, 2.5 U/mL dispase II, 225 U/mL collagenase type I, 100 U/mL DNase I, 5% FBS, 2 mM MgCl2, 2 mM CaCl2, and 20 mM HEPES for 60 minutes at 37°C. The tissue homogenate was filtered through a 70 μm strainer, and red blood cells were removed using RBC Lysis Buffer (BioLegend, 420301). The single-cell suspensions from above were pelleted and resuspended in PBS buffer containing 5% FBS and anti-human HTII-280 (5 μg/mL) (Terrace Biotech, TB-27AHT2-280). Cells were stained for 30 minutes at 4°C and further sorted using anti-mouse IgM microbeads (Miltenyi Biotec) with AutoMACS Pro.
Human organoid culture.
Human AEC2 and MRC5 cells (ATCC, CCL-171) (5,000:30,000) were diluted in equal volumes of Matrigel, seeded in Transwells (TCS002024), and solidified at 30 minutes at 37°C. CK+DCI medium is composed of the following components: IMDM (Thermo Fisher Scientific, 12440053), Ham’s F12 medium (CellGro, 10-080-CV), 3 μM CHIR99021 (Sigma-Aldrich, SML1046-5MG), 10 ng/mL rhKGF (Novoprotein, CH73), B27 supplement (Invitrogen, 12587–010), N2 supplement (Invitrogen, 17502–048), GlutaMAX (Invitrogen, 35050061), penicillin/streptomycin (Thermo Fisher Scientific, 15140122), 0.2% BSA (Sigma-Aldrich, B2064), 10 μg/mL 1-thioglycerol (MTG, Sigma-Aldrich, M6145), 50 μg/mL ascorbic acid (Sigma-Aldrich, A4544), 50 nM dexamethasone (Sigma-Aldrich, D4902), 0.1 mM 8-Bromo-cAMP (Sigma-Aldrich, B7880), and 0.1 mM 3-isobutyl-1-methylxanthine (IBMX) (Sigma-Aldrich, I5879) with 10 μM TGF-β inhibitor (A8301, Stemcell, 72024), and 10%FBS was added in the first 14 days to form spheres; 10 μM Y27632 was added in the first 2 days, and culture medium was replenished every other day. CHIR99021 was retracted from the culture medium from day 12, and 50 ng/mL human recombinant IFN-γ (Novoprotein, C014) or PBS was added from day 15 for 4 days. At day 18, the recombinant IFN-γ was reduced to 25 ng/mL, and organoids were collected at day 21 for analysis. For inhibitor treatment, cells were pretreated with indicated inhibitors from day 12. Forty-eight hours later, fresh medium containing IFN-γ and indicated inhibitors was replenished. The following molecules were used: 10 μM FAK inhibitor PF-573228 and 10 μM Src inhibitor dasatinib. Human MRC5 cells were cultured in medium containing DMEM-F12, 10%FBS, GlutaMAX, and 1% penicillin/streptomycin. MRC5 cells with passage of less than 5 were used. MRC5 cells with higher passages showed reduced ability to support human organoid growth.
Human lung tissue.
Human lung tissues were obtained from the Cardiothoracic Surgery Department, Children’s Hospital of Fudan University, and the Department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. For the complete list of human lung tissues, refer to Supplemental Tables 1 and 2.
Quantitative reverse-transcription PCR.
For quantitative reverse-transcription PCR (qRT-PCR), the left lung tissues were collected and stored in Trizol (Vazyme, REC03B-100) prior to RNA isolation. cDNA was then synthesized using the HiScript III RT SuperMix for qPCR Kit (Vazyme, R323-01). ChamQ Universal SYBR qPCR Master Mix standard program was run following the manufacturer’s protocol (Vazyme, Q711-03) on a Roche LC480-384 instrument. Gene expression was calculated relative to tubulin within that sample and expressed as fold change over the average expression. All primers are listed in Supplemental Table 3.
Statistics.
As indicated in the figure legends, 2-tailed t test, 2-tailed Mann-Whitney U test, or multiple t test was used for 2 groups. Levene’s test was used to check the equality of variances across groups, and then 1-way ANOVA or Brown-Forsythe and Welch’s ANOVA followed by the original FDR method of the Benjamini and Hochberg multiple-comparison test were used for more than 2 groups. A P value of less than 0.05 was considered significant. Data are represented as mean ± SEM. Statistical analysis was performed using Prism, version 10.2.0 (GraphPad Inc.).
Study approval.
All animal experiments were conducted under IACUC-approved protocols at the Center for Excellence in Molecular Cell Science and the CAS Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Science. Human lung tissues were collected under the surgical criteria of the Cardiothoracic Surgery Department, Children’s Hospital of Fudan University, and the department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. Human research was approved by the institutional review boards of Fudan University and the Department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. All participants provided written, informed consent.
Data availability.
The scRNA-Seq data generated in this study have been deposited in the NCBI’s Gene Expression Omnibus database (GEO GSE234082). Previously published scRNA-Seq data that are reanalyzed here are available at GSE171524. All analyses associated with this study used preexisting R packages. No custom code was generated. Values for all data points in graphs are reported in the Supporting Data Values file.
Sex as a biological variable.
Our study examined male and female animals, and similar findings are reported for both sexes. Human samples were obtained from both male and female subjects.
Mice.
Shhcre, Ifngr1fl/fl, Sox2creERT, Krt5creERT, R26-tdTomato(R26-tdT), Yapfl/fl, and Mst1fl/fl;Mst2–/– mice have all been described previously (10, 17, 20, 34, 47–49). Stat1fl/fl mice (T051946) were purchased from GemPharmatech. All mice were bred on the C57BL/6 background and kept in specific pathogen–free conditions with a 12-hour light/12-hour dark cycle.
Influenza- and bleomycin-injury models.
Influenza A/H1N1/Puerto Rico/8/34 (PR8) was propagated in embryonated chicken eggs. Male (8–10 weeks) and female (10–12 weeks) mice were anesthetized by intraperitoneal injection of 200 mg/kg sodium pentobarbital and then intratracheally administered with 100 PFU PR8 in 40 μL sterile PBS (pH = 7.4). For bleomycin treatment, male (6–8 weeks) and female (8–10 weeks) mice were anesthetized by isoflurane and intranasally administered with 1 mg/kg bleomycin (Hushi, XW90419342) in 40 μL sterile PBS once a week for 4 weeks.
Tamoxifen treatment.
Tamoxifen was completely dissolved in anhydrous ethanol at a concentration of 100 mg/mL (need vortex and instantaneous heating), then diluted in corn oil to 10 mg/mL. Mice were treated with 100 mg/kg tamoxifen (ABCONE, T56488) by intraperitoneal injection at indicated time points.
Inhibitor treatment.
Src inhibitor saracatinib (20 mg/kg/time, Selleck, S1006-100 mg), FAK inhibitor PND-1186 (150 mg/kg/time, Selleck, S7653-200 mg), or JAK1/JAK2 inhibitor baricitinib phosphate (10 mg/kg/time, MCE, HY-15315A-50 mg) was delivered via oral gavage from day 7 to day 11 after infection. Mice were sacrificed at day 14 after infection.
MVV detection.
Mice were anesthetized by intraperitoneal injection of 200 mg/kg sodium pentobarbital and then MVV was detected using the RoVent Advanced Small Animal Ventilator (Kent Scientific).
Pulmonary function test.
Lung-function parameters were measured using the EMMS eSpira Forced Manoeuvers System. Mice were anesthetized before an endotracheal cannula was inserted into their trachea. The dynamic compliance results were obtained from the resistance and compliance test. FVC results were obtained from the pressure volume test.
Histology staining.
Mice were sacrificed with CO2, and lungs were inflated with 4% paraformaldehyde (PFA) and fixed overnight, followed by 3 washes with PBS. Human lung tissues were fixed in 4% PFA overnight, followed by 3 washes with PBS. Organoids were collected and fixed with 4%PFA for 30 minutes at 4°C. Lungs tissues and organoids were then prepared for paraffin (8 μm) or cryo (10 μm) sectioning. Goblet cells were stained using a Periodic Acid–Schiff (PAS) Staining Kit (MilliporeSigma). Collagen fibers were stained using the Picro Sirius Red Stain Kit (Phygene). To quantify PAS-positive area and fibrosis area, the left lung lobes were imaged using Olympus BX51 and an image mosaic was created using ImageJ software (NIH). The PAS-positive areas and fibrosis areas were measured using ImageJ.
Immunostaining.
The following primary antibodies were used at the indicated concentrations for immunofluorescence staining: goat anti-SCGB3A2 polyclonal antibody (5 μg/mL) (R&D Systems, AF3465), rat anti-ITGB4 monoclonal antibody (5 μg/mL) (BioLegend, 123615), rat anti-F4/80 monoclonal antibody (5 μg/mL) (BioLegend, 111603), rabbit anti-CCR2 monoclonal antibody (5 μg/mL) (Abcam, ab216863), rabbit anti-KRT5 polyclonal antibody (5 μg/mL) (BioLegend, 905504), chicken anti-KRT5 polyclonal antibody (5 μg/mL) (BioLegend, 905903), mouse anti-SCGB1A1 monoclonal antibody (5 μg/mL) (Santa Cruz Biotechnology Inc., sc-365992), rabbit anti-CCSP polyclonal antibody (5 μg/mL) (Seven Hills, WRAB-3950), mouse anti-FOXJ1 monoclonal antibody (5 μg/mL) (Invitrogen, 14-9965-82), hamster anti-PDPN monoclonal antibody (5 μg/mL) (Invitrogen, 14-5381-82), rat anti-CD4 monoclonal antibody (5 μg/mL) (BioLegend, 100427), rat anti-CD8a monoclonal antibody (5 μg/mL) (BioLegend, 100707), mouse anti-NK1.1 monoclonal antibody (5 μg/mL) (BioLegend, 108729), rabbit anti-DCAMKL1 polyclonal antibody (5 μg/mL) (Abcam, ab31704), rabbit anti-proSPC polyclonal antibody (5 μg/mL) (MilliporeSigma, AB3786), rabbit anti-phospho-Src(Tyr416) polyclonal antibody (5 μg/mL) (Cell Signaling Technology, 2101S), rabbit anti-YAP polyclonal antibody (5 μg/mL) (Cell Signaling Technology, 4912S), mouse anti-KI67 monoclonal antibody(5 μg/mL) (BioLegend, 151204), rabbit anti-KI67 monoclonal antibody (5 μg/mL) (Cell Signaling Technology, 9129S), mouse anti-P63 monoclonal antibody (5 μg/mL) (Abcam, ab735), mouse anti-KRT17 monoclonal antibody (5 μg/mL) (Santa Cruz Biotechnology Inc., sc-393002), rat anti-KRT8 polyclonal antibody (5 μg/mL) (DSHB, Troma-1), mouse anti-human HTII-280 (5 μg/mL) (Terrace Biotech, TB-27AHT2-280), and mouse anti-human CD8a monoclonal antibody (5 μg/mL) (Invitrogen, 12-0088-42). The following secondary antibodies were from Jackson Immunoresearch: Cy3-conjugated goat anti-rabbit IgG (5 μg/mL) (code 111-165-003), FITC-conjugated goat anti-rabbit IgG (5 μg/mL) (code 111-095-003), Cy3-conjugated goat anti-mouse IgG (5 μg/mL) (code 115-165-003), FITC-conjugated goat anti-mouse IgG (5 μg/mL) (code 115-095-003), Cy3-conjugated goat anti-rat IgG (5 μg/mL) (code 112-125-003), and FITC-conjugated goat anti-rat IgG (5 μg/mL) (code 112-545-003). The following secondary antibodies were from Abcam: Alexa Fluor 555–conjugated donkey anti-goat IgG H&L (5 μg/mL) (ab150130), Alexa Fluor 647–conjugated goat anti-chicken IgY (5 μg/mL) (ab150171), Alexa Fluor 488–conjugated goat anti-chicken IgY (5 μg/mL) (ab150169), and Alexa Fluor 405–conjugated goat anti-chicken IgY (5 μg/mL) (ab175674). The following secondary antibodies were from Invitrogen: Alexa Fluor 488–conjugated goat anti-Syrian hamster IgG (5 μg/mL) (A-21110) and Alexa Fluor 546–conjugated goat anti-Syrian hamster IgG (5 μg/mL) (A-21111). DAPI solution (1 mg/mL) (Invitrogen) was used to counterstain nuclei. Images were acquired by Olympus BX51. Data were analyzed with ImageJ (NIH).
YAP immunofluorescent staining was performed using TSA fluorescein kits. The following antibodies were used: rabbit anti-YAP polyclonal antibody (5 μg/mL) (Cell Signaling Technology, 4912S) and goat anti rabbit IgG (H+L) HRP (5 μg/mL) (Invitrogen, 31430). Alexa Fluor 488 tyramide reagent (Invitrogen, B40953) was used to amplify the signal.
Quantification of KRT5+ lung area.
To quantify KRT5+ or PDPN– area, the left lung lobes were sectioned and stained for KRT5 and PDPN antibodies, then were imaged by Olympus BX51, and an image mosaic was created using ImageJ software. The KRT5+ or PDPN– areas were measured using outline spline in the measure menu of Axiovision 4.8.
Quantification of pods with different expression levels of YAP, SRC, and KI67.
To quantify number of different types of pods in lung, the left lung lobes were sectioned and stained for KRT5 and p-SRC antibodies or KRT5 and KI67 and YAP antibodies, then were imaged using Olympus BX51, and an image mosaic was created using ImageJ software. We defined more than 85% of cells as KRT5+p-SRC+ cells or KRT5+KI67+YAP+ cells as high pods, 30% to 85% of cells as KRT5+p-SRC+ cells or KRT5+KI67+YAP+ cells as medium pods, and less than 30% of cells as KRT5+p-SRC+ cells or KRT5+KI67+YAP+ cells as low pods.
Western blot.
Organoids were collected and washed in PBS to remove Matrigel and then lysed in RIPA (50 mM tris-HCl [pH 8.0], 150 mM NaCl, 0.1% SDS, 0.15% Na-Deoxycholate, 1% NP-40, and 2 mM EDTA [pH 8.0]) containing protease inhibitors (MCE, 317717) and phosphatase inhibitor PhosSTOP (Roche, 04906845001). Protein content was quantified by standard bicinchoninic acid (BCA) assay using the Omni-Easy BCA Protein Assay Kit (epizyme, ZJ102). Equal protein amounts (10 μg/sample) were run on 4.5%–10% bis-tris protein gels (epizyme, PG112) and transferred to PVDF Transfer Membranes (Thermo Scientific, 88518). Membranes were blocked in 5% milk solution for 1 hour at room temperature and probed overnight at 4°C with YAP antibodies (Cell Signaling Technology, 4912S) in 1:1,000 dilution and β-actin antibody (Cell Signaling Technology, 4967) in 1:5,000. After 3 washes in 1× Tris-buffered saline with 0.1% Tween 20 detergent (TBST) buffer (10 minutes each), membranes were incubated with peroxidase-labeled anti-rabbit (Invitrogen, 31460) secondary antibody (1:5,000) and peroxidase-labeled anti-mouse (Invitrogen, 31430) secondary antibody (1:5,000) for 1 hour at room temperature. After subsequent washes in 1× TBST buffer, the Enhanced Chemiluminescence Substrate Kit (Thermo Fisher Scientific, 32132) was used for band detection according to the manufacturer’s instructions. Membranes were imaged on the ChemiDoc MP Imaging System (Bio-Rad).
Sircol collagen assay.
Collagen quantification was performed using the Sirius Red Total Collagen Detection Kit following the manufacturer’s protocol (Chondrex, 9062). Briefly, the left lung was homogenized and digested overnight at 4°C in 2 mL 0.05 M acetic acid with 0.1% v/m pepsin and centrifuged at 18,400g for 30 minutes. The supernatant was collected, and 0.5 mL Sircol dye was incubated with 100 μL of the supernatant for 20 minutes. This sample was centrifuged at 9,600g for 3 minutes, and the resulting pellet was washed in the acid salt wash buffer and resuspended in 250 μL extraction buffer. Absorbance was measured at a wavelength of 530 nm in a microplate reader. The collagen content was determined by comparing it to a standard curve, represented as mg/mL of left lung homogenate.
BALF and lung homogenate cytokine panel.
Mice were euthanized by intraperitoneal injection of a lethal dose of sodium pentobarbital, after which a catheter was introduced into the trachea. A 1 mL syringe was loaded with 1 mL of sterile balanced salt solution with protease inhibitors, and then BALF was collected. This was repeated 3 times. Lavage fluid was centrifuged for 7 minutes at 400g and 4°C. Supernatant was collected and stored at –80°C. Mice were euthanized and whole lung was collected in PBS with protease inhibitors, then homogenate, and centrifuged at 18,400g for 30 minutes. The supernatant was collected and stored at –80°C. We detected cytokines using the Mouse Inflammation Panel (13-plex) with V-bottom Plate Flow Cytometry Kit following the manufacturer’s protocol (BioLegend, 740446). Briefly, samples were incubated with beads and shaken at 800 rpm on a plate shaker for 2 hours at room temperature. Samples were washed before being incubated with detection antibodies and shaken at 800 rpm on a plate shaker for 1 hour at room temperature. SA-PE was added directly, and samples were shaken at 800 rpm on a plate shaker for 0.5 hours at room temperature. Beads were washed and resuspended by pipetting and samples were read on flow cytometer BD Fortessa (BD Biosciences). The assay FCS files were analyzed using BioLegend’s LEGENDplex data analysis software, version 8.0.
Flow cytometry.
Mice were sacrificed with CO2 and perfused with 5 mL of cold PBS to eliminate blood. Whole lungs were digested by gentle shaking in lysis buffer containing DMEM, 225 U/mL collagenase type II, 25 U/mL DNase I, 5% FBS, 2 mM MgCl2, 2 mM CaCl2, and 20 mM HEPES for 60 minutes at 37°C, and then mechanically dissociated using GentleMACS C tubes (Miltenyi Biotec), followed by straining through a 70 μm filter resuspended in 40% Percoll, underlining with 66% Percoll. We centrifuged at 700g for 20 minutes and set deceleration (DEC) to 0” in this step. Hematopoietic cells were isolated from the interphase for analysis. The single-cell suspensions from above were pelleted, suspended (PBS), aliquoted at approximately 1 × 106 cells per tube, and stained with Fc blocking antibody (5 mg/mL, BD) and the Live/Dead Fixable Dead Cell Stain Kit (Invitrogen) at room temperature for 10 minutes. The cells were washed and then incubated with antibody cocktail for 45 minutes at 4°C. The following antibodies were used: Pacific blue–conjugated rat anti-CD4 monoclonal antibody (5 μg/mL) (BioLegend, 100427), PE-conjugated rat anti-CD8a monoclonal antibody (5 μg/mL) (BioLegend, 100707), Alexa Fluor 700–conjugated mouse anti-NK1.1 monoclonal antibody (5 μg/mL) (BioLegend, 108729), Apc-cy7–conjugated rat anti-CD90.2 monoclonal antibody (5 μg/mL) (BioLegend, 105327), FITC-conjugated rat anti-CD45 monoclonal antibody (5 μg/mL) (BioLegend, 103122), Alexa Fluor 700–conjugated mouse anti-CD11b monoclonal antibody (5 μg/mL) (BioLegend, 111222), PE-conjugated rat anti-F4/80 monoclonal antibody (5 μg/mL) (BioLegend, 111603), and APC-conjugated rat anti-Ly6c monoclonal antibody (5 μg/mL) (BioLegend, 128015). Absolute counting beads were used to count cell numbers (Invitrogen, C36950). Lymphocytes were sorted as AmCyan–FITC+Apc-cy7+ cells. NK cells were sorted as AmCyan–FITC+Alexa Fluor 700+ cells, CD4+ T cells were sorted as AmCyan–FITC+Apc-cy7+Pacific blue+ cells, and CD8+ T cells were sorted as AmCyan–FITC+Apc-cy7+PE+ cells. Alveolar macrophages were sorted as AmCyan–FITC+Apc-cy7+Alexa Fluor 700+PE+ cells, and monocyte-derived inflammatory macrophages were sorted as AmCyan–FITC+Apc-cy7+Alexa Fluor 700+APC+ cells. Samples were analyzed using BD Fortessa (BD Biosciences). Data were analyzed with FlowJo software, version 10.4
Mouse tissue digestion and cell sorting.
Mice were sacrificed using CO2 at 14 days post infection (dpi) after IAV challenge and transcardially perfused with 5 mL of cold PBS. Whole lungs were digested by gentle shaking in lysis buffer containing DMEM, 2.5 U/mL dispase II, 225 U/mL collagenase type I, 25 U/mL DNase I, 5% FBS, 2 mM MgCl2, 2 mM CaCl2, and 20 mM HEPES for 30 minutes at 37°C. Cell suspension was filtered through a 70 μm strainer, and red blood cells were removed using RBC lysis buffer (BioLegend, 420301). The single-cell suspensions from above were pelleted and stained for indicated antibodies.
For scRNA-Seq, the following antibodies were used: FITC-conjugated rat anti-CD45 (5 μg/mL) (BioLegend, 103107), FITC- conjugated rat anti-CD31 (5 μg/mL) (BioLegend, 102405), and APC-conjugated rat anti-EPCAM (5 μg/mL) (BioLegend, 118214). DAPI solution (1 mg/mL) (Invitrogen) was used to counterstain nuclei. Anti-FITC microbeads (Miltenyi Biotec) were used to clear endothelial cells and immune cells by AutoMACS Pro. Target cells were sorted as DAPI–FITC–APC+ using BD FACSAria III.
For sorting LNEP cells and fibroblast, the following antibodies were used: FITC-conjugated rat anti-CD45 (5 μg/mL) (BioLegend, 103107), FITC-conjugated rat anti-CD31 (5 μg/mL) (BioLegend, 102405), APC-cy7–conjugated rat anti-EPCAM (5 μg/mL) (BioLegend, 118217), APC-conjugated rat anti-CD200 (5 μg/mL) (BioLegend, 123809), and PE-cy7–conjugated rat anti-integrin β4 (5 μg/mL) (BioLegend, 123615). DAPI solution (1 mg/mL) (Invitrogen) was used to counterstain nuclei. Anti-FITC microbeads (Miltenyi Biotec) were used to clear endothelial cells and immune cells by AutoMACS Pro. LNEP cells were sorted as DAPI–FITC–APC-cy7+APC+PE-cy7+ cells, and fibroblasts were sorted as DAPI–FITC–APC-cy7– cells.
Mouse organoid culture.
LNEP cells and fibroblasts (20,000:50,000) were cocultured in Pneumacult-ALI Medium (STEMCELL Technologies, 5001) diluted 1:1 in growth factor–reduced Matrigel (Corning-Biocoat, 356231) and seeded in trans wells (TCS002024). Pneumacult-ALI Medium with 5% FBS was added in the first 7 days to form spheres; 10 μM Y27632 (MCE, HY-10583) was added at the first 2 days, and culture medium was replenished every other day. For IFN-γ treatment, 100 ng/mL mouse recombinant IFN-γ (novoprotein, C746) or PBS was added at day 8, and organoids were collected at day 11 for analysis. For inhibitor treatment, cells were pretreated with indicated inhibitors at day 7. Twenty-four hours later, fresh medium containing IFN-γ and indicated inhibitors were replenished and organoids were maintained for another 72 hours. The following molecules were used for LNEP organoid treatment: 10 μM JAK1/JAK2 inhibitor fedratinib (MCE, HY-10409), 10 μM JAK1/JAK2 inhibitor baricitinib (MCE, HY-15315), 10 μM FAK inhibitor PF-573228 (MCE, HY-10461), and 10 μM Src inhibitor dasatinib (MCE, HY-10181).
scRNA-Seq and analysis.
Sorted epithelial cells were used to perform scRNA-Seq with the 10x Genomics Chromium Platform at 14 dpi. FASTQ files were processed using the Cell Ranger pipeline. For further downstream analysis, R package Seurat (version 4.2.0) was used. Low-quality cells (with fewer than 200 or more than 7,500 genes and with more than 10% mitochondrial transcripts) were removed. ScaleData(), RunPCA(), FindNeighbors(), FindClusters() and RunTSNE() functions were used to cluster and visualize cells. Cell annotation was achieved according to the marker gene expression. Immune receptor expression was plotted using the Dotplot() function. The upregulated expression gene set was used to perform KEGG enrichment analysis using the clusterProfiler R package after removal of ribosomal genes. Visualization of YAP downstream gene expression was shown using Nebulosa (version 1.8.0) R package. The trajectory analysis was inferred using the R package Monocle 3 (version 1.0.0) by importing the counts from the Seurat object. For reanalysis of human RNA-Seq data, only data from COVID-19–infected lungs were used.
Human AEC2 sorting.
Human lung tissue was dissected into 1 cm3 pieces and washed with 50 mL cold PBS, then dissected into smaller pieces (1–2 mm3). Human lung pieces were digested by gentle shaking in lysis buffer containing DMEM, 2.5 U/mL dispase II, 225 U/mL collagenase type I, 100 U/mL DNase I, 5% FBS, 2 mM MgCl2, 2 mM CaCl2, and 20 mM HEPES for 60 minutes at 37°C. The tissue homogenate was filtered through a 70 μm strainer, and red blood cells were removed using RBC Lysis Buffer (BioLegend, 420301). The single-cell suspensions from above were pelleted and resuspended in PBS buffer containing 5% FBS and anti-human HTII-280 (5 μg/mL) (Terrace Biotech, TB-27AHT2-280). Cells were stained for 30 minutes at 4°C and further sorted using anti-mouse IgM microbeads (Miltenyi Biotec) with AutoMACS Pro.
Human organoid culture.
Human AEC2 and MRC5 cells (ATCC, CCL-171) (5,000:30,000) were diluted in equal volumes of Matrigel, seeded in Transwells (TCS002024), and solidified at 30 minutes at 37°C. CK+DCI medium is composed of the following components: IMDM (Thermo Fisher Scientific, 12440053), Ham’s F12 medium (CellGro, 10-080-CV), 3 μM CHIR99021 (Sigma-Aldrich, SML1046-5MG), 10 ng/mL rhKGF (Novoprotein, CH73), B27 supplement (Invitrogen, 12587–010), N2 supplement (Invitrogen, 17502–048), GlutaMAX (Invitrogen, 35050061), penicillin/streptomycin (Thermo Fisher Scientific, 15140122), 0.2% BSA (Sigma-Aldrich, B2064), 10 μg/mL 1-thioglycerol (MTG, Sigma-Aldrich, M6145), 50 μg/mL ascorbic acid (Sigma-Aldrich, A4544), 50 nM dexamethasone (Sigma-Aldrich, D4902), 0.1 mM 8-Bromo-cAMP (Sigma-Aldrich, B7880), and 0.1 mM 3-isobutyl-1-methylxanthine (IBMX) (Sigma-Aldrich, I5879) with 10 μM TGF-β inhibitor (A8301, Stemcell, 72024), and 10%FBS was added in the first 14 days to form spheres; 10 μM Y27632 was added in the first 2 days, and culture medium was replenished every other day. CHIR99021 was retracted from the culture medium from day 12, and 50 ng/mL human recombinant IFN-γ (Novoprotein, C014) or PBS was added from day 15 for 4 days. At day 18, the recombinant IFN-γ was reduced to 25 ng/mL, and organoids were collected at day 21 for analysis. For inhibitor treatment, cells were pretreated with indicated inhibitors from day 12. Forty-eight hours later, fresh medium containing IFN-γ and indicated inhibitors was replenished. The following molecules were used: 10 μM FAK inhibitor PF-573228 and 10 μM Src inhibitor dasatinib. Human MRC5 cells were cultured in medium containing DMEM-F12, 10%FBS, GlutaMAX, and 1% penicillin/streptomycin. MRC5 cells with passage of less than 5 were used. MRC5 cells with higher passages showed reduced ability to support human organoid growth.
Human lung tissue.
Human lung tissues were obtained from the Cardiothoracic Surgery Department, Children’s Hospital of Fudan University, and the Department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. For the complete list of human lung tissues, refer to Supplemental Tables 1 and 2.
Quantitative reverse-transcription PCR.
For quantitative reverse-transcription PCR (qRT-PCR), the left lung tissues were collected and stored in Trizol (Vazyme, REC03B-100) prior to RNA isolation. cDNA was then synthesized using the HiScript III RT SuperMix for qPCR Kit (Vazyme, R323-01). ChamQ Universal SYBR qPCR Master Mix standard program was run following the manufacturer’s protocol (Vazyme, Q711-03) on a Roche LC480-384 instrument. Gene expression was calculated relative to tubulin within that sample and expressed as fold change over the average expression. All primers are listed in Supplemental Table 3.
Statistics.
As indicated in the figure legends, 2-tailed t test, 2-tailed Mann-Whitney U test, or multiple t test was used for 2 groups. Levene’s test was used to check the equality of variances across groups, and then 1-way ANOVA or Brown-Forsythe and Welch’s ANOVA followed by the original FDR method of the Benjamini and Hochberg multiple-comparison test were used for more than 2 groups. A P value of less than 0.05 was considered significant. Data are represented as mean ± SEM. Statistical analysis was performed using Prism, version 10.2.0 (GraphPad Inc.).
Study approval.
All animal experiments were conducted under IACUC-approved protocols at the Center for Excellence in Molecular Cell Science and the CAS Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Science. Human lung tissues were collected under the surgical criteria of the Cardiothoracic Surgery Department, Children’s Hospital of Fudan University, and the department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. Human research was approved by the institutional review boards of Fudan University and the Department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. All participants provided written, informed consent.
Data availability.
The scRNA-Seq data generated in this study have been deposited in the NCBI’s Gene Expression Omnibus database (GEO GSE234082). Previously published scRNA-Seq data that are reanalyzed here are available at GSE171524. All analyses associated with this study used preexisting R packages. No custom code was generated. Values for all data points in graphs are reported in the Supporting Data Values file.
Author contributions
XL and PS conceived and designed the experimental approach. XL, WC, HW, and PS performed experiments. XL, GY, JZ, and PS analyzed the data. WC provided human lung samples. ZL and TR provided COVID-19 samples. BL provided resources for IAV infection experiments. GY, YX, and BL reviewed and edited the manuscript. XL and PS wrote the manuscript with input from other authors.
Supplementary Material
Supplemental data
Unedited blot and gel images
Supporting data values |
Title: Understanding overfitting in random forest for probability estimation: a visualization and simulation study | Body:
Background
Random Forests (RF) is an ensemble learning method introduced by Leo Breiman in 2001 [1]. The difference between RF and other tree ensemble methods such as bagging or boosting is that the trees in RF are independent. A bootstrap sample is selected at each tree and at each node of each tree a random subset of predictors is considered for the best split. This reduces the correlation between trees.
RF is used across a variety of clinical problems [2] and in recent years it has become very popular for clinical prediction modeling [3–6]. Its popularity has risen due to its good reported performance in applied studies, and its claimed robustness against overfitting in combination with the limited need for hyperparameter tuning [7]. It has been reported that RF models have better performance when the individual trees in the ensemble are overfitted [8–10]. Although RF has been widely investigated as a “classifier”, the literature about their performance as probability estimation trees (PET) is scarce.
In a recent study of women with an ovarian tumor, we compared the performance of different machine learning algorithms to estimate the probability of five tumor types (benign, borderline malignant, stage I primary invasive, stage II–IV primary invasive, and secondary metastatic) [11]. We developed prediction models on training data (n = 5909) using multinomial logistic regression (MLR), ridge multinomial logistic regression, RF, XGBoost, neural networks, and support vector machines with Gaussian kernel. We evaluated discrimination performance using the Polytomous Discrimination Index (PDI) as a multiclass area under the receiver operating characteristic curve (AUC) [12, 13]. The PDI is the probability that, when presented with a random patient from each category, the model can correctly identify the patient from a randomly selected category. With five outcome categories, the PDI is 1/5 for an uninformative or random model and 1 for a model with perfect discrimination. We observed that RF had near-perfect discrimination on the training data (PDI 0.93 for RF vs 0.47–0.70 for other models), and was competitive on the external validation data (PDI 0.54 for RF vs 0.41–0.55 for other models; n = 3199) (Table S1). The observation that the RF model had near-perfect (i.e., highly suspicious) discrimination on training data, yet performed competitively during external validation, may be somewhat counterintuitive. Such high training set performance suggests strong overfitting by modeling considerable amounts of noise, which would lead to reduced performance on new data [14, 15]. This was an interesting observation for us: the training results suggest a suspiciously high degree of overfitting by RF compared to other models, such that we would have expected a stronger reduction in performance on new data for RF. In this study, we aimed to understand the behavior of random forests for probability estimation by (1) visualizing data space in three real world case studies and (2) conducting a simulation study.
The paper outline is as follows. In the “Random forest for probability estimation” section, we summarize the RF algorithm for probability estimation, in the “Case studies” section, we visualize the predictions for the ovarian tumor data, and present two additional case studies. In the “Simulation study” section, we present a simulation study to explore the effect of tree depth and training sample size and the data generation mechanism (DGM) to better understand the behavior of the RF algorithm. In the “Overall discussion” section, we discuss our findings.
Random forest for probability estimation
When the outcome is categorical, RF can be used for classification or probability estimation. In this work we will use random forest for probability estimation [16, 17]. RF is a tree-based ensemble method, and when used for probability estimation it works as follows:Draw ntree bootstrap samples from the original training dataset, where ntree denotes the number of trees in the forest.On each bootstrap sample, construct a tree using recursive binary splits. To reduce the correlation between trees, a number of predictors (mtry) are chosen randomly at each split. mtry is a hyperparameter and can be tuned, but often a default value equal to the square root of the total predictors (P) is used. A split on one of these predictors is chosen so that the selected splitting criterion (e.g., Gini index) is optimized.Splits are consecutively created as long as all child nodes contain a specific minimum number of observations (min.node.size). When a node cannot be split without violating this condition, the node becomes a final leaf node. Other stopping criteria can be defined [18]. For each leaf node, the proportion of cases from each outcome class can be calculated. Alternatively, the majority vote can be determined: the outcome class that has the most cases in the leaf.To obtain a probability estimate for each outcome class i for a new case, we first determine the new case’s appropriate leaf node for each of the ntree trees. Then, two basic approaches are possible. The first uses the proportion of the ntree majority votes (cf step 3) that equal i. The second averages the proportion of cases from class i (cf step 3) across the ntree trees [16].
In the seminal books “The Elements of Statistical Learning” and “An Introduction to Statistical Learning”, the authors highlight the simplicity of training RF models [14, 19]. Regarding the commonly encountered claim that RF cannot overfit, the authors indicate that increasing ntree does not cause overfitting. It has been suggested that ntree does not need to be tuned, but that too low values lead to suboptimal performance [7, 20]. A value of 500 or even 250 has shown to be sufficient in most applications [7]. A typical value for mtry is \documentclass[12pt]{minimal}
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\begin{document}$$\sqrt{P}$$\end{document}P, as recommended by Breiman, or lower values to maximize decorrelation [14]. Hastie and colleagues suggest that min.node.size can be set to a very low value, even 1 and that mtry is a more important tuning parameter: “when the number of variables is large, but the fraction of relevant variables small, random forests are likely to perform poorly with small mtry. … Our experience is that using full-grown trees seldom costs much, and results in one less tuning parameter” [14].
Case studies
Methods
We aimed to visualize the estimated probabilities in data space to obtain a better understanding of the phenomenon where RF models with near-perfect discrimination also performed competitively during external validation. We followed a typical random train-test split used in machine learning procedures. We developed RF and MLR prediction models on the training set using two continuous and a number of categorical predictors. We use only two continuous predictors because if we set the categorical predictors to a fixed value, e.g., the most common one, we can show a two-dimensional subset of the complete data space by showing the two continuous predictors on the x-axis and y-axis. We can show estimated probabilities in this subset as a heatmap, and show individual cases (from training or test set) as a scatter plot. This allows us to visualize how RF and MLR transform predictor values into probability estimates, for example in terms of smoothness. Obviously, only cases for which the categorical values equal the chosen fixed value can be shown. By choosing different fixed values for categorical variables, we can visualize different subsets of data space. We noticed that the range of estimated probabilities was larger for RF than MLR. Therefore, to ensure a proper visualization of the high- and low-risk estimates, the greyscale in the heatmaps is bounded to the minimum and maximum predicted probabilities by each model in each panel. We also include figures using the same scale for all heatmaps in Additional file 1.
The RF models were trained with ranger package, with ntree = 500, mtry = \documentclass[12pt]{minimal}
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\begin{document}$$\lceil\sqrt{P}\rceil$$\end{document}⌈P⌉, and min.node.size = 2. Ranger estimates the probabilities with Malley’s probability machine methods which averages the proportion of cases from each class over the terminal nodes from each of the trees [16, 21]. In MLR models, we modeled continuous predictors using restricted cubic splines (rcs) with 3 knots to allow nonlinear associations [22, 23]. For each model, we calculated the train and test PDI and multinomial calibration plots. The code for training the models and generating the plots is available in the OSF repository (https://osf.io/y5tqv/).
Ovarian cancer diagnosis
This prospective study collected data on patients between 1999 and 2012. All patients had at least one adnexal (ovarian, para-ovarian, or tubal) mass that was judged not to be a physiological cyst, provided consent for transvaginal ultrasound examination, were not pregnant, and underwent surgical removal of the adnexal mass within 120 days after the ultrasound examination. We randomly split the data (N = 8398) into training (n = 5900, 70%) and test parts (n = 2498, 30%), and developed models on the training data using patient age (in years) and CA125 (in IU/L) as continuous variables, and five ultrasound based categorical variables (proportion of solid tissue, number of papillary projections, if the mass has more than 10 locules, if the mass has shadows and if the mass has ascites). Note that the proportion of solid tissue is a continuous variable that can be seen as a semi-categorical variable with 75% of observations having values 0 or 1. The distribution of classes in the dataset was 66% (5524) for benign tumors, 6% (531) for a borderline ovarian tumor, 6% (529) for stage I ovarian cancer, 17% (1434) for stage II–IV ovarian cancer, and 5% (380) for metastatic cancer to the ovaries (detailed information in Table S2). The apparent PDI was 0.97 for RF and 0.52 for MLR. In the test set the PDI decreased to 0.56 for the RF model and remained 0.52 for the MLR.
Figure 1 shows heatmaps for the estimated probabilities of a benign, borderline, stage I invasive, stage II–IV invasive, and secondary metastatic tumor, with training data cases superimposed (see Figures S1–2 for extended visualizations). Cases belonging to the class to which the probabilities refer are shown in red, and other cases in green. One set of heatmaps refers to the fitted RF model, and the other to the fitted MLR model. Whereas estimated probabilities from the regression model change smoothly according to the values of the continuous predictors, the estimated probabilities from the RF model peak where events from the training data were located. Where many events were found in proximity, these peaks combined into a larger area with increased probability. For events in less densely populated areas of data space, these peaks were idiosyncratic: in test data, events in these areas of data space tend to be located in different places (Fig. 2 and Figures S3–S4). The calibration performance of the RF model was very poor in the training set: high probabilities were underestimated and low probabilities were overestimated (Fig. 3). Calibration in the test set was much better.Fig. 1Random Forest and logistic regression probability estimation in data space for 4 subtypes of ovarian malignancy diagnosis with cases in training set superimposed. CA125 bounded to 500Fig. 2Random forest and logistic regression probability estimation in data space for 4 subtypes of ovarian malignancy diagnosis with cases in test set superimposed. CA125 bounded to 500Fig. 3Calibration plots for random forest model in ovarian cancer data. Observed proportion is estimated with a LOESS model. The plots only show observed proportions for predicted probabilities between quantiles 5th and 95th
CRASH3: traumatic brain injury prognosis
CRASH-3 data was collected between 2012 and 2019 for a multicenter, randomized, placebo-controlled trial to measure the effects of tranexamic acid on death, disability, vascular occlusive events, and other morbidities in 12,660 patients with acute traumatic brain injury (TBI) [24]. We used age (years) and systolic blood pressure (mmHg) as continuous variables and sex, Glasgow Coma Scale (GCS) eye-opening (4 levels), and pupillary reaction (4 levels) as categorical variables. We performed a complete case analysis (CCA) removing patients for which one or more values were missing obtaining a complete dataset of 12,548 patients. CCA was used for simplicity and because the phenomenon under study should not be affected importantly by this. The outcome was measured 28 days after randomization: alive (n = 10,022, 80%), death due to head injury (n = 2309, 18%), or death of other cause (n = 217, 2%) (detailed information in Table S3). The training set included 8783 patients (70%), and the test set was 3765 (30%).
For RF, the PDI was 0.96 in train data and 0.54 in test data. For MLR, the PDI was 0.61 and 0.60, respectively. The heatmaps drew a similar picture compared with the previous case study: RF had clear probability peaks whilst MLR had smoothly changing probabilities (Figures S5–S8). The calibration plots for RF were also similar: poor calibration in training data but decent in the test data for the 2 most common outcomes (Figure S9).
IST: type of stroke diagnosis
The International Stroke Trial (IST) database was designed with the aim of establishing whether early administration of aspirin, heparin, or both or neither influenced the clinical course of acute ischaemic stroke [25]. Data for the 19,435 patients with suspected acute ischaemic stroke were recruited between 1992 and 1996. We use age (years) and systolic blood pressure (mmHg) as continuous variables, and conscious state (fully alert vs drowsy), deficit of face (yes vs no), deficit of arm/hand (yes vs no), deficit of leg/foot (yes vs no), dysphasia (yes vs no), and hemianopia (yes vs no) as categorical variables. We again performed a CCA retaining 15,141 patients. The outcome is the type of stroke: ischaemic (n = 13,622, 90%), indeterminate (n = 736, 5%), hemorrhagic (n = 439, 3%), or no stroke (n = 344, 2%) (detailed information in Table S4). The training set included 10,598 patients (70%), and the test set 4543 (30%).
The RF model had a training PDI of 0.89 and a test PDI of 0.35. For MLR, the training set PDI was 0.39, the test set PDI was 0.41. In this dataspace, the phenomenon is notorious, with very local peaks around train cases (FiguresS10–S13). The calibration plots for training and test data showed poor calibration (Figure S14).
Methods
We aimed to visualize the estimated probabilities in data space to obtain a better understanding of the phenomenon where RF models with near-perfect discrimination also performed competitively during external validation. We followed a typical random train-test split used in machine learning procedures. We developed RF and MLR prediction models on the training set using two continuous and a number of categorical predictors. We use only two continuous predictors because if we set the categorical predictors to a fixed value, e.g., the most common one, we can show a two-dimensional subset of the complete data space by showing the two continuous predictors on the x-axis and y-axis. We can show estimated probabilities in this subset as a heatmap, and show individual cases (from training or test set) as a scatter plot. This allows us to visualize how RF and MLR transform predictor values into probability estimates, for example in terms of smoothness. Obviously, only cases for which the categorical values equal the chosen fixed value can be shown. By choosing different fixed values for categorical variables, we can visualize different subsets of data space. We noticed that the range of estimated probabilities was larger for RF than MLR. Therefore, to ensure a proper visualization of the high- and low-risk estimates, the greyscale in the heatmaps is bounded to the minimum and maximum predicted probabilities by each model in each panel. We also include figures using the same scale for all heatmaps in Additional file 1.
The RF models were trained with ranger package, with ntree = 500, mtry = \documentclass[12pt]{minimal}
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\begin{document}$$\lceil\sqrt{P}\rceil$$\end{document}⌈P⌉, and min.node.size = 2. Ranger estimates the probabilities with Malley’s probability machine methods which averages the proportion of cases from each class over the terminal nodes from each of the trees [16, 21]. In MLR models, we modeled continuous predictors using restricted cubic splines (rcs) with 3 knots to allow nonlinear associations [22, 23]. For each model, we calculated the train and test PDI and multinomial calibration plots. The code for training the models and generating the plots is available in the OSF repository (https://osf.io/y5tqv/).
Ovarian cancer diagnosis
This prospective study collected data on patients between 1999 and 2012. All patients had at least one adnexal (ovarian, para-ovarian, or tubal) mass that was judged not to be a physiological cyst, provided consent for transvaginal ultrasound examination, were not pregnant, and underwent surgical removal of the adnexal mass within 120 days after the ultrasound examination. We randomly split the data (N = 8398) into training (n = 5900, 70%) and test parts (n = 2498, 30%), and developed models on the training data using patient age (in years) and CA125 (in IU/L) as continuous variables, and five ultrasound based categorical variables (proportion of solid tissue, number of papillary projections, if the mass has more than 10 locules, if the mass has shadows and if the mass has ascites). Note that the proportion of solid tissue is a continuous variable that can be seen as a semi-categorical variable with 75% of observations having values 0 or 1. The distribution of classes in the dataset was 66% (5524) for benign tumors, 6% (531) for a borderline ovarian tumor, 6% (529) for stage I ovarian cancer, 17% (1434) for stage II–IV ovarian cancer, and 5% (380) for metastatic cancer to the ovaries (detailed information in Table S2). The apparent PDI was 0.97 for RF and 0.52 for MLR. In the test set the PDI decreased to 0.56 for the RF model and remained 0.52 for the MLR.
Figure 1 shows heatmaps for the estimated probabilities of a benign, borderline, stage I invasive, stage II–IV invasive, and secondary metastatic tumor, with training data cases superimposed (see Figures S1–2 for extended visualizations). Cases belonging to the class to which the probabilities refer are shown in red, and other cases in green. One set of heatmaps refers to the fitted RF model, and the other to the fitted MLR model. Whereas estimated probabilities from the regression model change smoothly according to the values of the continuous predictors, the estimated probabilities from the RF model peak where events from the training data were located. Where many events were found in proximity, these peaks combined into a larger area with increased probability. For events in less densely populated areas of data space, these peaks were idiosyncratic: in test data, events in these areas of data space tend to be located in different places (Fig. 2 and Figures S3–S4). The calibration performance of the RF model was very poor in the training set: high probabilities were underestimated and low probabilities were overestimated (Fig. 3). Calibration in the test set was much better.Fig. 1Random Forest and logistic regression probability estimation in data space for 4 subtypes of ovarian malignancy diagnosis with cases in training set superimposed. CA125 bounded to 500Fig. 2Random forest and logistic regression probability estimation in data space for 4 subtypes of ovarian malignancy diagnosis with cases in test set superimposed. CA125 bounded to 500Fig. 3Calibration plots for random forest model in ovarian cancer data. Observed proportion is estimated with a LOESS model. The plots only show observed proportions for predicted probabilities between quantiles 5th and 95th
CRASH3: traumatic brain injury prognosis
CRASH-3 data was collected between 2012 and 2019 for a multicenter, randomized, placebo-controlled trial to measure the effects of tranexamic acid on death, disability, vascular occlusive events, and other morbidities in 12,660 patients with acute traumatic brain injury (TBI) [24]. We used age (years) and systolic blood pressure (mmHg) as continuous variables and sex, Glasgow Coma Scale (GCS) eye-opening (4 levels), and pupillary reaction (4 levels) as categorical variables. We performed a complete case analysis (CCA) removing patients for which one or more values were missing obtaining a complete dataset of 12,548 patients. CCA was used for simplicity and because the phenomenon under study should not be affected importantly by this. The outcome was measured 28 days after randomization: alive (n = 10,022, 80%), death due to head injury (n = 2309, 18%), or death of other cause (n = 217, 2%) (detailed information in Table S3). The training set included 8783 patients (70%), and the test set was 3765 (30%).
For RF, the PDI was 0.96 in train data and 0.54 in test data. For MLR, the PDI was 0.61 and 0.60, respectively. The heatmaps drew a similar picture compared with the previous case study: RF had clear probability peaks whilst MLR had smoothly changing probabilities (Figures S5–S8). The calibration plots for RF were also similar: poor calibration in training data but decent in the test data for the 2 most common outcomes (Figure S9).
IST: type of stroke diagnosis
The International Stroke Trial (IST) database was designed with the aim of establishing whether early administration of aspirin, heparin, or both or neither influenced the clinical course of acute ischaemic stroke [25]. Data for the 19,435 patients with suspected acute ischaemic stroke were recruited between 1992 and 1996. We use age (years) and systolic blood pressure (mmHg) as continuous variables, and conscious state (fully alert vs drowsy), deficit of face (yes vs no), deficit of arm/hand (yes vs no), deficit of leg/foot (yes vs no), dysphasia (yes vs no), and hemianopia (yes vs no) as categorical variables. We again performed a CCA retaining 15,141 patients. The outcome is the type of stroke: ischaemic (n = 13,622, 90%), indeterminate (n = 736, 5%), hemorrhagic (n = 439, 3%), or no stroke (n = 344, 2%) (detailed information in Table S4). The training set included 10,598 patients (70%), and the test set 4543 (30%).
The RF model had a training PDI of 0.89 and a test PDI of 0.35. For MLR, the training set PDI was 0.39, the test set PDI was 0.41. In this dataspace, the phenomenon is notorious, with very local peaks around train cases (FiguresS10–S13). The calibration plots for training and test data showed poor calibration (Figure S14).
Simulation study
Aim
We conducted a simulation study to assess which key factors of the modeling setup (dataset and minimum node size) contribute to the phenomenon of having an exaggerated AUC in the training data without strong signs of overfitting in test data. We report the simulation study using the ADEMP (aims, data-generating mechanisms, estimands, methods, and performance measures) structure [26]. The code for the simulation study can be found in the OSF repository (https://osf.io/y5tqv/).
Data-generating mechanism (DGM)
For the simulation study, we generated data by assuming that the true model was in the form of an MLR with an outcome event fraction of 0.2 (see Additional file 1: Appendix 1: Simulation Algorithm for details).
The 48 DGMs differed according to the following parameters:i.
Predictor distribution: predictors were either all continuous with multivariate normal distribution or all binary with 50% prevalence.ii.
Number of predictors: there were either 4 true predictors (0 noise predictors), 16 true predictors (0 noise predictors), or 16 predictors of which 12 noise predictors. Noise predictors had a true regression coefficient of 0.iii.
Correlation between predictors: Pearson correlations between all predictors were either 0 or 0.4.iv.
True AUC: this was either 0.75 or 0.90.v.
Balance of regression coefficients: the true model coefficients that were not 0 were either all the same (balanced) or not (imbalanced). When imbalanced, one-fourth of the predictors have a coefficient that is 4 times larger than the others.
The true model coefficients for generating the data were obtained by trial error and are available in Table S5.
Estimands
For both training and test data, we estimate model discrimination, whether risk estimates have too high (overconfidence) or too low (underconfidence) spread and the prediction error. Underconfidence reflects the situation in Fig. 3 (Train): high probabilities are underestimated, and low probabilities are overestimated. Overconfidence is the opposite: high probabilities are overestimated, and low probabilities are underestimated. For test data, we also calculate discrimination loss vs the true model and the relative contribution of bias and variance to the prediction error. As the training and test samples are based on the same DGM, the test results reflect internal rather than external validation.
Methods
We fitted models on training datasets of size 200 (small) or 4000 (large), and for RF models we used values for min.node.size of 2 or 20 and ranger package for training. As a result, there were 2 × 2 × 3 × 2 × 2 × 2 × 2 = 192 scenarios. For each scenario, 1000 simulation runs were performed (i.e., 1000 different training datasets). For RF, ntree was fixed at 500, and mtry at the square root of the number of predictors (default value). Models were validated on a single large test dataset per DGM (N = 100,000) to avoid sampling variability.
Performance
For discrimination we calculated the AUC and for confidence of risk estimates the calibration slope (slope < 1 means overconfidence, slope > 1 underconfidence). The calibration slope is calculated as the slope of a logistic regression (LR) model fitting the outcome to the logit of the estimated probabilities as the only predictor. Calibration intercept is calculated fitting the same model for a slope of 1 by setting the predicted probabilities as an offset term. Discrimination loss was calculated as the difference between true AUC and median test AUC. Finally, the mean squared error (MSE) of the predicted probabilities was calculated according to [27] as the sum of squared bias and variance (see Additional file 1: Appendix 2: Simulation metrics for details).
Aim
We conducted a simulation study to assess which key factors of the modeling setup (dataset and minimum node size) contribute to the phenomenon of having an exaggerated AUC in the training data without strong signs of overfitting in test data. We report the simulation study using the ADEMP (aims, data-generating mechanisms, estimands, methods, and performance measures) structure [26]. The code for the simulation study can be found in the OSF repository (https://osf.io/y5tqv/).
Data-generating mechanism (DGM)
For the simulation study, we generated data by assuming that the true model was in the form of an MLR with an outcome event fraction of 0.2 (see Additional file 1: Appendix 1: Simulation Algorithm for details).
The 48 DGMs differed according to the following parameters:i.
Predictor distribution: predictors were either all continuous with multivariate normal distribution or all binary with 50% prevalence.ii.
Number of predictors: there were either 4 true predictors (0 noise predictors), 16 true predictors (0 noise predictors), or 16 predictors of which 12 noise predictors. Noise predictors had a true regression coefficient of 0.iii.
Correlation between predictors: Pearson correlations between all predictors were either 0 or 0.4.iv.
True AUC: this was either 0.75 or 0.90.v.
Balance of regression coefficients: the true model coefficients that were not 0 were either all the same (balanced) or not (imbalanced). When imbalanced, one-fourth of the predictors have a coefficient that is 4 times larger than the others.
The true model coefficients for generating the data were obtained by trial error and are available in Table S5.
Estimands
For both training and test data, we estimate model discrimination, whether risk estimates have too high (overconfidence) or too low (underconfidence) spread and the prediction error. Underconfidence reflects the situation in Fig. 3 (Train): high probabilities are underestimated, and low probabilities are overestimated. Overconfidence is the opposite: high probabilities are overestimated, and low probabilities are underestimated. For test data, we also calculate discrimination loss vs the true model and the relative contribution of bias and variance to the prediction error. As the training and test samples are based on the same DGM, the test results reflect internal rather than external validation.
Methods
We fitted models on training datasets of size 200 (small) or 4000 (large), and for RF models we used values for min.node.size of 2 or 20 and ranger package for training. As a result, there were 2 × 2 × 3 × 2 × 2 × 2 × 2 = 192 scenarios. For each scenario, 1000 simulation runs were performed (i.e., 1000 different training datasets). For RF, ntree was fixed at 500, and mtry at the square root of the number of predictors (default value). Models were validated on a single large test dataset per DGM (N = 100,000) to avoid sampling variability.
Performance
For discrimination we calculated the AUC and for confidence of risk estimates the calibration slope (slope < 1 means overconfidence, slope > 1 underconfidence). The calibration slope is calculated as the slope of a logistic regression (LR) model fitting the outcome to the logit of the estimated probabilities as the only predictor. Calibration intercept is calculated fitting the same model for a slope of 1 by setting the predicted probabilities as an offset term. Discrimination loss was calculated as the difference between true AUC and median test AUC. Finally, the mean squared error (MSE) of the predicted probabilities was calculated according to [27] as the sum of squared bias and variance (see Additional file 1: Appendix 2: Simulation metrics for details).
Results
The aggregated simulation results using median and interquartile range for discrimination and calibration and mean and standard deviation for mean squared error are available in Additional file 1 (Table S6). The complete simulation including the code and 1000 simulations for each of the 192 scenarios is available in the OSF repository (https://osf.io/y5tqv/).
Discrimination
In the simulation study, the median training AUCs were close to 1 in most of the cases. The median training AUC was between 0.97 and 1 unless there were 4 binary predictors, or 16 binary predictors combined with a minimum node size of 20 (Fig. 4). Higher min.node.size resulted in less extreme training AUCs.Fig. 4Training AUC by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength because this simulation factor had minimal effect
In general, median test AUCs were higher when there was a large vs small training dataset, high vs low min.node.size, high vs low correlation between predictors, binary versus continuous predictors, and 4 versus 16 predictors (except with correlated continuous predictors) (Fig. 5). All other simulation factors being equal, the scenarios with 4 true and 12 noise predictors had results for the AUC that was identical to scenarios with 16 predictors (Figs. 4 and 5 and S15–16).Fig. 5Test AUC by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength
The Spearman correlation between median training AUC and discrimination loss was 0.72 for scenarios with a true AUC of 0.9, and 0.69 for scenarios with a true AUC of 0.75 (Figure S17). The median discrimination loss was 0.025 (range 0.00 to 0.13). In the 114 scenarios where the median training AUC was ≥ 0.99, the median discrimination loss was 0.036 (range 0.003 to 0.13). In the other scenarios, the median discrimination loss was 0.013 (0.00 to 0.069).
Calibration
Median training calibration slopes ranged between 1.10 and 19.4 (Fig. 6 and Figure S18): the probability estimates were always underconfident where high probabilities were underestimated and low probabilities overestimated. This is the consequence of perfect separation between events and non-events in training data (i.e., AUC = 1) which means that any estimation above 0 or below 1 is underconfident (Figure S19). The median slope was lowest in scenarios with few binary predictors or higher min.node.size. Median test calibration slopes ranged between 0.45 and 2.34. Across all scenarios, the Spearman correlation between the median training slope and median test slope was − 0.11 (Figure S17). Median test slopes were mainly higher when the true AUC or min.node.size was higher. In addition, median test slopes tended to be higher with binary predictors, uncorrelated predictors, and higher sample sizes (Fig. 7 and Figure S18). Calibration slopes were similar in scenarios with 16 true predictors, 4 true predictors, and 12 noise predictors (Figs. 6 and 7 and Figure S20–21). In the 78 scenarios without perfect training (AUC < 0.99) the median test calibration slope was between 0.59 and 2.34 with a median of 1.10. In the 114 scenarios with almost perfect training AUC (≥ 0.99), the median calibration slope was 0.92.Fig. 6Training set calibration log slope by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength. The ideal value for the log slope is 0Fig. 7Test set calibration slope by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength. The ideal value for the slope is 1
Mean squared error (MSE)
Median MSE across scenarios was 0.008 (range 0.000–0.045) with a median squared bias of 0.002 (range 0.000–0.038) and median variance of 0.005 (range 0.000–0.017). For the 114 scenarios with median training AUC ≥ 0.99, we observed a median test MSE of 0.010 with a median squared bias of 0.004 (range 0.0004–0.0384) and a median variance of 0.006 (range 0.001–0.017). For the rest of the scenarios, the median test MSE was 0.006. Across all scenarios, the Spearman correlation of mean test squared bias and mean test variance with median training AUC were 0.47 and 0.43, respectively. The correlation with the discrimination loss was 0.51 for the squared bias and 0.70 for the variance. Lower sample size in training was associated with higher median test variance, and with higher median test squared bias in scenarios with continuous predictors. Lower min.node.size (i.e., deeper trees) was associated with a lower variance but higher bias in test data when the training sample size was small. More predictors were associated with higher bias, whereas the correlation between predictors and higher true AUC was associated with lower bias (Fig. 8). The models with noise predictors had lower variance and higher bias compared to scenarios with 4 true and no noise predictors (Figure S22).Fig. 8Mean squared error across scenarios without noise aggregated by strength
Discrimination
In the simulation study, the median training AUCs were close to 1 in most of the cases. The median training AUC was between 0.97 and 1 unless there were 4 binary predictors, or 16 binary predictors combined with a minimum node size of 20 (Fig. 4). Higher min.node.size resulted in less extreme training AUCs.Fig. 4Training AUC by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength because this simulation factor had minimal effect
In general, median test AUCs were higher when there was a large vs small training dataset, high vs low min.node.size, high vs low correlation between predictors, binary versus continuous predictors, and 4 versus 16 predictors (except with correlated continuous predictors) (Fig. 5). All other simulation factors being equal, the scenarios with 4 true and 12 noise predictors had results for the AUC that was identical to scenarios with 16 predictors (Figs. 4 and 5 and S15–16).Fig. 5Test AUC by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength
The Spearman correlation between median training AUC and discrimination loss was 0.72 for scenarios with a true AUC of 0.9, and 0.69 for scenarios with a true AUC of 0.75 (Figure S17). The median discrimination loss was 0.025 (range 0.00 to 0.13). In the 114 scenarios where the median training AUC was ≥ 0.99, the median discrimination loss was 0.036 (range 0.003 to 0.13). In the other scenarios, the median discrimination loss was 0.013 (0.00 to 0.069).
Calibration
Median training calibration slopes ranged between 1.10 and 19.4 (Fig. 6 and Figure S18): the probability estimates were always underconfident where high probabilities were underestimated and low probabilities overestimated. This is the consequence of perfect separation between events and non-events in training data (i.e., AUC = 1) which means that any estimation above 0 or below 1 is underconfident (Figure S19). The median slope was lowest in scenarios with few binary predictors or higher min.node.size. Median test calibration slopes ranged between 0.45 and 2.34. Across all scenarios, the Spearman correlation between the median training slope and median test slope was − 0.11 (Figure S17). Median test slopes were mainly higher when the true AUC or min.node.size was higher. In addition, median test slopes tended to be higher with binary predictors, uncorrelated predictors, and higher sample sizes (Fig. 7 and Figure S18). Calibration slopes were similar in scenarios with 16 true predictors, 4 true predictors, and 12 noise predictors (Figs. 6 and 7 and Figure S20–21). In the 78 scenarios without perfect training (AUC < 0.99) the median test calibration slope was between 0.59 and 2.34 with a median of 1.10. In the 114 scenarios with almost perfect training AUC (≥ 0.99), the median calibration slope was 0.92.Fig. 6Training set calibration log slope by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength. The ideal value for the log slope is 0Fig. 7Test set calibration slope by simulation factors and modeling hyperparameters in scenarios without noise. Scenarios are aggregated by strength. The ideal value for the slope is 1
Mean squared error (MSE)
Median MSE across scenarios was 0.008 (range 0.000–0.045) with a median squared bias of 0.002 (range 0.000–0.038) and median variance of 0.005 (range 0.000–0.017). For the 114 scenarios with median training AUC ≥ 0.99, we observed a median test MSE of 0.010 with a median squared bias of 0.004 (range 0.0004–0.0384) and a median variance of 0.006 (range 0.001–0.017). For the rest of the scenarios, the median test MSE was 0.006. Across all scenarios, the Spearman correlation of mean test squared bias and mean test variance with median training AUC were 0.47 and 0.43, respectively. The correlation with the discrimination loss was 0.51 for the squared bias and 0.70 for the variance. Lower sample size in training was associated with higher median test variance, and with higher median test squared bias in scenarios with continuous predictors. Lower min.node.size (i.e., deeper trees) was associated with a lower variance but higher bias in test data when the training sample size was small. More predictors were associated with higher bias, whereas the correlation between predictors and higher true AUC was associated with lower bias (Fig. 8). The models with noise predictors had lower variance and higher bias compared to scenarios with 4 true and no noise predictors (Figure S22).Fig. 8Mean squared error across scenarios without noise aggregated by strength
Overall discussion
We tried to better understand and visualize the behavior of random forests for probability estimation. We make three key observations from this work. First, RF models learn local probability peaks around training set events, in particular when the trees are very deep (i.e., low min.node.size) and when there are continuous predictors. Where a group of events is located close to one another in ‘data space’, the probability peaks are combined into a region of increased probability. Where events are isolated in data space, the probability peaks are very local. Learning through peaks leads to very optimistic (often near perfect) discrimination in training data, but also to reduced discrimination in new data compared to models that rely on less deep trees (cf. simulation settings where RF models with min.node.size 2 vs 20 were fitted on a set of continuous predictors). Probably, the reduction in discrimination on new data is modest because the local peaks for isolated events are often harmless for new data: it is unlikely to see an event in the exact same location. Second, RF also suffers from “classical overfitting” in which models with higher discrimination on training data tend to have lower discrimination on new data than models with less optimistic discrimination (cf. simulation settings where RF models are learned on 4 binary predictors using 200 vs 4000 training cases). Third, in training and test data, calibration performance for RF models is different from what we commonly observe for LR models. Whereas LR based on maximum likelihood leads by definition to calibration slopes of 1 on training data, calibration slopes for RF models were always above 1 on training data. Also, as opposed to LR, calibration slopes for RF models do not converge to 1 on new data. This different behavior is probably caused by the pragmatic way in which probabilities are obtained for RF models, whilst LR estimates probabilities in a principled way through maximum likelihood.
The simulation results regarding discrimination and calibration go against fitting very deep trees when using RF for probability estimation. This is in line with recent work that illustrated that RF using deeply grown trees results in risk estimates that are particularly unstable [28]. The heatmaps for our case studies illustrate how RF models with deeply grown trees lead to probabilities that change non-smoothly with changes in the values of predictors. It has been suggested to set min.node.size to 5% or 10% of the sample size [16, 29]. Alternatively, min.node.size can be tuned. Although this was not the focus of our study, it seems natural to tune with logloss (also known as the negative loglikelihood or cross-entropy) or Brier score as the loss function since they capture calibration [18]. In their work, Ledger and colleagues tuned min.node.size by optimizing logloss based on tenfold cross-validation for 30 random values for mtry and min.node.size using the trainControl and train functions from the caret R package [11]. This resulted in mtry = 3 and min.node.size = 15, with competitive results in test data. Applying the tuneRanger R package with 200 iterations for our case studies based on logloss yielded an optimal min.node.size of 8 (0.1% of training set size) for ovarian cancer data, 261 (2.1%) for CRASH3 data and 591 (3.9%) for IST data [18]. These values are higher than the default values in many statistical software programs.
Three comments regarding the interpretation of discrimination and calibration results for RF models are worth making. First, it is well known that apparent performance, i.e., performance assessment on the exact same dataset that was used to train the model, is overly optimistic [30, 31]. Our work indicates that this is a fortiori case for RF. Unfortunately, some studies present their RF models with “excellent” performance because they only present discrimination for the training data [4, 32, 33]. Instead, proper internal and external validation results should be reported. Second, from a regression modeling perspective, a calibration slope that is clearly below 1 on internal validation is a symptom of overfitting. This cannot be applied in the same way for RF models. Models based on larger training samples had higher calibration slopes in our simulation study, but a calibration slope of 1 does not appear to have a special meaning. Models with a calibration slope above 1 on test data when trained on 200 training samples, had an even higher slope when trained on 4000 samples. The interpretation of risk estimates based on RF requires caution, probably because probabilities are generated in a very ad hoc way. Of course, the calibration slope still quantifies in a descriptive manner whether the risk estimates are on average too confident (slope < 1), not confident enough (slope > 1), or fine (slope = 1). Third, despite that RF models had high discrimination results in the training data (suggestive of overfitting), the calibration slope in the training data was always above 1 (risk estimates show too little spread, suggestive of underfitting). This appears to be a consequence of the bootstrapping procedure in combination with the low min.node.size. Due to the bootstrapping, a training set case was part of approximately 63% of bootstrap samples and therefore was used for 63% of the trees in the forest. When averaging the proportion of events in the appropriate leaf nodes over all ntree trees to get a probability estimate for a given training set case, 63% of these proportions are near perfect: close to 1 if the training case is an event, close to 0 if the training cases is a non-event. The remaining 37% are more variable and often far less good. The 63% near-perfect proportions cause discrimination to be very high because most events will end up with an estimated probability of an event that is higher than that for most non-events. The remaining 37% of the proportions pull the probability estimates away from 0 (if the case is a non-event) or 1 (if the case is an event), leading to calibration slopes > 1.
Although the aim of our paper was largely educational, it links to previous more fundamental work and fills a gap in the literature by explicitly studying factors that contribute to better discrimination and calibration of new data. Wyner and colleagues (2017) argued that RF has excellent performance because it is an “interpolating classifier”, i.e., it is fitted with little to no error to the train data [34]. They argue that the interpolation should not be confused with overfitting. Even if the individual trees are overfitting, each training set case is not used in about 37% of the individual trees, such that averaging over trees partially solves overfitting. Belkin and colleagues have linked this to a double descent curve for highly flexible algorithms: when the complexity of the model increases, test set performance first improves, then deteriorates, and finally improves again once the ‘interpolation threshold’ (where perfect training performance is achieved) is exceeded [35]. Recently, however, Buschjäeger and Morik opposed the existence of double descent in RF [36]. Mentch and Zhou linked the success of RF to the signal-to-noise ratio (SNR) of the data. In their work, they present that the randomness of RF is beneficial in situations with low SNR whereas bagging is preferred if SNR is high [37]. They explain the success of RF by the low SNR of many real-world datasets. This view contradicts the view that flexible algorithms work best when the SNR is high [38]. Finally, the issue of calibration of estimated probabilities in the context of RF models received little attention in the literature, although it is key for optimal clinical decision making [39]. It has been suggested that using fully grown trees in RF leads to suboptimal risk estimates [16, 29]. However, this is rarely mentioned and hence it is common to see that low minimum node sizes are recommended because the problem is treated as a classification problem instead of a probability estimation problem (e.g., see default for randomForest package or scikit-learn). We think that the current study sheds further light on probability estimation in RF. Of course, a generic alternative that works for any miscalibrated model is to recalibrate the probabilities of the RF afterward using new data [40].
We identified the following limitations of our study. Firstly, a simulation study is always limited by the included scenarios. It would be of interest to include more simulation factors and values per simulation factor (e.g., for min.node.size) in the simulation study or to include scenarios where RF hyperparameters are tuned rather than fixed. Tuning could improve the calibration of the models [18, 41]. However, the simulation study already had 192 scenarios, and adding more factors or values would increase the computational cost exponentially and would overcomplicate the interpretation of the results. Topics that could be investigated in further simulation studies include varying other hyperparameters than min.node.soze (e.g., mtry, sampling fraction, splitting rule) and investigating more values for sample size, number of predictors, the proportion of noise predictors, and min.node.size. Secondly, we were using logistic DGMs without nonlinear or nonadditive associations with the outcome. We assumed that the impact of this would be limited, because the focus was not on the comparison of RF with LR, and any nonlinearity or nonadditivity (including the absence of it) has to be learned by the algorithm. Thirdly, the traditional RF algorithm selects variables at each split in a way that favors continuous over binary variables [42]. Continuous variables can split in many ways, such that there is often a split that, perhaps by chance, has a better Gini impurity reduction than the Gini impurity reduction for a binary variable. The splits for continuous variables may often overfit, thereby increasing training discrimination but decreasing test discrimination. It is well documented that this affects variable importance measures [42], but it may also be relevant for model performance. The problem can be addressed by using an adapted RF algorithm such as cforest from the partykit package [43]. These adapted algorithms grow conditional inference trees (CIT) instead of classification trees. For the case studies, we observed that tuning and using the adapted RF yields a less optimistic training AUC and similar or slightly better test performance (see OSF Repository, https://osf.io/y5tqv/). However, our aim was to understand how the characteristics of the data and the modeling process affected the models, hence we did not systematically explore the effects of tuning or alternative algorithms.
We conclude that RF tends to exhibit local overfitting by learning probability peaks, in particular when the RF model is based on deeply grown trees. This local overfitting can lead to highly optimistic (near perfect) discrimination on the training data but to reduced discrimination on new data compared to RF models based on less deeply grown trees. In line with the work of Kruppa and colleagues [41], our results go against the recommendation to use fully grown trees when using RF for probability estimation.
Supplementary Information
Additional file 1: Train and test performance of different machine learning algorithms on predicting ovarian type of tumour in terms of polytomous discrimination index (PDI) and ovarian malignancy in terms of AUC (Benign vs Malignant). AUC, Area Under receiver operating curve; MLR, Multiple Linear Regression; RF, Random Forest; XGBoost, Extreme gradient boosting; NN, Neural network; SVM, Support Vector Machine. Table S2. Distribution of different classes in the ovarian cancer dataset. Table S3. Distribution of different classes in the ovarian CRASH dataset. Table S4. Distribution of different classes in the ovarian IST dataset. Table S5. Coefficients and simulation factors. Table S6. Main simulation results. Figure S1. Random forest probability estimation in data space for ovarian malignancy diagnosis with random forest (left) and multinomial logistic regression (right). Squares refer to train cases. Figure S2. Random forest probability estimation in data space for ovarian malignancy diagnosis with random forest (left) and multinomial logistic regression (right) using same scale for all panels. Squares refer to train cases. Figure S3. Random forest probability estimation in data space for ovarian malignancy diagnosis with random forest (left) and multinomial logistic regression (right). Squares refer to test cases. Figure S4. Random forest probability estimation in data space for ovarian malignancy diagnosis with random forest (left) and multinomial logistic regression (right) using same scale for all panels. Squares refer to test cases. Figure S5 Figure S5 Figure S5. Figure S5. Random forest probability estimation in data space for 3 possible outcomes in CRASH 3 dataset. Squares refer to train cases. Figure S6 Figure S6 Figure S6. Figure S6. Random forest probability estimation in data space for 3 possible outcomes in CRASH 3 dataset using same scale for all panels. Squares refer to train cases. Figure S7. Random forest probability estimation in data space for 3 possible outcomes in CRASH 3 dataset. Squares refer to test cases. Figure S8. Random forest probability estimation in data space for 3 possible outcomes in CRASH 3 dataset. Squares refer to test cases using same scale for all panels. Figure S9. Multinomial calibration plots of CRASH training and test data. Observed proration is estimated with a LOESS model. The plot shows only predicted probabilities between quantiles 5th and 95th. Figure S10. Random forest probability estimation in data space for 4 possible type of strokes in IST dataset. Squares refer to training cases. Figure S11. Random forest probability estimation in data space for 4 possible type of strokes in IST dataset. Squares refer to training cases using same scale for all panels. Figure S12. Random forest probability estimation in data space for 4 possible type of strokes in IST dataset. Squares refer to test cases. Figure S13. Random forest probability estimation in data space for 4 possible type of strokes in IST dataset using same scale for all panels. Squares refer to test cases. Figure S14. Calibration plot in training for IST dataset. Observed proration is estimated with a LOESS model. The plot shows only predicted probabilities between quantiles 5th and 95th. Figure S15. Training AUC by simulation factors and modelling hyperparameters in scenarios with noise. Scenarios are aggregated by strength. Figure S16. Test AUC by simulation factors and modelling hyperparameters in scenarios with noise. Scenarios are aggregated by strength. Figure S17. Spearman correlations of principal metrics across all scenarios, scenarios with true AUCs 0.9 and scenarios with true AUC 0.75. Figure S18. Train and test calibration log(slope) in scenarios without noise. Scenarios are summarised by simulation factors that had minor effect. Figure S19. Histogram of predicted probabilities in training for different simulation scenarios. Minimum node size was always 2 and training sample size 4000. Figure S20. Training set calibration log slope by simulation factors and modelling hyperparameters in scenarios with noise. Scenarios are aggregated by strength. Perfect calibration is 0. Figure S21. Test set calibration slope by simulation factors and modelling hyperparameters in scenarios with noise. Scenarios are aggregated by strength. Perfect calibration is 1. Figure S22. Mean squared error across scenarios with noise aggregated by strength. |
Title: Differential Cellular Response to Mercury in Non-Farmed Fish Species Based on Mitochondrial DNA Copy Number Variation Analysis | Body:
1. Introduction
The Mediterranean Sea is subjected to the continuous input of numerous anthropogenic chemical compounds that pose serious threats to the balance of the entire marine ecosystem. Many studies, especially in recent decades, have focused on better understanding the toxicity of these compounds. Some contaminants, in fact, have bioaccumulation capacity in different animal tissues and this represents a hazard factor with consequences observable even long after the period of actual exposure. Contaminants can be also transferred through trophic chain through the “biomagnification” phenomenon [1]. Furthermore, it should be taken into account that many organic and/or inorganic compounds can induce severe impacts on living systems even at sub-lethal concentrations [2,3].
Osteichthyes, or bony fish, are the most numerous and widely distributed group of organisms within marine ecosystems, occupying all available ecological niches in such environments. Therefore, different species have often been studied and used as bioindicators of the quality of aquatic systems being able to reflect even small changes in environmental parameters [4]. In particular, fish are the group most exposed to the uptake of Hg through gills, skin and gut [5].
The detection of environmental contaminants relying on sensitive analytical methods, does not provide evidence on their actual or potential impact on exposed organisms. Therefore, the scientific community is focusing on the definition of biomarkers as possible primary responses of organisms at the cellular, molecular and biochemical level which are indicative of a physiological dysfunction [6] caused by a disease, a stress condition, or exposure to environmental contaminants. Thus, biomarkers are a predictive tool of the impact that environmental contaminants might have at the organism level and subsequently at the population, community and ecosystem level. In this context, a possible source of early molecular biomarker could be the mitochondrial genome (mtDNA), which has a particular vulnerability to oxidative stress induced by Reactive Oxygen Species (ROS) because it lacks histones and nucleotide excision repair mechanisms unlike to nuclear DNA (nDNA). However, mitochondria are the main site of ROS generation both endogenously, from cellular respiration, and exogenously, from pro-oxidant substances (such as heavy metals). For these reasons, mitochondrial DNA (mtDNA) is more prone to possible mutations which can in turn lead to the synthesis of functionally impaired respiratory chain subunits and respiratory chain dysfunction causing a further increase in ROS production [7]. Therefore, dysfunctional mtDNA could indicate potential exposure to xenobiotic.
The number of mitochondria within a cell is highly variable in different animal tissues, depending, for example, on energy requirements [8], vulnerability to oxidative stress [9], activation of pathways that determine mitochondrial fission and fusion events which regulate fundamental processes such as cell migration and division, embryogenesis, and are responsible of the onset of a variety of diseases [10]. An indirect estimate of the number of mitochondria in each cell is the mitochondrial DNA copy number (mtDNAcn) which is closely related to the well-being of mitochondria [11].
In this context, many studies have shown how the variation of mtDNAcn can be used as an efficient predictive biomarker for the onset of cellular dysfunction and a variety of pathological conditions such as diabetes, cancer, and neurodegenerative diseases in humans [12,13,14,15,16]. In particular, several studies show a positive correlation between mtDNAcn variation and carcinogenesis or occupational diseases [17]. On the other hand, because damaged mtDNA can be removed by autophagy [18], oxidative damage to the mitochondrial genome could also manifest itself through a significant decrease in mitochondrial mass and, consequently, in the copy number of the genome itself, with inevitable bioenergetic and cellular dysfunction [19,20].
Therefore, mtDNAcn variation could be a promising biomarker also for defining the impact of xenobiotics and environmental insults of various nature on animal systems although, to date, there are few studies on the topic, both with application on model [21,22,23] and non-model [24] organisms.
Based on these evidences, in the present study the mtDNAcn variation will be taken into consideration to better understand how environmental contamination affects the mitochondrial function in three fish: red mullet, Mullus barbatus (Linnaeus, 1758), annular seabream, Diplodus annularis (Linnaeus, 1758) and common pandora, Pagellus erythrinus (Linnaeus, 1758) commonly used in the biomonitoring of the marine environment to study the effects of various pollutants such as metals, polycyclic aromatic hydrocarbons, phthalates and microplastics [25,26,27,28].
2. Materials and Methods
2.1. Area of Study
The sampling areas were chosen in order to have specimens of the three target species (M. barbatus, D. annularis and P. erythrinus) both from the area with a higher level of pollution detected in the Augusta bay (declared Site of National Interest SNI) and from a control site (Portopalo di Capo Passero for M. barbatus, Marzamemi for D. annularis and P. erythrinus). Since the 1970s, Augusta Bay has gained recognition on a global scale as a polluted marine environment [29]. Beginning in the 1950s, industrial activity quickly expanded until the 1980s: in particular, a chlor-alkali plant based on Hg-cell technology was deemed to pose a serious environmental risk to the marine environment because it released Hg into the water until environmental regulations were adopted in Italy [30]. Therefore, mercury contamination represents the main issue for this area, considering that heavy metals can bioaccumulate in tissues of aquatic organisms and, by domino effects, be transported to humans through the food chain [31,32,33].
Two sites, Portopalo di Capo Passero and Marzamemi, both further south, were chosen as control sites. Indeed, according to the report on the biological and physico-chemical quality of marine coastal waters published in 2018 by the Regional Agency for Environmental Protection of Sicily [34], these sites were found to have good water and sediment quality. The sampling sites, which will be referred to hereafter as SNI (Augusta bay) and CTR (control sites), are shown in Figure 1.
2.2. Sampling
Fish sampling was carried out along the south-eastern Sicilian coast between July and early September 2021 through experimental fishing and the support of professional local fishermen.
The biota was collected in the southern section of Augusta Bay (collection stations F1, F2; Figure 1) since it shows higher levels of Hg both in the sediments and in the water column [35] and in the petrochemical district of Priolo (collection station F3; Figure 1) that is well-known to higher levels of Hg in sediments, too [37]. Two southern control areas were defined in front of Marzamemi (collection stations F4–F7; Figure 1) and off the south-east of Portopalo di Capo Passero (collection station F8; Figure 1) both declared in “good ecological and chemical quality status” [34].
In the SNI and off Marzamemi, the sampling was carried out using trammel net with length of 400–500 m (2–3 km in Marzamemi), height of 1.80 m and mesh size 25 of mm on sandy and gravelly bottoms with bathymetry between 10 and 18 m (10–50 m in Marzamemi). Both in the study and in the control area, fishing gears were lowered during the night, stationed for a few hours and retrieved at sunrise. Instead, the sampling of control specimens of M. barbatus took place about 12 miles south-east off Portopalo di Capo Passero through a trawl net set on a muddy seabed with bathymetry of 90–100 m. The net had a total length of 78 m, length of the codend of 8 m, vertical and horizontal opening of the mouth of 50 and 70 m respectively; initial mesh of the body of the net 80 mm; diagonal type codend mesh with a diameter of 50 mm. Once the presence of a shoal was detected with the aid of an echo-sounder, the net was lowered, towed for approximately 2 miles with a course 180° to the south at a speed of 3 mph, and then hoisted on board.
After collection, all fish specimens were immediately sacrificed by an overdose of MS222 (100 mg/L, Sandoz), according to [38], then were stored with flake ice and landed as quickly as possible. Once in laboratory morphological recognition of fishes was carried out following the analytical keys reported by [39], biometric parameters (total length, standard length and weight) were measured and a 1-cm fragment of muscular caudal fin was taken from each specimen, with sterile instruments in order to avoid contamination, and then stored in 96% ethanol before further processing.
The number of specimens of the three selected species (M. barbatus, D. annularis and P. erythrinus) from the study area and control area is shown in Table 1.
2.3. Chemical Analysis and Mercury Bioaccumulation
The total mercury concentration in fish species was measured using a direct mercury analyzer (Milestone DMA-80 atomic absorption spectrophotometry. Milestone, Bergamo, Italy). About 100 mg of wet sample (fish muscle of each specimen) were loaded into specific nickel boats and analyzed following the US-EPA 7473 method [40]. A Reference Standard Material (TORT-2 Lobster Hepatopancreas) was analyzed to assess accuracy (% recovery = 91–105%) and precision (routinely better than 8%; RSD%, n = 5).
2.4. DNA Extraction
Total DNA extraction was performed using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. Each DNA sample was then analyzed using the Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) in order to quantify the DNA concentration: measurements of 260/280 nm and 260/230 nm absorbance ratios were used to determine the quality and degree of purity.
2.5. mtDNAcn Evaluation
Quantitative real-time PCR (qPCR) analysis using the comparative Ct method were performed with the QuantStudio 1 Real-Time PCR System (Thermo Fisher Scientific, Walthman, MA, USA) to estimate the relative mtDNAcn. At this aim, the mitochondrial Cytb gene was selected as target gene while the nuclear 18S gene was used as reference gene (Table 2).
In each reaction 250 ng of total DNA were used in 20 µL of final volume containing 10 µL of SensiFAST SYBR master mix (Meridian BIOSCIENCE, Cincinnati, OH, USA) with ROX reference dye at low concentration (INVITROGEN, Waltham, MA, USA). Each set of reactions included a no template control and three technical replicates. qPCR conditions were performed following [24] experimental setup: 95 °C for 3 min, followed by 40 cycles of 95 °C for 20 s and 60 °C for 20 s. A final step of 95 °C for 15 s, 60 °C for 1 min and 95 °C for 1 s was included. Melt curves analysis were performed to detect non-specific PCR products.
Relative mtDNAcn for each sample was estimated according to Hartmann [43] and Rooney [44] equation: where cycle threshold (Ct) values of mitochondrial PCR products (mtDNA) were normalized to Ct-values of the nuclear locus (nDNA).
relative mtDNAcn = 2 × 2ΔCt
where ΔCt = Ct (nDNA gene) − Ct (mtDNA gene)
The variation in mtDNAcn in each fish species was obtained from the ratio between the mean of relative mtDNAcn value observed in the study area specimens and that exhibited by specimens of the same species taken in the control area. Finally, basing on the level of Hg bioaccumulation detected in each species collected in the SNI, when possible, the mtDNAcn variation will be assessed in fish showing medio-high level and high level of Hg bioaccumulation.
2.6. Statistical Analysis
All data obtained were subjected to statistical analysis using GraphPad Prism version 8.3.0. for Windows (GraphPad Software, San Diego, CA, USA, www.graphpad.com). The Mann-Whitney test was conducted to test the significance of the differences in mean observed between the two groups of specimens (polluted and control site samples) for M. barbatus and P. erythrinus, accepting those with p < 0.05 as significant values. For D. annularis statistically significant differences between the group SNI high Hg fish level, the group SNI medium-high Hg bioaccumulation fish level and the CTR group, was conducted thought the Tukey’s multiple comparisons test, accepting those with p < 0.05.
2.1. Area of Study
The sampling areas were chosen in order to have specimens of the three target species (M. barbatus, D. annularis and P. erythrinus) both from the area with a higher level of pollution detected in the Augusta bay (declared Site of National Interest SNI) and from a control site (Portopalo di Capo Passero for M. barbatus, Marzamemi for D. annularis and P. erythrinus). Since the 1970s, Augusta Bay has gained recognition on a global scale as a polluted marine environment [29]. Beginning in the 1950s, industrial activity quickly expanded until the 1980s: in particular, a chlor-alkali plant based on Hg-cell technology was deemed to pose a serious environmental risk to the marine environment because it released Hg into the water until environmental regulations were adopted in Italy [30]. Therefore, mercury contamination represents the main issue for this area, considering that heavy metals can bioaccumulate in tissues of aquatic organisms and, by domino effects, be transported to humans through the food chain [31,32,33].
Two sites, Portopalo di Capo Passero and Marzamemi, both further south, were chosen as control sites. Indeed, according to the report on the biological and physico-chemical quality of marine coastal waters published in 2018 by the Regional Agency for Environmental Protection of Sicily [34], these sites were found to have good water and sediment quality. The sampling sites, which will be referred to hereafter as SNI (Augusta bay) and CTR (control sites), are shown in Figure 1.
2.2. Sampling
Fish sampling was carried out along the south-eastern Sicilian coast between July and early September 2021 through experimental fishing and the support of professional local fishermen.
The biota was collected in the southern section of Augusta Bay (collection stations F1, F2; Figure 1) since it shows higher levels of Hg both in the sediments and in the water column [35] and in the petrochemical district of Priolo (collection station F3; Figure 1) that is well-known to higher levels of Hg in sediments, too [37]. Two southern control areas were defined in front of Marzamemi (collection stations F4–F7; Figure 1) and off the south-east of Portopalo di Capo Passero (collection station F8; Figure 1) both declared in “good ecological and chemical quality status” [34].
In the SNI and off Marzamemi, the sampling was carried out using trammel net with length of 400–500 m (2–3 km in Marzamemi), height of 1.80 m and mesh size 25 of mm on sandy and gravelly bottoms with bathymetry between 10 and 18 m (10–50 m in Marzamemi). Both in the study and in the control area, fishing gears were lowered during the night, stationed for a few hours and retrieved at sunrise. Instead, the sampling of control specimens of M. barbatus took place about 12 miles south-east off Portopalo di Capo Passero through a trawl net set on a muddy seabed with bathymetry of 90–100 m. The net had a total length of 78 m, length of the codend of 8 m, vertical and horizontal opening of the mouth of 50 and 70 m respectively; initial mesh of the body of the net 80 mm; diagonal type codend mesh with a diameter of 50 mm. Once the presence of a shoal was detected with the aid of an echo-sounder, the net was lowered, towed for approximately 2 miles with a course 180° to the south at a speed of 3 mph, and then hoisted on board.
After collection, all fish specimens were immediately sacrificed by an overdose of MS222 (100 mg/L, Sandoz), according to [38], then were stored with flake ice and landed as quickly as possible. Once in laboratory morphological recognition of fishes was carried out following the analytical keys reported by [39], biometric parameters (total length, standard length and weight) were measured and a 1-cm fragment of muscular caudal fin was taken from each specimen, with sterile instruments in order to avoid contamination, and then stored in 96% ethanol before further processing.
The number of specimens of the three selected species (M. barbatus, D. annularis and P. erythrinus) from the study area and control area is shown in Table 1.
2.3. Chemical Analysis and Mercury Bioaccumulation
The total mercury concentration in fish species was measured using a direct mercury analyzer (Milestone DMA-80 atomic absorption spectrophotometry. Milestone, Bergamo, Italy). About 100 mg of wet sample (fish muscle of each specimen) were loaded into specific nickel boats and analyzed following the US-EPA 7473 method [40]. A Reference Standard Material (TORT-2 Lobster Hepatopancreas) was analyzed to assess accuracy (% recovery = 91–105%) and precision (routinely better than 8%; RSD%, n = 5).
2.4. DNA Extraction
Total DNA extraction was performed using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. Each DNA sample was then analyzed using the Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) in order to quantify the DNA concentration: measurements of 260/280 nm and 260/230 nm absorbance ratios were used to determine the quality and degree of purity.
2.5. mtDNAcn Evaluation
Quantitative real-time PCR (qPCR) analysis using the comparative Ct method were performed with the QuantStudio 1 Real-Time PCR System (Thermo Fisher Scientific, Walthman, MA, USA) to estimate the relative mtDNAcn. At this aim, the mitochondrial Cytb gene was selected as target gene while the nuclear 18S gene was used as reference gene (Table 2).
In each reaction 250 ng of total DNA were used in 20 µL of final volume containing 10 µL of SensiFAST SYBR master mix (Meridian BIOSCIENCE, Cincinnati, OH, USA) with ROX reference dye at low concentration (INVITROGEN, Waltham, MA, USA). Each set of reactions included a no template control and three technical replicates. qPCR conditions were performed following [24] experimental setup: 95 °C for 3 min, followed by 40 cycles of 95 °C for 20 s and 60 °C for 20 s. A final step of 95 °C for 15 s, 60 °C for 1 min and 95 °C for 1 s was included. Melt curves analysis were performed to detect non-specific PCR products.
Relative mtDNAcn for each sample was estimated according to Hartmann [43] and Rooney [44] equation: where cycle threshold (Ct) values of mitochondrial PCR products (mtDNA) were normalized to Ct-values of the nuclear locus (nDNA).
relative mtDNAcn = 2 × 2ΔCt
where ΔCt = Ct (nDNA gene) − Ct (mtDNA gene)
The variation in mtDNAcn in each fish species was obtained from the ratio between the mean of relative mtDNAcn value observed in the study area specimens and that exhibited by specimens of the same species taken in the control area. Finally, basing on the level of Hg bioaccumulation detected in each species collected in the SNI, when possible, the mtDNAcn variation will be assessed in fish showing medio-high level and high level of Hg bioaccumulation.
2.6. Statistical Analysis
All data obtained were subjected to statistical analysis using GraphPad Prism version 8.3.0. for Windows (GraphPad Software, San Diego, CA, USA, www.graphpad.com). The Mann-Whitney test was conducted to test the significance of the differences in mean observed between the two groups of specimens (polluted and control site samples) for M. barbatus and P. erythrinus, accepting those with p < 0.05 as significant values. For D. annularis statistically significant differences between the group SNI high Hg fish level, the group SNI medium-high Hg bioaccumulation fish level and the CTR group, was conducted thought the Tukey’s multiple comparisons test, accepting those with p < 0.05.
3. Results
Determination of mtDNAcn performed by qPCR amplification showed consistently higher Ct values for the mitochondrial target gene (Cytb) than for the nuclear target gene, which corresponds to a lower amplification rate. This result aligns with the fact that the 18S gene, used as the nuclear reference gene for calculating the relative mtDNAcn, is a tandemly repeated locus, so it shows higher amplification rates. Notably then, while Ct values for the nuclear gene were similar in specimens from both SNI and CTR sites in all species investigated, those for the mitochondrial target were very different in the two groups of organisms from the same species (Figure 2).
Since the variation of mtDNAcn between the two survey areas is given by the ratio of the relative copy number in each, a higher value of this ratio in the contaminated site will result in an increase of mtDNAcn in the SNI compared CTR. Conversely, a lower relative mtDNAcn in the SNI will correspond to a decrease in mtDNA compared to CTR. Analysis of the melting curves shows specific amplification products (Figure 3).
Analysis of the variation of mtDNAcn among the SNI specimens belonging to M. barbatus and P. erythrinus compared with their respective CTR shows a drastic reduction of the biomarker by 55% and 61%, respectively. The differences in mean values of mtDNAcn observed in the two groups (SNI vs. CTR) are statistically significant in both species with p < 0.005 in M. barbatus and p < 0.05 in P. erythrinus. It is noteworthy that, for both species in the study area, the bioaccumulation contents of Hg in muscle of SNI specimens, although significantly higher than those detected in the CTR specimens do not exceed the legal threshold of 1 µg/g fixed by the European Community Regulation (EC Reg.) 1881/2006, for the species under consideration (Figure 4). In D. annularis the legal threshold of bioaccumulation contents of Hg in muscle is set at 0.5 µg/g by EC Reg. 1881/2006. For this species two subgroups of specimens from SNI were analyzed having Hg levels above the legal threshold: the first subgroup with very high bioaccumulation content, comparable to those detected in M. barbatus and P. erythrinus and a second subgroup with medium to high levels of bioaccumulation.
Comparison between SNI and CTR specimens reveals a dramatic increase in mtDNAcn in the former. Specifically, in the first subgroup, an increase in copy number of about 20 times the average value observed in the CTR specimens is observed; in the second subgroup, the ratio of mtDNAcn is as much as 35 times higher in SNI than CTR. In both cases, the Mann-Whitney test reported statistically significant differences with p < 0.005 (Figure 4).
4. Discussion
The results obtained in the three target species (M. barbatus, P. erythrinus and D. annularis), indicate the existence of statistically significant variation in mitochondrial genome copy number as a cellular response to chronic exposure to chemical contaminants present in a highly impacted area such as the Augusta bay [30,37,45].
Fish species, in general, are particularly useful for assessing health status in the aquatic environment given their sensitivity to anthropogenic contaminants [33,46]. The choice of investigating the three mentioned species stems, on the one hand, from their commercial importance in Southeastern Sicily, which configures them as possible vectors of transfer of contaminants from the environment to humans through food; on the other hand, from the evaluation of their respective biological characteristics that place them in different ecological niches and trophic levels [47]. Such peculiarities could allow a better understanding of the mechanisms of contaminant transfer in biota (not considered in this study) and a more careful and integrated assessment of biological responses.
From the analysis of copy number variation carried out in the present study, apparently contrasting results emerge in the three target species. Specifically, in the specimens of M. barbatus and P. erythrinus from SNI, a significant reduction in the mitochondrial biomarker is observed compared with those from CTR, while D. annularis from SNI shows a significant increase in mtDNAcn. These results are not unexpected as both cellular responses are plausible. In fact, they appear to be compatible with the processes of mitochondrial fission and fusion, i.e., processes that lead, respectively, to the division of a mitochondrion into two smaller organelles and the union of two mitochondria into a larger one [48,49,50]. An increase in mitochondrial copy number occurs to accommodate the increased energy demand required to restore a condition of homeostasis; conversely, a decrease in mitochondrial genome copy number occurs when, at the cellular level, the system fails to compensate for the ongoing damage and mitophagy pathways are consequently activated in order to eliminate any damaged mitochondria [51,52].
More specifically, damage to mtDNA induces excessive ROS production through alteration of electron transport chain components, which in turn generates additional ROS fueling a vicious cycle. Such damage initially promotes mitochondrial genome replication, and thus an increase in mtDNAcn, sustained by mitochondrial fission events as a compensatory mechanism to increase energy production and remove damage to the mitogenome. However, following prolonged exposure to stressors, there is such an accumulation of mutations to mtDNA that this compensatory mechanism is irreversibly impaired, copy number decreases, and, as a result, mitochondria become dysfunctional by undergoing mitophagy [53,54,55].
It should be noted that a recent investigation [24] to a population of the sap-feeder insect, Opsius heydeni, from SNI the same contaminated area considered in this investigation, showed a significant decrease in mtDNAcn compared to specimens of the same species taken from a control site.
Several model organisms, from the nematode Caenorhabditis elegans [56] to higher organisms (mice, hamster), have shown both in vitro and in vivo, an increase in mtDNAcn after exposure to infrared radiation [57,58,59]. Among terrestrial invertebrates, change in mtDNAcn, as a biomarker of mitochondrial dysfunction, has been studied, for example, in the fruit fly Drosophila melanogaster in response to exposure to ochratoxin A. Again, exposure to the pro-oxidant agent resulted in an increase in mtDNAcn [21].
In the aquatic environment, the implementation of mtDNAcn variation for environmental monitoring has been validated on different animal groups. Kim et al. observed that exposure to polycyclic aromatic hydrocarbons (particularly fluoranthene and pyrene) resulted in a significant increase in mitochondrial genome copy number in the zebrafish Danio rerio [60].
In contrast, exposure to sub-lethal doses of the broad-spectrum insecticide bifenthrin in Puntius sophore, an edible fish species widespread in Asian freshwater, resulted in significant reduction of mtDNAcn in brain, liver, and muscle tissue [61]. In the sea turtle Lepidochelys olivacea, the change in copy number was related to the presence of a congenital malformation related to exposure to high levels of Hg. However, the investigation showed an apparent absence of association between the parameters investigated in the experimental groups [62]. Bowman et al., next determined mitogenome levels, in association with other cellular biomarkers (apoptosis and mutation rates in mtDNA), to define the effects of ultraviolet radiation on the skin of large marine mammals. The study showed a decrease in the mitochondrial genome in correlation with increased levels of UV-induced skin vesicles suggesting mtDNAcn variation as a valid biomarker of the effects of global warming in the marine environment [63]. Furthermore, the effects of exposure to environmentally important levels of dibutyl phthalate were investigates in the early life stage of zebrafish. High levels of oxidative stress damaging mitochondrial ultrastructure and function were observed which induce also the inhibition of mtDNAcn. In this case a downregulation of mRNA expression of mitochondrial fusion-related genes and an upregulation of fission-related genes were also detected [64].
Though studies on mtDNAcn variation in wildlife are scarce and often related to ageing [42,65,66], numerous investigations on the variation of this biomarker of oxidative stress induced by chemical, physical and environmental insults have been conducted on murine systems both in vitro and in vivo. In such model organisms, chronic exposure to chemical contaminants has shown, for example, a dose-dependent reduction between mtDNAcn and exposure to heavy metals [67] or ultrafine particulate matter [68]. In other cases, however, there has been an increase in mtDNAcn levels in response to exposure to secondhand smoke [69], infrared radiation [57] and proton beams [70]. Such evidence reinforces the theory that increased levels of mtDNAcn may help counteract mitochondrial dysfunction associated with increased oxidative stress, promoting, among others, the development of neurodegenerative diseases and the induction of carcinogenesis [70].
In some cases, the mtDNAcn levels can both increase and decrease within the same species in relation to, for example, the chronicity of the insult to which the organism is exposed. In this regard, a relationship between mtDNAcn and the effects of global warming was observed in the study by Li et al. in the white shrimp (Palaemon carinicauda). As water temperature and salinity increased, an initial increase in mtDNAcn was reported. However, a further increase in salinity induced a decrease in mtDNAcn consistent with irreversible impairment of mitochondrial function. In fact, ROS are generated during osmotic pressure regulation, which in turn induces oxidative stress. The authors therefore suggest that once the increase in salinity (and the simultaneous stimulation of mtDNA replication and ROS accumulation) exceeds a threshold value, mtDNA replication is disrupted due to polymerase blockade and, consequently, copy number is decreased [23]. The study by Syromyatnikov et al. reported the change in mtDNAcn as an effect of pesticide exposure in Bombus terrestris, one of the most important pollinators for agriculture, which is currently threatened with severe extinction risk mainly due to the intensive use of these xenobiotics. Interestingly, a different cellular response has been detected in relation to pesticide toxicity. Specifically, the action of systemic pesticides results in an increase in mtDNAcn, as a compensatory mechanism to the inhibition of mitochondrial respiration, while broad-spectrum pesticides act more heavily resulting in a significant decrease in the biomarker indicative, in turn, of irreversible mitochondrial dysfunction [71].
In the present study, as already described, at the same Hg bioaccumulation contents in the muscle tissue of specimens from the Augusta Bay, mtDNAcn levels are found to be halved for M. barbatus and P. erythrinus, while they are greatly increased in D. annularis. In this context, it is therefore necessary to clarify two aspects: (i) to interpret the differences found between the species analyzed and (ii) to identify a possible trend in the response observed in D. annularis. Regarding the first point, it is useful to consider the growth curves of the three species in order to highlight that D. annularis specimens being younger (about 1 year of age) than those of the other two target species sampled in the impacted area (3–4 years of age), but showing the same level of Hg bioaccumulation, probably exhibit a more efficient cellular response system towards damage from oxidative stress [42]. This response would be part of the compensatory mechanism of inhibition of cellular respiration resulting in increased mtDNAcn. In contrast, for M. barbatus and P. erythrinus, the longer exposure time to contaminants related to older age may have already impaired mitochondrial function resulting in a significant decrease in mtDNAcn. Taking the second point into consideration, it is useful to compare the mitochondrial copy number variation values observed in the D. annularis specimens with medium to high Hg levels with those detected in the group of specimens with high bioaccumulation values. It is observed that an even more pronounced increase in copy number is appreciated in the former than in the latter, confirming the trend of change in mtDNAcn [62], namely a rapid initial increase that undergoes a gradual decrease as bioaccumulation levels increase.
5. Conclusions
The present study represents one of the first investigation aimed at filling knowledge gap on the effect of mercury on mitochondrial stability in non-farmed fish species living in contaminated areas. To this end, a reproducible experimental setup was developed to detect mtDNAcn variation in three fish species characterized by high levels of bioaccumulation of Hg that is a potent pro-oxidant agent. The results obtained were interpreted considering that the primary cellular response to ROS generation is sustained by an increased mitogenome replication rate: in this case the increased mtDNAcn is suggested to be a valid early warning biomarker of contaminant exposure. Furthermore, it has been highlighted that upon chronic exposure to the contaminant, albeit at sub-lethal doses, the compensatory mechanism is compromised resulting in a reduction of mtDNAcn: in this case the decreased mtDNAcn is suggested to be a possible biomarker of effect to chronic xenobiotic exposure in marine environments. |
Title: Pneumovesical vesicovaginal fistula repair: Lessons learned from an initial series of 25 patients | Body:
INTRODUCTION
Vesicovaginal fistulas (VVF) are common genitourinary fistulas with an incidence ranging from 0.3% to 2%, causing considerable distress.
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Although local trauma or prolonged labor can lead to VVF, the primary known cause is hysterectomy, accounting for approximately 85% of cases, occurring in approximately 1 out of 1800 hysterectomies.
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Conservative treatments, such as prolonged bladder drainage or fulguration, may be considered in cases that are small or non‐malignant and do not involve radiation therapy.
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However, the majority of VVF cases necessitate surgical intervention. Currently, there is no consensus on the optimal approach for VVF repair that is universally applicable. Generally, VVF repair can be accomplished via abdominal or vaginal route, each having its own merits. The choice of surgical method depends on various factors such as fistula size, location, and surgeon preference. The transvaginal approach is mainly employed for infratrigonal fistulas, offering less invasiveness and cost‐effectiveness. Nevertheless, it proves challenging when the vagina is short or when the fistula is situated above the trigone plane or near the ureteral orifice.
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With recent advancements in laparoscopic and robotic surgeries, the preference for the transabdominal approach has gained favor. This technique is commonly employed for supratrigonal fistulas and enables fistula tract removal through layer‐by‐layer repair.
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,
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However, despite these endeavors, accessing the fistula remains problematic, and bladder closure after fistula removal via bladder incision presents a drawback. Consequently, the surgical procedure is inherently intricate, leading to increased complexity and extended operative time.
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More recently, innovative surgeries utilizing pneumovesicum through CO2 insufflation into the bladder during laparoscopic procedures have been explored for VVF.
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This surgical approach offers a minimally invasive means to directly and conveniently access bladder lesions without complications such as postoperative bladder dysfunction, ureteral reimplantation for vesicoureteral reflux, removal of bladder diverticulum, or removal of bladder stones and foreign bodies.
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This study aimed to present the outcomes of laparoscopic pneumovesical VVF repair performed at a single institution, with a specific focus on the success rate of the procedure.
MATERIALS AND METHODS
Study population and selection
This retrospective study included 26 patients who had undergone laparoscopic pneumovesical repair for VVF at a single institution, Korea University Ansan Hospital, between July 2013 and July 2023. This study was approved by the Institutional Review Board (IRB) (IRB No. 2018AS0221). Patient data, including age, history of gynecological surgery, intra‐operative parameters, and postoperative outcomes, were reviewed and analyzed from medical records. This study included patients who had undergone both primary and secondary repairs. All patients analyzed in this study had a history of gynecological surgery and developed fistulas after surgery; 15 patients had undergone abdominal hysterectomy, five had undergone laparoscopic hysterectomy, and three had undergone robotic hysterectomy. Three patients developed fistulas after cesarean sections. Five patients had a history of radiation therapy for malignancies. The fistula size and location were measured using preoperative cystoscopy and computed tomography urogram. With one exception, all fistulas were located in the supratrigonal region, and one patient had multiple fistulas. Surgical data included operative time, complications during surgery, and postoperative details such as hospitalization period, duration of urinary drainage, and postoperative complications. Following surgery, urinary leakage was confirmed via cystography before removal of the urinary catheter, and surgical success was defined as the absence of urinary leakage observed during follow‐up monitoring.
Surgical procedure
All surgeries were performed by a urologist (J.H.B.) using either the traditional open or pneumovesical approach. The surgeon had previously performed five cases of the pneumovesical approach for Politano‐Leadbetter ureteric reimplantation before this surgery. During the period of this study, in addition to VVF repairs, the physician conducted 40 cases of Politano‐Leadbetter ureteric reimplantation, and 10 other pneumovesical surgeries including foreign body or stone removal, and bladder diverticulectomy. All surgical procedures were performed under general anesthesia, and the patient was placed in the modified lithotomy position with slight Trendelenburg and straight hands. After the bladder was filled with normal saline, three 5‐mm laparoscopic ports were inserted through the bladder wall under cystoscopic guidance. To prevent air leakage, the port was secured internally within the bladder using the Eagle‐port laparoscopic port (Dalim Medical, Seoul, Korea), which features a fixation mechanism (Figure 1). After port placement, normal saline was drained while simultaneously filling the bladder with gas (pressure, 8–12 mmHg and flow rate, 2–3 L/min). The vagina was packed with 30 cc balloon 20Fr Foley catheter to prevent gas leakage. After dissecting the peri‐fistula tissue, the mucosa surrounding the fistula was circumferentially debrided using endoscissors (Figure 2a). Fresh vaginal tissue was then obtained, and the vaginal layer was repaired in a layered fashion using vicryl 4‐0 sutures (Figure 2b,c). Subsequently, the bladder mucosal layer was reconstructed layer‐by‐layer using vicryl 4‐0 sutures (Figure 2d). After confirming bilateral jetting, the bladder was catheterized. The Foley catheter was removed after confirming the absence of leakage using cystography. No ureteral stents were placed.
FIGURE 1
Port placement during surgery.
FIGURE 2
Intraoperative picture of laparoscopic pneumovesical vesicovaginal fistula repair (a) Debridement of peri‐fistula tissue with surrounding mucosa. (b) Marginal resection until clear vaginal tissue is obtained (c) Closure of the vaginal layer using vicryl 4‐0 sutures (d) Closure of the bladder mucosal layer using vicryl 4‐0 sutures.
Statistical analysis
The cumulative sum (CUSUM) series was defined as Sn = ∑(X
i − X
0), where X
i represent an individual measurement, and X
0 was a predetermined reference level that was set as the mean value of the total operation time for all cases overseen. Sn is plotted against the sequence of operations. The cutoff values were selected according to the points of upward inflection revealed by the plots. Continuous data are expressed as mean ± SD. Statistical analyses were performed using IBM SPSS Statistics (version 21.0; IBM Corp., Armonk, NY, USA).
Study population and selection
This retrospective study included 26 patients who had undergone laparoscopic pneumovesical repair for VVF at a single institution, Korea University Ansan Hospital, between July 2013 and July 2023. This study was approved by the Institutional Review Board (IRB) (IRB No. 2018AS0221). Patient data, including age, history of gynecological surgery, intra‐operative parameters, and postoperative outcomes, were reviewed and analyzed from medical records. This study included patients who had undergone both primary and secondary repairs. All patients analyzed in this study had a history of gynecological surgery and developed fistulas after surgery; 15 patients had undergone abdominal hysterectomy, five had undergone laparoscopic hysterectomy, and three had undergone robotic hysterectomy. Three patients developed fistulas after cesarean sections. Five patients had a history of radiation therapy for malignancies. The fistula size and location were measured using preoperative cystoscopy and computed tomography urogram. With one exception, all fistulas were located in the supratrigonal region, and one patient had multiple fistulas. Surgical data included operative time, complications during surgery, and postoperative details such as hospitalization period, duration of urinary drainage, and postoperative complications. Following surgery, urinary leakage was confirmed via cystography before removal of the urinary catheter, and surgical success was defined as the absence of urinary leakage observed during follow‐up monitoring.
Surgical procedure
All surgeries were performed by a urologist (J.H.B.) using either the traditional open or pneumovesical approach. The surgeon had previously performed five cases of the pneumovesical approach for Politano‐Leadbetter ureteric reimplantation before this surgery. During the period of this study, in addition to VVF repairs, the physician conducted 40 cases of Politano‐Leadbetter ureteric reimplantation, and 10 other pneumovesical surgeries including foreign body or stone removal, and bladder diverticulectomy. All surgical procedures were performed under general anesthesia, and the patient was placed in the modified lithotomy position with slight Trendelenburg and straight hands. After the bladder was filled with normal saline, three 5‐mm laparoscopic ports were inserted through the bladder wall under cystoscopic guidance. To prevent air leakage, the port was secured internally within the bladder using the Eagle‐port laparoscopic port (Dalim Medical, Seoul, Korea), which features a fixation mechanism (Figure 1). After port placement, normal saline was drained while simultaneously filling the bladder with gas (pressure, 8–12 mmHg and flow rate, 2–3 L/min). The vagina was packed with 30 cc balloon 20Fr Foley catheter to prevent gas leakage. After dissecting the peri‐fistula tissue, the mucosa surrounding the fistula was circumferentially debrided using endoscissors (Figure 2a). Fresh vaginal tissue was then obtained, and the vaginal layer was repaired in a layered fashion using vicryl 4‐0 sutures (Figure 2b,c). Subsequently, the bladder mucosal layer was reconstructed layer‐by‐layer using vicryl 4‐0 sutures (Figure 2d). After confirming bilateral jetting, the bladder was catheterized. The Foley catheter was removed after confirming the absence of leakage using cystography. No ureteral stents were placed.
FIGURE 1
Port placement during surgery.
FIGURE 2
Intraoperative picture of laparoscopic pneumovesical vesicovaginal fistula repair (a) Debridement of peri‐fistula tissue with surrounding mucosa. (b) Marginal resection until clear vaginal tissue is obtained (c) Closure of the vaginal layer using vicryl 4‐0 sutures (d) Closure of the bladder mucosal layer using vicryl 4‐0 sutures.
Statistical analysis
The cumulative sum (CUSUM) series was defined as Sn = ∑(X
i − X
0), where X
i represent an individual measurement, and X
0 was a predetermined reference level that was set as the mean value of the total operation time for all cases overseen. Sn is plotted against the sequence of operations. The cutoff values were selected according to the points of upward inflection revealed by the plots. Continuous data are expressed as mean ± SD. Statistical analyses were performed using IBM SPSS Statistics (version 21.0; IBM Corp., Armonk, NY, USA).
RESULTS
The patient characteristics and results are outlined in Tables 1 and 2. Of the 26 patients, 23 (88.5%) achieved successful surgical outcomes. However, one patient required conversion to open surgery owing to difficulties related to a small bladder volume resulting from radiation and a relatively large fistula size. These factors led to difficulties in maintaining an airtight space in the bladder. Although leakage was observed on post‐surgery cystography in the remaining two patients, both patients were successfully treated through reoperation using the same method. Mean patient age was 47.4 years (range 33–77), and the mean body mass index was 24 kg/m2 (range 18–29.8). The average interval between gynecological surgery and VVF repair was 7 months (range 1–34). The fistula sizes ranged from 3 to 15 mm. The mean operative time was 99.9 min, with no cases necessitating blood transfusions owing to minimal blood loss during surgery. The average hospital stay was 9.1 days, and the mean duration of indwelling urinary catheter insertion was 11 days. During hospitalization, six patients experienced minor hematuria, one experienced vaginal bleeding, and two had urinary tract infections. In the follow‐up period, one patient had remnant suture material; however, no case of recurrence occurred. The average follow‐up period was 6.96 months.
TABLE 1
Demographic and clinical characteristics of the patients who underwent VVF repair.
Factor
Value
The number of patients
26
Age (years), mean
47.4 (33–77)
BMI (kg/m2)
24 (18–29.8)
Interval between gynecologic injury and VVF repair (months), mean
7 (1–34)
Fistula history, n (%)
Primary
21 (80.8)
Recurrent
5 (19.2)
Previous radiotherapy history
5
Size (mm)
6.1 (3–15)
Fistula number, n (%)
Single
25 (96.2)
Multiple
1 (3.8)
Fistula location n (%)
Supratrigonal
26 (96.3)
Infratrigonal
1 (3.7)
Etiology of fistula n (%)
Abdominal hysterectomy
15 (57.7)
Laparoscopic hysterectomy
5 (19.2)
Robotic hysterectomy
3 (11.5)
Cesarean
3 (11.5)
Abbreviations: BMI, body mass index; VVF, vesicovaginal fistula.
TABLE 2
Operative and postoperative results.
Parameters
Value
Operative time (min), mean ± SD
99.9 ± 22.9
Postoperative stay (days), mean ± SD
9.1 ± 3.1
Duration of Catheterization (days), mean ± SD
11 ± 4.5
Success rate n (%)
Primary repair
23/26 (88.5)
Secondary repair
2/2 (100)
Postoperative follow‐up period (months), mean
6.96 (1–30)
Complications, n (%)
Clavien‐Dindo grade I
8 (30.8)
Clavien‐Dindo grade II
2 (7.7)
Clavien‐Dindo grade III–V
0 (0)
Figure 3 displays an upward CUSUM chart illustrating operative time. Initially, a decreasing slope was observed from the first to the third cases, followed by an ascending trend up to the sixth case. Subsequently, fluctuations were noted until the 19th case, beyond which an ascending curve emerged, signifying an acceptable level of performance.
FIGURE 3
The upward cumulative sum (CUSUM) analysis for total operative time. The CUSUM chart shows an increasing slope from the third case, changed to a fluctuating pattern from the sixth case, and an upward shift from the 19th case.
DISCUSSION
VVF primarily results from iatrogenic injuries associated with gynecological surgeries, notably hysterectomies, causing considerable physical and psychological distress to both patients and surgeons.
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Although some cases respond to conservative treatments like prolonged urinary catheterization, most require surgical repair, predominantly via transvaginal or transabdominal approaches. However, the absence of randomized prospective trials has prevented the establishment of a gold standard surgical method for VVF repair, making the choice of technique dependent on various factors including fistula size, location, and the surgeon's expertise and preference.
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Despite different etiologies, the success rate of non‐complicated VVF repair in the recent era, whether through a vaginal or abdominal approach, is >90%. For improving the surgical success rate, the study has also reported that preoperative phenazopyridine, physical therapy, and intraoperative antibiotics may be beneficial methods. The transvaginal approach is often used for VVF repair when the fistula is in the lower vagina, given its benefits of shorter surgery times and quicker recovery.
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However, it may not be suitable for deeply located fistulas or in patients with narrow or compromised vaginal tissues due to conditions like radiation therapy.
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For urologists who may not be familiar with the transvaginal approach, the transabdominal approach can be a preferred alternative in cases of high‐lying, inaccessible vaginal fistulas, large fistulas, or coexisting intra‐abdominal surgical conditions such as synchronous ureteric involvement.
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It allows for extensive mobilization of tissues, complete fistula excision, and effective closure, often with omental interposition for added support collection.
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Laparoscopic or robotic‐assisted transabdominal approaches offer enhanced visualization and shorter recovery times but can be challenging due to increased operative time and intraperitoneal adhesions.
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The transvesical approach, while offering improved fistula identification, carries risks such as postoperative bladder dysfunction and bleeding.
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Recently, many studies have reported the use of the pneumovesical approach in various situations such as ureter reimplantation in vesicoureteral reflux or removal of bladder diverticulum, bladder stones, and foreign body.
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The pneumovesical approach is even utilized in surgeries related to the prostate, such as surgeries for benign prostatic hyperplasia.
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Compared with traditional laparoscopic surgery, this approach provides quick and straightforward access to the fistula lesion without causing injury to the intra‐abdominal organs or unnecessary bladder incision. This approach allows for fast recovery and a satisfactory cure rate and reduces bladder trauma, postoperative hematuria, and detrusor spasm. Our case series demonstrated promising results using the pneumovesical approach for VVF repair in patients with fistulas located in the supratrigonal region, a common site of bladder damage during abdominal hysterectomy.
19
This approach is particularly advantageous for fistulas located deep within the vagina or adjacent to ureteral orifices. Furthermore, most patients undergoing VVF repair have a history of abdominal surgery, particularly hysterectomy, which may lead to intra‐abdominal adhesions. In cases where patients have coexisting cancer and have undergone radiation therapy, the condition of the vaginal tissues may not be favorable. Considering these factors, accessing the fistula site can be challenging via both the transabdominal and transvaginal approaches. However, the pneumovesical approach offers a distinct advantage, as it allows direct access to the fistula through the bladder, significantly reducing preoperative considerations.
Ultimately, despite two patients requiring reoperation, there was no recurrence of urinary leakage observed in any of the cases included in this study. This is a noteworthy outcome, especially considering the challenging and complicated overall patient condition (20% had undergone previous VVF repair and 16% had undergone radiation therapy). Regarding VVF repair, secondary surgery after the initial procedure often leads to lower success rates.
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However, in our study, regardless of whether the initial surgery was transvaginal or transabdominal, no patient experienced leakage. Notably, if the previous surgical approach was the pneumovesical approach, reoperation can still be performed using the same method.
The timing of the VVF repair in this study was expedited as much as possible upon referral to our department, considering the distress of patients with urinary leakage. However, owing to patient referrals from other hospitals post‐surgery, a relatively extended mean interval of 7 months occurred between VVF repair and gynecological surgery. Moreover, in our case series, four cases experienced VVF within 1 month after gynecological surgery. Recent reports have indicated that delaying surgical repair offers no advantage in the absence of an inflammatory reaction, and the success rates of early repairs are comparable with those of the conventional strategy of delayed operation for 3–6 months after fistula formation.
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The upward CUSUM chart for operative time showed a trend of gradual improvement after the first three cases, but a fluctuating pattern was observed up to the 19th case. This is because surgery was initially intuitive and easy to approach, but there have been many trials and errors owing to concerns regarding the surgical method. These challenges include port placement, addressing gas leakage into the peritoneal cavity through Veress needle aspiration, and attempting various vaginal packing techniques. In the initial cases of this study and similar research, vaginal packing involved the use of Betadine or Vaseline gauze.
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However, this method resulted in gas leakage to some extent, necessitating manual intervention by the assistant, which disrupted the surgical procedure. In our subsequent cases, we employed a 30 cc balloon Foley catheter to prevent gas leakage. This was achieved by placing the balloon beyond the fistula site at the vaginal entrance. The restructured surgical approach pointed to a learning curve encompassing approximately 19 cases.
The successful execution of a pneumovesical VVF repair hinges on refined laparoscopic expertise, demanding meticulous dissection, resection, and intracorporeal suturing. Despite technical difficulties, the selected patient pool and the prevailing familiarity among urologists with laparoscopic surgery indicate a formidable yet surmountable learning curve. Recent instances involving robotic systems for pneumovesical VVF repair have emerged. Although there are some limitations, such as the relatively large size of robotic instruments compared with conventional laparoscopic systems and longer operative times, the robotic approach offers clear advantages, including high magnification and a three‐dimensional view. Given suturing post‐fistula excision is a critical procedure in VVF repair, the use of multi‐joint robotic systems presents clear advantages. If tailored robotic systems emerge for pneumovesical surgery, the steep learning curve associated with laparoscopic pneumovesical surgery could be mitigated, rendering it more accessible.
Our study had several limitations. First, its retrospective nature necessitates future research for comparison with conventional surgical methods. Second, the relatively low VVF incidence led to a limited surgical case pool. Third, in scenarios featuring coexisting ureteral injuries or inadequate bladder air pressure retention owing to extensive fistula size or reduced bladder capacity, the pneumovesical approach might not be suitable. Nonetheless, the favorable results in terms of cure and low recurrence rates in our study demonstrate the potential benefits of this approach for VVF repair. Previous studies utilizing the pneumovesical approach for VVF repair predominantly involved cases characterized by modified methods or a relatively limited number of selected patients.
10
,
11
,
12
However, our study encompassed a diverse range of cases, regardless of a prior history of VVF repair or radiation therapy, fistula location, or number. As an initial experience, our study demonstrates the effectiveness of the pneumovesical approach, which can be applied to the majority of VVF cases without significant limitations and yields successful outcomes with less bleeding and a short recovery period. The longer hospitalization period observed in our study was influenced by the healthcare insurance policy in Korea, resulting in relatively lower medical costs compared with other countries.
In conclusion, pneumovesical VVF repair emerges as a viable and secure clinical option, boasting a minimal recurrence rate. Owing to the prime benefit of diminished morbidity through the non‐peritoneal approach, our pneumovesicoscopic procedure could potentially assume an alternative role in VVF repair, potentially supplanting other surgical methods.
AUTHOR CONTRIBUTIONS
Byeong Jo Jeon: Writing—original draft; conceptualization; methodology; data curation; formal analysis. Bum Sik Tae: Writing—review & editing; investigation; validation; formal analysis; data curation; supervision. Jeong Wan Yoo: Data curation. Ho Young Koo: Data curation. Cheol Young Oh: Resources; data curation. Jae Young Park: Data curation; supervision. Jae Hyun Bae: Writing—review & editing; conceptualization; methodology; project administration; resources; data curation; supervision.
CONFLICT OF INTEREST STATEMENT
No conflicts of interest.
APPROVAL OF THE RESEARCH PROTOCOL BY AN INSTITUTIONAL REVIEWER BOARD
The study protocol was approved by the Institutional Review Board of Korea University Medicine (IRB No. 2018AS0221) and was conducted in accordance with the Declaration of Helsinki.
INFORMED CONSENT
Not applicable.
REGISTRY AND THE REGISTRATION NO. OF THE STUDY/TRIAL
Not applicable.
ANIMAL STUDIES
Not applicable. |
Title: Contrasting pathogen prevalence between tick and dog populations at Chornobyl | Body:
Background
In 1986, the steam explosion and meltdown that destroyed reactor 4 at the Chornobyl Nuclear Power Plant (NPP) complex caused massive radionuclide contamination of the surrounding territories of Ukraine, Belarus and Russia. More than 5000 petabecquerels (PBq) of radionuclides were released by the explosion and subsequent fires, including large quantities of long-lived 137Cs and 90Sr radioisotopes, whose adverse environmental effect will persist for greater than eight half-lives, about 240 years [1]. Radionuclide contamination, however, is only one of many serious adverse environmental effects resulting from the disaster and the decades-long cleanup efforts that followed. These include widespread contamination by heavy metals, organics, pesticides, asbestos and other pollutants introduced by remediation efforts, as well as the abandonment of an industrial complex containing, in addition to the four operating nuclear power plants, power substations, industrial facilities, a military base and massive construction projects, including a new cooling tower and two more nuclear power plants [2–6]. The large numbers of diverse contaminants make the environment surrounding the Chornobyl NPP unique. Nevertheless, in the absence of human habitation, and despite the hazardous conditions, many wildlife species now have established populations within the roughly 4000 km2 Chornobyl Exclusion Zone (CEZ) [7]. The re-emergence of flora and fauna within the CEZ has provided a unique opportunity for scientists to study the multi-generational effects of exposure to the contaminants at Chornobyl and has been the focus of numerous research studies.
Specifically, it is important to understand and characterize the potential health risks to human populations that continue to work or visit within the CEZ and to the resident animal and plant species. Over the past decades, animal studies in the CEZ have highlighted decreased population abundances (e.g. [8]), severe impacts on fertility of certain species (e.g. [9]), ocular pathologies (e.g. [10]) and morphological changes (e.g. [11]), while work in other species fails to note any significant effects in exposed populations (e.g. [7, 12–15]). In addition to population-level assessments, other scientists have investigated vector-borne pathogen levels in the areas just outside of the Chornobyl NPP in the CEZ [16–18]. Two of these studies reported that the prevalence of certain pathogens is different at sites closer to the NPP when compared to regions outside of the CEZ [16, 18]. Understanding the prevalence of pathogens in local vectors is another way to assess health risks in both animal and human populations arising from the disaster.
Studies that have focused on hard ticks within the CEZ highlighted increased levels of certain pathogens, such as Anaplasma phagocytophilum and Babesia canis in Dermacentor reticulatus ticks along with Borrelia burgdorferi sensu lato (B. burgdorferi s.l.) and Bartonella spp. in Ixodes ricinus ticks [16–18]. Increased prevalence of pathogens within the tick populations across the region could result in greater spread of diseases in the wild populations upon which the ticks feed. The increased presence of B. canis, B. burgdorferi and A. phagocytophilum are specifically of interest, as the respective diseases these organisms cause can be fatal in some species when left untreated [19–21]. Other authors have focused directly on the effect of radiation and other contaminants on infectious diseases and their vectors [22–27]. Exposure to radioisotopes was reported to reduce infectivity and survival of ticks based on the level of exposure and, in some cases, affected the diversity, prevalence and pathogenicity of pathogens (reviewed in [28]). Other studies also reported that small mammals, which can serve as reservoir hosts for different tick-borne pathogens, had elevated levels of pathogens and vectors in areas affected by radioactive contamination (reviewed in [28]). These authors noted that effects in reservoir species and vectors can have great impacts on the exposure of host species to vector-borne pathogens, including accidental hosts like companion animals and people. In addition to the importance of understanding how the Chornobyl disaster may have impacted pathogens and the risks they may pose to the local communities, it has been noted that the recent Russian invasion into Ukraine could facilitate the spread of vector-borne pathogens into other regions as animals are relocated [29]. It is, therefore, critical to understand the prevalence of these pathogens in the tick populations within the CEZ to better assess possible health effects for the resident populations and to understand how this may affect surrounding regions.
Along with the numerous wildlife species that have re-established in this area, several hundred free-breeding dogs now inhabit the CEZ, descendants of household pets that survived the extreme contamination and concerted extermination efforts [6, 30]. In previous studies, we found high levels of genetic differentiation and low levels of migration between two geographically close populations of these dogs: one population at the NPP and one 16 km away in Chornobyl City (CC) [31, 32]. As an expansion of our previous work, in the study reported here we used samples from these two distinct populations of dogs, characterized by their proximity to the NPP, to better understand the impact of radiation on vector-borne pathogen levels. We addressed this by determining the species and level of pathogens detected in ticks and their canine hosts when sampled at the NPP and further away at CC. We hypothesized that there are different prevalences of pathogens in ticks and their canine hosts when sampled at the NPP as compared to the less contaminated site in CC.
Methods
Sample collection
Ticks were collected from free-breeding dogs during spay/neuter/vaccination efforts conducted at the NPP and CC sample sites in 2018 sponsored by the Clean Futures Fund (described in [31, 32]). Collected ticks were immediately preserved in 70% ethanol on site until processed. We acquired a total of 106 ticks from 36 different dogs that were captured either at the NPP (n = 17 dogs; 54 ticks) or in CC (n = 19 dogs; 52 ticks). The majority (78%) of the ticks were visibly engorged upon collection and the remainder (22%) were flat. After removal from 70% ethanol, each tick was weighed and photographed prior to DNA extraction. All of the collected ticks were morphologically assessed for life-stage identification. Since the majority of ticks were engorged, tick species identification was conducted via molecular methods. Six of the sampled ticks were collected while mating to the feeding tick, but these males were separated out before DNA extraction.
In a previous study we also previously acquired blood-derived DNA samples, taken in 2019 from 111 unique dogs, of which 55 were sampled at the NPP and 56 sampled in CC (detailed further in [31]), along with DNA samples taken in 2018 for 24 dogs (20 at NPP and 4 at CC) that were sampled again in 2019. To maintain continuity between sampling years and provide equal representation for both populations, we focused on the 2019 samples for assessment of pathogen prevalence. We also determined pathogen presence in the 2018 samples, but these samples were used primarily to assess persistence and not for overall population prevalence. The ticks collected from dogs in 2018 overlapped with five of these dogs for which we have a blood sample from 2018. The 2019 dog samples used in the pathogen prevalence analysis did not have corresponding tick samples in the present analysis.
DNA extraction
Prior to extraction, we dissected each tick with a sterilized scalpel, removing the mouthparts and the front-most part of the scutum (hereafter, ‘head’) and bilaterally dissecting the ‘body’ (Fig. 1). We then extracted DNA from each ‘head’ and each half of the bisected ‘body’ so that we could make an attempt to differentiate which pathogens were only present in the blood meal, i.e. only in ‘body’ extracts, from those that may be in the salivary glands, which could be in both ‘head’ and ‘body’ extracts. DNA was extracted from the ticks following the protocol of the Qiagen Blood and Tissue DNA extraction kit (Qiagen, Hilden, Germany) with a few alterations. We increased the digestion time, allowing the tissue to incubate at 56 °C in buffer ATL and proteinase K overnight, together with the inclusion of additional proteinase K after 12 h, vortexing intermittently. For the largest engorged ticks, a second dose of buffer ATL and proteinase K was added after the initial 12-h incubation and the incubation time was extended for another 12 h. DNA was eluted twice with same 60 µL of high-performance liquid chromatography water. DNA integrity and purity was assessed by agarose gel electrophoresis and spectrophotometry (NanoDrop One spectrophotometer; Thermo Fisher Scientific, Waltham, MA, USA). All extracts were stored at - 20 °C prior to analysis.Fig. 1Schematic of tick dissection prior to DNA extraction, where DNA was extracted from one half of the body and from the front-most portion of the scutum. Created with BioRender (https://www.biorender.com/)
Tick species identification
For species identification, DNA isolated from each tick ‘head’ was used as the template for PCR amplification of an 820-bp region of the mitochondrial cytochrome c oxidase subunit I gene (COI) (all primer sequences are included in Table 1) [33]. The PCRs were performed in reaction volumes of 20 µl total, containing 20 ng of template DNA, 1× Taq RED Master Mix (Apex Bioresearch), and 300 nM of each primer. PCR assays were performed following the protocol described in Lv et al. [33]. Amplicons were sequenced using conventional bidirectional Sanger sequencing by the North Carolina State University Genomic Sciences Laboratory (NCSU GSL). Each sequenced amplicon was analyzed using National Center for Biotechnology Information (NCBI) BLAST to determine species identity, based on the lowest e-value of the matching NCBI sequence that shared > 95% identity with the amplicon.Table 1Primers and probes used in PCR assaysEndpoint PCR primersAssay targetPrimer nameSequence (5′–3′)ReferencesTick COI (820 bp)Cox1-FGGAACAATATATTTAATTTTTGGLv et al. [33]Cox1-RATCTATCCCTACTGTAAATATATGAnaplasmataceae (257 bp)AnaSpp-FGGGGATGATGTCAARTCAGCAYKrücken et al. [35]AnaSpp-RCACCAGCTTCGAGTTAAGCCAATFrancisella tularensis (357 bp)FrancTul-FGCAGGTTTAGCGAGCTGTTCTACTCKormilitsyna et al. [36]FrancTul-RAGCTGTCCACTTACCGCTACAGAAGBorrelia spp. (210 bp)Borr16s-FAGTGGCGAACGGGTGAGTAQurollo et al. [38]Borr16s-RCTCTCAGGCCGGTTACTTATCQuantitative PCR primers and probesAssay targetPrimer nameSequence (5'-3')ReferencesIxodes ricinus (probe-based qPCR; 130 bp)IxRic-FCTGGAGCTTCCGTTGACATAGRobinson et al. [34]IxRic-RGGTATTCGTTCTAAAGATAGTCCTGGTIxRic-P-HEXTCCCTTCATTTAGCAGGAATTTCATCAAnaplasma phagocytophilum (probe-based qPCR/ddPCR; 76 bp)ApMSP2-FTGGAAGGTAGTGTTGGTTATGGTATTCourtney et al. [37]ApMSP2-RTTGGTCTTGAAGCGCTCGTAApMSP2-FAMTGGTGCCAGGGTTGAGCTTGAGATTGBorrelia burgdorferi (probe-based qPCR; 77 bp)Bb23s-FCGAGTCTTAAAAGGGCGATTTAGTCourtney et al. [37]Bb23s-RGCTTCAGCCTGGCCATAAATAGBb23s-P-FAMAGATGTGGTAGACCCGAAGCCGAGTGRickettsia raoultii (probe-based qPCR; 107 bp)RrOmpB-FGTGGTGGTGTTCCTAATACTCCJiang et al. [39]RrOmpB-RACCTAAGTTGTTATAGTCTGTAGTAAACRrOmpB-P-FAMTATTGGCACTGTACAGTTAAAGCABartonella spp. (probe-based qPCR; 253 bp)BssrA-FGCTATGGTAATAAATGGACAATGAAATAADiaz et al. [40]BssrA-RGCTTCTGTTGCCAGGTGBssrA-P-FAMACCCCGCTTAAACCTGCGACGBabesia spp. (SYBR qPCR w/ melt Curve; 150 bp)B-lsu-FACCTGTCAARTTCCTTCACTAAMTTQurollo et al. [41]Bmic-FTTGCGATAGTAATAGATTTACTGCB-lsu-RTCTTAACCCAACTCACGTACCAPiroplasmid (SYBR qPCR w/ melt curve; 200 bp)Piro18s-FGCAGTTAAAAAGCTCGTAGTTGAATTTyrrell et al. [42]Piro18s-RGTTAAATACGAATGCCCCCAABabesia canis (ddPCR; 88 bp)Bcanis-FTAGTTTGAAACCCGCCTTKuo et al. [43]Bcanis-RGATGGGTCAGAAACTTGAABcanis-P-HEXCATCGCTAAATGCGATTCGCCACanis lupus familiaris STXBP6 (ddPCR; 104 bp)STXBP6-FCCAGGATTCTGCAGAGTTTGATMochizuki et al. [44]STXBP6-RGTGGTGGAGGATTTGGAAGAAGSTXBP6-P-HEXAATGCCTTTGACCAGTGGGTAGCCCOI mitochondrial cytochrome c oxidase subunit I, dd PCR droplet digital PCR, F forward qPCR quantitative PCR, R reverse
We additionally used a quantitative PCR (qPCR) assay for the detection and quantification of I. ricinus DNA, which was performed as described in Robinson et al. [34]. Although the primers and hydrolysis probe (IxRic) were designed to detect I. ricinus, initial testing indicated that the assay was also able to detect D. reticulatus DNA at a higher cycle threshold. This served as a confirmation for the COI species identification method. We tested the reaction on the three different cycle conditions described for each pathogen probe-based qPCR. The IxRic assay then served as an internal positive control (IPC) in all four of the hydrolysis probe-based qPCRs to verify that each test DNA sample was PCR-competent. All qPCR assays were conducted using a CFX384 Touch Real-Time PCR System (Bio-Rad Laboratories, Hercules, CA, USA).
PCR detection of pathogens
Two traditional endpoint PCRs were used to detect tick-borne pathogens, one for Anaplasmataceae spp. and one for Francisella tularensis. For both PCRs, we used the undiluted ‘head’ DNA extract as template, as the ‘body’ DNA extracts contained high concentrations of DNA from both tick and host. Using the ‘head’ DNA extracts allowed for more accurate detection of pathogens that were present in the tick and not simply present in the blood meal.
The Anaplasmataceae PCR primers were used to detect different species within the Anaplasmataceae family [35]. Each 20-µl reaction volume contained 1× GoTaq G2 Master Mix (Promega, Madison, WI, USA), 300 nM of each forward and reverse primer, and 2 µl of template DNA. PCR was performed as described in Krücken et al. [35].
The PCR for F. tularensis was used to detect the presence of different subspecies of F. tularensis [36]. The final 20-µl reaction volume contained 1× GoTaq G2 Master Mix, 480 nM of each primer and 2 µl of template DNA. The PCR thermal cycling profile was as described in Kormilitsyna et al. [36]. All products, for both the Anaplasmataceae and the F. tularensis PCR were visualized in a 2% agarose gel in 1× TAE, and amplicons were bidirectionally sequenced at the NCSU GSL. NCBI BLAST was used to identify the origins of each amplicon.
qPCR detection of pathogens
We conducted five qPCRs targeting pathogen species or species complexes that had been documented previously as being detected in ticks located around the CEZ. This included hydrolysis probe-based qPCRs for A. phagocytophilum (ApMSP2), B. burgdorferi s.l. (Bb23s), Rickettsia raoultii (RrOmpB), and Bartonella spp. (BssrA), in addition to a SYBR qPCR for detection of Babesia spp. (Table 1). Each of the hydrolysis probe-based qPCRs designed to detect pathogens was duplexed with the IxRic assay, which served as an IPC to ensure amplification competency in all reactions. As qPCR allows for increased sensitivity and accuracy, we first analyzed undiluted DNA extracts from the ‘body’ of the ticks, despite the higher DNA concentrations. We then followed up by testing each corresponding ‘head’ DNA extract for each ‘body’ sample that was PCR positive. All samples, including unknowns and controls (positive/negative/no-template), were run in duplicate. Presence of pathogen was indicated when both duplicate unknown samples produced an amplicon of the expected size for matched ‘body’ and ‘head’ extracts, as determined by agarose gel electrophoresis.
We used species-specific primers and a hydrolysis probe targeting the MSP2 gene for the detection and quantification of A. phagocytophilum [37]. The final 25-µl reaction volume contained 1× PerfeCTa qPCR ToughMix (QuantaBio, Beverly, MA, USA), 2 µl of template DNA, 900 nM of each AsMSP2 primer, 125 nM of AsMSP2 probe and 250 nM of IxRic primers and probe. PCR for both ApMSP2 and Bb23s were performed following the protocol described in Courtney et al. [37]. Two dog-derived blood samples positive for A. phagocytophilum were used as positive controls for this analysis, and these samples were acquired from the NCSU Vector Borne Disease Diagnostic Laboratory (VBDDL). We used a synthetic control, at a range of 106 copies to 101, for standard curve calculations.
For the detection and quantification of B. burgdorferi, we used primer and a hydrolysis probe sequences targeting the 23S gene [37]. The final 25-µl reaction volume contained 1× PerfeCTa qPCR ToughMix, 2 µl of template DNA, 700 nM of each Bb23s primer, 175 nM of Bb23s probe and 250 nM of IxRic primers and probe. We used a synthetic control designed for a 490-bp region surrounding the targeted amplicon as the positive control, diluted to 103 copies. We used the synthetic control to calculate a standard curve for dilutions from 106 to 101. For tick samples yielding amplicons from this assay, we then assessed the genospecies using endpoint PCR and Sanger sequencing. Primers used for this assay targeted the 16S gene for Borrelia spp. [38]. Each 25-µl reaction volume contained 1× GoTaq G2 Master Mix, 600 nM of each Bor16S primer and 2 µl of template DNA. The PCR thermal cycling profile followed the protocol described in Qurollo et al. [38]. Products were visualized in a 2% agarose gel in 1× TAE. Amplicons were then sequenced bidirectionally at the NCSU GSL, and BLAST was used to type the genospecies of B. burgdorferi within the positive ticks.
For the detection and quantification of R. raoultii, we used primers and a probe that was modified from a molecular beacon to a hydrolysis probe to target the OmpB gene [39]. Each 25-µl reaction volume for RrOmpB and IxRic contained 1× PerfeCTa qPCR ToughMix, 2 µl of template DNA, 500 nM of each RrOmpB primer, 400 nM of RrOmpB probe and 250 nM of IxRic primers and probe. The PCR thermal cycling profile following that described in Jiang et al. [39]. We used a synthetic control designed for a 414-bp region surrounding the targeted amplicon as the positive control, diluted to 103 copies. We also used the synthetic control to calculate a standard curve for dilutions from 106 to 101.
For cross-genus detection of Bartonella, we used primers and a probe targeting the ssrA gene [40]. This assay (BssrA) was designed for species identification upon sequencing of the amplicon. The final 25-µl reaction volume, duplexed with IxRic, contained 1× PerfeCTa qPCR ToughMix, 2 µl of template DNA, 500 nM of each BssrA primer, 400 nM of BssrA probe and 250 nM of IxRic primers and probe. The PCR thermal cycling profile followed the protocol described in Diaz et al. [40]. Two feline-derived samples that were known to harbor Bartonella henselae were used as positive controls for this analysis, which were acquired from the NCSU VBDDL.
For the detection of Babesia spp., we used a SYBR qPCR assay described in Qurollo et al. [41]. The 12.5-µl reaction volume contained 1× SsoAdvanced Universal SYBR Green Supermix (Bio-Rad Laboratories), 600 nM of B-LSU-F and B-LSU-R2, 400 nM of BMic-F and 2 µl of template DNA. PCR and melt curve analysis were performed as described in Qurollo et al. [41]. We looked specifically for melting temperatures between 76.5 °C and 77 °C for confirmation of Babesia spp. presence, and sequenced amplicons at the NCSU GSL to ensure specificity in amplification and to identify the Babesia species. Two canine-derived samples positive for Babesia vogeli, acquired from the NCSU VBDDL, were used as positive controls. Since this region shows high genetic similarity between B. canis and B. vogeli, we performed an additional SYBR qPCR to better identify these species of B. canis for ticks identified as positive through the aforementioned Babesia assay. This piroplasmid assay targets a 188-bp region of the 18S gene and has increased specificity towards B. canis over B. vogeli [42]. Each 25-µl reaction volume contained 1× SsoAdvanced Universal SYBR Green Supermix, 100 nM of each piroplasmid primer, and 2 µL of template DNA. The PCR and melt curve analysis were performed as described in Tyrrell et al. [42]. Resultant amplicons were sequenced bidirectionally at NCSU GSL and assessed with BLAST to further confirm the species identity of Babesia within positive ticks.
Pathogen droplet digital PCR assay for dogs
Using droplet digital PCR (ddPCR), we assessed the presence of pathogens in DNA samples from the peripheral blood of dogs located at either the NPP or in CC. The use of ddPCR specifically allowed for greater sensitivity for detecting circulating pathogens in these blood samples. We focused on two pathogens prevalent in the tick populations that infect canine blood cells: A. phagocytophilum and Babesia spp. We first tested a random canine DNA sample for the presence of these two pathogens using the ApMSP2 and the Babesia spp. qPCR assays described above for pathogen detection in the ticks, and then designed a more sensitive ddPCR assay to test each of the canine DNA samples. The preliminary assessment indicated the presence of both of these pathogens within dogs from both populations, and further identified that the species of Babesia present was B. canis (via Sanger sequencing of the positive amplicons). Based on these results from the test samples, we adapted qPCR assays for the detection of A. phagocytophilum (FAM) and B. canis (HEX), as described in [43], to form a single duplexed two-color ddPCR assay, thereby providing increased sensitivity in our level of detection. We established the optimum annealing temperature of 60 °C for the duplexed ddPCR, and this ddPCR assay was then used to detect these two prominent pathogens in blood-derived DNA samples from dogs of both populations.
Initially, we assessed all 135 blood-derived Chornobyl DNA samples (111 unique dogs) and 100 DNA samples from healthy, uninfected dogs for amplification competency with a probe-based ddPCR assay targeting a region on dog chromosome 8 (described in [44]). To examine any evidence of spurious hydrolysis of the probe in the reaction mixture, we first tested the competent DNA isolates from the 100 uninfected dogs. We then tested all competent Chornobyl dog samples for the presence of A. phagocytophilum and B. canis with the duplexed ddPCR assay. We performed the analysis using the manufacturer’s recommended protocol for primer and probe concentrations (900 nM of primers, 250 nM of probe), 1× ddPCR Supermix for probes [no dUTP]; Bio-Rad Laboratories), 2 µl of undiluted DNA extract as template (equivalent to approx. 10 µl of whole dog blood) and water for a final reaction volume of 22 µl. Droplets were generated with an Automated Droplet Generator (Bio-Rad Laboratories). PCR thermal cycling was performed using a C1000 Thermal Cycler (Bio-Rad Laboratories), starting with an initial denaturation step of 95 °C for 10 min, followed by 40 cycles of 94 °C for 30 s and 60 °C for 60 s, with a final extension at 98 °C for 10 min. Droplets were then analyzed using a QX200 Droplet Reader (Bio-Rad Laboratories). Each analysis plate contained positive controls (synthetic DNA controls at a concentration of 103 copies), no-template controls and negative controls (uninfected dog DNA). The positive controls were used to determine the expected amplitude for each channel.
Statistical analysis
For each pathogen, we calculated a Z-score to compare the proportion of positivity between the two populations. We used the following equation to calculate the Z-scores, based on the null hypothesis that the difference between the proportion for each population is zero.\documentclass[12pt]{minimal}
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\begin{document}$${\rm Z}= \frac{\left({\widehat{p}}_{1}- {\widehat{p}}_{2}\right)-0}{\sqrt{\widehat{p}\left(1- \widehat{p}\right) \left(\frac{1}{{n}_{1}}+\frac{1}{{n}_{2}}\right)}}$$\end{document}Z=p^1-p^2-0p^1-p^1n1+1n2where:\documentclass[12pt]{minimal}
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\begin{document}$$\widehat{p}= \frac{{Y}_{1}+{Y}_{2}}{{n}_{1}+ {n}_{2}}$$\end{document}p^=Y1+Y2n1+n2
A significant difference in proportions of population positivity, based on an alpha value of 0.05 and a two-tailed hypothesis, would require a Z-score with an absolute value > 2.
Sample collection
Ticks were collected from free-breeding dogs during spay/neuter/vaccination efforts conducted at the NPP and CC sample sites in 2018 sponsored by the Clean Futures Fund (described in [31, 32]). Collected ticks were immediately preserved in 70% ethanol on site until processed. We acquired a total of 106 ticks from 36 different dogs that were captured either at the NPP (n = 17 dogs; 54 ticks) or in CC (n = 19 dogs; 52 ticks). The majority (78%) of the ticks were visibly engorged upon collection and the remainder (22%) were flat. After removal from 70% ethanol, each tick was weighed and photographed prior to DNA extraction. All of the collected ticks were morphologically assessed for life-stage identification. Since the majority of ticks were engorged, tick species identification was conducted via molecular methods. Six of the sampled ticks were collected while mating to the feeding tick, but these males were separated out before DNA extraction.
In a previous study we also previously acquired blood-derived DNA samples, taken in 2019 from 111 unique dogs, of which 55 were sampled at the NPP and 56 sampled in CC (detailed further in [31]), along with DNA samples taken in 2018 for 24 dogs (20 at NPP and 4 at CC) that were sampled again in 2019. To maintain continuity between sampling years and provide equal representation for both populations, we focused on the 2019 samples for assessment of pathogen prevalence. We also determined pathogen presence in the 2018 samples, but these samples were used primarily to assess persistence and not for overall population prevalence. The ticks collected from dogs in 2018 overlapped with five of these dogs for which we have a blood sample from 2018. The 2019 dog samples used in the pathogen prevalence analysis did not have corresponding tick samples in the present analysis.
DNA extraction
Prior to extraction, we dissected each tick with a sterilized scalpel, removing the mouthparts and the front-most part of the scutum (hereafter, ‘head’) and bilaterally dissecting the ‘body’ (Fig. 1). We then extracted DNA from each ‘head’ and each half of the bisected ‘body’ so that we could make an attempt to differentiate which pathogens were only present in the blood meal, i.e. only in ‘body’ extracts, from those that may be in the salivary glands, which could be in both ‘head’ and ‘body’ extracts. DNA was extracted from the ticks following the protocol of the Qiagen Blood and Tissue DNA extraction kit (Qiagen, Hilden, Germany) with a few alterations. We increased the digestion time, allowing the tissue to incubate at 56 °C in buffer ATL and proteinase K overnight, together with the inclusion of additional proteinase K after 12 h, vortexing intermittently. For the largest engorged ticks, a second dose of buffer ATL and proteinase K was added after the initial 12-h incubation and the incubation time was extended for another 12 h. DNA was eluted twice with same 60 µL of high-performance liquid chromatography water. DNA integrity and purity was assessed by agarose gel electrophoresis and spectrophotometry (NanoDrop One spectrophotometer; Thermo Fisher Scientific, Waltham, MA, USA). All extracts were stored at - 20 °C prior to analysis.Fig. 1Schematic of tick dissection prior to DNA extraction, where DNA was extracted from one half of the body and from the front-most portion of the scutum. Created with BioRender (https://www.biorender.com/)
Tick species identification
For species identification, DNA isolated from each tick ‘head’ was used as the template for PCR amplification of an 820-bp region of the mitochondrial cytochrome c oxidase subunit I gene (COI) (all primer sequences are included in Table 1) [33]. The PCRs were performed in reaction volumes of 20 µl total, containing 20 ng of template DNA, 1× Taq RED Master Mix (Apex Bioresearch), and 300 nM of each primer. PCR assays were performed following the protocol described in Lv et al. [33]. Amplicons were sequenced using conventional bidirectional Sanger sequencing by the North Carolina State University Genomic Sciences Laboratory (NCSU GSL). Each sequenced amplicon was analyzed using National Center for Biotechnology Information (NCBI) BLAST to determine species identity, based on the lowest e-value of the matching NCBI sequence that shared > 95% identity with the amplicon.Table 1Primers and probes used in PCR assaysEndpoint PCR primersAssay targetPrimer nameSequence (5′–3′)ReferencesTick COI (820 bp)Cox1-FGGAACAATATATTTAATTTTTGGLv et al. [33]Cox1-RATCTATCCCTACTGTAAATATATGAnaplasmataceae (257 bp)AnaSpp-FGGGGATGATGTCAARTCAGCAYKrücken et al. [35]AnaSpp-RCACCAGCTTCGAGTTAAGCCAATFrancisella tularensis (357 bp)FrancTul-FGCAGGTTTAGCGAGCTGTTCTACTCKormilitsyna et al. [36]FrancTul-RAGCTGTCCACTTACCGCTACAGAAGBorrelia spp. (210 bp)Borr16s-FAGTGGCGAACGGGTGAGTAQurollo et al. [38]Borr16s-RCTCTCAGGCCGGTTACTTATCQuantitative PCR primers and probesAssay targetPrimer nameSequence (5'-3')ReferencesIxodes ricinus (probe-based qPCR; 130 bp)IxRic-FCTGGAGCTTCCGTTGACATAGRobinson et al. [34]IxRic-RGGTATTCGTTCTAAAGATAGTCCTGGTIxRic-P-HEXTCCCTTCATTTAGCAGGAATTTCATCAAnaplasma phagocytophilum (probe-based qPCR/ddPCR; 76 bp)ApMSP2-FTGGAAGGTAGTGTTGGTTATGGTATTCourtney et al. [37]ApMSP2-RTTGGTCTTGAAGCGCTCGTAApMSP2-FAMTGGTGCCAGGGTTGAGCTTGAGATTGBorrelia burgdorferi (probe-based qPCR; 77 bp)Bb23s-FCGAGTCTTAAAAGGGCGATTTAGTCourtney et al. [37]Bb23s-RGCTTCAGCCTGGCCATAAATAGBb23s-P-FAMAGATGTGGTAGACCCGAAGCCGAGTGRickettsia raoultii (probe-based qPCR; 107 bp)RrOmpB-FGTGGTGGTGTTCCTAATACTCCJiang et al. [39]RrOmpB-RACCTAAGTTGTTATAGTCTGTAGTAAACRrOmpB-P-FAMTATTGGCACTGTACAGTTAAAGCABartonella spp. (probe-based qPCR; 253 bp)BssrA-FGCTATGGTAATAAATGGACAATGAAATAADiaz et al. [40]BssrA-RGCTTCTGTTGCCAGGTGBssrA-P-FAMACCCCGCTTAAACCTGCGACGBabesia spp. (SYBR qPCR w/ melt Curve; 150 bp)B-lsu-FACCTGTCAARTTCCTTCACTAAMTTQurollo et al. [41]Bmic-FTTGCGATAGTAATAGATTTACTGCB-lsu-RTCTTAACCCAACTCACGTACCAPiroplasmid (SYBR qPCR w/ melt curve; 200 bp)Piro18s-FGCAGTTAAAAAGCTCGTAGTTGAATTTyrrell et al. [42]Piro18s-RGTTAAATACGAATGCCCCCAABabesia canis (ddPCR; 88 bp)Bcanis-FTAGTTTGAAACCCGCCTTKuo et al. [43]Bcanis-RGATGGGTCAGAAACTTGAABcanis-P-HEXCATCGCTAAATGCGATTCGCCACanis lupus familiaris STXBP6 (ddPCR; 104 bp)STXBP6-FCCAGGATTCTGCAGAGTTTGATMochizuki et al. [44]STXBP6-RGTGGTGGAGGATTTGGAAGAAGSTXBP6-P-HEXAATGCCTTTGACCAGTGGGTAGCCCOI mitochondrial cytochrome c oxidase subunit I, dd PCR droplet digital PCR, F forward qPCR quantitative PCR, R reverse
We additionally used a quantitative PCR (qPCR) assay for the detection and quantification of I. ricinus DNA, which was performed as described in Robinson et al. [34]. Although the primers and hydrolysis probe (IxRic) were designed to detect I. ricinus, initial testing indicated that the assay was also able to detect D. reticulatus DNA at a higher cycle threshold. This served as a confirmation for the COI species identification method. We tested the reaction on the three different cycle conditions described for each pathogen probe-based qPCR. The IxRic assay then served as an internal positive control (IPC) in all four of the hydrolysis probe-based qPCRs to verify that each test DNA sample was PCR-competent. All qPCR assays were conducted using a CFX384 Touch Real-Time PCR System (Bio-Rad Laboratories, Hercules, CA, USA).
PCR detection of pathogens
Two traditional endpoint PCRs were used to detect tick-borne pathogens, one for Anaplasmataceae spp. and one for Francisella tularensis. For both PCRs, we used the undiluted ‘head’ DNA extract as template, as the ‘body’ DNA extracts contained high concentrations of DNA from both tick and host. Using the ‘head’ DNA extracts allowed for more accurate detection of pathogens that were present in the tick and not simply present in the blood meal.
The Anaplasmataceae PCR primers were used to detect different species within the Anaplasmataceae family [35]. Each 20-µl reaction volume contained 1× GoTaq G2 Master Mix (Promega, Madison, WI, USA), 300 nM of each forward and reverse primer, and 2 µl of template DNA. PCR was performed as described in Krücken et al. [35].
The PCR for F. tularensis was used to detect the presence of different subspecies of F. tularensis [36]. The final 20-µl reaction volume contained 1× GoTaq G2 Master Mix, 480 nM of each primer and 2 µl of template DNA. The PCR thermal cycling profile was as described in Kormilitsyna et al. [36]. All products, for both the Anaplasmataceae and the F. tularensis PCR were visualized in a 2% agarose gel in 1× TAE, and amplicons were bidirectionally sequenced at the NCSU GSL. NCBI BLAST was used to identify the origins of each amplicon.
qPCR detection of pathogens
We conducted five qPCRs targeting pathogen species or species complexes that had been documented previously as being detected in ticks located around the CEZ. This included hydrolysis probe-based qPCRs for A. phagocytophilum (ApMSP2), B. burgdorferi s.l. (Bb23s), Rickettsia raoultii (RrOmpB), and Bartonella spp. (BssrA), in addition to a SYBR qPCR for detection of Babesia spp. (Table 1). Each of the hydrolysis probe-based qPCRs designed to detect pathogens was duplexed with the IxRic assay, which served as an IPC to ensure amplification competency in all reactions. As qPCR allows for increased sensitivity and accuracy, we first analyzed undiluted DNA extracts from the ‘body’ of the ticks, despite the higher DNA concentrations. We then followed up by testing each corresponding ‘head’ DNA extract for each ‘body’ sample that was PCR positive. All samples, including unknowns and controls (positive/negative/no-template), were run in duplicate. Presence of pathogen was indicated when both duplicate unknown samples produced an amplicon of the expected size for matched ‘body’ and ‘head’ extracts, as determined by agarose gel electrophoresis.
We used species-specific primers and a hydrolysis probe targeting the MSP2 gene for the detection and quantification of A. phagocytophilum [37]. The final 25-µl reaction volume contained 1× PerfeCTa qPCR ToughMix (QuantaBio, Beverly, MA, USA), 2 µl of template DNA, 900 nM of each AsMSP2 primer, 125 nM of AsMSP2 probe and 250 nM of IxRic primers and probe. PCR for both ApMSP2 and Bb23s were performed following the protocol described in Courtney et al. [37]. Two dog-derived blood samples positive for A. phagocytophilum were used as positive controls for this analysis, and these samples were acquired from the NCSU Vector Borne Disease Diagnostic Laboratory (VBDDL). We used a synthetic control, at a range of 106 copies to 101, for standard curve calculations.
For the detection and quantification of B. burgdorferi, we used primer and a hydrolysis probe sequences targeting the 23S gene [37]. The final 25-µl reaction volume contained 1× PerfeCTa qPCR ToughMix, 2 µl of template DNA, 700 nM of each Bb23s primer, 175 nM of Bb23s probe and 250 nM of IxRic primers and probe. We used a synthetic control designed for a 490-bp region surrounding the targeted amplicon as the positive control, diluted to 103 copies. We used the synthetic control to calculate a standard curve for dilutions from 106 to 101. For tick samples yielding amplicons from this assay, we then assessed the genospecies using endpoint PCR and Sanger sequencing. Primers used for this assay targeted the 16S gene for Borrelia spp. [38]. Each 25-µl reaction volume contained 1× GoTaq G2 Master Mix, 600 nM of each Bor16S primer and 2 µl of template DNA. The PCR thermal cycling profile followed the protocol described in Qurollo et al. [38]. Products were visualized in a 2% agarose gel in 1× TAE. Amplicons were then sequenced bidirectionally at the NCSU GSL, and BLAST was used to type the genospecies of B. burgdorferi within the positive ticks.
For the detection and quantification of R. raoultii, we used primers and a probe that was modified from a molecular beacon to a hydrolysis probe to target the OmpB gene [39]. Each 25-µl reaction volume for RrOmpB and IxRic contained 1× PerfeCTa qPCR ToughMix, 2 µl of template DNA, 500 nM of each RrOmpB primer, 400 nM of RrOmpB probe and 250 nM of IxRic primers and probe. The PCR thermal cycling profile following that described in Jiang et al. [39]. We used a synthetic control designed for a 414-bp region surrounding the targeted amplicon as the positive control, diluted to 103 copies. We also used the synthetic control to calculate a standard curve for dilutions from 106 to 101.
For cross-genus detection of Bartonella, we used primers and a probe targeting the ssrA gene [40]. This assay (BssrA) was designed for species identification upon sequencing of the amplicon. The final 25-µl reaction volume, duplexed with IxRic, contained 1× PerfeCTa qPCR ToughMix, 2 µl of template DNA, 500 nM of each BssrA primer, 400 nM of BssrA probe and 250 nM of IxRic primers and probe. The PCR thermal cycling profile followed the protocol described in Diaz et al. [40]. Two feline-derived samples that were known to harbor Bartonella henselae were used as positive controls for this analysis, which were acquired from the NCSU VBDDL.
For the detection of Babesia spp., we used a SYBR qPCR assay described in Qurollo et al. [41]. The 12.5-µl reaction volume contained 1× SsoAdvanced Universal SYBR Green Supermix (Bio-Rad Laboratories), 600 nM of B-LSU-F and B-LSU-R2, 400 nM of BMic-F and 2 µl of template DNA. PCR and melt curve analysis were performed as described in Qurollo et al. [41]. We looked specifically for melting temperatures between 76.5 °C and 77 °C for confirmation of Babesia spp. presence, and sequenced amplicons at the NCSU GSL to ensure specificity in amplification and to identify the Babesia species. Two canine-derived samples positive for Babesia vogeli, acquired from the NCSU VBDDL, were used as positive controls. Since this region shows high genetic similarity between B. canis and B. vogeli, we performed an additional SYBR qPCR to better identify these species of B. canis for ticks identified as positive through the aforementioned Babesia assay. This piroplasmid assay targets a 188-bp region of the 18S gene and has increased specificity towards B. canis over B. vogeli [42]. Each 25-µl reaction volume contained 1× SsoAdvanced Universal SYBR Green Supermix, 100 nM of each piroplasmid primer, and 2 µL of template DNA. The PCR and melt curve analysis were performed as described in Tyrrell et al. [42]. Resultant amplicons were sequenced bidirectionally at NCSU GSL and assessed with BLAST to further confirm the species identity of Babesia within positive ticks.
Pathogen droplet digital PCR assay for dogs
Using droplet digital PCR (ddPCR), we assessed the presence of pathogens in DNA samples from the peripheral blood of dogs located at either the NPP or in CC. The use of ddPCR specifically allowed for greater sensitivity for detecting circulating pathogens in these blood samples. We focused on two pathogens prevalent in the tick populations that infect canine blood cells: A. phagocytophilum and Babesia spp. We first tested a random canine DNA sample for the presence of these two pathogens using the ApMSP2 and the Babesia spp. qPCR assays described above for pathogen detection in the ticks, and then designed a more sensitive ddPCR assay to test each of the canine DNA samples. The preliminary assessment indicated the presence of both of these pathogens within dogs from both populations, and further identified that the species of Babesia present was B. canis (via Sanger sequencing of the positive amplicons). Based on these results from the test samples, we adapted qPCR assays for the detection of A. phagocytophilum (FAM) and B. canis (HEX), as described in [43], to form a single duplexed two-color ddPCR assay, thereby providing increased sensitivity in our level of detection. We established the optimum annealing temperature of 60 °C for the duplexed ddPCR, and this ddPCR assay was then used to detect these two prominent pathogens in blood-derived DNA samples from dogs of both populations.
Initially, we assessed all 135 blood-derived Chornobyl DNA samples (111 unique dogs) and 100 DNA samples from healthy, uninfected dogs for amplification competency with a probe-based ddPCR assay targeting a region on dog chromosome 8 (described in [44]). To examine any evidence of spurious hydrolysis of the probe in the reaction mixture, we first tested the competent DNA isolates from the 100 uninfected dogs. We then tested all competent Chornobyl dog samples for the presence of A. phagocytophilum and B. canis with the duplexed ddPCR assay. We performed the analysis using the manufacturer’s recommended protocol for primer and probe concentrations (900 nM of primers, 250 nM of probe), 1× ddPCR Supermix for probes [no dUTP]; Bio-Rad Laboratories), 2 µl of undiluted DNA extract as template (equivalent to approx. 10 µl of whole dog blood) and water for a final reaction volume of 22 µl. Droplets were generated with an Automated Droplet Generator (Bio-Rad Laboratories). PCR thermal cycling was performed using a C1000 Thermal Cycler (Bio-Rad Laboratories), starting with an initial denaturation step of 95 °C for 10 min, followed by 40 cycles of 94 °C for 30 s and 60 °C for 60 s, with a final extension at 98 °C for 10 min. Droplets were then analyzed using a QX200 Droplet Reader (Bio-Rad Laboratories). Each analysis plate contained positive controls (synthetic DNA controls at a concentration of 103 copies), no-template controls and negative controls (uninfected dog DNA). The positive controls were used to determine the expected amplitude for each channel.
Statistical analysis
For each pathogen, we calculated a Z-score to compare the proportion of positivity between the two populations. We used the following equation to calculate the Z-scores, based on the null hypothesis that the difference between the proportion for each population is zero.\documentclass[12pt]{minimal}
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\begin{document}$${\rm Z}= \frac{\left({\widehat{p}}_{1}- {\widehat{p}}_{2}\right)-0}{\sqrt{\widehat{p}\left(1- \widehat{p}\right) \left(\frac{1}{{n}_{1}}+\frac{1}{{n}_{2}}\right)}}$$\end{document}Z=p^1-p^2-0p^1-p^1n1+1n2where:\documentclass[12pt]{minimal}
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\begin{document}$$\widehat{p}= \frac{{Y}_{1}+{Y}_{2}}{{n}_{1}+ {n}_{2}}$$\end{document}p^=Y1+Y2n1+n2
A significant difference in proportions of population positivity, based on an alpha value of 0.05 and a two-tailed hypothesis, would require a Z-score with an absolute value > 2.
Results
Tick species identification
All 106 ticks were identified via COI amplification and sequencing; of these, 102 (93.6%) were I. ricinus and four (3.8%) were D. reticulatus. All four of the D. reticulatus ticks were sampled from the NPP population. Morphological assessment indicated that all collected ticks were adults.
All ticks amplified via the IxRic qPCR when tested in conjunction with each of the pathogen assays, despite the design targeting only I. ricinus. The four D. reticulatus ticks amplified at a higher cycle threshold (Ct) collectively than the I. ricinus (average Ct 32 vs 16, respectively). The consistent amplification confirmed the competency of the template DNA to amplify when duplexed with the pathogen qPCR assays.
PCR detection of pathogens
The general Anaplasmataceae PCR yielded positive results for 25 of the 106 ticks. Sequencing matched to A. phagocytophilum (n = 15), Anaplasma platys (n = 6) and Neoehrlichia mikurensis (n = 4) with a percent identity > 97% (> 99% on average) and high query coverage (Fig. 2; Additional file 1: Table S1). As there was a high percent identity in the sequenced region for both A. platys and A. phagocytophilum, we corroborated the Anaplasma sequencing results with a species-specific qPCR, ApMSP2, which does not amplify A. platys in silico. Tick samples positive for N. mikurensis were distributed evenly across the two populations, but we found that four of the six A. platys-positive ticks were sampled at CC.Fig. 2Pathogen prevalence in Ixodes ricinus ticks across the two sampled populations, Chornobyl City (CC; blue bars) and the Nuclear Power Plant (NPP; gold bars), along with population measures for co-infection and general positivity rate. The asterisk (*) indicates a Z-score > 2 and a significant difference at P < 0.05
The F. tularensis PCR yielded one positive amplicon in a D. reticulatus specimen from the NPP, which sequencing identified as a Francisella-like endosymbiont of D. reticulatus with 99% sequence identity. No other ticks were positive for F. tularensis by the PCR.
qPCR detection of pathogens
A total of 33 I. ricinus ticks were positive for A. phagocytophilum for both the ‘head’ and ‘body’ extracts, with a higher incidence of positive ticks at the NPP (44.0%; n = 22) than at CC (23.1%; n = 11; Fig. 2; Additional file 1: Table S1). For this assay, five additional ticks, including one D. reticulatus sample, were positive only when the ‘body’ was considered; the ‘head’ extract did not yield any amplification. Thirty-one I. ricinus ticks were positive for B. burgdorferi, of which 21 (34.0%) were from the NPP and 10 were from CC (11.5%). Three additional ticks were only positive for the ‘body’ extract and did not generate an amplicon for the ‘head’ of the respective tick. For the genospecies assessment, amplicon sequences of 24 of these 31 positive ticks (‘head’ and ‘body’) provided support for B. burgdorferi genospecies identification with > 95% sequence identity. The positive samples from CC were identified as Borrelia afzelii (n = 6), Borrelia garinii (n = 1), and Borrelia valaisiana (n = 1) with > 99% sequence identity. The predominant species in NPP samples was B. afzelii (n = 16). One I. ricinus tick from CC (1.9%) and all four sampled D. reticulatus ticks from the NPP were positive for R. raoultii. The Bartonella assay did not yield positive results for any tick extract. For Babesia spp., a total of nine I. ricinus ticks were positive at similar proportions across the two populations (NPP 8.0%; CC 9.6%). Sequencing from the general Babesia spp. qPCR indicated that the eight positives matched most closely to B. canis-like with > 97% sequence identity and that one matched most closely to B. caballi-like with > 97% sequence identity. The piroplasmid qPCR generated amplicons for four of the eight samples that previously matched closest to B. canis. Melt curve analysis of the products indicated that each product had the expected melting temperature for B. canis (80.5 °C). Sequencing results from the piroplasmid PCR indicated that the previous B. canis positives matched again to B. canis with > 99% identity.
Overall tick positivity was also higher at the NPP than at CC, where 62% and 51.8% of ticks were positive for any of the assessed pathogens, respectively. The proportion of ticks from the NPP and CC that were co-infected with two pathogens was 34% and 9.6%, respectively, including one NPP tick for which three different pathogens were detected.
Pathogen ddPCR assay for dogs
When assessed by ddPCR for the presence of a canine autosomal marker, all dog samples had positive droplets, proving competency of the DNA samples to amplify and thus allowing us to move forward with the pathogen assay. We detected A. phagocytophilum in eight of our 111 (7.2%) unique dogs via ddPCR. Seven of the positive dogs were sampled in CC, and one NPP dog had positive droplets. These data indicate that 1.8% of the NPP dogs and 11.7% of the CC dogs were positive for A. phagocytophilum (Fig. 3). We noted no persistence for this pathogen’s presence in individuals sampled in both 2018–2019. For B. canis, we found that 24.3% (27/111) of the dogs were positive and these were evenly distributed across the sample populations, with 14 of the positive dogs from the CC population. When we considered the dogs sampled in 2018 and 2019, we did find that both CC dogs positive for B. canis in 2018 were positive for both years, but of the seven NPP dogs that were positive in 2018, only one retained positivity from 2018 to 2019. Two CC individuals were positive for both A. phagocytophilum and B. canis, but we found no other overlap in positivity.Fig. 3Pathogen prevalence in dogs from Chornobyl City (CC; blue bars) and the Nuclear Power Plant (NPP; gold bars). The asterisk (*) indicates a Z-score > 2 and a significant difference at P < 0.05
We validated the lower positivity samples by assessment of 100 unaffected dog DNA samples, which yielded only one positive droplet out of over 2,000,000 droplets for each of the B. canis and A. phagocytophilum probes. This set the background positive droplet rate for each ddPCR assay at < 0.00005%. In addition, the single positive droplets for each assay within the negative control samples did not reach the fluorescence level of droplets in a positive sample (amplitude of approx. 6000 for A. phagocytophilum, approx. 3500 for B. canis; see Fig. 4) and was therefore not consistent with the amplitude or position of droplets from known positive samples.Fig. 4Droplet digital PCR (ddPCR) amplitude plots with selected samples. From left to right, the top row contains a positive control containing a gBlock fragment synthetic control for both Anaplasma phagocytophilum and Babesia canis (left) and a dog that had positive droplets for A. phagocytophilum (right). The bottom row contains a dog with positive droplets for B. canis only (left) and a dog negative for both pathogens (right). Fluorescein amidites (FAM; A. phagocytophilum) fluorescence is measured on channel 1, and hexachlorofluorescein (HEX; B. canis) fluorescence is measured on channel 2
Statistical analysis
We calculated the Z-score for each pathogen, to determine differences in pathogen prevalence between the two populations. For ticks, the NPP population had a significantly higher proportion of positivity for A. phagocytophilum and B. burgdorferi than the CC population, with Z-scores of 2.26 and 2.50, respectively (P = 0.012; P = 0.0062). For the other pathogens, the differences did not reach significance. The difference in proportion of co-infected ticks was significantly higher for the NPP population, with a Z-score of 3.07 (P = 0.00107). Overall pathogen positivity levels, however, were not significantly different (Z = 1.03; P = 0.15).
When the dogs were considered, we found that the CC population had a significantly higher proportion of individuals that were positive for A. phagocytophilum, with a Z-score of 2.23 (P = 0.013). There was no significant difference in B. canis presence between the NPP and CC populations. We additionally calculated the Z-score for persistence of B. canis, and found that the difference in B. canis retention between the populations was significant (Z = 2.27; P = 0.012).
Tick species identification
All 106 ticks were identified via COI amplification and sequencing; of these, 102 (93.6%) were I. ricinus and four (3.8%) were D. reticulatus. All four of the D. reticulatus ticks were sampled from the NPP population. Morphological assessment indicated that all collected ticks were adults.
All ticks amplified via the IxRic qPCR when tested in conjunction with each of the pathogen assays, despite the design targeting only I. ricinus. The four D. reticulatus ticks amplified at a higher cycle threshold (Ct) collectively than the I. ricinus (average Ct 32 vs 16, respectively). The consistent amplification confirmed the competency of the template DNA to amplify when duplexed with the pathogen qPCR assays.
PCR detection of pathogens
The general Anaplasmataceae PCR yielded positive results for 25 of the 106 ticks. Sequencing matched to A. phagocytophilum (n = 15), Anaplasma platys (n = 6) and Neoehrlichia mikurensis (n = 4) with a percent identity > 97% (> 99% on average) and high query coverage (Fig. 2; Additional file 1: Table S1). As there was a high percent identity in the sequenced region for both A. platys and A. phagocytophilum, we corroborated the Anaplasma sequencing results with a species-specific qPCR, ApMSP2, which does not amplify A. platys in silico. Tick samples positive for N. mikurensis were distributed evenly across the two populations, but we found that four of the six A. platys-positive ticks were sampled at CC.Fig. 2Pathogen prevalence in Ixodes ricinus ticks across the two sampled populations, Chornobyl City (CC; blue bars) and the Nuclear Power Plant (NPP; gold bars), along with population measures for co-infection and general positivity rate. The asterisk (*) indicates a Z-score > 2 and a significant difference at P < 0.05
The F. tularensis PCR yielded one positive amplicon in a D. reticulatus specimen from the NPP, which sequencing identified as a Francisella-like endosymbiont of D. reticulatus with 99% sequence identity. No other ticks were positive for F. tularensis by the PCR.
qPCR detection of pathogens
A total of 33 I. ricinus ticks were positive for A. phagocytophilum for both the ‘head’ and ‘body’ extracts, with a higher incidence of positive ticks at the NPP (44.0%; n = 22) than at CC (23.1%; n = 11; Fig. 2; Additional file 1: Table S1). For this assay, five additional ticks, including one D. reticulatus sample, were positive only when the ‘body’ was considered; the ‘head’ extract did not yield any amplification. Thirty-one I. ricinus ticks were positive for B. burgdorferi, of which 21 (34.0%) were from the NPP and 10 were from CC (11.5%). Three additional ticks were only positive for the ‘body’ extract and did not generate an amplicon for the ‘head’ of the respective tick. For the genospecies assessment, amplicon sequences of 24 of these 31 positive ticks (‘head’ and ‘body’) provided support for B. burgdorferi genospecies identification with > 95% sequence identity. The positive samples from CC were identified as Borrelia afzelii (n = 6), Borrelia garinii (n = 1), and Borrelia valaisiana (n = 1) with > 99% sequence identity. The predominant species in NPP samples was B. afzelii (n = 16). One I. ricinus tick from CC (1.9%) and all four sampled D. reticulatus ticks from the NPP were positive for R. raoultii. The Bartonella assay did not yield positive results for any tick extract. For Babesia spp., a total of nine I. ricinus ticks were positive at similar proportions across the two populations (NPP 8.0%; CC 9.6%). Sequencing from the general Babesia spp. qPCR indicated that the eight positives matched most closely to B. canis-like with > 97% sequence identity and that one matched most closely to B. caballi-like with > 97% sequence identity. The piroplasmid qPCR generated amplicons for four of the eight samples that previously matched closest to B. canis. Melt curve analysis of the products indicated that each product had the expected melting temperature for B. canis (80.5 °C). Sequencing results from the piroplasmid PCR indicated that the previous B. canis positives matched again to B. canis with > 99% identity.
Overall tick positivity was also higher at the NPP than at CC, where 62% and 51.8% of ticks were positive for any of the assessed pathogens, respectively. The proportion of ticks from the NPP and CC that were co-infected with two pathogens was 34% and 9.6%, respectively, including one NPP tick for which three different pathogens were detected.
Pathogen ddPCR assay for dogs
When assessed by ddPCR for the presence of a canine autosomal marker, all dog samples had positive droplets, proving competency of the DNA samples to amplify and thus allowing us to move forward with the pathogen assay. We detected A. phagocytophilum in eight of our 111 (7.2%) unique dogs via ddPCR. Seven of the positive dogs were sampled in CC, and one NPP dog had positive droplets. These data indicate that 1.8% of the NPP dogs and 11.7% of the CC dogs were positive for A. phagocytophilum (Fig. 3). We noted no persistence for this pathogen’s presence in individuals sampled in both 2018–2019. For B. canis, we found that 24.3% (27/111) of the dogs were positive and these were evenly distributed across the sample populations, with 14 of the positive dogs from the CC population. When we considered the dogs sampled in 2018 and 2019, we did find that both CC dogs positive for B. canis in 2018 were positive for both years, but of the seven NPP dogs that were positive in 2018, only one retained positivity from 2018 to 2019. Two CC individuals were positive for both A. phagocytophilum and B. canis, but we found no other overlap in positivity.Fig. 3Pathogen prevalence in dogs from Chornobyl City (CC; blue bars) and the Nuclear Power Plant (NPP; gold bars). The asterisk (*) indicates a Z-score > 2 and a significant difference at P < 0.05
We validated the lower positivity samples by assessment of 100 unaffected dog DNA samples, which yielded only one positive droplet out of over 2,000,000 droplets for each of the B. canis and A. phagocytophilum probes. This set the background positive droplet rate for each ddPCR assay at < 0.00005%. In addition, the single positive droplets for each assay within the negative control samples did not reach the fluorescence level of droplets in a positive sample (amplitude of approx. 6000 for A. phagocytophilum, approx. 3500 for B. canis; see Fig. 4) and was therefore not consistent with the amplitude or position of droplets from known positive samples.Fig. 4Droplet digital PCR (ddPCR) amplitude plots with selected samples. From left to right, the top row contains a positive control containing a gBlock fragment synthetic control for both Anaplasma phagocytophilum and Babesia canis (left) and a dog that had positive droplets for A. phagocytophilum (right). The bottom row contains a dog with positive droplets for B. canis only (left) and a dog negative for both pathogens (right). Fluorescein amidites (FAM; A. phagocytophilum) fluorescence is measured on channel 1, and hexachlorofluorescein (HEX; B. canis) fluorescence is measured on channel 2
Statistical analysis
We calculated the Z-score for each pathogen, to determine differences in pathogen prevalence between the two populations. For ticks, the NPP population had a significantly higher proportion of positivity for A. phagocytophilum and B. burgdorferi than the CC population, with Z-scores of 2.26 and 2.50, respectively (P = 0.012; P = 0.0062). For the other pathogens, the differences did not reach significance. The difference in proportion of co-infected ticks was significantly higher for the NPP population, with a Z-score of 3.07 (P = 0.00107). Overall pathogen positivity levels, however, were not significantly different (Z = 1.03; P = 0.15).
When the dogs were considered, we found that the CC population had a significantly higher proportion of individuals that were positive for A. phagocytophilum, with a Z-score of 2.23 (P = 0.013). There was no significant difference in B. canis presence between the NPP and CC populations. We additionally calculated the Z-score for persistence of B. canis, and found that the difference in B. canis retention between the populations was significant (Z = 2.27; P = 0.012).
Discussion
In this work, we investigated the potential impact of contrasting types and relative levels of environmental contamination on vectors and vector-borne pathogens at two locations within the CEZ. This study is the first to examine tick pathogens sampled at the NPP and within CC. Furthermore, no previous studies evaluated pathogen prevalence in both ticks and dogs in the CEZ. Pathogen prevalence was higher in ticks at the NPP than in ticks recovered from CC, and the relative proportion of certain pathogens also differed significantly between the two locations. Comparative analysis of the dogs at the two sites also revealed differential pathogen positivity for A. phagocytophilum, but, in contrast to the findings with the ticks, the dog population at CC had a higher proportion of positivity. The findings from this study highlight differential pathogen positivity rates in both tick and dog populations sampled at the NPP and in CC, which supports our initial hypothesis.
In terms of pathogen positivity overall, 56.8% of our 102 sampled I. ricinus ticks were positive for at least one of the eight pathogens assessed. This level of infection is markedly higher than the 11% positivity rate reported previously in almost 700 I. ricinus ticks sampled in Kyiv, Ukraine [45]. The positivity rate we identified in the present study is, however, more similar to that found in ticks collected from pet dogs and cats in Southwest Poland, where 65.4% of the I. ricinus ticks were positive for pathogens [46]. Comparing pathogen-specific positivity, we note an increased pathogen presence for A. phagocytophilum in the I. ricinus ticks sampled for our study when compared to the results of other studies in Ukraine (Table 2; Fig. 5). This level is also markedly higher than those found in regions of Poland, which ranged from 1.1% to 21.3% positivity [47–49]. Surprisingly, we did not detect Bartonella spp., despite previous reports of Bartonella spp. in I. ricinus in the CEZ [18]. Pathogen levels of B. burgdorferi and Babesia spp. were higher than those sampled at sites in close proximity to the CEZ, but these levels are more congruent when compared to studies conducted in more distant oblasts (administrative divisions) of Ukraine and, for Babesia, in Southwest Poland [46, 50]. The authors of other studies hypothesized higher than expected pathogen levels for ticks within the CEZ, supporting our findings for A. phagocytophilum and B. burgdorferi [16, 18]. These two zoonotic pathogens cause two notable emerging tick-borne diseases in Europe: granulocytic anaplasmosis and Lyme disease [51–53].Table 2Summary of findings for three tick-borne pathogens of interest identified in Ixodes ricinus ticks sampled in the UkraineLocationAnaplasma phagocytophilumBorrelia burgdorferiBabesia spp.Molecular methodNo. ticks analyzedCollection methodReferenceCEZ (NPP and CC)32.4%30.4%8.8%xqPCR106From dogsThis study CEZ (CC)0.4%13.5%0%PCR260QuestingRogovskyy et al. [18]Kyiv, Ukraine2.7%10.4%0.5%PCR182QuestingRogovskyy et al. [54]Kyiv, Ukraine (2)5.2%4.0%1.9%yPCR696QuestingDidyk et al. [45]Kyiv Oblast, Ukraine10%20%0%PCR20Questing and animalsLevytska et al. [50]Chernivtsy Oblast, Ukraine22%26%0%PCR23Questing and animalsLevytska et al. [50]Vinnytsya Oblast, Ukraine5%25%5%PCR20Questing and animalsLevytska et al. [50]Khmelnytskyi Oblast, Ukraine5%29%10%PCR21Questing and animalsLevytska et al. [50]Ternopil Oblast, Ukraine8%31%0%PCR13Questing and animalsLevytska et al. [50]South-Eastern Ukraine (Zaporizhzhya Oblast)4.2–7.7%8.6–12.7%NTaqPCR452 (pooled)Questing and animals/humansKovryha et al. [55] For our study, results are reported across both sample locations within the Chornobyl Exclusion ZoneCC Chornobyl City, CEZ Chornobyl Exclusion Zone, NPP Nuclear Power Plant, qPCR quantitative PCRaNT indicates that pathogen presence was not investigated; for species level identification of Babeisa spp., x corresponds to Babesia canis/B. caballi, y corresponds to B. microtiFig. 5Comparison of pathogen findings in Ixodes ricinus ticks for this study (blue) to other studies in Ukraine for three pathogens of interest. Babesia spp. not tested for South-Eastern Ukraine. See Table 2 for more detailed summary of these studies
Our study reports similar rates of infection by Babesia spp. and general Anaplasmataceae spp. (including A. platys and N. mikurensis) at the two sample locations. Interestingly, all but one of the Babesia amplicons closely matched B. canis. Vector competency for the transmission of B. canis in I. ricinus has not been confirmed, despite the presence of the piroplasm in I. ricinus in other studies [56, 57]. Therefore, our results cannot be directly linked to transmissibility from tick to host. This species of Babesia is typically transmitted by the ornate dog tick, D. reticulatus, of which we found very few samples [56, 58].
Of 106 collected ticks, only four were D. reticulatus, while the remaining 102 were I. ricinus. Previous dragging surveys for questing ticks around the CEZ noted a higher prevalence of D. reticulatus [16, 59]. As the sampled ticks were actively feeding on hosts, this may have impacted the species collected and is therefore not representative of all questing ticks that inhabit the CEZ. Also, it is important to note that since the ticks assessed in this study were feeding, pathogen presence may also be affected by the tick’s blood meal. Comparisons between the tick ‘body’ positivity and tick ‘head’ positivity suggest that there are instances where the blood meal was positive for pathogen but that the tick may not be actively transmitting.
In addition to the higher pathogen rates for A. phagocytophilum and B. burgdorferi between our study sites and other sample locations in Ukraine, we found differential proportions of ticks positive for these pathogens at the two sample sites. There were significantly higher rates of infections in ticks at the NPP than in ticks at CC, and the prevalence rates for the NPP (A. phagocytophilum: 44%; B. burgdorferi: 42%) were markedly higher than those reported in tick studies conducted in other parts of Ukraine (Table 2). Our findings also indicate an increased overall level of pathogen prevalence in the ticks at the NPP, along with a significantly higher proportion of co-infection with two or more pathogens. As the pool of assessed ticks did not contain any larval or nymphal ticks, the differences in pathogens between the two populations may be due to other environmental or ecological causes. Some studies suggest that certain mammal species, both small [14] and large [7, 15], have rebounded in this area following the 1986 disaster. Additional information on population densities and pathogen prevalence in each pathogen’s respective reservoir species, such as red deer, small rodent species and red foxes, would provide more contextual evidence for these findings and allow for a better assessment of the sylvatic interactions. The area around the NPP is more heavily forested than that around CC, which may facilitate an increase in sylvatic transmission and a higher pathogen positivity rate for the tick population [60]. The most prevalent B. burgdorferi genospecies in both populations of ticks was B. afzelii, which may be associated with a higher prevalence of pathogen in rodent species; however, further study is required for any conclusion to be drawn [61]. With small rodent species playing a role in the tick-borne pathogen cycle as both reservoir species and early hosts for maturing ticks, it is critical to study the pathogen prevalence in these populations. The radiation releases and widespread destruction of flora and fauna, as well as the subsequent cleanup, likely impacted the populations of small invertebrates, such as ticks, along with the small rodent hosts, which may have affected pathogen spread in the vicinity of the destroyed reactor [62].
In contrast to pathogen prevalence within the ticks themselves, we found that both of the dog populations sampled around Chornobyl had a higher prevalence of B. canis but contradictory levels of A. phagocytophilum. The proportion of dogs positive for B. canis was higher than the proportion of positive ticks, although this difference may be linked to the possibility of vertical transmission of B. canis in dogs [63, 64]. Our data also indicate corresponding positivity levels between Babesia spp. in ticks and B. canis in dogs for both sample locations (Fig. 6). In contrast, dog positivity for A. phagocytophilum in both populations was found to be inversely related to tick positivity. The dogs from the CC population had a significantly higher proportion of positivity than those from the NPP population, whereas the ticks at CC had a significantly lower pathogen prevalence than those at the NPP. The persistence of B. canis in the dog populations sampled in both 2018 and 2019 also differed. While the sample numbers are small and unevenly distributed between the populations, it is notable that both of the dogs in the CC population which tested positive for B. canis in 2018 also tested positive in 2019. Of the seven dogs in the NPP population that tested positive for B. canis in 2018, however, only one still tested positive the following year. The reasons for the difference are unclear, and larger sample sizes are needed to validate this observation. It is important to also note that B. burgdorferi prevalence could not be compared between the two dog populations despite differential prevalence in the tick populations, as this pathogen localizes in tissues and organs and could not be detected in the dog blood samples analyzed.Fig. 6Comparative view of positivity for Anaplasma phagocytophilum and Babesia spp./B. canis in both ticks and dogs. Asterisk (*) indicates Z-score > 2 and P-value < 0.05
Overall, it is notable that significantly different pathogen levels were detected in the two geographically close sample sites (CC and NPP) within the CEZ and that despite the high levels of A. phagocytophilum in the tick populations, we failed to detect a similarly high pathogen load in the dogs. This work also highlights interesting trends regarding the persistence of B. canis from year to year and the contrasting levels of A. phagocytophilum between dog and tick populations. There are a several possible explanations for this reduced A. phagocytophilum presence in the dogs at the NPP despite the high level of tick positivity. Possible explanations include differences in the reservoir species, in the vectors or in the dogs themselves, all of which could impact pathogen transmission from ticks to the dogs. Differential tick density in the sampling years could contribute to this difference, but we were unable to assess tick density with dragging surveys for the sampling years and thus are limited in drawing definitive conclusions. Additionally, although all dogs in the present study were unowned and free-breeding, anecdotal evidence suggests that a larger proportion of dogs at the NPP may be given regular doses of tick-preventatives than dogs in CC. This could explain the lower proportion of A. phagocytophilum in the dog population, but it does not seem to differentially alter the distribution of B. canis between the two populations.
Differing conditions surrounding both these study sites may also help to explain the differences detected between the sample locations. Compared to CC, the NPP site was more highly contaminated by radioactive isotopes, heavy metals and other toxic compounds[65]. Ionizing radiation exposure can diminish microbiome diversity as well as influence pathogen and vector dynamics, which could have contributed to a higher tick pathogen load (reviewed in [28, 66]). Ionizing radiation exposure, along with the other hazardous contaminants present, may impact the immune and inflammatory responses, which, in turn, could influence host response to ticks and tick-borne pathogens [67, 68]. Our earlier published studies suggest directional selection between the dog populations at the NPP and CC sites in genomic regions containing immune regulatory genes. If validated, this observation may reflect the animals’ response to the harsh environmental stressors, and their selective responses could therefore influence susceptibility to infection or persistence of tick-borne pathogens.
Conclusions
In this study we found high levels of pathogens in feeding ticks collected from the NPP and CC, the latter some 16 km away. A significantly higher prevalence of A. phagocytophilum and B. burgdorferi were found in ticks at the NPP compared to the CC site, which may reflect very different levels of environmental contamination. In contrast to the ticks themselves, the dogs sampled at the NPP had lower levels of A. phagocytophilum compared to those from CC, suggesting that the higher level of toxic materials at the NPP may have affected the dogs’ response to these pathogens or their vectors. Vector-borne pathogens pose serious health risks to both susceptible animal species and the human populations that come into contact with these animals and vectors. Continued study of the tick-borne pathogens and other micro-organisms and parasites, as well as the ways in which animal populations may have adapted to different pathogen prevalence, will add to our understanding of the effects of chronic exposure to radiation and/or toxic chemical exposures and better prepare medical and public health professionals for future environmental disasters.
Supplementary Information
Additional file 1.
Table S1. Assay results for each tick, including results for both the 'body' compared to the respective 'head' forpositive 'body' samples. |
Title: A deeper look at long-term effects of COVID-19 on myocardial function in survivors with no prior heart diseases: a GRADE approach systematic review and meta-analysis | Body:
1
Introduction
Since the beginning of December 2019, the COVID-19 pandemic, caused by a novel virus known as SARS-CoV-2 that originated in Wuhan, China, has presented a major challenge to global health and healthcare systems. Although COVID-19 is predominantly associated with lung-related symptoms and distinct functional and morphological changes, it has become evident that the infection can also lead to a multi-systemic disease affecting various organs including heart (1–3).
There is growing evidence of COVID-19's harmful effects on the heart, including acute events like heart attacks and long-term consequences even after recovery. The precise mechanisms underlying the cardiac damage caused by COVID-19 remain incompletely understood. COVID-19 has been associated with various patterns of cardiovascular dysfunction, including myocarditis, ischemic heart disease (e.g., myocardial infarction), hypovolemia, RV dysfunction resulting from pulmonary embolism, and, in some cases, cardiovascular dysfunction due to superimposed bacterial or fungal sepsis (4). The pathological findings suggest that SARS-CoV-2 can trigger hyper myocardial inflammation by infecting cardiomyocytes, leading to myocyte necrosis. This, in turn, may contribute to an increased risk of acute myocardial infarction, heart failure, arrhythmia, cardiac arrest, and acute coronary syndrome. Furthermore, apart from the potential harm caused by the illness itself, certain medications used in the treatment of COVID-19 and drug interactions may also have specific side effects on the heart (5).
Despite the advancements in treatments for COVID-19, it is anticipated that long-term consequences of the disease, particularly those affecting the heart, will persist in survivors. Therefore, the investigation of myocardial dysfunction following recovery from COVID-19 plays a vital role in the development of post-discharge monitoring programs and the formulation of public health, economic, and social policies (6). Cardiac imaging studies can serve as valuable predictive tools and aid in the comprehension of the underlying mechanisms of cardiac involvement. Echocardiography has been recognized as an available, non-invasive, and informative diagnostic tool, to identify cardiac manifestation (7). Echocardiographic findings in individuals with COVID-19 may exhibit variability, ranging from specific regional wall motion abnormalities of the LV or RV to varying degrees of global cardiac dysfunction associated with myocarditis or a systemic dysregulated inflammatory response to viral infection. Echocardiography is therefore essential in distinguishing these patterns, guiding treatment strategies, and monitoring the clinical progression over time (8).
Existing research has extensively focused on the impact of acute COVID-19 on cardiac function and complications, with numerous reviews and studies providing valuable insights into this area (9). However, there is a lack of information in the research on the lasting impacts of COVID-19, known as long covid, on heart function. Previous reviews have tried to examine this connection, but they have been restricted in their coverage and have not carried out thorough meta-analyses. Furthermore, these reviews have not taken into consideration possible influencing factors like the severity of the initial COVID-19 infection, the time elapsed since the infection, and the presence of other medical conditions. Hence, it is essential to conduct more in-depth and thorough research that specifically looks into the lasting effects of COVID-19 on the heart, considering important factors. To tackle this issue, we carried out a systematic review and meta-analysis that focused on echocardiographic imaging to study the long-term impact of SARS-CoV-2 infection on heart function and the risk of future cardiac complications.
2
Materials and methods
This systematic review and meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (10). The PRISMA checklist is provided as supplement (Supplementary S1 document). The protocol for this work was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (identifier: CRD42024481337). This review also followed the published protocol for evaluating risk factors and prognostic implications of imaging left ventricular diastolic dysfunction in individuals diagnosed with COVID-19, adhering to a systematic approach (8).
2.1
Eligibility criteria
To be considered for inclusion, published studies had to meet the following criteria: (1) studies employed valid research designs with clearly defined methodology, (2) studies assessed cardiac function using Echocardiography in COVID-19 patient after recovery, (3) studies identified COVID-19 infection according to the World Health Organization interim guidance, (4) studies reported at least one echocardiographic parameter measuring myocardial function and/or structure, (5) studies excluded cases with pre-existing cardiac disease including ischemic heart disease, valvular disease, arrhythmias-conduction disorders, heart failure, cardiomyopathies, pericarditis, pericardial effusion, pulmonary hypertension, pulmonary embolism, sarcoidosis, amyloidosis, active cancer, recent pregnancy, postpartum. The overall exclusion criteria were as follows: (1) studies involved cases during acute stage of COVID-19, (2) studies evaluated cardiac function using any other imaging technique other than echocardiography, (3) studies reported as abstracts, case reports, case series, reviews, or practice guidelines.
2.2
Information sources
A thorough search was conducted in the PubMed, Scopus, Web of Science, and Cochrane databases to locate relevant studies published until March 2024. Additionally, a manual search of the reference lists of the identified articles was carried out.
2.3
Search strategy
The search strategy of Scopus was conducted as follows: (TITLE-ABS-KEY (((“left ventric*” OR “right ventric*” OR “left cardiac*” OR “right heart” OR “right cardiac” OR “left heart” OR atri* OR myocardi* OR diastol* OR systol*) PRE/1 (dysfunction OR function OR remodeling OR impair* OR hypertroph* OR active* OR volume OR mass* OR dimension* OR diameter OR thickness OR index* OR “ejection time” OR “ejection fraction”)) OR echo OR echocardiograph*)) AND [TITLE-ABS-KEY (“covid-19” OR “sars cov 2”)]. The search strategy employed for PubMed, Web of Science, and the Cochrane Library was similar to that used for Scopus and its table is provided as supplement (Supplementary S2 document). Furthermore, three reviewers independently reviewed the reference lists of systematic reviews and selected studies to ensure that all pertinent articles were included in the analysis.
2.4
Study selection
Three reviewers independently assessed each title and abstract, and if the articles fulfilled the inclusion criteria, the full text was reviewed. The eligibility of the selected articles was then assessed by the same three reviewers through an evaluation of their full texts. Any discrepancies were resolved through discussion with a fourth reviewer. The study selection process was summarized using the PRISMA flow diagram.
2.5
Data extraction and data items
Following the extraction of data, the information was gathered through Microsoft Excel spreadsheets. The subsequent dataset comprises: studies' basic characteristics (study design, year of publication, country, and first author), participant characteristics (age, body mass index, number of cases and control groups), echocardiographic indices and major findings of each study. Potential confounding factors were carefully considered to ensure the robustness of the study findings. These factors included severity of COVID-19 infection, persistent post-COVID symptoms, duration from COVID acute phase to echocardiography examination in recovery phase, presence of comorbid disease. Data related to these factors were extracted from the studies to address their potential influence on the findings.
2.6
Risk of bias assessment
ROBINS-I was employed to evaluate the methodological quality and risk of bias for non-randomized control trials. This tool encompasses the assessment of seven potential sources of bias, including confounding bias, bias in participant selection, bias in intervention classification, bias due to deviations from intended interventions, bias resulting from missing data, bias in outcome measurement, and bias in the selection of reported results (11). Importantly, no studies were excluded based on the assessment of bias risk.
2.7
Outcome quality assessment
The certainty of overall evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method (12). The assessment of evidence certainty for individual outcomes relied on five distinct criteria: (1) limitations of the study design; (2) consistency of results; (3) directness; (4) precision and (5) potential for publication bias. A decrement of one level in certainty was implemented for each unfulfilled criterion.
2.8
Synthesis methods
The mean differences (MD) pooled the data, with 95% confidence intervals (CIs). The I2 statistic was used to analyze the interstudy statistical heterogeneity. To calculate the pooled effect, either fixed-effects or random-effects model was used according to the heterogeneity, study design and sample size. I2 values of 25%, 50%, and 75% were considered to represent low, moderate, and high levels of heterogeneity, respectively. Subgroup meta-analyses were conducted to uncover the underlying heterogeneity. A univariate meta-regression model was used to explore the impact of age and BMI as potential moderators. A sensitivity analysis was carried out in cases where the decision-making values had arbitrary or unclear ranges. Publication bias was assessed by visually inspecting funnel plots of MD vs. standard error. When at least 10 studies were available for analysis, Begg's tests and Egger's tests were employed to evaluate the potential publication bias. If there was an obvious publication bias, a trim-and-fill analysis was used to determine the underlying origin of the publication bias. All analyses were conducted using Comprehensive Meta-Analysis Version 3. P-value < 0.05 was considered significant in all tests.
2.1
Eligibility criteria
To be considered for inclusion, published studies had to meet the following criteria: (1) studies employed valid research designs with clearly defined methodology, (2) studies assessed cardiac function using Echocardiography in COVID-19 patient after recovery, (3) studies identified COVID-19 infection according to the World Health Organization interim guidance, (4) studies reported at least one echocardiographic parameter measuring myocardial function and/or structure, (5) studies excluded cases with pre-existing cardiac disease including ischemic heart disease, valvular disease, arrhythmias-conduction disorders, heart failure, cardiomyopathies, pericarditis, pericardial effusion, pulmonary hypertension, pulmonary embolism, sarcoidosis, amyloidosis, active cancer, recent pregnancy, postpartum. The overall exclusion criteria were as follows: (1) studies involved cases during acute stage of COVID-19, (2) studies evaluated cardiac function using any other imaging technique other than echocardiography, (3) studies reported as abstracts, case reports, case series, reviews, or practice guidelines.
2.2
Information sources
A thorough search was conducted in the PubMed, Scopus, Web of Science, and Cochrane databases to locate relevant studies published until March 2024. Additionally, a manual search of the reference lists of the identified articles was carried out.
2.3
Search strategy
The search strategy of Scopus was conducted as follows: (TITLE-ABS-KEY (((“left ventric*” OR “right ventric*” OR “left cardiac*” OR “right heart” OR “right cardiac” OR “left heart” OR atri* OR myocardi* OR diastol* OR systol*) PRE/1 (dysfunction OR function OR remodeling OR impair* OR hypertroph* OR active* OR volume OR mass* OR dimension* OR diameter OR thickness OR index* OR “ejection time” OR “ejection fraction”)) OR echo OR echocardiograph*)) AND [TITLE-ABS-KEY (“covid-19” OR “sars cov 2”)]. The search strategy employed for PubMed, Web of Science, and the Cochrane Library was similar to that used for Scopus and its table is provided as supplement (Supplementary S2 document). Furthermore, three reviewers independently reviewed the reference lists of systematic reviews and selected studies to ensure that all pertinent articles were included in the analysis.
2.4
Study selection
Three reviewers independently assessed each title and abstract, and if the articles fulfilled the inclusion criteria, the full text was reviewed. The eligibility of the selected articles was then assessed by the same three reviewers through an evaluation of their full texts. Any discrepancies were resolved through discussion with a fourth reviewer. The study selection process was summarized using the PRISMA flow diagram.
2.5
Data extraction and data items
Following the extraction of data, the information was gathered through Microsoft Excel spreadsheets. The subsequent dataset comprises: studies' basic characteristics (study design, year of publication, country, and first author), participant characteristics (age, body mass index, number of cases and control groups), echocardiographic indices and major findings of each study. Potential confounding factors were carefully considered to ensure the robustness of the study findings. These factors included severity of COVID-19 infection, persistent post-COVID symptoms, duration from COVID acute phase to echocardiography examination in recovery phase, presence of comorbid disease. Data related to these factors were extracted from the studies to address their potential influence on the findings.
2.6
Risk of bias assessment
ROBINS-I was employed to evaluate the methodological quality and risk of bias for non-randomized control trials. This tool encompasses the assessment of seven potential sources of bias, including confounding bias, bias in participant selection, bias in intervention classification, bias due to deviations from intended interventions, bias resulting from missing data, bias in outcome measurement, and bias in the selection of reported results (11). Importantly, no studies were excluded based on the assessment of bias risk.
2.7
Outcome quality assessment
The certainty of overall evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method (12). The assessment of evidence certainty for individual outcomes relied on five distinct criteria: (1) limitations of the study design; (2) consistency of results; (3) directness; (4) precision and (5) potential for publication bias. A decrement of one level in certainty was implemented for each unfulfilled criterion.
2.8
Synthesis methods
The mean differences (MD) pooled the data, with 95% confidence intervals (CIs). The I2 statistic was used to analyze the interstudy statistical heterogeneity. To calculate the pooled effect, either fixed-effects or random-effects model was used according to the heterogeneity, study design and sample size. I2 values of 25%, 50%, and 75% were considered to represent low, moderate, and high levels of heterogeneity, respectively. Subgroup meta-analyses were conducted to uncover the underlying heterogeneity. A univariate meta-regression model was used to explore the impact of age and BMI as potential moderators. A sensitivity analysis was carried out in cases where the decision-making values had arbitrary or unclear ranges. Publication bias was assessed by visually inspecting funnel plots of MD vs. standard error. When at least 10 studies were available for analysis, Begg's tests and Egger's tests were employed to evaluate the potential publication bias. If there was an obvious publication bias, a trim-and-fill analysis was used to determine the underlying origin of the publication bias. All analyses were conducted using Comprehensive Meta-Analysis Version 3. P-value < 0.05 was considered significant in all tests.
3
Results
3.1
Study selection
The study flowchart is shown in Figure 1; our search strategy revealed 2,602 studies in PubMed, 6,502 in Scopus, 2,994 in WOS and 42 in Cochrane. After removing duplications, 5,942 studies underwent title assessment. Of these, 2,321 studies were eligible for abstract review. After surveying abstracts, 107 studies were perused for full text. Finally, 66 studies were qualified to be included in this systematic review and meta-analysis, and the rest did not meet the inclusion criteria; the reasons for their exclusions are provided in the supporting information section (Supplementary S3 document).
Figure 1
Identification of studies via databases.
3.2
Characteristics of studies
Table 1 presents the key features of the sixty-six studies (13–76) included in this research. The search process resulted in the identification of 66 studies, out of which 41 were designed as cohort studies, 16 were cross-sectional studies, 8 were case-control studies, and one study (20) was a combined cross-sectional and longitudinal cohort. The majority of these studies utilized real-time PCR (rt-PCR) as the diagnostic method for COVID-19, while a few employed the IgG antibody titer for diagnosis (32, 37). Most of the studies focused on adult patients who had recovered from COVID-19, whereas 6 studies specifically examined athletes who had overcome the disease (13, 17, 19, 29, 52, 75). A total of 32 studies were conducted comparing post-COVID patients to a control group of individuals who tested negative for COVID-19. All 32 studies were included in a meta-analysis, with the exception of one study (39) where patients and controls were not matched, and two studies (42, 43) where matching status was unknown. Four studies (16, 27, 28, 40) categorized COVID-19 cases into groups based on the severity of the infection. To maintain consistency with the other included studies, we treated these studies as separate entities, each having a common healthy control group. Furthermore, two studies (30, 43) assessed COVID-19 cases based on the presence of dyspnea symptoms in patients. These studies were also divided into two distinct studies. Three studies (16, 24, 37) divided COVID-19 cases based on the time duration between diagnosis and echocardiography. Similarly, one study (17) divided COVID-19 cases into male and female athletes. Each of these studies was separated into two distinct studies as well.
Table 1
Characteristics of included studies.
First author, year, Country
Study design
Subgroup
Covid-19 patient category
Severity of Covid-19 infection
Covid-19 vaccination
Symptoms at study enrollment
Duration from acute COVID to Echo examination in recovery phase
No. Covid-19 cases/groups
Echocardiographic parameters
Comorbid disease
Main findings
Studies with post-COVID cases compared to non-COVID control group (meta-analysis)
Duration from acute COVID to echo examination in recovery phase (<1 Month)
Lakatos et al. (2021) Hungary (13)
Case-control
None
Athletes
Mild
Unknown
Asymptomatic (88.7%), loss of taste and/or smell (11.2%)
Mean 22 days (17–25)
107/107 matched
IVSD, PWD, LAVI, mitral E wave, mitral A wave, E/A, E/e’, RVD, TAPSE, LVEF, LVGLS
None (excluded from the study)
LV systolic and diastolic impairment in post-COVID athletes compared to control group. (↑E/A, ↓E/e’, ↓PWD, ↓IVSD, ↑LVEF)
Duration from acute COVID to echo examination in recovery phase (1–3 Months)
Günay et al. (2021) Turkey (14)
Prospective cohort
None
Adults
Moderate and Severe
Unknown
None
1 months
51/32 matched
LVESD, LVEDD, LAD, LVEF, IVSD, PWD, mitral E wave, mitral A wave, RV-GLS, RVD, sPAP, TAPSE, RV-MPI
None (Excluded from the study)
RV impairment in post-COVID patients compared to control group. (↑RV-MPI, ↑RV end-diastolic, end-systolic area and ↑sPAP)
Gul et al. (2022) Turkey (15)
Cross-sectional
None
Adults
Mild and Moderate
Unknown
None
58.39 ± 39.1 days (10–180)
126/98 matched
LVEF, LAD, LVEDD
HTN: 18.3% smoking: 30.2%
LV systolic impairment in post-COVID athletes compared to control group. (↓LVEF, ↑LVEDD)
Tryfou et al. (2021) Greece (16)
Prospective cohort
Tryfou et al. (2021) (1)
Hospitalized adults
Moderate and severe
Unknown
Not mentioned
At least 10 days (10–29)
67/37 matched
IVSD, PWD, LVEDD, LVESD, E/A, E/E’, LVEF, LVGLS, RVGLS
HTN, T2DM, Hyperlipidemia, smoking
LV and RV systolic impairment (↓LV-GLS, ↓RV-GLS)
Tryfou et al. (2021) (2)
Non- hospitalized adults
Mild
1.5 month
33/37 matched
LV and RV systolic impairment (↓LV-GLS, ↓RV-GLS)
Turpin et al. (2023) USA (17)
prospective, case-control
Turpin et al. (2023) (1)
Female athletes
Asymptomatic and mild
Unknown
None
1.5–3 months (51 ± 43)
24/24 matched
IVSD, PWD, LVM, LVMI, LVEDD, LVEDV, LVEF, mitral E wave, mitral A wave, E/A, E/e’
None (excluded from the study)
No significant finding
Turpin et al. (2023) (2)
Male athletes
1–2 months (31 ± 32)
37/37 matched
Kurtoğlu et al. (2022) Turkey (18)
Retrospective cohort
None
Adults
Mild
Unknown
None
At least 2 months (5 ± 2.8)
50/50 matched
LAD, LVEF, LVEDD, LVESD, IVSD, PWD, E/A, E/e’
None (Excluded from the study)
No significant finding
Schellenberg et al. (2023) Germany (19)
Prospective cohort
None
Athletes
Not mentioned
Unknown
Fever, cough, rhinorrhea, sore throat, resting dyspnea, exertional dyspnea, palpitation, chest pain, increased resting heart rate, dizziness, subjective perceived performance limitation
2 months
88/52 matched
LVM, LVEF, E/e', E/A
None (Excluded from the study)
LV systolic and diastolic impairment in post-COVID athletes compared to control group. (↑LVEF, ↓LVM, ↑E/e’, ↓E/A)
Honchar et al. (2023) Ukraine (20)
Combined cross-sectional and longitudinal cohort
None
Hospitalized adults
Moderate and severe
Unknown
Not mentioned
1.5–2 months
176/88 matched
LAD, IVSD, PWD, LVEDD, LVESD, LVMI, LVEF, LV-GLS, mitral e’ wave, mitral E wave, E/A, E/e’, RAD, RVD, TAPSE
HTN, T2DM, smoking
LV systolic and diastolic, RV diastolic impairment in post-COVID athletes compared to control group. (↑LAVI, ↑IVSD, ↑PWD, ↓LVEDD, ↓LVESD, ↑LVMI, ↓LV-GLS, ↓TAPSE)
Mahajan et al. (2021) India (21)
Prospective cohort
None
Adults
Mixed (mild, moderate, and sever)
Unknown
Palpitations, dyspnea, fatigue, cough, syncope, pedal oedema, fever
1–1.5 month
94/40 matched
LV-GLS
T2DM, HTN, Smoking
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS) RV impairment in impairment in post-COVID athletes with reduced LV-GLS compared to in post-COVID athletes with Normal LV-GLS. (↓TAPSE, ↑RVD)
Turan et al. (2021) Turkey (22)
Prospective cross-sectional
None
Adults
Asymptomatic and mild
Unknown
None
1 month (11–89 days)
70/70 matched
LVGLS, LVEF, LVEDD, LVESD, LVEDV, LVESV, IVSD, PWD, LAD, E, A, E’, A′, E/E’, RAD, RVD, TAPSE
HTN, Smoking, Asthma, Alcohol addiction
LV systolic and diastolic impairment in post-COVID patients compared to control group. (↓LV-GLS, ↑A wave)
Ardahanli et al. (2022) Turkey (23)
Cross-sectional
None
Adults
Mild and moderate
Unknown
Exertional dyspnea, chest pain, palpitation
2 months
200/182 matched
LVEDD, PWD, IVSD, LV-MPI, E mitral, A mitral, E/A, TAPSE, RV diameter
None (excluded from the study)
RV and LV systolic impairment in post-COVID patients compared to control group. (↑LV-MPI, ↓TAPSE)
Duration from acute COVID to echo examination in recovery phase (≥3 Months)
De et al. (2023) India (24)
Cross-sectional
De et al. (2023) (24)
Adults
Majority was mild
First dose
Asymptomatic: 16.5% palpitations: 3.6% exertional dyspnea: 60.4% Chest pain: 5.9%Weakness:11.9%
>3 months
232/100 matched
LVEF, LV-GLS, sPAP, E/e’
HTN: 59.1% T2DM: 34.1% Pre-existing Airway obstruction:5.6%
LV systolic and diastolic impairment in post-COVID patients compared to control group. (↓LVEF, ↓LV-GLS, ↓E/e’)
Baltodano-Arellano et al. (2021) Peru (25)
Cross-sectional
None
Adults
Mild
Unknown
None
3–6 months
33/31 matched
LVEF, LV-GLS
None (excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS)
Akkaya et al. (2021) Turkey (26)
Cross-sectional
None
Adults
Mild
Unknown
None
3 months
105/105 matched
LVEDD, LVESD, LVEF, E/e’, PWD, IVSD, LAD, RV-GLS, RV MPI, TAPSE, sPAP, RV diameter
None (excluded from the study)
RV systolic and diastolic impairment in post-COVID athletes compared to control group (↓RV-GLS, ↑ RV-MPI, ↓TAPSE, ↑sPAP, ↑RV diameter)
Akbulut et al. (2022) Turkey (27)
Prospective cohort
Akbulut et al. (2022) (1)
Adults
Moderate and Severe
Unknown
None
6 months
16/20 matched
LVEDD, LVESD, LVM, E mitral, A mitral, E/e’, PWD, IVSD, LV-GLS, RV-GLS, TAPSE, LVEF
HTN, T2DM, Smoking
No significant finding
Akbulut et al. (2022) (2)
Mild
42/20 matched
LVEDD, LVESD, LVM, E mitral, A mitral, E/e’, PWD, IVSD, LV-GLS, RV-GLS, TAPSE, LVEF
Baykiz et al. (2021) Turkey (28)
Prospective cohort
Baykiz et al. (2021) (1)
Adults
Mild
Unknown (29)
None
6 months
34/44 matched
LAD, LVEDD, LVEDV, LVEF, RAD, RAD, E/e’, E/A, TAPSE, sPAP, LAVI, LV-GLS
HTN (35%), T2DM (15%), Smoking (23.5%)
RV and LV diastolic impairment in post-COVID athletes compared to control group. (↑LAD, ↓LV-GLS, ↓TAPSE, ↑sPAP)
Baykiz et al. (2021) (2)
Adults
Moderate
None
30/44 matched
HTN (23%), T2DM (20%), Smoking (27%)
Baykiz et al. (2021) (3)
Adults
Severe
None
11/44 matched
HTN (36%), T2DM (18%), Smoking (9%)
Beaudry et al. (2022) Canada (30)
Cross-sectional
Beaudry et al. (2022) (1)
Adults
Not mentioned
Vaccinated patients excluded from the study
Dyspnea
At least 3 months (219 ± 82 days)
16/16 matched
E/e’, TAPSE
Smoking (14%)Pre-existing Airway obstruction (17%)CardiovascularComorbid except obesity and CAD (10%)
No significant finding
Beaudry et al. (2022) (2)
Not mentioned
None
16/16 matched
E/e’, TAPSE
Smoking (14%)Pre-existing Airway obstruction (7%)CardiovascularComorbid except obesity and CAD (14%)
Gherbesi et al. (2022) Italy (31)
Retrospective cohort
None
Young adults
Mild
Unknown
None
At least 3 months (15 ± 1.4 weeks)
40/40 Matched
LVEDD, LVESD, LVEF, IVSD, PWD, LAVI, LVMI, E/e’, E/A, LV-GLS, sPAP, TAPSE, RV-GLS, RVD
None (Excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS)
Gumanova et al. (2023) Russia (32)
Cross-sectional
None
Adults
Not mentioned
Unknown
None
At least 3 months
70/237 matched
LAD, LVEF, LVEDD, LVESD, LVESV, LVM, LVMI, LVEDV, PWD, IVSD, LAVI, sPAP, E/A, E/e’
None (excluded from the study)
RV and LV diastolic impairment in post-COVID athletes compared to control group. (↑LAD, ↑LVEDD, ↑LVESV, ↑LVEDV, ↑LVM, ↑LVMI ↑sPAP, ↑E/A, ↑E/e’)
Küçük et al. (2022) Turkey (33)
Cross-sectional
None
Adults
Moderate and Severe
Unknown
None
3–6 months
50/50 matched
LAD, LVEF, LVEDD, LVESD, LV-GLS, IVSD, PWD, TAPSE, sPAP, E/A, RVD, RAD
None (Excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS)
Lambadiari et al. (2021) Greece (34)
Case-control prospective
None
Adults
Mild (34.4%)Moderate (32.8%)Sever (32.8%)
Unknown
No symptoms (62.9%),Fatigue (15.71%), dyspnea (12.8%),Cough (4.3%),chest pain (4.3%)
4 months
70/70 Matched
LV-GLS, RV-GLS, TAPSE
None (excluded from the study)
LV systolic, RV systolic and diastolic impairment in post-COVID athletes compared to control group. (↓LV-GLS, ↓RV-GLS, ↓TAPSE)
Barros et al. (2023) Brazil (35)
Retrospective cohort
None
Adults
Severe
Unknown
None
11.9 ± 7.0 Months
35/26 Matched
LVEF, RV-GLS, TAPSE, RVD
HTN (62.3%)DM (19.7%)CKD (3.3%)Excluded disease: chronic lung diseases, PAH prior to COVID-19, previously known RV echo
RV systolic impairment in post-COVID athletes compared to control group (↓RV-GLS)
Tudoran et al. (2023) Romania (36)
Case-control
None
Adult women (18–55)
Majority was mild and few was moderate
Unknown
Dyspnea, persistent cough, unexplained and long-lasting fatigue, reduced effort capacity, tachycardia, chest pain, increased BP values, insomnia, vertigo, concentration difficulties, and memory impairments
3 months
54/40 matched
LVEF, LVMI, LAVI, E/A, E/e’
None (Excluded from the study)
LV systolic and diastolic impairment in post-COVID athletes compared to control group (↓LVEF, ↓LV-GLS, ↑LVMI, ↑LAVI, ↓E/A. ↑ E/e’)
Yang et al. (2022) China (37)
Case-control
Yang et al. (1)
Adults
Not mentioned
Unknown
Palpitation (10%), angina pectoris (10%), vertigo (15%)
3 months
40/40 Matched
LV-GLS, TAPSE
HTN
No significant finding
Yang et al. (2)
6 months
Yang et al. (3)
6 months
Rácz et al. (2022) Hungary (38)
Case-control
None
Adults
Mild
Unknown
Chronic fatigue, difficulty of carrying out previously undemanding physical activity, and palpitations
3 months
86/60 Matched
LAD, LAVI, LVEDD, LVESD, LVEDV, LVESV, ISVD, PWD, LVEF, LV-GLS, mitral E wave, mitral A wave, E/A, E/e’, RAD,RVD
HTN, mixed connective tissue disease
LV systolic impairment in post-COVID athletes compared to control group. (↓LVEF, ↓LV-GLS, ↑LVEDD)
Rajotiya et al. (2024) India (39)
Prospective case-control
None
Adults
Severe
Unknown
Not mentioned
21 months
23/20 Not matched
LVEF
Smoking, alcohol consumption
LV systolic impairment in post-COVID athletes compared to control group. (↓LVEF)
Ozer et al. (2021) Turkey (40)
Prospective case-control
Ozer et al. (2021) (1)
Hospitalized adults
Moderate and severe
Unknown
Not mentioned
4.5 months
36/41 Matched
LVEDV, LVESV, LVEDD, LVEF, LAVI, mitral E wave, E/e’, RVD, RAD,TAPSE, sPAP, RV-GLS
HTN, T2DM, Smoking
RV systolic and diastolic impairment in post-COVID athletes compared to control group. (↓RV-GLS, ↓TAPSE, ↑RVD, ↑sPAP)
Ozer et al. (2021) (2)
Home-recovered adults
Mild
4 months
43/41 Matched
Uziębło-Życzkowska et al. (2022) Poland (41)
Observational cohort
None
Adults
Mild
Unknown
Fever, cough, myalgia, anosmia/ageusia, chest pain, dyspnea
3.5–4 months
31/28 Matched
LVGLS, LVEF, TAPSE, E/e', E/A
HTN, T2DM, hypothyroidism, COPD, Smoking
No significant finding
Wood et al. (2022) Denmark (42)
Retrospective cohort
None
Adults
Mixed (mild, moderate, severe)
Unknown
Chest pain, dyspnea, palpitation
13–15 month
22/22 Unknown
LVESD, LVEDD, LVESV, LVEDV, LVEF, IVSD, PWD, LAD, LAVI, E/e’, E/A, LV-GLS, TAPSE
None (excluded from the study)
LV diastolic impairment in post-COVID athletes compared to control group. (↓E/A, ↑E/e’)
Cotella et al. (2022) South America (43)
Cross-sectional
Cotella et al. (2022) (1)
Adults
Mild
Unknown
None
At least 14 days but less than months
46/25 Unknown
LV-GLS
None (Excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group. (↓LV-GLS)
Cotella et al. (2022) (2)
Moderate-Severe
30/25 Unknown
Taş et al. (2023) Turkey (44)
Prospective cohort
None
Adults
Mild
Unknown
Palpitation, chest pain, fatigue, dyspnea, joint pain, cough, headache, insomnia
6 months
51/95 Matched
LVEDD, LVESD, LVEF, LVEF, LAVI, E wave, A wave, E/A, E/e’, RVD, RV-MPI, TAPSE
None (excluded from the study)
No significant finding
Hamdy et al. (2023) Egypt (45)
Cross-sectional
None
Adults
Not mentioned
Unknown
Dyspnea
Mean 3 Months (3 ± 1.7)
60/30 matched
LVEF, LVEDD, LVESD, LVEDV, LVESV, IVSD, PWD, LAD, LAVI, LVEF, E/A, E/e’
None (excluded from the study)
LV diastolic impairment in post-COVID patients compared to control group. (↑E/e’, ↑LAD, ↑LAVI)
Studies with only post-COVID cases (not included in the meta-analysis)
Duration from acute COVID to Echo examination in recovery phase (<1 Month)
Rasmusen et al. (2022) Denmark (29)
Prospective cohort
None
Young athletes
Not mentioned
None
None
2 weeks
121cases/comparison of patients based on symptoms and duration of acute phase of COVID-19
LVEDD, LVEF, LVGLS, E/e’, TAPSE
Asthma (14%), electrical cardiac disease (2%)
No significant finding.
Duration from acute COVID to echo examination in recovery phase (1–3 Months)
ZeinElabdeen et al. (2023) Egypt (46)
Prospective cohort
None
Adults
Not mentioned
Unknown
Asymptomatic, exertional dyspnea, fatigue, exercise intolerance (NYHA class ≥2)
1–3 months
63 cases/comparison of symptomatic patients and patients without any residual symptoms
LVESD, LVEDD, LVEF, LAVI, E wave, E/A, IVRT, LVGLS
None (excluded from the study)
LA strain and LA stiffness are early affected in patients with unexplained persistent dyspnea and exercise intolerance post- COVID-19, attributing to the impaired left ventricular diastolic function
ZeinELAbdeen et al. (2023) Egypt (47)
Cross-sectional
None
Adults
Mild and Moderate
1 dose (100%)2 dose (90.4%)3 dose (17.02%)
Palpitations (36.17%), dyspnea (26.6%), cough (22.34%), fatigue (27.65%), fever (3.19%), chest pain (7.44%)
1 month
94 cases/Comparison of post-COVID-19 patients with Postural orthostatic tachycardia and normal heart rate
LVEF, LVESD, LVEDD, LAD, LAVI, E/e'
None(Excluded from the study)
No significant finding
Sarıçam et al. (2021) Turkey (48)
cross-sectional
None
Adults
Not mentioned
Unknown
Palpitation, fatigue
3–8 weeks
105 cases/Comparison of asymptomatic patients with symptomatic patients
LVEF
None (Excluded from the study)
No significant finding
Tabacof et al. (2023) USA (49)
Retrospective observational cohort
None
Adults
Not mentioned (Not severe)
Unknown
Breathlessness, quality of life changes, fatigue, physical activity changes, cognitive function changes, anxiety, depression
1 month
203 cases/COVID-91 patients
LAD, RAD, LVEF, LVMI
Not mentioned
No significant finding
Samiei et al. (2023) Iran (50)
Cross-sectional
None
Adults
Mild, moderate, and severe
Unknown
Not mentioned
1.5 months
100 cases/Comparison according to the severity of symptoms defined by clinical features and lung CT
LVEF, LVGLS, E/e', RVEF, TAPSE, LAVI, LA peak strain, RA peak strain
None (Excluded from the study)
↓LV-GLS in patients with severe covid-19.Trend in reducing EF from 61% in milder groups to 55% in the severe group
Özer et al. (2021) Turkey (51)
Prospective cohort
None
Adults
Moderate and severe
Unknown
Not mentioned
1 month
74 cases/comparison of patients according to their hs-TnI levels at hospitalization
LVESDV, LVEDV, IVSD, PWD, LAD, LVEF, LVGLS
HTN (43.3%), T2DM (10.8%), smoking (8.1%)
↓LV-GLS in 1/3 patients recovered from COVID-19 infection.
Sollazzo et al. (2022) Italy (52)
Retrospective cohort
None
Athletes
Mild (98.6%), moderate (1.4%)
None (53%), First dose (39.2%), second dose (7.8%)
Chest pain (1.9%), palpitations (0.9%), shortness of breath (2.8%)
1 month
217 cases/Comparison of parameters assessed during pre-participation evaluation and return to play
RAD, LVEF, LVEDD, LVESD, IVSD, PWD, E/A
None (excluded from the study)
↓E/A ratio which commonly change according to the athletes’ training level.
Tudoran et al. (2021) (1) Romania (53)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
Persistent fatigue, shortness of breath, chest discomfort/pain, palpitations, reduced effort capacity
1–3 months
150 cases/comparison of patients with and without Significant Cardiac Abnormalities due to covid-19 infection
LVMI, LAVI, E/A, E/e', TAPSE, RVGLS, LVGLS, LVEF
None (excluded from the study)
LV systolic and diastolic dysfunction was present in a subset of patients. (↓RV-GLS, ↓LVEF, ↑LAVI, ↓E/A, ↑E/e’)
Tudoran et al. (2021) (2)Romania (54)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
fatigue, dyspnea, and palpitations
1.5–2.5 months
125 cases/Comparison of patients with diastolic dysfunction and normal cardiac function
LAVI, LVMI, LVEF, E/A, E/e', LVGLS, TAPSE, RVGLS
None (excluded from the study)
LV systolic and diastolic function were within normal limits, although we identified in 7 individuals mild LVH and another 4 patients had borderline values of RV-GLS.
Tudoran et al. (2021) (3) Romania (55)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
Not mentioned
2 months
91 cases/COVID-91 patients
RAD, RVD, TAPSE, RVGLS
None (excluded from the study)
RV dysfunction were seen even after the recovery from mild Covid-19 pulmonary infections. (↓RV-GLS, ↑sPAP)
Tudoran et al. (2021) (4)Romania (56)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Dyspnea, fatigability, palpitations, chest pain/discomfort, and reduced exercise tolerance
1 month3 months6 months
116 cases/Comparison of patients with and without pulmonary hypertension following covid-19 infection
LAVI, LVEF, RAD, RVD, TAPSE, RVGLS
None (excluded from the study)
49 cases had ↑ RVD and two borderline values of ↓TAPSE and/or ↓RV-GLS.
Tudoran et al. (2022) (1) Romania (57)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Long-lasting fatigue, reduced exercise capacity, dyspnea, chest pain/discomfort, palpitations, increased blood pressure values, dizziness, concentration issues, foggy brain, and sleep disturbances
1 month3 months6 months
203 cases/comparison of patients according to their BMI and Metabolic syndrome history
LVMI, LVGLS, LVEF, LAVI, E/A, E/e', TAPSE, RVGLS
Metabolic syndrome
Severe forms of diastolic dysfunction were diagnosed, suggesting irreversible cardiac damages, such as interstitial fibrosis.
Tudoran et al. (2022) (2) Romania (58)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Long-lasting fatigue, dyspnea, chest pain/discomfort, palpitations, and reduced exercise capacity
1 month3 months6 months
383 cases/Comparison of patients according to their type and severity of the prevailing cardiac dysfunction
LVMI, LAVI, LVEF, LVGLS, TAPSE, RVGLS, E/A, E/e', sPAP
None (excluded from the study)
LV systolic and diastolic impairment (↓LVEF, ↓LV-GLS, ↓ E/A ↑sPAP)
Tudoran et al. (2023) Romania (59)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Reduced physical exertion capacity, persisting fatigue, palpitations, elevated blood pressure levels, chest discomfort or even pain, dyspnea, dry cough, sleep distur- bances, foggy brain, and concentration issues
1 month3 months6 months
203 cases/comparison of patients with and without T2DM and MS
LVMI, LAVI, LVEF, LVGLS, TAPSE, RVGLS, E/A, E/e'
T2DM, MS
RV, LV systolic and LV diastolic impairment in patients with MS and/or T2DM compared to healthy controls. (↑LVMI, ↑LAVI, ↓LVEF, ↓LV-GLS, ↓TAPSE, ↓RV-GLS, ↓E/A, ↑E/e')
Bende et al. (2021) Romania (60)
Retrospective cohort
None
Adults
Not mentioned
Unknown
Fatigue, shortness of breath, chest discomfort, palpitations, reduced exercise capacity
2–3 months
97 cases/comparison of patients with and without pulmonary injury
LVMI, LAVI, E/A, E/e', TAPSE, LVEF
HTN (23.7%), T2DM (4.12%),
LV systolic and diastolic impairment (↓LVEF, ↑E/e’) Only 3.09% of patients had ↓LVEF, and 31.95% ↑E/e’
Erdem et al. (2022) Turkey (61)
Retrospective cohort
None
Adults
Mild, moderate, and severe
Unknown
Exertional dyspnea (52.7%), palpitations (48.3%), chest pain (31.8%), and dyspnea at rest (15.3%), back pain (59.3%)
2–3 months
91 cases/Comparison of patients according to their hospitalization status and pulmonary involvement (ICU, covid-19 ward, outpatient)
RVD, TAPSE, LVEF, LAD, LVESD, LVEDD
HTN (21.9%), T2DM (14.2%), smoking (14.2%)
RV impairment in severe post-COVID cases. (↑RVD, ↓TAPSE)
Kujur et al. (2021) India (62)
Cross-sectional
None
Adults
Mild, moderate, and severe
Unknown
Not mentioned
1–3 months
100 cases/comparison of patients according to the disease severity
LVEF
HTN (27%), T2DM (24%), obesity (27%), CKD (4%)
Myocardial dysfunction is common in covid-19 regardless of disease severity. (↓LVEF)
Vera-Pineda et al. (2023) Mexico (63)
Cross-sectional
None
Adults
Mild (63%), moderate (15%), and severe (22%)
Unknown
Not mentioned (dyspnea, cough, palpitations, or fatigue)
At least 1.5 months
100 cases/comparison of patients according to the severity of the covid-19 infection and
LAD, LAVI, LVMI, TAPSE, LVEF, LV-GLS, RV-GLS
T2DM (22%), HTN (13%), dyslipidemia (8%), smoking (23%)
RV and LV systolic impairment in 70% cases (↑LAD, ↑LVMI, ↓TAPSE, ↓LVEF, ↓ LV-GLS, ↓RV-GLS)
Osada et al. (2022) USA (64)
Prospective cohort
None
Adults
Mild
50%One/both doses of SARS-CoV-2Vaccine during the study(Three Moderna and one Pfizer)
Chest pain, chills, diarrhea, dizziness or vertigo, dry cough, dry eyes, dry mouth, fatigue, fever, headache, lack of appetite, anosmia, muscle or body aches, nasal congestion or runny nose, nausea or vomiting, shortness of breath, difficulty breathing, dyspnea, sore joints, or sore throat
1–6 months
18 cases/follow up from 1 to 6 months after COVID-19 infection
IVSD, LVM, LVMI, LVEF, LAD, E/e'
None (excluded from the study)
No significant finding
Can et al. (2024) Turkey (65)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
Not mentioned
1 month 7 months
70 cases/Comparison of changes between the 1st and 7th month's follow up
LVEF, LVEDD, LVESD, IVSD, LAD, IVRT, E/A, E/e'
None (excluded from the study)
(↓LAD, ↑IVRT)
Duration from acute COVID to echo examination in recovery phase (≥3 Months)
Yaroslavskaya et al. (2023) Russia (66)
Prospective cohort
None
Adults
Not mentioned
Unknown
Not mentioned
3 months, 12 months
156 cases/Comparison of patients with normal LVGLS and reduced LVGLS
LVGLS, LVEDV, LVESV, LVM, LVEF, IVRT, E/A, E/e', TAPSE
Not mentioned
LV systolic impairment 27.6% of patients after 1-year post-infection (↓LV-GLS)
Luchian et al. (2021) Belgium (67)
Prospective cohort
None
Adults
Moderate and sever
Unknown
Dyspnea (34.8%), Other symptoms were not mentioned.
12 months
66 cases/Comparison of patients with and without persistent dyspnea at the one-year follow-up
LVEF, E/A, E/e’, TAPSE
Dyslipidemia (19.7%)T2DM (16.7%)Obesity (25.8%)Smoking (12.1%)Cancer (3%)Chronic autoimmune disease (6.1%)
LV systolic impairment after 1-year post-infection (↓LV-GLS)
Matejova et al. (2022) Czechia (68)
Prospective observational cohort
None
Adults
Mild (74.4%)Moderate (4.7%)Severe (20.7%)
Unknown
Breathing problems, palpitations, exercise intolerance, fatigue in >50% of cases
3 months, 12 months
106 cases/COVID-19 patients
LVEF, LAD, LVEDD
HTN (20.2%)Obesity (11.5%)Bronchial asthma (5.8%)Renal insufficiency (5.8%)Depression (2.9%)Thromboembolic disease (1.9%)
No Significant finding.
Wu et al. (2021) China (69)
Prospective cohort
None
Adults
Mild (59.3%)Severe (40.7%)
Unknown
No cardiopulmonary symptoms.
6 months
27 cases/Comparison of patients with and without cardiac injury due to covid-19 infection
LAD, RAD, RVD, IVSD, LVEF, TAPSE
HTN (14.8%)T2DM (18.5%)
No Significant finding.
Stavileci et al. (2022) Turkey (70)
Retrospective cohort
None
Adults
Mild
Unknown
Cough (7.25%)Fever (19.35%)Joint Pain (30.64%)Fatigue (31.45%)Chest pain (9.67%)Dyspnea (20.96%)Taste abnormalities (29.83%)Smell abnormalities (26.1%)
6 months
248 cases/Comparison of patients with fragmented QRS (fQRS) wave and non- fragmented QRS wave
LVEF, LVESD, LVEDD, LAD, PWD, IVSD
Smoking (25%)
LVEF was statistically significant lower in the fQRS+ group compared to the non-fQRS group. Presence of fQRS was related also with wider: LVEDD, LVESD, septum thickness, and LAD
Sharma et al. (2022) India (71)
Prospective observational cohort
None
Adults
Mild, moderate, and severe
Unknown
Dyspnea (57.14%), Chest pain (19.04%), Palpitations (3.17%), Fatigue (25.39%)
6 months
63 cases/Comparison of patients with mild symptoms and moderate/severe symptoms
LVEF, E/A, E/e', TAPSE
HTN (28.57%), T2DM (14.28%)
LV dysfunction in moderate/severe group patients as compared to mild cases. No RV dysfunction. (↓LVEF, ↓E/A, ↑E/e')
Ro ´denas-Alesina et al. (2022) Spain (72)
Prospective cohort
None
Adults
Not mentioned
Unknown
Fever (89%), dyspnea (71.8%), diarrhea (24%)
4.3 months
109 cases/Comparison of patients with elevated biomarker (hs-TnI, NT-pro-BNP, D-dimer) with controls
LVEDD, LVMI, LVEF, TAPSE, LAVI, LVGLS, E/e'
Tobacco use, HTN, Dyslipidemia, T2DM, COPD, cancer,
Minimal changes were observed in LV function.
Chamtouri et al. (2022) Tunisia (77)
Prospective cohort
None
Adults
Not mentioned
Unknown
Not mentioned
3 months
111 cases/Comparison of patients according to CT scan lesions
LVEF, LVEDD, LVESD, IVSD, TAPSE, LVGLS, RVGLS
T2DM (31.8%), Hyperlipidemia (4.6%). HTN (45.8%), COPD (3.6%), smoking (7.8%)
This study showed that patients with severe CT scan pulmonary lesions were more likely to develop sub-clinical myocardial damage at mid-term follow-up. (↓LV-GLS, ↓RV-GLS)
Chudzik et al. (2022) Poland (73)
Prospective cohort
None
Adults
Mild, moderate, and severe
Unknown
Weakness (73%), impaired exercise tolerance (65.88%), palpitations (54%), memory and concentration disturbances (53.75%), chest pain (44%), Headache (34.39%), Dyspnea (32.02%), Excessive sweating (29.25%), Hair loss (28.06%), Muscle pain (24.6%), Anosmia and ageusia (24.12%), Cough (23.23%), Raynaud syndrome (14.29%), Ascites (swelling) (11.51%), Skin lesions (10.67%), Conjunctivitis (8.3%), Varicose veins of lower extremities (6.35%), Neurological disturbances (5.88%), Syncope (3.57%), Arthralgia (1.55%)
3 months
488 cases/Comparison of recovered patients with long covid-19 and no long covid-19
LAD, RVD, TAPSE, LVM, LVESD, LVEDD, IVSD
None (Excluded from the study)
Not significant finding
Flores et al. (2023) Braga (77)
Prospective cohort
None
Adults
Mild, moderate, and severe
Unknown
Not mentioned
6 months
88 cases/Comparison of patients admitted and not admitted to ICUs
LAD, LVEF, TAPSE
Not mentioned
Not significant finding
Kattamuri et al. (2023) India (74)
Prospective cohort
None
Adults
Mild and severe
Unknown
Not mentioned
3–6 months 6–12 months
53 cases/Comparison of patients with mild and severe covid-19
LVEF, E/A, E/e'
HTN (30%), T2DM (34%), Thyroid disease (3.7%)
No significant finding.
Hamburger et al. (2023) USA (75)
Prospective cohort
None
Athletes
Mild
Unknown
None
21 months
82 cases/Comparison of athletes pre- and post-training
LVEF, LAVI, LVEDD, LVESD, PWD, IVSD, LVMI, TAPSE, E/A, E/e'
None (Excluded from the study)
↑LVESD, ↑LAVI, ↑LVEDD
D’Ávila et al. (2023) Brazil (76)
Retrospective cohort
None
Adults
Moderate and severe
Unknown
Fatigue (71.4%), muscle pain (21.4%), Peripheral muscle weakness (19.6%), Dyspnea (17.9%)
7.9 months
56 cases/comparison of patients according to covid-19 severity
LVEDV, LVESV, LVEF, LVMI, TAPSE, LV-GLS, E/e'
HTN (63%), T2DM (20%), obesity (57%)
Despite having a similar EF and GLS, patients with a history of the critical manifestation in the acute phase of the disease had subclinical LV dysfunction according to other parameters. (↑global wasted work, ↓global work efficiency)
LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; PWD, posterior wall diameter; IVSD, interventricular septum diameter; LVM, left ventricular mass; LVMI, left ventricular mass index; LV-GLS, left ventricular global longitudinal strain; LAD, left atrium diameter; LAVI, left atrium volume index; LV-MPI, left ventricular myocardial performance index; E/A, the ratio of peak velocity blood flow from left ventricular relaxation in early diastole (the E wave) to peak velocity flow in late diastole caused by atrial contraction (the A wave); E/e’, ratio of E wave to early diastolic mitral annular velocity (e’); RVD, right ventricular diameter; RAD, right atrium diameter; RV-GLS, right ventricular global longitudinal strain; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; RV-MPI, right ventricular myocardial performance index; MS, metabolic syndrome; T2DM, type 2 diabetes mellitus.
The severity of COVID-19 infection was not addressed in 12 studies (19, 29, 30, 32, 37, 45, 46, 48, 60, 66, 72, 77). Regarding the COVID-19 vaccination, only six studies (24, 29, 30, 46, 52, 64) provided information on the vaccination status of patients. It is noteworthy that data collection in most of the studies was conducted before the availability of any vaccines. Thirteen studies lacked information on post-COVID symptoms at the time of study enrollment (16, 20, 39, 40, 50, 51, 55, 62, 65, 66, 74, 77, 78). In most studies, the time interval between the acute phase of COVID-19 and echocardiography during the recovery phase was over 1 month, except for 3 studies (13, 16, 29) that were conducted within at least 10 days. Thirty studies reported the exclusion of patients with comorbid disease. On the other hand, three studies did not provide any information regarding the comorbid diseases (49, 66, 78). The primary focus of the studies pertained to the evaluation of LV function, with a secondary emphasis on RV function. A subset of studies also conducted concurrent assessments of both LV and RV function. Majority of the studies found significant changes in echocardiographic parameters, indicating subclinical alterations in the function of the LV and/or RV in post-COVID patients. However, 17 studies (17, 18, 27, 29, 30, 37, 41, 44, 46, 47, 49, 64, 68, 69, 73, 74, 78) did not report any significant findings.
3.3
Studies' risk of bias
Figure 2 depicts a summary of the RoB-1 assessment. The overall risk of bias was found to be low to moderate. A low percentage (<15%) of serious risk of bias was identified in various domains, including confounding, selection of participants, classification of interventions, deviations from intended interventions, and missing data bias. Moderate risk of bias (25%–50%) was noted in confounding and deviations from intended interventions. There was no significant bias detected in the selection of reported results. Among 32 studies enrolled in the meta-analysis, five were found to have a serious risk of confounding bias (19, 30, 32, 37, 45). These studies did not provide information on the severity of COVID-19 infection in the patients. Additionally, 12 studies were rated as having a moderate risk of bias due to the presence of comorbid diseases that could impact heart function (15, 16, 20–22, 24, 27, 28, 35, 38, 40, 41). Three studies found a serious risk of bias in participant selection due to an unmatched case-control group (39, 42, 43), while five studies indicated a moderate risk due to the inclusion of specific populations such as athletes, women, and young adults that may not accurately represent the general population (13, 17, 19, 31, 36). Concerning bias due to classification of interventions, one study (21) found a serious risk of bias in comparing echo findings between two groups with reduced and normal-LVGLS, while two (24, 43) were deemed to have moderate risk due to incorrectly classifying post-COVID patients and comparing echo measures between them instead of with controls. Three studies demonstrated a moderate risk of bias due to deviations from intended interventions (30, 34, 39). Their main focus was on evaluating cardiopulmonary function rather than cardiac alone. Seven studies were found to have a moderate risk of bias due to missing data, and they reported small amounts of echocardiographic indices (15, 25, 30, 34, 37, 39, 43). Regarding bias in outcome measurement, 10 studies (14, 19, 22, 24, 26, 27, 33, 36, 42, 45) found a moderate risk of bias in reporting certain echo indices that deviated from the ranges reported in other studies (detailed in Table 2). Thirty-four studies only had post-COVID cases. Regarding the confounding factors, one study had a serious risk of bias as it did not provide information about the comorbid diseases of the patients (66). Twenty studies had moderate risks due to the presence of comorbid diseases (29, 51, 57, 59, 61–63, 67–69, 71, 72, 74, 76, 77) and lack of information on the severity of COVID-19 infection (29, 46, 48, 49, 72, 77). Considering the risk of bias in participant selection, 6 studies had moderate risks for reasons of inclusion of specific populations such as athletes (29, 52, 75) and having no classification and comparison among patients (49, 55, 68). Regarding bias due to deviations from intended interventions, one study had a serious risk of bias as its main focus was on hepatic abnormalities rather than cardiac alone (60). Twenty-one studies revealed moderate risk due to laboratory and biomarker evaluations, electrocardiogram evaluations, various surveys and lifestyle changes, return to play evaluation of athletes, chest computer tomography, post-COVID-19 functional status scale, cardio-ankle vascular index, ankle-brachial index, myocardial work analysis, walk test, pulmonary function tests, and cardiopulmonary exercise tests (48, 49, 51–59, 65, 67–71, 74–77). Bias due to missing data was serious in 3 studies as they reported small amounts of echocardiographic indices (48, 62, 74). Figure 3 represents the traffic light plot of risk of bias assessment for each included study.
Figure 2
Overall risk of bias.
Table 2
GRADE approach.
Outcome quality assessment
Outcome
No. studies/methodology
Subgroups
Risk of bias
Limitation
Inconsistency
Indirectness
Imprecision
Other consideration
Mean difference (95% CI)
Certainty
LVESD
Overall
156 prospective cohort4 retrospective cohort5 cross-sectional
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.325 [−0.119, 0.352]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Moderated
Highe
Low
Moderatef
None
−0.397 [−1.353, 0.560]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Low
Low
Low
Reporting biasc
0.928 [0.566, 1.289]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Low
Low
Moderateg
None
0.908 [0.488, 1.32]
ꚚꚚꚚꚚHighꚚꚚꚚOModerate
Moderate-Severe
Low
Moderate4
Highe
Low
Moderatef
Reporting biasc
−0.272 [−1.42, 0.877]
Grouped by comorbid disease
Present
Low
Low
Low
Low
Moderateg
None
−1.292 [−2.089, −0.495]
HighꚚꚚꚚO
Absent
Low
Low
Low
Low
Low
Reporting biasc
0.905 [0.567, 1.24]
HighꚚꚚꚚO
LVESV
Overall
62 cross-sectional3 prospective cohort1 case control
–
Low
Low
Low
Low
Moderatef
None
0.608 [−1.24, 2.45]
HighꚚꚚꚚO
Grouped by duration
≥3 months
Low
Moderated
Moderatea
Low
Moderatef
None
1.69 [−1.95, 5.33]
ꚚꚚꚚO Moderate
Grouped by COVID-19 severity
Mild
Highh
Highh
Moderatea
Low
Moderatef
None
2.23 [−1.43, 5.88]
ꚚꚚOO Low
Grouped by comorbid disease
Present
Highh
Highh
Low
Low
Moderatef
None
5.55 [1.15, 9.96]
ꚚꚚOO Low
Absent
Low
Moderated
Low
Low
Moderatef
None
−0.451 [−2.48, 1.58]
ꚚꚚꚚO Moderate
LVEDD
Overall
2610 prospective cohort3 retrospective cohort1 Observational cohort9 Cross-sectional4 case control
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.440 [−0.092, 0.155]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Moderated
Low
Low
Moderatef
None
−0.232 [−1.29, 0.835]
ꚚꚚOOLow
1–3 months
Low
Low
Highe
Low
Moderateb
None
0.542 [−0.524, 1.608]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.516 [−0.315, 1.346]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.580 [−0.199, 1.35]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Low
Highe
Low
Moderatef
None
0.620 [−0.421, 1.662]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderatec
Reporting biasc
0.605 [−0.324, 1.533]
ꚚꚚꚚOModerate
Absent
Low
Low
Moderatea
Low
Moderatec
Reporting biasc
0.325 [−0.484, 1.133]
ꚚꚚꚚOModerate
LVEDV
Overall
114 Prospective Cohort1 Retrospective Cohort2 Cross-Sectional4 Case-Control
Highi
Low
Moderatea
Low
Moderatee
None
4.79 [−0.341, 9.93]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Moderated
Low
Low
Moderateg
None
6.87 [0.605, 13.13]
ꚚꚚꚚO Moderate
≥3 months
Lowj
Low
Moderatea
Low
Moderatef
Reporting biasc
3.88 [−2.35, 10.11]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Moderated
Low
Low
Moderateg
None
8.39 [3.57, 13.20]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
Reporting biasd
10.09 [2.29, 17.89]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Present
Low
Moderated
Moderatea
Low
Moderateg
Reporting biask
10.35 [4.93, 15.76]
ꚚꚚꚚOModerate
Absent
Lowj
Low
Low
Low
Moderatef
None
0.602 [−4.87, 6.07]
ꚚꚚꚚOModerate
IVSD
Overall
196 ProspectiveCohort3 Retrospective Cohort7 Cross-Sectional4 Case-Control
Lowl
Low
Highe
Low
Moderateb
None
−0.203 [−0.526, 0.119]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
−0.108 [−1.16, 0.944]
ꚚOOOVery Low
1–3 months
Low
Low
Highe
Low
Moderateb
None
−0.108 [−1.16, 0.944]
ꚚꚚꚚOModerate
≥3 months
Lowl
Low
Low
Low
Moderatef
Reporting biasd
−0.256 [−0.796, 0.284]
ꚚꚚOOLow
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderateb
None
−0.411 [−0.830, 0.007]
ꚚꚚꚚOModerate
Moderate-Severe
Highm
Moderated
Highe
Low
Moderatef
Reporting biasd
0.250 [−0.399, 0.899]
ꚚꚚOOLow
Grouped by comorbid disease
Present
Highm
Moderated
Highe
Low
Moderatef
None
0.098 [−0.520, 0.715]
ꚚꚚꚚOModerate
Absent
Highm
Low
Highe
Low
Moderatef
None
−0.315 [−0.691, 0.061]
ꚚꚚOOLow
PWD
Overall
196 ProspectiveCohort3 Retrospective Cohort7 Cross-Sectional3 Case-Control
Low
Low
Highe
Low
Moderateb
None
0.086 [−0.139, 0.311]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
−0.127 [−0.762, 0.497]
ꚚꚚOOLow
1–3 months
Low
Low
Low
Low
Moderatef
None
0.273 [−0.118, 0.663]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highf
Low
Moderatef
None
−0.102 [−0.211, 0.007]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Moderateb
None
−0.149 [−0.405, 0.106]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Moderatea
Low
Moderateg
None
0.614 [0.259, 0.969]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderatef
None
0.311 [−0.072, 0.695]
ꚚꚚꚚOModerate
Absent
Low
Low
Moderatea
Low
Moderateb
None
−0.006 [−0.248, 0.237]
ꚚꚚꚚOModerate
LVM
Overall
74 ProspectiveCohort2 Cross-Sectional1 Case-Control
Lown
Low
Moderatea
Low
Moderatef
None
−7.630 [−21.7, 6.50]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Lown
Moderated
Highe
Low
Moderatef
None
−3.59 [−19.7, 12.53]
ꚚꚚꚚOModerate
≥3 months
Lown
Moderated
Low
Low
Moderateg
None
−19.37 [−29.8, −8.92]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Lown
Moderated
Low
Low
Moderateg
None
−13.71 [−25.30, −2.11]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
None
9.018 [0.458, 17.57]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Moderated
Low
Low
Moderatef
None
7.54 [−0.720, 15.81]
ꚚꚚꚚOModerate
Absent
Lown
Moderated
Low
Low
Moderateg
None
−18.28 [−26.72, −9.85]
ꚚꚚꚚOModerate
LVMI
Overall
71 Retrospective Cohort1 ProspectiveCohort2 Cross-Sectional2 Case-Control1 Observational Cohort
Lowo
Low
Highe
Low
Moderateb
None
−1.65 [−6.62, 3.31]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Lowo
Moderated
Low
Low
Moderatef
None
−0.251 [−1.95, 1.45]
ꚚꚚꚚOModerateꚚꚚꚚOModerate
≥3 months
Low
Moderated
Highe
Low
Moderatef
None
−1.023 [−9.48, 7.44]
Grouped by COVID-19 Severity
Mild
Lowo
Moderated
Highe
Low
Moderatef
None
2.408 [−1.11, 5.93]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Absent
Lowo
Low
Highe
Low
Moderatef
None
−2.29 [−9.69, 5.11]
ꚚꚚꚚOModerate
LVEF
Overall
3211 Prospective Cohort4 Retrospective cohort10 Cross-Sectional7 Case-Control1 Observational Cohort
Highp
Low
Moderatea
Low
Low
Reporting biasc
−0.829 [−1.397, −0.262]
ꚚꚚOOLow
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
0.667 [−1.42, 2.76]
ꚚꚚOOLow
1–3 months
Lowq
Low
Highe
Low
Moderateb
None
−0.615 [−1.75, 0.527]
ꚚꚚꚚOModerate
≥3 months
Lowq
Low
Moderatea
Low
Low
Reporting biasc
−1.16 [ −1.94, −0.375]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Highp
Low
Moderatea
Low
Low
None
−0.886 [−1.64, −0.128]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Low
Moderatea
Low
Moderatef
Reporting biasc
−0.900 [−1.96, 0.169]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Low
Reporting biasc
−0.852 [−1.66, −0.038]
ꚚꚚꚚꚚHigh
Absent
Highp
Low
Highe
Low
Low
None
−0.833 [−1.64, −0.005]
ꚚꚚOOLow
LV-GLS
Overall
2611 Prospective Cohort 2 Retrospective cohort6 Cross-Sectional6 Case-Control1 Observational Cohort
Lowr
Low
Highe
Low
Low
None
1.21 [0.681, 1.75]
ꚚꚚꚚꚚHigh
Grouped by duration
<1 month
Low
Moderated
Highe
Low
Moderatef
None
0.615 [−0.776, 2.00]
ꚚꚚꚚOModerate
1–3 months
Low
Low
Moderatea
Low
Low
None
1.22 [0.181, 2.27]
ꚚꚚꚚꚚHigh
≥3 months
Lowr
Low
Highe
Low
Low
None
1.37 [0.637, 2.11]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Lowr
Low
Highe
Low
Low
None
0.815 [0.047, 1.58]
ꚚꚚꚚꚚHigh
Moderate-Severe
Lowr
Low
Highe
Low
Moderateg
None
1.84 [0.751, 2.94]
ꚚꚚꚚꚚHigh
Grouped by comorbid disease
Present
Lowr
Low
Highe
Low
Low
None
0.919 [0.07, 1.76]
ꚚꚚꚚꚚHigh
Absent
Low
Low
Highe
Low
Low
None
1.43 [0.703, 2.16]
ꚚꚚꚚꚚHigh
E/A ratio
Overall
217 ProspectiveCohort3 Retrospective cohort6 Cross-Sectional4 Case-Control1 Observational Cohort
Highs
Low
Highe
Low
Moderateb
None
−0.058 [−0.118, 0.002]
ꚚꚚOOLow
Grouped by duration
1–3 months
Low
Low
Low
Low
Low
None
−0.084 [−0.129, −0.039]
ꚚꚚꚚꚚHigh
≥3 months
Highs
Low
Highe
Low
Moderateb
None
−0.043 [−0.122, 0.035]
ꚚꚚOOLow
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Low
None
−0.042 [−0.076, −0.007]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Highe
Low
Moderateg
None
−0.134 [−0.258, −0.010]
ꚚꚚꚚꚚHigh
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Moderateg
None
−0.137 [−0.424, −0.032]
ꚚꚚOOLow
Absent
Lowt
Low
Highe
Low
Moderateb
None
−0.021 [−0.093, 0.050]
ꚚꚚꚚꚚHigh
E/e’ ratio
Overall
279 ProspectiveCohort2 Retrospective cohort9 Cross-Sectional6 Case-Control1 Observational Cohort
Lowu
Low
Highe
Low
Moderateb
None
0.116 [−0.275, 0.507]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
−0.412 [−1.79, 0.968]
ꚚꚚOOLow
1–3 months
Low
Low
Moderatea
Low
Moderateb
None
−0.010 [−0.753, 0.732]
ꚚꚚꚚꚚHigh
≥3 months
Lowu
Low
Moderatea
Low
Moderateb
Reporting biasc
0.315 [−0.213, 0.843]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderateb
None
0.105 [−0.410, 0.620]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Low
Low
Low
Moderatef
Reporting biasc
0.380 [−0.009, 0.768]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Low
Low
Moderateb
Reporting biasc
0.164 [−0.052, 0.381]
ꚚꚚꚚꚚHigh
Absent
Lowu
Low
Highe
Low
Moderateb
None
0.129 [−0.470, 0.729]
ꚚꚚꚚꚚHigh
E wave
Overall
134 Prospective Cohort4 Cross-Sectional5 Case-Control
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
−0.013 [−0.029, 0.002]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Low
Moderatea
Low
Moderateb
None
−0.021 [−0.052, 0.009]
ꚚꚚꚚꚚHigh
≥3 months
Low
Moderated
Low
Low
Moderatef
Reporting biasc
0.009 [−0.025, 0.043]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Low
Low
Moderateb
None
0.001 [−0.017, 0.020]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
Reporting biasc
−0.052 [−0.082, −0.022]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Moderatef
Reporting biasc
−0.013 [−0.051, 0.026]
ꚚꚚꚚOModerate
Absent
Low
Low
Low
Low
Moderateb
None
−0.008 [−0.027, 0.011]
ꚚꚚꚚꚚHigh
A wave
Overall
104 ProspectiveCohort3 Cross-Sectional3 Case-control
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.026 [ −0.018, 0.070]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Low
Low
Low
Moderateg
None
0.039 [0.017, 0.060]
ꚚꚚꚚꚚHigh
≥3 months
Low
Moderated
Low
Low
Moderatef
None
0.034 [−0.034, 0.102]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderateb
None
−0.005 [−0.061, 0.050]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Low
Low
Moderatef
None
0.086 [0.022, 0.150]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Moderated
Low
Low
Moderatef
None
0.035 [−0.036, 0.105]
ꚚꚚꚚOModerate
Absent
Low
Low
Highe
Low
Moderateb
None
0.014 [−0.051, 0.080]
ꚚꚚꚚꚚ High
LAD
Overall
124 ProspectiveCohort1 Retrospective cohort3 Cross-Sectional4 Case-Control
Low
Low
Highe
Low
Low
Reporting biasc
1.603 [0.696, 2.511]
ꚚꚚꚚꚚHigh
Grouped by duration
1–3 months
Low
Moderated
Moderatea
Low
Moderatef
None
1.127 [−0.571, 2.826]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highe
Low
Moderateg
Reporting biasc
1.863 [0.694, 3.032]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Moderated
Moderatea
Low
Moderatef
Reporting biasc
0.937 [−0.183, 2.057]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Moderatea
Low
Moderateg
Reporting biasc
2.305 [1.058, 3.74]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Moderated
Highe
Low
Moderateg
None
2.287 [0.910, 3.664]
ꚚꚚꚚOModerate
Absent
Low
Moderated
Highe
Low
Moderateg
Reporting biasc
1.064 [−0.167, 2.295]
ꚚꚚꚚOModerate
LAVI
Overall
154 ProspectiveCohort2 Retrospective cohort4 Cross-Sectional4 Case-Control1 Observational Cohort
Lowc
Low
Highe
Low
Moderateb
Reporting biasc
0.895 [−0.509, 2.29]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Highh
Highh
Low
Low
Moderateg
None
1.95 [0.728, 3.17]
ꚚꚚOO Low
≥3 months
Lowt
Low
Highe
Low
Moderatef
Reporting biasc
0.941 [−0.712, 2.59]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Moderate1
Low
Moderateg
None
0.922 [0.139, 1.845]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
Reporting biasc
1.475 [0.374, 2.575]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Low
Low
Moderateg
Reporting biasc
1.135 [0.290, 1.980]
ꚚꚚꚚꚚHigh
Absent
Lowt
Low
Highe
Low
Moderatef
None
1.078 [−1.032, 3.187]
ꚚꚚꚚꚚHigh
RV-GLS
Overall
165 Prospective Cohort2 Retrospective cohort1 Cross-Sectional6 Case-Control1 Observational Cohort
Low
Low
Highe
Low
Low
None
2.179 [1.099, 3.260]
ꚚꚚꚚꚚHigh
Grouped by duration
1–3 months
Low
Moderated
Highe
Low
Moderatef
None
1.547 [−0.335, 3.430]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highe
Low
Low
None
1.842 [0.853, 2.831]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderatef
None
1.27 [−0.283, 2.73]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Highe
Low
Moderateg
None
4.306 [2.398, 6.214]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderateg
None
2.228 [0.377, 4.079]
ꚚꚚꚚOModerate
Absent
Low
Low
Highe
Low
Low
None
2.152 [0.807, 3.498]
ꚚꚚꚚꚚHigh
RV-MPI
Overall
61 Prospective Cohort2 Cross-Sectional3 Case-Control
Low
Low
Highe
Low
Moderateg
None
0.060 [0.030, 0.089]
ꚚꚚꚚOModerate
Grouped by duration
≥3 months
Low
Moderated
Highe
Low
Moderateg
None
0.035 [0.008, 0.062]
ꚚꚚOOLow
Grouped by COVID-19 severity
Mild
Highh
Highh
Highe
Low
Moderateg
None
0.063 [0.021, 0.106]
ꚚꚚOOLow
Grouped by comorbid disease
Absent
Lowv
Low
Highe
Low
Moderateg
None
0.060 [0.030, 0.089]
ꚚꚚꚚOModerate
RV diameter
Overall
154 ProspectiveCohort2 Retrospective cohort5 Cross-Sectional4 Case-Control
Highw
Low
Highe
Low
Moderateb
Reporting biasc
0.306 [−0.566, 1.178]
ꚚꚚOOLow
Grouped by duration
1–3 months
Highw
Moderatee
Highe
Low
Moderateg
None
−1.820 [−3.406, −0.234]
ꚚꚚOOLow
≥3 months
Low
Low
Low
Low
Low
Reporting biasc
0.900 [0.510, 1.290]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Low
Low
Low
Reporting biasc
0.865 [0.412, 1.317]
ꚚꚚꚚꚚHigh
Moderate-Severe
Lowv
Low
Highe
Low
Moderatef
None
−0.268 [−1.506, 0.970]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Moderatef
Reporting biasc
0.694 [0.242, 1.146]
ꚚꚚꚚOModerate
Absent
Highw
Low
Highe
Low
Moderateb
None
−0.227 [−1.467, 1.012]
ꚚꚚOOLow
TAPSE
Overall
268 Prospective Cohort3 Retrospective cohort7 Cross-Sectional8 Case-Control1 Observational Cohort
Low
Low
Highe
Low
Low
None
−1.01 [−1.621, −0.402]
ꚚꚚꚚꚚHigh
Grouped by duration
1–3 months
Low
Low
Highe
Low
Moderateb
None
−0.983 [−2.225, 0.285]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highe
Low
Low
None
−1.160 [−1.885, −0.466]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Moderatef
None
−0.283 [−1.050, 0.483]
ꚚꚚꚚꚚ High
Moderate-Severe
Low
Low
Moderatea
Low
Moderateg
Reporting biasc
−1.234 [−2.197, −0.270]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderateb
Reporting biasc
−0.510 [−1.409, 0.390]
ꚚꚚꚚOModerate
Absent
Low
Low
Highe
Low
Low
None
−1.440 [−2.274, −0.608]
ꚚꚚꚚꚚHigh
sPAP
Overall
124 Prospective Cohort1 Retrospective cohort2 Cross-Sectional5 Case-Control
Low
Low
Highe
Low
Low
Reporting biasc
4.37 [2.378, 6.380]
ꚚꚚꚚꚚHigh
Grouped by duration
≥3 months
Low
Low
Highe
Low
Moderateg
Reporting biasc
5.172 [2.668, 7.676]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Low
Reporting biasc
3.749 [0.817, 6.682]
ꚚꚚꚚꚚOModerate
Moderate-Severe
Lowx
Low
Highe
Low
Moderateg
Reporting biasc
6.686 [3.109, 9.662]
ꚚꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Lowy
Low
Highe
Low
Low
Reporting biasc
6.777 [4.463, 9.091]
ꚚꚚꚚꚚHigh
Absent
Low
Low
Highe
Low
Moderatef
Reporting biasc
2.039 [−0.181, 4.258]
ꚚꚚꚚꚚOModerate
LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, eft ventricular ejection fraction; PWD, posterior wall diameter; IVSD, interventricular septum diameter; LVM, left ventricular mass; LVMI, left ventricular mass index; LV-GLS, left ventricular global longitudinal strain; LAD, left atrium diameter; LAVI, left atrium volume index; LV-MPI, left ventricular myocardial performance index; E/A, the ratio of peak velocity blood flow from left ventricular relaxation in early diastole (the E wave) to peak velocity flow in late diastole caused by atrial contraction (the A wave); E/e’, ratio of E wave to early diastolic mitral annular velocity (e’); RVD, right ventricular diameter; RAD, right atrium diameter; RV-GLS, right ventricular global longitudinal strain; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; RV-MPI, right ventricular myocardial performance index; CI, confidence interval.
a
The level heterogeneity is moderate.
b
Confidence interval of the summary estimate included 0.
c
Majority of the studies are from Turkey.
d
The number of studies is less than 6.
e
The level heterogeneity is high.
f
The overall sample size was less than 500 and confidence interval of the summary estimate included 0.
g
The overall sample size was less than 500.
h
The number of studies is 2.
i
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the statistical significance of the summary estimate to significant result (42).
j
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the direction or statistical significance of the summary estimate (42).
k
All of the studies are from Turkey.
l
Even though two of the included studies were at high risk of bias for this outcome, a sensitivity analysis by excluding this study did not change the magnitude, direction, or statistical significance of the summary estimate (23, 27).
m
Two of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the statistical significance of the summary estimate to significant result (23, 27).
n
Two of the included studies evaluated athletes as their cases (17, 19) and excluding them, did not change the statistical significance of summary estimate.
o
One of the included studies evaluated athletes as their cases (17) and excluding them, did not change the statistical significance of summary estimate.
p
Three studies were at high risk of bias for LVEF and excluding them changed the significancy of the summary estimate to an insignificant result (17, 27, 36).
q
Three studies were at high risk of bias for LVEF and excluding them did not change the significancy of the summary estimate (17, 27, 36).
r
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the direction or statistical significance of the summary estimate (27).
s
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the insignificancy of the summary estimate to a significant result (45).
t
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the insignificancy of the summary estimate (45).
u
Even though two of the included studies were at high risk of bias for this outcome, a sensitivity analysis by excluding this study did not change the magnitude, direction, or statistical significance of the summary estimate (42, 45).
v
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the significancy of the results (14).
w
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the significancy of the results (14).
x
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the significancy of the results (33).
y
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the significancy of the result (24).
Figure 3
Risk-of-bias assessment (traffic light plot).
3.4
Outcome quality assessment
The certainty of evidence for outcomes, as assessed by GRADE framework, is delineated in Table 2. The meta-analysis indicates a moderate level of certainty in the majority of outcomes, primarily attributable to the inherent susceptibility to bias in observational studies. Outcomes with low certainty are typically caused by a small number of studies, significant heterogeneity, and the existence of potential biases.
3.5
Result of synthesis
3.5.1
Overall outcomes
Among the echocardiographic measures of LV systolic function, LV-GLS and LVEF were found to be significantly different between the two groups being compared. The analysis of 26 studies showed a notable decrease in LV-GLS (less negative) in post-COVID patients (n = 1,810) compared to controls (n = 1,254), with a mean difference of 1.21 [95%CI (0.681, 1.75), p = 0.000, I2 = 91%]. Post-COVID patients (n = 2,173) exhibited a lower LVEF compared to controls (n = 1,770), with a MD of −0.829 [95%CI (−1.397, −0.262), p = 0.004, I2 = 73%]. Additionally, the meta-analysis of 12 studies revealed that LAD was significantly increased in post-COVID patients (n = 833) comparing to controls (n = 892) with a MD of 1.603 [95%CI (0.696, 2.511), p = 0.001, I2 = 80.7%]. However, LAVI was not significantly different comparing two groups with a MD of 0.895 [95% CI (−0.509, 2.29), p = 0.211, I2 = 82.7%]. In terms of RV evaluation, post-COVID patients showed significantly lower RV-GLS (less negative) and higher RV-MPI values compared to controls, with mean differences of 2.179 [95%CI (1.099, 3.260), p = 0.000, I2 = 85.4%] and 0.060 [95% CI (0.030, 0.089), p = 0.009, I2 = 99%], respectively. No significant differences were found in the diastolic and geometric indices of the left ventricle between the two groups being compared (Table 3). Forest plots are provided in supporting information (Supplementary S4 document).
Table 3
Result of synthesis.
No. studies
No. cases
No. controls
Effect model
MD (CI: 95%)
P value
Heterogeneity
I2
P value
LV geometric indices
LVEDD
Overall
26
1,597
1,612
Random
0.440 [−0.092, 0.155]
0.148
65%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
2
174
144
Fixed
−0.232 [−1.29, 0.835]
0.670
0%
0.558
1–3 months
8
733
558
Random
0.542 [−0.524, 1.608]
0.319
80%
0.000
≥3 months
16
690
910
Random
0.516 [−0.315, 1.346]
0.223
58.6%
0.002
Grouped by severity of COVID-19 infectiona
Mild
15
1,079
1,205
Random
0.580 [−0.199, 1.35]
0.145
51.4%
0.011
Moderate-Sever
8
437
356
Random
0.620 [−0.421, 1.662]
0.243
78.8%
0.000
Mixed
2
81
91
Fixed
0.128 [−1.537, 1.792]
0.881
0%
0.955
Grouped by presence of comorbid diseases
Present
12
645
542
Random
0.605 [−0.324, 1.533]
0.202
75.3%
0.000
Absent
14
952
1,070
Random
0.325 [−0.484, 1.133]
0.431
54%
0.008
LVEDV
Overall
11
453
624
Random
4.79 [−0.341, 9.93]
0.067
56.9%
0.010
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
3
147
121
Fixed
6.87 [0.605, 13.13]
0.032
0%
0.536
≥3 months
8
306
503
Random
3.88 [−2.35, 10.11]
0.223
66%
0.004
Grouped by severity of COVID-19 infectiona
Mild
5
224
206
Fixed
8.39 [3.57, 13.20]
0.001
0%
0.753
Moderate-Sever
3
77
129
Fixed
10.09 [2.29, 17.89]
0.011
0%
0.728
Mixed
2
81
51
Random
−3.40 [−12.02, 5.22]
0.440
71.2%
0.062
Grouped by presence of comorbid diseases
Present
5
154
214
Random
10.35 [4.93, 15.76]
0.000
62.6%
0.020
Absent
6
299
410
Fixed
0.602 [−4.87, 6.07]
0.829
0%
0.945
LVESD
Overall
15
918
952
Random
0.325 [−0.119, 0.352]
0.346
73.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
67
37
–
–
–
–
1–3 months
4
346
217
Random
−0.397 [−1.353, 0.560]
0.417
80%
0.000
≥3 months
10
505
698
Fixed
0.928 [0.566, 1.289]
0.000
0%
0.541
Grouped by Severity of COVID-19 infectiona
Mild
7
407
437
Fixed
0.908 [0.488, 1.32]
0.000
32.1%
0.183
Moderate-Sever
5
360
227
Random
−0.272 [−1.42, 0.877]
0.642
84.5%
0.000
Mixed
2
81
51
Random
0.776 [−0.701, 2.254]
0.303
0%
0.772
Grouped by presence of comorbid diseases
Present
5
334
202
Random
−1.292 [−2.089, −0.495]
0.001
54%
0.069
Absent
10
584
750
Fixed
0.905 [0.567, 1.24]
0.000
0%
0.570
LVESV
Overall
6
317
431
Fixed
0.608 [−1.24, 2.45]
0.519
41.8%
0.127
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
1
86
60
–
0.500 [−2.77, 3.77]
–
–
–
≥3 months
5
231
371
Random
1.69 [−1.95, 5.33]
0.363
53%
0.0072
Grouped by Severity of COVID-19 infectiona
Mild
2
129
101
Random
2.23 [−1.43, 5.88]
0.230
57.7%
0.124
Moderate-Sever
1
36
41
–
6.20 [−2.17, 14.57]
–
–
–
Mixed
2
82
52
Fixed
1.40 [−3.08, 5.90]
0.539
0%
0.525
Grouped by presence of comorbid diseases
Present
2
79
82
Fixed
5.55 [1.15, 9.96]
0.013
0%
0.859
Absent
4
238
349
Fixed
−0.451 [−2.48, 1.58]
0.664
0%
0.445
IVSD
Overall
19
1,305
1,247
Random
−0.203 [−0.526, 0.119]
0.217
95.6%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
2
174
144
Random
−0.108 [−1.16, 0.944]
0.840
92.8%
0.000
1–3 months
8
677
530
Random
−0.256 [−0.796, 0.284]
0.352
98.2%
0.000
≥3 months
9
454
573
Fixed
−0.132 [−0.258, −0.007]
0.039
0%
0.456
Grouped by severity of COVID-19 infectiona
Mild
11
794
732
Random
−0.411 [−0.830, 0.007]
0.054
97.1%
0.000
Moderate-Sever
5
360
227
Random
0.250 [−0.399, 0.899]
0.451
80.4%
0.000
Mixed
2
81
51
Fixed
−0.128 [−0.427, 0.172]
0.403
0%
0.633
Grouped by presence of comorbid diseases
Present
6
404
272
Random
0.098 [−0.520, 0.715]
0.756
88.4%
0.000
Absent
13
901
975
Random
−0.315 [−0.691, 0.061]
0.101
96.5%
0.000
PWD
Overall
19
1,305
1,247
0.086 [−0.139, 0.311]
0.455
79.8%
0.000
Grouped by duration from Acute COVID to echo examination in recovery phase
<1 months
2
174
144
Random
−0.127 [−0.762, 0.497]
0.690
96.3%
0.000
1–3 months
8
677
530
Random
0.273 [−0.118, 0.663]
0.171
0%
0.497
≥3 months
9
454
573
Fixed
−0.102 [−0.211, 0.007]
0.068
77.5%
0.000
Grouped by severity of COVID-19 infectiona
Mild
11
794
732
Random
−0.149 [−0.405, 0.106]
0.251
64.8%
0.002
Moderate-Sever
5
360
227
Random
0.614 [0.259, 0.969]
0.001
52.7%
0.076
Mixed
2
81
51
Fixed
−0.159 [−0.552, 0.233]
0.426
0%
0.496
Grouped by presence of comorbid diseases
Present
6
404
272
Random
0.311 [−0.072, 0.695]
0.112
85.2%
0.000
Absent
13
901
975
Random
−0.006 [−0.248, 0.237]
0.962
63.7%
0.001
LVM
Overall
7
453
478
Random
−7.630 [−21.7, 6.50]
0.290
74.8%
0.001
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
4
325
201
Random
−3.59 [−19.7, 12.53]
0.663
74.1%
0.009
≥3 months
3
128
277
Fixed
−19.37 [−29.8, −8.92]
0.000
0%
0.423
Grouped by Severity of COVID-19 infectiona
Mild
4
191
139
Fixed
−13.71 [−25.30, −2.11]
0.020
0%
0.426
Moderate-Sever
2
192
102
Fixed
9.018 [0.458, 17.57]
0.039
0%
0.845
Grouped by presence of comorbid diseases
Present
3
234
128
Fixed
7.54 [−0.720, 15.81]
0.074
0%
0.420
Absent
4
219
350
Fixed
−18.28 [−26.72, −9.85]
0.000
19%
0.295
LVMI
Overall
7
432
494
Random
−1.65 [−6.62, 3.31]
0.513
81.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
3
237
149
Fixed
−0.251 [−1.95, 1.45]
0.772
0%
0.408
≥3 months
4
195
345
Random
−1.023 [−9.48, 7.44]
0.813
89.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
5
186
169
Random
2.408 [−1.11, 5.93]
0.181
73.1%
0.005
Moderate-Sever
1
176
88
–
0.00 [−1.74, 1.74]
–
–
–
Grouped by presence of comorbid diseases
Present
1
176
88
–
0.00 [−1.74, 1.74]
–
–
–
Absent
6
256
406
Random
−2.29 [−9.69, 5.11]
0.544
83.6%
0.000
LV systolic function
LVEF
Overall
32
2,173
1,770
Random
−0.829 [−1.397, −0.262]
0.004
73.8%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
2
174
144
Random
0.667 [−1.42, 2.76]
0.533
90.3%
0.001
1–3 months
9
691
498
Random
−0.615 [−1.75, 0.527]
0.291
83.5%
0.000
≥3 months
20
836
1,172
Random
−1.16 [ −1.94, −0.375]
0.004
60.1%
0.000
Grouped by severity of COVID-19 infectiona
Mild
20
1,586
1,109
Random
−0.886 [−1.64, −0.128]
0.022
78%
0.000
Moderate-Sever
10
495
402
Random
−0.900 [−1.96, 0.169]
0.098
69%
0.001
Mixed
1
22
22
–
−1.58 [−4.78, 1.62]
–
–
–
Grouped by presence of comorbid diseases
Present
16
1,245
663
Random
−0.852 [−1.66, −0.038]
0.040
56.5%
0.002
Absent
16
928
1,107
Random
−0.833 [−1.64, −0.005]
0.049
79.6%
0.000
LV-GLS
Overall
26
1,810
1,254
Random
1.21 [0.681, 1.75]
0.000
91%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
4
250
194
Random
0.615 [−0.776, 2.00]
0.386
95.9%
0.000
1–3 months
7
827
427
Random
1.22 [0.181, 2.27]
0.021
57.5%
0.028
≥3 months
15
733
633
Random
1.37 [0.637, 2.11]
0.000
92.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
13
1,084
714
Random
0.815 [0.047, 1.58]
0.038
88.1%
0.000
Moderate-Sever
7
380
308
Random
1.84 [0.751, 2.94]
0.001
90.1%
0.000
Mixed
3
226
112
Random
1.07 [−0.629, 2.77]
0.217
74.5%
0.020
Grouped by presence of comorbid diseases
Present
12
884
552
Random
0.919 [0.07, 1.76]
0.033
88.8%
0.000
Absent
14
926
702
Random
1.43 [0.703, 2.16]
0.000
93.7%
0.000
LV diastolic function
E/A ratio
Overall
21
1,321
1,258
−0.058 [−0.118, 0.002]
0.057
79.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
1
107
17
–
0.190 [−0.017, 0.397]
–
–
–
1–3 months
8
711
517
Fixed
−0.084 [−0.129, −0.039]
0.000
32%
0.167
≥3 months
12
503
724
Random
−0.043 [−0.122, 0.035]
0.280
85.6%
0.000
Grouped by severity of COVID-19 infectiona
Mild
13
835
706
Fixed
−0.042 [−0.076, −0.007]
0.017
45%
0.037
Moderate-Sever
5
334
263
Random
−0.134 [−0.258, −0.010]
0.034
82%
0.000
Mixed
2
82
52
Random
0.133 [−0.085, 0.351]
0.233
97%
0.000
Grouped by presence of comorbid diseases
Present
7
382
322
Random
−0.137 [−0.424, −0.032]
0.010
57.9%
0.027
Absent
14
939
936
Random
−0.021 [−0.093, 0.050]
0.563
82%
0.000
E/e’ ration
Overall
27
1,799
1,493
0.116 [−0.275, 0.507]
0.561
85.2%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
2
174
144
Random
−0.412 [−1.79, 0.968]
0.558
80.7%
0.000
1–3 months
7
496
553
Random
−0.010 [−0.753, 0.732]
0.978
58.1
0.000
≥3 months
17
657
696
Random
0.315 [−0.213, 0.843]
0.243
87%
0.000
Grouped by Severity of COVID-19 infectiona
Mild
16
1,279
898
Random
0.105 [−0.410, 0.620]
0.689
82.6%
0.000
Moderate-Sever
6
336
274
Fixed
0.380 [−0.009, 0.768]
0.055
0%
0.808
Mixed
2
82
52
Random
0.379 [−1.15, 1.91]
0.629
98.5
0.000
Grouped by presence of comorbid diseases
Present
16
1,179
706
Fixed
0.164 [−0.052, 0.381]
0.137
0%
0.521
Absent
11
620
787
Random
0.129 [−0.470, 0.729]
0.672
93.5%
0.000
Mitral E wave
Overall
13
939
817
Fixed
−0.013 [−0.029, 0.002]
0.099
44%
0.040
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
−0.010 [−0.059, 0.039]
–
–
1–3 months
7
644
493
Random
−0.021 [−0.052, 0.009]
0.161
55.9%
0.043
≥3 months
5
188
217
Fixed
0.009 [−0.025, 0.043]
0.599
31.2%
0.213
Grouped by severity of COVID-19 infection
Mild
9
660
636
Fixed
0.001 [−0.017, 0.020]
0.884
23.4%
0.234
Moderate-Sever
4
279
181
Fixed
−0.052 [−0.082, −0.022]
0.001
0%
0.437
Grouped by presence of comorbid diseases
Present
6
383
280
Random
−0.013 [−0.051, 0.026]
0.527
62.1%
0.022
Absent
7
556
537
fixed
−0.008 [−0.027, 0.011]
0.426
21.1%
0.268
Mitral A wave
Overall
10
684
647
Random
0.026 [ −0.018, 0.070]
0.245
72.8%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
−0.70 [−0.106, −0.034]
–
–
–
1–3 months
6
468
405
Fixed
0.039 [0.017, 0.060]
0.000
30.6%
0.206
≥3 months
3
109
135
Fixed
0.034 [−0.034, 0.102]
0.332
0%
0.912
Grouped by severity of COVID-19 infection
Mild
7
566
500
Random
0.015 [−0.031, 0.062]
0.521
77.2%
0.000
Moderate-Sever
3
118
147
Fixed
0.086 [0.022, 0.150]
0.008
0%
0.575
Grouped by presence of comorbid diseases
Present
3
128
110
Fixed
0.035 [−0.036, 0.105]
0.336
0%
0.913
Absent
7
556
537
Random
0.014 [−0.051, 0.080]
0.673
75%
0.001
Left atrium
LAD
Overall
12
833
892
Random
1.603 [0.696, 2.511]
0.001
80.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
4
423
288
Random
1.127 [−0.571, 2.826]
0.193
50.8%
0.107
≥3 months
8
410
604
Random
1.863 [0.694, 3.032]
0.002
86.2%
0.000
Grouped by severity of COVID-19 infectiona
Mild
5
385
367
Random
0.937 [−0.183, 2.057]
0.101
63%
0.029
Moderate-Sever
5
318
258
Random
2.305 [1.058, 3.74]
0.000
63.7%
0.026
Mixed
1
60
30
Random
3.40 [1.850, 4.95]
–
–
–
Grouped by presence of comorbid diseases
Present
6
447
388
Random
2.287 [0.910, 3.664]
0.001
77.4%
0.000
Absent
6
386
507
Random
1.064 [−0.167, 2.295]
0.090
83%
0.000
LAVI
Overall
15
821
983
Random
0.895 [−0.509, 2.29]
0.211
82.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
−1.50 [−3.54, 0.543]
–
–
–
1–3 months
2
236
174
Fixed
1.95 [0.728, 3.17]
0.002
8.4%
0.296
≥3 months
12
478
702
Random
0.941 [−0.712, 2.59]
0.256
84%
0.000
Grouped by severity of COVID-19 infectiona
Mild
8
420
481
Fixed
0.922 [0.139, 1.845]
0.023
46%
0.073
Moderate-Sever
4
253
217
Fixed
1.475 [0.374, 2.575]
0.009
35.1%
0.201
Mixed
2
78
48
Random
3.06 [−0.174, 6.30]
0.064
93.8%
0.000
Grouped by presence of comorbid diseases
Present
8
421
416
Fixed
1.135 [0.290, 1.980]
0.008
0%
0.462
Absent
7
400
567
Random
1.078 [−1.032, 3.187]
0.317
91.4%
0.000
Right heart function
RV-GLS
Overall
16
775
677
Random
2.179 [1.099, 3.260]
0.000
85.4%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
67
37
–
7.860 [6.33, 9.38]
–
–
–
1–3 months
3
170
129
Random
1.547 [−0.335, 3.430]
0.107
77.8%
0.011
≥3 months
12
538
511
Random
1.842 [0.853, 2.831]
0.000
74.3%
0.000
Grouped by severity of COVID-19 infectiona
Mild
7
380
331
Random
1.27 [−0.283, 2.73]
0.111
79.2%
0.000
Moderate-Sever
5
205
156
Random
4.306 [2.398, 6.214]
0.000
83.7%
0.000
Mixed
1
70
70
–
3.520 [2.61, 4.42]
–
–
–
Grouped by presence of comorbid diseases
Present
7
268
224
Random
2.228 [0.377, 4.079]
0.018
90%
0.000
Absent
9
507
453
Random
2.152 [0.807, 3.498]
0.002
79%
0.000
RV-MPI
Overall
6
327
352
Random
0.060 [0.030, 0.089]
0.009
99%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
1
51
32
–
0.190 [0.166, 0.214]
–
–
–
≥3 months
5
276
320
Random
0.035 [0.008, 0.062]
0.012
98.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
2
156
200
Random
0.063 [0.021, 0.106]
0.004
93.8%
0.000
Moderate-Sever
1
51
32
–
0.190 [0.166, 0.214]
–
–
–
Grouped by presence of comorbid diseases
Present
0
–
–
–
–
–
–
–
Absent
6
327
352
Random
0.060 [0.030, 0.089]
0.009
99%
0.000
RVD
Overall
15
1,055
999
Random
0.306 [−0.566, 1.178]
0.492
85.5%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
0.600 [−0.539, 1.739]
–
–
–
1–3 months
3
283
180
Random
−1.820 [−3.406, −0.234]
0.025
93.4%
0.000
≥3 months
11
665
712
Fixed
0.900 [0.510, 1.290]
0.000
0%
0.703
Grouped by severity of COVID-19 infection
Mild
8
666
674
Fixed
0.865 [0.412, 1.317]
0.000
0%
0.998
Moderate-Sever
7
389
325
Random
−0.268 [−1.506, 0.970]
0.672
92.6%
0.000
Grouped by presence of comorbid diseases
Present
7
365
328
Fixed
0.694 [0.242, 1.146]
0.003
48%
0.001
Absent
8
690
671
Random
−0.227 [−1.467, 1.012]
0.719
90.8%
0.000
RAD
Overall
8
450
422
Fixed
0.212 [−0.266, 0.689]
0.385
45.6%
0.075
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
2
246
158
Random
−0.329 [−1.710, 1.015]
0.640
70.5%
0.065
≥3 months
6
204
264
Fixed
0.499 [−0.096, 0.995]
0.107
21.4%
0.272
Grouped by severity of COVID-19 infection
Mild
3
147
155
Fixed
0.695 [−0.237, 1.62]
0.144
0%
0.572
Moderate-Sever
5
309
267
Random
0.173 [−0.647, 0.994]
0.679
61.3%
0.035
Grouped by presence of comorbid diseases
Present
7
400
372
Fixed
0.419 [−0.112, 0.951]
0.122
38.9%
0.132
Absent
1
50
50
–
−0.660 [−1.749, 0.429]
–
–
–
TAPSE
Overall
26
1,458
1,381
Random
−1.01 [−1.621, −0.402]
0.001
82%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
1.100 [0.014, 2.186]
–
–
–
1–3 months
5
583
432
Random
−0.983 [−2.225, 0.285]
0.121
76.7%
0.002
≥3 months
20
768
842
Random
−1.160 [−1.885, −0.466]
0.001
80.3%
0.000
Grouped by severity of COVID-19 infectiona
Mild
11
809
792
Random
−0.283 [−1.050, 0.483]
0.469
70.3%
0.000
Moderate-Sever
8
405
345
Random
−1.234 [−2.197, −0.270]
0.012
57%
0.022
Mixed
2
92
92
Random
−3.564 [−5.727, −1.400]
0.001
67.6%
0.079
Grouped by presence of comorbid diseases
Present
13
556
498
Random
−0.510 [−1.409, 0.390]
0.267
67.6%
0.000
Absent
13
902
883
Random
−1.440 [−2.274, −0.608]
0.001
87%
0.000
sPAP
Overall
12
1,049
885
Random
4.37 [2.378, 6.380]
0.000
94.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phasea
<1 months
1
107
107
–
0.300 [−0.979, 1.579]
–
–
–
1–3 months
1
51
32
–
5.70 [−2.010, 13.41]
–
–
–
≥3 months
9
419
646
Random
5.172 [2.668, 7.676]
0.000
95.2%
0.000
Grouped by severity of COVID-19 infectiona
Mild
6
801
437
Random
3.749 [0.817, 6.682]
0.012
92.3%
0.000
Moderate-Sever
5
178
211
Random
6.686 [3.109, 9.662]
0.000
95.2%
0.000
Grouped by presence of comorbid diseases
Present
6
626
314
Random
6.777 [4.463, 9.091]
0.000
91.2%
0.000
Absent
6
423
571
Random
2.039 [−0.181, 4.258]
0.072
91%
0.000
LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; PWD, posterior wall diameter; IVSD, interventricular septum diameter; LVM, left ventricular mass; LVMI, left ventricular mass index; LV-GLS, eft ventricular global longitudinal strain; LAD, left atrium diameter; LAVI, left atrium volume index; LV-MPI, left ventricular myocardial performance index; E/A, the ratio of peak velocity blood flow from left ventricular relaxation in early diastole (the E wave) to peak velocity flow in late diastole caused by atrial contraction (the A wave); E/e’, ratio of E wave to early diastolic mitral annular velocity (e’); RVD, right ventricular diameter; RAD, right atrium diameter; RV-GLS, right ventricular global longitudinal strain; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; RV-MPI, right ventricular myocardial performance index; CI, confidence interval.
Bold values show significant results (p < 0.05).
a
The studies conducted by Gumanova et al. (32), Beaudry et al. (30) and Yang et al. (37) did not report severity of COVID-19 infection.
b
One study did not report the timeframe from after recovering from COVID-19 to the echocardiography examination (24).
3.5.2
Subgroup analysis
3.5.2.1
Grouped by duration from acute COVID to echo examination in recovery phase
The subgroup meta-analysis found that among LV geometric indices, LVESD was notably higher in post-COVID patients (n = 505) compared to controls (n = 698) for a duration of ≥3 months, showing a MD of 0.928 [95% CI (0.566, 1.289), p = 0.000, I2 = 0%]. Moreover, post-COVID patients exhibited a significant decrease in IVSD and LVM compared to controls for a duration of ≥3 months, with a MD of −0.132 [95% CI (−0.258, −0.007), p = 0.039, I2 = 0%] and −19.37 [95%CI (−29.8, −8.92), p = 0.000, I2 = 0%], respectively.
In terms of systolic function lasting ≥3 months, LVEF was found to be significantly lower in post-COVID patients (n = 836) compared to controls (n = 1,172), with a MD of −1.16 [95% CI (−1.94, −0.375), p = 0.004, I2 = 60.1%]. Furthermore, post-COVID patients exhibited a significantly decreased LV-GLS (less negative) compared to controls for durations of both 1–3 months and ≥3 months, with MDs of 1.22 [95% CI (0.181, 2.27), p = 0.021, I2 = 57.5%] and 1.37 [95% CI (0.637, 2.11), p = 0.000, I2 = 92.9%], respectively.
There were significant differences in the E/A ratio and mitral A wave among diastolic function indices. Within a period of ≥3 months, post-COVID patients exhibited a significant reduction in the E/A ratio and an increase in the mitral A wave compared to the control group. The MDs were −0.084 [95%CI (−0.129, −0.039), p = 0.000, I2 = 32%] for the E/A ratio and 0.039 [95% CI (0.017, 0.060), p = 0.000, I2 = 30.6%] for the mitral A wave. Additionally, post-COVID patients (n = 410) exhibited a significant elevation in LAD in comparison to the control subjects (n = 604), over a duration of ≥3 months, with a MD of 1.863 [95% CI (0.694, 3.032), p = 0.002, I2 = 86.2%]. However, a meta-analysis of 2 studies showed a significant increase in LAVI in post-COVID patients (n = 236) compared to controls (n = 174) within a timeframe of 1–3 months, with a MD of 1.95 [95% CI (0.728, 3.17), p = 0.002, I2 = 8.4%].
In subgroup meta-analysis of RV function, RV-MPI, RVD and sPAP were significantly higher in post-COVID patients compared to control group for a duration of ≥3 months, with MDs of 0.035 [95% CI (0.008, 0.062), p = 0.012, I2 = 98.9], 0.900 [95% CI (0.510, 1.290), p = 0.000, I2 = 0%] and 5.172 [95%CI (2.668, 7.676), p = 0.000, I2 = 95.2%], respectively. Moreover, a significant decrease in TAPSE and RV-GLS (less negative) were observed in post-COVID patients compared to controls with a MD of −1.160 [95% CI (−1.885, −0.466), p = 0.001, I2 = 80.3%] and 1.842 [95%CI (0.853, 2.831), p = 0.000, I2 = 74.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2
Grouped by severity of COVID-19 infection
3.5.2.2.1
Mild COVID-19 infection
In terms of mild COVID-19 infection and LV geometric indices, significant increase was observed in LVEDV and LVESD in post-COVID patients compared to controls with MDs of 8.39 [95% CI (3.57, 13.20), p = 0.001, I2 = 0%], and 0.908 [95% CI (0.488, 1.32), p = 0.000, I2 = 32.1%], respectively. LVM was significantly lower in post-COVID patients (n = 191) compared to controls (n = 139), with a MD of −13.71 [95 CI% (−25.30, −2.11), p = 0.020, I2 = 0%]. Moreover, significant changes in systolic function were observed in mild infection cases. Post-COVID patients reveled to have a decrease in LVEF and LV-GLS (less negative) compared to control groups, with MDs of −0.886 [95% CI (−1.64, −0.128), p = 0.022, I2 = 78%] and 0.815 [95% CI (0.047, 1.58), 0.038, I2 = 88.1%], respectively. Among LV diastolic indices, E/A ratio was significantly lower and LAVI was significantly increased in mild infection compared to controls, with MDs of −0.042 [95% CI (−0.076, −0.007), p = 0.017, I2 = 45%] and 0.922 [95% CI (0.139, 1.845), p = 0.023, I2 = 46%], respectively. RVD and sPAP were significantly higher in post-COVID patients compared to controls with MDs of 0.865 [95% CI (0.412, 1.317), p = 0.000, I2 = 0%] and 3.749 [95% CI (0.817, 6.682), p = 0.012, I2 = 0.012, I2 = 92.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2.2
Moderate and/or severe COVID-19 infection
Post-COVID patients exhibited higher values of LVEDV, PWD, and LVM compared to the control group. The MDs for LVEDV, PWD, and LVM were 10.09 [95% CI (2.29, 17.89), p = 0.011, I2 = 0%], 0.614 [95% CI (0.259, 0.969), p = 0.001, I2 = 52.7%], and 9.018 [95% CI (0.458, 17.57), p = 0.039, I2 = 0%], respectively. Concerning systolic function, there was no significant difference in LVEF between the two groups, as indicated by a MD of −0.900 [95% CI (−1.96, 0.169), p = 0.098, I2 = 69%]. Conversely, LV-GLS exhibited significantly lower (less negative) values in post-COVID patients in comparison to the control group, with a MD of 1.84 [95% CI (0.751, 2.94), I2 = 90.1%]. Among LV diastolic indices, E/A ratio and mitral E wave values were significantly decreased and mitral A wave was significantly increased in post-COVID patients compared to controls. The MDs for E/A, E wave and A wave were −0.134 [95% CI (−0.258, −0.010), p = 0.034, I2 = 82%], −0.052 [95% CI (−0.082, −0.022), p = 0.001, I2 = 0%] and 0.086 [95%CI (0.022, 0.150), p = 0.008, I2 = 0%], respectively.
Additionally, significantly higher values were found in both LAD and LAVI in post-COVID patients compared to controls. The MD for LAD was 2.305 [95% CI (1.058, 3.74), p = 0.000, I2 = 63.7%], and for LAVI it was 1.475 [95% CI (0.374, 2.575), p = 0.009, I2 = 32.1%].
Regarding RV indices, post-COVID patients showed significantly increased value in sPAP with MDs of 4.306 [95% CI (2.398, 6.214), p = 0.000, I2 = 83.7%]. Moreover, TAPSE and RV-GLS values were significantly lower in post-COVID patients compared to controls with MDs of −1.234 [95% CI (−2.197, −0.270), p = 0.012, I2 = 57%] and 6.686 [95% CI (3.109, 9.662), p = 0.000, I2 = 95.2%], respectively. Detailed information is provided in Table 3.
3.5.2.3
Grouped by presence of comorbid diseases
Post-COVID patients with comorbidities showed higher values of LVEDV and LVESV compared to comorbid-matched control group with MDs of 10.35 [95% CI (4.93, 15.76), p = 0.000, I2 = 62.6%] and 5.55 [95% CI (1.15, 9.96), p = 0.013, I2 = 0%], respectively. There was a significant decrease in LVESD in post-COVID patients with comorbidities and an increase in cases without comorbidities compared to their comorbid-matched controls with MDs of −1.292 95% CI [−2.089, −0.495], p = 0.001, I2 = 43.2%) and 0.905 [95% CI (0.567, 1.24), p = 0.000, I2 = 0%], respectively. LVEF exhibited a significant decrease in post-COVID patients with comorbidities and those without comorbidities when compared to their comorbid-matched controls. The MDs were −0.852 [95% CI (−1.66, −0.038), p = 0.040, I2 = 56.5%] and −0.833 [95%CI (−1.64, −0.005), p = 0.049, I2 = 79.6%], respectively. Furthermore, LV-GLS was significantly decreased (less negative) in both groups of post-COVID patients, with MDs of 0.919 [95% CI (0.07, 1.76), p = 0.033, I2 = 88.8%] and 1.43 [95% CI (0.703, 2.16), p = 0.000, I2 = 93.7%] compared to their respective controls. E/A ratio was significantly lower in post-COVID patients with comorbidities compare to its comorbid-matched controls, with a MD of −0.137 [95% CI (−0.424, −0.032), p = 0.010, I2 = 57.9%]. Significantly higher values of both LAD and LAVI were observed in post-COVID patients with comorbidities compared to their matched controls. The MDs were 2.287 [95% CI (0.910, 3.664), p = 0.001, I2 = 77.4%] and 1.135 [95% CI (0.290, 1.980), p = 0.008, I2 = 0%], respectively.
Regarding RV function, RV-GLS was notably decreased (less negative) in both post-COVID patients with and without comorbidities compared to their controls, with MDs of 2.228 [95% CI (0.377, 4.079), p = 0.018, I2 = 90%] and 2.152 [95% CI (0.807, 3.498), p = 0.002, I2 = 79%]. Additionally, post-COVID patients without comorbidities presented higher values of RV-MPI with a MD of 0.060 [95% CI (0.030, 0.089), p = 0.009, I2 = 99%], compared to matched-controls. In post-COVID patients without comorbidities, TAPSE values were significantly lower, whereas no significant difference was found in cases with comorbidities when compared to their matched controls. The MDs were −1.440 [95%CI (−2.296, −0.585), p = 0.001, I2 = 87.4%] and −0.337 [95% CI (−1.213, 0.540), p = 0.452, I2 = 76%], respectively. Moreover, sPAP presented higher values in post-COVID patients with comorbidities and no significant result in cases without comorbidities compared to their matched controls with MDs of 6.777 [95% CI (4.463, 9.091), p = 0.000, I2 = 91.2%] and 2.039 [95%CI (−0.181, 4.258), p = 0.072, I2 = 91%], respectively. Detailed information is provided in Table 3. Forest plots are provided in supporting information (Supplementary S4 document).
Table 4 represent the summary of quantitative synthesis.
Table 4
Summary of quantitative synthesis.
Chamber function
Overall
Based on recovery phase
Based on severity of prior Covid-19 infection
Based on status of cardiovascular risk factors
Certainty of evidence
LV systolic function
SubclinicalImpairment(↓LVEF, ↓LV-GLS)
•Long-Covid (≥3 months): ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD, ↑LAD•Post-acute Covid (1–3 months): ↓LV-GLS
• Mild Infection: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVEDV•Moderate- Severe Infection: ↓LV-GLS, ↑LVMa
• Cardiovascular risk factors present: ↓LVEF, ↓LV-GLS, ↓LVESD, ↑LVESV•Cardiovascular risk factors absent: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD
Moderate ꚚꚚꚚO
LV diastolic function
SubclinicalImpairment(↑LAD, ↓E/A)
•Long-Covid (≥3 months): ↑LAD, ↑IVSD, ↓E/Aa•Post-acute Covid (1–3 months): ↑LVEDV, ↓E/A, ↑A wave, ↑LAVI
• Mild Infection: ↑LVESD, ↓E/A•Moderate- Severe Infection: ↑LVEDV, ↑PWD, ↓E/A, ↓E wave, ↑A wave, ↑LAD
•Cardiovascular risk factors present: ↑LVEDV, ↓E/A, ↑LAD, ↑LAVI•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
RV systolic function
SubclinicalImpairment(↓RV-GLS,↑RV-MPI,↓TAPSE)
•Long-Covid (≥3 months): ↓RV-GLS, ↑RV-MPI, ↑RVD, ↓TAPSE•Post-acute Covid (1–3 months)
• Mild infection: ↑RV-MPI, ↑RVD•Moderate- severe infection: ↓RV-GLS, ↓TAPSE
• Cardiovascular risk factors present: ↓RV-GLS, ↑RVD•Cardiovascular risk factors absent: ↓RV-GLS, ↑RV-MPI, ↓TAPSE
Moderate ꚚꚚꚚO
RV diastolic function
SubclinicalImpairment(↑sPAP)
•Long-Covid (≥3 months): ↑sPAP•Post-acute Covid (1–3 months): ↓RVD
• Mild infection: ↑sPAP•Moderate- severe Infection: ↑sPAP
•Cardiovascular risk factors present: ↑sPAP•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
a
Significant results were obtained during sensitivity analysis.
3.6
Sensitivity analysis
3.6.1
LVEDV
The study by Wood et al. (42), showed a high risk of bias for LVEDV in overall result of synthesis. Excluding this study revealed a significant difference between two groups of comparison with a MD of 4.732 [95% CI (1.367, 8.096), p = 0.006, I2 = 46.3%]. However, no significant difference was observed between two groups when grouped by duration ≥3 months and absence of comorbidities with MDs of 5.727 [95% CI (−0.209, 11.66), p = 0.059, I2 = 59.7%] and 1.964 [95% CI (−3.076, 7.00), 0.445, I2 = 57%], respectively.
3.6.2
IVSD
The studies by Ardahanli et al. (23) and Akbulut et al. (27) were found to have a high risk of bias for IVSD for in overall result of synthesis and duration of ≥3 months. Excluding these studies did not change the direction, or statistical significance of the summary estimate with MDs 0.011 [95% CI (−0.147, 0.170), p = 0.891, I2 = 77%] and 0.135 [95%CI (−0.124, 0.394), p = 0.307, I2 = 88.5%], respectively. However, excluding these studies revealed significant difference between two groups of comparison in moderate-severe COVID-19 infection, presence and absence of comorbid disease with MDs of 0.539 [95%CI (0.281, 0.798), p = 0.000, I2 = 77%] and 0.320 [95%CI (0.019, 0.620), p = 0.037, I2 = 88.5%], −0.083 [95%CI (−0.143, −0.023), p = 0.007, I2 = 36%], respectively.
3.6.3
LVM
Two studies (17, 19) were at high risk of bias for LVM due to involving athletes as their cases. A sensitivity analysis by excluding them did not change the direction, or statistical significance of the summary estimate with effect size of −5.78 [95%CI (−27.2, 15.3), p = 0.597, I2 = 83%].
3.6.4
LVMI
The study by Turpin et al. (17) was deemed to have a high risk of bias for LVMI due to the inclusion of athletes as study participants. However, excluding this study did not change the direction, or statistical significance of the summary estimate with an effect size of −0.722 [95%CI (−6.575, 5.123), p = 0.809, I2 = 86.9%]. Furthermore, subgroup analyses focusing on mild COVID-19 infection and the absence of comorbid diseases also showed no change in the significance of the results when excluding this study. The effect sizes for mild COVID-19 infection and absence of comorbid diseases were 2.07 [95%CI (−7.21, 11.36), p = 0.622, I2 = 80.2%] and −1.06 [95%CI (−10.82, 8.70), p = 0.831, I 2 = 89%], respectively.
3.6.5
LVEF
Three studies, conducted by Turpin et al. (17), Tudoran et al. (36) and Akbulut et al. (27), were deemed to have a high risk of bias in relation to LVEF. In a sensitivity analysis focusing on overall, mild COVID-19 and cases without comorbidities, the exclusion of these studies resulted in a change in the significance of the summary estimate. The effect size was found to be −0.499 [95% CI (−0.935, 0.037), p = 0.070, I2 = 63%] for overall cases, −0.229 [95% CI (−0.842, 0.383), p = 0.463, I2 = 62%] for mild cases, and −0.036 [95% CI (−0.686, 0.613), p = 0.913, I2 = 56.5%] for cases with absent comorbidities. However, excluding these studies did not change the direction, or statistical significance of the summary estimate for meta-analysis of duration ≥3 months with a MD of −0.693 [95% CI (−1.298, −0.087), p = 0.025, I2 = 47%].
3.6.6
LV-GLS
Akkabulut et al. (27) was found to have a high risk of bias in the meta-analysis of LV-GLS for both overall results and durations of ≥3 months. Conducting a sensitivity analysis by excluding this study did not affect the significance of the results, with effect sizes of 1.43 [95%CI (0.900, 1.961), p = 0.000, I2 = 91%] and 1.78 [95%CI (1.049, 2.516), p = 0.000, I2 = 92%], respectively. Furthermore, excluding this study did not alter the significant findings in the subgroup analysis of severity of COVID-19 infection. The effect sizes remained significant at 1.021 [95%CI (0.265, 1.776), p = 0.008, I2 = 87%] for mild infection and 2.289 [95%CI (1.201, 2.314), p = 0.000, I2 = 89%] for moderate-severe infection.
3.6.7
E/A ratio
Hamdy et al. (45) showed a high risk of bias in relation to this specific outcome. A sensitivity analysis was conducted by removing this study, changed the significancy of summary estimate for overall outcome and a duration of ≥3 months. The effect sizes were −0.079 [95%CI (−0.127, −0.032), p = 0.001, I2 = 64.6%] and −0.079 [95%CI (−0.141, −0.018), p = 0.011, I2 = 71.8%], respectively. However, excluding this study did not change the direction, or statistical significance of the summary estimate for the absence of comorbid diseases, with an effect size of −0.053 [95%CI (−0.109, 0.002), p = 0.061, I2 = 63.2%].
3.6.8
E/e' ratio
Hamdy et al. (45) and Wood et al. (42) were found to have a high risk of bias regarding this outcome. Excluding these studies did not change the direction, or statistical significance of the summary estimate for the overall outcome and duration of ≥3 months. The effect sizes remained at 0.092 [95%CI (−0.229, 0.412), p = 0.575, I2 = 76.3%] and 0.333 [95%CI (−0.094, 0.759), p = 0.126, I2 = 72.4%] for each respective outcome.
3.6.9
LAVI
Hamdy et al. (45) was found to have a high risk of bias for the outcome. Excluding this study did not change the direction, or statistical significance of the summary estimate for the overall outcome and duration of ≥3 months, with effect sizes of 0.578 [95%CI (−0.361, 1.517), p = 0.227, I2 = 51.4%] and 0.674 [95%CI (−0.077, 1.424), p = 0.079, I2 = 42.2%], respectively. The exclusion of this study also did not alter the lack of significance for the absence of comorbid disease, with an effect size of 0.214 [95%CI (−1.261, 1.688), p = 0.776, I2 = 73.4%].
3.6.10
RV-MPI
The study by Günay et al. (14) had a high risk of bias for this particular outcome. A sensitivity analysis was conducted by removing this study did not change the significancy of summary estimate for the overall outcome, showing an effect size of 0.035 [95% CI (0.008, 0.062), p = 0.012, I2 = 98.9%].
3.6.11
RVD
The study by Günay et al. (14) was found to have a high risk of bias for this particular outcome. Excluding this study changed the statistical significancy of summery estimates for the overall outcome, duration of 1–3 months, and the absence of comorbid disease, with effect sizes of 0.654 [95%CI (0.321, 0.987), p = 0.000, I2 = 17%], −0.277 [95%CI (−1.046, 0.493), p = 0.481, I2 = 21.3%] and 0.607 [95%CI (0.115, 1.099), p = 0.016, I2 = 0%], respectively. However, the sensitivity analysis for moderate-severe COVID-19 infection did not alter the direction or statistical significance of the summary estimate of the results. The effect sizes for these outcomes and 0.444 [95%CI (−0.099, 0.987), p = 0.109, I2 = 61.8%], respectively.
3.6.12
sPAP
Küçük et al. (33) had a high risk of bias for moderate to severe COVID-19 infection. A sensitivity analysis that excluded this study showed that the result remained significant with an effect size of 8.016 [95%CI (6.800, 9.232), p = 0.000, I2 = 26.5%]. De et al. (24) was also at high risk of bias for the presence of comorbid disease. However, excluding this study in a sensitivity analysis did not change the direction or statistical significance of the summary estimate, with an effect size of 8.097 [95%CI (7.08, 9.113), p = 0.000, I2 = 0%].
Forest plots of sensitivity analysis are provided in supporting information (Supplementary S5 document).
3.7
Meta-regression
The results of the univariate meta-regression showed a significant positive correlation between MDs of RV-GLS and age. The effect size was 0.150 [95% CI (0.027, 0.272), p = 0.016, R2 = 0.32]. Moreover, MDs of TAPSE was negatively correlated with post-COVID patients' age with an effect size of −0.077 [95%CI (−0.152, −0.003), p = 0.04, R2 = 0.09]. There were no other significant correlations observed between echocardiographic variables and age or BMI. Detailed information is presented in Table 5. Scatter plots are provided in supporting information (Supplementary S6 document).
Table 5
Meta-regression results between echocardiographic indices and baseline characteristics of patients.
Moderator
No. studies
Coefficient
SE
Z value
P value
95% CI
R
2
E/A ratio
Age
21
−0.004
0.003
−1.28
0.200
[−0.010, 0.002]
0.00
BMI
16
−0.004
0.017
0.23
0.815
[−0.038, 0.030]
0.00
LV-GLS
Age
26
0.017
0.032
0.55
0.584
[−0.045, 0.080]
0.02
BMI
12
−0.062
0.162
−0.38
0.700
[−0.382, 0.256]
0.00
LAVI
Age
15
−0.004
0.079
−0.05
0.960
[−0.158, 0.150]
0.00
BMI
13
−0.030
0.418
−0.07
0.941
[−0.850, 0.789]
0.00
LAD
Age
13
−0.019
0.093
−0.21
0.833
[−0.202, 0.163]
0.00
BMI
11
0.465
0.301
1.54
0.122
[−0.125, 1.055]
0.00
RV-GLS
Age
16
0.150
0.062
2.41
0.016
[0.027, 0.272]
0.32
BMI
6
−0.048
0.294
0.17
0.868
[−0.625, 0.528]
0.00
TAPSE
Age
26
−0.077
0.041
−2.05
0.04
[−0.152, −0.003]
0.09
BMI
16
−0.133
0.129
−1.03
0.304
[−0.388, 0.121]
0.00
sPAP
Age
12
0.162
0.105
1.55
0.122
[−0.043, 0.368]
0.00
BMI
11
0.730
0.502
1.45
0.145
[−0.253, 1.714]
0.00
3.8
Publication bias
A clear publication bias was observed when examining LVEF, LAVI, LAD and sPAP. After applying Duval and Tweedie's trim and fill method, it was determined that 9 studies needed to be added on the right side of the scatter plot for LVEF analysis. Following this adjustment, the effect size was calculated to be −0.120, with a 95%CI of (−0.711, 0.471). In the case of LAVI analysis, 5 studies needed to be imputed on the right side of the scatter plot, resulting in a summary effect size of 1.92, with a 95%CI of (0.689, 3.168). For LAD, 4 studies needed to be added on the left side of the scatter plot. The adjusted effect size was calculated 0.800 with a 95%CI of (−0.115, 1.716). Lastly, for sPAP correction analysis, 5 studies required imputation on the left side of the scatter plot, leading to a summary effect size of 1.29, with a 95%CI of (0.882, 1.717). Funnel plots and findings of Egger's and Begg's tests for all indices are provided in supporting information (Supplementary S7 document).
3.1
Study selection
The study flowchart is shown in Figure 1; our search strategy revealed 2,602 studies in PubMed, 6,502 in Scopus, 2,994 in WOS and 42 in Cochrane. After removing duplications, 5,942 studies underwent title assessment. Of these, 2,321 studies were eligible for abstract review. After surveying abstracts, 107 studies were perused for full text. Finally, 66 studies were qualified to be included in this systematic review and meta-analysis, and the rest did not meet the inclusion criteria; the reasons for their exclusions are provided in the supporting information section (Supplementary S3 document).
Figure 1
Identification of studies via databases.
3.2
Characteristics of studies
Table 1 presents the key features of the sixty-six studies (13–76) included in this research. The search process resulted in the identification of 66 studies, out of which 41 were designed as cohort studies, 16 were cross-sectional studies, 8 were case-control studies, and one study (20) was a combined cross-sectional and longitudinal cohort. The majority of these studies utilized real-time PCR (rt-PCR) as the diagnostic method for COVID-19, while a few employed the IgG antibody titer for diagnosis (32, 37). Most of the studies focused on adult patients who had recovered from COVID-19, whereas 6 studies specifically examined athletes who had overcome the disease (13, 17, 19, 29, 52, 75). A total of 32 studies were conducted comparing post-COVID patients to a control group of individuals who tested negative for COVID-19. All 32 studies were included in a meta-analysis, with the exception of one study (39) where patients and controls were not matched, and two studies (42, 43) where matching status was unknown. Four studies (16, 27, 28, 40) categorized COVID-19 cases into groups based on the severity of the infection. To maintain consistency with the other included studies, we treated these studies as separate entities, each having a common healthy control group. Furthermore, two studies (30, 43) assessed COVID-19 cases based on the presence of dyspnea symptoms in patients. These studies were also divided into two distinct studies. Three studies (16, 24, 37) divided COVID-19 cases based on the time duration between diagnosis and echocardiography. Similarly, one study (17) divided COVID-19 cases into male and female athletes. Each of these studies was separated into two distinct studies as well.
Table 1
Characteristics of included studies.
First author, year, Country
Study design
Subgroup
Covid-19 patient category
Severity of Covid-19 infection
Covid-19 vaccination
Symptoms at study enrollment
Duration from acute COVID to Echo examination in recovery phase
No. Covid-19 cases/groups
Echocardiographic parameters
Comorbid disease
Main findings
Studies with post-COVID cases compared to non-COVID control group (meta-analysis)
Duration from acute COVID to echo examination in recovery phase (<1 Month)
Lakatos et al. (2021) Hungary (13)
Case-control
None
Athletes
Mild
Unknown
Asymptomatic (88.7%), loss of taste and/or smell (11.2%)
Mean 22 days (17–25)
107/107 matched
IVSD, PWD, LAVI, mitral E wave, mitral A wave, E/A, E/e’, RVD, TAPSE, LVEF, LVGLS
None (excluded from the study)
LV systolic and diastolic impairment in post-COVID athletes compared to control group. (↑E/A, ↓E/e’, ↓PWD, ↓IVSD, ↑LVEF)
Duration from acute COVID to echo examination in recovery phase (1–3 Months)
Günay et al. (2021) Turkey (14)
Prospective cohort
None
Adults
Moderate and Severe
Unknown
None
1 months
51/32 matched
LVESD, LVEDD, LAD, LVEF, IVSD, PWD, mitral E wave, mitral A wave, RV-GLS, RVD, sPAP, TAPSE, RV-MPI
None (Excluded from the study)
RV impairment in post-COVID patients compared to control group. (↑RV-MPI, ↑RV end-diastolic, end-systolic area and ↑sPAP)
Gul et al. (2022) Turkey (15)
Cross-sectional
None
Adults
Mild and Moderate
Unknown
None
58.39 ± 39.1 days (10–180)
126/98 matched
LVEF, LAD, LVEDD
HTN: 18.3% smoking: 30.2%
LV systolic impairment in post-COVID athletes compared to control group. (↓LVEF, ↑LVEDD)
Tryfou et al. (2021) Greece (16)
Prospective cohort
Tryfou et al. (2021) (1)
Hospitalized adults
Moderate and severe
Unknown
Not mentioned
At least 10 days (10–29)
67/37 matched
IVSD, PWD, LVEDD, LVESD, E/A, E/E’, LVEF, LVGLS, RVGLS
HTN, T2DM, Hyperlipidemia, smoking
LV and RV systolic impairment (↓LV-GLS, ↓RV-GLS)
Tryfou et al. (2021) (2)
Non- hospitalized adults
Mild
1.5 month
33/37 matched
LV and RV systolic impairment (↓LV-GLS, ↓RV-GLS)
Turpin et al. (2023) USA (17)
prospective, case-control
Turpin et al. (2023) (1)
Female athletes
Asymptomatic and mild
Unknown
None
1.5–3 months (51 ± 43)
24/24 matched
IVSD, PWD, LVM, LVMI, LVEDD, LVEDV, LVEF, mitral E wave, mitral A wave, E/A, E/e’
None (excluded from the study)
No significant finding
Turpin et al. (2023) (2)
Male athletes
1–2 months (31 ± 32)
37/37 matched
Kurtoğlu et al. (2022) Turkey (18)
Retrospective cohort
None
Adults
Mild
Unknown
None
At least 2 months (5 ± 2.8)
50/50 matched
LAD, LVEF, LVEDD, LVESD, IVSD, PWD, E/A, E/e’
None (Excluded from the study)
No significant finding
Schellenberg et al. (2023) Germany (19)
Prospective cohort
None
Athletes
Not mentioned
Unknown
Fever, cough, rhinorrhea, sore throat, resting dyspnea, exertional dyspnea, palpitation, chest pain, increased resting heart rate, dizziness, subjective perceived performance limitation
2 months
88/52 matched
LVM, LVEF, E/e', E/A
None (Excluded from the study)
LV systolic and diastolic impairment in post-COVID athletes compared to control group. (↑LVEF, ↓LVM, ↑E/e’, ↓E/A)
Honchar et al. (2023) Ukraine (20)
Combined cross-sectional and longitudinal cohort
None
Hospitalized adults
Moderate and severe
Unknown
Not mentioned
1.5–2 months
176/88 matched
LAD, IVSD, PWD, LVEDD, LVESD, LVMI, LVEF, LV-GLS, mitral e’ wave, mitral E wave, E/A, E/e’, RAD, RVD, TAPSE
HTN, T2DM, smoking
LV systolic and diastolic, RV diastolic impairment in post-COVID athletes compared to control group. (↑LAVI, ↑IVSD, ↑PWD, ↓LVEDD, ↓LVESD, ↑LVMI, ↓LV-GLS, ↓TAPSE)
Mahajan et al. (2021) India (21)
Prospective cohort
None
Adults
Mixed (mild, moderate, and sever)
Unknown
Palpitations, dyspnea, fatigue, cough, syncope, pedal oedema, fever
1–1.5 month
94/40 matched
LV-GLS
T2DM, HTN, Smoking
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS) RV impairment in impairment in post-COVID athletes with reduced LV-GLS compared to in post-COVID athletes with Normal LV-GLS. (↓TAPSE, ↑RVD)
Turan et al. (2021) Turkey (22)
Prospective cross-sectional
None
Adults
Asymptomatic and mild
Unknown
None
1 month (11–89 days)
70/70 matched
LVGLS, LVEF, LVEDD, LVESD, LVEDV, LVESV, IVSD, PWD, LAD, E, A, E’, A′, E/E’, RAD, RVD, TAPSE
HTN, Smoking, Asthma, Alcohol addiction
LV systolic and diastolic impairment in post-COVID patients compared to control group. (↓LV-GLS, ↑A wave)
Ardahanli et al. (2022) Turkey (23)
Cross-sectional
None
Adults
Mild and moderate
Unknown
Exertional dyspnea, chest pain, palpitation
2 months
200/182 matched
LVEDD, PWD, IVSD, LV-MPI, E mitral, A mitral, E/A, TAPSE, RV diameter
None (excluded from the study)
RV and LV systolic impairment in post-COVID patients compared to control group. (↑LV-MPI, ↓TAPSE)
Duration from acute COVID to echo examination in recovery phase (≥3 Months)
De et al. (2023) India (24)
Cross-sectional
De et al. (2023) (24)
Adults
Majority was mild
First dose
Asymptomatic: 16.5% palpitations: 3.6% exertional dyspnea: 60.4% Chest pain: 5.9%Weakness:11.9%
>3 months
232/100 matched
LVEF, LV-GLS, sPAP, E/e’
HTN: 59.1% T2DM: 34.1% Pre-existing Airway obstruction:5.6%
LV systolic and diastolic impairment in post-COVID patients compared to control group. (↓LVEF, ↓LV-GLS, ↓E/e’)
Baltodano-Arellano et al. (2021) Peru (25)
Cross-sectional
None
Adults
Mild
Unknown
None
3–6 months
33/31 matched
LVEF, LV-GLS
None (excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS)
Akkaya et al. (2021) Turkey (26)
Cross-sectional
None
Adults
Mild
Unknown
None
3 months
105/105 matched
LVEDD, LVESD, LVEF, E/e’, PWD, IVSD, LAD, RV-GLS, RV MPI, TAPSE, sPAP, RV diameter
None (excluded from the study)
RV systolic and diastolic impairment in post-COVID athletes compared to control group (↓RV-GLS, ↑ RV-MPI, ↓TAPSE, ↑sPAP, ↑RV diameter)
Akbulut et al. (2022) Turkey (27)
Prospective cohort
Akbulut et al. (2022) (1)
Adults
Moderate and Severe
Unknown
None
6 months
16/20 matched
LVEDD, LVESD, LVM, E mitral, A mitral, E/e’, PWD, IVSD, LV-GLS, RV-GLS, TAPSE, LVEF
HTN, T2DM, Smoking
No significant finding
Akbulut et al. (2022) (2)
Mild
42/20 matched
LVEDD, LVESD, LVM, E mitral, A mitral, E/e’, PWD, IVSD, LV-GLS, RV-GLS, TAPSE, LVEF
Baykiz et al. (2021) Turkey (28)
Prospective cohort
Baykiz et al. (2021) (1)
Adults
Mild
Unknown (29)
None
6 months
34/44 matched
LAD, LVEDD, LVEDV, LVEF, RAD, RAD, E/e’, E/A, TAPSE, sPAP, LAVI, LV-GLS
HTN (35%), T2DM (15%), Smoking (23.5%)
RV and LV diastolic impairment in post-COVID athletes compared to control group. (↑LAD, ↓LV-GLS, ↓TAPSE, ↑sPAP)
Baykiz et al. (2021) (2)
Adults
Moderate
None
30/44 matched
HTN (23%), T2DM (20%), Smoking (27%)
Baykiz et al. (2021) (3)
Adults
Severe
None
11/44 matched
HTN (36%), T2DM (18%), Smoking (9%)
Beaudry et al. (2022) Canada (30)
Cross-sectional
Beaudry et al. (2022) (1)
Adults
Not mentioned
Vaccinated patients excluded from the study
Dyspnea
At least 3 months (219 ± 82 days)
16/16 matched
E/e’, TAPSE
Smoking (14%)Pre-existing Airway obstruction (17%)CardiovascularComorbid except obesity and CAD (10%)
No significant finding
Beaudry et al. (2022) (2)
Not mentioned
None
16/16 matched
E/e’, TAPSE
Smoking (14%)Pre-existing Airway obstruction (7%)CardiovascularComorbid except obesity and CAD (14%)
Gherbesi et al. (2022) Italy (31)
Retrospective cohort
None
Young adults
Mild
Unknown
None
At least 3 months (15 ± 1.4 weeks)
40/40 Matched
LVEDD, LVESD, LVEF, IVSD, PWD, LAVI, LVMI, E/e’, E/A, LV-GLS, sPAP, TAPSE, RV-GLS, RVD
None (Excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS)
Gumanova et al. (2023) Russia (32)
Cross-sectional
None
Adults
Not mentioned
Unknown
None
At least 3 months
70/237 matched
LAD, LVEF, LVEDD, LVESD, LVESV, LVM, LVMI, LVEDV, PWD, IVSD, LAVI, sPAP, E/A, E/e’
None (excluded from the study)
RV and LV diastolic impairment in post-COVID athletes compared to control group. (↑LAD, ↑LVEDD, ↑LVESV, ↑LVEDV, ↑LVM, ↑LVMI ↑sPAP, ↑E/A, ↑E/e’)
Küçük et al. (2022) Turkey (33)
Cross-sectional
None
Adults
Moderate and Severe
Unknown
None
3–6 months
50/50 matched
LAD, LVEF, LVEDD, LVESD, LV-GLS, IVSD, PWD, TAPSE, sPAP, E/A, RVD, RAD
None (Excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group (↓LV-GLS)
Lambadiari et al. (2021) Greece (34)
Case-control prospective
None
Adults
Mild (34.4%)Moderate (32.8%)Sever (32.8%)
Unknown
No symptoms (62.9%),Fatigue (15.71%), dyspnea (12.8%),Cough (4.3%),chest pain (4.3%)
4 months
70/70 Matched
LV-GLS, RV-GLS, TAPSE
None (excluded from the study)
LV systolic, RV systolic and diastolic impairment in post-COVID athletes compared to control group. (↓LV-GLS, ↓RV-GLS, ↓TAPSE)
Barros et al. (2023) Brazil (35)
Retrospective cohort
None
Adults
Severe
Unknown
None
11.9 ± 7.0 Months
35/26 Matched
LVEF, RV-GLS, TAPSE, RVD
HTN (62.3%)DM (19.7%)CKD (3.3%)Excluded disease: chronic lung diseases, PAH prior to COVID-19, previously known RV echo
RV systolic impairment in post-COVID athletes compared to control group (↓RV-GLS)
Tudoran et al. (2023) Romania (36)
Case-control
None
Adult women (18–55)
Majority was mild and few was moderate
Unknown
Dyspnea, persistent cough, unexplained and long-lasting fatigue, reduced effort capacity, tachycardia, chest pain, increased BP values, insomnia, vertigo, concentration difficulties, and memory impairments
3 months
54/40 matched
LVEF, LVMI, LAVI, E/A, E/e’
None (Excluded from the study)
LV systolic and diastolic impairment in post-COVID athletes compared to control group (↓LVEF, ↓LV-GLS, ↑LVMI, ↑LAVI, ↓E/A. ↑ E/e’)
Yang et al. (2022) China (37)
Case-control
Yang et al. (1)
Adults
Not mentioned
Unknown
Palpitation (10%), angina pectoris (10%), vertigo (15%)
3 months
40/40 Matched
LV-GLS, TAPSE
HTN
No significant finding
Yang et al. (2)
6 months
Yang et al. (3)
6 months
Rácz et al. (2022) Hungary (38)
Case-control
None
Adults
Mild
Unknown
Chronic fatigue, difficulty of carrying out previously undemanding physical activity, and palpitations
3 months
86/60 Matched
LAD, LAVI, LVEDD, LVESD, LVEDV, LVESV, ISVD, PWD, LVEF, LV-GLS, mitral E wave, mitral A wave, E/A, E/e’, RAD,RVD
HTN, mixed connective tissue disease
LV systolic impairment in post-COVID athletes compared to control group. (↓LVEF, ↓LV-GLS, ↑LVEDD)
Rajotiya et al. (2024) India (39)
Prospective case-control
None
Adults
Severe
Unknown
Not mentioned
21 months
23/20 Not matched
LVEF
Smoking, alcohol consumption
LV systolic impairment in post-COVID athletes compared to control group. (↓LVEF)
Ozer et al. (2021) Turkey (40)
Prospective case-control
Ozer et al. (2021) (1)
Hospitalized adults
Moderate and severe
Unknown
Not mentioned
4.5 months
36/41 Matched
LVEDV, LVESV, LVEDD, LVEF, LAVI, mitral E wave, E/e’, RVD, RAD,TAPSE, sPAP, RV-GLS
HTN, T2DM, Smoking
RV systolic and diastolic impairment in post-COVID athletes compared to control group. (↓RV-GLS, ↓TAPSE, ↑RVD, ↑sPAP)
Ozer et al. (2021) (2)
Home-recovered adults
Mild
4 months
43/41 Matched
Uziębło-Życzkowska et al. (2022) Poland (41)
Observational cohort
None
Adults
Mild
Unknown
Fever, cough, myalgia, anosmia/ageusia, chest pain, dyspnea
3.5–4 months
31/28 Matched
LVGLS, LVEF, TAPSE, E/e', E/A
HTN, T2DM, hypothyroidism, COPD, Smoking
No significant finding
Wood et al. (2022) Denmark (42)
Retrospective cohort
None
Adults
Mixed (mild, moderate, severe)
Unknown
Chest pain, dyspnea, palpitation
13–15 month
22/22 Unknown
LVESD, LVEDD, LVESV, LVEDV, LVEF, IVSD, PWD, LAD, LAVI, E/e’, E/A, LV-GLS, TAPSE
None (excluded from the study)
LV diastolic impairment in post-COVID athletes compared to control group. (↓E/A, ↑E/e’)
Cotella et al. (2022) South America (43)
Cross-sectional
Cotella et al. (2022) (1)
Adults
Mild
Unknown
None
At least 14 days but less than months
46/25 Unknown
LV-GLS
None (Excluded from the study)
LV systolic impairment in post-COVID athletes compared to control group. (↓LV-GLS)
Cotella et al. (2022) (2)
Moderate-Severe
30/25 Unknown
Taş et al. (2023) Turkey (44)
Prospective cohort
None
Adults
Mild
Unknown
Palpitation, chest pain, fatigue, dyspnea, joint pain, cough, headache, insomnia
6 months
51/95 Matched
LVEDD, LVESD, LVEF, LVEF, LAVI, E wave, A wave, E/A, E/e’, RVD, RV-MPI, TAPSE
None (excluded from the study)
No significant finding
Hamdy et al. (2023) Egypt (45)
Cross-sectional
None
Adults
Not mentioned
Unknown
Dyspnea
Mean 3 Months (3 ± 1.7)
60/30 matched
LVEF, LVEDD, LVESD, LVEDV, LVESV, IVSD, PWD, LAD, LAVI, LVEF, E/A, E/e’
None (excluded from the study)
LV diastolic impairment in post-COVID patients compared to control group. (↑E/e’, ↑LAD, ↑LAVI)
Studies with only post-COVID cases (not included in the meta-analysis)
Duration from acute COVID to Echo examination in recovery phase (<1 Month)
Rasmusen et al. (2022) Denmark (29)
Prospective cohort
None
Young athletes
Not mentioned
None
None
2 weeks
121cases/comparison of patients based on symptoms and duration of acute phase of COVID-19
LVEDD, LVEF, LVGLS, E/e’, TAPSE
Asthma (14%), electrical cardiac disease (2%)
No significant finding.
Duration from acute COVID to echo examination in recovery phase (1–3 Months)
ZeinElabdeen et al. (2023) Egypt (46)
Prospective cohort
None
Adults
Not mentioned
Unknown
Asymptomatic, exertional dyspnea, fatigue, exercise intolerance (NYHA class ≥2)
1–3 months
63 cases/comparison of symptomatic patients and patients without any residual symptoms
LVESD, LVEDD, LVEF, LAVI, E wave, E/A, IVRT, LVGLS
None (excluded from the study)
LA strain and LA stiffness are early affected in patients with unexplained persistent dyspnea and exercise intolerance post- COVID-19, attributing to the impaired left ventricular diastolic function
ZeinELAbdeen et al. (2023) Egypt (47)
Cross-sectional
None
Adults
Mild and Moderate
1 dose (100%)2 dose (90.4%)3 dose (17.02%)
Palpitations (36.17%), dyspnea (26.6%), cough (22.34%), fatigue (27.65%), fever (3.19%), chest pain (7.44%)
1 month
94 cases/Comparison of post-COVID-19 patients with Postural orthostatic tachycardia and normal heart rate
LVEF, LVESD, LVEDD, LAD, LAVI, E/e'
None(Excluded from the study)
No significant finding
Sarıçam et al. (2021) Turkey (48)
cross-sectional
None
Adults
Not mentioned
Unknown
Palpitation, fatigue
3–8 weeks
105 cases/Comparison of asymptomatic patients with symptomatic patients
LVEF
None (Excluded from the study)
No significant finding
Tabacof et al. (2023) USA (49)
Retrospective observational cohort
None
Adults
Not mentioned (Not severe)
Unknown
Breathlessness, quality of life changes, fatigue, physical activity changes, cognitive function changes, anxiety, depression
1 month
203 cases/COVID-91 patients
LAD, RAD, LVEF, LVMI
Not mentioned
No significant finding
Samiei et al. (2023) Iran (50)
Cross-sectional
None
Adults
Mild, moderate, and severe
Unknown
Not mentioned
1.5 months
100 cases/Comparison according to the severity of symptoms defined by clinical features and lung CT
LVEF, LVGLS, E/e', RVEF, TAPSE, LAVI, LA peak strain, RA peak strain
None (Excluded from the study)
↓LV-GLS in patients with severe covid-19.Trend in reducing EF from 61% in milder groups to 55% in the severe group
Özer et al. (2021) Turkey (51)
Prospective cohort
None
Adults
Moderate and severe
Unknown
Not mentioned
1 month
74 cases/comparison of patients according to their hs-TnI levels at hospitalization
LVESDV, LVEDV, IVSD, PWD, LAD, LVEF, LVGLS
HTN (43.3%), T2DM (10.8%), smoking (8.1%)
↓LV-GLS in 1/3 patients recovered from COVID-19 infection.
Sollazzo et al. (2022) Italy (52)
Retrospective cohort
None
Athletes
Mild (98.6%), moderate (1.4%)
None (53%), First dose (39.2%), second dose (7.8%)
Chest pain (1.9%), palpitations (0.9%), shortness of breath (2.8%)
1 month
217 cases/Comparison of parameters assessed during pre-participation evaluation and return to play
RAD, LVEF, LVEDD, LVESD, IVSD, PWD, E/A
None (excluded from the study)
↓E/A ratio which commonly change according to the athletes’ training level.
Tudoran et al. (2021) (1) Romania (53)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
Persistent fatigue, shortness of breath, chest discomfort/pain, palpitations, reduced effort capacity
1–3 months
150 cases/comparison of patients with and without Significant Cardiac Abnormalities due to covid-19 infection
LVMI, LAVI, E/A, E/e', TAPSE, RVGLS, LVGLS, LVEF
None (excluded from the study)
LV systolic and diastolic dysfunction was present in a subset of patients. (↓RV-GLS, ↓LVEF, ↑LAVI, ↓E/A, ↑E/e’)
Tudoran et al. (2021) (2)Romania (54)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
fatigue, dyspnea, and palpitations
1.5–2.5 months
125 cases/Comparison of patients with diastolic dysfunction and normal cardiac function
LAVI, LVMI, LVEF, E/A, E/e', LVGLS, TAPSE, RVGLS
None (excluded from the study)
LV systolic and diastolic function were within normal limits, although we identified in 7 individuals mild LVH and another 4 patients had borderline values of RV-GLS.
Tudoran et al. (2021) (3) Romania (55)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
Not mentioned
2 months
91 cases/COVID-91 patients
RAD, RVD, TAPSE, RVGLS
None (excluded from the study)
RV dysfunction were seen even after the recovery from mild Covid-19 pulmonary infections. (↓RV-GLS, ↑sPAP)
Tudoran et al. (2021) (4)Romania (56)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Dyspnea, fatigability, palpitations, chest pain/discomfort, and reduced exercise tolerance
1 month3 months6 months
116 cases/Comparison of patients with and without pulmonary hypertension following covid-19 infection
LAVI, LVEF, RAD, RVD, TAPSE, RVGLS
None (excluded from the study)
49 cases had ↑ RVD and two borderline values of ↓TAPSE and/or ↓RV-GLS.
Tudoran et al. (2022) (1) Romania (57)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Long-lasting fatigue, reduced exercise capacity, dyspnea, chest pain/discomfort, palpitations, increased blood pressure values, dizziness, concentration issues, foggy brain, and sleep disturbances
1 month3 months6 months
203 cases/comparison of patients according to their BMI and Metabolic syndrome history
LVMI, LVGLS, LVEF, LAVI, E/A, E/e', TAPSE, RVGLS
Metabolic syndrome
Severe forms of diastolic dysfunction were diagnosed, suggesting irreversible cardiac damages, such as interstitial fibrosis.
Tudoran et al. (2022) (2) Romania (58)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Long-lasting fatigue, dyspnea, chest pain/discomfort, palpitations, and reduced exercise capacity
1 month3 months6 months
383 cases/Comparison of patients according to their type and severity of the prevailing cardiac dysfunction
LVMI, LAVI, LVEF, LVGLS, TAPSE, RVGLS, E/A, E/e', sPAP
None (excluded from the study)
LV systolic and diastolic impairment (↓LVEF, ↓LV-GLS, ↓ E/A ↑sPAP)
Tudoran et al. (2023) Romania (59)
Prospective cohort
None
Adults
Mild and moderate
Unknown
Reduced physical exertion capacity, persisting fatigue, palpitations, elevated blood pressure levels, chest discomfort or even pain, dyspnea, dry cough, sleep distur- bances, foggy brain, and concentration issues
1 month3 months6 months
203 cases/comparison of patients with and without T2DM and MS
LVMI, LAVI, LVEF, LVGLS, TAPSE, RVGLS, E/A, E/e'
T2DM, MS
RV, LV systolic and LV diastolic impairment in patients with MS and/or T2DM compared to healthy controls. (↑LVMI, ↑LAVI, ↓LVEF, ↓LV-GLS, ↓TAPSE, ↓RV-GLS, ↓E/A, ↑E/e')
Bende et al. (2021) Romania (60)
Retrospective cohort
None
Adults
Not mentioned
Unknown
Fatigue, shortness of breath, chest discomfort, palpitations, reduced exercise capacity
2–3 months
97 cases/comparison of patients with and without pulmonary injury
LVMI, LAVI, E/A, E/e', TAPSE, LVEF
HTN (23.7%), T2DM (4.12%),
LV systolic and diastolic impairment (↓LVEF, ↑E/e’) Only 3.09% of patients had ↓LVEF, and 31.95% ↑E/e’
Erdem et al. (2022) Turkey (61)
Retrospective cohort
None
Adults
Mild, moderate, and severe
Unknown
Exertional dyspnea (52.7%), palpitations (48.3%), chest pain (31.8%), and dyspnea at rest (15.3%), back pain (59.3%)
2–3 months
91 cases/Comparison of patients according to their hospitalization status and pulmonary involvement (ICU, covid-19 ward, outpatient)
RVD, TAPSE, LVEF, LAD, LVESD, LVEDD
HTN (21.9%), T2DM (14.2%), smoking (14.2%)
RV impairment in severe post-COVID cases. (↑RVD, ↓TAPSE)
Kujur et al. (2021) India (62)
Cross-sectional
None
Adults
Mild, moderate, and severe
Unknown
Not mentioned
1–3 months
100 cases/comparison of patients according to the disease severity
LVEF
HTN (27%), T2DM (24%), obesity (27%), CKD (4%)
Myocardial dysfunction is common in covid-19 regardless of disease severity. (↓LVEF)
Vera-Pineda et al. (2023) Mexico (63)
Cross-sectional
None
Adults
Mild (63%), moderate (15%), and severe (22%)
Unknown
Not mentioned (dyspnea, cough, palpitations, or fatigue)
At least 1.5 months
100 cases/comparison of patients according to the severity of the covid-19 infection and
LAD, LAVI, LVMI, TAPSE, LVEF, LV-GLS, RV-GLS
T2DM (22%), HTN (13%), dyslipidemia (8%), smoking (23%)
RV and LV systolic impairment in 70% cases (↑LAD, ↑LVMI, ↓TAPSE, ↓LVEF, ↓ LV-GLS, ↓RV-GLS)
Osada et al. (2022) USA (64)
Prospective cohort
None
Adults
Mild
50%One/both doses of SARS-CoV-2Vaccine during the study(Three Moderna and one Pfizer)
Chest pain, chills, diarrhea, dizziness or vertigo, dry cough, dry eyes, dry mouth, fatigue, fever, headache, lack of appetite, anosmia, muscle or body aches, nasal congestion or runny nose, nausea or vomiting, shortness of breath, difficulty breathing, dyspnea, sore joints, or sore throat
1–6 months
18 cases/follow up from 1 to 6 months after COVID-19 infection
IVSD, LVM, LVMI, LVEF, LAD, E/e'
None (excluded from the study)
No significant finding
Can et al. (2024) Turkey (65)
Retrospective cohort
None
Adults
Mild and moderate
Unknown
Not mentioned
1 month 7 months
70 cases/Comparison of changes between the 1st and 7th month's follow up
LVEF, LVEDD, LVESD, IVSD, LAD, IVRT, E/A, E/e'
None (excluded from the study)
(↓LAD, ↑IVRT)
Duration from acute COVID to echo examination in recovery phase (≥3 Months)
Yaroslavskaya et al. (2023) Russia (66)
Prospective cohort
None
Adults
Not mentioned
Unknown
Not mentioned
3 months, 12 months
156 cases/Comparison of patients with normal LVGLS and reduced LVGLS
LVGLS, LVEDV, LVESV, LVM, LVEF, IVRT, E/A, E/e', TAPSE
Not mentioned
LV systolic impairment 27.6% of patients after 1-year post-infection (↓LV-GLS)
Luchian et al. (2021) Belgium (67)
Prospective cohort
None
Adults
Moderate and sever
Unknown
Dyspnea (34.8%), Other symptoms were not mentioned.
12 months
66 cases/Comparison of patients with and without persistent dyspnea at the one-year follow-up
LVEF, E/A, E/e’, TAPSE
Dyslipidemia (19.7%)T2DM (16.7%)Obesity (25.8%)Smoking (12.1%)Cancer (3%)Chronic autoimmune disease (6.1%)
LV systolic impairment after 1-year post-infection (↓LV-GLS)
Matejova et al. (2022) Czechia (68)
Prospective observational cohort
None
Adults
Mild (74.4%)Moderate (4.7%)Severe (20.7%)
Unknown
Breathing problems, palpitations, exercise intolerance, fatigue in >50% of cases
3 months, 12 months
106 cases/COVID-19 patients
LVEF, LAD, LVEDD
HTN (20.2%)Obesity (11.5%)Bronchial asthma (5.8%)Renal insufficiency (5.8%)Depression (2.9%)Thromboembolic disease (1.9%)
No Significant finding.
Wu et al. (2021) China (69)
Prospective cohort
None
Adults
Mild (59.3%)Severe (40.7%)
Unknown
No cardiopulmonary symptoms.
6 months
27 cases/Comparison of patients with and without cardiac injury due to covid-19 infection
LAD, RAD, RVD, IVSD, LVEF, TAPSE
HTN (14.8%)T2DM (18.5%)
No Significant finding.
Stavileci et al. (2022) Turkey (70)
Retrospective cohort
None
Adults
Mild
Unknown
Cough (7.25%)Fever (19.35%)Joint Pain (30.64%)Fatigue (31.45%)Chest pain (9.67%)Dyspnea (20.96%)Taste abnormalities (29.83%)Smell abnormalities (26.1%)
6 months
248 cases/Comparison of patients with fragmented QRS (fQRS) wave and non- fragmented QRS wave
LVEF, LVESD, LVEDD, LAD, PWD, IVSD
Smoking (25%)
LVEF was statistically significant lower in the fQRS+ group compared to the non-fQRS group. Presence of fQRS was related also with wider: LVEDD, LVESD, septum thickness, and LAD
Sharma et al. (2022) India (71)
Prospective observational cohort
None
Adults
Mild, moderate, and severe
Unknown
Dyspnea (57.14%), Chest pain (19.04%), Palpitations (3.17%), Fatigue (25.39%)
6 months
63 cases/Comparison of patients with mild symptoms and moderate/severe symptoms
LVEF, E/A, E/e', TAPSE
HTN (28.57%), T2DM (14.28%)
LV dysfunction in moderate/severe group patients as compared to mild cases. No RV dysfunction. (↓LVEF, ↓E/A, ↑E/e')
Ro ´denas-Alesina et al. (2022) Spain (72)
Prospective cohort
None
Adults
Not mentioned
Unknown
Fever (89%), dyspnea (71.8%), diarrhea (24%)
4.3 months
109 cases/Comparison of patients with elevated biomarker (hs-TnI, NT-pro-BNP, D-dimer) with controls
LVEDD, LVMI, LVEF, TAPSE, LAVI, LVGLS, E/e'
Tobacco use, HTN, Dyslipidemia, T2DM, COPD, cancer,
Minimal changes were observed in LV function.
Chamtouri et al. (2022) Tunisia (77)
Prospective cohort
None
Adults
Not mentioned
Unknown
Not mentioned
3 months
111 cases/Comparison of patients according to CT scan lesions
LVEF, LVEDD, LVESD, IVSD, TAPSE, LVGLS, RVGLS
T2DM (31.8%), Hyperlipidemia (4.6%). HTN (45.8%), COPD (3.6%), smoking (7.8%)
This study showed that patients with severe CT scan pulmonary lesions were more likely to develop sub-clinical myocardial damage at mid-term follow-up. (↓LV-GLS, ↓RV-GLS)
Chudzik et al. (2022) Poland (73)
Prospective cohort
None
Adults
Mild, moderate, and severe
Unknown
Weakness (73%), impaired exercise tolerance (65.88%), palpitations (54%), memory and concentration disturbances (53.75%), chest pain (44%), Headache (34.39%), Dyspnea (32.02%), Excessive sweating (29.25%), Hair loss (28.06%), Muscle pain (24.6%), Anosmia and ageusia (24.12%), Cough (23.23%), Raynaud syndrome (14.29%), Ascites (swelling) (11.51%), Skin lesions (10.67%), Conjunctivitis (8.3%), Varicose veins of lower extremities (6.35%), Neurological disturbances (5.88%), Syncope (3.57%), Arthralgia (1.55%)
3 months
488 cases/Comparison of recovered patients with long covid-19 and no long covid-19
LAD, RVD, TAPSE, LVM, LVESD, LVEDD, IVSD
None (Excluded from the study)
Not significant finding
Flores et al. (2023) Braga (77)
Prospective cohort
None
Adults
Mild, moderate, and severe
Unknown
Not mentioned
6 months
88 cases/Comparison of patients admitted and not admitted to ICUs
LAD, LVEF, TAPSE
Not mentioned
Not significant finding
Kattamuri et al. (2023) India (74)
Prospective cohort
None
Adults
Mild and severe
Unknown
Not mentioned
3–6 months 6–12 months
53 cases/Comparison of patients with mild and severe covid-19
LVEF, E/A, E/e'
HTN (30%), T2DM (34%), Thyroid disease (3.7%)
No significant finding.
Hamburger et al. (2023) USA (75)
Prospective cohort
None
Athletes
Mild
Unknown
None
21 months
82 cases/Comparison of athletes pre- and post-training
LVEF, LAVI, LVEDD, LVESD, PWD, IVSD, LVMI, TAPSE, E/A, E/e'
None (Excluded from the study)
↑LVESD, ↑LAVI, ↑LVEDD
D’Ávila et al. (2023) Brazil (76)
Retrospective cohort
None
Adults
Moderate and severe
Unknown
Fatigue (71.4%), muscle pain (21.4%), Peripheral muscle weakness (19.6%), Dyspnea (17.9%)
7.9 months
56 cases/comparison of patients according to covid-19 severity
LVEDV, LVESV, LVEF, LVMI, TAPSE, LV-GLS, E/e'
HTN (63%), T2DM (20%), obesity (57%)
Despite having a similar EF and GLS, patients with a history of the critical manifestation in the acute phase of the disease had subclinical LV dysfunction according to other parameters. (↑global wasted work, ↓global work efficiency)
LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; PWD, posterior wall diameter; IVSD, interventricular septum diameter; LVM, left ventricular mass; LVMI, left ventricular mass index; LV-GLS, left ventricular global longitudinal strain; LAD, left atrium diameter; LAVI, left atrium volume index; LV-MPI, left ventricular myocardial performance index; E/A, the ratio of peak velocity blood flow from left ventricular relaxation in early diastole (the E wave) to peak velocity flow in late diastole caused by atrial contraction (the A wave); E/e’, ratio of E wave to early diastolic mitral annular velocity (e’); RVD, right ventricular diameter; RAD, right atrium diameter; RV-GLS, right ventricular global longitudinal strain; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; RV-MPI, right ventricular myocardial performance index; MS, metabolic syndrome; T2DM, type 2 diabetes mellitus.
The severity of COVID-19 infection was not addressed in 12 studies (19, 29, 30, 32, 37, 45, 46, 48, 60, 66, 72, 77). Regarding the COVID-19 vaccination, only six studies (24, 29, 30, 46, 52, 64) provided information on the vaccination status of patients. It is noteworthy that data collection in most of the studies was conducted before the availability of any vaccines. Thirteen studies lacked information on post-COVID symptoms at the time of study enrollment (16, 20, 39, 40, 50, 51, 55, 62, 65, 66, 74, 77, 78). In most studies, the time interval between the acute phase of COVID-19 and echocardiography during the recovery phase was over 1 month, except for 3 studies (13, 16, 29) that were conducted within at least 10 days. Thirty studies reported the exclusion of patients with comorbid disease. On the other hand, three studies did not provide any information regarding the comorbid diseases (49, 66, 78). The primary focus of the studies pertained to the evaluation of LV function, with a secondary emphasis on RV function. A subset of studies also conducted concurrent assessments of both LV and RV function. Majority of the studies found significant changes in echocardiographic parameters, indicating subclinical alterations in the function of the LV and/or RV in post-COVID patients. However, 17 studies (17, 18, 27, 29, 30, 37, 41, 44, 46, 47, 49, 64, 68, 69, 73, 74, 78) did not report any significant findings.
3.3
Studies' risk of bias
Figure 2 depicts a summary of the RoB-1 assessment. The overall risk of bias was found to be low to moderate. A low percentage (<15%) of serious risk of bias was identified in various domains, including confounding, selection of participants, classification of interventions, deviations from intended interventions, and missing data bias. Moderate risk of bias (25%–50%) was noted in confounding and deviations from intended interventions. There was no significant bias detected in the selection of reported results. Among 32 studies enrolled in the meta-analysis, five were found to have a serious risk of confounding bias (19, 30, 32, 37, 45). These studies did not provide information on the severity of COVID-19 infection in the patients. Additionally, 12 studies were rated as having a moderate risk of bias due to the presence of comorbid diseases that could impact heart function (15, 16, 20–22, 24, 27, 28, 35, 38, 40, 41). Three studies found a serious risk of bias in participant selection due to an unmatched case-control group (39, 42, 43), while five studies indicated a moderate risk due to the inclusion of specific populations such as athletes, women, and young adults that may not accurately represent the general population (13, 17, 19, 31, 36). Concerning bias due to classification of interventions, one study (21) found a serious risk of bias in comparing echo findings between two groups with reduced and normal-LVGLS, while two (24, 43) were deemed to have moderate risk due to incorrectly classifying post-COVID patients and comparing echo measures between them instead of with controls. Three studies demonstrated a moderate risk of bias due to deviations from intended interventions (30, 34, 39). Their main focus was on evaluating cardiopulmonary function rather than cardiac alone. Seven studies were found to have a moderate risk of bias due to missing data, and they reported small amounts of echocardiographic indices (15, 25, 30, 34, 37, 39, 43). Regarding bias in outcome measurement, 10 studies (14, 19, 22, 24, 26, 27, 33, 36, 42, 45) found a moderate risk of bias in reporting certain echo indices that deviated from the ranges reported in other studies (detailed in Table 2). Thirty-four studies only had post-COVID cases. Regarding the confounding factors, one study had a serious risk of bias as it did not provide information about the comorbid diseases of the patients (66). Twenty studies had moderate risks due to the presence of comorbid diseases (29, 51, 57, 59, 61–63, 67–69, 71, 72, 74, 76, 77) and lack of information on the severity of COVID-19 infection (29, 46, 48, 49, 72, 77). Considering the risk of bias in participant selection, 6 studies had moderate risks for reasons of inclusion of specific populations such as athletes (29, 52, 75) and having no classification and comparison among patients (49, 55, 68). Regarding bias due to deviations from intended interventions, one study had a serious risk of bias as its main focus was on hepatic abnormalities rather than cardiac alone (60). Twenty-one studies revealed moderate risk due to laboratory and biomarker evaluations, electrocardiogram evaluations, various surveys and lifestyle changes, return to play evaluation of athletes, chest computer tomography, post-COVID-19 functional status scale, cardio-ankle vascular index, ankle-brachial index, myocardial work analysis, walk test, pulmonary function tests, and cardiopulmonary exercise tests (48, 49, 51–59, 65, 67–71, 74–77). Bias due to missing data was serious in 3 studies as they reported small amounts of echocardiographic indices (48, 62, 74). Figure 3 represents the traffic light plot of risk of bias assessment for each included study.
Figure 2
Overall risk of bias.
Table 2
GRADE approach.
Outcome quality assessment
Outcome
No. studies/methodology
Subgroups
Risk of bias
Limitation
Inconsistency
Indirectness
Imprecision
Other consideration
Mean difference (95% CI)
Certainty
LVESD
Overall
156 prospective cohort4 retrospective cohort5 cross-sectional
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.325 [−0.119, 0.352]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Moderated
Highe
Low
Moderatef
None
−0.397 [−1.353, 0.560]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Low
Low
Low
Reporting biasc
0.928 [0.566, 1.289]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Low
Low
Moderateg
None
0.908 [0.488, 1.32]
ꚚꚚꚚꚚHighꚚꚚꚚOModerate
Moderate-Severe
Low
Moderate4
Highe
Low
Moderatef
Reporting biasc
−0.272 [−1.42, 0.877]
Grouped by comorbid disease
Present
Low
Low
Low
Low
Moderateg
None
−1.292 [−2.089, −0.495]
HighꚚꚚꚚO
Absent
Low
Low
Low
Low
Low
Reporting biasc
0.905 [0.567, 1.24]
HighꚚꚚꚚO
LVESV
Overall
62 cross-sectional3 prospective cohort1 case control
–
Low
Low
Low
Low
Moderatef
None
0.608 [−1.24, 2.45]
HighꚚꚚꚚO
Grouped by duration
≥3 months
Low
Moderated
Moderatea
Low
Moderatef
None
1.69 [−1.95, 5.33]
ꚚꚚꚚO Moderate
Grouped by COVID-19 severity
Mild
Highh
Highh
Moderatea
Low
Moderatef
None
2.23 [−1.43, 5.88]
ꚚꚚOO Low
Grouped by comorbid disease
Present
Highh
Highh
Low
Low
Moderatef
None
5.55 [1.15, 9.96]
ꚚꚚOO Low
Absent
Low
Moderated
Low
Low
Moderatef
None
−0.451 [−2.48, 1.58]
ꚚꚚꚚO Moderate
LVEDD
Overall
2610 prospective cohort3 retrospective cohort1 Observational cohort9 Cross-sectional4 case control
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.440 [−0.092, 0.155]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Moderated
Low
Low
Moderatef
None
−0.232 [−1.29, 0.835]
ꚚꚚOOLow
1–3 months
Low
Low
Highe
Low
Moderateb
None
0.542 [−0.524, 1.608]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.516 [−0.315, 1.346]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.580 [−0.199, 1.35]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Low
Highe
Low
Moderatef
None
0.620 [−0.421, 1.662]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderatec
Reporting biasc
0.605 [−0.324, 1.533]
ꚚꚚꚚOModerate
Absent
Low
Low
Moderatea
Low
Moderatec
Reporting biasc
0.325 [−0.484, 1.133]
ꚚꚚꚚOModerate
LVEDV
Overall
114 Prospective Cohort1 Retrospective Cohort2 Cross-Sectional4 Case-Control
Highi
Low
Moderatea
Low
Moderatee
None
4.79 [−0.341, 9.93]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Moderated
Low
Low
Moderateg
None
6.87 [0.605, 13.13]
ꚚꚚꚚO Moderate
≥3 months
Lowj
Low
Moderatea
Low
Moderatef
Reporting biasc
3.88 [−2.35, 10.11]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Moderated
Low
Low
Moderateg
None
8.39 [3.57, 13.20]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
Reporting biasd
10.09 [2.29, 17.89]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Present
Low
Moderated
Moderatea
Low
Moderateg
Reporting biask
10.35 [4.93, 15.76]
ꚚꚚꚚOModerate
Absent
Lowj
Low
Low
Low
Moderatef
None
0.602 [−4.87, 6.07]
ꚚꚚꚚOModerate
IVSD
Overall
196 ProspectiveCohort3 Retrospective Cohort7 Cross-Sectional4 Case-Control
Lowl
Low
Highe
Low
Moderateb
None
−0.203 [−0.526, 0.119]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
−0.108 [−1.16, 0.944]
ꚚOOOVery Low
1–3 months
Low
Low
Highe
Low
Moderateb
None
−0.108 [−1.16, 0.944]
ꚚꚚꚚOModerate
≥3 months
Lowl
Low
Low
Low
Moderatef
Reporting biasd
−0.256 [−0.796, 0.284]
ꚚꚚOOLow
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderateb
None
−0.411 [−0.830, 0.007]
ꚚꚚꚚOModerate
Moderate-Severe
Highm
Moderated
Highe
Low
Moderatef
Reporting biasd
0.250 [−0.399, 0.899]
ꚚꚚOOLow
Grouped by comorbid disease
Present
Highm
Moderated
Highe
Low
Moderatef
None
0.098 [−0.520, 0.715]
ꚚꚚꚚOModerate
Absent
Highm
Low
Highe
Low
Moderatef
None
−0.315 [−0.691, 0.061]
ꚚꚚOOLow
PWD
Overall
196 ProspectiveCohort3 Retrospective Cohort7 Cross-Sectional3 Case-Control
Low
Low
Highe
Low
Moderateb
None
0.086 [−0.139, 0.311]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
−0.127 [−0.762, 0.497]
ꚚꚚOOLow
1–3 months
Low
Low
Low
Low
Moderatef
None
0.273 [−0.118, 0.663]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highf
Low
Moderatef
None
−0.102 [−0.211, 0.007]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Moderateb
None
−0.149 [−0.405, 0.106]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Moderatea
Low
Moderateg
None
0.614 [0.259, 0.969]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderatef
None
0.311 [−0.072, 0.695]
ꚚꚚꚚOModerate
Absent
Low
Low
Moderatea
Low
Moderateb
None
−0.006 [−0.248, 0.237]
ꚚꚚꚚOModerate
LVM
Overall
74 ProspectiveCohort2 Cross-Sectional1 Case-Control
Lown
Low
Moderatea
Low
Moderatef
None
−7.630 [−21.7, 6.50]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Lown
Moderated
Highe
Low
Moderatef
None
−3.59 [−19.7, 12.53]
ꚚꚚꚚOModerate
≥3 months
Lown
Moderated
Low
Low
Moderateg
None
−19.37 [−29.8, −8.92]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Lown
Moderated
Low
Low
Moderateg
None
−13.71 [−25.30, −2.11]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
None
9.018 [0.458, 17.57]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Moderated
Low
Low
Moderatef
None
7.54 [−0.720, 15.81]
ꚚꚚꚚOModerate
Absent
Lown
Moderated
Low
Low
Moderateg
None
−18.28 [−26.72, −9.85]
ꚚꚚꚚOModerate
LVMI
Overall
71 Retrospective Cohort1 ProspectiveCohort2 Cross-Sectional2 Case-Control1 Observational Cohort
Lowo
Low
Highe
Low
Moderateb
None
−1.65 [−6.62, 3.31]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Lowo
Moderated
Low
Low
Moderatef
None
−0.251 [−1.95, 1.45]
ꚚꚚꚚOModerateꚚꚚꚚOModerate
≥3 months
Low
Moderated
Highe
Low
Moderatef
None
−1.023 [−9.48, 7.44]
Grouped by COVID-19 Severity
Mild
Lowo
Moderated
Highe
Low
Moderatef
None
2.408 [−1.11, 5.93]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Absent
Lowo
Low
Highe
Low
Moderatef
None
−2.29 [−9.69, 5.11]
ꚚꚚꚚOModerate
LVEF
Overall
3211 Prospective Cohort4 Retrospective cohort10 Cross-Sectional7 Case-Control1 Observational Cohort
Highp
Low
Moderatea
Low
Low
Reporting biasc
−0.829 [−1.397, −0.262]
ꚚꚚOOLow
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
0.667 [−1.42, 2.76]
ꚚꚚOOLow
1–3 months
Lowq
Low
Highe
Low
Moderateb
None
−0.615 [−1.75, 0.527]
ꚚꚚꚚOModerate
≥3 months
Lowq
Low
Moderatea
Low
Low
Reporting biasc
−1.16 [ −1.94, −0.375]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Highp
Low
Moderatea
Low
Low
None
−0.886 [−1.64, −0.128]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Low
Moderatea
Low
Moderatef
Reporting biasc
−0.900 [−1.96, 0.169]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Low
Reporting biasc
−0.852 [−1.66, −0.038]
ꚚꚚꚚꚚHigh
Absent
Highp
Low
Highe
Low
Low
None
−0.833 [−1.64, −0.005]
ꚚꚚOOLow
LV-GLS
Overall
2611 Prospective Cohort 2 Retrospective cohort6 Cross-Sectional6 Case-Control1 Observational Cohort
Lowr
Low
Highe
Low
Low
None
1.21 [0.681, 1.75]
ꚚꚚꚚꚚHigh
Grouped by duration
<1 month
Low
Moderated
Highe
Low
Moderatef
None
0.615 [−0.776, 2.00]
ꚚꚚꚚOModerate
1–3 months
Low
Low
Moderatea
Low
Low
None
1.22 [0.181, 2.27]
ꚚꚚꚚꚚHigh
≥3 months
Lowr
Low
Highe
Low
Low
None
1.37 [0.637, 2.11]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Lowr
Low
Highe
Low
Low
None
0.815 [0.047, 1.58]
ꚚꚚꚚꚚHigh
Moderate-Severe
Lowr
Low
Highe
Low
Moderateg
None
1.84 [0.751, 2.94]
ꚚꚚꚚꚚHigh
Grouped by comorbid disease
Present
Lowr
Low
Highe
Low
Low
None
0.919 [0.07, 1.76]
ꚚꚚꚚꚚHigh
Absent
Low
Low
Highe
Low
Low
None
1.43 [0.703, 2.16]
ꚚꚚꚚꚚHigh
E/A ratio
Overall
217 ProspectiveCohort3 Retrospective cohort6 Cross-Sectional4 Case-Control1 Observational Cohort
Highs
Low
Highe
Low
Moderateb
None
−0.058 [−0.118, 0.002]
ꚚꚚOOLow
Grouped by duration
1–3 months
Low
Low
Low
Low
Low
None
−0.084 [−0.129, −0.039]
ꚚꚚꚚꚚHigh
≥3 months
Highs
Low
Highe
Low
Moderateb
None
−0.043 [−0.122, 0.035]
ꚚꚚOOLow
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Low
None
−0.042 [−0.076, −0.007]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Highe
Low
Moderateg
None
−0.134 [−0.258, −0.010]
ꚚꚚꚚꚚHigh
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Moderateg
None
−0.137 [−0.424, −0.032]
ꚚꚚOOLow
Absent
Lowt
Low
Highe
Low
Moderateb
None
−0.021 [−0.093, 0.050]
ꚚꚚꚚꚚHigh
E/e’ ratio
Overall
279 ProspectiveCohort2 Retrospective cohort9 Cross-Sectional6 Case-Control1 Observational Cohort
Lowu
Low
Highe
Low
Moderateb
None
0.116 [−0.275, 0.507]
ꚚꚚꚚOModerate
Grouped by duration
<1 month
Highh
Highh
Highe
Low
Moderatef
None
−0.412 [−1.79, 0.968]
ꚚꚚOOLow
1–3 months
Low
Low
Moderatea
Low
Moderateb
None
−0.010 [−0.753, 0.732]
ꚚꚚꚚꚚHigh
≥3 months
Lowu
Low
Moderatea
Low
Moderateb
Reporting biasc
0.315 [−0.213, 0.843]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderateb
None
0.105 [−0.410, 0.620]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Low
Low
Low
Moderatef
Reporting biasc
0.380 [−0.009, 0.768]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Low
Low
Moderateb
Reporting biasc
0.164 [−0.052, 0.381]
ꚚꚚꚚꚚHigh
Absent
Lowu
Low
Highe
Low
Moderateb
None
0.129 [−0.470, 0.729]
ꚚꚚꚚꚚHigh
E wave
Overall
134 Prospective Cohort4 Cross-Sectional5 Case-Control
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
−0.013 [−0.029, 0.002]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Low
Moderatea
Low
Moderateb
None
−0.021 [−0.052, 0.009]
ꚚꚚꚚꚚHigh
≥3 months
Low
Moderated
Low
Low
Moderatef
Reporting biasc
0.009 [−0.025, 0.043]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Low
Low
Moderateb
None
0.001 [−0.017, 0.020]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
Reporting biasc
−0.052 [−0.082, −0.022]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Moderatef
Reporting biasc
−0.013 [−0.051, 0.026]
ꚚꚚꚚOModerate
Absent
Low
Low
Low
Low
Moderateb
None
−0.008 [−0.027, 0.011]
ꚚꚚꚚꚚHigh
A wave
Overall
104 ProspectiveCohort3 Cross-Sectional3 Case-control
Low
Low
Moderatea
Low
Moderateb
Reporting biasc
0.026 [ −0.018, 0.070]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Low
Low
Low
Low
Moderateg
None
0.039 [0.017, 0.060]
ꚚꚚꚚꚚHigh
≥3 months
Low
Moderated
Low
Low
Moderatef
None
0.034 [−0.034, 0.102]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderateb
None
−0.005 [−0.061, 0.050]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Low
Low
Moderatef
None
0.086 [0.022, 0.150]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Moderated
Low
Low
Moderatef
None
0.035 [−0.036, 0.105]
ꚚꚚꚚOModerate
Absent
Low
Low
Highe
Low
Moderateb
None
0.014 [−0.051, 0.080]
ꚚꚚꚚꚚ High
LAD
Overall
124 ProspectiveCohort1 Retrospective cohort3 Cross-Sectional4 Case-Control
Low
Low
Highe
Low
Low
Reporting biasc
1.603 [0.696, 2.511]
ꚚꚚꚚꚚHigh
Grouped by duration
1–3 months
Low
Moderated
Moderatea
Low
Moderatef
None
1.127 [−0.571, 2.826]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highe
Low
Moderateg
Reporting biasc
1.863 [0.694, 3.032]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Moderated
Moderatea
Low
Moderatef
Reporting biasc
0.937 [−0.183, 2.057]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Moderatea
Low
Moderateg
Reporting biasc
2.305 [1.058, 3.74]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Moderated
Highe
Low
Moderateg
None
2.287 [0.910, 3.664]
ꚚꚚꚚOModerate
Absent
Low
Moderated
Highe
Low
Moderateg
Reporting biasc
1.064 [−0.167, 2.295]
ꚚꚚꚚOModerate
LAVI
Overall
154 ProspectiveCohort2 Retrospective cohort4 Cross-Sectional4 Case-Control1 Observational Cohort
Lowc
Low
Highe
Low
Moderateb
Reporting biasc
0.895 [−0.509, 2.29]
ꚚꚚꚚOModerate
Grouped by duration
1–3 months
Highh
Highh
Low
Low
Moderateg
None
1.95 [0.728, 3.17]
ꚚꚚOO Low
≥3 months
Lowt
Low
Highe
Low
Moderatef
Reporting biasc
0.941 [−0.712, 2.59]
ꚚꚚꚚOModerate
Grouped by COVID-19 severity
Mild
Low
Low
Moderate1
Low
Moderateg
None
0.922 [0.139, 1.845]
ꚚꚚꚚꚚHigh
Moderate-Severe
Low
Moderated
Low
Low
Moderateg
Reporting biasc
1.475 [0.374, 2.575]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Low
Low
Moderateg
Reporting biasc
1.135 [0.290, 1.980]
ꚚꚚꚚꚚHigh
Absent
Lowt
Low
Highe
Low
Moderatef
None
1.078 [−1.032, 3.187]
ꚚꚚꚚꚚHigh
RV-GLS
Overall
165 Prospective Cohort2 Retrospective cohort1 Cross-Sectional6 Case-Control1 Observational Cohort
Low
Low
Highe
Low
Low
None
2.179 [1.099, 3.260]
ꚚꚚꚚꚚHigh
Grouped by duration
1–3 months
Low
Moderated
Highe
Low
Moderatef
None
1.547 [−0.335, 3.430]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highe
Low
Low
None
1.842 [0.853, 2.831]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Moderatef
None
1.27 [−0.283, 2.73]
ꚚꚚꚚOModerate
Moderate-Severe
Low
Moderated
Highe
Low
Moderateg
None
4.306 [2.398, 6.214]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderateg
None
2.228 [0.377, 4.079]
ꚚꚚꚚOModerate
Absent
Low
Low
Highe
Low
Low
None
2.152 [0.807, 3.498]
ꚚꚚꚚꚚHigh
RV-MPI
Overall
61 Prospective Cohort2 Cross-Sectional3 Case-Control
Low
Low
Highe
Low
Moderateg
None
0.060 [0.030, 0.089]
ꚚꚚꚚOModerate
Grouped by duration
≥3 months
Low
Moderated
Highe
Low
Moderateg
None
0.035 [0.008, 0.062]
ꚚꚚOOLow
Grouped by COVID-19 severity
Mild
Highh
Highh
Highe
Low
Moderateg
None
0.063 [0.021, 0.106]
ꚚꚚOOLow
Grouped by comorbid disease
Absent
Lowv
Low
Highe
Low
Moderateg
None
0.060 [0.030, 0.089]
ꚚꚚꚚOModerate
RV diameter
Overall
154 ProspectiveCohort2 Retrospective cohort5 Cross-Sectional4 Case-Control
Highw
Low
Highe
Low
Moderateb
Reporting biasc
0.306 [−0.566, 1.178]
ꚚꚚOOLow
Grouped by duration
1–3 months
Highw
Moderatee
Highe
Low
Moderateg
None
−1.820 [−3.406, −0.234]
ꚚꚚOOLow
≥3 months
Low
Low
Low
Low
Low
Reporting biasc
0.900 [0.510, 1.290]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Low
Low
Low
Reporting biasc
0.865 [0.412, 1.317]
ꚚꚚꚚꚚHigh
Moderate-Severe
Lowv
Low
Highe
Low
Moderatef
None
−0.268 [−1.506, 0.970]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Moderatea
Low
Moderatef
Reporting biasc
0.694 [0.242, 1.146]
ꚚꚚꚚOModerate
Absent
Highw
Low
Highe
Low
Moderateb
None
−0.227 [−1.467, 1.012]
ꚚꚚOOLow
TAPSE
Overall
268 Prospective Cohort3 Retrospective cohort7 Cross-Sectional8 Case-Control1 Observational Cohort
Low
Low
Highe
Low
Low
None
−1.01 [−1.621, −0.402]
ꚚꚚꚚꚚHigh
Grouped by duration
1–3 months
Low
Low
Highe
Low
Moderateb
None
−0.983 [−2.225, 0.285]
ꚚꚚꚚOModerate
≥3 months
Low
Low
Highe
Low
Low
None
−1.160 [−1.885, −0.466]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Moderatea
Low
Moderatef
None
−0.283 [−1.050, 0.483]
ꚚꚚꚚꚚ High
Moderate-Severe
Low
Low
Moderatea
Low
Moderateg
Reporting biasc
−1.234 [−2.197, −0.270]
ꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Low
Low
Highe
Low
Moderateb
Reporting biasc
−0.510 [−1.409, 0.390]
ꚚꚚꚚOModerate
Absent
Low
Low
Highe
Low
Low
None
−1.440 [−2.274, −0.608]
ꚚꚚꚚꚚHigh
sPAP
Overall
124 Prospective Cohort1 Retrospective cohort2 Cross-Sectional5 Case-Control
Low
Low
Highe
Low
Low
Reporting biasc
4.37 [2.378, 6.380]
ꚚꚚꚚꚚHigh
Grouped by duration
≥3 months
Low
Low
Highe
Low
Moderateg
Reporting biasc
5.172 [2.668, 7.676]
ꚚꚚꚚꚚHigh
Grouped by COVID-19 severity
Mild
Low
Low
Highe
Low
Low
Reporting biasc
3.749 [0.817, 6.682]
ꚚꚚꚚꚚOModerate
Moderate-Severe
Lowx
Low
Highe
Low
Moderateg
Reporting biasc
6.686 [3.109, 9.662]
ꚚꚚꚚꚚOModerate
Grouped by comorbid disease
Present
Lowy
Low
Highe
Low
Low
Reporting biasc
6.777 [4.463, 9.091]
ꚚꚚꚚꚚHigh
Absent
Low
Low
Highe
Low
Moderatef
Reporting biasc
2.039 [−0.181, 4.258]
ꚚꚚꚚꚚOModerate
LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, eft ventricular ejection fraction; PWD, posterior wall diameter; IVSD, interventricular septum diameter; LVM, left ventricular mass; LVMI, left ventricular mass index; LV-GLS, left ventricular global longitudinal strain; LAD, left atrium diameter; LAVI, left atrium volume index; LV-MPI, left ventricular myocardial performance index; E/A, the ratio of peak velocity blood flow from left ventricular relaxation in early diastole (the E wave) to peak velocity flow in late diastole caused by atrial contraction (the A wave); E/e’, ratio of E wave to early diastolic mitral annular velocity (e’); RVD, right ventricular diameter; RAD, right atrium diameter; RV-GLS, right ventricular global longitudinal strain; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; RV-MPI, right ventricular myocardial performance index; CI, confidence interval.
a
The level heterogeneity is moderate.
b
Confidence interval of the summary estimate included 0.
c
Majority of the studies are from Turkey.
d
The number of studies is less than 6.
e
The level heterogeneity is high.
f
The overall sample size was less than 500 and confidence interval of the summary estimate included 0.
g
The overall sample size was less than 500.
h
The number of studies is 2.
i
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the statistical significance of the summary estimate to significant result (42).
j
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the direction or statistical significance of the summary estimate (42).
k
All of the studies are from Turkey.
l
Even though two of the included studies were at high risk of bias for this outcome, a sensitivity analysis by excluding this study did not change the magnitude, direction, or statistical significance of the summary estimate (23, 27).
m
Two of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the statistical significance of the summary estimate to significant result (23, 27).
n
Two of the included studies evaluated athletes as their cases (17, 19) and excluding them, did not change the statistical significance of summary estimate.
o
One of the included studies evaluated athletes as their cases (17) and excluding them, did not change the statistical significance of summary estimate.
p
Three studies were at high risk of bias for LVEF and excluding them changed the significancy of the summary estimate to an insignificant result (17, 27, 36).
q
Three studies were at high risk of bias for LVEF and excluding them did not change the significancy of the summary estimate (17, 27, 36).
r
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the direction or statistical significance of the summary estimate (27).
s
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the insignificancy of the summary estimate to a significant result (45).
t
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the insignificancy of the summary estimate (45).
u
Even though two of the included studies were at high risk of bias for this outcome, a sensitivity analysis by excluding this study did not change the magnitude, direction, or statistical significance of the summary estimate (42, 45).
v
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the significancy of the results (14).
w
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study changed the significancy of the results (14).
x
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the significancy of the results (33).
y
One of the include studies was at high risk of bias for this outcome and sensitivity analysis by excluding this study did not change the significancy of the result (24).
Figure 3
Risk-of-bias assessment (traffic light plot).
3.4
Outcome quality assessment
The certainty of evidence for outcomes, as assessed by GRADE framework, is delineated in Table 2. The meta-analysis indicates a moderate level of certainty in the majority of outcomes, primarily attributable to the inherent susceptibility to bias in observational studies. Outcomes with low certainty are typically caused by a small number of studies, significant heterogeneity, and the existence of potential biases.
3.5
Result of synthesis
3.5.1
Overall outcomes
Among the echocardiographic measures of LV systolic function, LV-GLS and LVEF were found to be significantly different between the two groups being compared. The analysis of 26 studies showed a notable decrease in LV-GLS (less negative) in post-COVID patients (n = 1,810) compared to controls (n = 1,254), with a mean difference of 1.21 [95%CI (0.681, 1.75), p = 0.000, I2 = 91%]. Post-COVID patients (n = 2,173) exhibited a lower LVEF compared to controls (n = 1,770), with a MD of −0.829 [95%CI (−1.397, −0.262), p = 0.004, I2 = 73%]. Additionally, the meta-analysis of 12 studies revealed that LAD was significantly increased in post-COVID patients (n = 833) comparing to controls (n = 892) with a MD of 1.603 [95%CI (0.696, 2.511), p = 0.001, I2 = 80.7%]. However, LAVI was not significantly different comparing two groups with a MD of 0.895 [95% CI (−0.509, 2.29), p = 0.211, I2 = 82.7%]. In terms of RV evaluation, post-COVID patients showed significantly lower RV-GLS (less negative) and higher RV-MPI values compared to controls, with mean differences of 2.179 [95%CI (1.099, 3.260), p = 0.000, I2 = 85.4%] and 0.060 [95% CI (0.030, 0.089), p = 0.009, I2 = 99%], respectively. No significant differences were found in the diastolic and geometric indices of the left ventricle between the two groups being compared (Table 3). Forest plots are provided in supporting information (Supplementary S4 document).
Table 3
Result of synthesis.
No. studies
No. cases
No. controls
Effect model
MD (CI: 95%)
P value
Heterogeneity
I2
P value
LV geometric indices
LVEDD
Overall
26
1,597
1,612
Random
0.440 [−0.092, 0.155]
0.148
65%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
2
174
144
Fixed
−0.232 [−1.29, 0.835]
0.670
0%
0.558
1–3 months
8
733
558
Random
0.542 [−0.524, 1.608]
0.319
80%
0.000
≥3 months
16
690
910
Random
0.516 [−0.315, 1.346]
0.223
58.6%
0.002
Grouped by severity of COVID-19 infectiona
Mild
15
1,079
1,205
Random
0.580 [−0.199, 1.35]
0.145
51.4%
0.011
Moderate-Sever
8
437
356
Random
0.620 [−0.421, 1.662]
0.243
78.8%
0.000
Mixed
2
81
91
Fixed
0.128 [−1.537, 1.792]
0.881
0%
0.955
Grouped by presence of comorbid diseases
Present
12
645
542
Random
0.605 [−0.324, 1.533]
0.202
75.3%
0.000
Absent
14
952
1,070
Random
0.325 [−0.484, 1.133]
0.431
54%
0.008
LVEDV
Overall
11
453
624
Random
4.79 [−0.341, 9.93]
0.067
56.9%
0.010
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
3
147
121
Fixed
6.87 [0.605, 13.13]
0.032
0%
0.536
≥3 months
8
306
503
Random
3.88 [−2.35, 10.11]
0.223
66%
0.004
Grouped by severity of COVID-19 infectiona
Mild
5
224
206
Fixed
8.39 [3.57, 13.20]
0.001
0%
0.753
Moderate-Sever
3
77
129
Fixed
10.09 [2.29, 17.89]
0.011
0%
0.728
Mixed
2
81
51
Random
−3.40 [−12.02, 5.22]
0.440
71.2%
0.062
Grouped by presence of comorbid diseases
Present
5
154
214
Random
10.35 [4.93, 15.76]
0.000
62.6%
0.020
Absent
6
299
410
Fixed
0.602 [−4.87, 6.07]
0.829
0%
0.945
LVESD
Overall
15
918
952
Random
0.325 [−0.119, 0.352]
0.346
73.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
67
37
–
–
–
–
1–3 months
4
346
217
Random
−0.397 [−1.353, 0.560]
0.417
80%
0.000
≥3 months
10
505
698
Fixed
0.928 [0.566, 1.289]
0.000
0%
0.541
Grouped by Severity of COVID-19 infectiona
Mild
7
407
437
Fixed
0.908 [0.488, 1.32]
0.000
32.1%
0.183
Moderate-Sever
5
360
227
Random
−0.272 [−1.42, 0.877]
0.642
84.5%
0.000
Mixed
2
81
51
Random
0.776 [−0.701, 2.254]
0.303
0%
0.772
Grouped by presence of comorbid diseases
Present
5
334
202
Random
−1.292 [−2.089, −0.495]
0.001
54%
0.069
Absent
10
584
750
Fixed
0.905 [0.567, 1.24]
0.000
0%
0.570
LVESV
Overall
6
317
431
Fixed
0.608 [−1.24, 2.45]
0.519
41.8%
0.127
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
1
86
60
–
0.500 [−2.77, 3.77]
–
–
–
≥3 months
5
231
371
Random
1.69 [−1.95, 5.33]
0.363
53%
0.0072
Grouped by Severity of COVID-19 infectiona
Mild
2
129
101
Random
2.23 [−1.43, 5.88]
0.230
57.7%
0.124
Moderate-Sever
1
36
41
–
6.20 [−2.17, 14.57]
–
–
–
Mixed
2
82
52
Fixed
1.40 [−3.08, 5.90]
0.539
0%
0.525
Grouped by presence of comorbid diseases
Present
2
79
82
Fixed
5.55 [1.15, 9.96]
0.013
0%
0.859
Absent
4
238
349
Fixed
−0.451 [−2.48, 1.58]
0.664
0%
0.445
IVSD
Overall
19
1,305
1,247
Random
−0.203 [−0.526, 0.119]
0.217
95.6%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
2
174
144
Random
−0.108 [−1.16, 0.944]
0.840
92.8%
0.000
1–3 months
8
677
530
Random
−0.256 [−0.796, 0.284]
0.352
98.2%
0.000
≥3 months
9
454
573
Fixed
−0.132 [−0.258, −0.007]
0.039
0%
0.456
Grouped by severity of COVID-19 infectiona
Mild
11
794
732
Random
−0.411 [−0.830, 0.007]
0.054
97.1%
0.000
Moderate-Sever
5
360
227
Random
0.250 [−0.399, 0.899]
0.451
80.4%
0.000
Mixed
2
81
51
Fixed
−0.128 [−0.427, 0.172]
0.403
0%
0.633
Grouped by presence of comorbid diseases
Present
6
404
272
Random
0.098 [−0.520, 0.715]
0.756
88.4%
0.000
Absent
13
901
975
Random
−0.315 [−0.691, 0.061]
0.101
96.5%
0.000
PWD
Overall
19
1,305
1,247
0.086 [−0.139, 0.311]
0.455
79.8%
0.000
Grouped by duration from Acute COVID to echo examination in recovery phase
<1 months
2
174
144
Random
−0.127 [−0.762, 0.497]
0.690
96.3%
0.000
1–3 months
8
677
530
Random
0.273 [−0.118, 0.663]
0.171
0%
0.497
≥3 months
9
454
573
Fixed
−0.102 [−0.211, 0.007]
0.068
77.5%
0.000
Grouped by severity of COVID-19 infectiona
Mild
11
794
732
Random
−0.149 [−0.405, 0.106]
0.251
64.8%
0.002
Moderate-Sever
5
360
227
Random
0.614 [0.259, 0.969]
0.001
52.7%
0.076
Mixed
2
81
51
Fixed
−0.159 [−0.552, 0.233]
0.426
0%
0.496
Grouped by presence of comorbid diseases
Present
6
404
272
Random
0.311 [−0.072, 0.695]
0.112
85.2%
0.000
Absent
13
901
975
Random
−0.006 [−0.248, 0.237]
0.962
63.7%
0.001
LVM
Overall
7
453
478
Random
−7.630 [−21.7, 6.50]
0.290
74.8%
0.001
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
4
325
201
Random
−3.59 [−19.7, 12.53]
0.663
74.1%
0.009
≥3 months
3
128
277
Fixed
−19.37 [−29.8, −8.92]
0.000
0%
0.423
Grouped by Severity of COVID-19 infectiona
Mild
4
191
139
Fixed
−13.71 [−25.30, −2.11]
0.020
0%
0.426
Moderate-Sever
2
192
102
Fixed
9.018 [0.458, 17.57]
0.039
0%
0.845
Grouped by presence of comorbid diseases
Present
3
234
128
Fixed
7.54 [−0.720, 15.81]
0.074
0%
0.420
Absent
4
219
350
Fixed
−18.28 [−26.72, −9.85]
0.000
19%
0.295
LVMI
Overall
7
432
494
Random
−1.65 [−6.62, 3.31]
0.513
81.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
3
237
149
Fixed
−0.251 [−1.95, 1.45]
0.772
0%
0.408
≥3 months
4
195
345
Random
−1.023 [−9.48, 7.44]
0.813
89.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
5
186
169
Random
2.408 [−1.11, 5.93]
0.181
73.1%
0.005
Moderate-Sever
1
176
88
–
0.00 [−1.74, 1.74]
–
–
–
Grouped by presence of comorbid diseases
Present
1
176
88
–
0.00 [−1.74, 1.74]
–
–
–
Absent
6
256
406
Random
−2.29 [−9.69, 5.11]
0.544
83.6%
0.000
LV systolic function
LVEF
Overall
32
2,173
1,770
Random
−0.829 [−1.397, −0.262]
0.004
73.8%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
2
174
144
Random
0.667 [−1.42, 2.76]
0.533
90.3%
0.001
1–3 months
9
691
498
Random
−0.615 [−1.75, 0.527]
0.291
83.5%
0.000
≥3 months
20
836
1,172
Random
−1.16 [ −1.94, −0.375]
0.004
60.1%
0.000
Grouped by severity of COVID-19 infectiona
Mild
20
1,586
1,109
Random
−0.886 [−1.64, −0.128]
0.022
78%
0.000
Moderate-Sever
10
495
402
Random
−0.900 [−1.96, 0.169]
0.098
69%
0.001
Mixed
1
22
22
–
−1.58 [−4.78, 1.62]
–
–
–
Grouped by presence of comorbid diseases
Present
16
1,245
663
Random
−0.852 [−1.66, −0.038]
0.040
56.5%
0.002
Absent
16
928
1,107
Random
−0.833 [−1.64, −0.005]
0.049
79.6%
0.000
LV-GLS
Overall
26
1,810
1,254
Random
1.21 [0.681, 1.75]
0.000
91%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
4
250
194
Random
0.615 [−0.776, 2.00]
0.386
95.9%
0.000
1–3 months
7
827
427
Random
1.22 [0.181, 2.27]
0.021
57.5%
0.028
≥3 months
15
733
633
Random
1.37 [0.637, 2.11]
0.000
92.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
13
1,084
714
Random
0.815 [0.047, 1.58]
0.038
88.1%
0.000
Moderate-Sever
7
380
308
Random
1.84 [0.751, 2.94]
0.001
90.1%
0.000
Mixed
3
226
112
Random
1.07 [−0.629, 2.77]
0.217
74.5%
0.020
Grouped by presence of comorbid diseases
Present
12
884
552
Random
0.919 [0.07, 1.76]
0.033
88.8%
0.000
Absent
14
926
702
Random
1.43 [0.703, 2.16]
0.000
93.7%
0.000
LV diastolic function
E/A ratio
Overall
21
1,321
1,258
−0.058 [−0.118, 0.002]
0.057
79.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
1
107
17
–
0.190 [−0.017, 0.397]
–
–
–
1–3 months
8
711
517
Fixed
−0.084 [−0.129, −0.039]
0.000
32%
0.167
≥3 months
12
503
724
Random
−0.043 [−0.122, 0.035]
0.280
85.6%
0.000
Grouped by severity of COVID-19 infectiona
Mild
13
835
706
Fixed
−0.042 [−0.076, −0.007]
0.017
45%
0.037
Moderate-Sever
5
334
263
Random
−0.134 [−0.258, −0.010]
0.034
82%
0.000
Mixed
2
82
52
Random
0.133 [−0.085, 0.351]
0.233
97%
0.000
Grouped by presence of comorbid diseases
Present
7
382
322
Random
−0.137 [−0.424, −0.032]
0.010
57.9%
0.027
Absent
14
939
936
Random
−0.021 [−0.093, 0.050]
0.563
82%
0.000
E/e’ ration
Overall
27
1,799
1,493
0.116 [−0.275, 0.507]
0.561
85.2%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
2
174
144
Random
−0.412 [−1.79, 0.968]
0.558
80.7%
0.000
1–3 months
7
496
553
Random
−0.010 [−0.753, 0.732]
0.978
58.1
0.000
≥3 months
17
657
696
Random
0.315 [−0.213, 0.843]
0.243
87%
0.000
Grouped by Severity of COVID-19 infectiona
Mild
16
1,279
898
Random
0.105 [−0.410, 0.620]
0.689
82.6%
0.000
Moderate-Sever
6
336
274
Fixed
0.380 [−0.009, 0.768]
0.055
0%
0.808
Mixed
2
82
52
Random
0.379 [−1.15, 1.91]
0.629
98.5
0.000
Grouped by presence of comorbid diseases
Present
16
1,179
706
Fixed
0.164 [−0.052, 0.381]
0.137
0%
0.521
Absent
11
620
787
Random
0.129 [−0.470, 0.729]
0.672
93.5%
0.000
Mitral E wave
Overall
13
939
817
Fixed
−0.013 [−0.029, 0.002]
0.099
44%
0.040
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
−0.010 [−0.059, 0.039]
–
–
1–3 months
7
644
493
Random
−0.021 [−0.052, 0.009]
0.161
55.9%
0.043
≥3 months
5
188
217
Fixed
0.009 [−0.025, 0.043]
0.599
31.2%
0.213
Grouped by severity of COVID-19 infection
Mild
9
660
636
Fixed
0.001 [−0.017, 0.020]
0.884
23.4%
0.234
Moderate-Sever
4
279
181
Fixed
−0.052 [−0.082, −0.022]
0.001
0%
0.437
Grouped by presence of comorbid diseases
Present
6
383
280
Random
−0.013 [−0.051, 0.026]
0.527
62.1%
0.022
Absent
7
556
537
fixed
−0.008 [−0.027, 0.011]
0.426
21.1%
0.268
Mitral A wave
Overall
10
684
647
Random
0.026 [ −0.018, 0.070]
0.245
72.8%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
−0.70 [−0.106, −0.034]
–
–
–
1–3 months
6
468
405
Fixed
0.039 [0.017, 0.060]
0.000
30.6%
0.206
≥3 months
3
109
135
Fixed
0.034 [−0.034, 0.102]
0.332
0%
0.912
Grouped by severity of COVID-19 infection
Mild
7
566
500
Random
0.015 [−0.031, 0.062]
0.521
77.2%
0.000
Moderate-Sever
3
118
147
Fixed
0.086 [0.022, 0.150]
0.008
0%
0.575
Grouped by presence of comorbid diseases
Present
3
128
110
Fixed
0.035 [−0.036, 0.105]
0.336
0%
0.913
Absent
7
556
537
Random
0.014 [−0.051, 0.080]
0.673
75%
0.001
Left atrium
LAD
Overall
12
833
892
Random
1.603 [0.696, 2.511]
0.001
80.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
4
423
288
Random
1.127 [−0.571, 2.826]
0.193
50.8%
0.107
≥3 months
8
410
604
Random
1.863 [0.694, 3.032]
0.002
86.2%
0.000
Grouped by severity of COVID-19 infectiona
Mild
5
385
367
Random
0.937 [−0.183, 2.057]
0.101
63%
0.029
Moderate-Sever
5
318
258
Random
2.305 [1.058, 3.74]
0.000
63.7%
0.026
Mixed
1
60
30
Random
3.40 [1.850, 4.95]
–
–
–
Grouped by presence of comorbid diseases
Present
6
447
388
Random
2.287 [0.910, 3.664]
0.001
77.4%
0.000
Absent
6
386
507
Random
1.064 [−0.167, 2.295]
0.090
83%
0.000
LAVI
Overall
15
821
983
Random
0.895 [−0.509, 2.29]
0.211
82.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
−1.50 [−3.54, 0.543]
–
–
–
1–3 months
2
236
174
Fixed
1.95 [0.728, 3.17]
0.002
8.4%
0.296
≥3 months
12
478
702
Random
0.941 [−0.712, 2.59]
0.256
84%
0.000
Grouped by severity of COVID-19 infectiona
Mild
8
420
481
Fixed
0.922 [0.139, 1.845]
0.023
46%
0.073
Moderate-Sever
4
253
217
Fixed
1.475 [0.374, 2.575]
0.009
35.1%
0.201
Mixed
2
78
48
Random
3.06 [−0.174, 6.30]
0.064
93.8%
0.000
Grouped by presence of comorbid diseases
Present
8
421
416
Fixed
1.135 [0.290, 1.980]
0.008
0%
0.462
Absent
7
400
567
Random
1.078 [−1.032, 3.187]
0.317
91.4%
0.000
Right heart function
RV-GLS
Overall
16
775
677
Random
2.179 [1.099, 3.260]
0.000
85.4%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
67
37
–
7.860 [6.33, 9.38]
–
–
–
1–3 months
3
170
129
Random
1.547 [−0.335, 3.430]
0.107
77.8%
0.011
≥3 months
12
538
511
Random
1.842 [0.853, 2.831]
0.000
74.3%
0.000
Grouped by severity of COVID-19 infectiona
Mild
7
380
331
Random
1.27 [−0.283, 2.73]
0.111
79.2%
0.000
Moderate-Sever
5
205
156
Random
4.306 [2.398, 6.214]
0.000
83.7%
0.000
Mixed
1
70
70
–
3.520 [2.61, 4.42]
–
–
–
Grouped by presence of comorbid diseases
Present
7
268
224
Random
2.228 [0.377, 4.079]
0.018
90%
0.000
Absent
9
507
453
Random
2.152 [0.807, 3.498]
0.002
79%
0.000
RV-MPI
Overall
6
327
352
Random
0.060 [0.030, 0.089]
0.009
99%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
1
51
32
–
0.190 [0.166, 0.214]
–
–
–
≥3 months
5
276
320
Random
0.035 [0.008, 0.062]
0.012
98.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
2
156
200
Random
0.063 [0.021, 0.106]
0.004
93.8%
0.000
Moderate-Sever
1
51
32
–
0.190 [0.166, 0.214]
–
–
–
Grouped by presence of comorbid diseases
Present
0
–
–
–
–
–
–
–
Absent
6
327
352
Random
0.060 [0.030, 0.089]
0.009
99%
0.000
RVD
Overall
15
1,055
999
Random
0.306 [−0.566, 1.178]
0.492
85.5%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
0.600 [−0.539, 1.739]
–
–
–
1–3 months
3
283
180
Random
−1.820 [−3.406, −0.234]
0.025
93.4%
0.000
≥3 months
11
665
712
Fixed
0.900 [0.510, 1.290]
0.000
0%
0.703
Grouped by severity of COVID-19 infection
Mild
8
666
674
Fixed
0.865 [0.412, 1.317]
0.000
0%
0.998
Moderate-Sever
7
389
325
Random
−0.268 [−1.506, 0.970]
0.672
92.6%
0.000
Grouped by presence of comorbid diseases
Present
7
365
328
Fixed
0.694 [0.242, 1.146]
0.003
48%
0.001
Absent
8
690
671
Random
−0.227 [−1.467, 1.012]
0.719
90.8%
0.000
RAD
Overall
8
450
422
Fixed
0.212 [−0.266, 0.689]
0.385
45.6%
0.075
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
2
246
158
Random
−0.329 [−1.710, 1.015]
0.640
70.5%
0.065
≥3 months
6
204
264
Fixed
0.499 [−0.096, 0.995]
0.107
21.4%
0.272
Grouped by severity of COVID-19 infection
Mild
3
147
155
Fixed
0.695 [−0.237, 1.62]
0.144
0%
0.572
Moderate-Sever
5
309
267
Random
0.173 [−0.647, 0.994]
0.679
61.3%
0.035
Grouped by presence of comorbid diseases
Present
7
400
372
Fixed
0.419 [−0.112, 0.951]
0.122
38.9%
0.132
Absent
1
50
50
–
−0.660 [−1.749, 0.429]
–
–
–
TAPSE
Overall
26
1,458
1,381
Random
−1.01 [−1.621, −0.402]
0.001
82%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
1.100 [0.014, 2.186]
–
–
–
1–3 months
5
583
432
Random
−0.983 [−2.225, 0.285]
0.121
76.7%
0.002
≥3 months
20
768
842
Random
−1.160 [−1.885, −0.466]
0.001
80.3%
0.000
Grouped by severity of COVID-19 infectiona
Mild
11
809
792
Random
−0.283 [−1.050, 0.483]
0.469
70.3%
0.000
Moderate-Sever
8
405
345
Random
−1.234 [−2.197, −0.270]
0.012
57%
0.022
Mixed
2
92
92
Random
−3.564 [−5.727, −1.400]
0.001
67.6%
0.079
Grouped by presence of comorbid diseases
Present
13
556
498
Random
−0.510 [−1.409, 0.390]
0.267
67.6%
0.000
Absent
13
902
883
Random
−1.440 [−2.274, −0.608]
0.001
87%
0.000
sPAP
Overall
12
1,049
885
Random
4.37 [2.378, 6.380]
0.000
94.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phasea
<1 months
1
107
107
–
0.300 [−0.979, 1.579]
–
–
–
1–3 months
1
51
32
–
5.70 [−2.010, 13.41]
–
–
–
≥3 months
9
419
646
Random
5.172 [2.668, 7.676]
0.000
95.2%
0.000
Grouped by severity of COVID-19 infectiona
Mild
6
801
437
Random
3.749 [0.817, 6.682]
0.012
92.3%
0.000
Moderate-Sever
5
178
211
Random
6.686 [3.109, 9.662]
0.000
95.2%
0.000
Grouped by presence of comorbid diseases
Present
6
626
314
Random
6.777 [4.463, 9.091]
0.000
91.2%
0.000
Absent
6
423
571
Random
2.039 [−0.181, 4.258]
0.072
91%
0.000
LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; PWD, posterior wall diameter; IVSD, interventricular septum diameter; LVM, left ventricular mass; LVMI, left ventricular mass index; LV-GLS, eft ventricular global longitudinal strain; LAD, left atrium diameter; LAVI, left atrium volume index; LV-MPI, left ventricular myocardial performance index; E/A, the ratio of peak velocity blood flow from left ventricular relaxation in early diastole (the E wave) to peak velocity flow in late diastole caused by atrial contraction (the A wave); E/e’, ratio of E wave to early diastolic mitral annular velocity (e’); RVD, right ventricular diameter; RAD, right atrium diameter; RV-GLS, right ventricular global longitudinal strain; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; RV-MPI, right ventricular myocardial performance index; CI, confidence interval.
Bold values show significant results (p < 0.05).
a
The studies conducted by Gumanova et al. (32), Beaudry et al. (30) and Yang et al. (37) did not report severity of COVID-19 infection.
b
One study did not report the timeframe from after recovering from COVID-19 to the echocardiography examination (24).
3.5.2
Subgroup analysis
3.5.2.1
Grouped by duration from acute COVID to echo examination in recovery phase
The subgroup meta-analysis found that among LV geometric indices, LVESD was notably higher in post-COVID patients (n = 505) compared to controls (n = 698) for a duration of ≥3 months, showing a MD of 0.928 [95% CI (0.566, 1.289), p = 0.000, I2 = 0%]. Moreover, post-COVID patients exhibited a significant decrease in IVSD and LVM compared to controls for a duration of ≥3 months, with a MD of −0.132 [95% CI (−0.258, −0.007), p = 0.039, I2 = 0%] and −19.37 [95%CI (−29.8, −8.92), p = 0.000, I2 = 0%], respectively.
In terms of systolic function lasting ≥3 months, LVEF was found to be significantly lower in post-COVID patients (n = 836) compared to controls (n = 1,172), with a MD of −1.16 [95% CI (−1.94, −0.375), p = 0.004, I2 = 60.1%]. Furthermore, post-COVID patients exhibited a significantly decreased LV-GLS (less negative) compared to controls for durations of both 1–3 months and ≥3 months, with MDs of 1.22 [95% CI (0.181, 2.27), p = 0.021, I2 = 57.5%] and 1.37 [95% CI (0.637, 2.11), p = 0.000, I2 = 92.9%], respectively.
There were significant differences in the E/A ratio and mitral A wave among diastolic function indices. Within a period of ≥3 months, post-COVID patients exhibited a significant reduction in the E/A ratio and an increase in the mitral A wave compared to the control group. The MDs were −0.084 [95%CI (−0.129, −0.039), p = 0.000, I2 = 32%] for the E/A ratio and 0.039 [95% CI (0.017, 0.060), p = 0.000, I2 = 30.6%] for the mitral A wave. Additionally, post-COVID patients (n = 410) exhibited a significant elevation in LAD in comparison to the control subjects (n = 604), over a duration of ≥3 months, with a MD of 1.863 [95% CI (0.694, 3.032), p = 0.002, I2 = 86.2%]. However, a meta-analysis of 2 studies showed a significant increase in LAVI in post-COVID patients (n = 236) compared to controls (n = 174) within a timeframe of 1–3 months, with a MD of 1.95 [95% CI (0.728, 3.17), p = 0.002, I2 = 8.4%].
In subgroup meta-analysis of RV function, RV-MPI, RVD and sPAP were significantly higher in post-COVID patients compared to control group for a duration of ≥3 months, with MDs of 0.035 [95% CI (0.008, 0.062), p = 0.012, I2 = 98.9], 0.900 [95% CI (0.510, 1.290), p = 0.000, I2 = 0%] and 5.172 [95%CI (2.668, 7.676), p = 0.000, I2 = 95.2%], respectively. Moreover, a significant decrease in TAPSE and RV-GLS (less negative) were observed in post-COVID patients compared to controls with a MD of −1.160 [95% CI (−1.885, −0.466), p = 0.001, I2 = 80.3%] and 1.842 [95%CI (0.853, 2.831), p = 0.000, I2 = 74.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2
Grouped by severity of COVID-19 infection
3.5.2.2.1
Mild COVID-19 infection
In terms of mild COVID-19 infection and LV geometric indices, significant increase was observed in LVEDV and LVESD in post-COVID patients compared to controls with MDs of 8.39 [95% CI (3.57, 13.20), p = 0.001, I2 = 0%], and 0.908 [95% CI (0.488, 1.32), p = 0.000, I2 = 32.1%], respectively. LVM was significantly lower in post-COVID patients (n = 191) compared to controls (n = 139), with a MD of −13.71 [95 CI% (−25.30, −2.11), p = 0.020, I2 = 0%]. Moreover, significant changes in systolic function were observed in mild infection cases. Post-COVID patients reveled to have a decrease in LVEF and LV-GLS (less negative) compared to control groups, with MDs of −0.886 [95% CI (−1.64, −0.128), p = 0.022, I2 = 78%] and 0.815 [95% CI (0.047, 1.58), 0.038, I2 = 88.1%], respectively. Among LV diastolic indices, E/A ratio was significantly lower and LAVI was significantly increased in mild infection compared to controls, with MDs of −0.042 [95% CI (−0.076, −0.007), p = 0.017, I2 = 45%] and 0.922 [95% CI (0.139, 1.845), p = 0.023, I2 = 46%], respectively. RVD and sPAP were significantly higher in post-COVID patients compared to controls with MDs of 0.865 [95% CI (0.412, 1.317), p = 0.000, I2 = 0%] and 3.749 [95% CI (0.817, 6.682), p = 0.012, I2 = 0.012, I2 = 92.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2.2
Moderate and/or severe COVID-19 infection
Post-COVID patients exhibited higher values of LVEDV, PWD, and LVM compared to the control group. The MDs for LVEDV, PWD, and LVM were 10.09 [95% CI (2.29, 17.89), p = 0.011, I2 = 0%], 0.614 [95% CI (0.259, 0.969), p = 0.001, I2 = 52.7%], and 9.018 [95% CI (0.458, 17.57), p = 0.039, I2 = 0%], respectively. Concerning systolic function, there was no significant difference in LVEF between the two groups, as indicated by a MD of −0.900 [95% CI (−1.96, 0.169), p = 0.098, I2 = 69%]. Conversely, LV-GLS exhibited significantly lower (less negative) values in post-COVID patients in comparison to the control group, with a MD of 1.84 [95% CI (0.751, 2.94), I2 = 90.1%]. Among LV diastolic indices, E/A ratio and mitral E wave values were significantly decreased and mitral A wave was significantly increased in post-COVID patients compared to controls. The MDs for E/A, E wave and A wave were −0.134 [95% CI (−0.258, −0.010), p = 0.034, I2 = 82%], −0.052 [95% CI (−0.082, −0.022), p = 0.001, I2 = 0%] and 0.086 [95%CI (0.022, 0.150), p = 0.008, I2 = 0%], respectively.
Additionally, significantly higher values were found in both LAD and LAVI in post-COVID patients compared to controls. The MD for LAD was 2.305 [95% CI (1.058, 3.74), p = 0.000, I2 = 63.7%], and for LAVI it was 1.475 [95% CI (0.374, 2.575), p = 0.009, I2 = 32.1%].
Regarding RV indices, post-COVID patients showed significantly increased value in sPAP with MDs of 4.306 [95% CI (2.398, 6.214), p = 0.000, I2 = 83.7%]. Moreover, TAPSE and RV-GLS values were significantly lower in post-COVID patients compared to controls with MDs of −1.234 [95% CI (−2.197, −0.270), p = 0.012, I2 = 57%] and 6.686 [95% CI (3.109, 9.662), p = 0.000, I2 = 95.2%], respectively. Detailed information is provided in Table 3.
3.5.2.3
Grouped by presence of comorbid diseases
Post-COVID patients with comorbidities showed higher values of LVEDV and LVESV compared to comorbid-matched control group with MDs of 10.35 [95% CI (4.93, 15.76), p = 0.000, I2 = 62.6%] and 5.55 [95% CI (1.15, 9.96), p = 0.013, I2 = 0%], respectively. There was a significant decrease in LVESD in post-COVID patients with comorbidities and an increase in cases without comorbidities compared to their comorbid-matched controls with MDs of −1.292 95% CI [−2.089, −0.495], p = 0.001, I2 = 43.2%) and 0.905 [95% CI (0.567, 1.24), p = 0.000, I2 = 0%], respectively. LVEF exhibited a significant decrease in post-COVID patients with comorbidities and those without comorbidities when compared to their comorbid-matched controls. The MDs were −0.852 [95% CI (−1.66, −0.038), p = 0.040, I2 = 56.5%] and −0.833 [95%CI (−1.64, −0.005), p = 0.049, I2 = 79.6%], respectively. Furthermore, LV-GLS was significantly decreased (less negative) in both groups of post-COVID patients, with MDs of 0.919 [95% CI (0.07, 1.76), p = 0.033, I2 = 88.8%] and 1.43 [95% CI (0.703, 2.16), p = 0.000, I2 = 93.7%] compared to their respective controls. E/A ratio was significantly lower in post-COVID patients with comorbidities compare to its comorbid-matched controls, with a MD of −0.137 [95% CI (−0.424, −0.032), p = 0.010, I2 = 57.9%]. Significantly higher values of both LAD and LAVI were observed in post-COVID patients with comorbidities compared to their matched controls. The MDs were 2.287 [95% CI (0.910, 3.664), p = 0.001, I2 = 77.4%] and 1.135 [95% CI (0.290, 1.980), p = 0.008, I2 = 0%], respectively.
Regarding RV function, RV-GLS was notably decreased (less negative) in both post-COVID patients with and without comorbidities compared to their controls, with MDs of 2.228 [95% CI (0.377, 4.079), p = 0.018, I2 = 90%] and 2.152 [95% CI (0.807, 3.498), p = 0.002, I2 = 79%]. Additionally, post-COVID patients without comorbidities presented higher values of RV-MPI with a MD of 0.060 [95% CI (0.030, 0.089), p = 0.009, I2 = 99%], compared to matched-controls. In post-COVID patients without comorbidities, TAPSE values were significantly lower, whereas no significant difference was found in cases with comorbidities when compared to their matched controls. The MDs were −1.440 [95%CI (−2.296, −0.585), p = 0.001, I2 = 87.4%] and −0.337 [95% CI (−1.213, 0.540), p = 0.452, I2 = 76%], respectively. Moreover, sPAP presented higher values in post-COVID patients with comorbidities and no significant result in cases without comorbidities compared to their matched controls with MDs of 6.777 [95% CI (4.463, 9.091), p = 0.000, I2 = 91.2%] and 2.039 [95%CI (−0.181, 4.258), p = 0.072, I2 = 91%], respectively. Detailed information is provided in Table 3. Forest plots are provided in supporting information (Supplementary S4 document).
Table 4 represent the summary of quantitative synthesis.
Table 4
Summary of quantitative synthesis.
Chamber function
Overall
Based on recovery phase
Based on severity of prior Covid-19 infection
Based on status of cardiovascular risk factors
Certainty of evidence
LV systolic function
SubclinicalImpairment(↓LVEF, ↓LV-GLS)
•Long-Covid (≥3 months): ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD, ↑LAD•Post-acute Covid (1–3 months): ↓LV-GLS
• Mild Infection: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVEDV•Moderate- Severe Infection: ↓LV-GLS, ↑LVMa
• Cardiovascular risk factors present: ↓LVEF, ↓LV-GLS, ↓LVESD, ↑LVESV•Cardiovascular risk factors absent: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD
Moderate ꚚꚚꚚO
LV diastolic function
SubclinicalImpairment(↑LAD, ↓E/A)
•Long-Covid (≥3 months): ↑LAD, ↑IVSD, ↓E/Aa•Post-acute Covid (1–3 months): ↑LVEDV, ↓E/A, ↑A wave, ↑LAVI
• Mild Infection: ↑LVESD, ↓E/A•Moderate- Severe Infection: ↑LVEDV, ↑PWD, ↓E/A, ↓E wave, ↑A wave, ↑LAD
•Cardiovascular risk factors present: ↑LVEDV, ↓E/A, ↑LAD, ↑LAVI•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
RV systolic function
SubclinicalImpairment(↓RV-GLS,↑RV-MPI,↓TAPSE)
•Long-Covid (≥3 months): ↓RV-GLS, ↑RV-MPI, ↑RVD, ↓TAPSE•Post-acute Covid (1–3 months)
• Mild infection: ↑RV-MPI, ↑RVD•Moderate- severe infection: ↓RV-GLS, ↓TAPSE
• Cardiovascular risk factors present: ↓RV-GLS, ↑RVD•Cardiovascular risk factors absent: ↓RV-GLS, ↑RV-MPI, ↓TAPSE
Moderate ꚚꚚꚚO
RV diastolic function
SubclinicalImpairment(↑sPAP)
•Long-Covid (≥3 months): ↑sPAP•Post-acute Covid (1–3 months): ↓RVD
• Mild infection: ↑sPAP•Moderate- severe Infection: ↑sPAP
•Cardiovascular risk factors present: ↑sPAP•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
a
Significant results were obtained during sensitivity analysis.
3.5.1
Overall outcomes
Among the echocardiographic measures of LV systolic function, LV-GLS and LVEF were found to be significantly different between the two groups being compared. The analysis of 26 studies showed a notable decrease in LV-GLS (less negative) in post-COVID patients (n = 1,810) compared to controls (n = 1,254), with a mean difference of 1.21 [95%CI (0.681, 1.75), p = 0.000, I2 = 91%]. Post-COVID patients (n = 2,173) exhibited a lower LVEF compared to controls (n = 1,770), with a MD of −0.829 [95%CI (−1.397, −0.262), p = 0.004, I2 = 73%]. Additionally, the meta-analysis of 12 studies revealed that LAD was significantly increased in post-COVID patients (n = 833) comparing to controls (n = 892) with a MD of 1.603 [95%CI (0.696, 2.511), p = 0.001, I2 = 80.7%]. However, LAVI was not significantly different comparing two groups with a MD of 0.895 [95% CI (−0.509, 2.29), p = 0.211, I2 = 82.7%]. In terms of RV evaluation, post-COVID patients showed significantly lower RV-GLS (less negative) and higher RV-MPI values compared to controls, with mean differences of 2.179 [95%CI (1.099, 3.260), p = 0.000, I2 = 85.4%] and 0.060 [95% CI (0.030, 0.089), p = 0.009, I2 = 99%], respectively. No significant differences were found in the diastolic and geometric indices of the left ventricle between the two groups being compared (Table 3). Forest plots are provided in supporting information (Supplementary S4 document).
Table 3
Result of synthesis.
No. studies
No. cases
No. controls
Effect model
MD (CI: 95%)
P value
Heterogeneity
I2
P value
LV geometric indices
LVEDD
Overall
26
1,597
1,612
Random
0.440 [−0.092, 0.155]
0.148
65%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
2
174
144
Fixed
−0.232 [−1.29, 0.835]
0.670
0%
0.558
1–3 months
8
733
558
Random
0.542 [−0.524, 1.608]
0.319
80%
0.000
≥3 months
16
690
910
Random
0.516 [−0.315, 1.346]
0.223
58.6%
0.002
Grouped by severity of COVID-19 infectiona
Mild
15
1,079
1,205
Random
0.580 [−0.199, 1.35]
0.145
51.4%
0.011
Moderate-Sever
8
437
356
Random
0.620 [−0.421, 1.662]
0.243
78.8%
0.000
Mixed
2
81
91
Fixed
0.128 [−1.537, 1.792]
0.881
0%
0.955
Grouped by presence of comorbid diseases
Present
12
645
542
Random
0.605 [−0.324, 1.533]
0.202
75.3%
0.000
Absent
14
952
1,070
Random
0.325 [−0.484, 1.133]
0.431
54%
0.008
LVEDV
Overall
11
453
624
Random
4.79 [−0.341, 9.93]
0.067
56.9%
0.010
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
3
147
121
Fixed
6.87 [0.605, 13.13]
0.032
0%
0.536
≥3 months
8
306
503
Random
3.88 [−2.35, 10.11]
0.223
66%
0.004
Grouped by severity of COVID-19 infectiona
Mild
5
224
206
Fixed
8.39 [3.57, 13.20]
0.001
0%
0.753
Moderate-Sever
3
77
129
Fixed
10.09 [2.29, 17.89]
0.011
0%
0.728
Mixed
2
81
51
Random
−3.40 [−12.02, 5.22]
0.440
71.2%
0.062
Grouped by presence of comorbid diseases
Present
5
154
214
Random
10.35 [4.93, 15.76]
0.000
62.6%
0.020
Absent
6
299
410
Fixed
0.602 [−4.87, 6.07]
0.829
0%
0.945
LVESD
Overall
15
918
952
Random
0.325 [−0.119, 0.352]
0.346
73.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
67
37
–
–
–
–
1–3 months
4
346
217
Random
−0.397 [−1.353, 0.560]
0.417
80%
0.000
≥3 months
10
505
698
Fixed
0.928 [0.566, 1.289]
0.000
0%
0.541
Grouped by Severity of COVID-19 infectiona
Mild
7
407
437
Fixed
0.908 [0.488, 1.32]
0.000
32.1%
0.183
Moderate-Sever
5
360
227
Random
−0.272 [−1.42, 0.877]
0.642
84.5%
0.000
Mixed
2
81
51
Random
0.776 [−0.701, 2.254]
0.303
0%
0.772
Grouped by presence of comorbid diseases
Present
5
334
202
Random
−1.292 [−2.089, −0.495]
0.001
54%
0.069
Absent
10
584
750
Fixed
0.905 [0.567, 1.24]
0.000
0%
0.570
LVESV
Overall
6
317
431
Fixed
0.608 [−1.24, 2.45]
0.519
41.8%
0.127
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
1
86
60
–
0.500 [−2.77, 3.77]
–
–
–
≥3 months
5
231
371
Random
1.69 [−1.95, 5.33]
0.363
53%
0.0072
Grouped by Severity of COVID-19 infectiona
Mild
2
129
101
Random
2.23 [−1.43, 5.88]
0.230
57.7%
0.124
Moderate-Sever
1
36
41
–
6.20 [−2.17, 14.57]
–
–
–
Mixed
2
82
52
Fixed
1.40 [−3.08, 5.90]
0.539
0%
0.525
Grouped by presence of comorbid diseases
Present
2
79
82
Fixed
5.55 [1.15, 9.96]
0.013
0%
0.859
Absent
4
238
349
Fixed
−0.451 [−2.48, 1.58]
0.664
0%
0.445
IVSD
Overall
19
1,305
1,247
Random
−0.203 [−0.526, 0.119]
0.217
95.6%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
2
174
144
Random
−0.108 [−1.16, 0.944]
0.840
92.8%
0.000
1–3 months
8
677
530
Random
−0.256 [−0.796, 0.284]
0.352
98.2%
0.000
≥3 months
9
454
573
Fixed
−0.132 [−0.258, −0.007]
0.039
0%
0.456
Grouped by severity of COVID-19 infectiona
Mild
11
794
732
Random
−0.411 [−0.830, 0.007]
0.054
97.1%
0.000
Moderate-Sever
5
360
227
Random
0.250 [−0.399, 0.899]
0.451
80.4%
0.000
Mixed
2
81
51
Fixed
−0.128 [−0.427, 0.172]
0.403
0%
0.633
Grouped by presence of comorbid diseases
Present
6
404
272
Random
0.098 [−0.520, 0.715]
0.756
88.4%
0.000
Absent
13
901
975
Random
−0.315 [−0.691, 0.061]
0.101
96.5%
0.000
PWD
Overall
19
1,305
1,247
0.086 [−0.139, 0.311]
0.455
79.8%
0.000
Grouped by duration from Acute COVID to echo examination in recovery phase
<1 months
2
174
144
Random
−0.127 [−0.762, 0.497]
0.690
96.3%
0.000
1–3 months
8
677
530
Random
0.273 [−0.118, 0.663]
0.171
0%
0.497
≥3 months
9
454
573
Fixed
−0.102 [−0.211, 0.007]
0.068
77.5%
0.000
Grouped by severity of COVID-19 infectiona
Mild
11
794
732
Random
−0.149 [−0.405, 0.106]
0.251
64.8%
0.002
Moderate-Sever
5
360
227
Random
0.614 [0.259, 0.969]
0.001
52.7%
0.076
Mixed
2
81
51
Fixed
−0.159 [−0.552, 0.233]
0.426
0%
0.496
Grouped by presence of comorbid diseases
Present
6
404
272
Random
0.311 [−0.072, 0.695]
0.112
85.2%
0.000
Absent
13
901
975
Random
−0.006 [−0.248, 0.237]
0.962
63.7%
0.001
LVM
Overall
7
453
478
Random
−7.630 [−21.7, 6.50]
0.290
74.8%
0.001
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
4
325
201
Random
−3.59 [−19.7, 12.53]
0.663
74.1%
0.009
≥3 months
3
128
277
Fixed
−19.37 [−29.8, −8.92]
0.000
0%
0.423
Grouped by Severity of COVID-19 infectiona
Mild
4
191
139
Fixed
−13.71 [−25.30, −2.11]
0.020
0%
0.426
Moderate-Sever
2
192
102
Fixed
9.018 [0.458, 17.57]
0.039
0%
0.845
Grouped by presence of comorbid diseases
Present
3
234
128
Fixed
7.54 [−0.720, 15.81]
0.074
0%
0.420
Absent
4
219
350
Fixed
−18.28 [−26.72, −9.85]
0.000
19%
0.295
LVMI
Overall
7
432
494
Random
−1.65 [−6.62, 3.31]
0.513
81.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
3
237
149
Fixed
−0.251 [−1.95, 1.45]
0.772
0%
0.408
≥3 months
4
195
345
Random
−1.023 [−9.48, 7.44]
0.813
89.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
5
186
169
Random
2.408 [−1.11, 5.93]
0.181
73.1%
0.005
Moderate-Sever
1
176
88
–
0.00 [−1.74, 1.74]
–
–
–
Grouped by presence of comorbid diseases
Present
1
176
88
–
0.00 [−1.74, 1.74]
–
–
–
Absent
6
256
406
Random
−2.29 [−9.69, 5.11]
0.544
83.6%
0.000
LV systolic function
LVEF
Overall
32
2,173
1,770
Random
−0.829 [−1.397, −0.262]
0.004
73.8%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
2
174
144
Random
0.667 [−1.42, 2.76]
0.533
90.3%
0.001
1–3 months
9
691
498
Random
−0.615 [−1.75, 0.527]
0.291
83.5%
0.000
≥3 months
20
836
1,172
Random
−1.16 [ −1.94, −0.375]
0.004
60.1%
0.000
Grouped by severity of COVID-19 infectiona
Mild
20
1,586
1,109
Random
−0.886 [−1.64, −0.128]
0.022
78%
0.000
Moderate-Sever
10
495
402
Random
−0.900 [−1.96, 0.169]
0.098
69%
0.001
Mixed
1
22
22
–
−1.58 [−4.78, 1.62]
–
–
–
Grouped by presence of comorbid diseases
Present
16
1,245
663
Random
−0.852 [−1.66, −0.038]
0.040
56.5%
0.002
Absent
16
928
1,107
Random
−0.833 [−1.64, −0.005]
0.049
79.6%
0.000
LV-GLS
Overall
26
1,810
1,254
Random
1.21 [0.681, 1.75]
0.000
91%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
4
250
194
Random
0.615 [−0.776, 2.00]
0.386
95.9%
0.000
1–3 months
7
827
427
Random
1.22 [0.181, 2.27]
0.021
57.5%
0.028
≥3 months
15
733
633
Random
1.37 [0.637, 2.11]
0.000
92.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
13
1,084
714
Random
0.815 [0.047, 1.58]
0.038
88.1%
0.000
Moderate-Sever
7
380
308
Random
1.84 [0.751, 2.94]
0.001
90.1%
0.000
Mixed
3
226
112
Random
1.07 [−0.629, 2.77]
0.217
74.5%
0.020
Grouped by presence of comorbid diseases
Present
12
884
552
Random
0.919 [0.07, 1.76]
0.033
88.8%
0.000
Absent
14
926
702
Random
1.43 [0.703, 2.16]
0.000
93.7%
0.000
LV diastolic function
E/A ratio
Overall
21
1,321
1,258
−0.058 [−0.118, 0.002]
0.057
79.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
1
107
17
–
0.190 [−0.017, 0.397]
–
–
–
1–3 months
8
711
517
Fixed
−0.084 [−0.129, −0.039]
0.000
32%
0.167
≥3 months
12
503
724
Random
−0.043 [−0.122, 0.035]
0.280
85.6%
0.000
Grouped by severity of COVID-19 infectiona
Mild
13
835
706
Fixed
−0.042 [−0.076, −0.007]
0.017
45%
0.037
Moderate-Sever
5
334
263
Random
−0.134 [−0.258, −0.010]
0.034
82%
0.000
Mixed
2
82
52
Random
0.133 [−0.085, 0.351]
0.233
97%
0.000
Grouped by presence of comorbid diseases
Present
7
382
322
Random
−0.137 [−0.424, −0.032]
0.010
57.9%
0.027
Absent
14
939
936
Random
−0.021 [−0.093, 0.050]
0.563
82%
0.000
E/e’ ration
Overall
27
1,799
1,493
0.116 [−0.275, 0.507]
0.561
85.2%
0.000
Grouped by duration from acute COVID to echo examination in recovery phaseb
<1 months
2
174
144
Random
−0.412 [−1.79, 0.968]
0.558
80.7%
0.000
1–3 months
7
496
553
Random
−0.010 [−0.753, 0.732]
0.978
58.1
0.000
≥3 months
17
657
696
Random
0.315 [−0.213, 0.843]
0.243
87%
0.000
Grouped by Severity of COVID-19 infectiona
Mild
16
1,279
898
Random
0.105 [−0.410, 0.620]
0.689
82.6%
0.000
Moderate-Sever
6
336
274
Fixed
0.380 [−0.009, 0.768]
0.055
0%
0.808
Mixed
2
82
52
Random
0.379 [−1.15, 1.91]
0.629
98.5
0.000
Grouped by presence of comorbid diseases
Present
16
1,179
706
Fixed
0.164 [−0.052, 0.381]
0.137
0%
0.521
Absent
11
620
787
Random
0.129 [−0.470, 0.729]
0.672
93.5%
0.000
Mitral E wave
Overall
13
939
817
Fixed
−0.013 [−0.029, 0.002]
0.099
44%
0.040
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
−0.010 [−0.059, 0.039]
–
–
1–3 months
7
644
493
Random
−0.021 [−0.052, 0.009]
0.161
55.9%
0.043
≥3 months
5
188
217
Fixed
0.009 [−0.025, 0.043]
0.599
31.2%
0.213
Grouped by severity of COVID-19 infection
Mild
9
660
636
Fixed
0.001 [−0.017, 0.020]
0.884
23.4%
0.234
Moderate-Sever
4
279
181
Fixed
−0.052 [−0.082, −0.022]
0.001
0%
0.437
Grouped by presence of comorbid diseases
Present
6
383
280
Random
−0.013 [−0.051, 0.026]
0.527
62.1%
0.022
Absent
7
556
537
fixed
−0.008 [−0.027, 0.011]
0.426
21.1%
0.268
Mitral A wave
Overall
10
684
647
Random
0.026 [ −0.018, 0.070]
0.245
72.8%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
−0.70 [−0.106, −0.034]
–
–
–
1–3 months
6
468
405
Fixed
0.039 [0.017, 0.060]
0.000
30.6%
0.206
≥3 months
3
109
135
Fixed
0.034 [−0.034, 0.102]
0.332
0%
0.912
Grouped by severity of COVID-19 infection
Mild
7
566
500
Random
0.015 [−0.031, 0.062]
0.521
77.2%
0.000
Moderate-Sever
3
118
147
Fixed
0.086 [0.022, 0.150]
0.008
0%
0.575
Grouped by presence of comorbid diseases
Present
3
128
110
Fixed
0.035 [−0.036, 0.105]
0.336
0%
0.913
Absent
7
556
537
Random
0.014 [−0.051, 0.080]
0.673
75%
0.001
Left atrium
LAD
Overall
12
833
892
Random
1.603 [0.696, 2.511]
0.001
80.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
4
423
288
Random
1.127 [−0.571, 2.826]
0.193
50.8%
0.107
≥3 months
8
410
604
Random
1.863 [0.694, 3.032]
0.002
86.2%
0.000
Grouped by severity of COVID-19 infectiona
Mild
5
385
367
Random
0.937 [−0.183, 2.057]
0.101
63%
0.029
Moderate-Sever
5
318
258
Random
2.305 [1.058, 3.74]
0.000
63.7%
0.026
Mixed
1
60
30
Random
3.40 [1.850, 4.95]
–
–
–
Grouped by presence of comorbid diseases
Present
6
447
388
Random
2.287 [0.910, 3.664]
0.001
77.4%
0.000
Absent
6
386
507
Random
1.064 [−0.167, 2.295]
0.090
83%
0.000
LAVI
Overall
15
821
983
Random
0.895 [−0.509, 2.29]
0.211
82.7%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
−1.50 [−3.54, 0.543]
–
–
–
1–3 months
2
236
174
Fixed
1.95 [0.728, 3.17]
0.002
8.4%
0.296
≥3 months
12
478
702
Random
0.941 [−0.712, 2.59]
0.256
84%
0.000
Grouped by severity of COVID-19 infectiona
Mild
8
420
481
Fixed
0.922 [0.139, 1.845]
0.023
46%
0.073
Moderate-Sever
4
253
217
Fixed
1.475 [0.374, 2.575]
0.009
35.1%
0.201
Mixed
2
78
48
Random
3.06 [−0.174, 6.30]
0.064
93.8%
0.000
Grouped by presence of comorbid diseases
Present
8
421
416
Fixed
1.135 [0.290, 1.980]
0.008
0%
0.462
Absent
7
400
567
Random
1.078 [−1.032, 3.187]
0.317
91.4%
0.000
Right heart function
RV-GLS
Overall
16
775
677
Random
2.179 [1.099, 3.260]
0.000
85.4%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
67
37
–
7.860 [6.33, 9.38]
–
–
–
1–3 months
3
170
129
Random
1.547 [−0.335, 3.430]
0.107
77.8%
0.011
≥3 months
12
538
511
Random
1.842 [0.853, 2.831]
0.000
74.3%
0.000
Grouped by severity of COVID-19 infectiona
Mild
7
380
331
Random
1.27 [−0.283, 2.73]
0.111
79.2%
0.000
Moderate-Sever
5
205
156
Random
4.306 [2.398, 6.214]
0.000
83.7%
0.000
Mixed
1
70
70
–
3.520 [2.61, 4.42]
–
–
–
Grouped by presence of comorbid diseases
Present
7
268
224
Random
2.228 [0.377, 4.079]
0.018
90%
0.000
Absent
9
507
453
Random
2.152 [0.807, 3.498]
0.002
79%
0.000
RV-MPI
Overall
6
327
352
Random
0.060 [0.030, 0.089]
0.009
99%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
1
51
32
–
0.190 [0.166, 0.214]
–
–
–
≥3 months
5
276
320
Random
0.035 [0.008, 0.062]
0.012
98.9%
0.000
Grouped by severity of COVID-19 infectiona
Mild
2
156
200
Random
0.063 [0.021, 0.106]
0.004
93.8%
0.000
Moderate-Sever
1
51
32
–
0.190 [0.166, 0.214]
–
–
–
Grouped by presence of comorbid diseases
Present
0
–
–
–
–
–
–
–
Absent
6
327
352
Random
0.060 [0.030, 0.089]
0.009
99%
0.000
RVD
Overall
15
1,055
999
Random
0.306 [−0.566, 1.178]
0.492
85.5%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
0.600 [−0.539, 1.739]
–
–
–
1–3 months
3
283
180
Random
−1.820 [−3.406, −0.234]
0.025
93.4%
0.000
≥3 months
11
665
712
Fixed
0.900 [0.510, 1.290]
0.000
0%
0.703
Grouped by severity of COVID-19 infection
Mild
8
666
674
Fixed
0.865 [0.412, 1.317]
0.000
0%
0.998
Moderate-Sever
7
389
325
Random
−0.268 [−1.506, 0.970]
0.672
92.6%
0.000
Grouped by presence of comorbid diseases
Present
7
365
328
Fixed
0.694 [0.242, 1.146]
0.003
48%
0.001
Absent
8
690
671
Random
−0.227 [−1.467, 1.012]
0.719
90.8%
0.000
RAD
Overall
8
450
422
Fixed
0.212 [−0.266, 0.689]
0.385
45.6%
0.075
Grouped by duration from acute COVID to echo examination in recovery phase
1–3 months
2
246
158
Random
−0.329 [−1.710, 1.015]
0.640
70.5%
0.065
≥3 months
6
204
264
Fixed
0.499 [−0.096, 0.995]
0.107
21.4%
0.272
Grouped by severity of COVID-19 infection
Mild
3
147
155
Fixed
0.695 [−0.237, 1.62]
0.144
0%
0.572
Moderate-Sever
5
309
267
Random
0.173 [−0.647, 0.994]
0.679
61.3%
0.035
Grouped by presence of comorbid diseases
Present
7
400
372
Fixed
0.419 [−0.112, 0.951]
0.122
38.9%
0.132
Absent
1
50
50
–
−0.660 [−1.749, 0.429]
–
–
–
TAPSE
Overall
26
1,458
1,381
Random
−1.01 [−1.621, −0.402]
0.001
82%
0.000
Grouped by duration from acute COVID to echo examination in recovery phase
<1 months
1
107
107
–
1.100 [0.014, 2.186]
–
–
–
1–3 months
5
583
432
Random
−0.983 [−2.225, 0.285]
0.121
76.7%
0.002
≥3 months
20
768
842
Random
−1.160 [−1.885, −0.466]
0.001
80.3%
0.000
Grouped by severity of COVID-19 infectiona
Mild
11
809
792
Random
−0.283 [−1.050, 0.483]
0.469
70.3%
0.000
Moderate-Sever
8
405
345
Random
−1.234 [−2.197, −0.270]
0.012
57%
0.022
Mixed
2
92
92
Random
−3.564 [−5.727, −1.400]
0.001
67.6%
0.079
Grouped by presence of comorbid diseases
Present
13
556
498
Random
−0.510 [−1.409, 0.390]
0.267
67.6%
0.000
Absent
13
902
883
Random
−1.440 [−2.274, −0.608]
0.001
87%
0.000
sPAP
Overall
12
1,049
885
Random
4.37 [2.378, 6.380]
0.000
94.3%
0.000
Grouped by duration from acute COVID to echo examination in recovery phasea
<1 months
1
107
107
–
0.300 [−0.979, 1.579]
–
–
–
1–3 months
1
51
32
–
5.70 [−2.010, 13.41]
–
–
–
≥3 months
9
419
646
Random
5.172 [2.668, 7.676]
0.000
95.2%
0.000
Grouped by severity of COVID-19 infectiona
Mild
6
801
437
Random
3.749 [0.817, 6.682]
0.012
92.3%
0.000
Moderate-Sever
5
178
211
Random
6.686 [3.109, 9.662]
0.000
95.2%
0.000
Grouped by presence of comorbid diseases
Present
6
626
314
Random
6.777 [4.463, 9.091]
0.000
91.2%
0.000
Absent
6
423
571
Random
2.039 [−0.181, 4.258]
0.072
91%
0.000
LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; PWD, posterior wall diameter; IVSD, interventricular septum diameter; LVM, left ventricular mass; LVMI, left ventricular mass index; LV-GLS, eft ventricular global longitudinal strain; LAD, left atrium diameter; LAVI, left atrium volume index; LV-MPI, left ventricular myocardial performance index; E/A, the ratio of peak velocity blood flow from left ventricular relaxation in early diastole (the E wave) to peak velocity flow in late diastole caused by atrial contraction (the A wave); E/e’, ratio of E wave to early diastolic mitral annular velocity (e’); RVD, right ventricular diameter; RAD, right atrium diameter; RV-GLS, right ventricular global longitudinal strain; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; RV-MPI, right ventricular myocardial performance index; CI, confidence interval.
Bold values show significant results (p < 0.05).
a
The studies conducted by Gumanova et al. (32), Beaudry et al. (30) and Yang et al. (37) did not report severity of COVID-19 infection.
b
One study did not report the timeframe from after recovering from COVID-19 to the echocardiography examination (24).
3.5.2
Subgroup analysis
3.5.2.1
Grouped by duration from acute COVID to echo examination in recovery phase
The subgroup meta-analysis found that among LV geometric indices, LVESD was notably higher in post-COVID patients (n = 505) compared to controls (n = 698) for a duration of ≥3 months, showing a MD of 0.928 [95% CI (0.566, 1.289), p = 0.000, I2 = 0%]. Moreover, post-COVID patients exhibited a significant decrease in IVSD and LVM compared to controls for a duration of ≥3 months, with a MD of −0.132 [95% CI (−0.258, −0.007), p = 0.039, I2 = 0%] and −19.37 [95%CI (−29.8, −8.92), p = 0.000, I2 = 0%], respectively.
In terms of systolic function lasting ≥3 months, LVEF was found to be significantly lower in post-COVID patients (n = 836) compared to controls (n = 1,172), with a MD of −1.16 [95% CI (−1.94, −0.375), p = 0.004, I2 = 60.1%]. Furthermore, post-COVID patients exhibited a significantly decreased LV-GLS (less negative) compared to controls for durations of both 1–3 months and ≥3 months, with MDs of 1.22 [95% CI (0.181, 2.27), p = 0.021, I2 = 57.5%] and 1.37 [95% CI (0.637, 2.11), p = 0.000, I2 = 92.9%], respectively.
There were significant differences in the E/A ratio and mitral A wave among diastolic function indices. Within a period of ≥3 months, post-COVID patients exhibited a significant reduction in the E/A ratio and an increase in the mitral A wave compared to the control group. The MDs were −0.084 [95%CI (−0.129, −0.039), p = 0.000, I2 = 32%] for the E/A ratio and 0.039 [95% CI (0.017, 0.060), p = 0.000, I2 = 30.6%] for the mitral A wave. Additionally, post-COVID patients (n = 410) exhibited a significant elevation in LAD in comparison to the control subjects (n = 604), over a duration of ≥3 months, with a MD of 1.863 [95% CI (0.694, 3.032), p = 0.002, I2 = 86.2%]. However, a meta-analysis of 2 studies showed a significant increase in LAVI in post-COVID patients (n = 236) compared to controls (n = 174) within a timeframe of 1–3 months, with a MD of 1.95 [95% CI (0.728, 3.17), p = 0.002, I2 = 8.4%].
In subgroup meta-analysis of RV function, RV-MPI, RVD and sPAP were significantly higher in post-COVID patients compared to control group for a duration of ≥3 months, with MDs of 0.035 [95% CI (0.008, 0.062), p = 0.012, I2 = 98.9], 0.900 [95% CI (0.510, 1.290), p = 0.000, I2 = 0%] and 5.172 [95%CI (2.668, 7.676), p = 0.000, I2 = 95.2%], respectively. Moreover, a significant decrease in TAPSE and RV-GLS (less negative) were observed in post-COVID patients compared to controls with a MD of −1.160 [95% CI (−1.885, −0.466), p = 0.001, I2 = 80.3%] and 1.842 [95%CI (0.853, 2.831), p = 0.000, I2 = 74.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2
Grouped by severity of COVID-19 infection
3.5.2.2.1
Mild COVID-19 infection
In terms of mild COVID-19 infection and LV geometric indices, significant increase was observed in LVEDV and LVESD in post-COVID patients compared to controls with MDs of 8.39 [95% CI (3.57, 13.20), p = 0.001, I2 = 0%], and 0.908 [95% CI (0.488, 1.32), p = 0.000, I2 = 32.1%], respectively. LVM was significantly lower in post-COVID patients (n = 191) compared to controls (n = 139), with a MD of −13.71 [95 CI% (−25.30, −2.11), p = 0.020, I2 = 0%]. Moreover, significant changes in systolic function were observed in mild infection cases. Post-COVID patients reveled to have a decrease in LVEF and LV-GLS (less negative) compared to control groups, with MDs of −0.886 [95% CI (−1.64, −0.128), p = 0.022, I2 = 78%] and 0.815 [95% CI (0.047, 1.58), 0.038, I2 = 88.1%], respectively. Among LV diastolic indices, E/A ratio was significantly lower and LAVI was significantly increased in mild infection compared to controls, with MDs of −0.042 [95% CI (−0.076, −0.007), p = 0.017, I2 = 45%] and 0.922 [95% CI (0.139, 1.845), p = 0.023, I2 = 46%], respectively. RVD and sPAP were significantly higher in post-COVID patients compared to controls with MDs of 0.865 [95% CI (0.412, 1.317), p = 0.000, I2 = 0%] and 3.749 [95% CI (0.817, 6.682), p = 0.012, I2 = 0.012, I2 = 92.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2.2
Moderate and/or severe COVID-19 infection
Post-COVID patients exhibited higher values of LVEDV, PWD, and LVM compared to the control group. The MDs for LVEDV, PWD, and LVM were 10.09 [95% CI (2.29, 17.89), p = 0.011, I2 = 0%], 0.614 [95% CI (0.259, 0.969), p = 0.001, I2 = 52.7%], and 9.018 [95% CI (0.458, 17.57), p = 0.039, I2 = 0%], respectively. Concerning systolic function, there was no significant difference in LVEF between the two groups, as indicated by a MD of −0.900 [95% CI (−1.96, 0.169), p = 0.098, I2 = 69%]. Conversely, LV-GLS exhibited significantly lower (less negative) values in post-COVID patients in comparison to the control group, with a MD of 1.84 [95% CI (0.751, 2.94), I2 = 90.1%]. Among LV diastolic indices, E/A ratio and mitral E wave values were significantly decreased and mitral A wave was significantly increased in post-COVID patients compared to controls. The MDs for E/A, E wave and A wave were −0.134 [95% CI (−0.258, −0.010), p = 0.034, I2 = 82%], −0.052 [95% CI (−0.082, −0.022), p = 0.001, I2 = 0%] and 0.086 [95%CI (0.022, 0.150), p = 0.008, I2 = 0%], respectively.
Additionally, significantly higher values were found in both LAD and LAVI in post-COVID patients compared to controls. The MD for LAD was 2.305 [95% CI (1.058, 3.74), p = 0.000, I2 = 63.7%], and for LAVI it was 1.475 [95% CI (0.374, 2.575), p = 0.009, I2 = 32.1%].
Regarding RV indices, post-COVID patients showed significantly increased value in sPAP with MDs of 4.306 [95% CI (2.398, 6.214), p = 0.000, I2 = 83.7%]. Moreover, TAPSE and RV-GLS values were significantly lower in post-COVID patients compared to controls with MDs of −1.234 [95% CI (−2.197, −0.270), p = 0.012, I2 = 57%] and 6.686 [95% CI (3.109, 9.662), p = 0.000, I2 = 95.2%], respectively. Detailed information is provided in Table 3.
3.5.2.3
Grouped by presence of comorbid diseases
Post-COVID patients with comorbidities showed higher values of LVEDV and LVESV compared to comorbid-matched control group with MDs of 10.35 [95% CI (4.93, 15.76), p = 0.000, I2 = 62.6%] and 5.55 [95% CI (1.15, 9.96), p = 0.013, I2 = 0%], respectively. There was a significant decrease in LVESD in post-COVID patients with comorbidities and an increase in cases without comorbidities compared to their comorbid-matched controls with MDs of −1.292 95% CI [−2.089, −0.495], p = 0.001, I2 = 43.2%) and 0.905 [95% CI (0.567, 1.24), p = 0.000, I2 = 0%], respectively. LVEF exhibited a significant decrease in post-COVID patients with comorbidities and those without comorbidities when compared to their comorbid-matched controls. The MDs were −0.852 [95% CI (−1.66, −0.038), p = 0.040, I2 = 56.5%] and −0.833 [95%CI (−1.64, −0.005), p = 0.049, I2 = 79.6%], respectively. Furthermore, LV-GLS was significantly decreased (less negative) in both groups of post-COVID patients, with MDs of 0.919 [95% CI (0.07, 1.76), p = 0.033, I2 = 88.8%] and 1.43 [95% CI (0.703, 2.16), p = 0.000, I2 = 93.7%] compared to their respective controls. E/A ratio was significantly lower in post-COVID patients with comorbidities compare to its comorbid-matched controls, with a MD of −0.137 [95% CI (−0.424, −0.032), p = 0.010, I2 = 57.9%]. Significantly higher values of both LAD and LAVI were observed in post-COVID patients with comorbidities compared to their matched controls. The MDs were 2.287 [95% CI (0.910, 3.664), p = 0.001, I2 = 77.4%] and 1.135 [95% CI (0.290, 1.980), p = 0.008, I2 = 0%], respectively.
Regarding RV function, RV-GLS was notably decreased (less negative) in both post-COVID patients with and without comorbidities compared to their controls, with MDs of 2.228 [95% CI (0.377, 4.079), p = 0.018, I2 = 90%] and 2.152 [95% CI (0.807, 3.498), p = 0.002, I2 = 79%]. Additionally, post-COVID patients without comorbidities presented higher values of RV-MPI with a MD of 0.060 [95% CI (0.030, 0.089), p = 0.009, I2 = 99%], compared to matched-controls. In post-COVID patients without comorbidities, TAPSE values were significantly lower, whereas no significant difference was found in cases with comorbidities when compared to their matched controls. The MDs were −1.440 [95%CI (−2.296, −0.585), p = 0.001, I2 = 87.4%] and −0.337 [95% CI (−1.213, 0.540), p = 0.452, I2 = 76%], respectively. Moreover, sPAP presented higher values in post-COVID patients with comorbidities and no significant result in cases without comorbidities compared to their matched controls with MDs of 6.777 [95% CI (4.463, 9.091), p = 0.000, I2 = 91.2%] and 2.039 [95%CI (−0.181, 4.258), p = 0.072, I2 = 91%], respectively. Detailed information is provided in Table 3. Forest plots are provided in supporting information (Supplementary S4 document).
Table 4 represent the summary of quantitative synthesis.
Table 4
Summary of quantitative synthesis.
Chamber function
Overall
Based on recovery phase
Based on severity of prior Covid-19 infection
Based on status of cardiovascular risk factors
Certainty of evidence
LV systolic function
SubclinicalImpairment(↓LVEF, ↓LV-GLS)
•Long-Covid (≥3 months): ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD, ↑LAD•Post-acute Covid (1–3 months): ↓LV-GLS
• Mild Infection: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVEDV•Moderate- Severe Infection: ↓LV-GLS, ↑LVMa
• Cardiovascular risk factors present: ↓LVEF, ↓LV-GLS, ↓LVESD, ↑LVESV•Cardiovascular risk factors absent: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD
Moderate ꚚꚚꚚO
LV diastolic function
SubclinicalImpairment(↑LAD, ↓E/A)
•Long-Covid (≥3 months): ↑LAD, ↑IVSD, ↓E/Aa•Post-acute Covid (1–3 months): ↑LVEDV, ↓E/A, ↑A wave, ↑LAVI
• Mild Infection: ↑LVESD, ↓E/A•Moderate- Severe Infection: ↑LVEDV, ↑PWD, ↓E/A, ↓E wave, ↑A wave, ↑LAD
•Cardiovascular risk factors present: ↑LVEDV, ↓E/A, ↑LAD, ↑LAVI•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
RV systolic function
SubclinicalImpairment(↓RV-GLS,↑RV-MPI,↓TAPSE)
•Long-Covid (≥3 months): ↓RV-GLS, ↑RV-MPI, ↑RVD, ↓TAPSE•Post-acute Covid (1–3 months)
• Mild infection: ↑RV-MPI, ↑RVD•Moderate- severe infection: ↓RV-GLS, ↓TAPSE
• Cardiovascular risk factors present: ↓RV-GLS, ↑RVD•Cardiovascular risk factors absent: ↓RV-GLS, ↑RV-MPI, ↓TAPSE
Moderate ꚚꚚꚚO
RV diastolic function
SubclinicalImpairment(↑sPAP)
•Long-Covid (≥3 months): ↑sPAP•Post-acute Covid (1–3 months): ↓RVD
• Mild infection: ↑sPAP•Moderate- severe Infection: ↑sPAP
•Cardiovascular risk factors present: ↑sPAP•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
a
Significant results were obtained during sensitivity analysis.
3.5.2.1
Grouped by duration from acute COVID to echo examination in recovery phase
The subgroup meta-analysis found that among LV geometric indices, LVESD was notably higher in post-COVID patients (n = 505) compared to controls (n = 698) for a duration of ≥3 months, showing a MD of 0.928 [95% CI (0.566, 1.289), p = 0.000, I2 = 0%]. Moreover, post-COVID patients exhibited a significant decrease in IVSD and LVM compared to controls for a duration of ≥3 months, with a MD of −0.132 [95% CI (−0.258, −0.007), p = 0.039, I2 = 0%] and −19.37 [95%CI (−29.8, −8.92), p = 0.000, I2 = 0%], respectively.
In terms of systolic function lasting ≥3 months, LVEF was found to be significantly lower in post-COVID patients (n = 836) compared to controls (n = 1,172), with a MD of −1.16 [95% CI (−1.94, −0.375), p = 0.004, I2 = 60.1%]. Furthermore, post-COVID patients exhibited a significantly decreased LV-GLS (less negative) compared to controls for durations of both 1–3 months and ≥3 months, with MDs of 1.22 [95% CI (0.181, 2.27), p = 0.021, I2 = 57.5%] and 1.37 [95% CI (0.637, 2.11), p = 0.000, I2 = 92.9%], respectively.
There were significant differences in the E/A ratio and mitral A wave among diastolic function indices. Within a period of ≥3 months, post-COVID patients exhibited a significant reduction in the E/A ratio and an increase in the mitral A wave compared to the control group. The MDs were −0.084 [95%CI (−0.129, −0.039), p = 0.000, I2 = 32%] for the E/A ratio and 0.039 [95% CI (0.017, 0.060), p = 0.000, I2 = 30.6%] for the mitral A wave. Additionally, post-COVID patients (n = 410) exhibited a significant elevation in LAD in comparison to the control subjects (n = 604), over a duration of ≥3 months, with a MD of 1.863 [95% CI (0.694, 3.032), p = 0.002, I2 = 86.2%]. However, a meta-analysis of 2 studies showed a significant increase in LAVI in post-COVID patients (n = 236) compared to controls (n = 174) within a timeframe of 1–3 months, with a MD of 1.95 [95% CI (0.728, 3.17), p = 0.002, I2 = 8.4%].
In subgroup meta-analysis of RV function, RV-MPI, RVD and sPAP were significantly higher in post-COVID patients compared to control group for a duration of ≥3 months, with MDs of 0.035 [95% CI (0.008, 0.062), p = 0.012, I2 = 98.9], 0.900 [95% CI (0.510, 1.290), p = 0.000, I2 = 0%] and 5.172 [95%CI (2.668, 7.676), p = 0.000, I2 = 95.2%], respectively. Moreover, a significant decrease in TAPSE and RV-GLS (less negative) were observed in post-COVID patients compared to controls with a MD of −1.160 [95% CI (−1.885, −0.466), p = 0.001, I2 = 80.3%] and 1.842 [95%CI (0.853, 2.831), p = 0.000, I2 = 74.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2
Grouped by severity of COVID-19 infection
3.5.2.2.1
Mild COVID-19 infection
In terms of mild COVID-19 infection and LV geometric indices, significant increase was observed in LVEDV and LVESD in post-COVID patients compared to controls with MDs of 8.39 [95% CI (3.57, 13.20), p = 0.001, I2 = 0%], and 0.908 [95% CI (0.488, 1.32), p = 0.000, I2 = 32.1%], respectively. LVM was significantly lower in post-COVID patients (n = 191) compared to controls (n = 139), with a MD of −13.71 [95 CI% (−25.30, −2.11), p = 0.020, I2 = 0%]. Moreover, significant changes in systolic function were observed in mild infection cases. Post-COVID patients reveled to have a decrease in LVEF and LV-GLS (less negative) compared to control groups, with MDs of −0.886 [95% CI (−1.64, −0.128), p = 0.022, I2 = 78%] and 0.815 [95% CI (0.047, 1.58), 0.038, I2 = 88.1%], respectively. Among LV diastolic indices, E/A ratio was significantly lower and LAVI was significantly increased in mild infection compared to controls, with MDs of −0.042 [95% CI (−0.076, −0.007), p = 0.017, I2 = 45%] and 0.922 [95% CI (0.139, 1.845), p = 0.023, I2 = 46%], respectively. RVD and sPAP were significantly higher in post-COVID patients compared to controls with MDs of 0.865 [95% CI (0.412, 1.317), p = 0.000, I2 = 0%] and 3.749 [95% CI (0.817, 6.682), p = 0.012, I2 = 0.012, I2 = 92.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2.2
Moderate and/or severe COVID-19 infection
Post-COVID patients exhibited higher values of LVEDV, PWD, and LVM compared to the control group. The MDs for LVEDV, PWD, and LVM were 10.09 [95% CI (2.29, 17.89), p = 0.011, I2 = 0%], 0.614 [95% CI (0.259, 0.969), p = 0.001, I2 = 52.7%], and 9.018 [95% CI (0.458, 17.57), p = 0.039, I2 = 0%], respectively. Concerning systolic function, there was no significant difference in LVEF between the two groups, as indicated by a MD of −0.900 [95% CI (−1.96, 0.169), p = 0.098, I2 = 69%]. Conversely, LV-GLS exhibited significantly lower (less negative) values in post-COVID patients in comparison to the control group, with a MD of 1.84 [95% CI (0.751, 2.94), I2 = 90.1%]. Among LV diastolic indices, E/A ratio and mitral E wave values were significantly decreased and mitral A wave was significantly increased in post-COVID patients compared to controls. The MDs for E/A, E wave and A wave were −0.134 [95% CI (−0.258, −0.010), p = 0.034, I2 = 82%], −0.052 [95% CI (−0.082, −0.022), p = 0.001, I2 = 0%] and 0.086 [95%CI (0.022, 0.150), p = 0.008, I2 = 0%], respectively.
Additionally, significantly higher values were found in both LAD and LAVI in post-COVID patients compared to controls. The MD for LAD was 2.305 [95% CI (1.058, 3.74), p = 0.000, I2 = 63.7%], and for LAVI it was 1.475 [95% CI (0.374, 2.575), p = 0.009, I2 = 32.1%].
Regarding RV indices, post-COVID patients showed significantly increased value in sPAP with MDs of 4.306 [95% CI (2.398, 6.214), p = 0.000, I2 = 83.7%]. Moreover, TAPSE and RV-GLS values were significantly lower in post-COVID patients compared to controls with MDs of −1.234 [95% CI (−2.197, −0.270), p = 0.012, I2 = 57%] and 6.686 [95% CI (3.109, 9.662), p = 0.000, I2 = 95.2%], respectively. Detailed information is provided in Table 3.
3.5.2.2.1
Mild COVID-19 infection
In terms of mild COVID-19 infection and LV geometric indices, significant increase was observed in LVEDV and LVESD in post-COVID patients compared to controls with MDs of 8.39 [95% CI (3.57, 13.20), p = 0.001, I2 = 0%], and 0.908 [95% CI (0.488, 1.32), p = 0.000, I2 = 32.1%], respectively. LVM was significantly lower in post-COVID patients (n = 191) compared to controls (n = 139), with a MD of −13.71 [95 CI% (−25.30, −2.11), p = 0.020, I2 = 0%]. Moreover, significant changes in systolic function were observed in mild infection cases. Post-COVID patients reveled to have a decrease in LVEF and LV-GLS (less negative) compared to control groups, with MDs of −0.886 [95% CI (−1.64, −0.128), p = 0.022, I2 = 78%] and 0.815 [95% CI (0.047, 1.58), 0.038, I2 = 88.1%], respectively. Among LV diastolic indices, E/A ratio was significantly lower and LAVI was significantly increased in mild infection compared to controls, with MDs of −0.042 [95% CI (−0.076, −0.007), p = 0.017, I2 = 45%] and 0.922 [95% CI (0.139, 1.845), p = 0.023, I2 = 46%], respectively. RVD and sPAP were significantly higher in post-COVID patients compared to controls with MDs of 0.865 [95% CI (0.412, 1.317), p = 0.000, I2 = 0%] and 3.749 [95% CI (0.817, 6.682), p = 0.012, I2 = 0.012, I2 = 92.3%], respectively. Detailed information is provided in Table 3.
3.5.2.2.2
Moderate and/or severe COVID-19 infection
Post-COVID patients exhibited higher values of LVEDV, PWD, and LVM compared to the control group. The MDs for LVEDV, PWD, and LVM were 10.09 [95% CI (2.29, 17.89), p = 0.011, I2 = 0%], 0.614 [95% CI (0.259, 0.969), p = 0.001, I2 = 52.7%], and 9.018 [95% CI (0.458, 17.57), p = 0.039, I2 = 0%], respectively. Concerning systolic function, there was no significant difference in LVEF between the two groups, as indicated by a MD of −0.900 [95% CI (−1.96, 0.169), p = 0.098, I2 = 69%]. Conversely, LV-GLS exhibited significantly lower (less negative) values in post-COVID patients in comparison to the control group, with a MD of 1.84 [95% CI (0.751, 2.94), I2 = 90.1%]. Among LV diastolic indices, E/A ratio and mitral E wave values were significantly decreased and mitral A wave was significantly increased in post-COVID patients compared to controls. The MDs for E/A, E wave and A wave were −0.134 [95% CI (−0.258, −0.010), p = 0.034, I2 = 82%], −0.052 [95% CI (−0.082, −0.022), p = 0.001, I2 = 0%] and 0.086 [95%CI (0.022, 0.150), p = 0.008, I2 = 0%], respectively.
Additionally, significantly higher values were found in both LAD and LAVI in post-COVID patients compared to controls. The MD for LAD was 2.305 [95% CI (1.058, 3.74), p = 0.000, I2 = 63.7%], and for LAVI it was 1.475 [95% CI (0.374, 2.575), p = 0.009, I2 = 32.1%].
Regarding RV indices, post-COVID patients showed significantly increased value in sPAP with MDs of 4.306 [95% CI (2.398, 6.214), p = 0.000, I2 = 83.7%]. Moreover, TAPSE and RV-GLS values were significantly lower in post-COVID patients compared to controls with MDs of −1.234 [95% CI (−2.197, −0.270), p = 0.012, I2 = 57%] and 6.686 [95% CI (3.109, 9.662), p = 0.000, I2 = 95.2%], respectively. Detailed information is provided in Table 3.
3.5.2.3
Grouped by presence of comorbid diseases
Post-COVID patients with comorbidities showed higher values of LVEDV and LVESV compared to comorbid-matched control group with MDs of 10.35 [95% CI (4.93, 15.76), p = 0.000, I2 = 62.6%] and 5.55 [95% CI (1.15, 9.96), p = 0.013, I2 = 0%], respectively. There was a significant decrease in LVESD in post-COVID patients with comorbidities and an increase in cases without comorbidities compared to their comorbid-matched controls with MDs of −1.292 95% CI [−2.089, −0.495], p = 0.001, I2 = 43.2%) and 0.905 [95% CI (0.567, 1.24), p = 0.000, I2 = 0%], respectively. LVEF exhibited a significant decrease in post-COVID patients with comorbidities and those without comorbidities when compared to their comorbid-matched controls. The MDs were −0.852 [95% CI (−1.66, −0.038), p = 0.040, I2 = 56.5%] and −0.833 [95%CI (−1.64, −0.005), p = 0.049, I2 = 79.6%], respectively. Furthermore, LV-GLS was significantly decreased (less negative) in both groups of post-COVID patients, with MDs of 0.919 [95% CI (0.07, 1.76), p = 0.033, I2 = 88.8%] and 1.43 [95% CI (0.703, 2.16), p = 0.000, I2 = 93.7%] compared to their respective controls. E/A ratio was significantly lower in post-COVID patients with comorbidities compare to its comorbid-matched controls, with a MD of −0.137 [95% CI (−0.424, −0.032), p = 0.010, I2 = 57.9%]. Significantly higher values of both LAD and LAVI were observed in post-COVID patients with comorbidities compared to their matched controls. The MDs were 2.287 [95% CI (0.910, 3.664), p = 0.001, I2 = 77.4%] and 1.135 [95% CI (0.290, 1.980), p = 0.008, I2 = 0%], respectively.
Regarding RV function, RV-GLS was notably decreased (less negative) in both post-COVID patients with and without comorbidities compared to their controls, with MDs of 2.228 [95% CI (0.377, 4.079), p = 0.018, I2 = 90%] and 2.152 [95% CI (0.807, 3.498), p = 0.002, I2 = 79%]. Additionally, post-COVID patients without comorbidities presented higher values of RV-MPI with a MD of 0.060 [95% CI (0.030, 0.089), p = 0.009, I2 = 99%], compared to matched-controls. In post-COVID patients without comorbidities, TAPSE values were significantly lower, whereas no significant difference was found in cases with comorbidities when compared to their matched controls. The MDs were −1.440 [95%CI (−2.296, −0.585), p = 0.001, I2 = 87.4%] and −0.337 [95% CI (−1.213, 0.540), p = 0.452, I2 = 76%], respectively. Moreover, sPAP presented higher values in post-COVID patients with comorbidities and no significant result in cases without comorbidities compared to their matched controls with MDs of 6.777 [95% CI (4.463, 9.091), p = 0.000, I2 = 91.2%] and 2.039 [95%CI (−0.181, 4.258), p = 0.072, I2 = 91%], respectively. Detailed information is provided in Table 3. Forest plots are provided in supporting information (Supplementary S4 document).
Table 4 represent the summary of quantitative synthesis.
Table 4
Summary of quantitative synthesis.
Chamber function
Overall
Based on recovery phase
Based on severity of prior Covid-19 infection
Based on status of cardiovascular risk factors
Certainty of evidence
LV systolic function
SubclinicalImpairment(↓LVEF, ↓LV-GLS)
•Long-Covid (≥3 months): ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD, ↑LAD•Post-acute Covid (1–3 months): ↓LV-GLS
• Mild Infection: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVEDV•Moderate- Severe Infection: ↓LV-GLS, ↑LVMa
• Cardiovascular risk factors present: ↓LVEF, ↓LV-GLS, ↓LVESD, ↑LVESV•Cardiovascular risk factors absent: ↓LVEF, ↓LV-GLS, ↓LVM, ↑LVESD
Moderate ꚚꚚꚚO
LV diastolic function
SubclinicalImpairment(↑LAD, ↓E/A)
•Long-Covid (≥3 months): ↑LAD, ↑IVSD, ↓E/Aa•Post-acute Covid (1–3 months): ↑LVEDV, ↓E/A, ↑A wave, ↑LAVI
• Mild Infection: ↑LVESD, ↓E/A•Moderate- Severe Infection: ↑LVEDV, ↑PWD, ↓E/A, ↓E wave, ↑A wave, ↑LAD
•Cardiovascular risk factors present: ↑LVEDV, ↓E/A, ↑LAD, ↑LAVI•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
RV systolic function
SubclinicalImpairment(↓RV-GLS,↑RV-MPI,↓TAPSE)
•Long-Covid (≥3 months): ↓RV-GLS, ↑RV-MPI, ↑RVD, ↓TAPSE•Post-acute Covid (1–3 months)
• Mild infection: ↑RV-MPI, ↑RVD•Moderate- severe infection: ↓RV-GLS, ↓TAPSE
• Cardiovascular risk factors present: ↓RV-GLS, ↑RVD•Cardiovascular risk factors absent: ↓RV-GLS, ↑RV-MPI, ↓TAPSE
Moderate ꚚꚚꚚO
RV diastolic function
SubclinicalImpairment(↑sPAP)
•Long-Covid (≥3 months): ↑sPAP•Post-acute Covid (1–3 months): ↓RVD
• Mild infection: ↑sPAP•Moderate- severe Infection: ↑sPAP
•Cardiovascular risk factors present: ↑sPAP•Cardiovascular risk factors absent: None
Moderate ꚚꚚꚚO
a
Significant results were obtained during sensitivity analysis.
3.6
Sensitivity analysis
3.6.1
LVEDV
The study by Wood et al. (42), showed a high risk of bias for LVEDV in overall result of synthesis. Excluding this study revealed a significant difference between two groups of comparison with a MD of 4.732 [95% CI (1.367, 8.096), p = 0.006, I2 = 46.3%]. However, no significant difference was observed between two groups when grouped by duration ≥3 months and absence of comorbidities with MDs of 5.727 [95% CI (−0.209, 11.66), p = 0.059, I2 = 59.7%] and 1.964 [95% CI (−3.076, 7.00), 0.445, I2 = 57%], respectively.
3.6.2
IVSD
The studies by Ardahanli et al. (23) and Akbulut et al. (27) were found to have a high risk of bias for IVSD for in overall result of synthesis and duration of ≥3 months. Excluding these studies did not change the direction, or statistical significance of the summary estimate with MDs 0.011 [95% CI (−0.147, 0.170), p = 0.891, I2 = 77%] and 0.135 [95%CI (−0.124, 0.394), p = 0.307, I2 = 88.5%], respectively. However, excluding these studies revealed significant difference between two groups of comparison in moderate-severe COVID-19 infection, presence and absence of comorbid disease with MDs of 0.539 [95%CI (0.281, 0.798), p = 0.000, I2 = 77%] and 0.320 [95%CI (0.019, 0.620), p = 0.037, I2 = 88.5%], −0.083 [95%CI (−0.143, −0.023), p = 0.007, I2 = 36%], respectively.
3.6.3
LVM
Two studies (17, 19) were at high risk of bias for LVM due to involving athletes as their cases. A sensitivity analysis by excluding them did not change the direction, or statistical significance of the summary estimate with effect size of −5.78 [95%CI (−27.2, 15.3), p = 0.597, I2 = 83%].
3.6.4
LVMI
The study by Turpin et al. (17) was deemed to have a high risk of bias for LVMI due to the inclusion of athletes as study participants. However, excluding this study did not change the direction, or statistical significance of the summary estimate with an effect size of −0.722 [95%CI (−6.575, 5.123), p = 0.809, I2 = 86.9%]. Furthermore, subgroup analyses focusing on mild COVID-19 infection and the absence of comorbid diseases also showed no change in the significance of the results when excluding this study. The effect sizes for mild COVID-19 infection and absence of comorbid diseases were 2.07 [95%CI (−7.21, 11.36), p = 0.622, I2 = 80.2%] and −1.06 [95%CI (−10.82, 8.70), p = 0.831, I 2 = 89%], respectively.
3.6.5
LVEF
Three studies, conducted by Turpin et al. (17), Tudoran et al. (36) and Akbulut et al. (27), were deemed to have a high risk of bias in relation to LVEF. In a sensitivity analysis focusing on overall, mild COVID-19 and cases without comorbidities, the exclusion of these studies resulted in a change in the significance of the summary estimate. The effect size was found to be −0.499 [95% CI (−0.935, 0.037), p = 0.070, I2 = 63%] for overall cases, −0.229 [95% CI (−0.842, 0.383), p = 0.463, I2 = 62%] for mild cases, and −0.036 [95% CI (−0.686, 0.613), p = 0.913, I2 = 56.5%] for cases with absent comorbidities. However, excluding these studies did not change the direction, or statistical significance of the summary estimate for meta-analysis of duration ≥3 months with a MD of −0.693 [95% CI (−1.298, −0.087), p = 0.025, I2 = 47%].
3.6.6
LV-GLS
Akkabulut et al. (27) was found to have a high risk of bias in the meta-analysis of LV-GLS for both overall results and durations of ≥3 months. Conducting a sensitivity analysis by excluding this study did not affect the significance of the results, with effect sizes of 1.43 [95%CI (0.900, 1.961), p = 0.000, I2 = 91%] and 1.78 [95%CI (1.049, 2.516), p = 0.000, I2 = 92%], respectively. Furthermore, excluding this study did not alter the significant findings in the subgroup analysis of severity of COVID-19 infection. The effect sizes remained significant at 1.021 [95%CI (0.265, 1.776), p = 0.008, I2 = 87%] for mild infection and 2.289 [95%CI (1.201, 2.314), p = 0.000, I2 = 89%] for moderate-severe infection.
3.6.7
E/A ratio
Hamdy et al. (45) showed a high risk of bias in relation to this specific outcome. A sensitivity analysis was conducted by removing this study, changed the significancy of summary estimate for overall outcome and a duration of ≥3 months. The effect sizes were −0.079 [95%CI (−0.127, −0.032), p = 0.001, I2 = 64.6%] and −0.079 [95%CI (−0.141, −0.018), p = 0.011, I2 = 71.8%], respectively. However, excluding this study did not change the direction, or statistical significance of the summary estimate for the absence of comorbid diseases, with an effect size of −0.053 [95%CI (−0.109, 0.002), p = 0.061, I2 = 63.2%].
3.6.8
E/e' ratio
Hamdy et al. (45) and Wood et al. (42) were found to have a high risk of bias regarding this outcome. Excluding these studies did not change the direction, or statistical significance of the summary estimate for the overall outcome and duration of ≥3 months. The effect sizes remained at 0.092 [95%CI (−0.229, 0.412), p = 0.575, I2 = 76.3%] and 0.333 [95%CI (−0.094, 0.759), p = 0.126, I2 = 72.4%] for each respective outcome.
3.6.9
LAVI
Hamdy et al. (45) was found to have a high risk of bias for the outcome. Excluding this study did not change the direction, or statistical significance of the summary estimate for the overall outcome and duration of ≥3 months, with effect sizes of 0.578 [95%CI (−0.361, 1.517), p = 0.227, I2 = 51.4%] and 0.674 [95%CI (−0.077, 1.424), p = 0.079, I2 = 42.2%], respectively. The exclusion of this study also did not alter the lack of significance for the absence of comorbid disease, with an effect size of 0.214 [95%CI (−1.261, 1.688), p = 0.776, I2 = 73.4%].
3.6.10
RV-MPI
The study by Günay et al. (14) had a high risk of bias for this particular outcome. A sensitivity analysis was conducted by removing this study did not change the significancy of summary estimate for the overall outcome, showing an effect size of 0.035 [95% CI (0.008, 0.062), p = 0.012, I2 = 98.9%].
3.6.11
RVD
The study by Günay et al. (14) was found to have a high risk of bias for this particular outcome. Excluding this study changed the statistical significancy of summery estimates for the overall outcome, duration of 1–3 months, and the absence of comorbid disease, with effect sizes of 0.654 [95%CI (0.321, 0.987), p = 0.000, I2 = 17%], −0.277 [95%CI (−1.046, 0.493), p = 0.481, I2 = 21.3%] and 0.607 [95%CI (0.115, 1.099), p = 0.016, I2 = 0%], respectively. However, the sensitivity analysis for moderate-severe COVID-19 infection did not alter the direction or statistical significance of the summary estimate of the results. The effect sizes for these outcomes and 0.444 [95%CI (−0.099, 0.987), p = 0.109, I2 = 61.8%], respectively.
3.6.12
sPAP
Küçük et al. (33) had a high risk of bias for moderate to severe COVID-19 infection. A sensitivity analysis that excluded this study showed that the result remained significant with an effect size of 8.016 [95%CI (6.800, 9.232), p = 0.000, I2 = 26.5%]. De et al. (24) was also at high risk of bias for the presence of comorbid disease. However, excluding this study in a sensitivity analysis did not change the direction or statistical significance of the summary estimate, with an effect size of 8.097 [95%CI (7.08, 9.113), p = 0.000, I2 = 0%].
Forest plots of sensitivity analysis are provided in supporting information (Supplementary S5 document).
3.6.1
LVEDV
The study by Wood et al. (42), showed a high risk of bias for LVEDV in overall result of synthesis. Excluding this study revealed a significant difference between two groups of comparison with a MD of 4.732 [95% CI (1.367, 8.096), p = 0.006, I2 = 46.3%]. However, no significant difference was observed between two groups when grouped by duration ≥3 months and absence of comorbidities with MDs of 5.727 [95% CI (−0.209, 11.66), p = 0.059, I2 = 59.7%] and 1.964 [95% CI (−3.076, 7.00), 0.445, I2 = 57%], respectively.
3.6.2
IVSD
The studies by Ardahanli et al. (23) and Akbulut et al. (27) were found to have a high risk of bias for IVSD for in overall result of synthesis and duration of ≥3 months. Excluding these studies did not change the direction, or statistical significance of the summary estimate with MDs 0.011 [95% CI (−0.147, 0.170), p = 0.891, I2 = 77%] and 0.135 [95%CI (−0.124, 0.394), p = 0.307, I2 = 88.5%], respectively. However, excluding these studies revealed significant difference between two groups of comparison in moderate-severe COVID-19 infection, presence and absence of comorbid disease with MDs of 0.539 [95%CI (0.281, 0.798), p = 0.000, I2 = 77%] and 0.320 [95%CI (0.019, 0.620), p = 0.037, I2 = 88.5%], −0.083 [95%CI (−0.143, −0.023), p = 0.007, I2 = 36%], respectively.
3.6.3
LVM
Two studies (17, 19) were at high risk of bias for LVM due to involving athletes as their cases. A sensitivity analysis by excluding them did not change the direction, or statistical significance of the summary estimate with effect size of −5.78 [95%CI (−27.2, 15.3), p = 0.597, I2 = 83%].
3.6.4
LVMI
The study by Turpin et al. (17) was deemed to have a high risk of bias for LVMI due to the inclusion of athletes as study participants. However, excluding this study did not change the direction, or statistical significance of the summary estimate with an effect size of −0.722 [95%CI (−6.575, 5.123), p = 0.809, I2 = 86.9%]. Furthermore, subgroup analyses focusing on mild COVID-19 infection and the absence of comorbid diseases also showed no change in the significance of the results when excluding this study. The effect sizes for mild COVID-19 infection and absence of comorbid diseases were 2.07 [95%CI (−7.21, 11.36), p = 0.622, I2 = 80.2%] and −1.06 [95%CI (−10.82, 8.70), p = 0.831, I 2 = 89%], respectively.
3.6.5
LVEF
Three studies, conducted by Turpin et al. (17), Tudoran et al. (36) and Akbulut et al. (27), were deemed to have a high risk of bias in relation to LVEF. In a sensitivity analysis focusing on overall, mild COVID-19 and cases without comorbidities, the exclusion of these studies resulted in a change in the significance of the summary estimate. The effect size was found to be −0.499 [95% CI (−0.935, 0.037), p = 0.070, I2 = 63%] for overall cases, −0.229 [95% CI (−0.842, 0.383), p = 0.463, I2 = 62%] for mild cases, and −0.036 [95% CI (−0.686, 0.613), p = 0.913, I2 = 56.5%] for cases with absent comorbidities. However, excluding these studies did not change the direction, or statistical significance of the summary estimate for meta-analysis of duration ≥3 months with a MD of −0.693 [95% CI (−1.298, −0.087), p = 0.025, I2 = 47%].
3.6.6
LV-GLS
Akkabulut et al. (27) was found to have a high risk of bias in the meta-analysis of LV-GLS for both overall results and durations of ≥3 months. Conducting a sensitivity analysis by excluding this study did not affect the significance of the results, with effect sizes of 1.43 [95%CI (0.900, 1.961), p = 0.000, I2 = 91%] and 1.78 [95%CI (1.049, 2.516), p = 0.000, I2 = 92%], respectively. Furthermore, excluding this study did not alter the significant findings in the subgroup analysis of severity of COVID-19 infection. The effect sizes remained significant at 1.021 [95%CI (0.265, 1.776), p = 0.008, I2 = 87%] for mild infection and 2.289 [95%CI (1.201, 2.314), p = 0.000, I2 = 89%] for moderate-severe infection.
3.6.7
E/A ratio
Hamdy et al. (45) showed a high risk of bias in relation to this specific outcome. A sensitivity analysis was conducted by removing this study, changed the significancy of summary estimate for overall outcome and a duration of ≥3 months. The effect sizes were −0.079 [95%CI (−0.127, −0.032), p = 0.001, I2 = 64.6%] and −0.079 [95%CI (−0.141, −0.018), p = 0.011, I2 = 71.8%], respectively. However, excluding this study did not change the direction, or statistical significance of the summary estimate for the absence of comorbid diseases, with an effect size of −0.053 [95%CI (−0.109, 0.002), p = 0.061, I2 = 63.2%].
3.6.8
E/e' ratio
Hamdy et al. (45) and Wood et al. (42) were found to have a high risk of bias regarding this outcome. Excluding these studies did not change the direction, or statistical significance of the summary estimate for the overall outcome and duration of ≥3 months. The effect sizes remained at 0.092 [95%CI (−0.229, 0.412), p = 0.575, I2 = 76.3%] and 0.333 [95%CI (−0.094, 0.759), p = 0.126, I2 = 72.4%] for each respective outcome.
3.6.9
LAVI
Hamdy et al. (45) was found to have a high risk of bias for the outcome. Excluding this study did not change the direction, or statistical significance of the summary estimate for the overall outcome and duration of ≥3 months, with effect sizes of 0.578 [95%CI (−0.361, 1.517), p = 0.227, I2 = 51.4%] and 0.674 [95%CI (−0.077, 1.424), p = 0.079, I2 = 42.2%], respectively. The exclusion of this study also did not alter the lack of significance for the absence of comorbid disease, with an effect size of 0.214 [95%CI (−1.261, 1.688), p = 0.776, I2 = 73.4%].
3.6.10
RV-MPI
The study by Günay et al. (14) had a high risk of bias for this particular outcome. A sensitivity analysis was conducted by removing this study did not change the significancy of summary estimate for the overall outcome, showing an effect size of 0.035 [95% CI (0.008, 0.062), p = 0.012, I2 = 98.9%].
3.6.11
RVD
The study by Günay et al. (14) was found to have a high risk of bias for this particular outcome. Excluding this study changed the statistical significancy of summery estimates for the overall outcome, duration of 1–3 months, and the absence of comorbid disease, with effect sizes of 0.654 [95%CI (0.321, 0.987), p = 0.000, I2 = 17%], −0.277 [95%CI (−1.046, 0.493), p = 0.481, I2 = 21.3%] and 0.607 [95%CI (0.115, 1.099), p = 0.016, I2 = 0%], respectively. However, the sensitivity analysis for moderate-severe COVID-19 infection did not alter the direction or statistical significance of the summary estimate of the results. The effect sizes for these outcomes and 0.444 [95%CI (−0.099, 0.987), p = 0.109, I2 = 61.8%], respectively.
3.6.12
sPAP
Küçük et al. (33) had a high risk of bias for moderate to severe COVID-19 infection. A sensitivity analysis that excluded this study showed that the result remained significant with an effect size of 8.016 [95%CI (6.800, 9.232), p = 0.000, I2 = 26.5%]. De et al. (24) was also at high risk of bias for the presence of comorbid disease. However, excluding this study in a sensitivity analysis did not change the direction or statistical significance of the summary estimate, with an effect size of 8.097 [95%CI (7.08, 9.113), p = 0.000, I2 = 0%].
Forest plots of sensitivity analysis are provided in supporting information (Supplementary S5 document).
3.7
Meta-regression
The results of the univariate meta-regression showed a significant positive correlation between MDs of RV-GLS and age. The effect size was 0.150 [95% CI (0.027, 0.272), p = 0.016, R2 = 0.32]. Moreover, MDs of TAPSE was negatively correlated with post-COVID patients' age with an effect size of −0.077 [95%CI (−0.152, −0.003), p = 0.04, R2 = 0.09]. There were no other significant correlations observed between echocardiographic variables and age or BMI. Detailed information is presented in Table 5. Scatter plots are provided in supporting information (Supplementary S6 document).
Table 5
Meta-regression results between echocardiographic indices and baseline characteristics of patients.
Moderator
No. studies
Coefficient
SE
Z value
P value
95% CI
R
2
E/A ratio
Age
21
−0.004
0.003
−1.28
0.200
[−0.010, 0.002]
0.00
BMI
16
−0.004
0.017
0.23
0.815
[−0.038, 0.030]
0.00
LV-GLS
Age
26
0.017
0.032
0.55
0.584
[−0.045, 0.080]
0.02
BMI
12
−0.062
0.162
−0.38
0.700
[−0.382, 0.256]
0.00
LAVI
Age
15
−0.004
0.079
−0.05
0.960
[−0.158, 0.150]
0.00
BMI
13
−0.030
0.418
−0.07
0.941
[−0.850, 0.789]
0.00
LAD
Age
13
−0.019
0.093
−0.21
0.833
[−0.202, 0.163]
0.00
BMI
11
0.465
0.301
1.54
0.122
[−0.125, 1.055]
0.00
RV-GLS
Age
16
0.150
0.062
2.41
0.016
[0.027, 0.272]
0.32
BMI
6
−0.048
0.294
0.17
0.868
[−0.625, 0.528]
0.00
TAPSE
Age
26
−0.077
0.041
−2.05
0.04
[−0.152, −0.003]
0.09
BMI
16
−0.133
0.129
−1.03
0.304
[−0.388, 0.121]
0.00
sPAP
Age
12
0.162
0.105
1.55
0.122
[−0.043, 0.368]
0.00
BMI
11
0.730
0.502
1.45
0.145
[−0.253, 1.714]
0.00
3.8
Publication bias
A clear publication bias was observed when examining LVEF, LAVI, LAD and sPAP. After applying Duval and Tweedie's trim and fill method, it was determined that 9 studies needed to be added on the right side of the scatter plot for LVEF analysis. Following this adjustment, the effect size was calculated to be −0.120, with a 95%CI of (−0.711, 0.471). In the case of LAVI analysis, 5 studies needed to be imputed on the right side of the scatter plot, resulting in a summary effect size of 1.92, with a 95%CI of (0.689, 3.168). For LAD, 4 studies needed to be added on the left side of the scatter plot. The adjusted effect size was calculated 0.800 with a 95%CI of (−0.115, 1.716). Lastly, for sPAP correction analysis, 5 studies required imputation on the left side of the scatter plot, leading to a summary effect size of 1.29, with a 95%CI of (0.882, 1.717). Funnel plots and findings of Egger's and Begg's tests for all indices are provided in supporting information (Supplementary S7 document).
4
Discussion
In the present systematic review and meta-analysis, we performed a pooled analysis of 66 studies to evaluate the effect of SARS-CoV-2 infection on cardiac function in post-COVID-19 survivors without a prior history of cardiac issues or abnormalities. Following strict inclusion and exclusion criteria, we identified 32 studies that met the eligibility criteria for meta-analysis. This meta-analysis revealed significant myocardial alterations in individuals who have recovered from COVID-19 when compared to control groups. Furthermore, differences were observed in the function of the right and left ventricles in post-COVID patients compared to controls, especially in subgroup analyses based on the time since the onset of acute COVID-19 and echocardiogram evaluation during recovery, the severity of the initial infection, and the presence of comorbidities.
4.1
Definition
“Long COVID” or “post-COVID syndrome” is the term used to describe the ongoing presence of symptoms after a SARS-CoV-2 infection, lasting for weeks or months, regardless of whether the virus is still present in the body. These symptoms can persist or come back intermittently and may consist of either lingering symptoms from the initial COVID infection or new symptoms (79). The National Institute for Health and Care Excellence (NICE), the Scottish Intercollegiate Guidelines Network, and the Royal College of General Practitioners have collaborated to develop guidelines for individuals who have recuperated from COVID-19 but are still facing symptoms. They have coined the terms “post-acute COVID-19” for symptoms persisting 4–12 weeks after the initial infection and “long-COVID” for symptoms lasting beyond 12 weeks (80).
4.2
Echocardiographic evaluation of long COVID
The current systematic review and meta-analysis found that chronic COVID-19 patients exhibit impaired cardiac function in both the right and left sides of the heart. Unlike in previous reviews and meta-analyses, these patients did not have any history of cardiac disease and/or comorbidities that could affect their cardiac function.
4.2.1
Left ventricular function
LV systolic dysfunction has been observed as a consequence of acute COVID-19 infection. Multiple studies have shown significant reductions in LVEF after 3 months of recovery from COVID-19, across a spectrum of symptoms and severity levels (24, 36, 39, 40). Additionally, there have been reports of reduced LVEF in chronic COVID-19 survivors, although these studies lacked a control group (70, 71, 75, 81).
LV-GLS provides valuable insight into LV function and is considered a more precise measure compared to LVEF (82). Long COVID patients, with and without a control group, were found to have reduced (less negative) LV-GLS (24, 25, 28, 31, 33, 34, 38, 66, 72, 77). However, there were reports of studies with no significant findings of LVEF and LV-GLS in long-COVID cases (26, 27, 37, 41, 74, 76, 78). The present meta-analysis revealed that individuals with long-COVID had significantly lower LV-GLS and LVEF compared to the control group. Unlike LVEF, decreased LV-GLS was also observed in COVID-19 patients with both mild and moderate-severe infections. Furthermore, reduced LVEF and LV-GLS were observed in COVID-19 patients with and without comorbidities compared to their matched groups.
Several studies have reported LV diastolic dysfunction in addition to LV systolic dysfunction. Long-COVID patients were found to have lower E/A and E/e' ratios compared to the control group (24, 28, 32, 36, 38, 40). In a study conducted by Sharma and colleagues (71), it was found that individuals with moderate to severe cases of COVID-19 had a greater likelihood of experiencing left ventricular diastolic dysfunction compared to those with mild cases when assessed through echocardiography six months post-infection (71). However, the present meta-analysis did not find any significant differences in E/e', E/A, mitral A wave, and mitral E wave between long-COVID patients and the control group.
Diastolic dysfunction is characterized by an irregular filling pattern in the left ventricle, often resulting in significant elevations in end-diastolic pressure during the filling of the ventricle (83). Left atrium enlargement is a key indicator of the structural remodeling process that occurs in reaction to chronically elevated LV end-diastolic pressure, typically resulting from diastolic dysfunction (83). In the current meta-analysis, it was found that LAD was significantly higher in long-COVID patients compared to the control group. However, there were no significant differences observed in LAVI between the two groups. In subgroup analysis, LAD and LAVI were increased in patients with history of moderate-severe COVID-19 infection and comorbid disease compared to their matched controls.
Additionally, abnormal LV shape can be a sign of both systolic and diastolic dysfunction. Several studies have shown that patients with long-lasting COVID-19 symptoms have significant alterations in LV geometric measurements (27, 32, 36, 38, 73, 75). In the current meta-analysis, it was observed that long-COVID patients exhibited lower LVM and IVSD compared to the control group, which could potentially suggest systolic dysfunction. Nevertheless, it is crucial to understand that lower LVM and IVSD levels may not necessarily signal systolic dysfunction. Instead, a decrease in LVM and IVSD may simply suggest a reduction in the size and thickness of the LV muscle. This decline could be attributed to factors like weight loss or reduced physical activity, which were not specifically examined in the present study (84).
Although long-COVID patients showed a decrease in LVM, further analysis by subgroup indicated an increase in LVM for those with moderate to severe COVID-19 infection and a decrease in LVM for those with mild infection. The pathophysiology of LV remodeling in the context of a SARS-CoV-2 infection is not fully understood, but it is likely related to the systemic inflammatory response triggered by the virus. It is suggested that COVID-19 can lead to a cytokine storm, where the immune system releases large amounts of pro-inflammatory cytokines in response to the infection. This excessive inflammation can damage the heart muscle and lead to LV hypertrophy over time (85). Moreover, these late pathological findings may be linked to the severity of the initial COVID-19 illness, the duration since the acute phase, and the presence of lingering symptoms (86).
4.2.2
Right ventricular function
Research suggests that individuals may be at increased risk for right ventricular dysfunction after experiencing a severe case of COVID-19. This vulnerability is thought to be caused by the damage to the lungs and the rise in pulmonary vascular resistance resulting from the virus (87, 88). Several studies have demonstrated evidence of impaired RV function in individuals who have recovered from acute COVID-19, ranging from mild to severe infection, despite having no pre-existing cardiac conditions, for a duration exceeding 3 months comparing to control group (26, 28, 32, 34, 35, 40). However, studies conducted without a control group found that RV function was preserved in long-COVID cases (66, 69, 71, 73, 75). In the study conducted by Chamtouri et al., patients with severe pulmonary lesions detected on CT scans had a higher probability of experiencing subclinical myocardial injury during the mid-term monitoring period (77). In the current meta-analysis, long-COVID patients showed significantly impaired RV echocardiographic indices including increased RV diameter, sPAP, RV-MPI and reduced RV-GLS (less negative) and TAPSE compared to the control group. Pulmonary remodeling can lead to increased sPAP and RV-MPI. This can have a negative impact on the function of the right ventricle, leading to reductions in TAPSE and RV-GLS (89). In the subgroup analysis grouped by COVID-19 severity of infection, reduced RV-GLS and TAPSE were only observed in moderate-severe post-COVID patients compared to the control group. Earlier research indicates that even mild cases of COVID-19 may result in lasting cardiovascular complications (90). There is a possible relationship between the severity of inflammation during acute infection and long-term RV-GLS measurements (26). In addition, previous studies have demonstrated that assessing RV-GLS can be beneficial in predicting outcomes for patients with ARDS (91). It is believed that inflammation contributes to an increased workload and damage to the RV, ultimately leading to RV failure, which can be evaluated through RV-GLS (92).
In patients with COVID-19, it is crucial to recognize subclinical RV dysfunction, as reduced RV strain has been associated with increased mortality (93). This type of dysfunction appears to be common during post-recovery monitoring, even in individuals without preexisting cardiovascular or respiratory conditions or signs of heart failure, indicating potential unrecognized heart damage and compromised circulation following COVID-19 (94, 95).
4.3
Echocardiographic evaluation of post-acute COVID-19
4.3.1
Left ventricular function
Among parameters indicating the LV systolic function, subgroup analysis showed impaired LV-GLS in post-COVID patients. Impaired LV-GLS was present in both mild infection and moderate-severe infections of COVID-19. Regarding the comorbidities, both patients with and without comorbid diseases had significant impairment in LV-GLS. These findings underscore the impact of COVID-19 on LVGLS, irrespective of disease severity or the presence of comorbidities. Samiei et al. (50) compared COVID-19 patients according to the severity of their infection. Reported LVGLS were in the normal range in all COVID-19 groups in their study (mild: −22.2 ± 2; moderate: −20.6 ± 2; severe: −19.3 ± 1). However, their investigation revealed that in the early recovery phase (1.5 months post-infection), LV-GLS was significantly lower in individuals who had suffered from a severe form of COVID-19 compared to others who experienced a milder clinical course. Özer et al. (51, 96) reported LVGLS below the normal range in the presence of COVID-19-caused myocardial injury (37) (−17.7 ± 2.6). They reported that myocardial LV-GLS values were impaired in one out of every three patients 1-month post-COVID-19 recovery. Tudoran et al. (58) conducted an assessment of the cardiac morphology and function in patients 1–3 months post-COVID-19 infection, analyzing and comparing the results based on the presence of cardiac abnormalities. They reported lower LV-GLS in 8.66% of patients with cardiac abnormalities. Since impaired LV-GLS shows subclinical myocardial deformation and is suggestive of LV-impaired systolic performance, post-acute COVID-19 patients are prone to the LV systolic dysfunction.
Moreover, the current meta-analysis on LV diastolic parameters found that the mitral A wave was significantly elevated and the E/A ratio was notably reduced when compared to the control group. This difference was both present in mild and moderate-severe COVID-19 infections. Furthermore, the difference was significant among patients with comorbid diseases. Sollazzo et al. (52) evaluated the cardiac function of athletes after mild or moderate COVID-19 infection. According to their findings, significant difference was observed in the E/A ratio, one month after the COVID-19 infection. Tudoran et al. (58) also reported an E/A ratio over 2, thus a type III diastolic dysfunction; and an E/A ratio of under 0.8, thus a type I diastolic dysfunction in a subset of their patients. According to these results, it can be concluded that COVID-19 causes impaired relaxation and consequently LV diastolic dysfunction in the early recovery phase.
Additionally, a notable disparity in LAVI was seen between post-acute COVID-19 patients and the control groups. The difference was seen in both mild infection and moderate-severe infection and the presence of comorbid diseases. Tudoran et al. (58) also reported increased LAVI in patients with cardiac abnormality 1–3 months after COVID-19 infection. COVID-19 infection might cause left atrial remodeling in the initial diastolic dysfunction phase by increased participation of left atrial active contraction to surpass the relaxation difficulty and thus, leading to A wave increase as well.
4.3.2
Right ventricular function
In the present meta-analysis, only RV diameter showed a significant decrease in COVID-19 patients compared to the control groups. Erdem et al. (61) compared COVID-19 patients according to their hospitalization status and pulmonary involvement. Unlike the results of our meta-analysis, their findings showed that 2–3 months after recovering from COVID-19, RVD is increased in patients without a history of risk factors. Furthermore, the increases correlate with the severity of COVID-19 and the extent of pulmonary involvement. Tudoran et al. (55) compared patients with and without pulmonary hypertension following COVID-19 infection. As expected, RV diameter was significantly higher in patients with pulmonary hypertension. This controversy in the results might be due to the low number of studies included in our meta-analysis (5 studies). Although the occurrence of diastolic dysfunction prior to the systolic dysfunction might contribute to the decreased LV diameter.
4.4
A review of previous systematic reviews
Our search identified three systematic reviews (97–99) and two meta-analyses (100, 101) that investigated cardiac function in COVID-19 survivors.
In the meta-analysis conducted by Rahmati et al., a total of 21 studies were included (100). The inclusion criteria were studies that examined individuals who had recovered from COVID-19, in comparison to a control group, and presented findings of cardiac indices measured through Cardiac Magnetic Resonance (CMR), or echocardiography. They reported significant decrease in LVEDV, LVSV and LVEF in COVID-19 survivors compared to controls [(standardized mean difference (SMD) = −0.39, 95% CI = −0.56 to −0.22, p = 0.00001), (MD = −4.33, 95% CI = −5.72 to −2.94, p = 0.0000), and (SMD = −0.18, 95% CI = −0.34 to −0.01, p = 0.04), respectively]. No significant results were observed in LVEF across different post-COVID-19 follow-up periods in subgroup analysis. However, a decrease in LVEF was only evident where patients had a prior history of ICU admission grouped by the severity of COVID-19. LVM was significantly increased in COVID-19 cases in comparison to controls [(SMD = 0.23, 95%CI = 0.05–0.40, p = 0.01)]. Subgroup analysis showed that LVM started to increase significantly 3 months after recovery from COVID-19 infection. A meta-analysis of 5 studies showed a reduction of LV-GLS in recovered COVID-19 patients compared to controls (MD = −1.52, 95% CI = −1.64 to −0.97, p = 0.00001). Subgroup analysis revealed a decrease in LV-GLS would exist 2 months to 1 year after recovery. In terms of right heart indices, there was a significant reduction in RVEF, RVEDV, RVESV, RVSV and TAPSE of COVID-19 survivors compared to controls [(SMD = −0.29, 95% CI = −0.50 to −0.09, p = 0.005), (SMD = −0.42, 95% CI = −0.55 to −0.29, p = 0.00001), (SMD = −0.16, 95% CI = −0.29 to −0.03, p = 0.02), (MD = −0.50, 95% CI = −0.75 to −0.205, p = 0.0001) and (SMD = −0.91, 95% CI = −1.30 to −0.51, p = 0.00001), respectively] (100). Subgroup analysis revealed that TAPSE was reduced between 2 months and 1year post-COVID-19 recovery, while RVEF was reduced between 2 and 6 months after recovery. Subgroup analysis based on the severity of the acute COVID-19 phase and subsequent chronic outcome demonstrated a decrease in RVEF and RVESV only in patients who had been admitted to the ICU.
In the meta-analysis by Herold et al., they reviewed 32 CMR studies involving patients with COVID-19 that utilized myocardial longitudinal magnetization relaxation time constant (T1), transverse magnetization relaxation time constant (T2) mapping, extracellular volume, and late gadolinium enhancement (101). The authors suggested that T1 and T2 provided dynamic measures of cardiac involvement in COVID-19 survivors, indicating the improvement of cardiomyocyte injury and myocardial inflammation during recovery. In contrast, late gadolinium enhancement and, to a lesser extent, extracellular volume are seen as more static markers influenced by preexisting risk factors linked to adverse changes in myocardial tissue (101).
Ramadan et al. performed a comprehensive review to evaluate heart complications following recovery from COVID-19 (99). Of the studies analyzed, 12 employed CMR while 9 utilized echocardiography for cardiac function assessment. The median time for CMR evaluation was 63 days. The results indicated higher T1 intensity in 19% of cases, late gadolinium enhancement in 12% of cases, increased T2 intensity in 7% of cases, reduced GLS in 5% of cases, and decreased LVEF in 1.5%. In the echocardiography evaluation, the median time was 41 days. Reported outcomes included reduced LVEF, global hypokinesis, LV hypertrophy, diastolic dysfunction, pulmonary hypertension, and reduced GLS. Moreover, in the 3 to 6-month follow-up period, results showed a 30% decrease in LV-GLS, late gadolinium enhancement in 10% of cases on CMR, and diastolic dysfunction in 40% of cases on echocardiography (99).
Elhiny et al. conducted a review focusing on post-COVID-19 complications, including cardiovascular complications, in adults (97). Out of the studies analyzed, only three studies provided information on cardiac function assessment via imaging techniques. Two of these studies highlighted abnormal findings in CMR imaging. One study reported myocardial edema in 54% of COVID-19 survivors and positive late gadolinium enhancement in 31% of patients. Another study documented elevated myocardial native T1 and T2 values, myocardial, late gadolinium enhancement, pericardial enhancement, and reduced LVEF (less than 50%) in a subset of patients. Additionally, one study utilized trans-thoracic echocardiography to assess cardiac function, revealing a high prevalence of diastolic dysfunction, pulmonary hypertension, and pericardial effusion. Overall, this systematic review primarily focused on post-COVID-19 symptoms and complications across various organs, and the evaluation of cardiac function was found to be limited in scope.
Hassani et al. conducted a systematic review summarizing the CMR findings of COVID-19 adult survivors as reported in all available case series and cohort studies (98). Median follow-up time to MRI was at least 2 weeks after hospital discharge or diagnosis. The authors pooled data from 12 cohorts and 10 case series. Regarding the cardiac function, in 4 out of 8 cohort studies, RVEF was significantly lower than that in the control group. Mean/median LVEF fell in the normal range in all studies. However, six studies also reported the number of cases with LVEF <50%. One study found that RV and LV strains significantly decreased in COVID-19 with late gadolinium enhancement compared to those without late gadolinium enhancement and normal controls.
In contrast to earlier systematic reviews, we implemented measures to eliminate the possible interference of pre-existing heart conditions on the cardiac function of individuals who have recovered from COVID-19. Instead of combining echocardiography and CMR in the present meta-analysis like previous assessments, we focused solely on echocardiography to reduce variability and improve the accuracy of our results. Additionally, we conducted subgroup analysis to provide a more thorough understanding of the data. Moreover, we have incorporated a greater quantity of recent studies to validate the strength of our conclusions.
4.5
Limitations
It is important to acknowledge certain limitations when comparing the provided reports. To eliminate the heterogeneity of studies and the reported echocardiographic measurements, a subgroup meta-analysis was conducted. However, it is important to note that due to intrinsic limitations of the studies included, some degree of heterogeneity was inevitable. The parameters evaluated in echocardiography exhibited a wide range of variability, making it impossible to assess all the reported parameters. Additionally, there are several factors that may impact the function of the LV and RV, such as the vaccination status of patients and the treatment approach for COVID-19 infection during the acute phase. Unfortunately, the absence of data on these two variables in the research it was not feasible to compare the data.
4.6
Implications for research, practice and policy
Further research should focus on identifying risk factors for developing cardiac complications following COVID-19, as well as potential preventive measures and investigate potential treatments and interventions to prevent or manage cardiac dysfunction in COVID-19 survivors. Healthcare providers should be aware of the increased risk of cardiac dysfunction in individuals who have recovered from COVID-19, even if they did not experience severe symptoms during their initial illness. Regular cardiac monitoring and follow-up assessments should be considered for COVID-19 survivors, especially those with pre-existing cardiovascular conditions or other risk factors for heart disease. Policy makers should consider implementing guidelines for cardiac screening and follow-up care for COVID-19 survivors, to ensure early detection and appropriate management of cardiac dysfunction.
4.1
Definition
“Long COVID” or “post-COVID syndrome” is the term used to describe the ongoing presence of symptoms after a SARS-CoV-2 infection, lasting for weeks or months, regardless of whether the virus is still present in the body. These symptoms can persist or come back intermittently and may consist of either lingering symptoms from the initial COVID infection or new symptoms (79). The National Institute for Health and Care Excellence (NICE), the Scottish Intercollegiate Guidelines Network, and the Royal College of General Practitioners have collaborated to develop guidelines for individuals who have recuperated from COVID-19 but are still facing symptoms. They have coined the terms “post-acute COVID-19” for symptoms persisting 4–12 weeks after the initial infection and “long-COVID” for symptoms lasting beyond 12 weeks (80).
4.2
Echocardiographic evaluation of long COVID
The current systematic review and meta-analysis found that chronic COVID-19 patients exhibit impaired cardiac function in both the right and left sides of the heart. Unlike in previous reviews and meta-analyses, these patients did not have any history of cardiac disease and/or comorbidities that could affect their cardiac function.
4.2.1
Left ventricular function
LV systolic dysfunction has been observed as a consequence of acute COVID-19 infection. Multiple studies have shown significant reductions in LVEF after 3 months of recovery from COVID-19, across a spectrum of symptoms and severity levels (24, 36, 39, 40). Additionally, there have been reports of reduced LVEF in chronic COVID-19 survivors, although these studies lacked a control group (70, 71, 75, 81).
LV-GLS provides valuable insight into LV function and is considered a more precise measure compared to LVEF (82). Long COVID patients, with and without a control group, were found to have reduced (less negative) LV-GLS (24, 25, 28, 31, 33, 34, 38, 66, 72, 77). However, there were reports of studies with no significant findings of LVEF and LV-GLS in long-COVID cases (26, 27, 37, 41, 74, 76, 78). The present meta-analysis revealed that individuals with long-COVID had significantly lower LV-GLS and LVEF compared to the control group. Unlike LVEF, decreased LV-GLS was also observed in COVID-19 patients with both mild and moderate-severe infections. Furthermore, reduced LVEF and LV-GLS were observed in COVID-19 patients with and without comorbidities compared to their matched groups.
Several studies have reported LV diastolic dysfunction in addition to LV systolic dysfunction. Long-COVID patients were found to have lower E/A and E/e' ratios compared to the control group (24, 28, 32, 36, 38, 40). In a study conducted by Sharma and colleagues (71), it was found that individuals with moderate to severe cases of COVID-19 had a greater likelihood of experiencing left ventricular diastolic dysfunction compared to those with mild cases when assessed through echocardiography six months post-infection (71). However, the present meta-analysis did not find any significant differences in E/e', E/A, mitral A wave, and mitral E wave between long-COVID patients and the control group.
Diastolic dysfunction is characterized by an irregular filling pattern in the left ventricle, often resulting in significant elevations in end-diastolic pressure during the filling of the ventricle (83). Left atrium enlargement is a key indicator of the structural remodeling process that occurs in reaction to chronically elevated LV end-diastolic pressure, typically resulting from diastolic dysfunction (83). In the current meta-analysis, it was found that LAD was significantly higher in long-COVID patients compared to the control group. However, there were no significant differences observed in LAVI between the two groups. In subgroup analysis, LAD and LAVI were increased in patients with history of moderate-severe COVID-19 infection and comorbid disease compared to their matched controls.
Additionally, abnormal LV shape can be a sign of both systolic and diastolic dysfunction. Several studies have shown that patients with long-lasting COVID-19 symptoms have significant alterations in LV geometric measurements (27, 32, 36, 38, 73, 75). In the current meta-analysis, it was observed that long-COVID patients exhibited lower LVM and IVSD compared to the control group, which could potentially suggest systolic dysfunction. Nevertheless, it is crucial to understand that lower LVM and IVSD levels may not necessarily signal systolic dysfunction. Instead, a decrease in LVM and IVSD may simply suggest a reduction in the size and thickness of the LV muscle. This decline could be attributed to factors like weight loss or reduced physical activity, which were not specifically examined in the present study (84).
Although long-COVID patients showed a decrease in LVM, further analysis by subgroup indicated an increase in LVM for those with moderate to severe COVID-19 infection and a decrease in LVM for those with mild infection. The pathophysiology of LV remodeling in the context of a SARS-CoV-2 infection is not fully understood, but it is likely related to the systemic inflammatory response triggered by the virus. It is suggested that COVID-19 can lead to a cytokine storm, where the immune system releases large amounts of pro-inflammatory cytokines in response to the infection. This excessive inflammation can damage the heart muscle and lead to LV hypertrophy over time (85). Moreover, these late pathological findings may be linked to the severity of the initial COVID-19 illness, the duration since the acute phase, and the presence of lingering symptoms (86).
4.2.2
Right ventricular function
Research suggests that individuals may be at increased risk for right ventricular dysfunction after experiencing a severe case of COVID-19. This vulnerability is thought to be caused by the damage to the lungs and the rise in pulmonary vascular resistance resulting from the virus (87, 88). Several studies have demonstrated evidence of impaired RV function in individuals who have recovered from acute COVID-19, ranging from mild to severe infection, despite having no pre-existing cardiac conditions, for a duration exceeding 3 months comparing to control group (26, 28, 32, 34, 35, 40). However, studies conducted without a control group found that RV function was preserved in long-COVID cases (66, 69, 71, 73, 75). In the study conducted by Chamtouri et al., patients with severe pulmonary lesions detected on CT scans had a higher probability of experiencing subclinical myocardial injury during the mid-term monitoring period (77). In the current meta-analysis, long-COVID patients showed significantly impaired RV echocardiographic indices including increased RV diameter, sPAP, RV-MPI and reduced RV-GLS (less negative) and TAPSE compared to the control group. Pulmonary remodeling can lead to increased sPAP and RV-MPI. This can have a negative impact on the function of the right ventricle, leading to reductions in TAPSE and RV-GLS (89). In the subgroup analysis grouped by COVID-19 severity of infection, reduced RV-GLS and TAPSE were only observed in moderate-severe post-COVID patients compared to the control group. Earlier research indicates that even mild cases of COVID-19 may result in lasting cardiovascular complications (90). There is a possible relationship between the severity of inflammation during acute infection and long-term RV-GLS measurements (26). In addition, previous studies have demonstrated that assessing RV-GLS can be beneficial in predicting outcomes for patients with ARDS (91). It is believed that inflammation contributes to an increased workload and damage to the RV, ultimately leading to RV failure, which can be evaluated through RV-GLS (92).
In patients with COVID-19, it is crucial to recognize subclinical RV dysfunction, as reduced RV strain has been associated with increased mortality (93). This type of dysfunction appears to be common during post-recovery monitoring, even in individuals without preexisting cardiovascular or respiratory conditions or signs of heart failure, indicating potential unrecognized heart damage and compromised circulation following COVID-19 (94, 95).
4.2.1
Left ventricular function
LV systolic dysfunction has been observed as a consequence of acute COVID-19 infection. Multiple studies have shown significant reductions in LVEF after 3 months of recovery from COVID-19, across a spectrum of symptoms and severity levels (24, 36, 39, 40). Additionally, there have been reports of reduced LVEF in chronic COVID-19 survivors, although these studies lacked a control group (70, 71, 75, 81).
LV-GLS provides valuable insight into LV function and is considered a more precise measure compared to LVEF (82). Long COVID patients, with and without a control group, were found to have reduced (less negative) LV-GLS (24, 25, 28, 31, 33, 34, 38, 66, 72, 77). However, there were reports of studies with no significant findings of LVEF and LV-GLS in long-COVID cases (26, 27, 37, 41, 74, 76, 78). The present meta-analysis revealed that individuals with long-COVID had significantly lower LV-GLS and LVEF compared to the control group. Unlike LVEF, decreased LV-GLS was also observed in COVID-19 patients with both mild and moderate-severe infections. Furthermore, reduced LVEF and LV-GLS were observed in COVID-19 patients with and without comorbidities compared to their matched groups.
Several studies have reported LV diastolic dysfunction in addition to LV systolic dysfunction. Long-COVID patients were found to have lower E/A and E/e' ratios compared to the control group (24, 28, 32, 36, 38, 40). In a study conducted by Sharma and colleagues (71), it was found that individuals with moderate to severe cases of COVID-19 had a greater likelihood of experiencing left ventricular diastolic dysfunction compared to those with mild cases when assessed through echocardiography six months post-infection (71). However, the present meta-analysis did not find any significant differences in E/e', E/A, mitral A wave, and mitral E wave between long-COVID patients and the control group.
Diastolic dysfunction is characterized by an irregular filling pattern in the left ventricle, often resulting in significant elevations in end-diastolic pressure during the filling of the ventricle (83). Left atrium enlargement is a key indicator of the structural remodeling process that occurs in reaction to chronically elevated LV end-diastolic pressure, typically resulting from diastolic dysfunction (83). In the current meta-analysis, it was found that LAD was significantly higher in long-COVID patients compared to the control group. However, there were no significant differences observed in LAVI between the two groups. In subgroup analysis, LAD and LAVI were increased in patients with history of moderate-severe COVID-19 infection and comorbid disease compared to their matched controls.
Additionally, abnormal LV shape can be a sign of both systolic and diastolic dysfunction. Several studies have shown that patients with long-lasting COVID-19 symptoms have significant alterations in LV geometric measurements (27, 32, 36, 38, 73, 75). In the current meta-analysis, it was observed that long-COVID patients exhibited lower LVM and IVSD compared to the control group, which could potentially suggest systolic dysfunction. Nevertheless, it is crucial to understand that lower LVM and IVSD levels may not necessarily signal systolic dysfunction. Instead, a decrease in LVM and IVSD may simply suggest a reduction in the size and thickness of the LV muscle. This decline could be attributed to factors like weight loss or reduced physical activity, which were not specifically examined in the present study (84).
Although long-COVID patients showed a decrease in LVM, further analysis by subgroup indicated an increase in LVM for those with moderate to severe COVID-19 infection and a decrease in LVM for those with mild infection. The pathophysiology of LV remodeling in the context of a SARS-CoV-2 infection is not fully understood, but it is likely related to the systemic inflammatory response triggered by the virus. It is suggested that COVID-19 can lead to a cytokine storm, where the immune system releases large amounts of pro-inflammatory cytokines in response to the infection. This excessive inflammation can damage the heart muscle and lead to LV hypertrophy over time (85). Moreover, these late pathological findings may be linked to the severity of the initial COVID-19 illness, the duration since the acute phase, and the presence of lingering symptoms (86).
4.2.2
Right ventricular function
Research suggests that individuals may be at increased risk for right ventricular dysfunction after experiencing a severe case of COVID-19. This vulnerability is thought to be caused by the damage to the lungs and the rise in pulmonary vascular resistance resulting from the virus (87, 88). Several studies have demonstrated evidence of impaired RV function in individuals who have recovered from acute COVID-19, ranging from mild to severe infection, despite having no pre-existing cardiac conditions, for a duration exceeding 3 months comparing to control group (26, 28, 32, 34, 35, 40). However, studies conducted without a control group found that RV function was preserved in long-COVID cases (66, 69, 71, 73, 75). In the study conducted by Chamtouri et al., patients with severe pulmonary lesions detected on CT scans had a higher probability of experiencing subclinical myocardial injury during the mid-term monitoring period (77). In the current meta-analysis, long-COVID patients showed significantly impaired RV echocardiographic indices including increased RV diameter, sPAP, RV-MPI and reduced RV-GLS (less negative) and TAPSE compared to the control group. Pulmonary remodeling can lead to increased sPAP and RV-MPI. This can have a negative impact on the function of the right ventricle, leading to reductions in TAPSE and RV-GLS (89). In the subgroup analysis grouped by COVID-19 severity of infection, reduced RV-GLS and TAPSE were only observed in moderate-severe post-COVID patients compared to the control group. Earlier research indicates that even mild cases of COVID-19 may result in lasting cardiovascular complications (90). There is a possible relationship between the severity of inflammation during acute infection and long-term RV-GLS measurements (26). In addition, previous studies have demonstrated that assessing RV-GLS can be beneficial in predicting outcomes for patients with ARDS (91). It is believed that inflammation contributes to an increased workload and damage to the RV, ultimately leading to RV failure, which can be evaluated through RV-GLS (92).
In patients with COVID-19, it is crucial to recognize subclinical RV dysfunction, as reduced RV strain has been associated with increased mortality (93). This type of dysfunction appears to be common during post-recovery monitoring, even in individuals without preexisting cardiovascular or respiratory conditions or signs of heart failure, indicating potential unrecognized heart damage and compromised circulation following COVID-19 (94, 95).
4.3
Echocardiographic evaluation of post-acute COVID-19
4.3.1
Left ventricular function
Among parameters indicating the LV systolic function, subgroup analysis showed impaired LV-GLS in post-COVID patients. Impaired LV-GLS was present in both mild infection and moderate-severe infections of COVID-19. Regarding the comorbidities, both patients with and without comorbid diseases had significant impairment in LV-GLS. These findings underscore the impact of COVID-19 on LVGLS, irrespective of disease severity or the presence of comorbidities. Samiei et al. (50) compared COVID-19 patients according to the severity of their infection. Reported LVGLS were in the normal range in all COVID-19 groups in their study (mild: −22.2 ± 2; moderate: −20.6 ± 2; severe: −19.3 ± 1). However, their investigation revealed that in the early recovery phase (1.5 months post-infection), LV-GLS was significantly lower in individuals who had suffered from a severe form of COVID-19 compared to others who experienced a milder clinical course. Özer et al. (51, 96) reported LVGLS below the normal range in the presence of COVID-19-caused myocardial injury (37) (−17.7 ± 2.6). They reported that myocardial LV-GLS values were impaired in one out of every three patients 1-month post-COVID-19 recovery. Tudoran et al. (58) conducted an assessment of the cardiac morphology and function in patients 1–3 months post-COVID-19 infection, analyzing and comparing the results based on the presence of cardiac abnormalities. They reported lower LV-GLS in 8.66% of patients with cardiac abnormalities. Since impaired LV-GLS shows subclinical myocardial deformation and is suggestive of LV-impaired systolic performance, post-acute COVID-19 patients are prone to the LV systolic dysfunction.
Moreover, the current meta-analysis on LV diastolic parameters found that the mitral A wave was significantly elevated and the E/A ratio was notably reduced when compared to the control group. This difference was both present in mild and moderate-severe COVID-19 infections. Furthermore, the difference was significant among patients with comorbid diseases. Sollazzo et al. (52) evaluated the cardiac function of athletes after mild or moderate COVID-19 infection. According to their findings, significant difference was observed in the E/A ratio, one month after the COVID-19 infection. Tudoran et al. (58) also reported an E/A ratio over 2, thus a type III diastolic dysfunction; and an E/A ratio of under 0.8, thus a type I diastolic dysfunction in a subset of their patients. According to these results, it can be concluded that COVID-19 causes impaired relaxation and consequently LV diastolic dysfunction in the early recovery phase.
Additionally, a notable disparity in LAVI was seen between post-acute COVID-19 patients and the control groups. The difference was seen in both mild infection and moderate-severe infection and the presence of comorbid diseases. Tudoran et al. (58) also reported increased LAVI in patients with cardiac abnormality 1–3 months after COVID-19 infection. COVID-19 infection might cause left atrial remodeling in the initial diastolic dysfunction phase by increased participation of left atrial active contraction to surpass the relaxation difficulty and thus, leading to A wave increase as well.
4.3.2
Right ventricular function
In the present meta-analysis, only RV diameter showed a significant decrease in COVID-19 patients compared to the control groups. Erdem et al. (61) compared COVID-19 patients according to their hospitalization status and pulmonary involvement. Unlike the results of our meta-analysis, their findings showed that 2–3 months after recovering from COVID-19, RVD is increased in patients without a history of risk factors. Furthermore, the increases correlate with the severity of COVID-19 and the extent of pulmonary involvement. Tudoran et al. (55) compared patients with and without pulmonary hypertension following COVID-19 infection. As expected, RV diameter was significantly higher in patients with pulmonary hypertension. This controversy in the results might be due to the low number of studies included in our meta-analysis (5 studies). Although the occurrence of diastolic dysfunction prior to the systolic dysfunction might contribute to the decreased LV diameter.
4.3.1
Left ventricular function
Among parameters indicating the LV systolic function, subgroup analysis showed impaired LV-GLS in post-COVID patients. Impaired LV-GLS was present in both mild infection and moderate-severe infections of COVID-19. Regarding the comorbidities, both patients with and without comorbid diseases had significant impairment in LV-GLS. These findings underscore the impact of COVID-19 on LVGLS, irrespective of disease severity or the presence of comorbidities. Samiei et al. (50) compared COVID-19 patients according to the severity of their infection. Reported LVGLS were in the normal range in all COVID-19 groups in their study (mild: −22.2 ± 2; moderate: −20.6 ± 2; severe: −19.3 ± 1). However, their investigation revealed that in the early recovery phase (1.5 months post-infection), LV-GLS was significantly lower in individuals who had suffered from a severe form of COVID-19 compared to others who experienced a milder clinical course. Özer et al. (51, 96) reported LVGLS below the normal range in the presence of COVID-19-caused myocardial injury (37) (−17.7 ± 2.6). They reported that myocardial LV-GLS values were impaired in one out of every three patients 1-month post-COVID-19 recovery. Tudoran et al. (58) conducted an assessment of the cardiac morphology and function in patients 1–3 months post-COVID-19 infection, analyzing and comparing the results based on the presence of cardiac abnormalities. They reported lower LV-GLS in 8.66% of patients with cardiac abnormalities. Since impaired LV-GLS shows subclinical myocardial deformation and is suggestive of LV-impaired systolic performance, post-acute COVID-19 patients are prone to the LV systolic dysfunction.
Moreover, the current meta-analysis on LV diastolic parameters found that the mitral A wave was significantly elevated and the E/A ratio was notably reduced when compared to the control group. This difference was both present in mild and moderate-severe COVID-19 infections. Furthermore, the difference was significant among patients with comorbid diseases. Sollazzo et al. (52) evaluated the cardiac function of athletes after mild or moderate COVID-19 infection. According to their findings, significant difference was observed in the E/A ratio, one month after the COVID-19 infection. Tudoran et al. (58) also reported an E/A ratio over 2, thus a type III diastolic dysfunction; and an E/A ratio of under 0.8, thus a type I diastolic dysfunction in a subset of their patients. According to these results, it can be concluded that COVID-19 causes impaired relaxation and consequently LV diastolic dysfunction in the early recovery phase.
Additionally, a notable disparity in LAVI was seen between post-acute COVID-19 patients and the control groups. The difference was seen in both mild infection and moderate-severe infection and the presence of comorbid diseases. Tudoran et al. (58) also reported increased LAVI in patients with cardiac abnormality 1–3 months after COVID-19 infection. COVID-19 infection might cause left atrial remodeling in the initial diastolic dysfunction phase by increased participation of left atrial active contraction to surpass the relaxation difficulty and thus, leading to A wave increase as well.
4.3.2
Right ventricular function
In the present meta-analysis, only RV diameter showed a significant decrease in COVID-19 patients compared to the control groups. Erdem et al. (61) compared COVID-19 patients according to their hospitalization status and pulmonary involvement. Unlike the results of our meta-analysis, their findings showed that 2–3 months after recovering from COVID-19, RVD is increased in patients without a history of risk factors. Furthermore, the increases correlate with the severity of COVID-19 and the extent of pulmonary involvement. Tudoran et al. (55) compared patients with and without pulmonary hypertension following COVID-19 infection. As expected, RV diameter was significantly higher in patients with pulmonary hypertension. This controversy in the results might be due to the low number of studies included in our meta-analysis (5 studies). Although the occurrence of diastolic dysfunction prior to the systolic dysfunction might contribute to the decreased LV diameter.
4.4
A review of previous systematic reviews
Our search identified three systematic reviews (97–99) and two meta-analyses (100, 101) that investigated cardiac function in COVID-19 survivors.
In the meta-analysis conducted by Rahmati et al., a total of 21 studies were included (100). The inclusion criteria were studies that examined individuals who had recovered from COVID-19, in comparison to a control group, and presented findings of cardiac indices measured through Cardiac Magnetic Resonance (CMR), or echocardiography. They reported significant decrease in LVEDV, LVSV and LVEF in COVID-19 survivors compared to controls [(standardized mean difference (SMD) = −0.39, 95% CI = −0.56 to −0.22, p = 0.00001), (MD = −4.33, 95% CI = −5.72 to −2.94, p = 0.0000), and (SMD = −0.18, 95% CI = −0.34 to −0.01, p = 0.04), respectively]. No significant results were observed in LVEF across different post-COVID-19 follow-up periods in subgroup analysis. However, a decrease in LVEF was only evident where patients had a prior history of ICU admission grouped by the severity of COVID-19. LVM was significantly increased in COVID-19 cases in comparison to controls [(SMD = 0.23, 95%CI = 0.05–0.40, p = 0.01)]. Subgroup analysis showed that LVM started to increase significantly 3 months after recovery from COVID-19 infection. A meta-analysis of 5 studies showed a reduction of LV-GLS in recovered COVID-19 patients compared to controls (MD = −1.52, 95% CI = −1.64 to −0.97, p = 0.00001). Subgroup analysis revealed a decrease in LV-GLS would exist 2 months to 1 year after recovery. In terms of right heart indices, there was a significant reduction in RVEF, RVEDV, RVESV, RVSV and TAPSE of COVID-19 survivors compared to controls [(SMD = −0.29, 95% CI = −0.50 to −0.09, p = 0.005), (SMD = −0.42, 95% CI = −0.55 to −0.29, p = 0.00001), (SMD = −0.16, 95% CI = −0.29 to −0.03, p = 0.02), (MD = −0.50, 95% CI = −0.75 to −0.205, p = 0.0001) and (SMD = −0.91, 95% CI = −1.30 to −0.51, p = 0.00001), respectively] (100). Subgroup analysis revealed that TAPSE was reduced between 2 months and 1year post-COVID-19 recovery, while RVEF was reduced between 2 and 6 months after recovery. Subgroup analysis based on the severity of the acute COVID-19 phase and subsequent chronic outcome demonstrated a decrease in RVEF and RVESV only in patients who had been admitted to the ICU.
In the meta-analysis by Herold et al., they reviewed 32 CMR studies involving patients with COVID-19 that utilized myocardial longitudinal magnetization relaxation time constant (T1), transverse magnetization relaxation time constant (T2) mapping, extracellular volume, and late gadolinium enhancement (101). The authors suggested that T1 and T2 provided dynamic measures of cardiac involvement in COVID-19 survivors, indicating the improvement of cardiomyocyte injury and myocardial inflammation during recovery. In contrast, late gadolinium enhancement and, to a lesser extent, extracellular volume are seen as more static markers influenced by preexisting risk factors linked to adverse changes in myocardial tissue (101).
Ramadan et al. performed a comprehensive review to evaluate heart complications following recovery from COVID-19 (99). Of the studies analyzed, 12 employed CMR while 9 utilized echocardiography for cardiac function assessment. The median time for CMR evaluation was 63 days. The results indicated higher T1 intensity in 19% of cases, late gadolinium enhancement in 12% of cases, increased T2 intensity in 7% of cases, reduced GLS in 5% of cases, and decreased LVEF in 1.5%. In the echocardiography evaluation, the median time was 41 days. Reported outcomes included reduced LVEF, global hypokinesis, LV hypertrophy, diastolic dysfunction, pulmonary hypertension, and reduced GLS. Moreover, in the 3 to 6-month follow-up period, results showed a 30% decrease in LV-GLS, late gadolinium enhancement in 10% of cases on CMR, and diastolic dysfunction in 40% of cases on echocardiography (99).
Elhiny et al. conducted a review focusing on post-COVID-19 complications, including cardiovascular complications, in adults (97). Out of the studies analyzed, only three studies provided information on cardiac function assessment via imaging techniques. Two of these studies highlighted abnormal findings in CMR imaging. One study reported myocardial edema in 54% of COVID-19 survivors and positive late gadolinium enhancement in 31% of patients. Another study documented elevated myocardial native T1 and T2 values, myocardial, late gadolinium enhancement, pericardial enhancement, and reduced LVEF (less than 50%) in a subset of patients. Additionally, one study utilized trans-thoracic echocardiography to assess cardiac function, revealing a high prevalence of diastolic dysfunction, pulmonary hypertension, and pericardial effusion. Overall, this systematic review primarily focused on post-COVID-19 symptoms and complications across various organs, and the evaluation of cardiac function was found to be limited in scope.
Hassani et al. conducted a systematic review summarizing the CMR findings of COVID-19 adult survivors as reported in all available case series and cohort studies (98). Median follow-up time to MRI was at least 2 weeks after hospital discharge or diagnosis. The authors pooled data from 12 cohorts and 10 case series. Regarding the cardiac function, in 4 out of 8 cohort studies, RVEF was significantly lower than that in the control group. Mean/median LVEF fell in the normal range in all studies. However, six studies also reported the number of cases with LVEF <50%. One study found that RV and LV strains significantly decreased in COVID-19 with late gadolinium enhancement compared to those without late gadolinium enhancement and normal controls.
In contrast to earlier systematic reviews, we implemented measures to eliminate the possible interference of pre-existing heart conditions on the cardiac function of individuals who have recovered from COVID-19. Instead of combining echocardiography and CMR in the present meta-analysis like previous assessments, we focused solely on echocardiography to reduce variability and improve the accuracy of our results. Additionally, we conducted subgroup analysis to provide a more thorough understanding of the data. Moreover, we have incorporated a greater quantity of recent studies to validate the strength of our conclusions.
4.5
Limitations
It is important to acknowledge certain limitations when comparing the provided reports. To eliminate the heterogeneity of studies and the reported echocardiographic measurements, a subgroup meta-analysis was conducted. However, it is important to note that due to intrinsic limitations of the studies included, some degree of heterogeneity was inevitable. The parameters evaluated in echocardiography exhibited a wide range of variability, making it impossible to assess all the reported parameters. Additionally, there are several factors that may impact the function of the LV and RV, such as the vaccination status of patients and the treatment approach for COVID-19 infection during the acute phase. Unfortunately, the absence of data on these two variables in the research it was not feasible to compare the data.
4.6
Implications for research, practice and policy
Further research should focus on identifying risk factors for developing cardiac complications following COVID-19, as well as potential preventive measures and investigate potential treatments and interventions to prevent or manage cardiac dysfunction in COVID-19 survivors. Healthcare providers should be aware of the increased risk of cardiac dysfunction in individuals who have recovered from COVID-19, even if they did not experience severe symptoms during their initial illness. Regular cardiac monitoring and follow-up assessments should be considered for COVID-19 survivors, especially those with pre-existing cardiovascular conditions or other risk factors for heart disease. Policy makers should consider implementing guidelines for cardiac screening and follow-up care for COVID-19 survivors, to ensure early detection and appropriate management of cardiac dysfunction.
5
Conclusion
This systematic review discusses emerging research on the possible development of cardiac dysfunction following the resolution of acute COVID-19 infection. Findings revealed subclinical changes in both left and right ventricular systolic and diastolic function among post-acute and long COVID patients without a prior history of heart disease, including individuals lacking cardiovascular risk factors such as diabetes and hypertension, irrespective of the severity of their initial illness. While these changes remained within normal limits, they were markedly different from those in non-COVID control subjects, indicating potential underlying issues that warrant further exploration. |
Title: Recent Advancements in MXene-Based Biosensors for Health and Environmental Applications—A Review | Body:
1. Introduction
Nanomaterials (NMs), at the forefront of scientific research, have gathered tremendous interest among the scientific community due to their excellent properties and diverse applications. In 1959, Nobel laureate Richard Feynman coined the term nanomaterial for the first time which started from gold sols and developed beyond 2D-nanomaterials in current times [1]. 2D materials are extensively employed in various applications such as biosensors, nanogenerators, resonators, photonics, batteries, supercapacitors, and thermoelectrics. The outstanding properties of nanomaterials such as small size, high surface-to-volume ratio, and optical response are appropriate for their applications in electronics, medicine, energy, environmental remediation, etc. A rapid development of two-dimensional (2D)-based nanomaterials including graphene, dichalcogenides, and MXene has shown an impressive manifold growth owing to their exceptional optical, mechanical, electronic, and physicochemical properties [2]. 2D materials are crystalline solids consisting of multi-element single-atom-thick layers of allotropes or multi-covalent bond complexes [3]. 2D- materials with the invention of graphene, in 2014, have expanded not only to the plumbene family (silicene, phosphorene, antimonene, tellurene, selinene, etc.) but also consist of other materials including WS2, MoS2, SnSe, black phosphorus, carbon-based NMs, and MXene. To take full advantage of these nanomaterials, it is essential to transform them into devices for identification, characterization, and quantification purposes [4]. This demands the integration of life-science and electronic technology to develop pivotal pathways that understand the bio-molecular interactions with ease since it is crucial in the field of clinical diagnosis and chemical detection. The rapid technological enhancement to satisfy the growing needs of today’s modern society has enabled the development of rapid, viable, and efficient devices for sensing biomolecules called biosensors. Biosensors are effective, lucrative, and onsite detection tools to analyze and detect biomolecules with high sensitivity, repeatability, physicochemical stability, and low detection limit. A biosensor device comprises a bio-recognition component, which refers to the organic counterpart, i.e., enzyme, antibody, and nucleic acid, and a transducer that generates a signal corresponding to the specific interaction. The classification of the biosensor can be made based on either the biorecognition element or the type of transducer. For example, the enzyme-, antibody-, aptamer-, and whole-cell-biosensors are named based on their bio-recognition element. On the other hand, the electrochemical-, optical-, and fluorescence-biosensors are named based on their transducer [5]. Enzymatic biosensors utilize enzymes as biocatalysts, which are efficient at increasing the reaction rate of any biological process. Analyte detection takes place via different mechanisms: (1) The enzyme metabolizes the analyte and thus, the concentration of the enzyme can be determined through the measurement of catalytic transformation of the analyte; (2) the analyte can inhibit or activate enzymes, which provides the opportunity to quantify the analyte via estimating the enzymatic product formation; and (3) by observing the change in enzyme activity.
Electrochemical biosensors rely on the electrochemical properties of both the analyte and transducer. The electrochemical reaction between the bio-recognition element and analyte takes place on the surface of the transducer, which can be measured in terms of voltage, current, impedance, or capacitance. Based on the type of signal from the transducer, they are further classified as potentiometric, amperometric, impedimetric, and conductometric voltammetry [6]. In optical biosensors, the physical or chemical change caused by the bio-recognition element can be detected via a change in the physical properties of the transducer such as polarization, transmission, reflection, refraction, phase, amplitude, or frequency. Both label-based and label-free optical biosensors can be effectively used to quantify analyte by monitoring the optical signal generated through different methods (colorimetric and fluorescence methods) and analyte–transducer interaction, respectively. The fluorescence-based biosensors use fluorescent dyes, quantum dots, and fluorescent proteins as fluorescence probes. The specific fluorescence probe with a high affinity towards the analyte is selected. The interaction between the analyte and probe modulates the fluorescence properties in proportion to the analyte concentration. The interaction with the analytes resulted in the fluctuation of the emission intensity due to different processes, namely, photoinduced electron transfer (PET), Förster resonance energy transfer (FRET), intramolecular charge transfer (ICT), and excited state intramolecular proton transfer (ESIPT) [7].
MXenes, a relatively new class of fastest-growing two-dimensional (2D) transition metal carbides and nitrides, have garnered significant attention in the field of biosensing due to their unique properties and versatile applications. Since their discovery in 2011 by Prof. Yuri Gogotsi [8,9] at Drexel University, USA, MXenes have demonstrated remarkable potential in various fields, including energy storage [10], catalysis [11], electromagnetic shielding [12], and sensing [13]. MXenes are marvelous materials that offer outstanding prospects for transforming into novel products with enhanced technological capabilities [3]. In recent years, the development of MXene-based biosensors has emerged as a promising area of research, particularly for health monitoring and environmental detection applications [14,15]. MXenes possess several advantageous characteristics such as high electrical conductivity, large surface area, excellent hydrophilicity, ease of surface functionalization, and tunable surface chemistry that make them ideal candidates for biosensor development [12]. Moreover, their biocompatibility and stability in aqueous environments further enhance their suitability for biosensing applications [16]. The family of MXenes is generally derived from MAX phases through chemical etching, which leaves behind layered structured MXenes with Mn+1XnTx. Naguib et al. [17] synthesized Ti3C2 2D nanosheets for the first time from the Ti3AlC2 phase at room temperature using hydrofluoric (HF) acid, which opens the door to synthesize a variety of MXenes via Al etching. This distinctive layered structure offers high surface area, tunable electronic properties, and excellent mechanical strength, satisfying the essential criteria for various applications. A variety of high-quality MAX phases could be created by substituting M, A, and X, resulting in a diverse library of MAX phases available for their conversion into MXenes. Mono- (Ti3C2Tx) and di-transition metal-substituted MXenes (Mo2TiC2Tx, Mo2Ti2C3Tx, and Cr2TiC2Tx) have been created through the etching Al from their corresponding MAX phases. Similarly, two A elements have also been substituted to obtain the Ti3(Si,Al)C2 MAX phase. Likewise, X can be tuned to obtain MAX phases, e.g., Ti3AlCN. To further increase the family of the MXenes, the existing M of any MAX phases has also been substituted with different M partially (Ti2−yNbyCTx, Ti2−yVyCTx, and V2−yNbyCTx) to enlarge the family of MXenes [18]. Until now, more than 30 MAX phases have been converted to their respective MXenes among 100 experimentally known MAX phases [9]. MXene materials are also prepared on large scales in laboratory settings resulting in a rapid growth in MXene research [19]. Among the MXenes, Ti3C2 MXene exhibits a large surface area, high metallic conductivity, high hydrophilicity, and excellent absorption, which makes them favorable for biosensor applications [20]. In addition, the availability of the terminal groups on the MXene surface assists them in interacting with numerous biomolecules through non-covalent interactions, i.e., hydrogen bonds, coordination bonds, van der Waals forces, and electrostatic interactions [21]. Large UV-vis absorption capability along with the possibility of long-range electron and energy transfer makes them ideal candidates for the energy acceptor or quencher species [22].
In the healthcare sector, MXene-based biosensors have shown great promise for the detection of various biomarkers, pathogens, and metabolites [23]. Recent advancements have led to the development of highly sensitive and selective sensors for monitoring glucose levels [24], detecting cancer biomarkers [25], and identifying infectious agents [26]. These sensors offer the potential for rapid, point-of-care diagnostics and continuous health monitoring, which could revolutionize personalized medicine and early disease detection. Environmental applications of MXene-based biosensors have also seen significant progress. Researchers have developed sensors capable of detecting pollutants [15], heavy metals [27], and harmful chemicals in water and air samples with high sensitivity and specificity [28,29]. These advancements contribute to more effective environmental monitoring and pollution control strategies, addressing critical global challenges related to water quality and air pollution. The integration of MXenes with other nanomaterials and advanced sensing techniques has further enhanced their performance and expanded their applications. For instance, combining MXenes with aptamers, enzymes, or antibodies has led to the creation of highly specific and sensitive biosensing platforms [30]. Additionally, the incorporation of MXenes into electrochemical, optical, and field-effect transistor-based sensors has resulted in improved detection limits and response times [31].
In the past, numerous researchers have extensively covered synthesis, characterization, and different application aspects of MXene materials in their review articles. Sinha et al. [32] summed up the electrochemical, gas-absorptive, and piezoresistive sensors’ application. Kalambate et al. [33] briefly described the application of MXene for the detection of non-steroidal anti-inflammatory drugs (NSAIDs). The MXene materials application for the detection of biomarkers and pharmaceutical drugs was presented by Ozcan et al. [34], while the MXene nanocomposite-based electrochemical biosensors by Yoon et al. [26]. Similarly, the review about biomedical (bioimaging, photothermal cancer therapy) [35], photoelectrochemical and electrochemiluminescence sensors [36], physical sensors (pressure, strain, gas, humidity, and temperature sensors) [37], and in-vivo/vitro cancer diagnosis, photodynamic therapy (PDT) [38] could be found in the literature. However, the review specifically focused on the biosensing application of MXene material, especially within the span of the last 5–6 years is scarce. Since MXene materials are seen as promising materials for biosensing applications, their investigation in recent years has also proportionally increased; in contrast, there is a lack of relevant review that summarizes the current advancements. Therefore, in this review article, we have presented recent state-of-the-art and broad prospects of MXene nanomaterials for their application in biosensing. Firstly, the definition and basic properties of MXene along with their preparation methods are discussed with the prime focus on current findings related to the MXene materials for their application in electrochemical, enzymatic, optical, and fluorescence-based biosensors for the detection of key analytes.
2. Definition and Characteristics of MXenes
2.1. Definition of MXene
MXenes are a class of two-dimensional (2D) inorganic compounds that consist of layers of transition metals, carbides, nitrides, or carbonitrides. They are produced by selectively etching out the ‘A’ element layer from their parent three-dimensional (3D) layered materials called MAX phases [39,40,41,42,43]. MAX phases are represented by the formula Mn+1AXnTx, where ‘M’ (M = Ti, Mo, Nb, Hf, V, Sc, Cr, Ta, Zr, or W) represents an early transition metal, ‘A’ represents an element mainly from groups 13 and 14, and ‘X’ represents carbon or nitrogen [44]. The resulting MXenes have a general formula of Mn+1XnTx, where ‘Tx’ denotes surface terminations like –O, –OH, –F, and/or –Cl, which bond to the outer ‘M’ layers after the etching process [45]. The ‘n’ value in the formula can range from 1 to 4, resulting in different MXene structures with varying thicknesses [45]. For instance, M2X, M3X2, and M4X3 MXenes consist of 3, 5, and 7 atomic layers, respectively [46,47]. These structures are like their MAX phase precursors, exhibiting a hexagonal close-packed (hcp) arrangement with P63/mmc space group symmetry, where ‘M’ sites are closely packed with transition metals, while ‘X’ atoms fill the octahedral sites between the atomic planes [48]. There are a total of 60 experimentally synthesized members of the MAX family formed by using 14 M and 16 A elements; out of them, more than 40 separate thermodynamically stable forms of MXenes have been prepared at lab scale [49]. This diversity arises from the different possible combinations of M, X, n, and Tx, making them a highly versatile class of 2D materials [50].
2.2. Synthesis Method and Characteristics
2.2.1. HF Etching Method-Based MXenes and Characteristics
Hydrofluoric acid (HF) was the first etching agent used to synthesize MXenes from their MAX phase precursors. The HF reacts with the Al in the MAX phase, replacing it with surface terminations, such as –F, –OH, and –O, to form the MXene. The synthesis of MXene materials via the HF Etching method is highly efficient in terms of conversion of the MAX phase to MXene; in other words, the HF etches away Al from the MAX phase more efficiently and produces high-quality MXenes. Besides effectiveness, the HF process produces MXene in an easy and cost-efficient manner without the need for bulkier instruments and complicated reaction tools. In addition, the large quantity of MXene preparation is feasible via the HF etching process. However, the HF used in the synthesis of MXene is known for its high toxicity and corrosive properties, posing significant health and safety concerns. This process can be represented by the following chemical reactions: [8,39]
Ti3AlC2(s) + 3HF(l) → AlF3(s) + 3/2H2(g) + Ti3C2(s)
Ti3C2(s) + 2HF(l) → Ti3C2F2(s) + H2(g)
Ti3C2(s) + 2H2O(l) → Ti3C2 (OH)2(s) + H2(g)
Ti3C2(s) + 2H2O(l) → Ti3C2O(s) + 2H2(g)
This etching process can be used on a variety of MAX phases containing Al, such as Ti3AlC2, Ti2AlC, Ta4AlC3, TiNbAlC, and Ti3AlCN [51]. Additionally, HF can be used to etch non-MAX phases, such as Hf3Al4C6 and Zr3Al3C5. HF etching is influenced by factors such as HF concentration, etching temperature, and etching time. While a 5 wt% HF solution can produce Ti3C2Tx, higher concentrations of HF (10–30 wt%) can produce Ti3C2Tx with increased etching efficiency and larger lateral flake size [40]. In contrast, V– and Nb–based MXenes can only be produced using a 50 wt% HF solution. The final MXene product typically has an accordion-like, multilayered structure. To obtain single-layer MXene nanosheets, the synthesized multilayer MXenes need to undergo an intercalation and delamination process, typically using organic chemicals such as dimethyl sulfoxide (DMSO) or tetrabutylammonium hydroxide (TBAOH). This process weakens the van der Waals forces between the MXene layers to separate them. Although effective, HF etching has several drawbacks, including the corrosive, toxic, and hazardous nature of HF, which raises safety and environmental concerns [52].
2.2.2. In Situ HF Etching Method-Based MXenes and Characteristics
In situ HF etching has emerged as a safer alternative to using highly corrosive hydrofluoric acid directly for synthesizing MXenes. This method relies on generating HF in situ through the reaction of an acid with a fluoride salt [53,54]. For instance, a mixture of lithium fluoride (LiF) and hydrochloric acid (HCl) is commonly employed [53]. During the etching process, the acid-fluoride salt mixture selectively removes the A-element layers from MAX phases, leaving behind 2D sheets of transition metal carbides, nitrides, or carbonitrides, known as MXenes [43]. A notable advantage of the in situ HF etching, particularly using the LiF/HCl route, is its ability to directly produce delaminated MXene nanosheets that readily disperse in solutions [40].
One critical factor influencing the characteristics of the synthesized MXenes is the concentration of LiF and HCl in the etching solution. Studies have shown that a 5MLiF/6MHCl mixture yields Ti3C2Tx MXene nanosheets with lateral dimensions ranging from 200 to 500 nm. Interestingly, increasing the concentration to 7.5MLiF/9MHCl leads to significantly larger nanosheets with sizes ranging from 4 to 15 µm. This change in lateral size can be attributed to the higher concentration of LiF, which promotes the intercalation of Li+ ions into the interlayer spaces of the MXene structure, facilitating more efficient etching and larger nanosheet formation [54]. Beyond influencing the size, the etching conditions also affect the surface terminations of the resulting MXenes. For instance, using bifluoride salts like NH4HF2 as the etching agent can lead to MXenes with –OH terminations [55]. However, regardless of the specific in situ HF etching route, the produced MXenes generally possess surface groups such as –F, –OH, and =O [42]. These surface terminations significantly influence the properties of MXenes. For example, a higher proportion of –F groups can reduce the interlayer spacing and decrease water molecule intercalation due to their hydrophobic nature. However, it is important to note that the provided sources primarily focus on the various synthesis methods of MXenes, with less emphasis on the specific characteristics of MXenes produced solely via in situ HF etching.
2.2.3. Synthesis of MQDs and Characteristics
MQDs are typically synthesized using two primary approaches: top–down and bottom–up methods [56,57,58]. Top–down methods involve breaking down larger MXene structures into smaller MQDs using various physical or chemical techniques [56,57]. Common top–down methods include hydrothermal synthesis, solvothermal synthesis, ultrasonication, acid–base reflux, electrochemical methods, and combinations of these techniques. Of these methods, the hydrothermal method is the most widely used. It involves heating an aqueous solution of MXene nanosheets above the boiling point of water, typically between 100 and 200 °C. The size, morphology, and surface terminations of the resulting MQDs can be tuned by adjusting reaction parameters such as temperature, pH, and reaction time. For example, researchers synthesized Ti3C2Tx quantum dots with average lateral sizes ranging from 2.9 nm to 6.2 nm by varying the hydrothermal reaction temperature between 100 and 150 °C [59].
While top–down methods are prevalent for MQD synthesis, they have drawbacks such as longer reaction times, lower production yields, and the potential use of environmentally harmful chemicals, such as hydrofluoric acid. Bottom-up approaches, which involve assembling MQDs from smaller molecular precursors, offer advantages such as higher atomic utilization, greater control over size and morphology, and the potential for large-scale production. However, research on bottom-up methods for MQD synthesis is still limited. One reported bottom-up method is the pyrolysis method, in which a precursor material containing the desired elements is subjected to high temperatures in an inert atmosphere [56,57].
MQDs possess unique characteristics owing to their small size and quantum confinement effects. They exhibit improved properties compared to their parent MXenes, including better dispersibility, ease of functionalization, and enhanced photoluminescence. MQDs inherit the structural properties of 2D MXenes, typically forming hexagonal lattices [56]. The size of MQDs is a critical factor that influences their properties. Smaller MQDs possess a higher surface area-to-volume ratio, leading to enhanced reactivity and making them suitable for applications such as catalysis and sensing. Additionally, smaller MQDs exhibit a shift in bandgap energy towards the visible region, enhancing their fluorescence properties [56,60].
2.1. Definition of MXene
MXenes are a class of two-dimensional (2D) inorganic compounds that consist of layers of transition metals, carbides, nitrides, or carbonitrides. They are produced by selectively etching out the ‘A’ element layer from their parent three-dimensional (3D) layered materials called MAX phases [39,40,41,42,43]. MAX phases are represented by the formula Mn+1AXnTx, where ‘M’ (M = Ti, Mo, Nb, Hf, V, Sc, Cr, Ta, Zr, or W) represents an early transition metal, ‘A’ represents an element mainly from groups 13 and 14, and ‘X’ represents carbon or nitrogen [44]. The resulting MXenes have a general formula of Mn+1XnTx, where ‘Tx’ denotes surface terminations like –O, –OH, –F, and/or –Cl, which bond to the outer ‘M’ layers after the etching process [45]. The ‘n’ value in the formula can range from 1 to 4, resulting in different MXene structures with varying thicknesses [45]. For instance, M2X, M3X2, and M4X3 MXenes consist of 3, 5, and 7 atomic layers, respectively [46,47]. These structures are like their MAX phase precursors, exhibiting a hexagonal close-packed (hcp) arrangement with P63/mmc space group symmetry, where ‘M’ sites are closely packed with transition metals, while ‘X’ atoms fill the octahedral sites between the atomic planes [48]. There are a total of 60 experimentally synthesized members of the MAX family formed by using 14 M and 16 A elements; out of them, more than 40 separate thermodynamically stable forms of MXenes have been prepared at lab scale [49]. This diversity arises from the different possible combinations of M, X, n, and Tx, making them a highly versatile class of 2D materials [50].
2.2. Synthesis Method and Characteristics
2.2.1. HF Etching Method-Based MXenes and Characteristics
Hydrofluoric acid (HF) was the first etching agent used to synthesize MXenes from their MAX phase precursors. The HF reacts with the Al in the MAX phase, replacing it with surface terminations, such as –F, –OH, and –O, to form the MXene. The synthesis of MXene materials via the HF Etching method is highly efficient in terms of conversion of the MAX phase to MXene; in other words, the HF etches away Al from the MAX phase more efficiently and produces high-quality MXenes. Besides effectiveness, the HF process produces MXene in an easy and cost-efficient manner without the need for bulkier instruments and complicated reaction tools. In addition, the large quantity of MXene preparation is feasible via the HF etching process. However, the HF used in the synthesis of MXene is known for its high toxicity and corrosive properties, posing significant health and safety concerns. This process can be represented by the following chemical reactions: [8,39]
Ti3AlC2(s) + 3HF(l) → AlF3(s) + 3/2H2(g) + Ti3C2(s)
Ti3C2(s) + 2HF(l) → Ti3C2F2(s) + H2(g)
Ti3C2(s) + 2H2O(l) → Ti3C2 (OH)2(s) + H2(g)
Ti3C2(s) + 2H2O(l) → Ti3C2O(s) + 2H2(g)
This etching process can be used on a variety of MAX phases containing Al, such as Ti3AlC2, Ti2AlC, Ta4AlC3, TiNbAlC, and Ti3AlCN [51]. Additionally, HF can be used to etch non-MAX phases, such as Hf3Al4C6 and Zr3Al3C5. HF etching is influenced by factors such as HF concentration, etching temperature, and etching time. While a 5 wt% HF solution can produce Ti3C2Tx, higher concentrations of HF (10–30 wt%) can produce Ti3C2Tx with increased etching efficiency and larger lateral flake size [40]. In contrast, V– and Nb–based MXenes can only be produced using a 50 wt% HF solution. The final MXene product typically has an accordion-like, multilayered structure. To obtain single-layer MXene nanosheets, the synthesized multilayer MXenes need to undergo an intercalation and delamination process, typically using organic chemicals such as dimethyl sulfoxide (DMSO) or tetrabutylammonium hydroxide (TBAOH). This process weakens the van der Waals forces between the MXene layers to separate them. Although effective, HF etching has several drawbacks, including the corrosive, toxic, and hazardous nature of HF, which raises safety and environmental concerns [52].
2.2.2. In Situ HF Etching Method-Based MXenes and Characteristics
In situ HF etching has emerged as a safer alternative to using highly corrosive hydrofluoric acid directly for synthesizing MXenes. This method relies on generating HF in situ through the reaction of an acid with a fluoride salt [53,54]. For instance, a mixture of lithium fluoride (LiF) and hydrochloric acid (HCl) is commonly employed [53]. During the etching process, the acid-fluoride salt mixture selectively removes the A-element layers from MAX phases, leaving behind 2D sheets of transition metal carbides, nitrides, or carbonitrides, known as MXenes [43]. A notable advantage of the in situ HF etching, particularly using the LiF/HCl route, is its ability to directly produce delaminated MXene nanosheets that readily disperse in solutions [40].
One critical factor influencing the characteristics of the synthesized MXenes is the concentration of LiF and HCl in the etching solution. Studies have shown that a 5MLiF/6MHCl mixture yields Ti3C2Tx MXene nanosheets with lateral dimensions ranging from 200 to 500 nm. Interestingly, increasing the concentration to 7.5MLiF/9MHCl leads to significantly larger nanosheets with sizes ranging from 4 to 15 µm. This change in lateral size can be attributed to the higher concentration of LiF, which promotes the intercalation of Li+ ions into the interlayer spaces of the MXene structure, facilitating more efficient etching and larger nanosheet formation [54]. Beyond influencing the size, the etching conditions also affect the surface terminations of the resulting MXenes. For instance, using bifluoride salts like NH4HF2 as the etching agent can lead to MXenes with –OH terminations [55]. However, regardless of the specific in situ HF etching route, the produced MXenes generally possess surface groups such as –F, –OH, and =O [42]. These surface terminations significantly influence the properties of MXenes. For example, a higher proportion of –F groups can reduce the interlayer spacing and decrease water molecule intercalation due to their hydrophobic nature. However, it is important to note that the provided sources primarily focus on the various synthesis methods of MXenes, with less emphasis on the specific characteristics of MXenes produced solely via in situ HF etching.
2.2.3. Synthesis of MQDs and Characteristics
MQDs are typically synthesized using two primary approaches: top–down and bottom–up methods [56,57,58]. Top–down methods involve breaking down larger MXene structures into smaller MQDs using various physical or chemical techniques [56,57]. Common top–down methods include hydrothermal synthesis, solvothermal synthesis, ultrasonication, acid–base reflux, electrochemical methods, and combinations of these techniques. Of these methods, the hydrothermal method is the most widely used. It involves heating an aqueous solution of MXene nanosheets above the boiling point of water, typically between 100 and 200 °C. The size, morphology, and surface terminations of the resulting MQDs can be tuned by adjusting reaction parameters such as temperature, pH, and reaction time. For example, researchers synthesized Ti3C2Tx quantum dots with average lateral sizes ranging from 2.9 nm to 6.2 nm by varying the hydrothermal reaction temperature between 100 and 150 °C [59].
While top–down methods are prevalent for MQD synthesis, they have drawbacks such as longer reaction times, lower production yields, and the potential use of environmentally harmful chemicals, such as hydrofluoric acid. Bottom-up approaches, which involve assembling MQDs from smaller molecular precursors, offer advantages such as higher atomic utilization, greater control over size and morphology, and the potential for large-scale production. However, research on bottom-up methods for MQD synthesis is still limited. One reported bottom-up method is the pyrolysis method, in which a precursor material containing the desired elements is subjected to high temperatures in an inert atmosphere [56,57].
MQDs possess unique characteristics owing to their small size and quantum confinement effects. They exhibit improved properties compared to their parent MXenes, including better dispersibility, ease of functionalization, and enhanced photoluminescence. MQDs inherit the structural properties of 2D MXenes, typically forming hexagonal lattices [56]. The size of MQDs is a critical factor that influences their properties. Smaller MQDs possess a higher surface area-to-volume ratio, leading to enhanced reactivity and making them suitable for applications such as catalysis and sensing. Additionally, smaller MQDs exhibit a shift in bandgap energy towards the visible region, enhancing their fluorescence properties [56,60].
2.2.1. HF Etching Method-Based MXenes and Characteristics
Hydrofluoric acid (HF) was the first etching agent used to synthesize MXenes from their MAX phase precursors. The HF reacts with the Al in the MAX phase, replacing it with surface terminations, such as –F, –OH, and –O, to form the MXene. The synthesis of MXene materials via the HF Etching method is highly efficient in terms of conversion of the MAX phase to MXene; in other words, the HF etches away Al from the MAX phase more efficiently and produces high-quality MXenes. Besides effectiveness, the HF process produces MXene in an easy and cost-efficient manner without the need for bulkier instruments and complicated reaction tools. In addition, the large quantity of MXene preparation is feasible via the HF etching process. However, the HF used in the synthesis of MXene is known for its high toxicity and corrosive properties, posing significant health and safety concerns. This process can be represented by the following chemical reactions: [8,39]
Ti3AlC2(s) + 3HF(l) → AlF3(s) + 3/2H2(g) + Ti3C2(s)
Ti3C2(s) + 2HF(l) → Ti3C2F2(s) + H2(g)
Ti3C2(s) + 2H2O(l) → Ti3C2 (OH)2(s) + H2(g)
Ti3C2(s) + 2H2O(l) → Ti3C2O(s) + 2H2(g)
This etching process can be used on a variety of MAX phases containing Al, such as Ti3AlC2, Ti2AlC, Ta4AlC3, TiNbAlC, and Ti3AlCN [51]. Additionally, HF can be used to etch non-MAX phases, such as Hf3Al4C6 and Zr3Al3C5. HF etching is influenced by factors such as HF concentration, etching temperature, and etching time. While a 5 wt% HF solution can produce Ti3C2Tx, higher concentrations of HF (10–30 wt%) can produce Ti3C2Tx with increased etching efficiency and larger lateral flake size [40]. In contrast, V– and Nb–based MXenes can only be produced using a 50 wt% HF solution. The final MXene product typically has an accordion-like, multilayered structure. To obtain single-layer MXene nanosheets, the synthesized multilayer MXenes need to undergo an intercalation and delamination process, typically using organic chemicals such as dimethyl sulfoxide (DMSO) or tetrabutylammonium hydroxide (TBAOH). This process weakens the van der Waals forces between the MXene layers to separate them. Although effective, HF etching has several drawbacks, including the corrosive, toxic, and hazardous nature of HF, which raises safety and environmental concerns [52].
2.2.2. In Situ HF Etching Method-Based MXenes and Characteristics
In situ HF etching has emerged as a safer alternative to using highly corrosive hydrofluoric acid directly for synthesizing MXenes. This method relies on generating HF in situ through the reaction of an acid with a fluoride salt [53,54]. For instance, a mixture of lithium fluoride (LiF) and hydrochloric acid (HCl) is commonly employed [53]. During the etching process, the acid-fluoride salt mixture selectively removes the A-element layers from MAX phases, leaving behind 2D sheets of transition metal carbides, nitrides, or carbonitrides, known as MXenes [43]. A notable advantage of the in situ HF etching, particularly using the LiF/HCl route, is its ability to directly produce delaminated MXene nanosheets that readily disperse in solutions [40].
One critical factor influencing the characteristics of the synthesized MXenes is the concentration of LiF and HCl in the etching solution. Studies have shown that a 5MLiF/6MHCl mixture yields Ti3C2Tx MXene nanosheets with lateral dimensions ranging from 200 to 500 nm. Interestingly, increasing the concentration to 7.5MLiF/9MHCl leads to significantly larger nanosheets with sizes ranging from 4 to 15 µm. This change in lateral size can be attributed to the higher concentration of LiF, which promotes the intercalation of Li+ ions into the interlayer spaces of the MXene structure, facilitating more efficient etching and larger nanosheet formation [54]. Beyond influencing the size, the etching conditions also affect the surface terminations of the resulting MXenes. For instance, using bifluoride salts like NH4HF2 as the etching agent can lead to MXenes with –OH terminations [55]. However, regardless of the specific in situ HF etching route, the produced MXenes generally possess surface groups such as –F, –OH, and =O [42]. These surface terminations significantly influence the properties of MXenes. For example, a higher proportion of –F groups can reduce the interlayer spacing and decrease water molecule intercalation due to their hydrophobic nature. However, it is important to note that the provided sources primarily focus on the various synthesis methods of MXenes, with less emphasis on the specific characteristics of MXenes produced solely via in situ HF etching.
2.2.3. Synthesis of MQDs and Characteristics
MQDs are typically synthesized using two primary approaches: top–down and bottom–up methods [56,57,58]. Top–down methods involve breaking down larger MXene structures into smaller MQDs using various physical or chemical techniques [56,57]. Common top–down methods include hydrothermal synthesis, solvothermal synthesis, ultrasonication, acid–base reflux, electrochemical methods, and combinations of these techniques. Of these methods, the hydrothermal method is the most widely used. It involves heating an aqueous solution of MXene nanosheets above the boiling point of water, typically between 100 and 200 °C. The size, morphology, and surface terminations of the resulting MQDs can be tuned by adjusting reaction parameters such as temperature, pH, and reaction time. For example, researchers synthesized Ti3C2Tx quantum dots with average lateral sizes ranging from 2.9 nm to 6.2 nm by varying the hydrothermal reaction temperature between 100 and 150 °C [59].
While top–down methods are prevalent for MQD synthesis, they have drawbacks such as longer reaction times, lower production yields, and the potential use of environmentally harmful chemicals, such as hydrofluoric acid. Bottom-up approaches, which involve assembling MQDs from smaller molecular precursors, offer advantages such as higher atomic utilization, greater control over size and morphology, and the potential for large-scale production. However, research on bottom-up methods for MQD synthesis is still limited. One reported bottom-up method is the pyrolysis method, in which a precursor material containing the desired elements is subjected to high temperatures in an inert atmosphere [56,57].
MQDs possess unique characteristics owing to their small size and quantum confinement effects. They exhibit improved properties compared to their parent MXenes, including better dispersibility, ease of functionalization, and enhanced photoluminescence. MQDs inherit the structural properties of 2D MXenes, typically forming hexagonal lattices [56]. The size of MQDs is a critical factor that influences their properties. Smaller MQDs possess a higher surface area-to-volume ratio, leading to enhanced reactivity and making them suitable for applications such as catalysis and sensing. Additionally, smaller MQDs exhibit a shift in bandgap energy towards the visible region, enhancing their fluorescence properties [56,60].
3. MXenes in Wearable Devices
Electronic devices that are both flexible and wearable have tremendously alleviated human life and transformed into a rapidly growing industry in recent times. Wearable electronic devices are robust, lightweight, and durable devices that convert a non-electric physiological activity into electric signals (current/resistance or voltage) [61]. These devices can be used for managing human health and disease diagnosis via detecting important physical and chemical signals of the human body such as blood pressure, heart pulse rate, body temperature, throat moisture, or any other human body information such as joint activity and micro-expressions. Until now, different types of wearable devices have been extensively explored for sensing pressure, strain, and gas molecules in a continuous, onsite, and non-invasive manner [62,63]. For high performance, the electronic device should be able to, on the one hand, capture the body movement of the user and environmental changes including humidity, temperature, etc., and, on the other hand, should be able to cohere to the user skin [64]. The wearable device consists of the circuit board, power supply, electrochemical sensor, analytical transmission system, and user interface. The power supply is an important component of the device, which is a key for the overall stability and longer life of the device [65]. The conventional wearable devices were based on natural conductive cotton fibers and yarns, which are poor electric conductors and photo-insensitive; hence, the cotton fibers and yarns should have to be integrated with numerous other conductive materials [66]. On the other hand, modern wearable sensors are equipped with hydrogels and conductive materials (polymers, carbon-based materials, graphene, MXenes, etc.). MXene materials hold great prospects for their use in wearable devices owing to their biocompatibility, large surface area, high electrical conductivity, and hydrophilic nature and are excellent candidates for fiber-based textiles [67,68].
He et al. [66] used a composite of Ti3C2Tx MXene nanosheets and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) as the core. Introduction of PEDOT: PSS with MXene not only enhances the electrical conductivity but also protects MXene from oxidation. The cotton yarn was coated onto the surface of the core to form a core-shell structure that exhibited an electrical conductivity of 21.8 Ω cm−1. By using a core-shell structure, a 3D honeycomb-like heater is fabricated. The temperature of the heater reached up to 196 °C at 2.5 V and 64.2 °C at 130 mW cm−2 optical density. Chai et al. [68] prepared aerogels using cellulose, MXene nanosheets, and polyurethane composite. MXene nanosheets are evenly distributed in the cellulose matrix to maximize the MXene nanosheets interactions. The aerogel under study showed electromagnetic shielding of 48.59 dB in the X-band with 0.34 S⋅cm−1 electrical conductivity. The thermal conductivity of the aerogel reached up to 83.95 mW/(m⋅K) when the aerogels were placed on the surface for 30 min at 120 °C with 60.2 C aerogel surface temperature. The compressive strength and shape recovery of the aerogel were found to be 491.9 kPa and 73%, respectively. The Ti3C2Tx MXene nanosheets have also been used in strain sensor fabrication. Firstly, the self-polymerizable polydopamine (PDA) was coated onto the MXene nanosheets to simultaneously enhance the mechanical properties and protect MXene from oxidation. Secondly, the octadecyl trimethoxy silane (OTMS) was coated onto the PDA@Mxene to make them hydrophobic. Finally, the MXene@PDA@OTMS was immobilized on the cotton to fabricate the strain sensor. The hydrophobic MXene@PDA@OTMS with a contact angle of 156.3 ± 2.4° has achieved a 105 °C temperature at 100 mW/cm2 power density [69]. Zhou et al. [70] reported a flexible MXene-Leather composite-based sensor for pressure and joule heating sensing. With a simple vacuum filtration process, the MXene nanosheets were incorporated into the leather via hydrogen bonding. The incorporation of the MXene nanosheets provided a conductive network (24.6 Ω sq−1 resistance) and enhanced the mechanical property of the leather, i.e., Young’s modulus up to 9.64 MPa (45.6%). The pressure sensor fabricated using MXene-Leather composite had a sensitivity of 33.58 k/Pa over a wide range of 0.025−12.5 kPa. Han et al. [71] reported the MXene/PDA composite-based wearable pressure sensor, as shown in Figure 1. The addition of the PDA has significantly enhanced the sensitivity of the MXene films from 0.26 k/Pa to 24.7 k/Pa and 138.8 k/Pa for lower- (0.18–2.90 kPa) and higher-pressure (2.90–6.20 kPa) range, respectively. Also, the response and recovery time of the MXene/PDA composite-based sensor were relatively shorter, i.e., <100 ms and <50 ms, respectively.
Chen et al. [64] constructed MXene, polyacrylamide, and agar (MXene-PAM/Agar) composite double network structure via a one-pot process for their application in strain sensing. The acrylamide was polymerized in long chains and cross-linked with the MXenes to form a first layered network structure, while another layer was introduced by mixing agar at high temperature to fabricate MXene-PAM/Agar hydrogel. MXene-PAM/Agar hydrogel showed conductivity of 1.02 S/m and 1300% stretchability. The strain sensor could perform well under 80% and 0.35 MPa of compressive strain and stress, respectively. The authors measure the real-time moments of the different body parts, as shown in Figure 2.
Similarly, the sensor could withstand 1000% tensile strain and 0.25 MPa tensile stress. On the other hand, the MXene materials have also been used to fabricate MXene-bacterial cellulose (MXene-BC) supercapacitor [72]. The cross-linking of cellulose bacteria with MXene nanosheets provided a thin film with 88.94 MPa tensile strength, 6.8 GPa Young’s modulus, and 182 S/cm electrical conductivity. The MXene thin films having 10% cellulose bacteria worked well as a supercapacitor electrode and showed an aerial capacitance of 346 mF/cm2. MXenes in combination with polyvinyl alcohol (PVA) have also presented better results. By mixing the different concentrations (5–25 wt.%) of the PVA with MXenes, the tensile strength and thermal stability of the MXenes/PVA fibers have been improved [67]. MXenes/PVA fibers-based supercapacitors exhibited the gravimetric and areal capacitance of 119.3 F/g and 130.9 mF/cm2, respectively. Fan et al. [73] used the plasmonic silver NPs and polyurethane with MXene nanosheets to build a wearable sensor. The hybridization of the MXene@AgNPs causes enhanced photothermal conversion due to the combined plasmonic effect from AgNPs as well as the photothermal/thermal conductivity effects from MXene nanosheets. The low concentration, about 0.08 wt.%, of plasmonic MXene@AgNPs could produce increased temperature (~111 °C) after applying the 600 mW cm−2 light irradiation for 5 min. This increase in the temperature permits the polyurethane thereby healing the damaged coating by 97%. Also, the MXene@AgNPs thin film of ∼60 μm thickness has maintained ∼83% of transmittance. The AgNP@MXene hybrid functions as a highly effective photon captor, energy transformer, and molecular heater due to the amalgamation of (1) ultrahigh photothermal conversion efficiency, high thermal conductivity, and structural properties of MXene; (2) the outstanding plasmonic effect of Ag NPs; and (3) the synergistic effects from their hybrids. The resulting wearable composite coating with ultralow loading of plasmonic AgNP@MXene hybrids (0.08 wt.%wt % or 0.024 vol %) can produce a significant temperature increase of ∼111 ± 2.6 °C after the application of 600 mW cm−2 light irradiation for 5 min while maintaining a high optical transmittance of ∼83% at a thickness of ∼60 μm. This local temperature increase can rapidly heal the mechanical damage to the composite coating, with a healing efficiency above 97%.
4. MXenes in Volatile Organic Compound (VOC) Detection
In the present modern society, with the growing industrialization, hazardous volatile organic compounds (VOCs) emissions liberated from common pollution sources such as mining, manufacturing, and the automobile industry have become a challenging issue throughout the world. Some of these VOCs (toluene, xylene, phenol, and formaldehyde) at different concentration levels are commonly present in indoor home and office premises, hold neurotoxic and carcinogenic properties, and could yield toxic products via photochemical activation capable of deteriorating human health upon long time exposure [74]. In addition, the majority of the VOCs fall into the category of flammable substances. There is thus a current technological demand for a rapid and reproducible approach to the identification of VOCs [75]. Therefore, advanced sensors are essentially required for fast, sensitive, and accurate detection of hazardous VOC gases for environmental and healthcare applications. Detection of VOCs in parts per million (ppm) level from exhaled breath, which carries ~200 VOCs, is another prominent way to diagnose illness at an early stage [76]. It is known that the ammonia levels in the 50–2000 parts per billion (ppb) levels are critical for the diagnosis of peptic ulcers; similarly, the acetone levels between 300 and 1800 ppb can distinguish healthy and diabetic patients [77]. In literature, numerous oxide-based VOC sensors are reported that work well at high temperatures (300–500 °C), limiting their wide range of applications [74]. Ideally, the sensor for VOCs’ detection should be able to selectively detect the minute concentrations of VOCs, particularly in ppb levels at RT (under ambient conditions) [78]. Also, the VOC detection should be reproducible, recoverable, and cheaper. There are very few VOC sensors working at RT, for example, nickel oxide-based sensors, which can operate at RT but exhibit poor signal-to-noise ratio, sensitivity, and recovery time. The other RT VOC sensors based on graphene and carbon nanotubes need air/oxygen and ammonia supply and current (100 mA) or UV illumination for sensor recovery [74]. Many researchers have investigated the synthesis of 2D MXene materials for their use in VOC sensing.
Xu et al. [79] reported Ti3C2Tx/SnO2 sensors with a large surface area of about 45.186 m2/gm for ethanolamine (EA) sensing. The Ti3C2Tx/SnO2 sensor works well in the 20–600 ppm range, resulting in the sensitive detection of the EA with 76.76 ppb LOD, which is comparably lower than that of the Ti3C2Tx (527.70 ppb), SnO2 (187.19 ppb) when used exclusively. The addition of SnO2 to the pristine Ti3C2Tx not only enhanced the LOD by 7 times but also showed selectivity for EA among 12 different VOCs. Similarly, to detect the triethylamine (TEA), SnS2 nanoflakes were added as a sensitive channel to the Ti3C2Tx MXenes by Han et al. [75]. The SnS2/Ti3C2Tx showed 38% response to 50 ppm TEA in 12 s and sensitively detected the TEA in the 2–20 ppm range was 1.2% ppm−1. Bhardwaj et al. and Liu et al. [80] investigated the performance of the Ti3C2Tx/TiO2 for their application in VOC detection. The TiO2 was grown in situ because of the self-oxidation of MXene via the hydrothermal method. Obviously, the improved performance of the Ti3C2Tx/TiO2 as compared to the pristine counterpart was noted. The improved performance of the Ti3C2Tx/TiO2 is attributed to the interfacial Schottky barrier, which resulted in the improved response for NO2 gas up to 1.13 and 2.10–5 ppm (86 times higher than pristine) at RT and 175 °C, respectively. Bhardwaj et al. [81] utilized a SrTiO3 with MXenes to fabricate a SrTiO3/MXene-based composite sensor for VOC sensing. The authors created a TiO2 on MXene by oxidizing the MXene itself at 350 °C for 24 h and then coated it with the SrTiO3 moisture-resistant layer. The formation of the TiO2 layer has enhanced the response for acetone sensing, for 50 ppm acetone at 150 °C in air, from 175% to 217%. Under 80% relative humid conditions, the SrTiO3/MXene showed 68% response for 100 ppb of acetone and LOD of 40 ppb under 150 °C temperature. In another study, single-atom platinum (Pt) was coated onto the Ti3C2Tx nanosheets (Pt–Ti3C2Tx) for the detection of TEA. The Pt–Ti3C2Tx showed better response toward ppb-level at RT. Anchoring of the Pt on MXene not only provided a high specificity but also shortened the short response time in comparison with the pristine Ti3C2Tx, and thus, an LOD of 14 ppb for detecting TEA was achieved [82]. Doping sulfur (S) atoms into the Ti3C2Tx MXene has also shown enhanced gas sensing properties as compared to the undoped Ti3C2Tx MXene [74]. S-doped Ti3C2Tx MXene selectively detects toluene among other VOCs (namely, ethanol, hexane, toluene, and hexyl-acetate) with higher response for toluene than the undoped Ti3C2Tx MXene. With S-doped Ti3C2Tx MXene, the response of toluene has enhanced from ∼214% (1 ppm) to ∼312% (50 ppm) with a notable response even for 500 ppb of toluene. The gold (Au) and Pt atoms are also coated on the Ti3C2Tx MXene and their ability to sense NH3 gas is evaluated by Nam et al. [83]. It was observed that the 1.92 at% Au (under 5 V) and 0.83 at% Pt (under 3 V) showed good selectivity for NH3 gas as well as flexibility upon tilting (1000 times) and bending (5000 times). MXene/MoS2 nanocomposites prepared via the ultrasonic exfoliation (HIUE) method with 90% yield and significantly shorter etching time of 3 h have also been shown to sense NH3 with 21.1% response to the 100 ppm NH3 concentration [84]. Our group recently reported a Ti3C2Tx highly sensitive MXene-based sensor that responds in quick time (53 s) for the detection of acetone as low as 250 ppb at RT, which holds paramount importance in both environmental monitoring and identifying diabetes patients via their breath analysis. The sensor not only showed good selectivity in comparison with the other gases but was also found to be repeatable and highly stable [85]. Besides Ti3C2Tx MXene, we have also reported a chemiresistive gas sensor using thermally oxidized V2CTx MXene forming urchin-like vanadium oxide (V2Ox) hybrid structures, as seen in Figure 3. The V2CTx MXene displayed a 4.6% response toward 15 ppm of acetone, which improved to 11.9% upon the thermal treatment [86].
Rathi et al. [87] added cation surfactant cetyltrimethylammonium bromide (CTAB) during the exfoliation of Nb2CTx. The addition of CTAB has been shown to enhance NO2 gas sensing response by 3-fold from 0.543 ppm−1 for Nb2CTx to 1.686 ppm−1 for Nb2CTx–CTAB. The Nb2CTx–CTAB sensor is capable of detecting trace-level gas concentration even after 30 days. The double transition metal-based Ti2VC2Tx MXenes are reported to sense ammonia gas molecules with about 25% of sensor response to 100 ppm ammonia at RT within 4s, and the recovery time found to be 16.2 s [88].
5. MXenes in Biosensing
MXenes have attracted considerable interest in the fields of analytical nanoscience and biosensing because of their distinct characteristics. The groups on the surface such as OH, O, and F make MXenes water-loving, allowing them to interact with biomolecules through hydrogen bonding, Van der Waals forces, electrostatic interactions, and binding to ligands. This property makes MXenes excellent candidates as carriers in biosensor device applications [32,89,90]. Importantly, various compositions of MXenes have been shown to be biocompatible and non-cytotoxic, further enhancing their suitability for biomedical applications. This review summarizes the composition and analytical performance of MXene-based biosensors across different categories including electrochemical, optical, and other biosensors. Each subsection highlights specific examples showcasing MXenes’ versatility and effectiveness in biosensor fabrication. These case studies underscore MXenes’ broad applicability in sensing technologies, providing valuable insights and data for researchers in the field of biosensing. Readers can readily access detailed research and measurement data from the summarized tables, facilitating further exploration and development of MXene-based biosensors.
5.1. MXene-Based Electrochemical Biosensors
A biosensor is a measurement tool that merges a biological sensing component, like a protein or DNA, with a transducer that transforms the biological reaction into a measurable output. The pioneering electrochemical biosensors, introduced in 1967 for glucose monitoring, continue to play an important part in diabetes management. Usually, an electrochemical biosensor consists of three main parts: a working electrode, a reference electrode, and a counter electrode. The working electrode serves as the main detection element and can be made from different types of nanomaterials to identify certain biomolecules or pathogens. Advancements in electrochemical biosensor technology have been driven by the development of biomimetic and bioactive working electrodes. These improvements, coupled with effective signal amplification strategies, enable the conversion of molecular recognition events into measurable electrical signals, such as current, voltage, or impedance. The effectiveness of an electrochemical biosensor is greatly affected by the structure, surface characteristics, and biological operation of the active electrode. Selecting the right materials for the bioreceptor and transducer is crucial for improving the sensor’s ability to distinguish between different substances and its capacity to convert chemical signals into electrical signals. This careful selection of materials is vital for increasing the precision and dependability of detecting specific analytes.
There is an increasing fascination with using MXenes as bioreceptors or transducers in electrochemical biosensors because of their simplicity in making, creating, and modifying them. MXenes, known for their outstanding compatibility with biological materials, provide a flexible base for incorporating fully operational biomolecules like proteins and DNA into electrochemical bio-interfaces. This characteristic makes them perfect for enabling molecular recognition, electrochemical reactions, and signal enhancement—crucial steps for the efficient operation of electrochemical biosensors. MXenes bring notable benefits to biosensor use, such as a large surface area, superior electrical conductivity, and the capability to establish stable and functional connections with biological entities. These features allow MXenes to improve the sensitivity and selectivity of biosensors, enabling the detection of rare biomarkers and pathogens with great accuracy. Additionally, the unique layered structure of MXenes allows for the easy incorporation of various functional groups, further enhancing their versatility in biosensing applications. One of the key strengths of MXenes is their ability to support a wide range of signal amplification strategies. For example, MXenes can be combined with nanomaterials such as gold nanoparticles or quantum dots to enhance electrochemical signals, thereby improving the detection limits of biosensors. Moreover, MXenes’ excellent electrical conductivity ensures efficient electron transfer, which is critical for the rapid and accurate measurement of analyte concentrations.
Even though MXenes were first found more than 10 years ago and have been applied in many well-known areas, there is still a significant lack of thorough studies that cover the growing applications of MXenes in electrochemical biosensing. Current research has only scratched the surface of MXenes’ potential, and there is a need for systematic studies to fully understand and optimize their use in biosensor technologies. Such studies could explore the integration of MXenes with other nanomaterials, the development of new functionalization techniques, and the application of MXenes in real-world biosensing scenarios. As research continues to evolve, a systematic exploration of MXenes’ potential in this domain could pave the way for new innovations and advancements in biosensor technology. By addressing the existing gaps and exploring novel applications, researchers can unlock the full potential of MXenes, leading to the development of next-generation biosensors with unprecedented sensitivity, selectivity, and reliability.
Lately, materials with two dimensions (2D) have been widely used in creating the components that transmit signals in advanced biosensors. This allows for the accurate identification and measurement of DNA and RNA, which are essential for detecting and quantifying nucleic acids. Materials known as MXenes have shown potential as an alternative due to their distinct characteristics. Nonetheless, to create a dependable foundation for electrochemical detection of DNA using MXene 2D materials, it is important to fully comprehend how MXenes interact with DNA, particularly in the spaces between micro-sized MXene flakes where DNA can fit. MXenes have a large surface area, good electrical conductivity, and strong chemical stability, qualities that make them ideal for use in biosensors. Their capacity to establish robust connections with DNA can improve the sensitivity and accuracy of biosensors. However, the interaction between MXenes and DNA needs to be carefully studied to ensure that the structural integrity and biological activity of DNA are maintained during the sensing process. One critical aspect is the intercalation of DNA within the interlayer spaces of MXene flakes. This intercalation can influence the electrochemical properties of the biosensor, potentially enhancing the signal transduction and amplification capabilities. However, it is essential to elucidate how this intercalation affects the stability and functionality of the DNA. Studies should focus on assessing the binding affinity, structural changes, and potential denaturation of DNA when in contact with MXenes. Furthermore, the biocompatibility of MXenes needs to be evaluated to ensure that they do not induce any adverse effects on the DNA or other biological components used in the biosensors. This includes examining potential cytotoxicity, oxidative stress, and any unintended interactions with other biomolecules. Ensuring biocompatibility is paramount for developing reliable and effective MXene-based DNA biosensors for practical applications in medical diagnostics, environmental monitoring, and biotechnology.
Jialin Zhang et al. [89] reported the development of an electrochemical biosensor mediated by conductive Ti3C2 MXenes for detecting Mycobacterium tuberculosis. The biosensor targets the 16S rDNA strands, which contain highly conserved sequences across different species, suitable for general bacterial detection, as well as species-specific variable regions for precise species typing. In this study, a specific fragment of single-stranded DNA (ssDNA) from the species-specific variable region of the 16S rDNA of M. tuberculosis was selected as the target biomarker. The biosensor employs peptide nucleic acid (PNA) as the binding agent. PNA is a synthetic version of DNA, made up of a repeating sequence of neutral N–(2–aminoethyl) glycine units linked by peptide bonds, which also contain nucleotide bases. When the target DNA sequence is present, a DNA–PNA complex is formed inside the gold nanoparticle (AuNP) nanogap structure. Then, Ti3C2 MXenes are bonded to this nanogap structure with a Zr4+ cross-linking agent, which can interact with the phosphate groups in the DNA–PNA complex and the hydroxide groups in Ti3C2 MXenes. This shift from PNA to the conductive Ti3C2 MXenes within the nanogap structure results in a notable change in electrical conductivity. This shift acts as the basis for the accurate detection of the target DNA sequences, enabling the identification of M. tuberculosis. The suggested approach is quick, precise, and capable of detecting as few as 20 colony-forming units per milliliter (CFU/mL) in just 2 h. The biosensor was effectively utilized to identify M. tuberculosis in 40 simulated sputum samples, showcasing its real-world utility. Figure 4 provides a comprehensive overview of the preparation and application of Ti3C2 MXenes nanosheets in the biosensor for rapid detection of M. tuberculosis. (a) The Preparation of Ti3C2 MXenes Nanosheets: The synthesis of Ti3C2 MXenes nanosheets typically involves etching aluminum layers from the precursor material (Ti3AlC2) using hydrofluoric acid or a similar etchant.
This process results in the formation of two-dimensional Ti3C2 MXenes sheets with a high surface area and excellent conductivity. These nanosheets are then exfoliated to obtain a stable colloidal suspension suitable for biosensing applications. (b) The Pretreatment of Ti3C2 MXenes Nanosheets with ZrOCl2: To enhance the biosensor’s functionality, the Ti3C2 MXenes nanosheets undergo pretreatment with zirconium oxychloride (ZrOCl2). This step involves the reaction of Zr4+ ions with the hydroxide groups on the surface of the MXenes, facilitating the subsequent attachment of biomolecules. The pretreatment ensures effective cross-linking between the MXenes and the DNA–PNA complex, crucial for the biosensor’s performance. (c) The Constructed Strategy of the Sensor for Rapid Detection of M. tuberculosis: The biosensor’s construction integrates the Ti3C2 MXenes nanosheets into a gold nanoparticle (AuNP) nanogap network electrode.
C. Lorena Manzanares-Palenzuela et al. [91] conducted a comprehensive study to explore the interaction dynamics between MXene Ti3C2Tx and fluorophore-tagged DNA using a combination of fluorescence measurements and molecular dynamics simulations. Their research highlighted MXene Ti3C2Tx as a promising material due to its robust binding affinity toward fluorophore-tagged DNA, enabling highly sensitive biosensing capabilities capable of detecting picomole levels of target molecules with single-base discrimination. The study also uncovered a distinctive kinetic behavior in MXene Ti3C2Tx, characterized by its ability to effectively trap and release nucleic acids. This behavior suggests potential applications in controlled biomolecule delivery systems, where MXenes could play a crucial role in time-sensitive applications such as drug delivery or molecular sensing with temporal precision. Furthermore, the findings underscore MXenes’ versatility as platforms for nucleic acid interactions, positioning them at the forefront of hybridization–based biosensing technologies. By elucidating the mechanisms behind MXene-DNA interactions at a molecular level, this research contributes to expanding the understanding of MXenes’ role in biomedical applications, including diagnostics, therapeutics, and bioengineering. As shown in Figure 5, the way MXenes and DNA interact is marked by their varying degrees of attraction to single-stranded DNA (ssDNA) versus double-stranded DNA (dsDNA). MXenes usually show a stronger attraction to ssDNA than to dsDNA because they use different, non-covalent ways to bind.
These ways involve van der Waals forces, hydrogen bonds, and π stacking interactions that happen between the phosphate backbone and the nucleobases of DNA. In particular, π–π stacking, which is especially common in MXenes that have sp2 hybridized systems, leads to a stronger bond with ssDNA, making it easier for the unpaired bases to come into contact with the material surface. In contrast, dsDNA, with its rigid double helix structure and bases engaged in intra-strand hydrogen bonding, exhibits weaker adsorption onto MXene surfaces. This differential affinity plays a crucial role in biosensor applications, enabling MXenes to selectively detect specific nucleic acid sequences by leveraging the strong interaction with ssDNA to produce detectable changes in electrical properties, thus enhancing the sensitivity and specificity of MXene-based biosensors.
Jian Yang and his team [92] described a unique 3D-structured setup made of silver (Ag) nanoparticles and Ti3C2Tx, intended for use in surface-enhanced Raman scattering (SERS) and electrochemical impedance spectroscopy (EIS) biosensors. The combination of Ti3C2Tx and Ag nanoparticles significantly boosts the Raman signal strength in the Ag/Ti3C2Tx mixture, rendering it highly effective for SERS purposes. Leveraging the superior SERS capabilities of the Ag/Ti3C2Tx mixture, along with the magnetic characteristics of Fe3O4 and the specificity of antigen-antibody interactions, the researchers created a sandwich-like SERS biosensor. This biosensor shows exceptional sensitivity in identifying even small quantities of beta-human chorionic gonadotropin (β-hCG), spanning a wide linear range from 5.0 × 10−6 to 1.0 mIU mL−1 and a very low detection limit of 9.0 × 10−7 mIU mL−1. Moreover, Yang and his colleagues developed an EIS biosensor using the Ag/Ti3C2Tx mixture for the portable detection of β-hCG. This EIS biosensor also displays a wide linear range from 5.0 × 10−2 to 1.0 × 102 mIU mL−1 and a low detection limit of 9.5 × 10−3 mIU mL−1. The Ag/Ti3C2Tx mixture is made by a process called in situ reduction, where silver (Ag) nanoparticles are grown on the Ti3C2Tx surface through etching Ti3AlC2 with a solution of HCl and LiF. This synthesis method ensures the integration of Ag nanoparticles onto the Ti3C2Tx surface, enhancing its electrochemical properties for sensor applications.
For the creation of the electrochemical impedance spectroscopy (EIS) immunosensor designed for portable detection, the setup involves integrating three electrodes within a small electrolytic cell designed by the researchers. The detection of beta-human chorionic gonadotropin (β-hCG) is facilitated using a portable electrochemical workstation (PalmSens4). The immunosensor’s signal is transmitted wirelessly to a mobile phone via Bluetooth for real-time analysis. The foundation of EIS immunosensors is based on the connection of antigens to the Ag/Ti3C2Tx-based electrodes. This connection raises the resistance of the electrodes in a direct relationship with the amount of the target antigen in the sample. Through monitoring these changes in resistance, the immunosensors can precisely identify and measure β-hCG molecules.
The foundation of EIS immunosensors is based on the connection of antigens to the Ag/Ti3C2Tx-based electrodes. This connection raises the resistance of the electrodes in a direct relationship with the amount of the target antigen in the sample. Through monitoring these changes in resistance, the immunosensor can precisely identify and measure β-hCG molecules. This method takes advantage of the large surface area and conductivity of the Ag/Ti3C2Tx mixture, providing sensitive and dependable detection abilities that are ideal for point-of-care diagnostics and medical uses. The practical utility of both immunosensors was validated by successfully detecting β-hCG in actual serum samples, demonstrating satisfactory recovery rates ranging from 98.5% to 102.2%. These results highlight the potential of the Ag/Ti3C2Tx composite as a versatile platform for developing sensitive and specific immunosensors, promising advancements in clinical diagnostics and biomedical research.
5.2. Enzymatic MXene-Based Biosensor
DNAzymes, also referred to as catalytic DNA or deoxyribozymes, are brief DNA sequences that can facilitate different chemical processes. These reactions include DNA phosphorylation, RNA cleavage, and the formation of nucleopeptide linkages, among others [93,94]. The breakthrough in isolating DNAzymes came in 1994 when Breaker and Joyce pioneered their discovery through an innovative in vitro selection method [95]. This pioneering work opened new avenues in nucleic acid research by demonstrating that DNA, traditionally viewed as a carrier of genetic information, can also function as a catalyst akin to enzymes. DNAzymes have since garnered significant attention for their potential applications in biotechnology, medicine, and nanotechnology, owing to their programmable nature and ability to perform precise catalytic functions under diverse conditions.
A wealth of research in the field highlights MXenes and MXene composite materials as capable of maintaining enzyme activity when enzymes are incorporated into their structures. This is attributed to the numerous characteristics and distinctive structural characteristics of MXenes, making them promising candidates for enzyme-based biosensors. Maintaining the function of enzymes is essential for precise and sensitive identification of different substances in medical, environmental, and food safety fields. For instance, Ma and colleagues created an innovative enzyme sensor using Ti3C2 MXene-coated HRP enzyme complex combined with chitosan. This sensor reached a minimal detection threshold for hydrogen peroxide (H2O2) and was effectively used to identify small concentrations of H2O2 in food items [96]. Such applications highlight MXene-based biosensors as promising tools for ensuring food safety and quality control. Similarly, Xu et al. [97] demonstrated the direct integration of Ti3C2 MXene with HRP enzyme to develop a biosensor specifically designed for detecting H2O2. The biosensor was utilized to examine blood samples from patients suffering from acute myocardial infarction (AMI) both prior to and following their surgical procedures, demonstrating its capability for use in clinical diagnostics. In summary, the study suggests that MXenes not only maintain the activity of enzymes but also improve the sensitivity and effectiveness of biosensing systems. This quality positions them as useful in various areas, including medical diagnostics, environmental surveillance, and the analysis of food products.
Besides the applications previously mentioned, MXenes have been thoroughly investigated with various types of enzymes, including glucose oxidase [98], cholesterol oxidase [99], acetylcholinesterase [100], and tyrosinase [101] to detect different molecules. These research efforts highlight the wide-ranging and potent capabilities of MXenes in improving the performance of enzymatic biosensors for a variety of substances. Murugan et al. [100] dispersed MXene in 4-sulfocalix[4]arene (SCX)-doped PEDOT solution to prepare PEDOT: SCX/MXene nanocomposite films, and on top of that the glucose oxidase GOX was immobilized using chitosan (Chit) binder to detect glucose. The nanocomposite PEDOT: SCX/MXene/GOX was then coated on the glassy carbon electrode (GCE), which detects glucose in the 0.5–8 mM range with an LOD of 22.5 µM, as seen in Figure 6. Xia et al. [99] prepared accordion-like Chit/ChOx/Ti3C2Tx nanocomposite-based enzymatic electrochemical biosensor where the cholesterol oxidase (ChOx) immobilization on MXene. In the process of detection, the cholesterol is oxidized to cholest-4-en-3-one and H2O2 because of a reaction with ChOx. The levels of generation of cholesterol could be then indirectly estimated by measuring the electro-oxidation generated due to the H2O2 formation. Under the optimum conditions, the cholesterol can be detected in the linear range of 0.3 to 4.5 nM with a 0.11 nM detection limit and 132.66 μA nM−1 cm−2 sensitivity.
Song et al. [102] investigated electrochemical etching to create a fluorine-free Nb2CTx MXene with minimal toxicity and developed an Nb2CTx/acetylcholinesterase biosensor for the detection of sulfoxide, as shown in Figure 7. This biosensor showed enhanced enzymatic function and electron transfer capabilities compared to those of V2C and Ti3C2 MXene biosensors.
Wu et al. [101] also employed Ti3C2 MXene to attach tyrosinase, enabling direct electron transfer for sensitive and quick detection of phenols. The biosensor they created demonstrated outstanding analytical capabilities over a wide linear range (0.05–15.5 μM) with detection limits as low as 12 nM, proving the potential of Ti3C2 MXene as a stable and sensitive phenolic biosensor. In recent years, there has been significant exploration into enzyme electrochemical biosensors that offer enhanced efficiency and substrate specificity under mild conditions (refer to Table 1 [103]). Wu et al. [104] created a mixed PLL/Ti3C2/glucose oxidase biosensor for measuring glucose levels, as presented in Figure 8. This biosensor employs Ti3C2 MXene to speed up the decomposition of H2O2 produced during the oxidation of glucose, thus initiating a series of reactions. Furthermore, the Ti3C2–PLL–GOx nanosheets are deposited on GCE to build a biosensor, which detects the glucose effectively up to 2.6 μM.
These examples highlight MXenes as promising materials for developing advanced enzymatic biosensors with enhanced performance characteristics, including sensitivity, stability, and electron transfer efficiency. Continued research in this area could further expand the scope of MXene-based biosensing device applications in biomedical diagnostics, environmental monitoring, and food safety. The schematic illustration shows the enzymatic inhibition process for detecting sulfoxide using the HF-free Nb2CTx/AChE biosensor, where acetylcholinesterase (AChE) is immobilized on Nb2CTx MXene. This biosensor demonstrates enhanced enzymatic activity and electron transfer compared to other MXene counterparts [102]. These figures highlight the innovative use of MXenes in biosensing applications, showcasing their role in enhancing sensitivity and performance in detecting specific analytes.
5.3. Optical Biosensors
Optical biosensors are label-free, tiny, and compact yet powerful portable analytical devices that enable rapid, real-time, and simultaneous detection of multiple analytes (biological and chemical molecules) with high sensitivity, selectivity, and cost-effectiveness. They provide information about a biological interaction via a label-free method via the changes in optical properties of the material near the surface, such as refractive index and polarization, leading to a measurable signal without the need for an external label. Generally, surface-enhanced plasmon resonance (SEPR), interferometry, and waveguide-based biosensors fall into the category of optical biosensors. The research and development of optical biosensors have witnessed exponential growth recently in the fields of healthcare, environment, and biotechnology. The optical biosensors not only provide valuable information on the concentration of the analyte but also shed light on their biochemical interaction at the molecular level with negligible signal-to-noise ratio. Optical biosensors contain a biorecognition element integrated with an optical transducer, which together generates a signal in response to a specific reaction. The biological materials including enzymes, antibodies, antigens, receptors, nucleic acids, cells, and tissues are well-known biorecognition elements that are frequently used in optical biosensors. The analyte is accurately detected in real time with minimal false positive results due to the specific affinity of the biorecognition elements toward the target analyte, facilitating immediate analysis of biological processes for rapid decision-making in medical and research contexts. With their versatility across various biological and chemical sensing tasks, long-term stability, and compatibility with automation, optical biosensors represent a critical technology driving advancements in biomedicine, environmental science, and beyond.
The integration of nanotechnology with advanced materials like MXenes has propelled optical biosensors to new levels of performance and versatility. MXenes, as two-dimensional materials derived from transition metal carbides, nitrides, or carbonitrides, offer several distinct advantages such as high surface area (98 m2 g−1), superior electrical conductivity (2400 S cm−1), biocompatibility, hydrophilicity, and chemical stability in sulfuric acid solution (pH = 1) that enhance the capabilities of optical biosensors. These distinctive properties enable the efficient detection of biomolecules even at extremely low concentrations, which is critical for medical diagnostics and environmental monitoring. The high surface area of MXenes can increase the sensitivity of the biosensor due to the larger number of active sites available for analytes to bind. Moreover, MXenes can be tailored to exhibit tunable optical properties such as absorbance and fluorescence. Owing to their excellent biocompatibility, the MXenes are seamlessly used with biological systems without harming living organisms or clinical samples. Their mechanical robustness and stability under diverse environmental conditions further contribute to the reliability and longevity of biosensing devices.
Alsaif et al. [106] recently reported a refractive index-based biosensor for the detection of malaria. The sensor comprises a meta-surface with four equal quadrants at the center coated with MXene material and a square-shaped ring resonator coated with black phosphorus which are grown on the surface of the SiO2 substrate. At optimum conditions, the sensors exhibit high sensitivity up to 600 GHz/RIU and a low detection limit of 0.109 RIU for malaria. Pisani [4] designed a biosensor consisting of a gold nanoparticle (AuNPs)-functionalized Ti3C2Tx MXene (AuNPs@Ti3C2Tx) for detecting human IgG. To detect human IgG, the antihuman IgG was first coated on the AuNPs@Ti3C2Tx. In the presence of the human IgG, the antibody-antigen interaction takes place which results in the formation of AuNPs clusters. This Au aggregation-induced nanoplatform detects human IgG up to the ≈0.1 ng/mL limit. Singh et al. [107] reported a surface plasmon resonance (SPR)-based biosensor for sensing and differentiating among healthy and tumorous brain tissues based on their unique refractive index (RI). Surface plasmons are generated via Au particles’ coating on the rectangular open channel (ROC) part of the biosensor. Afterward, a thin Ti3C2Tx–MXene coated over Au particles, and an additional TiO2 layer, was placed on the ROC surface to hold the Au particles firmly. The sensitivity values for the gray matter, cerebrospinal fluid, and oligodendroglioma are observed to be 12,352.94, 2030.45, and 672.26 nm/refractive index unit (RIU), respectively. While for the tumorous tissues such as glioblastoma, lymphoma, and metastasis, the sensitivities are 800, 774.9, and 643.26 nm/RIU, respectively. The SPR-based biosensor worked well for the 1.25 × 10−4 to 8.09 × 10−6 RIU ranges with the maximum figure of merit (FoM) of 126.05 RIU−1. Sun et al. [108] investigated the performance of the hybrid dual-mode ratiometric biosensor by combining the fluorescent zinc(II) meso-tetra(4-carboxyphenyl) porphyrin (ZnTCPP) and electro-chemiluminescent Ti3C2Tx MXene (see Figure 9). The fluorescence of the ZnTCPP is quenched by the Ti3C2Tx MXene via energy transfer. Through the ZnTCPP/Ti3C2Tx system, the biosensing of alkaline phosphatase (ALP) is carried out through ALP-catalyzed (PO4)3− production in the linear detection range of 0.1–50 mU/mL and detection limit of 0.0083 mU/mL.
Lang et al. [109] developed a WaveFlex optical fiber biosensor for the detection of Xanthine using a tapered structure consisting of AuNPs and MQDs. The presence of the AuNPs and MQDs generated the SPR to enhance the detection sensitivity. Xanthine was detected via coating xanthine oxidase enzyme on the surface of optical fiber with a 1.93 nm/mM sensitivity, 0–800 mu M detection range, and 146.11 mu M limit of detection. Hasanah et al. [110] proposed a Kretschmann-configured SPR sensor modified by Ti3C2T2 MXene. Both the Ti3C2T2 MXene layers and terminal functional groups are tuned to obtain optimum parameters of MXene. The optimized sensor consisting of six layers of MXene and F2 as a terminal group showed the highest sensitivity of 150.131 degrees/RIU with an LOD of 0.01633 RIU. Similarly, as shown in Figure 10, the tyramine is also detected by coating AuNPs and MXene on the fiber surface, which provided the enhanced surface area for tyrosinase enzyme mobilization to sense tyramine with 6.96 mu M detection limit and a sensitivity of 0.0385 nm/mu M [111].
Zhang et al. [112] fabricated flexible W-shaped SPR-based optical fiber biosensor for detecting histamine in food. Besides the AuNPs and Nb2CTx MXene, molybdenum disulfide (MoS2) is also coated to improve the surface area and reaction sites. To enhance the specificity, the diamine oxidase (DAO) enzyme is additionally coated on the probe. The proposed biosensor detected histamine in the linear detection range of 0–1000 mu M and achieved 52.5 mu M detection limit along with 4.4 pm/mu M sensitivity [112]. MXene materials have been employed to detect the renal cancer protein biomarkers to diagnose cancer at an early stage. Li et al. [113] designed an optical microfiber coated with Ti3C2 and AuNPs for the sensing of the carbonic anhydrase IX (CAIX) protein and renal cancer cells with 13.8 zM, 0.19 aM detection limit in pure buffer, and 30% serum solution, respectively. A double S-tapered optical fiber biosensor is reported for tyramine detection. The fiber was coated with AuNPs@GO@tyrosinase and AuNPs@Nb2CTx@tyrosinase to detect tyramine with 17 and 34 pm/mu M in the 0–300 mu M tyramine concentrations range [114].
These studies collectively demonstrate the potential of MXene materials as optical biosensors owing to their versatility, sensitivity, and selectivity for biomedical, environmental, and industrial applications. MXenes materials perform a key role in advancing biosensing technologies through their unique properties and diverse applications, resulting in further enhancements in sensor parameters, along with lower detection limits, enhanced stability, and their ability to integrate into portable devices for on-site and point-of-care detection. MXene materials hold promise to expand the scope and effectiveness of biosensing technologies, addressing diverse analytical challenges, and contributing to advancements in health, environmental sustainability, and food safety.
5.4. Fluorescence-Based Biosensors
The fluorescence-based biosensing devices detect the biological interactions via the changes in the fluorescence properties of the fluorophore (fluorescence material) label attached to the analyte or receptor, through well-known methods such as FRET, fluorescent polarization, and dye-based detection. They offer sensitive and selective detection of biological targets by utilizing specific recognition elements such as antibodies or nucleic acids for detecting analytes with minimal interference and high accuracy, beneficial for detecting disease biomarkers, environmental pollutants, and food contamination. The real-time monitoring capabilities of fluorescence biosensors allow rapid analysis. The versatility of fluorescent probes, ranging from organic dyes to advanced nanostructures like quantum dots, further enhances their utility across various applications. These biosensors not only provide quantitative data through measurable fluorescence signals but also support multiplexed detection, allowing simultaneous analysis of multiple targets within a single sample. As technologies advance, integrating fluorescent-based biosensors into portable and miniaturized platforms continues to expand their use in diverse scientific, clinical, and industrial settings, driving innovation in biosensing and analytical chemistry.
MXenes, particularly titanium carbide and titanium nitride derivatives, are promising candidates for fluorescence-based biosensors owing to their exceptional properties such as high surface area, excellent conductivity, and biocompatibility. MXenes can not only serve as a support for immobilizing biomolecules like enzymes or antibodies due to their large surface area and ability to form stable interfaces with biological molecules but also could be integrated with fluorescent probes or quantum dots to enhance signal detection. MXene and fluorophores integration leads to improved sensitivity and signal-to-noise ratios, which is crucial for detecting low analyte concentrations. This technology holds significant potential in medical diagnostics, environmental monitoring, and biotechnology, offering advantages such as high sensitivity, real-time monitoring capabilities, and the potential for multiplexed detection.
5.4.1. Heavy Metal Ion Detection
Numerous MXene-based optical biosensors dealing with the detection of different types of metal ions via different mechanisms have been reported in the literature. Xue et al. [115] reported photoluminescent Ti3C2 MXene quantum dot (MQD) with 10% quantum yield (QY) and their potential application in multicolor cellular imaging probes via RAW264.7 cells and Zn2+ detection. The comparison of synthesis temperatures and the PL of the MQDs because of the Förster resonance energy transfer (FRET) was declined selectively for Zn2+, while the PL was unchanged for other metal ions such as Fe3+, Co2+, Ni2+, Cr2+, Pb2+, Al3+, Cu2+, Mn2+, and Sn2+. Guan et al. [116] prepared N– and P–functionalized Ti3C2 MQDs with the 2.93 nm size and 20.1% PLQY. N/P@MQDs nanoprobes were employed for the Cu2+ ions detection. The PL intensity of the N/P@MQDs was quenched linearly due to FRET upon the addition of Cu2+ ions in the 2–100 μM and 250–5000 μM ranges, providing 2 μM of sensitive Cu2+ detection. Ti3C2Tx MQDs are also used to detect Fe3+ ions [117]. The white, blue, and blue PL are observed from the Ti3C2Tx MQDs prepared by the solvothermal method in dimethyl sulfoxide (DMSO), dimethylformamide (DMF), and ethanol solvents, respectively. The solvent has also affected the PL QY (10.7, 6.9, and 4.1%) and average size (3.3, 2.5, and 1.8) of the Ti3C2Tx MQDs for DMF, ethanol, and DMSO, respectively. Various ions, i.e., Fe3+, Fe2+, Ni2+, Ag+, Al3+, Co2+, Cd2+, Mg2+, Cu2+, Mn2+, Hg2+, Zn2+, and Pb2+, are used to check the metal ion sensitivity of Ti3C2Tx MQDs probe. It was observed that the Ti3C2Tx MQDs probe selectively detects Fe3+ within a good linear relationship range of 5–470 and 510–750 μM and the lowest detection limit of 2 μM. In another study, the Ti3C2Tx MODs are blended with CsPbBr3 halide perovskite to form a nanocomposite-based turn-on sensor for Cd2+ detection. The PL intensity of the CsPbBr3 has been significantly reduced by Ti3C2Tx MQDs due to the charge transfer from CsPbBr3 to Ti3C2Tx MQDs. The quenched PL was then linearly recovered with the addition of the Cd2+ ions. The detection range for the Cd2+ ions was estimated to be from 9.9 × 10−5 to 5.9 × 10−4 [118]. Yang et al. [119] reported niobium-based Nb2C MQDs for the detection of Fe3+ ions (Figure 11). The PL intensity of the Nb2C MQDs probe quenched linearly (static quenching) with an increase in the Fe3+ concentration from 0 to 300 μM, which is attributed to the coordination interaction between the –OH and –COOH surface group present on the Nb2C MQDs and Fe3+ ions. The Nb2C MQDs probes detect the Fe3+ ions as low as 0.89 μM.
Desai et al. [120] reported a sensing platform comprising Ti3C2 nanosheets for the detection of Ag+ and Mn2+ ions based on PL quenching of Ti3C2. The hydroxyl– and fluorine–terminated Ti3C2 nanosheet shows affinity toward Ag+ and Mn2+ ions, which enables the selective sensing of Ag+ and Mn2+ ions even from the mixture of other competitive metal ions in environmental and food (tomato, rice, and spinach) samples (Figure 12). The Ti3C2 nanosheets-based nanoplatform detected the Ag+ and Mn2+ ions with the detection limit as low as 9.7 and 102 nM within the linear range of 0.1–40 µM and 0.5–60 µM, respectively.
Zhang et al. [121] utilized Ti3C2 MQDs of 1.75 nm particles with 7.7% quantum yield for the detection of Fe3+. The PL quenching in Ti3C2 MQDs was observed because of the IFE as a result of the redox reaction between Ti3C2 MQDs and Fe3+. Fe3+ can be effectively detected up to 310 nM limit in the 5–1000 μM range.
5.4.2. Detection of Biomolecules
MXene nanomaterials are widely used for the detection of biomolecules for clinical diagnostics in the field of biomedical research, where precise detection of biomolecules is essential. Xu et al. [122] prepared Ti3C2 MQDs using the hydrothermal method for the detection of intracellular glutathione (GSH), an important health and disease biomarker. The Ti3C2 MQDs produced a blue emission that arises from the surface defects and quantum confinement effects. In the presence of the GSH, the FRET occurs from Ti3C2 MQDs to GSH for the wide concentration range (1–100 μM), thereby detecting GSH up to 0.02 μM. Shi et al. [123] reported another method to detect GSH using luminescent Cu nanocrystals (NCs) functionalized Ti3C2Tx MXene flakes. The blue emission of Ti3C2Tx MXene flakes was quenched by the neighboring Cu NCs through IEF. When the GSH is added to the MXenes–Cu NCs composite, the quenched emission is recovered due to the specific interaction that took place between glutathione and Ti3C2Tx MXene flakes. The Ti3C2 MQDs (∼4.2 nm in size) are also used as a probe for detecting alkaline phosphatase (ALP) activity and embryonic stem cell (ESC) recognition. Owing to the overlap between the emission of Ti3C2 MQDs and the absorption spectrum of p-nitrophenol (p-NP), the energy transfers from Ti3C2 MQDs to p-NPs via IFE and detect ALP activity with 0.02 U L−1 limit of detection [124]. Liu et al. [125] investigated the Ti3C2 MQDs for cytochrome c detection. The deprotonated Ti3C2 MQDs exhibit a blue emission around 415 nm with a quantum yield of 22%. In the presence of cytochrome c, the intensity of the Ti3C2 MQDs has declined linearly in the 0.2–40 μM range with a 20.5 nM detection limit because of the IFE.
To detect an important biomarker, i.e., uric acid, GSH-functionalized Ti3C2 MQDs are also reported. In a process, the uric acid was first oxidized using uricase enzyme into the allantoin and hydrogen peroxide, whereas o-Phenylenediamine (OPD) was oxidized to the yellow-colored 2,3-diaminophenazine (oxOPD) by using horseradish peroxidase enzyme. The GSH@Ti3C2 MQDs emit effectively at 425 nm while the oxOPD product produces 568 nm emission. When the uric acid is added to the GSH@Ti3C2 MQDs and oxOPD composite, FRET takes place between them causing the 425 nm of GSH@Ti3C2 MQDs to decrease and 568 nm of oxOPD to increase. Using this nanoplatform, the uric acid is detected in the linear range of 1.2–75 μM and a detection limit of 125 nM [126]. In another study by Zhu et al. [127], Ti3C2 nanosheets were integrated into FRET-based assays with rhodamine B (RhB)-labeled phospholipids for detecting phospholipase D, achieving a sensitivity of 0.10 UL−1. The emission of the Ti3C2 nanosheets is strongly quenched by the neighboring RhB via FRET. Phospholipase D activity cleaves phospholipids, causing the RhB-labeled phospholipids to detach from Ti3C2 MXenes, which results in fluorescence recovery. Zhu et al. [128] developed a glucose sensor by using Ti3C2 MXene nanosheets and red-emitting carbon dots (RCDs). Ti3C2 nanosheets passively quenched the fluorescence intensity of RCDs (>96%) through the IFE, which is recovered upon interaction with glucose. Glucose oxidase catalyzed the oxidation of glucose along with the generation of H2O2, which further oxidized Ti3C2 to Ti (OH)4, thereby inhibiting IFE from RCDs to Ti3C2 MXene nanosheets. As presented in Figure 13, the Ti3C2 MXene-based sensor was also used to detect human papillomavirus HPV-18 DNA [129]. The dye-coated single-stranded (ss) DNA was used as a probe. When the Ti3C2 nanosheets were coated on the dye@ssDNA probe, the fluorescence of the dye was quenched, whereas the hybridization of the dye-labeled ssDNA with complementary target DNA forms dsDNA, which is responsible for the fluorescence recovery, thereby achieving a low detection limit up to 100 pM. Ti3C2 MXenes have been explored in complex biological interactions.
Wang et al. [130] investigated the use of chimeric peptide-coated Ti3C2 MXenes in FRET assays involving histone deacetylase sirtuin-1 and protein phosphatase 2C, showcasing their role in studying molecular interactions critical for understanding cellular mechanisms and disease pathways. Moreover, Ti3C2 nanosheets have been utilized in pathogen detection, for example, Hong et al. [131] developed a FRET-based sensitive assay for Vibrio parahaemolyticus (VP) detection by using aptamer-modified polyhedral oligomeric silsesquioxane-perovskite quantum dots (POSS-PQDs@Apt) as probe and Ti3C2 nanosheets as quencher. When the VP is added to the POSS-PQDs@Apt@Ti3C2 composite, the aptamer selectively binds to the VP leaving behind the Ti3C2 nanosheets due to the higher binding affinity of the aptamers toward VP than Ti3C2 nanosheets, which has recovered the quenched POSS-PQDs@Apt emission, thereby achieving a detection limit as low as 30 cfu mL−1 in the concentration range of 102–106 cfu/mL. For detecting the endotoxins and Escherichia coli pathogens, the Ti3C2Tx MXenes were integrated with CRISPR–Cas12a by Sheng et al. [132]. The aptamer used in this study initiated the trans-cleavage activity of CRISPR–Cas12a, resulting in the separation of Ti3C2Tx MXenes from ssDNA to recover the fluorescence, achieving detection limits of 11 pg mL−1 for endotoxins and 23 CFU mL−1 for E. coli. Kalkal et al. [21] reported an Ag/Ti3C2 composite-based sensor for detecting neuron-specific enolase (Figure 14). In this system, the fluorescence signal from antibody/amino-graphene quantum dots conjugated with Ag/Ti3C2 is quenched. Upon introduction of the antigen, the fluorescence signal is recovered, demonstrating the capability of the biosensor for sensitive and reproducible detection of neuron-specific enolase (NSE) with 0.05 pg mL−1 limit of detection in the 0.0001–1500 ng mL−1 detection range. Furthermore, the dual-signal-labeled DNA-functionalized Ti3C2 MXene nanoprobes were reported for the simultaneous analysis of MUC 1 and miRNA-21 at low concentrations in vitro, alongside in situ imaging of MCF-7 breast cancer cells by Wang et al. [133]. This dual-labeling strategy not only enhances the sensitivity of detection but also enables spatial imaging of biomarkers within cells, providing valuable insights into their expression levels and distribution.
5.4.3. Detection of Miscellaneous Compounds
MXene materials are utilized for the detection of different compounds, for example, Wang et al. [134] prepared uric acid (UA)-functionalized Ti3C2 MQDs for detecting 2,4,6-trinitrophenol (TNP). The emission of Ti3C2 MQDs is transferred to the TNP via IFE for a wide linear range of 0.01–40 μM and a detection limit is calculated to be 9.58 nM. The authors employed the UA@Ti3C2 MQDs for detecting TNP in water as well as on surfaces through a smartphone-based calorimetric method achieving a linear detection range of 10 to 100 ng. MXene materials are also used for detecting an important indicator used for monitoring numerous diseases and cellular health, i.e., intracellular pH. Chen et al. [135] developed a ratiometric fluorescence sensor by combining pH-sensitive polyethyleneimine (PEI)-coated Ti3C2 MQDs and pH non-sensitive [Ru(dpp)3]Cl2 for monitoring the intracellular pH changes. Lu et al. [136] used N-doped Ti3C2 MQDs for the quantitative detection of the H2O2 and xanthine, see Figure 15. The N-doped Ti3C2 MQDs were functionalized with 2,3-diaminophenazine (DAP) to build a dual-emissive radiometric sensor for H2O2 via a photoinduced electron transfer from MQDs to DAP. The composite N-Ti3C2@DAP nanoprobe could be used to detect H2O2 and xanthine up to 0.57 μM and 0.34 μM limit, respectively.
Similarly, the N-fluorescent Ti3C2 MQDs have also been employed in the detection of chromium (Cr(VI)) and ascorbic acid (AA) [137]. Figure 16 illustrates the synthesis procedure for the fluorescent N-Ti3C2 MQDs and the method of detection. The fluorescence of N-Ti3C2 MQDs is quenched (turn off) by the Cr(VI) via IEF and static quenching, while the addition of AA resulted in the redox reaction between the AA and Cr(VI), which is responsible for the recovery (turn on) of the N-Ti3C2 MQDs fluorescence. The N-Ti3C2 MQDs-based nanoplatform performed well in the linear detection range of 0.1–500 μM for both the AA and Cr(VI), with the detection limit of 0.012 (Cr(VI)) and 0.02 μM (AA). Ti3C2 MQDs have also been used for the detection of curcumin and hypochlorite (ClO−). The Ti3C2 MQDs emission effectively overlaps with the curcumin absorption, causing FRET and thus emission quenching. In the presence of the ClO−, curcumin is oxidized to the quinones and recovers the Ti3C2 MQDs fluorescence. The Ti3C2 MQDs probe was found to show a linear relationship in the 0.05–10 and 25–275 μM range as well as the detection limit of 20 nM and 5 μM, for curcumin and ClO−, respectively [138].
These studies underscore MXenes’ versatility and effectiveness in fluorescence–based biosensing applications, offering robust platforms for detecting enzymes, nucleic acids, and biomolecules with high sensitivity and specificity. The integration of MXenes into optical sensors enhances their capabilities in biomedical research, diagnostics, and cellular imaging, paving the way for advanced analytical techniques in biological and clinical sciences.
5.1. MXene-Based Electrochemical Biosensors
A biosensor is a measurement tool that merges a biological sensing component, like a protein or DNA, with a transducer that transforms the biological reaction into a measurable output. The pioneering electrochemical biosensors, introduced in 1967 for glucose monitoring, continue to play an important part in diabetes management. Usually, an electrochemical biosensor consists of three main parts: a working electrode, a reference electrode, and a counter electrode. The working electrode serves as the main detection element and can be made from different types of nanomaterials to identify certain biomolecules or pathogens. Advancements in electrochemical biosensor technology have been driven by the development of biomimetic and bioactive working electrodes. These improvements, coupled with effective signal amplification strategies, enable the conversion of molecular recognition events into measurable electrical signals, such as current, voltage, or impedance. The effectiveness of an electrochemical biosensor is greatly affected by the structure, surface characteristics, and biological operation of the active electrode. Selecting the right materials for the bioreceptor and transducer is crucial for improving the sensor’s ability to distinguish between different substances and its capacity to convert chemical signals into electrical signals. This careful selection of materials is vital for increasing the precision and dependability of detecting specific analytes.
There is an increasing fascination with using MXenes as bioreceptors or transducers in electrochemical biosensors because of their simplicity in making, creating, and modifying them. MXenes, known for their outstanding compatibility with biological materials, provide a flexible base for incorporating fully operational biomolecules like proteins and DNA into electrochemical bio-interfaces. This characteristic makes them perfect for enabling molecular recognition, electrochemical reactions, and signal enhancement—crucial steps for the efficient operation of electrochemical biosensors. MXenes bring notable benefits to biosensor use, such as a large surface area, superior electrical conductivity, and the capability to establish stable and functional connections with biological entities. These features allow MXenes to improve the sensitivity and selectivity of biosensors, enabling the detection of rare biomarkers and pathogens with great accuracy. Additionally, the unique layered structure of MXenes allows for the easy incorporation of various functional groups, further enhancing their versatility in biosensing applications. One of the key strengths of MXenes is their ability to support a wide range of signal amplification strategies. For example, MXenes can be combined with nanomaterials such as gold nanoparticles or quantum dots to enhance electrochemical signals, thereby improving the detection limits of biosensors. Moreover, MXenes’ excellent electrical conductivity ensures efficient electron transfer, which is critical for the rapid and accurate measurement of analyte concentrations.
Even though MXenes were first found more than 10 years ago and have been applied in many well-known areas, there is still a significant lack of thorough studies that cover the growing applications of MXenes in electrochemical biosensing. Current research has only scratched the surface of MXenes’ potential, and there is a need for systematic studies to fully understand and optimize their use in biosensor technologies. Such studies could explore the integration of MXenes with other nanomaterials, the development of new functionalization techniques, and the application of MXenes in real-world biosensing scenarios. As research continues to evolve, a systematic exploration of MXenes’ potential in this domain could pave the way for new innovations and advancements in biosensor technology. By addressing the existing gaps and exploring novel applications, researchers can unlock the full potential of MXenes, leading to the development of next-generation biosensors with unprecedented sensitivity, selectivity, and reliability.
Lately, materials with two dimensions (2D) have been widely used in creating the components that transmit signals in advanced biosensors. This allows for the accurate identification and measurement of DNA and RNA, which are essential for detecting and quantifying nucleic acids. Materials known as MXenes have shown potential as an alternative due to their distinct characteristics. Nonetheless, to create a dependable foundation for electrochemical detection of DNA using MXene 2D materials, it is important to fully comprehend how MXenes interact with DNA, particularly in the spaces between micro-sized MXene flakes where DNA can fit. MXenes have a large surface area, good electrical conductivity, and strong chemical stability, qualities that make them ideal for use in biosensors. Their capacity to establish robust connections with DNA can improve the sensitivity and accuracy of biosensors. However, the interaction between MXenes and DNA needs to be carefully studied to ensure that the structural integrity and biological activity of DNA are maintained during the sensing process. One critical aspect is the intercalation of DNA within the interlayer spaces of MXene flakes. This intercalation can influence the electrochemical properties of the biosensor, potentially enhancing the signal transduction and amplification capabilities. However, it is essential to elucidate how this intercalation affects the stability and functionality of the DNA. Studies should focus on assessing the binding affinity, structural changes, and potential denaturation of DNA when in contact with MXenes. Furthermore, the biocompatibility of MXenes needs to be evaluated to ensure that they do not induce any adverse effects on the DNA or other biological components used in the biosensors. This includes examining potential cytotoxicity, oxidative stress, and any unintended interactions with other biomolecules. Ensuring biocompatibility is paramount for developing reliable and effective MXene-based DNA biosensors for practical applications in medical diagnostics, environmental monitoring, and biotechnology.
Jialin Zhang et al. [89] reported the development of an electrochemical biosensor mediated by conductive Ti3C2 MXenes for detecting Mycobacterium tuberculosis. The biosensor targets the 16S rDNA strands, which contain highly conserved sequences across different species, suitable for general bacterial detection, as well as species-specific variable regions for precise species typing. In this study, a specific fragment of single-stranded DNA (ssDNA) from the species-specific variable region of the 16S rDNA of M. tuberculosis was selected as the target biomarker. The biosensor employs peptide nucleic acid (PNA) as the binding agent. PNA is a synthetic version of DNA, made up of a repeating sequence of neutral N–(2–aminoethyl) glycine units linked by peptide bonds, which also contain nucleotide bases. When the target DNA sequence is present, a DNA–PNA complex is formed inside the gold nanoparticle (AuNP) nanogap structure. Then, Ti3C2 MXenes are bonded to this nanogap structure with a Zr4+ cross-linking agent, which can interact with the phosphate groups in the DNA–PNA complex and the hydroxide groups in Ti3C2 MXenes. This shift from PNA to the conductive Ti3C2 MXenes within the nanogap structure results in a notable change in electrical conductivity. This shift acts as the basis for the accurate detection of the target DNA sequences, enabling the identification of M. tuberculosis. The suggested approach is quick, precise, and capable of detecting as few as 20 colony-forming units per milliliter (CFU/mL) in just 2 h. The biosensor was effectively utilized to identify M. tuberculosis in 40 simulated sputum samples, showcasing its real-world utility. Figure 4 provides a comprehensive overview of the preparation and application of Ti3C2 MXenes nanosheets in the biosensor for rapid detection of M. tuberculosis. (a) The Preparation of Ti3C2 MXenes Nanosheets: The synthesis of Ti3C2 MXenes nanosheets typically involves etching aluminum layers from the precursor material (Ti3AlC2) using hydrofluoric acid or a similar etchant.
This process results in the formation of two-dimensional Ti3C2 MXenes sheets with a high surface area and excellent conductivity. These nanosheets are then exfoliated to obtain a stable colloidal suspension suitable for biosensing applications. (b) The Pretreatment of Ti3C2 MXenes Nanosheets with ZrOCl2: To enhance the biosensor’s functionality, the Ti3C2 MXenes nanosheets undergo pretreatment with zirconium oxychloride (ZrOCl2). This step involves the reaction of Zr4+ ions with the hydroxide groups on the surface of the MXenes, facilitating the subsequent attachment of biomolecules. The pretreatment ensures effective cross-linking between the MXenes and the DNA–PNA complex, crucial for the biosensor’s performance. (c) The Constructed Strategy of the Sensor for Rapid Detection of M. tuberculosis: The biosensor’s construction integrates the Ti3C2 MXenes nanosheets into a gold nanoparticle (AuNP) nanogap network electrode.
C. Lorena Manzanares-Palenzuela et al. [91] conducted a comprehensive study to explore the interaction dynamics between MXene Ti3C2Tx and fluorophore-tagged DNA using a combination of fluorescence measurements and molecular dynamics simulations. Their research highlighted MXene Ti3C2Tx as a promising material due to its robust binding affinity toward fluorophore-tagged DNA, enabling highly sensitive biosensing capabilities capable of detecting picomole levels of target molecules with single-base discrimination. The study also uncovered a distinctive kinetic behavior in MXene Ti3C2Tx, characterized by its ability to effectively trap and release nucleic acids. This behavior suggests potential applications in controlled biomolecule delivery systems, where MXenes could play a crucial role in time-sensitive applications such as drug delivery or molecular sensing with temporal precision. Furthermore, the findings underscore MXenes’ versatility as platforms for nucleic acid interactions, positioning them at the forefront of hybridization–based biosensing technologies. By elucidating the mechanisms behind MXene-DNA interactions at a molecular level, this research contributes to expanding the understanding of MXenes’ role in biomedical applications, including diagnostics, therapeutics, and bioengineering. As shown in Figure 5, the way MXenes and DNA interact is marked by their varying degrees of attraction to single-stranded DNA (ssDNA) versus double-stranded DNA (dsDNA). MXenes usually show a stronger attraction to ssDNA than to dsDNA because they use different, non-covalent ways to bind.
These ways involve van der Waals forces, hydrogen bonds, and π stacking interactions that happen between the phosphate backbone and the nucleobases of DNA. In particular, π–π stacking, which is especially common in MXenes that have sp2 hybridized systems, leads to a stronger bond with ssDNA, making it easier for the unpaired bases to come into contact with the material surface. In contrast, dsDNA, with its rigid double helix structure and bases engaged in intra-strand hydrogen bonding, exhibits weaker adsorption onto MXene surfaces. This differential affinity plays a crucial role in biosensor applications, enabling MXenes to selectively detect specific nucleic acid sequences by leveraging the strong interaction with ssDNA to produce detectable changes in electrical properties, thus enhancing the sensitivity and specificity of MXene-based biosensors.
Jian Yang and his team [92] described a unique 3D-structured setup made of silver (Ag) nanoparticles and Ti3C2Tx, intended for use in surface-enhanced Raman scattering (SERS) and electrochemical impedance spectroscopy (EIS) biosensors. The combination of Ti3C2Tx and Ag nanoparticles significantly boosts the Raman signal strength in the Ag/Ti3C2Tx mixture, rendering it highly effective for SERS purposes. Leveraging the superior SERS capabilities of the Ag/Ti3C2Tx mixture, along with the magnetic characteristics of Fe3O4 and the specificity of antigen-antibody interactions, the researchers created a sandwich-like SERS biosensor. This biosensor shows exceptional sensitivity in identifying even small quantities of beta-human chorionic gonadotropin (β-hCG), spanning a wide linear range from 5.0 × 10−6 to 1.0 mIU mL−1 and a very low detection limit of 9.0 × 10−7 mIU mL−1. Moreover, Yang and his colleagues developed an EIS biosensor using the Ag/Ti3C2Tx mixture for the portable detection of β-hCG. This EIS biosensor also displays a wide linear range from 5.0 × 10−2 to 1.0 × 102 mIU mL−1 and a low detection limit of 9.5 × 10−3 mIU mL−1. The Ag/Ti3C2Tx mixture is made by a process called in situ reduction, where silver (Ag) nanoparticles are grown on the Ti3C2Tx surface through etching Ti3AlC2 with a solution of HCl and LiF. This synthesis method ensures the integration of Ag nanoparticles onto the Ti3C2Tx surface, enhancing its electrochemical properties for sensor applications.
For the creation of the electrochemical impedance spectroscopy (EIS) immunosensor designed for portable detection, the setup involves integrating three electrodes within a small electrolytic cell designed by the researchers. The detection of beta-human chorionic gonadotropin (β-hCG) is facilitated using a portable electrochemical workstation (PalmSens4). The immunosensor’s signal is transmitted wirelessly to a mobile phone via Bluetooth for real-time analysis. The foundation of EIS immunosensors is based on the connection of antigens to the Ag/Ti3C2Tx-based electrodes. This connection raises the resistance of the electrodes in a direct relationship with the amount of the target antigen in the sample. Through monitoring these changes in resistance, the immunosensors can precisely identify and measure β-hCG molecules.
The foundation of EIS immunosensors is based on the connection of antigens to the Ag/Ti3C2Tx-based electrodes. This connection raises the resistance of the electrodes in a direct relationship with the amount of the target antigen in the sample. Through monitoring these changes in resistance, the immunosensor can precisely identify and measure β-hCG molecules. This method takes advantage of the large surface area and conductivity of the Ag/Ti3C2Tx mixture, providing sensitive and dependable detection abilities that are ideal for point-of-care diagnostics and medical uses. The practical utility of both immunosensors was validated by successfully detecting β-hCG in actual serum samples, demonstrating satisfactory recovery rates ranging from 98.5% to 102.2%. These results highlight the potential of the Ag/Ti3C2Tx composite as a versatile platform for developing sensitive and specific immunosensors, promising advancements in clinical diagnostics and biomedical research.
5.2. Enzymatic MXene-Based Biosensor
DNAzymes, also referred to as catalytic DNA or deoxyribozymes, are brief DNA sequences that can facilitate different chemical processes. These reactions include DNA phosphorylation, RNA cleavage, and the formation of nucleopeptide linkages, among others [93,94]. The breakthrough in isolating DNAzymes came in 1994 when Breaker and Joyce pioneered their discovery through an innovative in vitro selection method [95]. This pioneering work opened new avenues in nucleic acid research by demonstrating that DNA, traditionally viewed as a carrier of genetic information, can also function as a catalyst akin to enzymes. DNAzymes have since garnered significant attention for their potential applications in biotechnology, medicine, and nanotechnology, owing to their programmable nature and ability to perform precise catalytic functions under diverse conditions.
A wealth of research in the field highlights MXenes and MXene composite materials as capable of maintaining enzyme activity when enzymes are incorporated into their structures. This is attributed to the numerous characteristics and distinctive structural characteristics of MXenes, making them promising candidates for enzyme-based biosensors. Maintaining the function of enzymes is essential for precise and sensitive identification of different substances in medical, environmental, and food safety fields. For instance, Ma and colleagues created an innovative enzyme sensor using Ti3C2 MXene-coated HRP enzyme complex combined with chitosan. This sensor reached a minimal detection threshold for hydrogen peroxide (H2O2) and was effectively used to identify small concentrations of H2O2 in food items [96]. Such applications highlight MXene-based biosensors as promising tools for ensuring food safety and quality control. Similarly, Xu et al. [97] demonstrated the direct integration of Ti3C2 MXene with HRP enzyme to develop a biosensor specifically designed for detecting H2O2. The biosensor was utilized to examine blood samples from patients suffering from acute myocardial infarction (AMI) both prior to and following their surgical procedures, demonstrating its capability for use in clinical diagnostics. In summary, the study suggests that MXenes not only maintain the activity of enzymes but also improve the sensitivity and effectiveness of biosensing systems. This quality positions them as useful in various areas, including medical diagnostics, environmental surveillance, and the analysis of food products.
Besides the applications previously mentioned, MXenes have been thoroughly investigated with various types of enzymes, including glucose oxidase [98], cholesterol oxidase [99], acetylcholinesterase [100], and tyrosinase [101] to detect different molecules. These research efforts highlight the wide-ranging and potent capabilities of MXenes in improving the performance of enzymatic biosensors for a variety of substances. Murugan et al. [100] dispersed MXene in 4-sulfocalix[4]arene (SCX)-doped PEDOT solution to prepare PEDOT: SCX/MXene nanocomposite films, and on top of that the glucose oxidase GOX was immobilized using chitosan (Chit) binder to detect glucose. The nanocomposite PEDOT: SCX/MXene/GOX was then coated on the glassy carbon electrode (GCE), which detects glucose in the 0.5–8 mM range with an LOD of 22.5 µM, as seen in Figure 6. Xia et al. [99] prepared accordion-like Chit/ChOx/Ti3C2Tx nanocomposite-based enzymatic electrochemical biosensor where the cholesterol oxidase (ChOx) immobilization on MXene. In the process of detection, the cholesterol is oxidized to cholest-4-en-3-one and H2O2 because of a reaction with ChOx. The levels of generation of cholesterol could be then indirectly estimated by measuring the electro-oxidation generated due to the H2O2 formation. Under the optimum conditions, the cholesterol can be detected in the linear range of 0.3 to 4.5 nM with a 0.11 nM detection limit and 132.66 μA nM−1 cm−2 sensitivity.
Song et al. [102] investigated electrochemical etching to create a fluorine-free Nb2CTx MXene with minimal toxicity and developed an Nb2CTx/acetylcholinesterase biosensor for the detection of sulfoxide, as shown in Figure 7. This biosensor showed enhanced enzymatic function and electron transfer capabilities compared to those of V2C and Ti3C2 MXene biosensors.
Wu et al. [101] also employed Ti3C2 MXene to attach tyrosinase, enabling direct electron transfer for sensitive and quick detection of phenols. The biosensor they created demonstrated outstanding analytical capabilities over a wide linear range (0.05–15.5 μM) with detection limits as low as 12 nM, proving the potential of Ti3C2 MXene as a stable and sensitive phenolic biosensor. In recent years, there has been significant exploration into enzyme electrochemical biosensors that offer enhanced efficiency and substrate specificity under mild conditions (refer to Table 1 [103]). Wu et al. [104] created a mixed PLL/Ti3C2/glucose oxidase biosensor for measuring glucose levels, as presented in Figure 8. This biosensor employs Ti3C2 MXene to speed up the decomposition of H2O2 produced during the oxidation of glucose, thus initiating a series of reactions. Furthermore, the Ti3C2–PLL–GOx nanosheets are deposited on GCE to build a biosensor, which detects the glucose effectively up to 2.6 μM.
These examples highlight MXenes as promising materials for developing advanced enzymatic biosensors with enhanced performance characteristics, including sensitivity, stability, and electron transfer efficiency. Continued research in this area could further expand the scope of MXene-based biosensing device applications in biomedical diagnostics, environmental monitoring, and food safety. The schematic illustration shows the enzymatic inhibition process for detecting sulfoxide using the HF-free Nb2CTx/AChE biosensor, where acetylcholinesterase (AChE) is immobilized on Nb2CTx MXene. This biosensor demonstrates enhanced enzymatic activity and electron transfer compared to other MXene counterparts [102]. These figures highlight the innovative use of MXenes in biosensing applications, showcasing their role in enhancing sensitivity and performance in detecting specific analytes.
5.3. Optical Biosensors
Optical biosensors are label-free, tiny, and compact yet powerful portable analytical devices that enable rapid, real-time, and simultaneous detection of multiple analytes (biological and chemical molecules) with high sensitivity, selectivity, and cost-effectiveness. They provide information about a biological interaction via a label-free method via the changes in optical properties of the material near the surface, such as refractive index and polarization, leading to a measurable signal without the need for an external label. Generally, surface-enhanced plasmon resonance (SEPR), interferometry, and waveguide-based biosensors fall into the category of optical biosensors. The research and development of optical biosensors have witnessed exponential growth recently in the fields of healthcare, environment, and biotechnology. The optical biosensors not only provide valuable information on the concentration of the analyte but also shed light on their biochemical interaction at the molecular level with negligible signal-to-noise ratio. Optical biosensors contain a biorecognition element integrated with an optical transducer, which together generates a signal in response to a specific reaction. The biological materials including enzymes, antibodies, antigens, receptors, nucleic acids, cells, and tissues are well-known biorecognition elements that are frequently used in optical biosensors. The analyte is accurately detected in real time with minimal false positive results due to the specific affinity of the biorecognition elements toward the target analyte, facilitating immediate analysis of biological processes for rapid decision-making in medical and research contexts. With their versatility across various biological and chemical sensing tasks, long-term stability, and compatibility with automation, optical biosensors represent a critical technology driving advancements in biomedicine, environmental science, and beyond.
The integration of nanotechnology with advanced materials like MXenes has propelled optical biosensors to new levels of performance and versatility. MXenes, as two-dimensional materials derived from transition metal carbides, nitrides, or carbonitrides, offer several distinct advantages such as high surface area (98 m2 g−1), superior electrical conductivity (2400 S cm−1), biocompatibility, hydrophilicity, and chemical stability in sulfuric acid solution (pH = 1) that enhance the capabilities of optical biosensors. These distinctive properties enable the efficient detection of biomolecules even at extremely low concentrations, which is critical for medical diagnostics and environmental monitoring. The high surface area of MXenes can increase the sensitivity of the biosensor due to the larger number of active sites available for analytes to bind. Moreover, MXenes can be tailored to exhibit tunable optical properties such as absorbance and fluorescence. Owing to their excellent biocompatibility, the MXenes are seamlessly used with biological systems without harming living organisms or clinical samples. Their mechanical robustness and stability under diverse environmental conditions further contribute to the reliability and longevity of biosensing devices.
Alsaif et al. [106] recently reported a refractive index-based biosensor for the detection of malaria. The sensor comprises a meta-surface with four equal quadrants at the center coated with MXene material and a square-shaped ring resonator coated with black phosphorus which are grown on the surface of the SiO2 substrate. At optimum conditions, the sensors exhibit high sensitivity up to 600 GHz/RIU and a low detection limit of 0.109 RIU for malaria. Pisani [4] designed a biosensor consisting of a gold nanoparticle (AuNPs)-functionalized Ti3C2Tx MXene (AuNPs@Ti3C2Tx) for detecting human IgG. To detect human IgG, the antihuman IgG was first coated on the AuNPs@Ti3C2Tx. In the presence of the human IgG, the antibody-antigen interaction takes place which results in the formation of AuNPs clusters. This Au aggregation-induced nanoplatform detects human IgG up to the ≈0.1 ng/mL limit. Singh et al. [107] reported a surface plasmon resonance (SPR)-based biosensor for sensing and differentiating among healthy and tumorous brain tissues based on their unique refractive index (RI). Surface plasmons are generated via Au particles’ coating on the rectangular open channel (ROC) part of the biosensor. Afterward, a thin Ti3C2Tx–MXene coated over Au particles, and an additional TiO2 layer, was placed on the ROC surface to hold the Au particles firmly. The sensitivity values for the gray matter, cerebrospinal fluid, and oligodendroglioma are observed to be 12,352.94, 2030.45, and 672.26 nm/refractive index unit (RIU), respectively. While for the tumorous tissues such as glioblastoma, lymphoma, and metastasis, the sensitivities are 800, 774.9, and 643.26 nm/RIU, respectively. The SPR-based biosensor worked well for the 1.25 × 10−4 to 8.09 × 10−6 RIU ranges with the maximum figure of merit (FoM) of 126.05 RIU−1. Sun et al. [108] investigated the performance of the hybrid dual-mode ratiometric biosensor by combining the fluorescent zinc(II) meso-tetra(4-carboxyphenyl) porphyrin (ZnTCPP) and electro-chemiluminescent Ti3C2Tx MXene (see Figure 9). The fluorescence of the ZnTCPP is quenched by the Ti3C2Tx MXene via energy transfer. Through the ZnTCPP/Ti3C2Tx system, the biosensing of alkaline phosphatase (ALP) is carried out through ALP-catalyzed (PO4)3− production in the linear detection range of 0.1–50 mU/mL and detection limit of 0.0083 mU/mL.
Lang et al. [109] developed a WaveFlex optical fiber biosensor for the detection of Xanthine using a tapered structure consisting of AuNPs and MQDs. The presence of the AuNPs and MQDs generated the SPR to enhance the detection sensitivity. Xanthine was detected via coating xanthine oxidase enzyme on the surface of optical fiber with a 1.93 nm/mM sensitivity, 0–800 mu M detection range, and 146.11 mu M limit of detection. Hasanah et al. [110] proposed a Kretschmann-configured SPR sensor modified by Ti3C2T2 MXene. Both the Ti3C2T2 MXene layers and terminal functional groups are tuned to obtain optimum parameters of MXene. The optimized sensor consisting of six layers of MXene and F2 as a terminal group showed the highest sensitivity of 150.131 degrees/RIU with an LOD of 0.01633 RIU. Similarly, as shown in Figure 10, the tyramine is also detected by coating AuNPs and MXene on the fiber surface, which provided the enhanced surface area for tyrosinase enzyme mobilization to sense tyramine with 6.96 mu M detection limit and a sensitivity of 0.0385 nm/mu M [111].
Zhang et al. [112] fabricated flexible W-shaped SPR-based optical fiber biosensor for detecting histamine in food. Besides the AuNPs and Nb2CTx MXene, molybdenum disulfide (MoS2) is also coated to improve the surface area and reaction sites. To enhance the specificity, the diamine oxidase (DAO) enzyme is additionally coated on the probe. The proposed biosensor detected histamine in the linear detection range of 0–1000 mu M and achieved 52.5 mu M detection limit along with 4.4 pm/mu M sensitivity [112]. MXene materials have been employed to detect the renal cancer protein biomarkers to diagnose cancer at an early stage. Li et al. [113] designed an optical microfiber coated with Ti3C2 and AuNPs for the sensing of the carbonic anhydrase IX (CAIX) protein and renal cancer cells with 13.8 zM, 0.19 aM detection limit in pure buffer, and 30% serum solution, respectively. A double S-tapered optical fiber biosensor is reported for tyramine detection. The fiber was coated with AuNPs@GO@tyrosinase and AuNPs@Nb2CTx@tyrosinase to detect tyramine with 17 and 34 pm/mu M in the 0–300 mu M tyramine concentrations range [114].
These studies collectively demonstrate the potential of MXene materials as optical biosensors owing to their versatility, sensitivity, and selectivity for biomedical, environmental, and industrial applications. MXenes materials perform a key role in advancing biosensing technologies through their unique properties and diverse applications, resulting in further enhancements in sensor parameters, along with lower detection limits, enhanced stability, and their ability to integrate into portable devices for on-site and point-of-care detection. MXene materials hold promise to expand the scope and effectiveness of biosensing technologies, addressing diverse analytical challenges, and contributing to advancements in health, environmental sustainability, and food safety.
5.4. Fluorescence-Based Biosensors
The fluorescence-based biosensing devices detect the biological interactions via the changes in the fluorescence properties of the fluorophore (fluorescence material) label attached to the analyte or receptor, through well-known methods such as FRET, fluorescent polarization, and dye-based detection. They offer sensitive and selective detection of biological targets by utilizing specific recognition elements such as antibodies or nucleic acids for detecting analytes with minimal interference and high accuracy, beneficial for detecting disease biomarkers, environmental pollutants, and food contamination. The real-time monitoring capabilities of fluorescence biosensors allow rapid analysis. The versatility of fluorescent probes, ranging from organic dyes to advanced nanostructures like quantum dots, further enhances their utility across various applications. These biosensors not only provide quantitative data through measurable fluorescence signals but also support multiplexed detection, allowing simultaneous analysis of multiple targets within a single sample. As technologies advance, integrating fluorescent-based biosensors into portable and miniaturized platforms continues to expand their use in diverse scientific, clinical, and industrial settings, driving innovation in biosensing and analytical chemistry.
MXenes, particularly titanium carbide and titanium nitride derivatives, are promising candidates for fluorescence-based biosensors owing to their exceptional properties such as high surface area, excellent conductivity, and biocompatibility. MXenes can not only serve as a support for immobilizing biomolecules like enzymes or antibodies due to their large surface area and ability to form stable interfaces with biological molecules but also could be integrated with fluorescent probes or quantum dots to enhance signal detection. MXene and fluorophores integration leads to improved sensitivity and signal-to-noise ratios, which is crucial for detecting low analyte concentrations. This technology holds significant potential in medical diagnostics, environmental monitoring, and biotechnology, offering advantages such as high sensitivity, real-time monitoring capabilities, and the potential for multiplexed detection.
5.4.1. Heavy Metal Ion Detection
Numerous MXene-based optical biosensors dealing with the detection of different types of metal ions via different mechanisms have been reported in the literature. Xue et al. [115] reported photoluminescent Ti3C2 MXene quantum dot (MQD) with 10% quantum yield (QY) and their potential application in multicolor cellular imaging probes via RAW264.7 cells and Zn2+ detection. The comparison of synthesis temperatures and the PL of the MQDs because of the Förster resonance energy transfer (FRET) was declined selectively for Zn2+, while the PL was unchanged for other metal ions such as Fe3+, Co2+, Ni2+, Cr2+, Pb2+, Al3+, Cu2+, Mn2+, and Sn2+. Guan et al. [116] prepared N– and P–functionalized Ti3C2 MQDs with the 2.93 nm size and 20.1% PLQY. N/P@MQDs nanoprobes were employed for the Cu2+ ions detection. The PL intensity of the N/P@MQDs was quenched linearly due to FRET upon the addition of Cu2+ ions in the 2–100 μM and 250–5000 μM ranges, providing 2 μM of sensitive Cu2+ detection. Ti3C2Tx MQDs are also used to detect Fe3+ ions [117]. The white, blue, and blue PL are observed from the Ti3C2Tx MQDs prepared by the solvothermal method in dimethyl sulfoxide (DMSO), dimethylformamide (DMF), and ethanol solvents, respectively. The solvent has also affected the PL QY (10.7, 6.9, and 4.1%) and average size (3.3, 2.5, and 1.8) of the Ti3C2Tx MQDs for DMF, ethanol, and DMSO, respectively. Various ions, i.e., Fe3+, Fe2+, Ni2+, Ag+, Al3+, Co2+, Cd2+, Mg2+, Cu2+, Mn2+, Hg2+, Zn2+, and Pb2+, are used to check the metal ion sensitivity of Ti3C2Tx MQDs probe. It was observed that the Ti3C2Tx MQDs probe selectively detects Fe3+ within a good linear relationship range of 5–470 and 510–750 μM and the lowest detection limit of 2 μM. In another study, the Ti3C2Tx MODs are blended with CsPbBr3 halide perovskite to form a nanocomposite-based turn-on sensor for Cd2+ detection. The PL intensity of the CsPbBr3 has been significantly reduced by Ti3C2Tx MQDs due to the charge transfer from CsPbBr3 to Ti3C2Tx MQDs. The quenched PL was then linearly recovered with the addition of the Cd2+ ions. The detection range for the Cd2+ ions was estimated to be from 9.9 × 10−5 to 5.9 × 10−4 [118]. Yang et al. [119] reported niobium-based Nb2C MQDs for the detection of Fe3+ ions (Figure 11). The PL intensity of the Nb2C MQDs probe quenched linearly (static quenching) with an increase in the Fe3+ concentration from 0 to 300 μM, which is attributed to the coordination interaction between the –OH and –COOH surface group present on the Nb2C MQDs and Fe3+ ions. The Nb2C MQDs probes detect the Fe3+ ions as low as 0.89 μM.
Desai et al. [120] reported a sensing platform comprising Ti3C2 nanosheets for the detection of Ag+ and Mn2+ ions based on PL quenching of Ti3C2. The hydroxyl– and fluorine–terminated Ti3C2 nanosheet shows affinity toward Ag+ and Mn2+ ions, which enables the selective sensing of Ag+ and Mn2+ ions even from the mixture of other competitive metal ions in environmental and food (tomato, rice, and spinach) samples (Figure 12). The Ti3C2 nanosheets-based nanoplatform detected the Ag+ and Mn2+ ions with the detection limit as low as 9.7 and 102 nM within the linear range of 0.1–40 µM and 0.5–60 µM, respectively.
Zhang et al. [121] utilized Ti3C2 MQDs of 1.75 nm particles with 7.7% quantum yield for the detection of Fe3+. The PL quenching in Ti3C2 MQDs was observed because of the IFE as a result of the redox reaction between Ti3C2 MQDs and Fe3+. Fe3+ can be effectively detected up to 310 nM limit in the 5–1000 μM range.
5.4.2. Detection of Biomolecules
MXene nanomaterials are widely used for the detection of biomolecules for clinical diagnostics in the field of biomedical research, where precise detection of biomolecules is essential. Xu et al. [122] prepared Ti3C2 MQDs using the hydrothermal method for the detection of intracellular glutathione (GSH), an important health and disease biomarker. The Ti3C2 MQDs produced a blue emission that arises from the surface defects and quantum confinement effects. In the presence of the GSH, the FRET occurs from Ti3C2 MQDs to GSH for the wide concentration range (1–100 μM), thereby detecting GSH up to 0.02 μM. Shi et al. [123] reported another method to detect GSH using luminescent Cu nanocrystals (NCs) functionalized Ti3C2Tx MXene flakes. The blue emission of Ti3C2Tx MXene flakes was quenched by the neighboring Cu NCs through IEF. When the GSH is added to the MXenes–Cu NCs composite, the quenched emission is recovered due to the specific interaction that took place between glutathione and Ti3C2Tx MXene flakes. The Ti3C2 MQDs (∼4.2 nm in size) are also used as a probe for detecting alkaline phosphatase (ALP) activity and embryonic stem cell (ESC) recognition. Owing to the overlap between the emission of Ti3C2 MQDs and the absorption spectrum of p-nitrophenol (p-NP), the energy transfers from Ti3C2 MQDs to p-NPs via IFE and detect ALP activity with 0.02 U L−1 limit of detection [124]. Liu et al. [125] investigated the Ti3C2 MQDs for cytochrome c detection. The deprotonated Ti3C2 MQDs exhibit a blue emission around 415 nm with a quantum yield of 22%. In the presence of cytochrome c, the intensity of the Ti3C2 MQDs has declined linearly in the 0.2–40 μM range with a 20.5 nM detection limit because of the IFE.
To detect an important biomarker, i.e., uric acid, GSH-functionalized Ti3C2 MQDs are also reported. In a process, the uric acid was first oxidized using uricase enzyme into the allantoin and hydrogen peroxide, whereas o-Phenylenediamine (OPD) was oxidized to the yellow-colored 2,3-diaminophenazine (oxOPD) by using horseradish peroxidase enzyme. The GSH@Ti3C2 MQDs emit effectively at 425 nm while the oxOPD product produces 568 nm emission. When the uric acid is added to the GSH@Ti3C2 MQDs and oxOPD composite, FRET takes place between them causing the 425 nm of GSH@Ti3C2 MQDs to decrease and 568 nm of oxOPD to increase. Using this nanoplatform, the uric acid is detected in the linear range of 1.2–75 μM and a detection limit of 125 nM [126]. In another study by Zhu et al. [127], Ti3C2 nanosheets were integrated into FRET-based assays with rhodamine B (RhB)-labeled phospholipids for detecting phospholipase D, achieving a sensitivity of 0.10 UL−1. The emission of the Ti3C2 nanosheets is strongly quenched by the neighboring RhB via FRET. Phospholipase D activity cleaves phospholipids, causing the RhB-labeled phospholipids to detach from Ti3C2 MXenes, which results in fluorescence recovery. Zhu et al. [128] developed a glucose sensor by using Ti3C2 MXene nanosheets and red-emitting carbon dots (RCDs). Ti3C2 nanosheets passively quenched the fluorescence intensity of RCDs (>96%) through the IFE, which is recovered upon interaction with glucose. Glucose oxidase catalyzed the oxidation of glucose along with the generation of H2O2, which further oxidized Ti3C2 to Ti (OH)4, thereby inhibiting IFE from RCDs to Ti3C2 MXene nanosheets. As presented in Figure 13, the Ti3C2 MXene-based sensor was also used to detect human papillomavirus HPV-18 DNA [129]. The dye-coated single-stranded (ss) DNA was used as a probe. When the Ti3C2 nanosheets were coated on the dye@ssDNA probe, the fluorescence of the dye was quenched, whereas the hybridization of the dye-labeled ssDNA with complementary target DNA forms dsDNA, which is responsible for the fluorescence recovery, thereby achieving a low detection limit up to 100 pM. Ti3C2 MXenes have been explored in complex biological interactions.
Wang et al. [130] investigated the use of chimeric peptide-coated Ti3C2 MXenes in FRET assays involving histone deacetylase sirtuin-1 and protein phosphatase 2C, showcasing their role in studying molecular interactions critical for understanding cellular mechanisms and disease pathways. Moreover, Ti3C2 nanosheets have been utilized in pathogen detection, for example, Hong et al. [131] developed a FRET-based sensitive assay for Vibrio parahaemolyticus (VP) detection by using aptamer-modified polyhedral oligomeric silsesquioxane-perovskite quantum dots (POSS-PQDs@Apt) as probe and Ti3C2 nanosheets as quencher. When the VP is added to the POSS-PQDs@Apt@Ti3C2 composite, the aptamer selectively binds to the VP leaving behind the Ti3C2 nanosheets due to the higher binding affinity of the aptamers toward VP than Ti3C2 nanosheets, which has recovered the quenched POSS-PQDs@Apt emission, thereby achieving a detection limit as low as 30 cfu mL−1 in the concentration range of 102–106 cfu/mL. For detecting the endotoxins and Escherichia coli pathogens, the Ti3C2Tx MXenes were integrated with CRISPR–Cas12a by Sheng et al. [132]. The aptamer used in this study initiated the trans-cleavage activity of CRISPR–Cas12a, resulting in the separation of Ti3C2Tx MXenes from ssDNA to recover the fluorescence, achieving detection limits of 11 pg mL−1 for endotoxins and 23 CFU mL−1 for E. coli. Kalkal et al. [21] reported an Ag/Ti3C2 composite-based sensor for detecting neuron-specific enolase (Figure 14). In this system, the fluorescence signal from antibody/amino-graphene quantum dots conjugated with Ag/Ti3C2 is quenched. Upon introduction of the antigen, the fluorescence signal is recovered, demonstrating the capability of the biosensor for sensitive and reproducible detection of neuron-specific enolase (NSE) with 0.05 pg mL−1 limit of detection in the 0.0001–1500 ng mL−1 detection range. Furthermore, the dual-signal-labeled DNA-functionalized Ti3C2 MXene nanoprobes were reported for the simultaneous analysis of MUC 1 and miRNA-21 at low concentrations in vitro, alongside in situ imaging of MCF-7 breast cancer cells by Wang et al. [133]. This dual-labeling strategy not only enhances the sensitivity of detection but also enables spatial imaging of biomarkers within cells, providing valuable insights into their expression levels and distribution.
5.4.3. Detection of Miscellaneous Compounds
MXene materials are utilized for the detection of different compounds, for example, Wang et al. [134] prepared uric acid (UA)-functionalized Ti3C2 MQDs for detecting 2,4,6-trinitrophenol (TNP). The emission of Ti3C2 MQDs is transferred to the TNP via IFE for a wide linear range of 0.01–40 μM and a detection limit is calculated to be 9.58 nM. The authors employed the UA@Ti3C2 MQDs for detecting TNP in water as well as on surfaces through a smartphone-based calorimetric method achieving a linear detection range of 10 to 100 ng. MXene materials are also used for detecting an important indicator used for monitoring numerous diseases and cellular health, i.e., intracellular pH. Chen et al. [135] developed a ratiometric fluorescence sensor by combining pH-sensitive polyethyleneimine (PEI)-coated Ti3C2 MQDs and pH non-sensitive [Ru(dpp)3]Cl2 for monitoring the intracellular pH changes. Lu et al. [136] used N-doped Ti3C2 MQDs for the quantitative detection of the H2O2 and xanthine, see Figure 15. The N-doped Ti3C2 MQDs were functionalized with 2,3-diaminophenazine (DAP) to build a dual-emissive radiometric sensor for H2O2 via a photoinduced electron transfer from MQDs to DAP. The composite N-Ti3C2@DAP nanoprobe could be used to detect H2O2 and xanthine up to 0.57 μM and 0.34 μM limit, respectively.
Similarly, the N-fluorescent Ti3C2 MQDs have also been employed in the detection of chromium (Cr(VI)) and ascorbic acid (AA) [137]. Figure 16 illustrates the synthesis procedure for the fluorescent N-Ti3C2 MQDs and the method of detection. The fluorescence of N-Ti3C2 MQDs is quenched (turn off) by the Cr(VI) via IEF and static quenching, while the addition of AA resulted in the redox reaction between the AA and Cr(VI), which is responsible for the recovery (turn on) of the N-Ti3C2 MQDs fluorescence. The N-Ti3C2 MQDs-based nanoplatform performed well in the linear detection range of 0.1–500 μM for both the AA and Cr(VI), with the detection limit of 0.012 (Cr(VI)) and 0.02 μM (AA). Ti3C2 MQDs have also been used for the detection of curcumin and hypochlorite (ClO−). The Ti3C2 MQDs emission effectively overlaps with the curcumin absorption, causing FRET and thus emission quenching. In the presence of the ClO−, curcumin is oxidized to the quinones and recovers the Ti3C2 MQDs fluorescence. The Ti3C2 MQDs probe was found to show a linear relationship in the 0.05–10 and 25–275 μM range as well as the detection limit of 20 nM and 5 μM, for curcumin and ClO−, respectively [138].
These studies underscore MXenes’ versatility and effectiveness in fluorescence–based biosensing applications, offering robust platforms for detecting enzymes, nucleic acids, and biomolecules with high sensitivity and specificity. The integration of MXenes into optical sensors enhances their capabilities in biomedical research, diagnostics, and cellular imaging, paving the way for advanced analytical techniques in biological and clinical sciences.
5.4.1. Heavy Metal Ion Detection
Numerous MXene-based optical biosensors dealing with the detection of different types of metal ions via different mechanisms have been reported in the literature. Xue et al. [115] reported photoluminescent Ti3C2 MXene quantum dot (MQD) with 10% quantum yield (QY) and their potential application in multicolor cellular imaging probes via RAW264.7 cells and Zn2+ detection. The comparison of synthesis temperatures and the PL of the MQDs because of the Förster resonance energy transfer (FRET) was declined selectively for Zn2+, while the PL was unchanged for other metal ions such as Fe3+, Co2+, Ni2+, Cr2+, Pb2+, Al3+, Cu2+, Mn2+, and Sn2+. Guan et al. [116] prepared N– and P–functionalized Ti3C2 MQDs with the 2.93 nm size and 20.1% PLQY. N/P@MQDs nanoprobes were employed for the Cu2+ ions detection. The PL intensity of the N/P@MQDs was quenched linearly due to FRET upon the addition of Cu2+ ions in the 2–100 μM and 250–5000 μM ranges, providing 2 μM of sensitive Cu2+ detection. Ti3C2Tx MQDs are also used to detect Fe3+ ions [117]. The white, blue, and blue PL are observed from the Ti3C2Tx MQDs prepared by the solvothermal method in dimethyl sulfoxide (DMSO), dimethylformamide (DMF), and ethanol solvents, respectively. The solvent has also affected the PL QY (10.7, 6.9, and 4.1%) and average size (3.3, 2.5, and 1.8) of the Ti3C2Tx MQDs for DMF, ethanol, and DMSO, respectively. Various ions, i.e., Fe3+, Fe2+, Ni2+, Ag+, Al3+, Co2+, Cd2+, Mg2+, Cu2+, Mn2+, Hg2+, Zn2+, and Pb2+, are used to check the metal ion sensitivity of Ti3C2Tx MQDs probe. It was observed that the Ti3C2Tx MQDs probe selectively detects Fe3+ within a good linear relationship range of 5–470 and 510–750 μM and the lowest detection limit of 2 μM. In another study, the Ti3C2Tx MODs are blended with CsPbBr3 halide perovskite to form a nanocomposite-based turn-on sensor for Cd2+ detection. The PL intensity of the CsPbBr3 has been significantly reduced by Ti3C2Tx MQDs due to the charge transfer from CsPbBr3 to Ti3C2Tx MQDs. The quenched PL was then linearly recovered with the addition of the Cd2+ ions. The detection range for the Cd2+ ions was estimated to be from 9.9 × 10−5 to 5.9 × 10−4 [118]. Yang et al. [119] reported niobium-based Nb2C MQDs for the detection of Fe3+ ions (Figure 11). The PL intensity of the Nb2C MQDs probe quenched linearly (static quenching) with an increase in the Fe3+ concentration from 0 to 300 μM, which is attributed to the coordination interaction between the –OH and –COOH surface group present on the Nb2C MQDs and Fe3+ ions. The Nb2C MQDs probes detect the Fe3+ ions as low as 0.89 μM.
Desai et al. [120] reported a sensing platform comprising Ti3C2 nanosheets for the detection of Ag+ and Mn2+ ions based on PL quenching of Ti3C2. The hydroxyl– and fluorine–terminated Ti3C2 nanosheet shows affinity toward Ag+ and Mn2+ ions, which enables the selective sensing of Ag+ and Mn2+ ions even from the mixture of other competitive metal ions in environmental and food (tomato, rice, and spinach) samples (Figure 12). The Ti3C2 nanosheets-based nanoplatform detected the Ag+ and Mn2+ ions with the detection limit as low as 9.7 and 102 nM within the linear range of 0.1–40 µM and 0.5–60 µM, respectively.
Zhang et al. [121] utilized Ti3C2 MQDs of 1.75 nm particles with 7.7% quantum yield for the detection of Fe3+. The PL quenching in Ti3C2 MQDs was observed because of the IFE as a result of the redox reaction between Ti3C2 MQDs and Fe3+. Fe3+ can be effectively detected up to 310 nM limit in the 5–1000 μM range.
5.4.2. Detection of Biomolecules
MXene nanomaterials are widely used for the detection of biomolecules for clinical diagnostics in the field of biomedical research, where precise detection of biomolecules is essential. Xu et al. [122] prepared Ti3C2 MQDs using the hydrothermal method for the detection of intracellular glutathione (GSH), an important health and disease biomarker. The Ti3C2 MQDs produced a blue emission that arises from the surface defects and quantum confinement effects. In the presence of the GSH, the FRET occurs from Ti3C2 MQDs to GSH for the wide concentration range (1–100 μM), thereby detecting GSH up to 0.02 μM. Shi et al. [123] reported another method to detect GSH using luminescent Cu nanocrystals (NCs) functionalized Ti3C2Tx MXene flakes. The blue emission of Ti3C2Tx MXene flakes was quenched by the neighboring Cu NCs through IEF. When the GSH is added to the MXenes–Cu NCs composite, the quenched emission is recovered due to the specific interaction that took place between glutathione and Ti3C2Tx MXene flakes. The Ti3C2 MQDs (∼4.2 nm in size) are also used as a probe for detecting alkaline phosphatase (ALP) activity and embryonic stem cell (ESC) recognition. Owing to the overlap between the emission of Ti3C2 MQDs and the absorption spectrum of p-nitrophenol (p-NP), the energy transfers from Ti3C2 MQDs to p-NPs via IFE and detect ALP activity with 0.02 U L−1 limit of detection [124]. Liu et al. [125] investigated the Ti3C2 MQDs for cytochrome c detection. The deprotonated Ti3C2 MQDs exhibit a blue emission around 415 nm with a quantum yield of 22%. In the presence of cytochrome c, the intensity of the Ti3C2 MQDs has declined linearly in the 0.2–40 μM range with a 20.5 nM detection limit because of the IFE.
To detect an important biomarker, i.e., uric acid, GSH-functionalized Ti3C2 MQDs are also reported. In a process, the uric acid was first oxidized using uricase enzyme into the allantoin and hydrogen peroxide, whereas o-Phenylenediamine (OPD) was oxidized to the yellow-colored 2,3-diaminophenazine (oxOPD) by using horseradish peroxidase enzyme. The GSH@Ti3C2 MQDs emit effectively at 425 nm while the oxOPD product produces 568 nm emission. When the uric acid is added to the GSH@Ti3C2 MQDs and oxOPD composite, FRET takes place between them causing the 425 nm of GSH@Ti3C2 MQDs to decrease and 568 nm of oxOPD to increase. Using this nanoplatform, the uric acid is detected in the linear range of 1.2–75 μM and a detection limit of 125 nM [126]. In another study by Zhu et al. [127], Ti3C2 nanosheets were integrated into FRET-based assays with rhodamine B (RhB)-labeled phospholipids for detecting phospholipase D, achieving a sensitivity of 0.10 UL−1. The emission of the Ti3C2 nanosheets is strongly quenched by the neighboring RhB via FRET. Phospholipase D activity cleaves phospholipids, causing the RhB-labeled phospholipids to detach from Ti3C2 MXenes, which results in fluorescence recovery. Zhu et al. [128] developed a glucose sensor by using Ti3C2 MXene nanosheets and red-emitting carbon dots (RCDs). Ti3C2 nanosheets passively quenched the fluorescence intensity of RCDs (>96%) through the IFE, which is recovered upon interaction with glucose. Glucose oxidase catalyzed the oxidation of glucose along with the generation of H2O2, which further oxidized Ti3C2 to Ti (OH)4, thereby inhibiting IFE from RCDs to Ti3C2 MXene nanosheets. As presented in Figure 13, the Ti3C2 MXene-based sensor was also used to detect human papillomavirus HPV-18 DNA [129]. The dye-coated single-stranded (ss) DNA was used as a probe. When the Ti3C2 nanosheets were coated on the dye@ssDNA probe, the fluorescence of the dye was quenched, whereas the hybridization of the dye-labeled ssDNA with complementary target DNA forms dsDNA, which is responsible for the fluorescence recovery, thereby achieving a low detection limit up to 100 pM. Ti3C2 MXenes have been explored in complex biological interactions.
Wang et al. [130] investigated the use of chimeric peptide-coated Ti3C2 MXenes in FRET assays involving histone deacetylase sirtuin-1 and protein phosphatase 2C, showcasing their role in studying molecular interactions critical for understanding cellular mechanisms and disease pathways. Moreover, Ti3C2 nanosheets have been utilized in pathogen detection, for example, Hong et al. [131] developed a FRET-based sensitive assay for Vibrio parahaemolyticus (VP) detection by using aptamer-modified polyhedral oligomeric silsesquioxane-perovskite quantum dots (POSS-PQDs@Apt) as probe and Ti3C2 nanosheets as quencher. When the VP is added to the POSS-PQDs@Apt@Ti3C2 composite, the aptamer selectively binds to the VP leaving behind the Ti3C2 nanosheets due to the higher binding affinity of the aptamers toward VP than Ti3C2 nanosheets, which has recovered the quenched POSS-PQDs@Apt emission, thereby achieving a detection limit as low as 30 cfu mL−1 in the concentration range of 102–106 cfu/mL. For detecting the endotoxins and Escherichia coli pathogens, the Ti3C2Tx MXenes were integrated with CRISPR–Cas12a by Sheng et al. [132]. The aptamer used in this study initiated the trans-cleavage activity of CRISPR–Cas12a, resulting in the separation of Ti3C2Tx MXenes from ssDNA to recover the fluorescence, achieving detection limits of 11 pg mL−1 for endotoxins and 23 CFU mL−1 for E. coli. Kalkal et al. [21] reported an Ag/Ti3C2 composite-based sensor for detecting neuron-specific enolase (Figure 14). In this system, the fluorescence signal from antibody/amino-graphene quantum dots conjugated with Ag/Ti3C2 is quenched. Upon introduction of the antigen, the fluorescence signal is recovered, demonstrating the capability of the biosensor for sensitive and reproducible detection of neuron-specific enolase (NSE) with 0.05 pg mL−1 limit of detection in the 0.0001–1500 ng mL−1 detection range. Furthermore, the dual-signal-labeled DNA-functionalized Ti3C2 MXene nanoprobes were reported for the simultaneous analysis of MUC 1 and miRNA-21 at low concentrations in vitro, alongside in situ imaging of MCF-7 breast cancer cells by Wang et al. [133]. This dual-labeling strategy not only enhances the sensitivity of detection but also enables spatial imaging of biomarkers within cells, providing valuable insights into their expression levels and distribution.
5.4.3. Detection of Miscellaneous Compounds
MXene materials are utilized for the detection of different compounds, for example, Wang et al. [134] prepared uric acid (UA)-functionalized Ti3C2 MQDs for detecting 2,4,6-trinitrophenol (TNP). The emission of Ti3C2 MQDs is transferred to the TNP via IFE for a wide linear range of 0.01–40 μM and a detection limit is calculated to be 9.58 nM. The authors employed the UA@Ti3C2 MQDs for detecting TNP in water as well as on surfaces through a smartphone-based calorimetric method achieving a linear detection range of 10 to 100 ng. MXene materials are also used for detecting an important indicator used for monitoring numerous diseases and cellular health, i.e., intracellular pH. Chen et al. [135] developed a ratiometric fluorescence sensor by combining pH-sensitive polyethyleneimine (PEI)-coated Ti3C2 MQDs and pH non-sensitive [Ru(dpp)3]Cl2 for monitoring the intracellular pH changes. Lu et al. [136] used N-doped Ti3C2 MQDs for the quantitative detection of the H2O2 and xanthine, see Figure 15. The N-doped Ti3C2 MQDs were functionalized with 2,3-diaminophenazine (DAP) to build a dual-emissive radiometric sensor for H2O2 via a photoinduced electron transfer from MQDs to DAP. The composite N-Ti3C2@DAP nanoprobe could be used to detect H2O2 and xanthine up to 0.57 μM and 0.34 μM limit, respectively.
Similarly, the N-fluorescent Ti3C2 MQDs have also been employed in the detection of chromium (Cr(VI)) and ascorbic acid (AA) [137]. Figure 16 illustrates the synthesis procedure for the fluorescent N-Ti3C2 MQDs and the method of detection. The fluorescence of N-Ti3C2 MQDs is quenched (turn off) by the Cr(VI) via IEF and static quenching, while the addition of AA resulted in the redox reaction between the AA and Cr(VI), which is responsible for the recovery (turn on) of the N-Ti3C2 MQDs fluorescence. The N-Ti3C2 MQDs-based nanoplatform performed well in the linear detection range of 0.1–500 μM for both the AA and Cr(VI), with the detection limit of 0.012 (Cr(VI)) and 0.02 μM (AA). Ti3C2 MQDs have also been used for the detection of curcumin and hypochlorite (ClO−). The Ti3C2 MQDs emission effectively overlaps with the curcumin absorption, causing FRET and thus emission quenching. In the presence of the ClO−, curcumin is oxidized to the quinones and recovers the Ti3C2 MQDs fluorescence. The Ti3C2 MQDs probe was found to show a linear relationship in the 0.05–10 and 25–275 μM range as well as the detection limit of 20 nM and 5 μM, for curcumin and ClO−, respectively [138].
These studies underscore MXenes’ versatility and effectiveness in fluorescence–based biosensing applications, offering robust platforms for detecting enzymes, nucleic acids, and biomolecules with high sensitivity and specificity. The integration of MXenes into optical sensors enhances their capabilities in biomedical research, diagnostics, and cellular imaging, paving the way for advanced analytical techniques in biological and clinical sciences.
6. MXene/MOF Composite-Based Biosensors
Metal-organic frameworks (MOFs) are hybrid compounds made from metal ions or clusters (inorganic nodes) and organic linkers. MOFs are crystalline, porous compounds with unique properties, including high surface area, adjustable porosity, and diverse chemical functionalities. These properties open the door for numerous applications, such as chemical detection, energy storage, drug delivery, catalysis, and photocatalysis [139,140,141,142]. Integrating two conductive materials such as MXene and MOFs has enhanced the conductivity and the electrochemical activity due to the swift electron transfer. For instance, anchoring an MOF-based material on MXene layers overcomes the limitations and enhances the poor conductivity and electrochemical surfaces of both materials [143]. Additionally, the incorporation of MXene and MOF provides unique advantages in different applications. These hybrid materials show synergistic effects by combining the unique properties of both materials [143,144,145].
6.1. HIV Detection
Human immunodeficiency virus (HIV) is one of the major global health issues that cause acquired immune deficiency syndrome (AIDS) [146]. According to the literature, every year around 1.3 million people die from HIV/AIDS-related diseases. For that, developing reliable and rapid sensors for early detection is critically important [146,147,148].
In 2021, Yunfei Wang and his colleagues successfully prepared an efficient electrochemical luminescence (ECL) biosensor using Ti3C2Tx/ZIF-8 composite as an ECL emitter to identify HIV-1 protein [149]. By incorporating the imidazole-based-MOF (ZIF-8) into the layered structure of Ti3C2Tx, the resulting nanocomposite exhibits an enhancement in electrical conductivity and excellent ECL response. The synthesis of Ti3C2Tx/ZIF-8 composite involves the addition of polyacrylic acid (PPA) to Ti3C2Tx to avoid MXene sheets agglomeration and to provide a high surface area, enabling ZIF-8 to integrate into an MXene layered structure, leading to rapid electron transfer. The resulting biosensor exhibits high performance, with a low detection limit of 0.3 fM and a linear range between 1 fM and 1 nM, revealing the potential for early and accurate HIV-1 detection [149].
6.2. Tyrosine Detection
Tyrosine is one of the important amino acids that helps to maintain nutritional balance in the human body. It plays a role in regulating emotions and stimulating the neurological system because it is found as a precursor to neurotransmitters such as dopamine and thyroxine [150]. Tyrosine is a key component of proteins found in foods like milk, meat, and soybeans [151]. Low levels of tyrosine can lead to conditions such as depression, hypothyroidism, and dementia, while excessively high concentration can contribute to Parkinson’s disease and hyperthyroidism [152,153]. Accurate and fast detection of tyrosine levels can assist in diagnosing these diseases.
Lu et al. [154] prepared an electrochemical sensor employing MXene/C nanotubes (CNTs)/Cu–MOF materials for tyrosine detection. The synthesis started by incorporating the CNT into Ti–MXene sheets to expand the space between the layers of the composites and prevent the aggregation of the MXene layers, then octahedral Cu–MOF (Cu–BTC) was added to MXene/CNT composite to enhance the porosity and catalytic performance of prepared material. The obtained SEM images of previous materials (Cu–MOF, MXene, MXene/CNT, and MXene/CNT/Cu–MOF) showed an octahedral structure of Cu–MOF (Cu–BTC), while Ti–MXene has an accordion-like multilayer structure. After the addition of CNTs to MXene, the resulting composite shows a loss of its multilayer structure and the Cu–MOF granules coating the MXene/CNT surface. Prepared MXene/CNT/Cu–MOF composite was layered on GCE to detect tyrosine, as seen in Figure 17. The sensor exhibited a broad linear detection range and low limit of detection (LOD) for tyrosine. It also showed remarkable selectivity, stability, and repeatability with a high rate of recovery and efficient detection of tyrosine, making it suitable for practical use.
6.3. Hygromycin B Detection
Aminoglycoside antibiotics (AGs) are antibiotics effective across a broad spectrum of bacterial types, including Gram-positive and Gram-negative bacteria. However, excessive intake of AGs can cause significant harm to the kidneys, brain, and hearing, potentially leading to irreversible organ damage [155,156,157,158]. Although the development of antibiotics over the decades has made AGs less common in modern treatment, they remain a promising area of study due to their potential to address viral and genetic diseases. Detecting aminoglycosides in food quickly and effectively is crucial for ensuring dietary safety and human health. Hygromycin B, an AG with potent antibacterial properties, is commonly used in animal husbandry for the treatment of intestinal nematode infections in pigs and chickens. Like other AGs, Hygromycin B is not metabolized in the body and is excreted in its unchanged molecular form [155,156].
Wang et al. [159] successfully fabricated a molecularly imprinted electrochemical sensor (MIES) with Ti3C2Tx and Cu–MOF (Cu–BTC) to detect hygromycin B in food. The sensor was prepared using the covalently imprinting technology by coating the molecularly imprinted polymers (MIP) film onto the gold electrode surface; during the electropolymerization, the hydroxyl group in the Hygromycin B and the functional monomer (3-theinylboroinc acid) formed the borate ester bond. Cu–MOF was used in the sensor to increase the adhesion surface of the MIP. The electrical conductivity of the fabricated sensor was enhanced by incorporating high-conductivity Ti3C2Tx, which improved the total sensing efficiency of the MIES. Figure 18 shows the preparation procedure of the MIP/Cu–MOF/Ti3C2Tx/GE sensor. This prepared sensor exhibits excellent selectivity with a linear range of 5 × 109–5 × 106 M, stability, and reproducibility, which make it suitable for hygromycin B detection in food with a good recovery rate.
6.4. CD44 Detection
CD44 is a family of cell adhesion molecules found on different cell surfaces and involved in various biological functions, such as division, cell migration, and cell proliferation [160]. Its expression level is directly connected with the metastasis and progression of cancer, with higher levels in more metastatic tumor cells compared to those with less metastatic potential. Therefore, the CD44 expression level serves as a biomarker for malignant diseases’ detection. For that, reliable and effective CD44 monitoring is a critical focus of cancer research [161,162].
In 2022, Lian and his colleagues designed a sandwich-type electrochemical sensor for effective CD44 detection [163]. The platform of the sensor was prepared using platelet membrane (PM)/Au NP/V2C MXene nanosheet (PM/AuNPs/d-V2C)-modified electrode, while methylene blue (MB)/aminated MOF (MB@NH2-Fe–MOF–Zn) was used as the signal indicator. V2C was used to boost the electrical conductivity of the electrode, and the gold nanoparticles (NPs) were then electrodeposited to enhance the electrochemical properties. The electrode’s surface was then enhanced with the incorporation of anti-CD44 antibodies (Ab1) and poly(melamine) (PM), which prevent nonspecific adsorption and enhance the selective capture of CD44 antigen. Additionally, methylene blue (MB)/aminated MOF (MB@NH2–Fe–MOF–Zn) was utilized as a signal indicator to facilitate the covalent binding of anti-CD44 Ab (Ab2) through differential pulse voltammetry (DPV). Figure 19 represents the synthesis and fabrication procedure of an electrochemical immunosensor for the detection of CD44. The resulting sensor exhibits an excellent antifouling capability and biocompatibility, making it highly effective to identify the target protein CD44 and detect CD44-positive cancer cells.
6.5. Arsenic (III) Detection
The high arsenic (As) level in drinking water leads to significant health risks, like skin diseases, keratosis, and bladder and lung cancer [164]. Arsenic exists in two forms, As (III) and As(V), which are found in drinking water, subsurface water, and surface water. Because of the high toxicity of As (III), the World Health Organization (WHO) proposes that the acceptable concentration of As (III) should be 10 μg·L−1 in drinking water and 50 μg·L−1 in surface water. Therefore, developing an accurate, sensitive, and rapid method for detecting arsenic in water is crucial [165,166].
Xiao et al. [165] constructed an electrochemical sensor for arsenic monitoring. The sensor was synthesized hydrothermally by depositing a Fe–MOF on the Ti3C2Tx matrix to prepare the Fe–MOF/MXene electrode, which possesses a large surface area and excellent conductivity. Due to the synergistic interaction between Fe–MOF and MXene, arsenic deposition significantly enhances and boosts the sensing response for As(III) at the Fe–MOF/MXene/GCE interface. This sensing system has achieved high sensitivity and the lowest limit of detection (LOD) at 0.58 ng·L−1, compared to previous methods. Moreover, the prepared sensor was successfully utilized to measure As (III) in real water samples with excellent recovery rates and high repeatability, confirming its practical applicability.
6.6. Dopamine Detection
Dopamine (DA) is a neurotransmitter that is essential in various biological functions, and imbalances in DA levels can lead to neuroendocrine diseases such as tardive dyskinesia, Parkinson’s, and schizophrenia. Therefore, selective and ultrasensitive DA–detecting methods are essential in diagnostic applications [167].
Boruah and his colleague [168] fabricated electrochemical sensor for dopamine detection. They used a simple in situ synthesis method of different MOFs (ZIF–67, MIL–100(Fe), MIL–101(Fe), and HKUST–1) in delaminated Nb4C3Tx sheets. The high affinity of MXene surface for dopamine, which was further enhanced by the addition of MOFs, leads to superior sensing performance. The MOF/Nb4C3Tx composites, particularly those with ZIF–8, demonstrated outstanding performance in selectively detecting small biomolecules like dopamine. Figure 20 shows the fabrication of MOF/Nb4C3Tx composites using different MOFs to detect several biomolecules such as dopamine (DA), ascorbic acid (AA), and uric acid (UA).
6.1. HIV Detection
Human immunodeficiency virus (HIV) is one of the major global health issues that cause acquired immune deficiency syndrome (AIDS) [146]. According to the literature, every year around 1.3 million people die from HIV/AIDS-related diseases. For that, developing reliable and rapid sensors for early detection is critically important [146,147,148].
In 2021, Yunfei Wang and his colleagues successfully prepared an efficient electrochemical luminescence (ECL) biosensor using Ti3C2Tx/ZIF-8 composite as an ECL emitter to identify HIV-1 protein [149]. By incorporating the imidazole-based-MOF (ZIF-8) into the layered structure of Ti3C2Tx, the resulting nanocomposite exhibits an enhancement in electrical conductivity and excellent ECL response. The synthesis of Ti3C2Tx/ZIF-8 composite involves the addition of polyacrylic acid (PPA) to Ti3C2Tx to avoid MXene sheets agglomeration and to provide a high surface area, enabling ZIF-8 to integrate into an MXene layered structure, leading to rapid electron transfer. The resulting biosensor exhibits high performance, with a low detection limit of 0.3 fM and a linear range between 1 fM and 1 nM, revealing the potential for early and accurate HIV-1 detection [149].
6.2. Tyrosine Detection
Tyrosine is one of the important amino acids that helps to maintain nutritional balance in the human body. It plays a role in regulating emotions and stimulating the neurological system because it is found as a precursor to neurotransmitters such as dopamine and thyroxine [150]. Tyrosine is a key component of proteins found in foods like milk, meat, and soybeans [151]. Low levels of tyrosine can lead to conditions such as depression, hypothyroidism, and dementia, while excessively high concentration can contribute to Parkinson’s disease and hyperthyroidism [152,153]. Accurate and fast detection of tyrosine levels can assist in diagnosing these diseases.
Lu et al. [154] prepared an electrochemical sensor employing MXene/C nanotubes (CNTs)/Cu–MOF materials for tyrosine detection. The synthesis started by incorporating the CNT into Ti–MXene sheets to expand the space between the layers of the composites and prevent the aggregation of the MXene layers, then octahedral Cu–MOF (Cu–BTC) was added to MXene/CNT composite to enhance the porosity and catalytic performance of prepared material. The obtained SEM images of previous materials (Cu–MOF, MXene, MXene/CNT, and MXene/CNT/Cu–MOF) showed an octahedral structure of Cu–MOF (Cu–BTC), while Ti–MXene has an accordion-like multilayer structure. After the addition of CNTs to MXene, the resulting composite shows a loss of its multilayer structure and the Cu–MOF granules coating the MXene/CNT surface. Prepared MXene/CNT/Cu–MOF composite was layered on GCE to detect tyrosine, as seen in Figure 17. The sensor exhibited a broad linear detection range and low limit of detection (LOD) for tyrosine. It also showed remarkable selectivity, stability, and repeatability with a high rate of recovery and efficient detection of tyrosine, making it suitable for practical use.
6.3. Hygromycin B Detection
Aminoglycoside antibiotics (AGs) are antibiotics effective across a broad spectrum of bacterial types, including Gram-positive and Gram-negative bacteria. However, excessive intake of AGs can cause significant harm to the kidneys, brain, and hearing, potentially leading to irreversible organ damage [155,156,157,158]. Although the development of antibiotics over the decades has made AGs less common in modern treatment, they remain a promising area of study due to their potential to address viral and genetic diseases. Detecting aminoglycosides in food quickly and effectively is crucial for ensuring dietary safety and human health. Hygromycin B, an AG with potent antibacterial properties, is commonly used in animal husbandry for the treatment of intestinal nematode infections in pigs and chickens. Like other AGs, Hygromycin B is not metabolized in the body and is excreted in its unchanged molecular form [155,156].
Wang et al. [159] successfully fabricated a molecularly imprinted electrochemical sensor (MIES) with Ti3C2Tx and Cu–MOF (Cu–BTC) to detect hygromycin B in food. The sensor was prepared using the covalently imprinting technology by coating the molecularly imprinted polymers (MIP) film onto the gold electrode surface; during the electropolymerization, the hydroxyl group in the Hygromycin B and the functional monomer (3-theinylboroinc acid) formed the borate ester bond. Cu–MOF was used in the sensor to increase the adhesion surface of the MIP. The electrical conductivity of the fabricated sensor was enhanced by incorporating high-conductivity Ti3C2Tx, which improved the total sensing efficiency of the MIES. Figure 18 shows the preparation procedure of the MIP/Cu–MOF/Ti3C2Tx/GE sensor. This prepared sensor exhibits excellent selectivity with a linear range of 5 × 109–5 × 106 M, stability, and reproducibility, which make it suitable for hygromycin B detection in food with a good recovery rate.
6.4. CD44 Detection
CD44 is a family of cell adhesion molecules found on different cell surfaces and involved in various biological functions, such as division, cell migration, and cell proliferation [160]. Its expression level is directly connected with the metastasis and progression of cancer, with higher levels in more metastatic tumor cells compared to those with less metastatic potential. Therefore, the CD44 expression level serves as a biomarker for malignant diseases’ detection. For that, reliable and effective CD44 monitoring is a critical focus of cancer research [161,162].
In 2022, Lian and his colleagues designed a sandwich-type electrochemical sensor for effective CD44 detection [163]. The platform of the sensor was prepared using platelet membrane (PM)/Au NP/V2C MXene nanosheet (PM/AuNPs/d-V2C)-modified electrode, while methylene blue (MB)/aminated MOF (MB@NH2-Fe–MOF–Zn) was used as the signal indicator. V2C was used to boost the electrical conductivity of the electrode, and the gold nanoparticles (NPs) were then electrodeposited to enhance the electrochemical properties. The electrode’s surface was then enhanced with the incorporation of anti-CD44 antibodies (Ab1) and poly(melamine) (PM), which prevent nonspecific adsorption and enhance the selective capture of CD44 antigen. Additionally, methylene blue (MB)/aminated MOF (MB@NH2–Fe–MOF–Zn) was utilized as a signal indicator to facilitate the covalent binding of anti-CD44 Ab (Ab2) through differential pulse voltammetry (DPV). Figure 19 represents the synthesis and fabrication procedure of an electrochemical immunosensor for the detection of CD44. The resulting sensor exhibits an excellent antifouling capability and biocompatibility, making it highly effective to identify the target protein CD44 and detect CD44-positive cancer cells.
6.5. Arsenic (III) Detection
The high arsenic (As) level in drinking water leads to significant health risks, like skin diseases, keratosis, and bladder and lung cancer [164]. Arsenic exists in two forms, As (III) and As(V), which are found in drinking water, subsurface water, and surface water. Because of the high toxicity of As (III), the World Health Organization (WHO) proposes that the acceptable concentration of As (III) should be 10 μg·L−1 in drinking water and 50 μg·L−1 in surface water. Therefore, developing an accurate, sensitive, and rapid method for detecting arsenic in water is crucial [165,166].
Xiao et al. [165] constructed an electrochemical sensor for arsenic monitoring. The sensor was synthesized hydrothermally by depositing a Fe–MOF on the Ti3C2Tx matrix to prepare the Fe–MOF/MXene electrode, which possesses a large surface area and excellent conductivity. Due to the synergistic interaction between Fe–MOF and MXene, arsenic deposition significantly enhances and boosts the sensing response for As(III) at the Fe–MOF/MXene/GCE interface. This sensing system has achieved high sensitivity and the lowest limit of detection (LOD) at 0.58 ng·L−1, compared to previous methods. Moreover, the prepared sensor was successfully utilized to measure As (III) in real water samples with excellent recovery rates and high repeatability, confirming its practical applicability.
6.6. Dopamine Detection
Dopamine (DA) is a neurotransmitter that is essential in various biological functions, and imbalances in DA levels can lead to neuroendocrine diseases such as tardive dyskinesia, Parkinson’s, and schizophrenia. Therefore, selective and ultrasensitive DA–detecting methods are essential in diagnostic applications [167].
Boruah and his colleague [168] fabricated electrochemical sensor for dopamine detection. They used a simple in situ synthesis method of different MOFs (ZIF–67, MIL–100(Fe), MIL–101(Fe), and HKUST–1) in delaminated Nb4C3Tx sheets. The high affinity of MXene surface for dopamine, which was further enhanced by the addition of MOFs, leads to superior sensing performance. The MOF/Nb4C3Tx composites, particularly those with ZIF–8, demonstrated outstanding performance in selectively detecting small biomolecules like dopamine. Figure 20 shows the fabrication of MOF/Nb4C3Tx composites using different MOFs to detect several biomolecules such as dopamine (DA), ascorbic acid (AA), and uric acid (UA).
7. Conclusions and Future Perspectives
MXenes are currently among the most promising materials for a variety of applications including biosensing and health and environmental monitoring. They exhibit unique physicochemical attributes such as high electrical conductivity, biocompatibility, and tunable surface chemistry, which make them highly suitable for biosensing. In this review, we have reviewed thoroughly the recent advancement of MXene-based biosensors encompassing the systematic discussion on the synthesis and characterizations and highlighting their potential in various applications including electrochemical biosensors, enzymatic biosensors, optical-based, and fluorescence-based biosensors. In addition, the role of MXenes as prospective wearable sensors and for VOC detection in environmental applications has been discussed. Their successful integration into these biosensors has demonstrated improved sensitivity, selectivity, and detection limits for a variety of environmental pollutants and biomarkers. Specifically, the ability of MXenes to improve sensor performance by acting as transducers and facilitating electron transfer has opened new avenues for real-time monitoring in medical diagnostics and environmental applications.
Despite these promising developments, challenges that still need attention are improving the large-scale production, reproducibility, and biocompatibility and reducing toxicity and long-term stability of MXene-based biosensors. While many MXene-based biosensors have shown high performance at the lab scale, their scalability for industrial production remains a great challenge. For large-scale production, existing synthesis methods, especially the HF etching procedure, need to be improved. The reproducibility of these sensors in real-world applications also needs more research to ensure consistency across large batches. Researchers must research alternate manufacturing procedures that are scalable and cost-effective, such as green synthesis approaches, without sacrificing MXenes’ inherent properties. Roll-to-roll processing and 3D printing are examples of automated and repeatable manufacturing processes that could provide viable avenues for increasing the supply of sensors. As these challenges are overcome, MXene-based biosensors are poised to play a crucial role in advancing healthcare diagnostics and environmental monitoring technologies.
MXene-based glucose and dopamine biosensors have demonstrated considerable promise, meeting clinical standards for high sensitivity. However, biosensors for environmental monitoring and cancer biomarker detection are still in the development stage and require further improvements in sensitivity and specificity.
In order to improve the performance of biosensors, research should also concentrate on the creation of hybrid MXene composites with MOFs, QDs, or nanoparticles (NPs). These combinations may result in the study of next-generation biosensors with improved sensitivity, the capacity to detect various analytes, and real-time data transfer. The range of detectable analytes for MXene-based biosensors will be expanded by further research into strategic surface functionalization processes, improving their suitability for environmental monitoring and customized treatment. More research should be focused on resolving the scalability of MXene synthesis for their commercialization. Future developments are needed to investigate the employment of MXene-based sensors for point-of-care (PoC) applications for wearable and portable devices. The continuous and non-invasive diagnostics using wearable biosensors, combined with biosensing technologies, artificial intelligence (AI), and the internet of things (IoT) have the potential to further improve personalized health monitoring. This comprehensive review aims to serve as a valuable resource for researchers and practitioners working in the areas of materials science, biosensing, and environmental monitoring. |
Title: Transforming Diagnostics: A Comprehensive Review of Advances in Digital Pathology | Body:
Introduction and background
The field of digital pathology is at the forefront of a significant transformation in the field of diagnostic medicine. Traditionally, pathology has relied on examining glass slides under a microscope, a practice that has been fundamental to diagnosing diseases for over a century. However, the advent of digital technologies has brought about a paradigm shift, enabling the conversion of glass slides into high-resolution digital images that can be stored, analyzed, and shared electronically [1]. This evolution from analog to digital has profound implications for how pathology is practiced and has opened up new avenues for enhancing diagnostic accuracy, efficiency, and collaboration. Various technological advancements support the shift to digital pathology. Developing whole slide imaging (WSI) systems, which allow for the digitization of glass slides at high resolution, is a key innovation. These digital slides can be easily archived, retrieved, and analyzed using sophisticated image analysis software. The ability to zoom in and out of these digital images and annotate specific areas enhances the precision and depth of analysis, providing pathologists with powerful tools to support their diagnostic decisions [2].
One of the most significant impacts of digital pathology is its potential to improve diagnostic accuracy. Traditional microscopy is subject to inter-observer variability, where different pathologists might interpret the same slide differently. Digital pathology solves this problem by allowing the utilization of computer-aided diagnostic (CAD) tools, which use artificial intelligence (AI) and machine learning (ML) algorithms to help pathologists identify and quantify pathological features. These algorithms can be trained to recognize patterns in tissue samples, such as the presence of tumor cells, which may not be easily discernible through manual examination. By augmenting the pathologist's expertise with AI, digital pathology has the potential to reduce diagnostic errors and ensure more consistent and accurate results [3].
Moreover, digital pathology facilitates remote consultations and telepathology, breaking down geographical barriers and allowing for expert opinions to be sought anywhere in the world. This capability is particularly valuable in regions with limited access to specialized pathologists. Telepathology enables real-time consultations, second opinions, and collaborative case reviews, enhancing patients' quality of care regardless of location. The ability to share digital slides easily also supports educational initiatives, enabling the dissemination of knowledge and the training of future pathologists in a more interactive and accessible manner [4]. In addition to enhancing diagnostic accuracy and collaboration, digital pathology plays an increasingly important role in research. The digitization of slides enables large-scale data collection and analysis, which is essential for developing new diagnostic markers and understanding disease mechanisms. Integrating digital pathology with other omics technologies, such as genomics and proteomics, leads to new insights into the molecular underpinnings of diseases, paving the way for personalized medicine. By correlating pathological findings with genetic data, researchers can identify specific biomarkers that can guide treatment decisions and improve patient outcomes [5].
However, the widespread adoption of digital pathology is not without challenges. The transition requires significant investment in IT infrastructure, including the implementation of secure data storage systems and high-speed networks to handle the large file sizes associated with digital slides [6]. Standardizing image formats and data integration protocols is also critical to ensure interoperability between different systems and institutions. Additionally, there are regulatory considerations, as the use of digital pathology for primary diagnosis is still subject to approval by health authorities in many countries [7]. Digital pathology represents a transformative shift in the field of diagnostic medicine. By enhancing diagnostic accuracy, enabling remote consultations, and supporting research initiatives, it promises to improve patient care on a global scale [8]. However, realizing the full potential of digital pathology will require addressing the technical, regulatory, and logistic challenges accompanying its implementation. As these hurdles are overcome, digital pathology is set to play a pivotal role in the future of healthcare, offering new possibilities for innovation in diagnostics and treatment. Digital pathology is rapidly transforming the landscape of diagnostic medicine by integrating cutting-edge technologies with traditional practices. This review explores the field's key advancements, applications, and challenges, highlighting digital pathology's pivotal role in modern healthcare. Figure 1 illustrates digital pathology's impact, highlighting benefits like enhanced diagnostic accuracy and remote consultations. It also outlines challenges such as financial costs, data management, and regulatory approval. Future directions include technological advancements and improved data management, which are crucial for overcoming these challenges and advancing the field.
Figure 1
Digital pathology in revolutionizing diagnostics
Providing high-resolution digital images improves diagnostic precision through AI-driven tools that reduce inter-observer variability and detect subtle patterns. This technology also facilitates remote consultations and global collaboration, bridging gaps in regions with limited access to specialized expertise. However, challenges such as significant financial investment, large data file management, and regulatory approval hurdles must be addressed. Financial constraints involve the costs of whole slide imaging systems and data storage solutions, while data management issues include handling large file sizes and ensuring efficient retrieval. Regulatory approval processes and the need for standardized image formats further complicate adoption. Future advancements in technology, improved data management, and establishing global standards are crucial for overcoming these challenges and fully realizing the potential of digital pathology in transforming healthcare.
Image Credits: Dr. Ghizal Fatima
Review
WSI technology
WSI represents a transformative technology at the heart of digital pathology, fundamentally altering how pathological specimens are examined, stored, and analyzed [9]. Traditionally, pathologists have relied on the microscopic examination of glass slides to diagnose diseases, a method that, while effective, comes with limitations in terms of accessibility, reproducibility, and the ability to share information across different geographical locations. WSI addresses these limitations by converting glass slides into high-resolution digital images, which can be stored, shared, and reviewed electronically, thereby enhancing the diagnostic process in numerous ways. The process of WSI involves scanning an entire tissue specimen on a glass slide to create a comprehensive digital image. This digital slide mimics the traditional glass slide but offers significant advantages. First, the image quality in WSI is often superior to what can be observed through a traditional microscope. The high-resolution nature of these digital images allows for a detailed examination of tissue architecture, cellular morphology, and other critical pathological features. Pathologists can zoom in and out of these images, much like they would with a microscope, but with the added benefit of digital tools that can enhance visualization and highlight specific areas of interest [10].
One of the primary advantages of WSI is its ability to facilitate remote consultations and collaborative diagnoses. In regions with limited access to specialized pathologists, WSI enables local healthcare providers to share digital slides with experts anywhere in the world, allowing for timely and accurate second opinions. This capability not only improves diagnostic accuracy but also speeds up the diagnostic process, which is critical in time-sensitive cases such as different forms of cancer. Additionally, the ease of sharing digital slides supports multidisciplinary team meetings, where specialists from different fields can review the same images simultaneously, leading to more informed and comprehensive patient care decisions. WSI is also essential for advancing research and the development of artificial intelligence (AI) in pathology. The ability to digitize slides creates vast datasets that can be used to train machine learning algorithms. These algorithms can be taught to recognize patterns, identify abnormalities, and even predict disease outcomes based on historical data. The large-scale WSI-generated data is crucial for developing and refining AI tools, which are increasingly integrated into the pathology workflow. These tools assist pathologists by automating routine tasks, such as tissue segmentation and cell counting, and provide diagnostic support through pattern recognition. The combination of WSI and AI has the potential to greatly enhance diagnostic accuracy and efficiency, reducing the workload on pathologists and enabling them to focus on more complex cases [11].
Moreover, WSI supports educational initiatives by allowing students and trainees to access high-quality digital slides from anywhere globally. This accessibility democratizes education, providing learners with the opportunity to study a wide variety of cases and gain experience with rare conditions that they might not encounter in their local practice. However, the adoption of WSI is not without challenges. The technology requires significant investment in scanning equipment, data storage solutions, and IT infrastructure to handle the large file sizes of digital slides. Additionally, there are ongoing concerns about data security and patient privacy, as well as the need for standardization in image formats and integration with existing laboratory information systems. WSI is a cornerstone of digital pathology, offering substantial benefits regarding image quality, remote collaboration, and the development of AI-driven diagnostic tools. As the technology continues to evolve and overcome current challenges, it is poised to play an increasingly vital role in both clinical practice and pathology research, ultimately improving patient outcomes and advancing the field of medicine [12].
Artificial intelligence and machine learning
Integrating AI and ML into digital pathology represents a transformative leap in diagnostic medicine, revolutionizing how pathologists interpret and analyze tissue samples. AI and ML technologies are increasingly being employed to automate routine tasks such as tissue segmentation, cell counting, and the identification of pathological features. These advancements not only enhance the efficiency of diagnostic workflows but also contribute significantly to the accuracy and consistency of diagnoses. One of the key benefits of AI-powered algorithms in pathology is their ability to reduce inter-observer variability, a common issue in traditional pathology. Variability in diagnoses can occur due to differences in individual pathologists' experience and interpretation, leading to inconsistencies in patient care. AI algorithms, trained on large datasets, provide standardized interpretations that help mitigate these discrepancies. For example, studies have demonstrated that AI can match or even surpass human pathologists in identifying certain cancer cells, such as those in breast and prostate cancers, thereby supporting more reliable diagnoses. AI also plays a crucial role in increasing diagnostic accuracy [13]. Machine learning models, particularly deep learning algorithms, have shown remarkable proficiency in identifying subtle patterns in tissue samples that human eyes might overlook. These patterns could include early signs of malignancy or other disease markers crucial for timely and accurate diagnosis of several diseases. By leveraging AI, pathologists can detect these early indicators, leading to more precise diagnoses and better patient outcomes. For instance, AI has been used to improve the detection of lymph node metastases in lung cancer, achieving higher sensitivity and specificity compared to traditional methods [14].
Moreover, AI and ML enable faster turnaround times for pathology reports. Traditional pathological analysis can be time-consuming, particularly in complex cases where multiple features must be examined. AI-driven tools can rapidly process and analyze large volumes of data, significantly reducing the time required to generate diagnostic reports. This is especially beneficial in busy clinical settings where timely diagnosis is critical for patient management [15].
However, the integration of AI into clinical practice is challenging. Deploying AI tools in pathology requires rigorous validation to ensure they are reliable and safe for clinical use. This includes extensive testing across diverse patient populations and tissue types to confirm that the AI algorithms can generalize well and produce accurate results consistently. Furthermore, regulatory approval is necessary before these tools can be widely adopted in clinical settings. Regulatory bodies such as the FDA have begun to establish guidelines for evaluating AI in medical devices, but the path to approval can be complex and time-consuming. Integrating AI and ML in digital pathology holds immense potential to enhance diagnostic accuracy, reduce variability, and improve efficiency in pathology workflows. As these technologies continue to evolve and undergo validation, they are poised to become indispensable tools in the practice of modern pathology, ultimately improving patient outcomes through more precise and timely diagnoses [16].
Telepathology and remote consultations
Telepathology, the practice of diagnosing diseases remotely through the use of digital images, has seen significant advancements with the advent of digital pathology. By leveraging high-resolution digital slides, telepathology enables pathologists to engage in real-time consultations, provide second opinions, and collaborate on complex cases without the constraints of physical location. This technological development has revolutionized the field of pathology, particularly benefiting regions with limited access to specialized medical expertise. One of the primary advantages of telepathology is its ability to bridge the gap between resource-rich and resource-limited areas. In many parts of the world, particularly in rural or underserved regions, access to specialized pathologists is limited or non-existent. Telepathology addresses this challenge by allowing digital slides to be shared instantaneously with experts located in different parts of the country or even the world. This capability ensures that patients in remote areas can receive high-quality diagnostic services, comparable to those available in major urban areas, without the need for expensive and time-consuming travel. The use of telepathology is particularly beneficial in scenarios requiring expert opinions, such as complex or rare cases where the local pathologist may seek a second opinion from a subspecialist [17]. Digital pathology platforms enable these consultations to occur rapidly, often in real-time, which is crucial in situations where prompt diagnosis is essential for effective treatment planning. The ability to quickly and efficiently consult with colleagues enhances the diagnostic process, leading to better patient outcomes in Neuropathologic Intraoperative Consultations [18].
Telepathology also plays a vital role in fostering global collaboration in both research and education. The ease of sharing digital slides across institutions facilitates the exchange of knowledge and expertise on an unprecedented scale. Researchers can collaborate on multicenter studies, pooling data and insights from different populations to achieve more robust and generalizable findings. This global exchange accelerates the pace of scientific discovery and the development of new diagnostic and therapeutic strategies. In education, telepathology offers an invaluable tool for training the next generation of pathologists. Medical students and trainees can access a diverse array of digital slides, including rare and complex cases that they might not encounter in their local practice. This exposure to a broader spectrum of pathology enhances their learning experience and prepares them for a wide range of diagnostic challenges. Telepathology also allows for interactive teaching sessions, where instructors and students can examine and discuss digital slides in real time, regardless of their physical locations [19].
Moreover, telepathology supports the standardization of diagnostic practices across different regions. By facilitating the sharing of digital slides and the exchange of diagnostic criteria, it helps harmonize pathology practices globally, reducing variability in diagnoses and improving the overall quality of care. This standardization is particularly important in the context of clinical trials and research studies, where consistent diagnostic criteria are essential for valid and reliable results. However, the widespread adoption of telepathology comes with certain challenges [20]. These include ensuring the security and privacy of patient data during digital transmission, the need for robust IT infrastructure, and the potential resistance from professionals accustomed to traditional microscopy. Addressing these challenges is crucial for the successful integration of telepathology into routine clinical practice. Telepathology, enhanced by digital pathology, is transforming the field of diagnostic medicine by enabling remote consultations, fostering global collaboration, and supporting educational initiatives. Its ability to connect pathologists across distances ensures that high-quality diagnostic services are accessible to patients everywhere, contributing to more equitable and effective healthcare delivery worldwide [5].
Impact on diagnostic accuracy
Telepathology, the practice of diagnosing diseases remotely through the use of digital images, has seen significant advancements with the advent of digital pathology. By leveraging high-resolution digital slides, telepathology enables pathologists to engage in real-time consultations, provide second opinions, and collaborate on complex cases without the constraints of physical location. This technological development has revolutionized the field of pathology, particularly benefiting regions with limited access to specialized medical expertise. One of the primary advantages of telepathology is its ability to bridge the gap between resource-rich and resource-limited areas. Access to specialized pathologists is limited or non-existent in many parts of the world, particularly in rural or underserved regions. Telepathology addresses this challenge by allowing digital slides to be shared instantaneously with experts located in different parts of the country or even the world. This capability ensures that patients in remote areas can receive high-quality diagnostic services comparable to those available in major urban areas without needing expensive and time-consuming travel. The use of telepathology is particularly beneficial in scenarios requiring expert opinions, such as complex or rare cases where the local pathologist may seek a second opinion from a subspecialist [17]. Digital pathology platforms enable these consultations to occur rapidly, often in real-time, which is crucial in situations where prompt diagnosis is essential for effective treatment planning. The ability to quickly and efficiently consult with colleagues enhances the diagnostic process, leading to better patient outcomes in Neuropathologic Intraoperative Consultations [18].
Telepathology also plays a vital role in fostering global collaboration in both research and education. The ease of sharing digital slides across institutions facilitates the exchange of knowledge and expertise on an unprecedented scale. Researchers can collaborate on multicenter studies, pooling data and insights from different populations to achieve more robust and generalizable findings. This global exchange accelerates the pace of scientific discovery and the development of new diagnostic and therapeutic strategies. In education, telepathology offers an invaluable tool for training the next generation of pathologists. Medical students and trainees can access diverse digital slides, including rare and complex cases they might not encounter in their local practice. This exposure to a broader spectrum of pathology enhances their learning experience and prepares them for a wide range of diagnostic challenges. Telepathology also allows for interactive teaching sessions, where instructors and students can examine and discuss digital slides in real time, regardless of their physical locations [19].
Moreover, telepathology supports the standardization of diagnostic practices across different regions. Sharing digital slides and exchanging diagnostic criteria helps harmonize pathology practices globally, reducing diagnosis variability and improving the overall quality of care. This standardization is particularly important in the context of clinical trials and research studies, where consistent diagnostic criteria are essential for valid and reliable results. However, the widespread adoption of telepathology comes with certain challenges [20]. These include ensuring the security and privacy of patient data during digital transmission, the need for robust IT infrastructure, and the potential resistance from professionals accustomed to traditional microscopy. Addressing these challenges is crucial for successfully integrating telepathology into routine clinical practice. Telepathology, enhanced by digital pathology, is transforming the field of diagnostic medicine by enabling remote consultations, fostering global collaboration, and supporting educational initiatives. Its ability to connect pathologists across distances ensures that high-quality diagnostic services are accessible to patients everywhere, contributing to more equitable and effective healthcare delivery worldwide [5].
Advances in personalized medicine
Digital pathology plays an increasingly pivotal role in advancing personalized medicine by enabling the precise characterization of tissue samples and facilitating a more individualized approach to patient care. Integrating digital pathology with omics technologies such as genomics, proteomics, and metabolomics provides a comprehensive understanding of disease mechanisms, which is crucial for developing targeted therapies. One of the primary ways digital pathology contributes to personalized medicine is by identifying and analyzing specific biomarkers. Digital pathology allows for the detailed examination of tissue samples, enabling pathologists to identify biomarkers indicative of particular disease states or responses to treatment. For instance, digital slides can be used to detect specific molecular markers in cancer tissues, such as hormone receptors or genetic mutations, which are critical for selecting appropriate targeted therapies. This capability is particularly valuable in oncology, where identifying biomarkers can guide the choice of targeted therapies and improve treatment outcomes for patients with cancers like breast, lung, and colorectal cancer [25].
Integrating digital pathology with other omic technologies enhances this process by providing a more holistic view of the disease. Genomics, which involves studying the complete set of DNA in an organism, can identify genetic mutations that contribute to disease development. When combined with digital pathology, genomics data can be mapped onto high-resolution digital images of tissue samples, allowing for the correlation of genetic alterations with specific histopathological features. Proteomics, the study of the proteome, is the entire set of proteins expressed by a genome, and it complements this by revealing how these genetic changes affect protein expression and function. This multi-layered approach helps understand how genetic and molecular alterations drive disease and how they can be targeted with specific treatments.
Moreover, digital pathology supports the development of precision medicine by enabling the stratification of patients based on their molecular profiles. This stratification allows for customized treatment plans according to each patient's disease's specific genetic and molecular characteristics. For example, patients with specific genetic mutations may benefit from targeted therapies designed to inhibit the activity of proteins produced by these mutations, while others may require different treatment approaches based on their unique molecular profiles [26].
Combining digital pathology with omics technologies also facilitates the development of new diagnostic and prognostic markers. By analyzing large datasets generated from digital slides and omics data, researchers can identify novel biomarkers that could be used for early detection, disease monitoring, and treatment response evaluation. This approach enhances the precision of diagnoses and contributes to developing personalized treatment regimens tailored to the individual patient. Digital pathology is crucial in advancing personalized medicine by enabling precise tissue characterization and integrating with omics technologies. This integration allows for a deeper understanding of disease mechanisms and supports the development of targeted therapies, ultimately leading to more individualized and effective patient care.
Advances and current limitations in cytological, histological, and IHC software implementations
Digital pathology has significantly enhanced cytological, histological, and immunohistochemical (IHC) analysis. However, the current software implementation level varies, with several promising developments yet to achieve widespread validation and clinical adoption. In cytology, integrating AI-based algorithms for automated cell detection and classification has shown promise in improving the sensitivity and specificity of diagnoses, particularly in screening programs for cervical and lung cancer [27]. However, fully automated cytological assessments are still in developmental phases, with challenges surrounding the heterogeneity of cytological samples and the need for high levels of manual oversight. Novel approaches using deep learning are currently being explored to refine the detection of subtle cellular abnormalities and reduce false positives, but more robust datasets and validation studies are required before these methods can be routinely applied in clinical settings.
In histology, digital pathology platforms have significantly improved image analysis through whole-slide imaging (WSI). This advancement allows pathologists to perform detailed tissue assessments with tools for measuring tumor margins, quantifying cell proliferation, and analyzing tissue architecture [28]. Despite these capabilities, the full potential of digital histopathology is hindered by the variability in tissue preparation and staining protocols, which can affect image quality and analysis outcomes. New approaches, such as integrating 3D histological reconstructions and multiplex staining, are being developed to address these limitations and provide deeper insights into tissue morphology.
In immunohistochemistry (IHC), digital pathology has enhanced the standardization of marker quantification, particularly for well-established markers like Ki-67 [29]. However, the current state of IHC software implementations is still evolving. While software-based analysis for certain markers has been validated, numerous other IHC markers lack the necessary validation for clinical use. Additionally, current algorithms often struggle with variations in staining intensity and background noise, leading to inconsistent results. Future innovations could focus on integrating AI to improve the accuracy of quantitative IHC analysis and develop universal protocols that standardize the staining and imaging process across laboratories. Looking forward, new approaches such as integrating multi-omics data (e.g., genomics, transcriptomics) with digital pathology platforms can potentially expand the diagnostic capabilities of software-driven analysis. However, these advances are still largely experimental and require significant validation before clinical application. Moreover, the digitization of pathology in resource-limited settings remains a distant goal, with the cost of infrastructure and the lack of trained personnel being major barriers.
Overall, while the technological revolution in digital pathology has introduced innovative methods in cytological, histological, and IHC analysis, the field continues to face challenges in validating, implementing, and standardizing these tools. Ongoing research and collaboration between software developers, pathologists, and regulatory bodies will be critical to advancing these methodologies and realizing the full potential of digital pathology in routine diagnostics.
Challenges and future directions
Despite its numerous benefits, the widespread adoption of digital pathology faces several challenges. The initial investment required for WSI systems, data storage, and IT infrastructure can be significant. Additionally, the large file sizes associated with digital slides require robust data management solutions to ensure efficient storage and retrieval. Standardizing image formats and data interoperability is also crucial for seamless integration across different systems and institutions. Regulatory approval is another hurdle, as health authorities in many countries still evaluate digital pathology use for primary diagnosis. Looking forward, the continued development of AI algorithms, improvements in data management, and the establishment of global standards will be key to overcoming these challenges. As digital pathology becomes more widely adopted, it has the potential to revolutionize the field of diagnostic medicine, offering new possibilities for improving patient care, advancing research, and reducing healthcare disparities [30].
Financial Investment
One of the primary barriers to widespread adoption is the substantial initial investment required for whole WSI systems, data storage solutions, and IT infrastructure. WSI systems, which convert glass slides into high-resolution digital images, are costly and require ongoing maintenance and upgrades. Additionally, the infrastructure needed to store and manage the large volumes of data generated by digital slides involves significant expenditures on servers, data storage solutions, and backup systems. Smaller laboratories and healthcare institutions may find these costs prohibitive, limiting the accessibility of digital pathology technologies [31].
Data Management and Interoperability
The large file sizes associated with digital slides necessitate robust data management solutions to ensure efficient storage, retrieval, and information sharing. Effective data management is critical to prevent data loss, ensure data integrity, and facilitate seamless access to digital slides across different platforms. Moreover, standardizing image formats and data interoperability is essential for integrating digital pathology systems across various institutions and systems. Without standardized protocols, sharing and comparing digital slides between different healthcare providers can be challenging, hindering collaboration and the potential benefits of digital pathology [32].
Regulatory Approval
Regulatory approval is another significant challenge, as health authorities in many countries still evaluate digital pathology's use for primary diagnosis. The process of gaining regulatory approval can be lengthy and complex, involving rigorous validation studies to ensure that digital pathology systems meet the required accuracy, reliability, and safety standards. Until clear guidelines and regulations are established, the adoption of digital pathology for diagnostic purposes may be limited [33].
Future Directions
Addressing these challenges requires ongoing efforts in several key areas. Continued development of AI algorithms is essential for improving diagnostic accuracy and reducing the workload on pathologists. Advances in AI can enhance the automation of routine tasks and support more accurate and efficient diagnoses. Improvements in data management technologies, such as cloud-based storage solutions and advanced data compression techniques, can help manage the large volumes of data generated by digital pathology systems. Establishing global standards for image formats, data interoperability, and regulatory requirements will facilitate the seamless integration of digital pathology across different systems and institutions. While digital pathology holds immense promise for revolutionizing diagnostic medicine, overcoming the challenges related to financial investment, data management, and regulatory approval is crucial for its widespread adoption. By addressing these challenges and continuing to advance technology, digital pathology has the potential to significantly improve patient care, advance research, and reduce healthcare disparities on a global scale.
Conclusions
Digital pathology transforms diagnostics, particularly by enhancing accuracy through high-resolution imaging and AI integration. This technology improves diagnostic precision, reducing inter-observer variability, especially in complex cases like cancer. While advancements in remote consultations and collaborative reviews are bridging geographical gaps, the technology's potential in personalized medicine, such as integrating with other omics, remains in the early stages. For now, validation for immunohistochemical (IHC) markers is limited, with Ki-67 being one of the few in use. Additionally, molecular diagnostics cannot yet be fully realized through software alone. Challenges such as the high cost of whole-slide imaging (WSI) systems, regulatory approval for primary diagnosis, and lack of widespread adoption remain barriers, particularly in resource-limited regions. Despite these hurdles, continued technological improvements and global standardization efforts are essential for digital pathology to reach its full potential and play a key role in advancing healthcare. |
Title: Comprehensive retrospect and future perspective on bacteriophage and cancer | Body:
Introduction
As one of the major threats to global public health security, cancer is currently considered as a genetic disorder caused by both exogenous and endogenous factors. Smoking, alcoholism, high-fat diet, and other bad lifestyle habits are closely related to the incidence of cancer [1–3]. In addition, biological factors, such as virus and bacteria, are also believed to be powerful promoters of the development of cancer especially gastrointestinal cancer [4, 5]. Due to the improvement of medical care and the popularity of early cancer screening, the mortality of melanoma and other cancers has decreased significantly in recent years [6]. However, the incidence of some cancers, including breast and uterine cancer, is on the rise, and the population of colorectal cancer (CRC) patients is becoming younger [6, 7]. Despite the rapid development of current cancer treatment methods, it is still difficult to effectively reduce the high mortality rate of cancer.
As an important member of the human viriome, bacteriophage (phage) has the effect of invading bacteria. The gut is rich in phages, about 30 billions of which enter the body every day through the gut epithelium [8]. Benler et al. identified 3,738 phage genomes out of 5,742 human gut metagenomes and discovered that they were involved in lipid biosynthesis pathways [9]. Phages can indirectly affect human health by infecting bacteria. Phage can change the immunity of gut mucosa, affect the production of interferon-gamma (IFN-γ), and is associated with the aggravation of colitis [10].
Bacteria play a “double-edged” role in the progression of cancer. According to the different effects on cancer, bacteria can be divided into cancer-promoting bacteria, cancer-suppressing bacteria, and cancer-related bacteria whose role is not clear yet. Bacteria and their metabolites can damage cellular DNA, interfere with host signal transduction pathways, and directly contribute to the development of cancer. In addition, the body's immune and metabolic functions can be disturbed by bacteria, which indirectly promotes the occurrence of cancer. The common cancer-promoting bacteria, such as Helicobacter pylori has been reported to induce inflammatory response and signal transduction process, leading to gastric mucosal disorder, chronic gastritis and cancer [11]. Recently, Clostridioides difficile strain, as new members of cancer-promoting bacteria, have been found to be significantly enriched in colon tissue and continuously secrete toxins TcdB to promote the occurrence of CRC [12]. On the other hand, different strains of the same bacteria may have opposite effects on cancer. The “double-edged” role of Escherichia coli in cancer has been widely reported. For example, E. coli with pks island can encode the production of colibactin, induce cell double-stranded DNA break and local inflammatory response in the body to promote the development of cancer [13, 14]. Butler et al. reported that uropathogenic E. coli reduced cellular MYC levels, significantly inhibited tumor growth, and prolonged survival in mouse tumor models of bladder and colon cancer [15].
Phage roles in at least some cancers have also been demonstrated. Phages are associated with CRC and may influence cancer progression by altering the bacterial host community. Geoffrey D Hannigan et al. analyzed gut phage structure and corresponding flora changes through 16S rRNA gene, fecal whole shotgun metagenomic sequencing, and purified virus metagenomic sequencing technology. Further studies found that intestinal phages were mainly mild phages, and proved that the phage community was related to CRC, which might affect cancer progression by changing the bacterial host community [16].
However, phages may also be used as tools against cancer. Phages might be used as tools for antimicrobial therapy and vaccine development [17]. The application of phages in the development of tumor vaccines can be divided into two modes, namely the direct use of phages as tumor vaccines and the use of phages to mediate specific immune stimulation against tumor cells. Phage surface antibodies can specifically recognize tumor cell antigens, providing potential strategies for tumor targeted therapy and tumor imaging [18, 19] (Fig. 1). Abnormal collagen fibers in cancer tissues, such as degeneration and degradation, lead to changes in the extracellular matrix. The remodeling of extracellular matrix may inhibit the anti-tumor immune response by acting on the tumor microbiome [20, 21]. Phages have been genetically engineered to image abnormal collagen in lung adenocarcinoma cells, which contributes to the imaging diagnosis of cancer [22]. Additionally, phage display technology is a highly efficient gene expression screening technology. Expressed specific foreign proteins or peptides on the phage surface is also expected to be applied in cancer imaging and molecular diagnosis. Rafael da Fonseca Alves et al. used phage display technology to select biotin-C3 and biotin-H2 peptides as biometric molecules to develop a biosensor that can distinguish serum samples from patients with breast cancer and those with benign breast disease [23]. Moreover, Xue Dong et al. built a personalized cancer vaccine platform based on M13 phage that is loaded with tumor antigens by electrostatic adsorption [24]. It was found that phage-based tumor vaccine could prevent tumor, enhance anti-tumor immune response, and inhibit tumor growth in both primary and metastatic mice almost without toxic side effects [24].Fig. 1Application of phage in the treatment and prevention of cancer. Phages have strong targeting ability and carrier potential, so they are mainly applied in five aspects, including cancer immunotherapy, cancer imaging, cancer vaccine, gene therapy, and drug delivery system
Phages have the ability to target bacteria and little toxic side effects, and can be used as carriers to deliver cytokines, tumor killer factors and chemotherapy drugs. Phages are therefore favored by immunotherapy and gene therapy. Paladd Asavarut et al. used the coat protein of tumor-targeting phages as a drug carrier to develop a transparticle system for cancer immunotherapy and gene delivery [25]. In xenografted tumor mouse models, the system delivered the cytokines tumor necrosis factor-α (TNF-α), interleukin 12 (IL12), interleukin 15 (IL15) and their encoded transgenes, aiming to achieve significant enrichment of anti-tumor cytokine at the tumor site and inhibition of tumor cell growth in a safe and efficient manner. Phages successfully circumvent the "cytokine storm" and are more economical cytokine carriers compared with specific antibodies, which indicates the great potential of phages for cancer immunotherapy. The application of nano delivery vector to the improvement of the tumor targeting ability is expected to provide a new method for improving the tumor killing ability of chemical drugs. Wang et al. produced mixed micelles assembled with polyethylene glycol-phosphati-dylethanolamine (PEG-PE) conjugate and a pancreatic cancer cell (PANC-1) specific phage protein (P38 and L1) loaded with the water-insoluble anticancer drug paclitaxel (PCT). This PCT-loaded targeting bacteriophage micelle had a better ability to target PANC-1 cells, and the cytotoxicity is significantly improved [26]. Phage-guided irinoconamil granules also significantly improved the therapeutic efficiency of first-line chemotherapy for CRC and inhibited the growth of the cancer-promoting bacterium Fusobacterium nucleatum in mouse models of spontaneous CRC tumors [27].
In the era of big data, bibliographic analysis is a popular approach to select many information documents to get the research hotspot and trend in a certain field. At present, bibliometric analysis is widely used in biomedicine, ecological environment, information science, and other fields [28, 29]. In recent years, many experimental studies on the development of phage and tumor vaccine and tumor therapy have been carried out in vitro and in vivo, and these preliminary research data highlight the potential value of clinical application of phage in anti-cancer therapies. However, there is a lack of bibliometric analysis focusing on gut phage and cancer to explore research trends in this area. In this study, CiteSpace software was used to conduct a bibliometric analysis of gut phage and cancer, which provided a comprehensive perspective on the research trend of phage and cancer. Considering that the study on phage is hierarchical, three topics in turn, including gut phage, phage and bacteria, and phage and tumor, were visually analyzed. The research progress was described from the perspective of dynamic change and evolution, and it would play an important guiding role for current researchers to explore the hot spots in this field.
Methods
Data sources
Web of Science (https://www.webofknowledge.com) is an important database of global academic information, and it contains over 13,000 authoritative and high-impact academic journals around the world, covering natural sciences, engineering technology, biomedicine, social sciences, arts and humanities [30–32]. In this study, Web of Science was used as a database to conduct three searches on gut phage, phage and bacteria, and phage and tumor. The search strategy is as follows: (1) The gut phage corresponds to TS = (bacteriophage* OR phage*) AND TS = (intestinal* OR intestine* OR bowel* OR gut*). (2) The phage and bacteria corresponds to (TI = (Bacteriophage* OR phage*) AND TS = (Gastrointestinal Microbiome* OR Gut Microbiome* OR Gut Microflora OR Gut Microbiota* OR Gastrointestinal Flora OR Gut Flora OR Gastrointestinal Microbiota* OR Gastrointestinal Microbial Community OR Gastrointestinal Microbial Communities OR Gastrointestinal Microflora OR Gastric Microbiome* OR Intestinal Microbiome* OR Intestinal Microbiota* OR Intestinal Microflora OR Intestinal Flora OR Enteric Bacteria OR Lactobacillus* OR Bifidobacterium* OR Escherichia coli*)) NOT TS = ("phage display"). (3) The phage and tumor corresponds to (TS = (neoplasm* OR tumor* OR neoplasia* OR cancer* OR malignant neoplasm* OR malignancy OR malignancies OR benign neoplasm*) AND TS = (bacteriophage* OR phage*)) NOT TS = ("phage display").
Visualized analysis
CiteSpace (https://citespace.podia.com/) is a bibliometric analysis software developed by JAVA [33]. The software supports the visual exploration of new trends and new dynamics of scientific development, with various functions, including research hot spot analysis, research frontier detection, research evolution path analysis, etc.
The parameters of CiteSpace (6.2 R1) were set as follows: The time slices of phage, phage and bacteria, and phage and tumor were chosen from January 2008 to June 2023, the time slicing parameters were set to 1 year, and the criteria were chosen (g-index, g2 ≤ k Σi≤gci, k ∈ Z+, k = 25). The included documents were visually analyzed by CiteSpace in terms of country/region, institution, author, reference, and keyword.
A visual analysis of the cooperation network for countries/regions, institutions and authors reflects the degree of cooperation among various units. Reference co-citation analysis is presented through network map, timeline graph and reference burst. If two or more papers are jointly cited by one or more later papers, the co-citation relationship is established, and the co-citation of references can measure the degree of correlation between articles. Cluster analysis, time zone analysis and burst analysis constitute visual analysis for keywords.
In the upper left corner of the generated figure, N represents the number of nodes, E refers to the number of connections, and density is the network density. Q value is the value of clustering module, and more than 0.3 is considered as significant clustering structure effect [34]. The silhouette score ranges from -1 to 1, and a higher score indicates a greater internal homogeneity.
The flowchart of the study is shown in Additional file 1: Fig. S1.
Data sources
Web of Science (https://www.webofknowledge.com) is an important database of global academic information, and it contains over 13,000 authoritative and high-impact academic journals around the world, covering natural sciences, engineering technology, biomedicine, social sciences, arts and humanities [30–32]. In this study, Web of Science was used as a database to conduct three searches on gut phage, phage and bacteria, and phage and tumor. The search strategy is as follows: (1) The gut phage corresponds to TS = (bacteriophage* OR phage*) AND TS = (intestinal* OR intestine* OR bowel* OR gut*). (2) The phage and bacteria corresponds to (TI = (Bacteriophage* OR phage*) AND TS = (Gastrointestinal Microbiome* OR Gut Microbiome* OR Gut Microflora OR Gut Microbiota* OR Gastrointestinal Flora OR Gut Flora OR Gastrointestinal Microbiota* OR Gastrointestinal Microbial Community OR Gastrointestinal Microbial Communities OR Gastrointestinal Microflora OR Gastric Microbiome* OR Intestinal Microbiome* OR Intestinal Microbiota* OR Intestinal Microflora OR Intestinal Flora OR Enteric Bacteria OR Lactobacillus* OR Bifidobacterium* OR Escherichia coli*)) NOT TS = ("phage display"). (3) The phage and tumor corresponds to (TS = (neoplasm* OR tumor* OR neoplasia* OR cancer* OR malignant neoplasm* OR malignancy OR malignancies OR benign neoplasm*) AND TS = (bacteriophage* OR phage*)) NOT TS = ("phage display").
Visualized analysis
CiteSpace (https://citespace.podia.com/) is a bibliometric analysis software developed by JAVA [33]. The software supports the visual exploration of new trends and new dynamics of scientific development, with various functions, including research hot spot analysis, research frontier detection, research evolution path analysis, etc.
The parameters of CiteSpace (6.2 R1) were set as follows: The time slices of phage, phage and bacteria, and phage and tumor were chosen from January 2008 to June 2023, the time slicing parameters were set to 1 year, and the criteria were chosen (g-index, g2 ≤ k Σi≤gci, k ∈ Z+, k = 25). The included documents were visually analyzed by CiteSpace in terms of country/region, institution, author, reference, and keyword.
A visual analysis of the cooperation network for countries/regions, institutions and authors reflects the degree of cooperation among various units. Reference co-citation analysis is presented through network map, timeline graph and reference burst. If two or more papers are jointly cited by one or more later papers, the co-citation relationship is established, and the co-citation of references can measure the degree of correlation between articles. Cluster analysis, time zone analysis and burst analysis constitute visual analysis for keywords.
In the upper left corner of the generated figure, N represents the number of nodes, E refers to the number of connections, and density is the network density. Q value is the value of clustering module, and more than 0.3 is considered as significant clustering structure effect [34]. The silhouette score ranges from -1 to 1, and a higher score indicates a greater internal homogeneity.
The flowchart of the study is shown in Additional file 1: Fig. S1.
Results
Research tendency
A total of 443 articles were selected focusing on gut phage from January 1, 2008, to June 5, 2023. The deadline for article selection was June 2023, and the whole year of publications in 2023 were not complete, so all line charts covered 2008 to 2023 only. Since 2013, the annual number of articles on gut phage had an upward trend, and the number of articles from 2021 to 2022 had the largest increase (Additional file 1: Fig. S2 A).
From January 1, 2008, to June 30, 2023, a total of 630 articles on phage and gut bacteria were selected. In the two periods of 2010–2012 and 2017–2022, the annual number of publications on phage and bacteria showed an increasing trend. Moreover, 2017 is an important time node, as the number of articles in the field decreased in 2016–2017, while the number of articles surged in 2017–2018 (Additional file 1: Fig. S2 B).
Compared with the other two topics, the number of articles on phage and tumor is relatively small from January 1, 2008, to June 21, 2023, with 141 articles. From 2014 to 2019, the number of annual publications on phage and tumor increased steadily. Furthermore, 2020 is also an important time node, because the number of articles declined in 2019–2020, while the number of articles surged in 2020–2021 (Additional file 1: Fig. S2 C).
Collaboration network
Gut phage
The articles on gut phage for this study were collected from 59 countries/regions, with a network density of 0.0479 (Fig. 2 A). From 2008 to 2023, the United States of America (USA), China, and France were the most frequent publishers, with 173 (39.05%), 63 (14.22%) and 43 (9.71%) articles, respectively (Table 1). The number of articles in the USA had a significant advantage, with a half-life of 10.5, which indicated that the USA had achieved excellent results in the quantity and quality of articles in the field of gut phage research. Additionally, Switzerland, Canada, USA, etc. tended to conduct collaborative research with other countries/regions. For example, Switzerland had international cooperation with 8 countries/regions, such as Russia, while Canada had international cooperation with 5 countries/regions, such as PRC.Fig. 2Cooperative network analysis of gut phage. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 1Top 5 of most productive countries,regions on gut phageRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA17339.050.2510.52China6314.220.313.53Germany439.710.135.53France439.71010.54Canada409.030.2311.55England327.220.176.5
A total of 295 institutions and 436 authors were screened for visual analysis. Harvard University (29 articles), UDICE -French research universities (29 articles), University of California System (28 articles), University College Cork (25 articles), Universite Paris Cite (20 articles) were the top 5 most frequently published institutions (Fig. 2 B). Most institutions were universities. Hill, Colin,Ross (12 articles), R Paul,Shkoporov (10 articles), Andrey N (7 articles), Debarbieux, Laurent (7 articles) were the top 4 most frequently published authors (Fig. 2 C).
Phage and bacteria
A total of 58 countries, including USA, China and Canada, were collected from January 1, 2008, to June 20, 2023 (Fig. 3 A). The USA ranked first with 193 articles (31.64%), followed by China with 92 articles (15.08%), and Canada with 48 articles (7.87%) ranked third. There were 16 important countries/regions with high centrality, including France, Canada, England and USA. Some developed countries, including the USA, Canada, Russia and France, started research earlier and carried out extensive international cooperation. For example, Canada had cooperation with 7 countries/regions, such as Finland, Poland and Chile. In terms of number, centrality and half-life of articles, USA and China occupied a dominant position in the quantity and quality of articles published in this field (Table 2).Fig. 3Cooperative network analysis of phage and bacteria. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 2Top 6 of most productive countries/regions on phage and bacteriaRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA19331.640.149.52China9215.080.1911.53Canada487.870.386.54France437.050.47.55Germany426.890.079.56England406.560.339.5
There were 331 nodes, 512 connections, and a network density of 0.0094 for institutions (Fig. 3 B). Ten institutions, including The Centre National de la Recherche Scientifique (CNRS), University of California System, and University of Guelph, played an important connecting role in the network of institutional cooperation. Among the 10 institutions in the forefront of annual article production (Table 3), 5 belonged to France (50%), 2 to the USA (20%), 2 to Poland (20%), and 1 to Finland (10%).Table 3Top 7 of most productive institutions on phage and bacteriaRankInstitutionCountry/regionFrequency1UDICE-French Research UniversitiesFrance292Centre National de la Recherche Scientifique (CNRS)France203Fahrenheit UniversitiesFinland184Polish Academy of SciencesPoland155University of California SystemUSA145University of Texas SystemUSA145University of GdanskPoland146Universite Paris CiteFrance137Le Reseau International des Instituts Pasteur (RIIP)France127Institut Pasteur ParisFrance12
There were 471 nodes, 475 connections, and a network density of 0.0043 for authors (Fig. 3 C). The productive authors include Wegrzyn Grzegorz, Bloch Sylwia, and Wu Vivian C H, and 14 others with at least 5 articles.
Phage and tumor
Articles on phage and tumor from 36 countries/regions, such as USA and PRC, were collected (Fig. 4 A). The USA held the top spot with 48 articles, followed by PRC with 36 articles and England with 16 articles (Table 4). Notably, the USA had an important influence in the network of national cooperation network, so it played a bridging role. What’s more, based on publication frequency and half-life, USA and China served as trailblazers in the field of phage and tumor research.Fig. 4Cooperative network analysis of phage and tumor. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 4Top 5 of most productive countries/regions on phage and tumorRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA4834.040.629.52China3625.530.0711.53England1611.350.268.54Germany107.090.159.55Italy96.380.19.5
There were 240 nodes, 510 connections, and a network density of 0.0178 for institutions (Fig. 4 B). Among them, a total of 7 institutions, including Imperial College London, Harvard University and Polish Academy of Sciences, published at least 5 articles.
There were 372 nodes, 903 connections, and a network density of 0.0131 for authors (Fig. 4 C). Hajitou Amin was the most productive author with considerable article quality and conducted extensive cooperation with other authors. Hajitou Amin had an early insight into the ability of phages to target tumors, and used them as vectors to deliver tumor necrosis factor, CRISPR-Cas9 transgene boxes, etc., for immunotherapy and gene delivery of various cancers such as melanoma [25, 35, 36].
Reference co-citation and research hotspot
Gut phage
There were 668 nodes, 1,414 connections, and a network density of 0.0063, and 13 clusters were presented, including #0 eggerthella lenta, #1 virus-bacteria linkages, #2 phage resistance, #3 human gut, #4 phage translocation, #5 vancomycin-resistant enterococcus faecalis, #6 correlation, #7 crassphage, #8 carrier state lifecycle, #9 probiotic bacteria, #10 microbiome, #11 campylobacter jejuni, and #12 symnioses (Fig. 5 A). #9 probiotic bacteria and #11 campylobacter jejuni started early, while #0 eggerthella lenta, #1 virus-bacteria linkages, and #2 phage resistance have received attention in recent years (Fig. 5 B).Fig. 5Reference co-citation analysis of gut phage. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
Totally, 6 citations burst starting in 2021 and beyond (Fig. 5 C). These citations explored the phage-dominated viral genome to discover its diversity and individual specificity and investigated potential associations among bacteriome, metabolome, and viriome [37–40]. For example, the citation (strength = 8.13) by Ann C Gregory et al. described a human enterovirus database (GVB) of 1,986 individuals from 16 countries, which confirmed the individual specificity of phages and revealed the shift in viral diversity from infancy to old age[39].
Phage and bacteria
The top 13 clusters were presented from 740 co-cited articles, including #0 bacteriophage, #1 jumbo phage, #2 shiga toxin, #3 feces, #4 human gut microbiome, #5 bacteriophage t4, #6 d6, #7 virulence, #8 h7, #9 genome analysis, #10 microbiota, #11 diarrhea, and #12 peptidoglycan hydrolase (Fig. 6 A). Among them, #1 jumbo phage, #4 human gut microbiome, #7 virulence, and #8 h7 were expected to attract more investment in future research. Additionally, #0 e. coli o157:h7 and #5 bacteriophage t4 started early with relatively profound research foundation (Fig. 6 B).Fig. 6Reference co-citation analysis of phage and bacteria. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
The timeline of the top 25 co-cited articles on phage and bacteria with the strongest bursts was shown in Fig. 6 C. The citation written by Alejandro Reyes et al. had the strongest burst strength (6.45) and focused on the concept of gut virome early [41]. Articles that might reveal the potential future research hotspots (burst end = 2023) mainly concentrated on the further exploration and application of phage properties. First, the current research on the characteristics of phages seemed to be more focused on the study of its survival mechanism and comparison between individuals, including its mechanism of targeting host and avoiding attack [42, 43], as well as the high individual specificity of the phage-dominated virome [37], which provides the premise of multi-disciplinary application of phages. Besides, based on the impacts of phage on gut bacteria and even gut microbiome, the application of phages was expected to attract more attention, such as the treatment of bacterial infection and food safety problems [44–46].
Phage and tumor
There were 465 nodes, 1,199 connections, and a network density of 0.0111 for citation on phage and tumor (Fig. 7 A). The top 13 clusters included #0 esophageal diseases, #1 ge11, #2 melanoma, #3 temozolomide, #4 crispr-cas9, #5 nanoparticle, #6 cec, #7 coli phagelysate, #8 combined therapy, #9 aptamers, #10 hpv vaccine, #11 immune checkpoint inhibitor, and #12 collagen. #2 melanoma, #3 temozolomide, #8 combined therapy, and #9 aptamers started early with a relatively in-depth study while #5 nanoparticle and #7 coli phagelysate appeared recently and could be the focus of the future research (Fig. 7 B).Fig. 7Reference co-citation analysis of phage and tumor. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
A total of 7 citation burst started in 2021 and lasted until the search deadline, which had a certain directional effect on future research hotspots (Fig. 7 C). The potential of phages in the diagnosis and treatment of cancer such as CRC has been emphasized, and the changes of intestinal mucosa have been revealed to be related to phages [5, 10, 16, 47–49]. For example, the citation by Lasha Gogokhia et al. (strength = 2.11) revealed that phages promoted mucosal IFN-γ through a TLR9-dependent pathway, which enhanced mucosal immunity and exacerbated the occurrence of colitis [10]. Rebekah M. Dedrick et al. reported for the first time a case study (strength = 2.07) of the use of engineered phages in human treatment of drug-resistant mycobacterium abscess with clinical improvements such as liver function recovery, which strongly suggested the potential of effective combination of phage and genetic engineering technology in clinical practice [49].
Keyword co-occurrence and burst
Gut phage
The top 13 clusters were presented from 394 keyword nodes (Q = 0.7519), including #0 phageome, #1 community, #2 antibiotic reststance, #3 dynamics, #4 enterococcus, #5 shiga toxin, #6 escherichia coli, #7 volunteers, #8 ulcerative colitis, #9 capsid proteins, #10 alignment, #11 in vitro, and #12 animal models (Fig. 8 A).Fig. 8Keyword analysis of gut phage. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Some keywords, such as “escherichia coli”, “identification” and “therapy”, appeared early, while others, such as “gut virome”, emerged in recent years (Fig. 8 B). The top 25 keywords with the strongest citation bursts were presented in Fig. 8 C. “Phage therapy” (strength = 3.46), “viral community” (strength = 3.76), “infection” (strength = 3.19), etc. were the hot spots of early research, while “gut microbiome” (strength = 2.94), “database” (strength = 3.83), “microbiota” (strength = 3.12), “expansion” (strength = 2.89), and “mice” (strength = 2.18) would be the future research hot spots.
Phage and bacteria
The top 13 clusters were presented from 413 keyword nodes (Q = 0.6972), including #0 operon, #1 antimicrobial resistance, #2 phage therapy, #3 rna polymerase, #4 shiga toxin, #5 bacteriophage t4, #6 biocontrol, #7 outbreak, #8 gut microbiota, #9 escherichia coli, #10 mutants, #11 antagonistic coevolution, and #12 mung bean seeds (Fig. 9 A). “Escherichia coli” had the highest frequency of 177, ranking the first keyword, followed by identification with a frequency of 81, and “bacteriophage” and “phage” with a frequency of 61.Fig. 9Keyword analysis of phage and bacteria. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Moreover, “escherichia coli”, “identification”, “protein”, “phage”, etc. were the earliest hotspot keywords since 2008, while “antimicrobial resistance (amr)”, “stability” and “1st step transfer dna” had emerged recently (Fig. 9 B). Among the keywords of high burst intensity, the keywords of burst beginning in 2021 or later were noticed, including “alignment” (strength = 2.93), “lipopolysaccharide” (strength = 2.6), “microbiota” (strength = 2.4), “human gut” (strength = 2.34), “escherichia coli 0157” (strength = 2.34), and “gut microbiota” (strength = 2.93) (Fig. 9 C).
Phage and tumor
There were 322 keyword nodes in total, and 13 clusters are presented (Q = 0.7077) including #0 gene therapy, #1 hpv vaccine, #2 virome, #3 short-wavelength infrared imaging, #4 cancer, #5 cancer gene therapy, #6 nanoparticle, #7 m13 bacteriophage, #8 carbohydrates, #9 biomaterials, #10 microbial network, #11 phage idiotype vaccination, and #12 immune checkpoint inhibitor (Fig. 10 A).Fig. 10Keyword analysis of phage and tumor. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Key words, including “delivery” (frequency = 19), “cancer” (frequency = 19), “expression” (frequency = 17), “cells” (frequency = 15), “therapy” (frequency = 12) and “colorectal cancer” (frequency = 18), had deep research foundation, and still are the topic of the current study (Fig. 10 B). Among the keywords with high burst intensity with the burst start time of 2021 and beyond, “colorectal cancer” (strength = 3.7) ranked first, followed by “proteins” (strength = 2.84) and “phage therapy” (strength = 3.7) (Fig. 10 C).
Research tendency
A total of 443 articles were selected focusing on gut phage from January 1, 2008, to June 5, 2023. The deadline for article selection was June 2023, and the whole year of publications in 2023 were not complete, so all line charts covered 2008 to 2023 only. Since 2013, the annual number of articles on gut phage had an upward trend, and the number of articles from 2021 to 2022 had the largest increase (Additional file 1: Fig. S2 A).
From January 1, 2008, to June 30, 2023, a total of 630 articles on phage and gut bacteria were selected. In the two periods of 2010–2012 and 2017–2022, the annual number of publications on phage and bacteria showed an increasing trend. Moreover, 2017 is an important time node, as the number of articles in the field decreased in 2016–2017, while the number of articles surged in 2017–2018 (Additional file 1: Fig. S2 B).
Compared with the other two topics, the number of articles on phage and tumor is relatively small from January 1, 2008, to June 21, 2023, with 141 articles. From 2014 to 2019, the number of annual publications on phage and tumor increased steadily. Furthermore, 2020 is also an important time node, because the number of articles declined in 2019–2020, while the number of articles surged in 2020–2021 (Additional file 1: Fig. S2 C).
Collaboration network
Gut phage
The articles on gut phage for this study were collected from 59 countries/regions, with a network density of 0.0479 (Fig. 2 A). From 2008 to 2023, the United States of America (USA), China, and France were the most frequent publishers, with 173 (39.05%), 63 (14.22%) and 43 (9.71%) articles, respectively (Table 1). The number of articles in the USA had a significant advantage, with a half-life of 10.5, which indicated that the USA had achieved excellent results in the quantity and quality of articles in the field of gut phage research. Additionally, Switzerland, Canada, USA, etc. tended to conduct collaborative research with other countries/regions. For example, Switzerland had international cooperation with 8 countries/regions, such as Russia, while Canada had international cooperation with 5 countries/regions, such as PRC.Fig. 2Cooperative network analysis of gut phage. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 1Top 5 of most productive countries,regions on gut phageRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA17339.050.2510.52China6314.220.313.53Germany439.710.135.53France439.71010.54Canada409.030.2311.55England327.220.176.5
A total of 295 institutions and 436 authors were screened for visual analysis. Harvard University (29 articles), UDICE -French research universities (29 articles), University of California System (28 articles), University College Cork (25 articles), Universite Paris Cite (20 articles) were the top 5 most frequently published institutions (Fig. 2 B). Most institutions were universities. Hill, Colin,Ross (12 articles), R Paul,Shkoporov (10 articles), Andrey N (7 articles), Debarbieux, Laurent (7 articles) were the top 4 most frequently published authors (Fig. 2 C).
Phage and bacteria
A total of 58 countries, including USA, China and Canada, were collected from January 1, 2008, to June 20, 2023 (Fig. 3 A). The USA ranked first with 193 articles (31.64%), followed by China with 92 articles (15.08%), and Canada with 48 articles (7.87%) ranked third. There were 16 important countries/regions with high centrality, including France, Canada, England and USA. Some developed countries, including the USA, Canada, Russia and France, started research earlier and carried out extensive international cooperation. For example, Canada had cooperation with 7 countries/regions, such as Finland, Poland and Chile. In terms of number, centrality and half-life of articles, USA and China occupied a dominant position in the quantity and quality of articles published in this field (Table 2).Fig. 3Cooperative network analysis of phage and bacteria. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 2Top 6 of most productive countries/regions on phage and bacteriaRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA19331.640.149.52China9215.080.1911.53Canada487.870.386.54France437.050.47.55Germany426.890.079.56England406.560.339.5
There were 331 nodes, 512 connections, and a network density of 0.0094 for institutions (Fig. 3 B). Ten institutions, including The Centre National de la Recherche Scientifique (CNRS), University of California System, and University of Guelph, played an important connecting role in the network of institutional cooperation. Among the 10 institutions in the forefront of annual article production (Table 3), 5 belonged to France (50%), 2 to the USA (20%), 2 to Poland (20%), and 1 to Finland (10%).Table 3Top 7 of most productive institutions on phage and bacteriaRankInstitutionCountry/regionFrequency1UDICE-French Research UniversitiesFrance292Centre National de la Recherche Scientifique (CNRS)France203Fahrenheit UniversitiesFinland184Polish Academy of SciencesPoland155University of California SystemUSA145University of Texas SystemUSA145University of GdanskPoland146Universite Paris CiteFrance137Le Reseau International des Instituts Pasteur (RIIP)France127Institut Pasteur ParisFrance12
There were 471 nodes, 475 connections, and a network density of 0.0043 for authors (Fig. 3 C). The productive authors include Wegrzyn Grzegorz, Bloch Sylwia, and Wu Vivian C H, and 14 others with at least 5 articles.
Phage and tumor
Articles on phage and tumor from 36 countries/regions, such as USA and PRC, were collected (Fig. 4 A). The USA held the top spot with 48 articles, followed by PRC with 36 articles and England with 16 articles (Table 4). Notably, the USA had an important influence in the network of national cooperation network, so it played a bridging role. What’s more, based on publication frequency and half-life, USA and China served as trailblazers in the field of phage and tumor research.Fig. 4Cooperative network analysis of phage and tumor. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 4Top 5 of most productive countries/regions on phage and tumorRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA4834.040.629.52China3625.530.0711.53England1611.350.268.54Germany107.090.159.55Italy96.380.19.5
There were 240 nodes, 510 connections, and a network density of 0.0178 for institutions (Fig. 4 B). Among them, a total of 7 institutions, including Imperial College London, Harvard University and Polish Academy of Sciences, published at least 5 articles.
There were 372 nodes, 903 connections, and a network density of 0.0131 for authors (Fig. 4 C). Hajitou Amin was the most productive author with considerable article quality and conducted extensive cooperation with other authors. Hajitou Amin had an early insight into the ability of phages to target tumors, and used them as vectors to deliver tumor necrosis factor, CRISPR-Cas9 transgene boxes, etc., for immunotherapy and gene delivery of various cancers such as melanoma [25, 35, 36].
Gut phage
The articles on gut phage for this study were collected from 59 countries/regions, with a network density of 0.0479 (Fig. 2 A). From 2008 to 2023, the United States of America (USA), China, and France were the most frequent publishers, with 173 (39.05%), 63 (14.22%) and 43 (9.71%) articles, respectively (Table 1). The number of articles in the USA had a significant advantage, with a half-life of 10.5, which indicated that the USA had achieved excellent results in the quantity and quality of articles in the field of gut phage research. Additionally, Switzerland, Canada, USA, etc. tended to conduct collaborative research with other countries/regions. For example, Switzerland had international cooperation with 8 countries/regions, such as Russia, while Canada had international cooperation with 5 countries/regions, such as PRC.Fig. 2Cooperative network analysis of gut phage. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 1Top 5 of most productive countries,regions on gut phageRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA17339.050.2510.52China6314.220.313.53Germany439.710.135.53France439.71010.54Canada409.030.2311.55England327.220.176.5
A total of 295 institutions and 436 authors were screened for visual analysis. Harvard University (29 articles), UDICE -French research universities (29 articles), University of California System (28 articles), University College Cork (25 articles), Universite Paris Cite (20 articles) were the top 5 most frequently published institutions (Fig. 2 B). Most institutions were universities. Hill, Colin,Ross (12 articles), R Paul,Shkoporov (10 articles), Andrey N (7 articles), Debarbieux, Laurent (7 articles) were the top 4 most frequently published authors (Fig. 2 C).
Phage and bacteria
A total of 58 countries, including USA, China and Canada, were collected from January 1, 2008, to June 20, 2023 (Fig. 3 A). The USA ranked first with 193 articles (31.64%), followed by China with 92 articles (15.08%), and Canada with 48 articles (7.87%) ranked third. There were 16 important countries/regions with high centrality, including France, Canada, England and USA. Some developed countries, including the USA, Canada, Russia and France, started research earlier and carried out extensive international cooperation. For example, Canada had cooperation with 7 countries/regions, such as Finland, Poland and Chile. In terms of number, centrality and half-life of articles, USA and China occupied a dominant position in the quantity and quality of articles published in this field (Table 2).Fig. 3Cooperative network analysis of phage and bacteria. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 2Top 6 of most productive countries/regions on phage and bacteriaRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA19331.640.149.52China9215.080.1911.53Canada487.870.386.54France437.050.47.55Germany426.890.079.56England406.560.339.5
There were 331 nodes, 512 connections, and a network density of 0.0094 for institutions (Fig. 3 B). Ten institutions, including The Centre National de la Recherche Scientifique (CNRS), University of California System, and University of Guelph, played an important connecting role in the network of institutional cooperation. Among the 10 institutions in the forefront of annual article production (Table 3), 5 belonged to France (50%), 2 to the USA (20%), 2 to Poland (20%), and 1 to Finland (10%).Table 3Top 7 of most productive institutions on phage and bacteriaRankInstitutionCountry/regionFrequency1UDICE-French Research UniversitiesFrance292Centre National de la Recherche Scientifique (CNRS)France203Fahrenheit UniversitiesFinland184Polish Academy of SciencesPoland155University of California SystemUSA145University of Texas SystemUSA145University of GdanskPoland146Universite Paris CiteFrance137Le Reseau International des Instituts Pasteur (RIIP)France127Institut Pasteur ParisFrance12
There were 471 nodes, 475 connections, and a network density of 0.0043 for authors (Fig. 3 C). The productive authors include Wegrzyn Grzegorz, Bloch Sylwia, and Wu Vivian C H, and 14 others with at least 5 articles.
Phage and tumor
Articles on phage and tumor from 36 countries/regions, such as USA and PRC, were collected (Fig. 4 A). The USA held the top spot with 48 articles, followed by PRC with 36 articles and England with 16 articles (Table 4). Notably, the USA had an important influence in the network of national cooperation network, so it played a bridging role. What’s more, based on publication frequency and half-life, USA and China served as trailblazers in the field of phage and tumor research.Fig. 4Cooperative network analysis of phage and tumor. A Cooperation network map for countries/regions. B Cooperation network map for institutions. C Cooperation network map for authorsTable 4Top 5 of most productive countries/regions on phage and tumorRankCountry/regionFrequencyPercentage (%)CentralityHalf-life1USA4834.040.629.52China3625.530.0711.53England1611.350.268.54Germany107.090.159.55Italy96.380.19.5
There were 240 nodes, 510 connections, and a network density of 0.0178 for institutions (Fig. 4 B). Among them, a total of 7 institutions, including Imperial College London, Harvard University and Polish Academy of Sciences, published at least 5 articles.
There were 372 nodes, 903 connections, and a network density of 0.0131 for authors (Fig. 4 C). Hajitou Amin was the most productive author with considerable article quality and conducted extensive cooperation with other authors. Hajitou Amin had an early insight into the ability of phages to target tumors, and used them as vectors to deliver tumor necrosis factor, CRISPR-Cas9 transgene boxes, etc., for immunotherapy and gene delivery of various cancers such as melanoma [25, 35, 36].
Reference co-citation and research hotspot
Gut phage
There were 668 nodes, 1,414 connections, and a network density of 0.0063, and 13 clusters were presented, including #0 eggerthella lenta, #1 virus-bacteria linkages, #2 phage resistance, #3 human gut, #4 phage translocation, #5 vancomycin-resistant enterococcus faecalis, #6 correlation, #7 crassphage, #8 carrier state lifecycle, #9 probiotic bacteria, #10 microbiome, #11 campylobacter jejuni, and #12 symnioses (Fig. 5 A). #9 probiotic bacteria and #11 campylobacter jejuni started early, while #0 eggerthella lenta, #1 virus-bacteria linkages, and #2 phage resistance have received attention in recent years (Fig. 5 B).Fig. 5Reference co-citation analysis of gut phage. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
Totally, 6 citations burst starting in 2021 and beyond (Fig. 5 C). These citations explored the phage-dominated viral genome to discover its diversity and individual specificity and investigated potential associations among bacteriome, metabolome, and viriome [37–40]. For example, the citation (strength = 8.13) by Ann C Gregory et al. described a human enterovirus database (GVB) of 1,986 individuals from 16 countries, which confirmed the individual specificity of phages and revealed the shift in viral diversity from infancy to old age[39].
Phage and bacteria
The top 13 clusters were presented from 740 co-cited articles, including #0 bacteriophage, #1 jumbo phage, #2 shiga toxin, #3 feces, #4 human gut microbiome, #5 bacteriophage t4, #6 d6, #7 virulence, #8 h7, #9 genome analysis, #10 microbiota, #11 diarrhea, and #12 peptidoglycan hydrolase (Fig. 6 A). Among them, #1 jumbo phage, #4 human gut microbiome, #7 virulence, and #8 h7 were expected to attract more investment in future research. Additionally, #0 e. coli o157:h7 and #5 bacteriophage t4 started early with relatively profound research foundation (Fig. 6 B).Fig. 6Reference co-citation analysis of phage and bacteria. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
The timeline of the top 25 co-cited articles on phage and bacteria with the strongest bursts was shown in Fig. 6 C. The citation written by Alejandro Reyes et al. had the strongest burst strength (6.45) and focused on the concept of gut virome early [41]. Articles that might reveal the potential future research hotspots (burst end = 2023) mainly concentrated on the further exploration and application of phage properties. First, the current research on the characteristics of phages seemed to be more focused on the study of its survival mechanism and comparison between individuals, including its mechanism of targeting host and avoiding attack [42, 43], as well as the high individual specificity of the phage-dominated virome [37], which provides the premise of multi-disciplinary application of phages. Besides, based on the impacts of phage on gut bacteria and even gut microbiome, the application of phages was expected to attract more attention, such as the treatment of bacterial infection and food safety problems [44–46].
Phage and tumor
There were 465 nodes, 1,199 connections, and a network density of 0.0111 for citation on phage and tumor (Fig. 7 A). The top 13 clusters included #0 esophageal diseases, #1 ge11, #2 melanoma, #3 temozolomide, #4 crispr-cas9, #5 nanoparticle, #6 cec, #7 coli phagelysate, #8 combined therapy, #9 aptamers, #10 hpv vaccine, #11 immune checkpoint inhibitor, and #12 collagen. #2 melanoma, #3 temozolomide, #8 combined therapy, and #9 aptamers started early with a relatively in-depth study while #5 nanoparticle and #7 coli phagelysate appeared recently and could be the focus of the future research (Fig. 7 B).Fig. 7Reference co-citation analysis of phage and tumor. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
A total of 7 citation burst started in 2021 and lasted until the search deadline, which had a certain directional effect on future research hotspots (Fig. 7 C). The potential of phages in the diagnosis and treatment of cancer such as CRC has been emphasized, and the changes of intestinal mucosa have been revealed to be related to phages [5, 10, 16, 47–49]. For example, the citation by Lasha Gogokhia et al. (strength = 2.11) revealed that phages promoted mucosal IFN-γ through a TLR9-dependent pathway, which enhanced mucosal immunity and exacerbated the occurrence of colitis [10]. Rebekah M. Dedrick et al. reported for the first time a case study (strength = 2.07) of the use of engineered phages in human treatment of drug-resistant mycobacterium abscess with clinical improvements such as liver function recovery, which strongly suggested the potential of effective combination of phage and genetic engineering technology in clinical practice [49].
Gut phage
There were 668 nodes, 1,414 connections, and a network density of 0.0063, and 13 clusters were presented, including #0 eggerthella lenta, #1 virus-bacteria linkages, #2 phage resistance, #3 human gut, #4 phage translocation, #5 vancomycin-resistant enterococcus faecalis, #6 correlation, #7 crassphage, #8 carrier state lifecycle, #9 probiotic bacteria, #10 microbiome, #11 campylobacter jejuni, and #12 symnioses (Fig. 5 A). #9 probiotic bacteria and #11 campylobacter jejuni started early, while #0 eggerthella lenta, #1 virus-bacteria linkages, and #2 phage resistance have received attention in recent years (Fig. 5 B).Fig. 5Reference co-citation analysis of gut phage. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
Totally, 6 citations burst starting in 2021 and beyond (Fig. 5 C). These citations explored the phage-dominated viral genome to discover its diversity and individual specificity and investigated potential associations among bacteriome, metabolome, and viriome [37–40]. For example, the citation (strength = 8.13) by Ann C Gregory et al. described a human enterovirus database (GVB) of 1,986 individuals from 16 countries, which confirmed the individual specificity of phages and revealed the shift in viral diversity from infancy to old age[39].
Phage and bacteria
The top 13 clusters were presented from 740 co-cited articles, including #0 bacteriophage, #1 jumbo phage, #2 shiga toxin, #3 feces, #4 human gut microbiome, #5 bacteriophage t4, #6 d6, #7 virulence, #8 h7, #9 genome analysis, #10 microbiota, #11 diarrhea, and #12 peptidoglycan hydrolase (Fig. 6 A). Among them, #1 jumbo phage, #4 human gut microbiome, #7 virulence, and #8 h7 were expected to attract more investment in future research. Additionally, #0 e. coli o157:h7 and #5 bacteriophage t4 started early with relatively profound research foundation (Fig. 6 B).Fig. 6Reference co-citation analysis of phage and bacteria. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
The timeline of the top 25 co-cited articles on phage and bacteria with the strongest bursts was shown in Fig. 6 C. The citation written by Alejandro Reyes et al. had the strongest burst strength (6.45) and focused on the concept of gut virome early [41]. Articles that might reveal the potential future research hotspots (burst end = 2023) mainly concentrated on the further exploration and application of phage properties. First, the current research on the characteristics of phages seemed to be more focused on the study of its survival mechanism and comparison between individuals, including its mechanism of targeting host and avoiding attack [42, 43], as well as the high individual specificity of the phage-dominated virome [37], which provides the premise of multi-disciplinary application of phages. Besides, based on the impacts of phage on gut bacteria and even gut microbiome, the application of phages was expected to attract more attention, such as the treatment of bacterial infection and food safety problems [44–46].
Phage and tumor
There were 465 nodes, 1,199 connections, and a network density of 0.0111 for citation on phage and tumor (Fig. 7 A). The top 13 clusters included #0 esophageal diseases, #1 ge11, #2 melanoma, #3 temozolomide, #4 crispr-cas9, #5 nanoparticle, #6 cec, #7 coli phagelysate, #8 combined therapy, #9 aptamers, #10 hpv vaccine, #11 immune checkpoint inhibitor, and #12 collagen. #2 melanoma, #3 temozolomide, #8 combined therapy, and #9 aptamers started early with a relatively in-depth study while #5 nanoparticle and #7 coli phagelysate appeared recently and could be the focus of the future research (Fig. 7 B).Fig. 7Reference co-citation analysis of phage and tumor. A Reference clustering map. B Reference co-citation time diagram. C The top 25 co-cited references with the strongest burst intensity
A total of 7 citation burst started in 2021 and lasted until the search deadline, which had a certain directional effect on future research hotspots (Fig. 7 C). The potential of phages in the diagnosis and treatment of cancer such as CRC has been emphasized, and the changes of intestinal mucosa have been revealed to be related to phages [5, 10, 16, 47–49]. For example, the citation by Lasha Gogokhia et al. (strength = 2.11) revealed that phages promoted mucosal IFN-γ through a TLR9-dependent pathway, which enhanced mucosal immunity and exacerbated the occurrence of colitis [10]. Rebekah M. Dedrick et al. reported for the first time a case study (strength = 2.07) of the use of engineered phages in human treatment of drug-resistant mycobacterium abscess with clinical improvements such as liver function recovery, which strongly suggested the potential of effective combination of phage and genetic engineering technology in clinical practice [49].
Keyword co-occurrence and burst
Gut phage
The top 13 clusters were presented from 394 keyword nodes (Q = 0.7519), including #0 phageome, #1 community, #2 antibiotic reststance, #3 dynamics, #4 enterococcus, #5 shiga toxin, #6 escherichia coli, #7 volunteers, #8 ulcerative colitis, #9 capsid proteins, #10 alignment, #11 in vitro, and #12 animal models (Fig. 8 A).Fig. 8Keyword analysis of gut phage. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Some keywords, such as “escherichia coli”, “identification” and “therapy”, appeared early, while others, such as “gut virome”, emerged in recent years (Fig. 8 B). The top 25 keywords with the strongest citation bursts were presented in Fig. 8 C. “Phage therapy” (strength = 3.46), “viral community” (strength = 3.76), “infection” (strength = 3.19), etc. were the hot spots of early research, while “gut microbiome” (strength = 2.94), “database” (strength = 3.83), “microbiota” (strength = 3.12), “expansion” (strength = 2.89), and “mice” (strength = 2.18) would be the future research hot spots.
Phage and bacteria
The top 13 clusters were presented from 413 keyword nodes (Q = 0.6972), including #0 operon, #1 antimicrobial resistance, #2 phage therapy, #3 rna polymerase, #4 shiga toxin, #5 bacteriophage t4, #6 biocontrol, #7 outbreak, #8 gut microbiota, #9 escherichia coli, #10 mutants, #11 antagonistic coevolution, and #12 mung bean seeds (Fig. 9 A). “Escherichia coli” had the highest frequency of 177, ranking the first keyword, followed by identification with a frequency of 81, and “bacteriophage” and “phage” with a frequency of 61.Fig. 9Keyword analysis of phage and bacteria. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Moreover, “escherichia coli”, “identification”, “protein”, “phage”, etc. were the earliest hotspot keywords since 2008, while “antimicrobial resistance (amr)”, “stability” and “1st step transfer dna” had emerged recently (Fig. 9 B). Among the keywords of high burst intensity, the keywords of burst beginning in 2021 or later were noticed, including “alignment” (strength = 2.93), “lipopolysaccharide” (strength = 2.6), “microbiota” (strength = 2.4), “human gut” (strength = 2.34), “escherichia coli 0157” (strength = 2.34), and “gut microbiota” (strength = 2.93) (Fig. 9 C).
Phage and tumor
There were 322 keyword nodes in total, and 13 clusters are presented (Q = 0.7077) including #0 gene therapy, #1 hpv vaccine, #2 virome, #3 short-wavelength infrared imaging, #4 cancer, #5 cancer gene therapy, #6 nanoparticle, #7 m13 bacteriophage, #8 carbohydrates, #9 biomaterials, #10 microbial network, #11 phage idiotype vaccination, and #12 immune checkpoint inhibitor (Fig. 10 A).Fig. 10Keyword analysis of phage and tumor. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Key words, including “delivery” (frequency = 19), “cancer” (frequency = 19), “expression” (frequency = 17), “cells” (frequency = 15), “therapy” (frequency = 12) and “colorectal cancer” (frequency = 18), had deep research foundation, and still are the topic of the current study (Fig. 10 B). Among the keywords with high burst intensity with the burst start time of 2021 and beyond, “colorectal cancer” (strength = 3.7) ranked first, followed by “proteins” (strength = 2.84) and “phage therapy” (strength = 3.7) (Fig. 10 C).
Gut phage
The top 13 clusters were presented from 394 keyword nodes (Q = 0.7519), including #0 phageome, #1 community, #2 antibiotic reststance, #3 dynamics, #4 enterococcus, #5 shiga toxin, #6 escherichia coli, #7 volunteers, #8 ulcerative colitis, #9 capsid proteins, #10 alignment, #11 in vitro, and #12 animal models (Fig. 8 A).Fig. 8Keyword analysis of gut phage. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Some keywords, such as “escherichia coli”, “identification” and “therapy”, appeared early, while others, such as “gut virome”, emerged in recent years (Fig. 8 B). The top 25 keywords with the strongest citation bursts were presented in Fig. 8 C. “Phage therapy” (strength = 3.46), “viral community” (strength = 3.76), “infection” (strength = 3.19), etc. were the hot spots of early research, while “gut microbiome” (strength = 2.94), “database” (strength = 3.83), “microbiota” (strength = 3.12), “expansion” (strength = 2.89), and “mice” (strength = 2.18) would be the future research hot spots.
Phage and bacteria
The top 13 clusters were presented from 413 keyword nodes (Q = 0.6972), including #0 operon, #1 antimicrobial resistance, #2 phage therapy, #3 rna polymerase, #4 shiga toxin, #5 bacteriophage t4, #6 biocontrol, #7 outbreak, #8 gut microbiota, #9 escherichia coli, #10 mutants, #11 antagonistic coevolution, and #12 mung bean seeds (Fig. 9 A). “Escherichia coli” had the highest frequency of 177, ranking the first keyword, followed by identification with a frequency of 81, and “bacteriophage” and “phage” with a frequency of 61.Fig. 9Keyword analysis of phage and bacteria. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Moreover, “escherichia coli”, “identification”, “protein”, “phage”, etc. were the earliest hotspot keywords since 2008, while “antimicrobial resistance (amr)”, “stability” and “1st step transfer dna” had emerged recently (Fig. 9 B). Among the keywords of high burst intensity, the keywords of burst beginning in 2021 or later were noticed, including “alignment” (strength = 2.93), “lipopolysaccharide” (strength = 2.6), “microbiota” (strength = 2.4), “human gut” (strength = 2.34), “escherichia coli 0157” (strength = 2.34), and “gut microbiota” (strength = 2.93) (Fig. 9 C).
Phage and tumor
There were 322 keyword nodes in total, and 13 clusters are presented (Q = 0.7077) including #0 gene therapy, #1 hpv vaccine, #2 virome, #3 short-wavelength infrared imaging, #4 cancer, #5 cancer gene therapy, #6 nanoparticle, #7 m13 bacteriophage, #8 carbohydrates, #9 biomaterials, #10 microbial network, #11 phage idiotype vaccination, and #12 immune checkpoint inhibitor (Fig. 10 A).Fig. 10Keyword analysis of phage and tumor. A Keyword clustering map. B Keyword time diagram. C The top 25 keywords with the strongest burst intensity
Key words, including “delivery” (frequency = 19), “cancer” (frequency = 19), “expression” (frequency = 17), “cells” (frequency = 15), “therapy” (frequency = 12) and “colorectal cancer” (frequency = 18), had deep research foundation, and still are the topic of the current study (Fig. 10 B). Among the keywords with high burst intensity with the burst start time of 2021 and beyond, “colorectal cancer” (strength = 3.7) ranked first, followed by “proteins” (strength = 2.84) and “phage therapy” (strength = 3.7) (Fig. 10 C).
Discussion
Phages, a major member of the human viriome, can specifically infect bacteria and have been found to be involved in the progression of tumors in recent years. This study used CiteSpace to explore three topics, including phage, phage and bacteria, and phage and tumor. Through the analysis of research trends, cooperation networks, reference co-citations, and keywords, the development of these three fields from 2008 to 2023 was found, and the research trends and hot topics in this field were presented.
The results proved that the annual publication number of the three fields was basically on the rise. The annual number of publications on phage and tumor decreased in 2020, which could be due to the outbreak of the COVID-19 pandemic. The upward trend could be attributed to the rapid development of artificial intelligence, bioinformatics, and sequencing methods, which provided strong technical support for researchers. The construction of virus database depends on the acquisition of phage sequences. At present, third-generation sequencing technology has been popularized in recent studies for its advantages of long read length and high precision [50]. In this study, it was found that "sequences" was the early research focus on gut phage, and "database" was a potential future research hotspot. The results revealed that the development of sequencing technology has promoted the shift from fragmented sequence collection to systematic database construction [51]. With the support of bioinformatics and artificial intelligence, the information resources in virus database are expected to exert their application potential. Menglu Li et al. collected over 3,000 phage-host pairs and their protein sequences and used deep convolutional neural networks to construct prediction models for phage-host interactions [52]. This model was expected to contribute to the development of phage therapy and provide suitable phage selection for personalized treatment.
The collaborative network analysis of countries/regions, institutions, and authors in the three fields of gut phage, phage and bacteria, and phage and tumor illustrated that the USA and China occupied an important position in each field. For countries stronger in various fields, early research time and extensive international cooperation are prerequisites for taking a leading position. Notably, half of the top 10 productive institutions in the field of phage and bacteria were from France, which suggested the strong research background of France and provided directions for future research collaborations in this field.
Reference co-citation and keyword analysis provided a comprehensive perspective for the interpretation of research trends and hot spots. “Phage therapy”, appeared in two topics including gut phage, and phage and tumor as a keyword with the high citation burst. Phage therapy is self-limiting, and once the host bacteria are eliminated, the number of phages decreases sharply. Moreover, when the host bacteria spread, it can be expanded autonomously and actively, so it can be administered in small doses. On the one hand, phage therapy offers a solution to wean ourselves off antibiotics, which may contribute to tackle antibiotic resistance. Little et al. reported the successful case of phage in the treatment of Mycobacterium chelonae infection for the first time, and the tested patient had excellent clinical results and developed neutralizing antibodies against the phage, without the bacterial resistance to the phage [53]. Another case of phages treating Mycobacterium abscess in the lungs was also groundbreaking, which provided a reference for solving bacterial infections in organs. Jerry A Nick et al. found that the ability of phage to dissolve Mycobacterium abscess was optimized by genetic engineering, and the drug-resistant bacterial infection disappeared successfully after intravenous infusion of the engineered phage [54].
As for the cancer, this study found that “colorectal cancer” was a recent hot spot in the field of phage and tumor. CRC is the third most common malignant tumors with high mortality, low five-year survival rate and heavy disease burden [7, 55]. In 2023, there were expected to be 153,020 new CRC cases and 52,550 deaths in the USA [7]. Phage differs in abundance and diversity between healthy people and CRC patients, and thus, as a biomarker, it has great application potential in the diagnosis of CRC. Siyuan Shen et al. included 317 metagenomic sequencing samples and identified that 5 phages significantly enriched in CRC, including Peptacetobacter hiranonis Phage, Fusobacterium nucleatum animalis 7_1 Phage, Fusobacterium nucleatum polymorphum Phage, Fusobacterium nucleatum animalis 4_8 Phage, and Parvimonas micra Phage through the random forest model (AUC = 0.8616) [56]. Given the interaction between phages and bacteria, phages can indirectly affect the progression of CRC through the regulation of CRC-related bacteria. In the process of CRC development, phage was found to be correlated with butyrate producing bacteria, including Eubacterium rectale, Eubacterium cellulosolvens, and Butyrivibrio proteoclasticus [57]. The changes of bacterial community structure mediated by phages play an important role in the development of CRC [16]. Phages can enhance the body's immune response to mitigate the effects of cancer-promoting bacteria on CRC. The M13 phage attached with silver nanoparticles was designed to eliminate symbiotic Fusobacterium nucleatum, with an inhibitory anti-tumor immune response, and activate antigen-presenting cells. In situ CRC mouse models, the survival rate of mice was significantly increased and the anti-tumor immune response was enhanced [58]. In addition, the powerful carrier potential and targeting ability of phages are also applied in CRC [27, 59, 60]. The efficiency of phage drug delivery is affected by gastrointestinal environmental factors, such as PH, temperature, stomach acid, etc., thus leading to premature degradation of phages [61–64]. With the rapid development of materials science, hydrogels, liposomes, electrospinning fibers, etc., are used for embedding and delivery of phages to protect the vitality of phages during gastrointestinal transport, aiming to achieve targeted release and accurate quantification [65–68].
Increasing evidence confirms the close relationship between phage and tumor, which suggests the value of phages in cancer prevention and treatment. Phage preparation is a heterologous protein for humans, and may cause transient allergic reactions such as fever, shortness of breath, and wheezing when taken orally, topically, by aerosol inhalation, or intravenously. The exponential proliferation of phages is a significant advantage of phage therapy. Under the right conditions, each phage will produce several hundred progeny phages in a lysis cycle. Therefore, the therapeutic effect can be achieved with a small amount of bacteriophage preparation. Phages themselves may carry virulence genes or drug-resistance coding genes, which may increase host virulence or produce novel pathogenic strains through general or specific transduction mechanisms. However, by modifying phages with molecular biological methods, the toxin genes can be eliminated. Phage targeting prevents damage to other organs. Therefore, phage preparation has little self-toxicity. However, phage therapy has not been widely used in clinical practice due to its own limitations, such as narrow antibacterial spectrum, possible allergic reactions, and drug resistance after frequent use. Moreover, the function of phage genes is still largely unknown. Through high-throughput sequencing technology, bioinformatics analysis, combined with multi-omics analysis, it is expected to accelerate the decoding of phage gene function puzzles. The keyword “proteins” had a high burst intensity in phage and tumor in this study. At present, most research focuses on phage display technology to screen specific peptides/proteins for application in cancer diagnosis and treatment. Phage gene coding products, such as lyase, are easy to be edited by genetic engineering technology, and do not proliferate, which provides a new direction for tumor phage therapy, but few studies have reported their discovery in the field of tumor. Phage lysin, as an "early protein", is a novel antimicrobial agent in recent years, and its host selectivity was reported [69]. With the gradual understanding of the function of phage genes, it is expected that the role of phage gene coding products in the field of cancer will be further studied, and their practical clinical application value will be explored.
In this study, in order to explore the relationship between phage and tumor from a dynamic and comprehensive perspective, CiteSpace was used to visually analyze gut phage, phage and bacteria, and phage and tumor in sequence, but with certain limitations. First, the exclusion of non-English articles may lead to the omission of valuable articles. Moreover, articles were only from the Web of Science, so this also leads to incomplete retrieval of articles. In addition, given the high content of phages in the gut virome, the topic will be narrowed down in the future to study the relationship between phage and gut disease, such as CRC, to provide a more granular perspective.
Conclusion
In this study, three topics, including gut phage, phage and bacteria, and phage and tumor were visually analyzed, and the research trends and hotspots in the three fields were provided. From a dynamic and comprehensive perspective, the role and application prospect of phages in cancer, especially CRC, were interpreted, which provided a new direction for future research.
Supplementary Information
Additional file 1. Figure S1. Flowchart of the study. Figure S2. Research tendency. A: Line chart of the annual publications on gut phage from 2008 to 2022. B: Line chart of the annual publications on phage and bacteria from 2008 to 2022. C: Line chart of the annual publications on phage and tumor from 2008 to 2022. |
Title: Highly selective and flexible silver nanoparticles-based paper sensor for on-site colorimetric detection of paraquat pesticide | Body:
1.
Introduction
Chemical pesticides have demonstrated their potential since the 1940s by increasing global agricultural productivity, reducing insect-borne diseases, and protecting wood products.1,2 However, the amount of pesticide reaching the target pest is 0.1% only, and the remaining 99.9% goes into the environment, water bodies and the food chain.2 Pesticides are non-degradable toxic chemicals, which can lead to diseases such as kidney dysfunction, birth defects, neuro disorder, and cancer.2,3 On the other hand, 50% of the world population directly depends on the agriculture profession and almost the entire global population indirectly depends on agriculture.3 Hence, pesticide detection, control use and pesticide destruction are some of the urgent issues in the agro sector.
In this regard, paper-based sensors, also known as paper based analytical devices (PAD), present a cost-effective, rapid detection solution for pesticide in agro field settings, due to the cheap cellulose, controlled porosity, and biocompatibility.3,4 Paper sensors need specific recognition elements to be added, such as enzymes, nanoparticles, and molecularly imprinted polymers (MIPs), to detect target pesticides in real samples.5–7 Paper based methods include colorimetry, fluorescence, surface enhancement Raman scattering (SERS), and electrochemical method, where the majority of techniques are time-consuming, costly and require complicated instrumentation.4 Among these, paper based colorimetric methods provide naked eye observation or by mobile based image analysis and without any sophisticated instruments. The colorimetric method mostly relies on the aggregation phenomena of nanoparticles, which leads to the color modulation of the nanoparticle on the sensing platform.
Silver nanoparticles (AgNPs) have become popular for colorimetric sensors,8 due to their size, shape, interparticle distance and functionalization dependent optical properties and also due to high surface energy or surface reactivity.9–11 AgNPs can be functionalized with specific receptors or ligands that selectively bind to target pesticide molecules, enabling the development of highly sensitive and selective sensors.8 AgNPs have potential in colorimetric sensors due to their aggregation properties through interaction with the analyte.12 The aggregation phenomena of AgNPs can be further tailored by capping agent and through its surface plasmon resonance phenomena.12 Dubas et al. synthesized poly(methacrylic acid)-AgNP and used it for colorimetric sensing of ammonia.13 Orouji et al. used uncapped AgNP at different pH for aggregation induced detection of organophosphorus pesticide.14 Jana et al. synthesized silver-carbon dot hybrid and used it for sensing of ethanol.15
Paraquat (1,1-dimethyl-4,4-pyridinium chloride, PQ) has been used worldwide as herbicide in agriculture since its discovery and after its commercial approval in 1955.1,16 Paraquat ions bind to the ferredoxin binding site of photosystems, destroying cell membranes and ultimately causing the death of the plant.1 The estimated oral lethal dose (LD) of paraquat in humans is 35 mg kg−1.17 PQ has long-term persistence and is toxic causing damage in brain, lungs, liver, and kidneys.18 Hence, analyzing herbicide quantitatively with a portable, low-cost, high-sensitive sensor is essential.
In literature, several attempts have been made to detect the PQ by various approaches, including spectroscopic,19 fluorimetric,20 electrochemical,21 and colorimetric,22 liquid chromatography/electrospray ionization-mass spectrometry, ultraperformance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS).23 Traiwatcharanon et al. prepared the PbO-NPs/SPE for sensing of PQ herbicide with a linear range of 1–5 mM using electrochemical technique (with PbO-NPs as an active compound).24 Somnet et al. prepared platinum nanoparticles coated with a molecularly imprinted polymer (PtNPs@MIP) for sensing PQ with a detection limit of 20 mM using electrochemical technique.25 Zhao et al. prepared a pyranine-based fluorescent “turn-off” method for PQ sensing with a linear range of 1–20 μM.26 Kong et al. prepared AuNP–GO composite for sensing of PQ using electrochemical method.27 Xiong et al. prepared a carboxyl group functionalized AuNP for electrochemical detection of PQ pesticide.28 Shan et al. prepared Au–chitosan composite for electrochemical detection of PQ pesticide.29 Chang et al. fabricated a paper-based and image analysis based sensor for sensing PQ with a detection limit of 28 μM.30 Chaikhan et al. designed a PQ sensor with chromatography paper with a detection limit of 1.24 mg L−1.31 The PAD-based sensors are more costly than paper-based sensors due to the use of inkjet printing and wax printing. Wang et al. reported a paper sensor modified by mesoporous silica coupled with carboxylatopillar[5]arene for PQ detection, however used surface enhanced Raman scattering (SERS) method, which is costly and not portable.32 To the best of our knowledge, there is no colorimetric paper-based PQ pesticide sensor has been reported in the literature. Also the existing PQ sensor rarely addressed sensing parameters like selectivity, interference and repeatability.30,31 The paper based pesticide sensors are struggling with selectivity issues, which have been addressed in the present work.
We have prepared a paper-based colorimetric sensor consisting of citrate-capped silver nanoparticles (cc-AgNPs) and chitosan for selective detection of PQ herbicide. Chitosan has been selected as a robust, biocompatible matrix for developing a sensing platform due to its film-forming ability through its cationic amino group and chelating property to metal ions.33,34 The nanocomposite paper film has been thoroughly characterized using UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and transmission electron microscopy (TEM). The selectivity for paraquat pesticide was attained using the modified paper-based sensor, with a limit of detection (LOD) and linear range (LR) of 10 μM and 10–100 μM, respectively. The other sensor parameters, such as reproducibility, interference, and real samples analysis, showed the RSD% values as 5%, 2%, and 3.5%, respectively, which are within the WHO recommendation of 20%. The present work will open up new avenues for advancement in flexible electronics; cost-effective, portable and on-site sensors, and sustainable device development.
2.
Experimental method
2.1
Materials
Chitosan (CS), ascorbic acid, and trisodium citrate were purchased from Sigma-Aldrich, USA. Silver nitrate (AgNO3), sodium hydroxide (NaOH), and acetic acid were purchased from SRL, India. Chlorpyrifos, pretilachlor, cypermethrin, paraquat, deltamethrin, and dimethoate were purchased from Shillong, India. Deionized water with pH = 6.5 and resistivity = 18.5 MΩ × cm, was used for making solutions for different pesticides.
2.2
Preparation of citrate-capped silver nanoparticles (cc-AgNPs) and chitosan solution (CS)
Citrate-capped silver nanoparticles (cc-AgNPs) were prepared using the procedure by Alula et al.12 Firstly, ascorbic acid (0.6 mM) and trisodium citrate (3 mM) were prepared in deionized water. These two solutions were mixed in the conical flask, and the pH (10.5) was adjusted using NaOH solution (0.1 M) under continuous stirring. Later, AgNO3 solution (0.1 M) was added to the conical flask containing the two solutions. The solution was heated to 30 °C for 15 minutes, resulting in a yellow color solution. The yellow solution indicates the preparation of the cc-AgNPs colloids, as shown in Fig. 1A. The CS solution with a concentration of 0.1 M was prepared by mixing chitosan flakes with acetic acid (0.1 M) under constant stirring for 6 hours at room temperature.
Fig. 1
(A) Flow chart for preparation of citrate-capped silver nanoparticles. (B) Working mechanism for citrate-capped AgNPs paper-based pesticide sensor.
2.3
Smartphone camera and imaging characterization
All images of the paper discs were taken by the smartphone camera. Smartphone model name (Redme Note 8 pro), camera pixel (64 × 106), camera F stop (f/1.9), color representation (RGB), and object distance (32 cm). Modified paper surfaces have been characterized with FTIR (model) and TEM (model).
2.4
Preparation of paper-based sensor and its measurement
Firstly, Whatman filter paper (44) was immersed in the chitosan solution for 15 seconds and allowed to vacuum dry overnight. In the second stage, the chitosan-attached Whatman filter paper (44) was immersed in the citrate-capped silver nanoparticle solution for 15 seconds and vacuum-dried overnight. The prepared paper-based sensor was used to sense paraquat (PQ) pesticides for further studies.
The detection of PQ relies on the reaction between the CS + cc-AgNPs probe and PQ pesticide. For this purpose, varying concentrations of PQ was treated with the designed paper probe and allowed to react for 9 min. A smartphone camera recorded the color changes on paper discs in photographic conditions. This was followed by image processing and colorimetric quantification, where the colored images of the paper surface were processed to obtain the corresponding RGB values. A uniform selective area was adapted for the image analysis to eliminate the errors in pixel values in the capturing condition. The selective area eliminates the dark rings and patches. It provides accurate changes in the pixel values by taking mean intensity values of the distributed uniform area of the CS + cc-AgNPs paper-based sensor. Fig. 1B shows a change in RGB intensity when CS + cc AgNPs interact with PQ pesticide.
2.1
Materials
Chitosan (CS), ascorbic acid, and trisodium citrate were purchased from Sigma-Aldrich, USA. Silver nitrate (AgNO3), sodium hydroxide (NaOH), and acetic acid were purchased from SRL, India. Chlorpyrifos, pretilachlor, cypermethrin, paraquat, deltamethrin, and dimethoate were purchased from Shillong, India. Deionized water with pH = 6.5 and resistivity = 18.5 MΩ × cm, was used for making solutions for different pesticides.
2.2
Preparation of citrate-capped silver nanoparticles (cc-AgNPs) and chitosan solution (CS)
Citrate-capped silver nanoparticles (cc-AgNPs) were prepared using the procedure by Alula et al.12 Firstly, ascorbic acid (0.6 mM) and trisodium citrate (3 mM) were prepared in deionized water. These two solutions were mixed in the conical flask, and the pH (10.5) was adjusted using NaOH solution (0.1 M) under continuous stirring. Later, AgNO3 solution (0.1 M) was added to the conical flask containing the two solutions. The solution was heated to 30 °C for 15 minutes, resulting in a yellow color solution. The yellow solution indicates the preparation of the cc-AgNPs colloids, as shown in Fig. 1A. The CS solution with a concentration of 0.1 M was prepared by mixing chitosan flakes with acetic acid (0.1 M) under constant stirring for 6 hours at room temperature.
Fig. 1
(A) Flow chart for preparation of citrate-capped silver nanoparticles. (B) Working mechanism for citrate-capped AgNPs paper-based pesticide sensor.
2.3
Smartphone camera and imaging characterization
All images of the paper discs were taken by the smartphone camera. Smartphone model name (Redme Note 8 pro), camera pixel (64 × 106), camera F stop (f/1.9), color representation (RGB), and object distance (32 cm). Modified paper surfaces have been characterized with FTIR (model) and TEM (model).
2.4
Preparation of paper-based sensor and its measurement
Firstly, Whatman filter paper (44) was immersed in the chitosan solution for 15 seconds and allowed to vacuum dry overnight. In the second stage, the chitosan-attached Whatman filter paper (44) was immersed in the citrate-capped silver nanoparticle solution for 15 seconds and vacuum-dried overnight. The prepared paper-based sensor was used to sense paraquat (PQ) pesticides for further studies.
The detection of PQ relies on the reaction between the CS + cc-AgNPs probe and PQ pesticide. For this purpose, varying concentrations of PQ was treated with the designed paper probe and allowed to react for 9 min. A smartphone camera recorded the color changes on paper discs in photographic conditions. This was followed by image processing and colorimetric quantification, where the colored images of the paper surface were processed to obtain the corresponding RGB values. A uniform selective area was adapted for the image analysis to eliminate the errors in pixel values in the capturing condition. The selective area eliminates the dark rings and patches. It provides accurate changes in the pixel values by taking mean intensity values of the distributed uniform area of the CS + cc-AgNPs paper-based sensor. Fig. 1B shows a change in RGB intensity when CS + cc AgNPs interact with PQ pesticide.
3.
Results and discussion
3.1
FTIR and TEM characterization of citrate-AgNP based paper sensor platform
FTIR spectra of the chitosan (CS), cc-AgNPs, and, CS + cc-AgNPs are shown in Fig. 2. The characteristic peaks of chitosan are present at 1020 cm−1 (C–O–C stretching vibrations) due to an ester group of CS.33 The band at 2920 cm−1, 1526 cm−1, and 750 cm−1 corresponds to the C–H stretching, amide III, and N–H bending vibration of chitosan, respectively.33,35,36 Also, the band at 3627 cm−1 corresponds to the O–H stretching vibration in chitosan.37 For cc-AgNPs, the presence of bands at 1582 cm−1 and 1405 cm−1 correspond to the asymmetric and symmetric C
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O stretch of carboxylate ion, respectively, and a wide band at 3393 cm−1 corresponding to the O–H stretch proves the surface capping of trisodium citrate on silver nanoparticles.38 A band at 882 cm−1 is present due to the stretching vibration of the CO bond in the carboxylate group of the citrate ligand.39 Also, the band at 2355 cm−1 is due to C–N stretching.40 The shift from 3393 cm−1 to 3350 cm−1 can be attributed to the interaction between citrate-capped silver nanoparticles and chitosan through hydrogen bonding, leading to a change in the hydrogen bonding environment of the hydroxyl groups. Peak assignments of the composite film are shown in Table S1.†
Fig. 2
FTIR graph of components of nanocomposite and nanocomposite film.
The TEM characterization has also been performed to study the pesticide interaction with cc-AgNPs. Fig. 3A and B show the TEM image of cc-AgNPs with PQ pesticide on a nanometer scale. When PQ (10 μM) was mixed with the cc-AgNPs solution, the aggregation of the cc-AgNPs took place, as shown in Fig. 3A. The interaction may be due to chemical groups in pesticides that interact with the surface of the nanoparticles.41 This interaction could lead to the formation of chemical bonds or attractive forces that cause the nanoparticles to aggregate. The energy dispersive X-ray analysis (EDX) shows the elemental analysis of the sample (Fig. 3C), which confirms more than 60% Ag atoms from AgNP, Cu atom due to the TEM grid, and the presence of other elements (Fe, Al) in low% is due to spurious X-rays.42 The d-value from the SAED pattern (Fig. 3D) is calculated for different planes ((111), (200), (220), (311)) as 0.24 nm, 0.19 nm, 0.14 nm, 0.12 nm, respectively, which aligns with the literature report.41,43,44
Fig. 3
TEM characterization of cc-AgNP with PQ, (A and B) TEM images, (C and D) EDX analysis, and SAED pattern.
3.2
Optimization of paper type, pH, and reaction time for sensing
We have spread the nanocomposite on the three different Whatman papers (42, 44, and 602). We checked color intensity of individual paper and Whatman (44) was optimized (Fig. 4A). The green intensity was found to be the highest, which was chosen for colorimetric sensing compared to other colors (red and blue) for all studies (Fig. 4B). To find out the color channel that provides maximum sensitivity for the analytical applications of the designed biosensor, we individually tested and compared the primary color channels.
Fig. 4
(A) Selection of the paper using RGB intensity and (B) the selection of the individual intensity (30 μM). (C) Graph showing the signal intensity change with the reaction time (selected for pH 7, 30 μM). (D) Graph showing the signal intensity change with the reaction time (selected for 9 minutes, 30 μM).
The pH study was carried out by varying the pH from 3 to 11. Fig. 4C shows the signal intensity variation with the different pH. The pH of the test solution was adjusted using HCl or NaOH. The concentration used for the study was 30 μM of PQ pesticide. Maximum responses were obtained at pH = 7, and the response decreased on acidic and alkaline pH (Fig. 4C). The prepared paper-based sensor was tested for different reaction times, and it was found that it gradually increased and got saturated and optimized after 9 min of reaction time for sensing pesticides (Fig. 4D). Water contact angle measurements were taken to assess the hydrophobicity and water absorption on Whatman paper, chitosan-modified paper, and CS + cc-AgNPs-modified paper as shown in Fig. S1.† While no contact angle was measurable for Whatman paper due to strong liquid–solid attraction, chitosan-modified paper showed a hydrophobic contact angle of 109°, and CS + cc-AgNPs-modified paper had a contact angle of 116°, indicating it is nearing superhydrophobicity.
3.3
Detection of PQ using the CS + cc-AgNPs paper-based sensor
To evaluate the colorimetric sensor, the RGB (red, green, and blue) convention of color image analysis was followed, where we first selected the most sensitive primary color channel, as seen in Fig. 4A. The effective intensity was calculated using eqn (1).451
I
RGB is the mean pixel intensities of all primary channels obtained from a selected area. In digital images, the intensity IRGB reflects pixel intensity of the actual color, composed of the intensities of three primary color channels: IRed, IGreen, and IBlue. In the colorimetric changes, gradual intensification of the appeared color signifies the absorption of the complementary color.46 To find out the color channel that provides maximum sensitivity for the analytical applications of the designed biosensor, we individually tested and compared the primary color channels as seen in Fig. 4B. The individual intensity was calculated using eqn (2).45 The IGreen was selected for all the further studies.2where, IGreen is the mean pixel intensity of the green channel obtained from the selected area.
3.4
UV-visible spectra of CS + cc-AgNPs and selectivity study
Fig. 5 shows the UV-visible spectra of cc-AgNPs and CS + cc-AgNPs in the presence of different pesticides, which shows an intense peak around 405 nm due to localized surface plasmon resonance (LSPR) band of metallic AgNP (Fig. 5A).18Fig. 5A and B indicate that the intensity of the 405 nm peak varied by adding different pesticides. However, a significant decrease in intensity was observed when PQ pesticide was added (Fig. 5A and B), which reflects a reduction in the concentration of individually dispersed cc-AgNPs in suspension and the red shift in the LSPR band (550 nm) reflects the increase of particle size or aggregation of cc-AgNP.18,47 Citrate (trisodium citrate) acts as a stabilizing agent, capping agent to AgNPs to prevent aggregation in normal condition and provide a negatively charged surface.48 Paraquat, a positively charged pesticide49 and hence can interact electrostatically with the negatively charged cc-AgNPs. The chitosan matrix may absorb paraquat due to its porous structure and it can make hydrogen bonding with paraquat through amine groups.50 The schematic diagram for the sensing mechanism or interaction of PQ pesticide with active platform, have been presented in Fig. S2.† The interaction of CS + cc-AgNP with paraquat, facilitates the aggregation of AgNP leading to color change and change in their localized surface plasmon resonance (LSPR) detectable by UV-visible spectroscopy or even by the naked eye.47 These types of intercation of PQ with the CS + cc-AgNP paper based platform, bring the selectivity of PQ sensing.
Fig. 5
(A) UV-visible spectra of cc-AgNP with different pesticides, (B) UV-visible spectra of CS + cc-AgNP with different pesticides, (C) selectivity of the prepared CS + cc-AgNPs paper-based sensor, (D) calibration curve of the prepared paper-based sensor, (E) color changes recorded of the CS + cc-AgNP modified paper at different concentration of PQ pesticide.
Fig. 5C shows the selectivity study of the PQ pesticide carried out by paper-based colorimetric measurements along with other pesticides such as pretilachlor, cypermethrin, PQ, chlorpyriphos, dimethoate, and deltamethrin. It was found that the effective intensity in presence of PQ was 3× higher signal compared to other pesticides. Therefore, PQ showed better selectivity and was selected as a target sensor analyte for determining parameters like linear range, detection limit, interference and real sample studies.
Fig. 5D shows the calibration curve of the prepared paper-based sensor with different concentrations of PQ pesticides. The limit of detection (LOD) has been calculated using the calibration curve's slope and standard deviation (SD). The LOD was determined using eqn (3) (Fig. 5D)33 and it was found to be 10 μM using a paper-based sensor. The LR value of a sensor is generally defined and calculated from the linear fitting of the calibration curve with R2 value > 0.95 or within a 5% deviation. The linear fitting of the signal response with a concentration of PQ pesticide has been done and LR value of the sensor have been found out to be 10–100 μM with the R2 value of fitting as 0.98. Fig. 5E shows the colour change of the CS + cc-AgNPs film after adding different amounts of PQ pesticides. Table 1 summarizes the available literature on PQ pesticide sensing using different composite and different techniques.3
Summary of PQ pesticide sensing parameters from literature data and present work
S. No.
Nanocomposite used
Technique
LOD
Linear range
Sensitivity
RSD% (real sample/reproducibility)
References
1
Citrate-AgNP
Paper-based colorimetric
10 μM
10–100 μM
NR
3%/4%
Present work
2
PbO NPs/SPE
Electrochemical
1.1 mM
1–5 mM
204.85 mA mM−1 cm−2
5.95%
51
3
GCE
Electrochemical
3.2 μM
3.9–31.0 μM
NR
NR
52
4
PtNPs/MIP
Electrochemical
0.02 μM
0.05–1000 μM
NR
NR
25
5
Tb-MOF
Photoluminescence
2.84 μM
0–50 μM
NR
NR
53
6
Carboxylatopillar[5]arene-SiO2 paper
SERS
0.000117 μM
NR
NR
NR
32
7
Nf/SPGE
Electrochemical
0.31 μM
5.0–125 μM
NR
4.78%
54
8
SiO2 modified GCE electrodes
Electrochemical
12 nM
10 nM to 10 μM
0.021 μA nM−1 cm−2
NR
55
9
Graphene-B-diamond electrode
Electrochemical
10 μM
0.2 and 1.2 μM
31.83 μA μM−1 cm−2
NR
56
10
Au NPs/caboxylato-pillar[5]arenes
Electrochemical
0.73 nM
3.8–10 μM
NR
NR
57
3.5
Repeatability, reproducibility, interference, and real samples study
In Fig. 6, panel A shows the reproducibility and repeatability of the paper based sensor. The reproducibility of the sensor was tested on 3 different CS + cc-AgNPs modified paper film with RSD value of 4% (Fig. 6A1). The films were prepared under the same ambient conditions and the same paper have been cut into three pieces of films. The CS + cc-AgNP paper acts as a platform for colorimetric sensing of PQ pesticide and the justification of its standardization and preparation are as follows. The cc-AgNP solution is the standard solution since it has been prepared following simple chemical mixing at a given standard condition and no further centrifugation or filtration. The paper used was Whatman paper (grade 44) and hence it is standard one. We have set the standards of the coating method of CS + cc-AgNP on paper by following protocol. To prepare CS + cc-AgNP, we have taken chitosan with standard molecular weight (MW 150–700 kDa, 90% DA) and cc-AgNP was prepared as mentioned section 2.2. The reaction time of cc-AgNP with paper has been optimized through visual inspection until the mechanical strength of the paper looses, as 15 s. The CS + cc-AgNP coating on paper was done by two steps i.e. 15 s dipping of paper in chitosan solution followed by overnight (12 h) dried at vacuum desiccator. Afterword, the chitosan-coated Whatman paper was dipped in cc-AgNPs for 15 s and similarly dried overnight (12 h) under vacuum. These two step methods made the coating process bias less and reproducible batch synthesis, with RSD value of reproducibility within 4.1% (Fig. 6A2). The repeatability of the reported sensor has been carried out (Fig. 6A3) using 4 numbers of paper-based sensors and the RSD value of the sensor responses was 5%, which indicates the reproducibility of the sensor.
Fig. 6
Panel-A: (A1) reproducibility study of the sensor with different CS + cc-AgNPs films in presence of PQ pesticide, (A2) reproducibility study of the sensor with different CS + cc-AgNPs batch in absence of PQ pesticide, (A3) repeatability study of the sensor; panel-B: interference studies in different interfering species and real samples: (B1) metal ions and organic interfering species, (B2) food samples analysis, (B3) soil samples analysis.
The Fig. 6, panel-B shows the studies of interference with metal oxide, organic species, food samples and soil samples, respectively. Fig. 6B1 shows the interference studies carried out using 10 μM of PQ pesticide in the presence of 2 μM metal ions (Zn2+, Cu2+, Ni2+, Al3+, Mg2+) as well as 2 μM organic interfering similar molecules (ascorbic acid, aspartic acid, glutamic acid, citric acid), with the variation of signal response within RSD value of 2% which represents the consistency of sensor response in the presence of interfering species. Fig. 6B2 shows the sensor response for food samples (tomato, distilled water, and tap water), with RSD value of 3%, which shows that the paper-based sensors could be used for real sample or matrix such as food sample analysis. In order to achieve real sample analysis in more versatile manner, the interference studies with soil sample was also conducted. The soil samples (black, red, brick, lake, and farm) were collected from various surface sources following an earlier report.58 The collected samples were sieved and a specific quantity (10 grams) of each soil, was dissolved in 25 mL of DI water and left for 8 hours for sedimentation of the dirty layer. The samples were filtered and collected in a 50 mL volumetric flask and then spiked with PQ pesticide solution (10 μM). The scheme for preparing soil samples is shown in Fig. S3.† The pre-treatment procedure for soil samples yielded favourable recovery results as shown in Table 2, which indicates a significant improvement for practical detection of PQ in soil samples. The paper-based flexible sensor demonstrated excellent average RSD value with 3.5% for soil sample analysis. The different sensing parameters obtained from the food and soil samples indicates the on-site sensing relevance.
Application of paper based sensor for the determination of PQ in spiked soil samples
S. No.
Soil samples
Spiked (μM)
Recovered (μM)
Recovery (%)
1
Farm soil
10
8.47
84.7
2
Lake soil
10
9.52
95.2
3
Black soil
10
8.75
87.5
4
Red soil
10
8.19
85.9
5
Brick soil
10
7.82
74.2
3.1
FTIR and TEM characterization of citrate-AgNP based paper sensor platform
FTIR spectra of the chitosan (CS), cc-AgNPs, and, CS + cc-AgNPs are shown in Fig. 2. The characteristic peaks of chitosan are present at 1020 cm−1 (C–O–C stretching vibrations) due to an ester group of CS.33 The band at 2920 cm−1, 1526 cm−1, and 750 cm−1 corresponds to the C–H stretching, amide III, and N–H bending vibration of chitosan, respectively.33,35,36 Also, the band at 3627 cm−1 corresponds to the O–H stretching vibration in chitosan.37 For cc-AgNPs, the presence of bands at 1582 cm−1 and 1405 cm−1 correspond to the asymmetric and symmetric C
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O stretch of carboxylate ion, respectively, and a wide band at 3393 cm−1 corresponding to the O–H stretch proves the surface capping of trisodium citrate on silver nanoparticles.38 A band at 882 cm−1 is present due to the stretching vibration of the CO bond in the carboxylate group of the citrate ligand.39 Also, the band at 2355 cm−1 is due to C–N stretching.40 The shift from 3393 cm−1 to 3350 cm−1 can be attributed to the interaction between citrate-capped silver nanoparticles and chitosan through hydrogen bonding, leading to a change in the hydrogen bonding environment of the hydroxyl groups. Peak assignments of the composite film are shown in Table S1.†
Fig. 2
FTIR graph of components of nanocomposite and nanocomposite film.
The TEM characterization has also been performed to study the pesticide interaction with cc-AgNPs. Fig. 3A and B show the TEM image of cc-AgNPs with PQ pesticide on a nanometer scale. When PQ (10 μM) was mixed with the cc-AgNPs solution, the aggregation of the cc-AgNPs took place, as shown in Fig. 3A. The interaction may be due to chemical groups in pesticides that interact with the surface of the nanoparticles.41 This interaction could lead to the formation of chemical bonds or attractive forces that cause the nanoparticles to aggregate. The energy dispersive X-ray analysis (EDX) shows the elemental analysis of the sample (Fig. 3C), which confirms more than 60% Ag atoms from AgNP, Cu atom due to the TEM grid, and the presence of other elements (Fe, Al) in low% is due to spurious X-rays.42 The d-value from the SAED pattern (Fig. 3D) is calculated for different planes ((111), (200), (220), (311)) as 0.24 nm, 0.19 nm, 0.14 nm, 0.12 nm, respectively, which aligns with the literature report.41,43,44
Fig. 3
TEM characterization of cc-AgNP with PQ, (A and B) TEM images, (C and D) EDX analysis, and SAED pattern.
3.2
Optimization of paper type, pH, and reaction time for sensing
We have spread the nanocomposite on the three different Whatman papers (42, 44, and 602). We checked color intensity of individual paper and Whatman (44) was optimized (Fig. 4A). The green intensity was found to be the highest, which was chosen for colorimetric sensing compared to other colors (red and blue) for all studies (Fig. 4B). To find out the color channel that provides maximum sensitivity for the analytical applications of the designed biosensor, we individually tested and compared the primary color channels.
Fig. 4
(A) Selection of the paper using RGB intensity and (B) the selection of the individual intensity (30 μM). (C) Graph showing the signal intensity change with the reaction time (selected for pH 7, 30 μM). (D) Graph showing the signal intensity change with the reaction time (selected for 9 minutes, 30 μM).
The pH study was carried out by varying the pH from 3 to 11. Fig. 4C shows the signal intensity variation with the different pH. The pH of the test solution was adjusted using HCl or NaOH. The concentration used for the study was 30 μM of PQ pesticide. Maximum responses were obtained at pH = 7, and the response decreased on acidic and alkaline pH (Fig. 4C). The prepared paper-based sensor was tested for different reaction times, and it was found that it gradually increased and got saturated and optimized after 9 min of reaction time for sensing pesticides (Fig. 4D). Water contact angle measurements were taken to assess the hydrophobicity and water absorption on Whatman paper, chitosan-modified paper, and CS + cc-AgNPs-modified paper as shown in Fig. S1.† While no contact angle was measurable for Whatman paper due to strong liquid–solid attraction, chitosan-modified paper showed a hydrophobic contact angle of 109°, and CS + cc-AgNPs-modified paper had a contact angle of 116°, indicating it is nearing superhydrophobicity.
3.3
Detection of PQ using the CS + cc-AgNPs paper-based sensor
To evaluate the colorimetric sensor, the RGB (red, green, and blue) convention of color image analysis was followed, where we first selected the most sensitive primary color channel, as seen in Fig. 4A. The effective intensity was calculated using eqn (1).451
I
RGB is the mean pixel intensities of all primary channels obtained from a selected area. In digital images, the intensity IRGB reflects pixel intensity of the actual color, composed of the intensities of three primary color channels: IRed, IGreen, and IBlue. In the colorimetric changes, gradual intensification of the appeared color signifies the absorption of the complementary color.46 To find out the color channel that provides maximum sensitivity for the analytical applications of the designed biosensor, we individually tested and compared the primary color channels as seen in Fig. 4B. The individual intensity was calculated using eqn (2).45 The IGreen was selected for all the further studies.2where, IGreen is the mean pixel intensity of the green channel obtained from the selected area.
3.4
UV-visible spectra of CS + cc-AgNPs and selectivity study
Fig. 5 shows the UV-visible spectra of cc-AgNPs and CS + cc-AgNPs in the presence of different pesticides, which shows an intense peak around 405 nm due to localized surface plasmon resonance (LSPR) band of metallic AgNP (Fig. 5A).18Fig. 5A and B indicate that the intensity of the 405 nm peak varied by adding different pesticides. However, a significant decrease in intensity was observed when PQ pesticide was added (Fig. 5A and B), which reflects a reduction in the concentration of individually dispersed cc-AgNPs in suspension and the red shift in the LSPR band (550 nm) reflects the increase of particle size or aggregation of cc-AgNP.18,47 Citrate (trisodium citrate) acts as a stabilizing agent, capping agent to AgNPs to prevent aggregation in normal condition and provide a negatively charged surface.48 Paraquat, a positively charged pesticide49 and hence can interact electrostatically with the negatively charged cc-AgNPs. The chitosan matrix may absorb paraquat due to its porous structure and it can make hydrogen bonding with paraquat through amine groups.50 The schematic diagram for the sensing mechanism or interaction of PQ pesticide with active platform, have been presented in Fig. S2.† The interaction of CS + cc-AgNP with paraquat, facilitates the aggregation of AgNP leading to color change and change in their localized surface plasmon resonance (LSPR) detectable by UV-visible spectroscopy or even by the naked eye.47 These types of intercation of PQ with the CS + cc-AgNP paper based platform, bring the selectivity of PQ sensing.
Fig. 5
(A) UV-visible spectra of cc-AgNP with different pesticides, (B) UV-visible spectra of CS + cc-AgNP with different pesticides, (C) selectivity of the prepared CS + cc-AgNPs paper-based sensor, (D) calibration curve of the prepared paper-based sensor, (E) color changes recorded of the CS + cc-AgNP modified paper at different concentration of PQ pesticide.
Fig. 5C shows the selectivity study of the PQ pesticide carried out by paper-based colorimetric measurements along with other pesticides such as pretilachlor, cypermethrin, PQ, chlorpyriphos, dimethoate, and deltamethrin. It was found that the effective intensity in presence of PQ was 3× higher signal compared to other pesticides. Therefore, PQ showed better selectivity and was selected as a target sensor analyte for determining parameters like linear range, detection limit, interference and real sample studies.
Fig. 5D shows the calibration curve of the prepared paper-based sensor with different concentrations of PQ pesticides. The limit of detection (LOD) has been calculated using the calibration curve's slope and standard deviation (SD). The LOD was determined using eqn (3) (Fig. 5D)33 and it was found to be 10 μM using a paper-based sensor. The LR value of a sensor is generally defined and calculated from the linear fitting of the calibration curve with R2 value > 0.95 or within a 5% deviation. The linear fitting of the signal response with a concentration of PQ pesticide has been done and LR value of the sensor have been found out to be 10–100 μM with the R2 value of fitting as 0.98. Fig. 5E shows the colour change of the CS + cc-AgNPs film after adding different amounts of PQ pesticides. Table 1 summarizes the available literature on PQ pesticide sensing using different composite and different techniques.3
Summary of PQ pesticide sensing parameters from literature data and present work
S. No.
Nanocomposite used
Technique
LOD
Linear range
Sensitivity
RSD% (real sample/reproducibility)
References
1
Citrate-AgNP
Paper-based colorimetric
10 μM
10–100 μM
NR
3%/4%
Present work
2
PbO NPs/SPE
Electrochemical
1.1 mM
1–5 mM
204.85 mA mM−1 cm−2
5.95%
51
3
GCE
Electrochemical
3.2 μM
3.9–31.0 μM
NR
NR
52
4
PtNPs/MIP
Electrochemical
0.02 μM
0.05–1000 μM
NR
NR
25
5
Tb-MOF
Photoluminescence
2.84 μM
0–50 μM
NR
NR
53
6
Carboxylatopillar[5]arene-SiO2 paper
SERS
0.000117 μM
NR
NR
NR
32
7
Nf/SPGE
Electrochemical
0.31 μM
5.0–125 μM
NR
4.78%
54
8
SiO2 modified GCE electrodes
Electrochemical
12 nM
10 nM to 10 μM
0.021 μA nM−1 cm−2
NR
55
9
Graphene-B-diamond electrode
Electrochemical
10 μM
0.2 and 1.2 μM
31.83 μA μM−1 cm−2
NR
56
10
Au NPs/caboxylato-pillar[5]arenes
Electrochemical
0.73 nM
3.8–10 μM
NR
NR
57
3.5
Repeatability, reproducibility, interference, and real samples study
In Fig. 6, panel A shows the reproducibility and repeatability of the paper based sensor. The reproducibility of the sensor was tested on 3 different CS + cc-AgNPs modified paper film with RSD value of 4% (Fig. 6A1). The films were prepared under the same ambient conditions and the same paper have been cut into three pieces of films. The CS + cc-AgNP paper acts as a platform for colorimetric sensing of PQ pesticide and the justification of its standardization and preparation are as follows. The cc-AgNP solution is the standard solution since it has been prepared following simple chemical mixing at a given standard condition and no further centrifugation or filtration. The paper used was Whatman paper (grade 44) and hence it is standard one. We have set the standards of the coating method of CS + cc-AgNP on paper by following protocol. To prepare CS + cc-AgNP, we have taken chitosan with standard molecular weight (MW 150–700 kDa, 90% DA) and cc-AgNP was prepared as mentioned section 2.2. The reaction time of cc-AgNP with paper has been optimized through visual inspection until the mechanical strength of the paper looses, as 15 s. The CS + cc-AgNP coating on paper was done by two steps i.e. 15 s dipping of paper in chitosan solution followed by overnight (12 h) dried at vacuum desiccator. Afterword, the chitosan-coated Whatman paper was dipped in cc-AgNPs for 15 s and similarly dried overnight (12 h) under vacuum. These two step methods made the coating process bias less and reproducible batch synthesis, with RSD value of reproducibility within 4.1% (Fig. 6A2). The repeatability of the reported sensor has been carried out (Fig. 6A3) using 4 numbers of paper-based sensors and the RSD value of the sensor responses was 5%, which indicates the reproducibility of the sensor.
Fig. 6
Panel-A: (A1) reproducibility study of the sensor with different CS + cc-AgNPs films in presence of PQ pesticide, (A2) reproducibility study of the sensor with different CS + cc-AgNPs batch in absence of PQ pesticide, (A3) repeatability study of the sensor; panel-B: interference studies in different interfering species and real samples: (B1) metal ions and organic interfering species, (B2) food samples analysis, (B3) soil samples analysis.
The Fig. 6, panel-B shows the studies of interference with metal oxide, organic species, food samples and soil samples, respectively. Fig. 6B1 shows the interference studies carried out using 10 μM of PQ pesticide in the presence of 2 μM metal ions (Zn2+, Cu2+, Ni2+, Al3+, Mg2+) as well as 2 μM organic interfering similar molecules (ascorbic acid, aspartic acid, glutamic acid, citric acid), with the variation of signal response within RSD value of 2% which represents the consistency of sensor response in the presence of interfering species. Fig. 6B2 shows the sensor response for food samples (tomato, distilled water, and tap water), with RSD value of 3%, which shows that the paper-based sensors could be used for real sample or matrix such as food sample analysis. In order to achieve real sample analysis in more versatile manner, the interference studies with soil sample was also conducted. The soil samples (black, red, brick, lake, and farm) were collected from various surface sources following an earlier report.58 The collected samples were sieved and a specific quantity (10 grams) of each soil, was dissolved in 25 mL of DI water and left for 8 hours for sedimentation of the dirty layer. The samples were filtered and collected in a 50 mL volumetric flask and then spiked with PQ pesticide solution (10 μM). The scheme for preparing soil samples is shown in Fig. S3.† The pre-treatment procedure for soil samples yielded favourable recovery results as shown in Table 2, which indicates a significant improvement for practical detection of PQ in soil samples. The paper-based flexible sensor demonstrated excellent average RSD value with 3.5% for soil sample analysis. The different sensing parameters obtained from the food and soil samples indicates the on-site sensing relevance.
Application of paper based sensor for the determination of PQ in spiked soil samples
S. No.
Soil samples
Spiked (μM)
Recovered (μM)
Recovery (%)
1
Farm soil
10
8.47
84.7
2
Lake soil
10
9.52
95.2
3
Black soil
10
8.75
87.5
4
Red soil
10
8.19
85.9
5
Brick soil
10
7.82
74.2
4.
Conclusion
We have developed a straightforward, single-step paper-based flexible sensor platform composed of chitosan and citrate-capped silver nanoparticles on Whatman paper for on-site detection of PQ pesticides in real samples. The nanocomposite was thoroughly characterized using UV-visible spectroscopy, FTIR, and TEM. The colorimetric sensing signal was quantified and effectively analyzed using Adobe Photoshop, which produces the calibration curve of the sensor. The prepared paper-based flexible sensor showed the LOD and LR as 10 μM and 10–100 μM, respectively. The prepared paper-based sensor could selectively sense PQ pesticide with an effective intensity of 3× compared to other pesticides. The paper-based flexible sensor demonstrated excellent RSD value with 5% for repeatability, 4% for reproducibility, 2% for metal ions/organic interference, and 4% for food sample analysis, and 3.5% for soil sample analysis, indicating high precision sensing capability. Therefore, all sensor parameters were within the RSD value of 20% recommended by the WHO, indicating reliable and consistent performance for detecting PQ pesticides. The present work will open up new avenues for advancement in flexible electronics, cost-effective, portable, on-site sensors, and sustainable device development.
Data availability
The datasets generated and/or analyzed during this study will be available in the Trepo, the institutional repository of Tampere University (https://trepo.tuni.fi/). Additionally, the data supporting this article have been included as part of the Main Manuscript and ESI.†
Conflicts of interest
The authors have no conflicts of interest to declare.
Supplementary Material
RA-014-D4RA04557B-s001 |
Title: The TDP‐43/TP63 Positive Feedback Circuit Promotes Esophageal Squamous Cell Carcinoma Progression | Body:
1
Introduction
Esophageal cancer is one of the most common malignancies and the sixth leading cause of cancer death globally. Its two histologic subtypes are esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). In Eastern Asian, about 90% of esophageal cancer cases are ESCC.[
1
,
2
] The prognosis of ESCC is poor since most patients were usually diagnosed at advanced disease stages. Although systematic therapies (chemoradiotherapy,[
3
] targeted therapy,[
4
] and immune checkpoint inhibitor therapy[
5
]) have achieved great progress for disease control, the 5‐year survival rate for advanced ESCC is only 15%.[
2
,
6
] Lacking a fully understanding of pathological mechanisms contributing to the malignant progression of ESCC might be the main cause.
As a group of highly conserved proteins, RNA‐binding proteins (RBPs) are essential for post‐transcriptional regulation of gene expression.[
7
] Forming RNA‐protein complexes (RNPs) is a frequent interaction between the conventional RBPs and particular RNA targets. Because the target RNAs are diverse and complicated, these RNPs play different roles during a variety of biological processes. It has been reported that multiple RBPs are dysregulated in cancers and involved in fine‐regulation of RNA metabolism of oncogenes and/or tumor suppressors.[
8
] TAR‐DNA‐binding protein‐43 (TDP‐43, also known as TARDBP) is an RNA/DNA binding protein which belongs to the heterogeneous nuclear ribonucleoproteins (hnRNPs) family. On one hand, TDP‐43 can act as a canonical RBP to bind target RNAs. On the other hand, TDP‐43 functions as a transcription factor (TF) to bind specific DNA sequences. Consequently, TDP‐43 is essential for controlling RNA metabolism and gene transcription in human cells.[
9
,
10
] TDP‐43 has been reported as a pathological aggregating protein in several neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobar degeneration, and amyotrophic lateral sclerosis, which are often associated with tau pathology.[
11
,
12
] Accumulated evidences indicated that TDP‐43 is also involved in development of malignancies, such as hepatocellular carcinoma (HCC), neuroblastoma and breast cancer, via regulating mRNA translation, RNA alternative splicing, RNA transport, RNA stability, and miRNA processing.[
13
,
14
,
15
] For instance, by inhibiting the translation of GSK3β protein and activating the Wnt/β‐catenin pathway, TDP‐43 promotes growth and distant spread of HCC cells.[
16
] However, it remains for the most part unclear how TDP‐43 contributes to ESCC progression.
As a member of the P53 family, tumor protein 63 (TP63) functions as a sequence‐specific DNA‐binding TF.[
17
,
18
] TP63 is involved in the regulation of cell metabolism, apoptosis, DNA repair, and proliferation.[
19
,
20
] During mammalian development, TP63 is essential for regenerative proliferation in limb, craniofacial, and epithelial development.[
21
] In ESCC, TP63 acts as a master TF, cooperates with other TFs (DLX5, SOX2, and KLF5) to regulate enhancers and/or promoters of target oncogenes, and activates multiple oncogenic signaling pathways.[
22
,
23
,
24
] Furthermore, TP63 also activates lncRNA transcription, such as LINC01503, by binding to its SCC‐specific super enhancer (SE).[
24
] Highly expression of LINC01503 enhances migration, invasion, colony formation, and proliferation of ESCC cells.[
24
] However, it is still largely unexplored how TP63 is transcriptionally and post‐ transcriptionally regulated in ESCC.
In the present study, we profiled expression levels of 150 classical RBP genes in multiple ESCC patient cohorts and discovered TDP‐43 as an oncogene with significantly upregulated expression in ESCC tissues. TDP‐43 promotes proliferation of ESCC cells ex vivo and in vivo. Importantly, TDP‐43 can significantly boost TP63 expression via both stabilizing TP63 mRNAs as a RBP at post‐transcriptional level and enhancing TP63 transcription as a TF binding to the TP63 promoter at transcriptional level in ESCC cells. In contrast, the TF TP63 attaches to the TDP‐43 promoter, speeds up transcription, and causes noticeably elevated TDP‐43 expression in ESCC. Our data identify a previously underappreciated TDP‐43/TP63 positive feedback circuit and illustrate that activation of the circuit promotes esophageal tumorigenesis.
2
Experimental Section
2.1
Cell Culture
The RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS, Gibco) was used for cultivating the KYSE‐450, KYSE‐510, KYSE‐30, KYSE‐180, KYSE‐520, and HEK293T cells. Prof. Dongxin Lin (Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) kindly contributed the human KYSE‐450, KYSE‐510, KYSE‐30, KYSE‐180 and KYSE‐520 ESCC cell lines. Dr. Yunshan Wang (Jinan Central Hospital, Shandong Province, China) generously contributed HEK293T cells. The cells were cultivated in an incubator with 5% CO2 at 37 °C, and their mycoplasma negative status was checked on a regular basis.
2.2
Overexpression, shRNA, and Mutant Constructs of TDP‐43
The pCDH‐CMV‐MCS‐EF1α‐Puro vector (Genewiz, Suzhou, China) was used to clone the full‐length TDP‐43 cDNA (NM_007375.4). The resulting plasmid was given the name TDP‐43. HA‐tagged wild‐type (WT) full‐length TDP‐43 cDNA was also cloned into pcDNA3.1. A negative control shRNA (shNC) and two TDP‐43 shRNAs (shT43‐1 or shT43‐2) were synthesized and cloned into the pLKO.1 vector (Table S3, Supporting Information). After deleting the 105–169 nt, 193–252 nt, or the 105–169 nt plus 193–252 nt regions of wild‐type (WT) full‐length TDP‐43 mRNA, three truncated TDP‐43 plasmids (ΔRRM1, ΔRRM2, or ΔRRM1&RRM2) with a HA‐tag were produced. To verify the orientation and integrity, all plasmids were sequenced.
2.3
Cell Transfection
Transfection of all plasmids was carried out using the jetPRIME reagent (Polyplus, USA). Small interference RNAs (siRNAs) of TP63 (siTP63‐1 and siTP63‐2), TDP‐43 (siT43‐1 and siT43‐2), PABPC1 (siPABPC1‐1 and siPABPC1‐2), HuR (siHuR‐1 and siHuR‐2) as well as the negative control RNA (NC RNA) were products of Genepharma (Shanghai, China) (Table S3, Supporting Information). Small RNAs were transfected using the INTERFERin reagent (Polyplus, USA), as previously reported.[
25
,
26
,
27
]
2.4
Lentiviral Transduction
The lentivirus plasmid TDP‐43, shT43‐1, or shT43‐2 was co‐transfected into HEK293T cells along with the psPAX2 (Addgene, #12260) and pMD2.G (Addgene, #12259) plasmids as reported previously.[
25
,
26
,
27
] Recombinant lentiviral particles in the viral supernatants were extracted 48 and 72 h after transfection. ESCC cells were infected with the viral particles of TDP‐43, shT43‐1, shT43‐2, NC, or shNC independently. Cells were then selected using blasticidin or puromycin at a concentration of 2 µg mL−1.
2.5
Quantitative Reverse Transcription PCR (RT‐qPCR)
Reverse transcribed total RNAs were used to determine the relative expression of candidate genes using the indicated primers (Table S2, Supporting Information) as reported previously.[
25
,
26
,
27
]
2.6
Western Blotting
Western blot was performed as described previously.[
25
,
26
,
27
,
28
] Using an SDS‐PAGE gel, total proteins were isolated and then transferred to a PVDF membrane (Millipore, ISEQ00010). Next, the indicated antibodies were incubated with the PVDF membrane (Table S1, Supporting Information). The ECL Western Blotting Substrate (Pierce, 32106) was used to visualize proteins.
2.7
Cell Proliferation and Colony Formation Assays
Stable TDP‐43‐knockdown (TDP‐43‐KD) or TDP‐43‐overexpression (TDP‐43‐OE) KYSE‐450 or KYSE‐510 cells were used to investigate the functions of TDP‐43 in cell proliferation and colony formation as reported previously.[
28
,
29
]
2.8
Wound Healing and Transwell Assays
As previously reported, transwell assays and wound healing assays were used to examine the effects of TDP‐43 on the migratory and invasion capacities of ESCC cells.[
25
,
26
,
27
]
2.9
Xenografts
In order to investigate the role of TDP‐43 in vivo, 4 × 106
TDP‐43‐KD KYSE‐450 cells, 4 × 106
TDP‐43‐OE KYSE‐450 cells, or 4 × 106 control cells were injected subcutaneously into the scapular area of 5 weeks old male BALB/c nude mice (Vital River Laboratory, Beijing, China) (n = 5 per group). Tumor volumes were assessed every two days as previously described.[
26
,
27
] All procedures involving mice were approved by the Ethics Committee of Shandong Hospital and Institute (SDTHEC2021012032).
2.10
RNAseq
NovaSeq6000 (Illumina, USA) was used to perform RNA‐seq of TDP‐43‐KD KYSE‐450 cells to identify the target genes of TDP‐43 in ESCC cells. Differentially expressed genes (|log2(Fold Change)|>2, P < 0.05) were subjected to the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses.
2.11
Chromatin Immunoprecipitation Sequencing (ChIP‐seq) and ChIP‐qPCR
The ChIP assays were performed using anti‐TDP‐43 antibody, anti‐TP63 antibody, or the IgG control as described previously.[
25
,
27
] Utilizing the Illumina NovaSeq 6000 platform (Illumina, USA), immunoprecipitated DNA was sequenced. The ChIP‐qPCR assays were performed using the ChIP‐qPCR primers (Table S2, Supporting Information) as previously described.[
25
,
27
,
28
,
29
]
2.12
RNA Immunoprecipitation Sequencing (RIP‐seq) and RIP‐qPCR
The Magna RIP RNA‐Binding Protein Immunoprecipitation Kit (Millipore, 17–700) with either the anti‐TDP‐43 antibody or the IgG control were used for RIP assays. The target RNPs were enriched with Dynabeads Protein G beads (Invitrogen). A magnet was used to immobilize the magnetic beads‐bound RNPs. After that, the beads were washed for six times. The immunoprecipitated RNAs were sequenced on an Illumina NovaSeq 6000 (Illumina, USA) or detected via RIP‐qPCR.
2.13
Dual Luciferase Reporter Gene Assays
The potential TP63 promoter region (chr3: 189629603–189629853 bp) or the TDP‐43 promoter region (chr1: 11025262–11025612 bp) was cloned into the pGL3‐Basic vector (Promega, Madison, WI), respectively. The TDP‐43 reporter construct was designated as pGL3‐TDP‐43‐WT. The pGL3‐TDP‐43‐WT construct with two deleted TP63‐binding motifs were named as pGL3‐TDP‐43‐del‐a and pGL3‐TDP‐43‐del‐b. The TP63 reporter construct was named as pGL3‐TP63‐WT. The construct with deleted TDP‐43‐binding motif was designated as pGL3‐TP63‐del. As a normalization control, the pRL‐SV40 plasmid was co‐transfected with these constructs into cells. Luciferase activities were measured with the Dual‐Luciferase Reporter Assay System (Promega).
2.14
mRNA Stability Assays
To determine the impacts of TDP‐43 as a RBP on TP63 mRNA stability, ESCC cells were treated with 0.25 mg mL−1 actinomycin D and harvested at the indicated time points after drug treatment. qRT‐PCR was performed to examine TP63 mRNA levels in ESCC cells.
2.15
Immunoprecipitation‐Mass Spectrometry (IP‐MS) and Co‐IP
To identify proteins interacting with TDP‐43 in ESCC cells, IP‐MS was carried out using the anti‐TDP‐43 antibody. The immunoprecipitated proteins were analyzed using liquid chromatography‐tandem mass spectrometry (LS‐MS/MS) (Hoogen Biotech Co., Shanghai, China) as described previously.[
30
] Co‐IP was performed between TDP‐43 and HuR, or TDP‐43 and PABPC1 in ESCC cells. Western blotting was used to examine the proteins that were retrieved with a total of 1% of inputs used.[
28
,
29
]
2.16
Immunofluorescence
The immunofluorescence assays were carried out as reported previously.[
25
,
27
,
30
] TDP‐43, HuR, and PABPC1 proteins were detected with the indicated antibodies and coraLite488‐conjugated or coraLite594‐conjugated secondary antibodies, followed by washing with PBS and staining with 4,6‐diamidino‐2‐phenylindole (DAPI). Cells images were visualized and recorded with a Zeiss LSM800 confocal microscope (Zeiss, Germany).
2.17
Statistics
The Student's t‐test was used to determine the difference between the two groups. One‐way ANOVA was employed to compare data from multiple groups. The significance of expression associations between different genes was examined using Spearman's correlation analyses. Statistical significance was defined as a p‐value of <0.05. The SPSS software package (Version 16.0, SPSS Inc.) or GraphPad Prism (Version 8, GraphPad Software, Inc.) were used for all analyses.
2.1
Cell Culture
The RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS, Gibco) was used for cultivating the KYSE‐450, KYSE‐510, KYSE‐30, KYSE‐180, KYSE‐520, and HEK293T cells. Prof. Dongxin Lin (Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) kindly contributed the human KYSE‐450, KYSE‐510, KYSE‐30, KYSE‐180 and KYSE‐520 ESCC cell lines. Dr. Yunshan Wang (Jinan Central Hospital, Shandong Province, China) generously contributed HEK293T cells. The cells were cultivated in an incubator with 5% CO2 at 37 °C, and their mycoplasma negative status was checked on a regular basis.
2.2
Overexpression, shRNA, and Mutant Constructs of TDP‐43
The pCDH‐CMV‐MCS‐EF1α‐Puro vector (Genewiz, Suzhou, China) was used to clone the full‐length TDP‐43 cDNA (NM_007375.4). The resulting plasmid was given the name TDP‐43. HA‐tagged wild‐type (WT) full‐length TDP‐43 cDNA was also cloned into pcDNA3.1. A negative control shRNA (shNC) and two TDP‐43 shRNAs (shT43‐1 or shT43‐2) were synthesized and cloned into the pLKO.1 vector (Table S3, Supporting Information). After deleting the 105–169 nt, 193–252 nt, or the 105–169 nt plus 193–252 nt regions of wild‐type (WT) full‐length TDP‐43 mRNA, three truncated TDP‐43 plasmids (ΔRRM1, ΔRRM2, or ΔRRM1&RRM2) with a HA‐tag were produced. To verify the orientation and integrity, all plasmids were sequenced.
2.3
Cell Transfection
Transfection of all plasmids was carried out using the jetPRIME reagent (Polyplus, USA). Small interference RNAs (siRNAs) of TP63 (siTP63‐1 and siTP63‐2), TDP‐43 (siT43‐1 and siT43‐2), PABPC1 (siPABPC1‐1 and siPABPC1‐2), HuR (siHuR‐1 and siHuR‐2) as well as the negative control RNA (NC RNA) were products of Genepharma (Shanghai, China) (Table S3, Supporting Information). Small RNAs were transfected using the INTERFERin reagent (Polyplus, USA), as previously reported.[
25
,
26
,
27
]
2.4
Lentiviral Transduction
The lentivirus plasmid TDP‐43, shT43‐1, or shT43‐2 was co‐transfected into HEK293T cells along with the psPAX2 (Addgene, #12260) and pMD2.G (Addgene, #12259) plasmids as reported previously.[
25
,
26
,
27
] Recombinant lentiviral particles in the viral supernatants were extracted 48 and 72 h after transfection. ESCC cells were infected with the viral particles of TDP‐43, shT43‐1, shT43‐2, NC, or shNC independently. Cells were then selected using blasticidin or puromycin at a concentration of 2 µg mL−1.
2.5
Quantitative Reverse Transcription PCR (RT‐qPCR)
Reverse transcribed total RNAs were used to determine the relative expression of candidate genes using the indicated primers (Table S2, Supporting Information) as reported previously.[
25
,
26
,
27
]
2.6
Western Blotting
Western blot was performed as described previously.[
25
,
26
,
27
,
28
] Using an SDS‐PAGE gel, total proteins were isolated and then transferred to a PVDF membrane (Millipore, ISEQ00010). Next, the indicated antibodies were incubated with the PVDF membrane (Table S1, Supporting Information). The ECL Western Blotting Substrate (Pierce, 32106) was used to visualize proteins.
2.7
Cell Proliferation and Colony Formation Assays
Stable TDP‐43‐knockdown (TDP‐43‐KD) or TDP‐43‐overexpression (TDP‐43‐OE) KYSE‐450 or KYSE‐510 cells were used to investigate the functions of TDP‐43 in cell proliferation and colony formation as reported previously.[
28
,
29
]
2.8
Wound Healing and Transwell Assays
As previously reported, transwell assays and wound healing assays were used to examine the effects of TDP‐43 on the migratory and invasion capacities of ESCC cells.[
25
,
26
,
27
]
2.9
Xenografts
In order to investigate the role of TDP‐43 in vivo, 4 × 106
TDP‐43‐KD KYSE‐450 cells, 4 × 106
TDP‐43‐OE KYSE‐450 cells, or 4 × 106 control cells were injected subcutaneously into the scapular area of 5 weeks old male BALB/c nude mice (Vital River Laboratory, Beijing, China) (n = 5 per group). Tumor volumes were assessed every two days as previously described.[
26
,
27
] All procedures involving mice were approved by the Ethics Committee of Shandong Hospital and Institute (SDTHEC2021012032).
2.10
RNAseq
NovaSeq6000 (Illumina, USA) was used to perform RNA‐seq of TDP‐43‐KD KYSE‐450 cells to identify the target genes of TDP‐43 in ESCC cells. Differentially expressed genes (|log2(Fold Change)|>2, P < 0.05) were subjected to the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses.
2.11
Chromatin Immunoprecipitation Sequencing (ChIP‐seq) and ChIP‐qPCR
The ChIP assays were performed using anti‐TDP‐43 antibody, anti‐TP63 antibody, or the IgG control as described previously.[
25
,
27
] Utilizing the Illumina NovaSeq 6000 platform (Illumina, USA), immunoprecipitated DNA was sequenced. The ChIP‐qPCR assays were performed using the ChIP‐qPCR primers (Table S2, Supporting Information) as previously described.[
25
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27
,
28
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]
2.12
RNA Immunoprecipitation Sequencing (RIP‐seq) and RIP‐qPCR
The Magna RIP RNA‐Binding Protein Immunoprecipitation Kit (Millipore, 17–700) with either the anti‐TDP‐43 antibody or the IgG control were used for RIP assays. The target RNPs were enriched with Dynabeads Protein G beads (Invitrogen). A magnet was used to immobilize the magnetic beads‐bound RNPs. After that, the beads were washed for six times. The immunoprecipitated RNAs were sequenced on an Illumina NovaSeq 6000 (Illumina, USA) or detected via RIP‐qPCR.
2.13
Dual Luciferase Reporter Gene Assays
The potential TP63 promoter region (chr3: 189629603–189629853 bp) or the TDP‐43 promoter region (chr1: 11025262–11025612 bp) was cloned into the pGL3‐Basic vector (Promega, Madison, WI), respectively. The TDP‐43 reporter construct was designated as pGL3‐TDP‐43‐WT. The pGL3‐TDP‐43‐WT construct with two deleted TP63‐binding motifs were named as pGL3‐TDP‐43‐del‐a and pGL3‐TDP‐43‐del‐b. The TP63 reporter construct was named as pGL3‐TP63‐WT. The construct with deleted TDP‐43‐binding motif was designated as pGL3‐TP63‐del. As a normalization control, the pRL‐SV40 plasmid was co‐transfected with these constructs into cells. Luciferase activities were measured with the Dual‐Luciferase Reporter Assay System (Promega).
2.14
mRNA Stability Assays
To determine the impacts of TDP‐43 as a RBP on TP63 mRNA stability, ESCC cells were treated with 0.25 mg mL−1 actinomycin D and harvested at the indicated time points after drug treatment. qRT‐PCR was performed to examine TP63 mRNA levels in ESCC cells.
2.15
Immunoprecipitation‐Mass Spectrometry (IP‐MS) and Co‐IP
To identify proteins interacting with TDP‐43 in ESCC cells, IP‐MS was carried out using the anti‐TDP‐43 antibody. The immunoprecipitated proteins were analyzed using liquid chromatography‐tandem mass spectrometry (LS‐MS/MS) (Hoogen Biotech Co., Shanghai, China) as described previously.[
30
] Co‐IP was performed between TDP‐43 and HuR, or TDP‐43 and PABPC1 in ESCC cells. Western blotting was used to examine the proteins that were retrieved with a total of 1% of inputs used.[
28
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]
2.16
Immunofluorescence
The immunofluorescence assays were carried out as reported previously.[
25
,
27
,
30
] TDP‐43, HuR, and PABPC1 proteins were detected with the indicated antibodies and coraLite488‐conjugated or coraLite594‐conjugated secondary antibodies, followed by washing with PBS and staining with 4,6‐diamidino‐2‐phenylindole (DAPI). Cells images were visualized and recorded with a Zeiss LSM800 confocal microscope (Zeiss, Germany).
2.17
Statistics
The Student's t‐test was used to determine the difference between the two groups. One‐way ANOVA was employed to compare data from multiple groups. The significance of expression associations between different genes was examined using Spearman's correlation analyses. Statistical significance was defined as a p‐value of <0.05. The SPSS software package (Version 16.0, SPSS Inc.) or GraphPad Prism (Version 8, GraphPad Software, Inc.) were used for all analyses.
3
Results
3.1
Elevated Expression of TDP‐43 in Malignant Tissues was Associated with Poor Prognosis of ESCC Patients
To explore the role of RBPs in ESCC progression, we examined the expression profiling in ESCC specimens of one hundred and fifty canonical RBP genes from TCGA and two other cohorts (GSE164158 and GSE45670) (Figure
1A). There were twenty‐one RBP genes with significantly elevated expression in ESCC tissues compared to normal specimens in all three cohorts. Eleven RBP genes were excluded due to their known roles in ESCC and ten candidate genes were identified (Figure 1A). Among these ten RBP genes, only high levels of TDP‐43 in ESCC tissues were remarkably associated with a shorten overall survival (OS) time in ESCC patients from the TCGA cohort (Figure 1B). Considering that, we focused on how TDP‐43 contributes to ESCC development.
Figure 1
TDP‐43 is highly expressed in ESCC and associated with poor prognosis of patients. A) An illustration showing the RNA‐binding proteins (RBPs) that are up‐regulated in ESCC of the TCGA and GEO (GSE164158 and GSE45670) cohorts. B) Kaplan–Meier curves showed that high levels of TDP‐43 were significantly associated with poor survival in the TCGA ESCA cohort. C–E) There were elevated TDP‐43 expression in ESCC tissues compared to normal specimens in the other three cohorts (GSE38129, GSE77861, and GSE32424). F) Expression of TDP‐43 in ESCC and normal tissue was determined by IHC (proteinatlas.org). G–I) Differential TDP‐43 expression in cancerous tissues of patients with esophageal carcinoma of different grades (G), clinical stages (H), and lymph nodal metastasis status (I) in the TCGA ESCA cohort. Data represent mean ± S.D., the p‐value was determined by a two‐tailed paired t‐test or unpaired Student's t‐test or one‐way ANOVA. Log‐rank test was used for survival comparison. *
p < 0.05, ***
p < 0.001.
In line with the TCGA, GSE164158, GSE45670, and the Cancer Cell Line Encyclopedia (CCLE) data (Figure S1, Supporting Information), TDP‐43 showed increased expression in cancerous tissues in comparation with normal tissues in other ESCC cohorts (GSE38129, GSE77861, and GSE32424) (all P < 0.05) (Figure 1C–E). Immunohistochemistry (IHC) data from the Human Protein Atlas (https://www.proteinatlas.org) demonstrated that the increased TDP‐43 protein expression exists in ESCC tissues, but not in normal esophageal tissues (Figure 1F). Furthermore, evidently elevated TDP‐43 expression in malignant tissues of ESCC patients with advanced diseases (higher cancer grades, higher AJCC stages, or more lymph node metastases) was observed compared to individuals with local diseases (lower cancer grades, lower AJCC stages, or less lymph node metastases) in TCGA (all P < 0.001) (Figure 1G–I). Collectively, these findings indicate that TDP‐43 might function as a novel oncogene in ESCC.
3.2
TDP‐43 Promotes ESCC Cell Proliferation In Vitro and In Vivo
To determine whether TDP‐43 is functionally involved in ESCC development, we employed lentiviral transduction strategies to either up‐ or down‐regulate TDP‐43 expression in KYSE‐450 and KYSE‐510 cells (Figure
2A,B; Figure S2A,B, Supporting Information). We found that the TDP‐43‐KD KYSE‐450 or KYSE‐510 cells showed inhibited proliferation ability in comparation with controls (all P < 0.001) (Figure 2C), whereas the TDP‐43‐OE ESCC cells exhibited increased proliferation capability compared to control cells (both P < 0.01) (Figure 2D). We also found that silencing of TDP‐43 significantly promoted apoptosis of ESCC cells (all P < 0.01) (Figure S2C,D, Supporting Information). Moreover, we found that depletion of TDP‐43 obviously increased the expression of the proapoptotic protein BAX and BID, and decreased the expression of antiapoptotic protein BCL2 in ESCC cells (Figure S2E, Supporting Information). That is, knockdown of TDP‐43 induced apoptosis of ESCC cells and, thus, cell death. We successfully validated oncogenic impacts of TDP‐43 on KYSE30, KYSE180 and KYSE520 ESCC cell lines (all P < 0.01) (Figure S2F–H, Supporting Information). Consistently, clone formation of ESCC cells was suppressed after knocking‐down TDP‐43 expression (all P < 0.001) (Figure 2E); while reinforced clonogenicity of ESCC cells was found after ectopic TDP‐43 expression (both P < 0.01) (Figure 2F). Next, we investigated the effects of TDP‐43 on ESCC cell motility and invasion. There were no evident impacts of TDP‐43 on motility and invasion of ESCC cells (Figure S3, Supporting Information). These data elucidate that TDP‐43 is essential for ESCC cell proliferation, but not for tumor migration and metastasis.
Figure 2
TDP‐43 promotes proliferation of ESCC cells in vivo and in vitro. A) Relative expression of TDP‐43 mRNA and protein levels in KYSE‐450 or KYSE‐510 cell lines that stabilized silenced TDP‐43 (by shRNAs). B) Relative expression of TDP‐43 mRNA and protein levels in the TDP‐43‐OE ESCC cells. C) Silencing of TDP‐43 inhibited proliferation of KYSE‐450 or KYSE‐510 cells. D) Overexpressed TDP‐43 promoted proliferation of ESCC cells. E,F) TDP‐43 promoted clonogenicity of KYSE‐450 or KYSE‐510 cells. G–J) In vivo growth of ESCC xenografts was accelerated by TDP‐43. Decreased or increased tumor growth were observed in nude mice via subcutaneous injection of ESCC cells after TDP‐43‐knockdown or TDP‐43‐overexpression (n = 5). Decreased or increased tumor weights were found in nude mice via subcutaneous injection of ESCC cells after TDP‐43‐knockdown or TDP‐43‐overexpression (n = 5). In (A–F) n = 3 biological replicates, G–J) n = 5 biological replicates. Data are the mean ± SD (A–F) and the mean ± SEM (G‐J). The p‐value was determined by a two‐tailed unpaired Student's t‐test. **
p < 0.01, ***
p < 0.001.
We next assessed the role of TDP‐43 in vivo using ESCC xenografts through injection of nude mice with the TDP‐43‐KD or TDP‐43‐OE KYSE‐450 cells. When comparing the TDP‐43‐KD xenografts to controls, there were clearly reduced tumor volumes and tumor weights (all P < 0.01) (Figure 2G,H). Conversely, the TDP‐43‐OE xenografts showed a significant increase in terms of tumor volumes and tumor weights compared to the control group (both P < 0.01) (Figure 2I,J). Together, these findings elucidate that TDP‐43 promotes malignant proliferation of ESCC cells in vitro and in vivo.
3.3
TP63 is a Crucial Target Gene of TDP‐43 in ESCC
We then investigate the mechanism that underlies the oncogenic functions of TDP‐43 in ESCC. Considering that TDP‐43 could function as a RBP and/or a TF, we performed RNA‐seq, TDP‐43 RIP‐seq, and TDP‐43 ChIP‐seq in ESCC cells (Figure
3A). For RNA‐seq, the TDP‐43‐KD KYSE‐450 cells (shT43‐1 and shT43‐2) and the control cells were utilized. To identify potential TDP‐43‐regulated genes, TDP‐43 RIP‐seq of KYSE‐450 cells as well as TDP‐43 ChIP‐seq in KYSE‐450 and KYSE‐510 cells were also carried out. After integration of the RNA‐seq, RIP‐seq, and ChIP‐seq data, we identified thirteen candidate TDP‐43 target genes in ESCC (Figure 3A). Among these thirteen genes, three genes (TP63, DHX9, and TBL1XR1) showed remarkably higher expression in ESCC tissues than that in normal tissues from the TCGA, GSE164158, or GSE45670 cohorts (all P < 0.05) (Figure 3A). To validate the potential regulation of TDP‐43 for these target genes, we measured expression levels of TP63, DHX9, and TBL1XR1 in the TDP‐43‐KD and wide‐type ESCC cells (Figure S4, Supporting Information). Considering that only TP63 levels could be evidently suppressed in ESCC cells with silenced TDP‐43 expression (Figure S4A,B, Supporting Information), we focused on how TDP‐43 regulates TP63 expression in ESCC.
Figure 3
TP63 is a target gene regulated by TDP‐43. A) A schematic view for identification of the candidate target genes of TDP‐43 in ESCC. B) In the TCGA and GEO cohorts, TP63 expression levels were higher in ESCC tissues than in normal specimens. C) The expression correlation between TDP‐43 and TP63 in ESCC tissues of the TCGA ESCA cohort. D) TP63 expression levels in different tumor histology of esophageal carcinoma of the TCGA ESCA cohort. E) There were elevated TP63 expression levels in LUSC, CESC, or HNSCC tissues compared to normal specimens in the TCGA cohorts. F) The expression correlations between TDP‐43 and TP63 in LUSC, CESC, or HNSCC tissues of TCGA. G–I) Differential TP63 expression in cancerous tissues of patients with esophageal carcinoma of different grades (G), clinical stages (H), and lymph nodal metastasis status I) in the TCGA ESCA cohort. Data represent mean ± S.D., the p‐value was determined by a two‐tailed paired t‐test or unpaired Student's t‐test or one‐way ANOVA. The expression correlation between genes were calculated with Pearson's correlation analyses. ns, not significant; *
P < 0.05, ***
p < 0.001.
There was an increase in TP63 expression in ESCC tissues relative to normal tissues in multiple cohorts (TCGA, GSE164158, or GSE45670, all P < 0.001) (Figure 3B). A significantly positive expression correlation between TDP‐43 and TP63 expression was observed in ESCC tissues (Figure 3C). Furthermore, TP63 was highly expressed only in ESCC tissues but not in EAC specimens in contrast to normal tissues (Figure 3D). We also analyzed TP63 and TDP‐43 expression levels in three other types of SCCs from TCGA, including lung squamous cell carcinoma (LUSC), cervical squamous cell carcinoma (CESC), and head and neck squamous cell carcinoma (HNSCC). Interestingly, there were higher levels of TDP‐43 and TP63 expression in tissues of LUSC, CESC, or HNSCC than those in normal tissues (all P < 0.05) (Figure 3E; Figure S5, Supporting Information). Moreover, positive correlations between TDP‐43 and TP63 expression were observed in TCGA LUSC, CESC or HNSCC tissues (all P < 0.05) (Figure 3F). Notably, there were strikingly up‐regulated TP63 levels in cancerous specimens of ESCC patients with advanced diseases (higher cancer grades, higher AJCC stages, or more lymph node metastases) compared with those of subjects with local diseases (lower cancer grades, lower AJCC stages, or less lymph node metastases) in TCGA (all P < 0.001) (Figure 3G–I). These data indicate that oncogenic TP63 might be a key target gene of TDP‐43 protein in ESCC cells.
3.4
TF TDP‐43 Promotes TP63 Transcription in ESCC
To examine the mechanism of how TDP‐43 transcriptionally up‐regulates TP63 expression, we first detected TP63 mRNA levels in either TDP‐43‐KD or TDP‐43‐OE ESCC cell lines (Figure
4A,B). Interestingly, silenced TDP‐43 evidently reduced TP63 mRNA levels in KYSE‐450 and KYSE‐510 cells (all P < 0.01) (Figure 4A). Conversely, enforced TDP‐43 expression increased TP63 mRNA levels in ESCC cells (both P<0.001) (Figure 4B). We then measured TP63 protein levels in either TDP‐43‐KD or TDP‐43‐OE ESCC cells (Figure 4C,D). Consistently, TDP‐43 led to significantly elevated TP63 protein levels in ESCC cells (all P<0.001) (Figure 4C,D; Figure S6A,B, Supporting Information).
Figure 4
TDP‐43 transcriptionally up‐regulates TP63 expression. A) Relative expression of TP63 mRNAs after knock‐down of TDP‐43 by shRNAs in KYSE‐450 and KYSE‐510 cells. B) Relative TP63 mRNA expression in the TDP‐43‐OE ESCC cells. C,D) Western blot assays showed TP63 protein levels in KYSE‐450 and KYSE‐510 cells that stabilized either silenced TDP‐43 or overexpressed TDP‐43. E) The ChIP assays showed that there was evident binding of TDP‐43 to the TP63 promoter in ESCC cells. F–H) The dual luciferase reporter assays indicated that the TDP‐43‐binding motif is essential for the TP63 promoter activities in KYSE‐450 and KYSE‐510 cells. Data represent mean ± S.D., the p‐value was determined by a two‐tailed unpaired Student's t‐test. ns, not significant; **
p < 0.01, ***
p < 0.001. Data shows one representative of three independent experiments with three biological replicates.
ChIP‐qPCR analyses showed evident TF TDP‐43 enrichments in the TP63 promoter of KYSE‐450 and KYSE0510 cell lines (both P < 0.01) (Figure 4E). We further investigated how TF TDP‐43 impacts TP63 promoter activities using reporter gene assays (Figure 4F). It has been shown that the promoter activities of the pGL3‐TP63‐WT construct with the TP63 promoter containing the TDP‐43 binding motif were significantly higher than those of the pGL3‐TP63‐del construct without the TF TDP‐43 binding motif in ESCC cells (Figure 4G–H). In particular, an obvious decrease of pGL3‐TP63‐WT luciferase activities in the TDP‐43‐KD cells was found compared to the control cells (all P < 0.01) (Figure 4G). Nevertheless, the TDP‐43‐KD cells transfected with pGL3‐TP63‐del did not show a similar decline in luciferase activities (Figure 4G). By contrast, enforced TDP‐43 expression markedly elevated luciferase activities of the pGL3‐TP63‐WT construct in ESCC cells (both P < 0.001). However, there was no such increased luciferase activities in the TDP‐43‐OE cells transfected with pGL3‐TP63‐del (Figure 4H). The rescue assays showed that overexpression of TDP‐43 in the ESCC cells with silenced TDP‐43 markedly restored luciferase activities of the pGL3‐TP63‐WT construct (Figure S6C,D, Supporting Information). Taken together, these results indicated that TF TDP‐43 binds to the TP63 promoter and activates TP63 expression in ESCC cells.
3.5
RBP TDP‐43 Stabilizes TP63 mRNAs in ESCC
Besides acting as a TF, TDP‐43 is also a multifunctional RBP controlling RNA metabolism. To further investigate whether TDP‐43 is involved in stabilizing TP63 mRNAs as an RBP, we performed RIP‐qPCR to verify the binding of TDP‐43 with TP63 mRNAs in ESCC cells. In KYSE‐450 and KYSE‐510 cells, there were significant enrichments of TP63 mRNAs in RNPs precipitated with antibody against TDP‐43 as compared with the IgG control (Figure
5A). RBP TDP‐43 contains two RNA recognition motif domains (RRM1 and RRM2), a nuclear localization signal (NLS) at N‐terminal domain (NTD), as well as two intrinsically disordered regions (IDR1 and IDR2) at the C‐terminal domain (CTD) (Figure 5B).[
31
] To explore the specific domains required for the interaction between TDP‐43 protein and TP63 mRNAs, we constructed various truncated TDP‐43 expression plasmids (Figure 5B,C). RIP‐qPCR assays revealed that the RRM‐1 domain (aa105‐169) and the RRM2 domain (aa193‐262) are required for binding of RBP TDP‐43 to TP63 mRNAs (Figure 5D). Next, we examined whether TDP‐43 affects the RNA stability of TP63 mRNAs in ESCC cells. Following actinomycin D treatments, the half‐life of TP63 mRNAs was markedly shortened in TDP‐43‐KD KYSE‐450 and KYSE‐510 cells in comparison with the control cells (both P<0.01) (Figure 5E).
Figure 5
RBP TDP‐43 stabilizes TP63 mRNAs via recruiting PABPC1 in cytoplasm or HuR in nucleus. A) The RIP assays showed relative enrichments of TP63 mRNAs associated with endogenous TDP‐43 relative to an input control in KYSE‐450 or KYSE‐510 cells. IgG served as the control. B,C) RBP TDP‐43 protein domains and various truncated TDP‐43 expression plasmids. Western blot assays showed expression of full length or truncated TDP‐43 proteins after cells transfected HA‐tagged plasmids encoding full length TDP‐43 or truncated TDP‐43. D) The RIP assays were performed using KYSE‐450 or KYSE‐510 cells transfected HA‐tagged plasmids encoding full length TDP‐43 or truncated TDP‐43. Relative enrichments of TP63 mRNAs associated with different HA‐tag‐TDP‐43 relative to an input control were shown. IgG served as the control. E) The TDP‐43‐KD KYSE‐450 or KYSE‐510 cells and the control cells were treated with actinomycin D. The TP63 mRNAs levels were determined by qRT‐PCR at different time points. F,G) The Co‐IP assays showed interactions between TDP‐43 and PABPC1 or TDP‐43 and HuR in KYSE‐450 and KYSE‐510 cells. H,I) The RIP‐qPCR analysis showed relative enrichments of TP63 mRNAs associated with endogenous PABPC1 or HuR relative to an input control in ESCC cells. IgG served as the control. J) Western blot assays showed expression of PABPC1 or TDP‐43 and HuR in in cytoplasm and the nucleus of KYSE‐450 or KYSE‐510 cells. K) The Co‐IP assays showed that TDP‐43 binds to HuR in the nucleus and binds to PABPC1 in the cytoplasm of KYSE‐450 or KYSE‐510 cells. L) The immunofluorescence assays showed co‐localization of TDP‐43 and PABPC1 or TDP‐43 and HuR proteins in ESCC cells. Data represent mean ± S.D., the p‐value was determined by a two‐tailed unpaired Student's t‐test. ns, not significant; **
p < 0.01, ***
p < 0.001. Data shows one representative of three independent experiments with three biological replicates.
Accumulated evidences indicated that multiple RBPs play essential part in maintaining RNA stability. Therefore, we hypothesized that TDP‐43 may affect TP63 mRNAs stability through recruitment of other RBPs to target RNAs. Using Co‐IP plus mass spectrometry (Co‐IP/MS), we identified PABPC1 and HuR, two well‐known RBPs involved in stabilizing mRNAs (Table S4, Supporting Information). Co‐IP assays consistently demonstrated that endogenous TDP‐43 in KYSE‐450 and KYSE‐510 cells could be precipitated with PABPC1 or HuR (Figure 5F). Additionally, endogenous PABPC1 or HuR could also be precipitated with TDP‐43 in ESCC cells (Figure 5G). RIP‐qPCR assays revealed significant enrichments of TP63 mRNAs in RNPs precipitated with antibody against PABPC1 or HuR in cells (Figure 5H,I). We then measured PABPC1 and HuR levels in cytoplasm and the nucleus of ESCC cells after separating the cytoplasm and the nucleus proteins of cells (Figure 5J). Interestingly, PABPC1 is mainly in the cytoplasm of cells, but HuR is dominantly in nucleus of cells (Figure 5J). Co‐IP assays showed that TDP‐43 interacts with HuR in the nucleus and interacts with PABPC1 in the cytoplasm (Figure 5K). In line with this, PABPC1 and TDP‐43 co‐localized mainly in the cytoplasm of KYSE450 and KYSE510 cells, in accordance with immunofluorescence assays (Figure 5L). However, HuR and TDP‐43 co‐localized mainly in nucleus of ESCC cells (Figure 5L). To investigate whether PABPC1 and HuR proteins are necessary for TDP‐43 to regulate TP63 mRNA stability, we examined half‐life of TP63 mRNAs in ESCC cells treated with actinomycin D (Figure S7, Supporting Information). The half‐life of TP63 mRNAs was significantly shortened in TDP‐43‐KD ESCC cells with silenced PABPC1 or HuR compared to the control cells (Figure S7, Supporting Information). Collectively, these data suggest that TDP‐43 recruits PABPC1 in the cytoplasm or HuR in the nucleus to TP63 mRNAs, facilitates TP63 mRNA stability in cells, and, thus, post‐transcriptionally elevates TP63 expression.
3.6
The Positive Feedback Regulation of TF TP63 on TDP‐43 Transcription
In light of the importance of TDP‐43 in ESCC, we investigated how TDP‐43 expression is regulated in cells. Using JASPAR (https://jaspar.genereg.net/) and hTFtarget (http://bioinfo.life.hust.edu.cn/hTFtarget#!/) algorithms, we firstly predicted the potential TFs binding to the 2500 bp TDP‐43 promoter region (−1 to −2500 bp from TSS) (Figure
6A). The expression levels of the one hundred and twenty‐two candidate TF genes were then profiled in ESCC tissues from the TCGA ESCC cohort and two ESCC cohorts (GSE164158 and GSE45670). Nine candidate TFs, including TP63, with significantly high expression in cancerous tissues were then identified (Figure 6A). It is worth noting that TP63 acts as a master TF during ESCC development. Therefore, we examined whether TF TP63 transcriptionally impacts TDP‐43 expression in ESCC cells. In fact, in KYSE‐450 and KYSE‐510 cells, silencing TP63 led to markedly decreased levels of TDP‐43 mRNA and protein (Figure 6B,C; Figure S8A, Supporting Information).
Figure 6
The master TF TP63 promotes transcription and expression of TDP‐43. A) Venn diagram analyses indicated potential TFs involved in transcriptional regulation of TDP‐43 by HTFtarget and JASPAR. B) Relative expression of TDP‐43 mRNAs after knock‐down of TP63 by siRNAs in KYSE‐450 and KYSE‐510 cells. C) Western blot assays showed expression of TDP‐43 protein after knock‐down of TP63 by siRNAs in ESCC cells. D,E) The dual luciferase reporter assays indicated that the TP63‐binding Motif a is essential for the TDP‐43 promoter activities. F) The ChIP‐qPCR analyses indicated evident enrichments of TF TP63 in the Motif a of the TDP‐43 promoter in ESCC cells. G) Graphical representation of the regulation and functions of TDP‐43 and TP63 in ESCC. TDP‐43 facilitated TP63 transcription and mRNA stability, which in turn markedly enhanced TP63 expression. The master TF TP63 in turn transcriptionally activated the TDP‐43 expression. The TDP‐43/TP63 positive feedback circuit promotes ESCC progression. Data represent mean ± S.D., the p‐value was determined by a two‐tailed unpaired Student's t‐test. ns, not significant; **
p < 0.01, ***
p < 0.001. Data shows one representative of three independent experiments with three biological replicates.
We next investigated whether TDP‐43 is a transcriptional target gene of TP63 in ESCC. There are two putative TF TP63 binding motifs (Motif a and Motif b) in the TDP‐43 promoter. To evaluate whether these motifs confer TP63‐dependent transcriptional activation of TDP‐43, we cloned the TDP‐43 promoter containing the wild‐type motifs into pGL3‐basic (pGL3‐TDP‐43‐WT). Three mutant constructs (pGL3‐TDP‐43‐del‐a, pGL3‐TDP‐43‐del‐b, and pGL3‐TDP‐43‐del‐a&b) were obtained after deletion of Motif a, Motif b, or both motifs from pGL3‐TDP‐43‐WT (Figure 6D). Dual luciferase reporter gene assays elucidated that silencing of TP63 expression led to reduced TDP‐43 promoter activities of KYSE‐450 and KYSE‐510 cells transfected with pGL3‐TDP‐43‐WT (both P < 0.001) (Figure 6E). Similar results were observed in cells transfected with pGL3‐TDP‐43‐del‐b (both P < 0.001) (Figure 6E). However, no such decrease in luciferase activities was observed in ESCC cells transfected with pGL3‐TDP‐43‐del‐a or pGL3‐TDP‐43‐del‐a&b (all P > 0.05) (Figure 6E). These findings revealed that the TP63 binding Motif a is essential for the TDP‐43 promoter. To further validate this, ChIP‐qPCR assays were performed using antibodies against TP63 or IgG. As shown in Figure 6F, evident enrichments of TF TP63 in the Motif a of the TDP‐43 promoter were observed, whereas no such enrichments existed at Motif b. The rescue assays indicated that the crosstalk between two TFs, TDP‐43, and TP‐63, are necessary for ESCC cell proliferation (Figure S8B–E, Supporting Information). Taken together, these data demonstrated that TF TP63 transcriptionally activates TDP‐43 expression through its binding of the Motif a in ESCC.
3.1
Elevated Expression of TDP‐43 in Malignant Tissues was Associated with Poor Prognosis of ESCC Patients
To explore the role of RBPs in ESCC progression, we examined the expression profiling in ESCC specimens of one hundred and fifty canonical RBP genes from TCGA and two other cohorts (GSE164158 and GSE45670) (Figure
1A). There were twenty‐one RBP genes with significantly elevated expression in ESCC tissues compared to normal specimens in all three cohorts. Eleven RBP genes were excluded due to their known roles in ESCC and ten candidate genes were identified (Figure 1A). Among these ten RBP genes, only high levels of TDP‐43 in ESCC tissues were remarkably associated with a shorten overall survival (OS) time in ESCC patients from the TCGA cohort (Figure 1B). Considering that, we focused on how TDP‐43 contributes to ESCC development.
Figure 1
TDP‐43 is highly expressed in ESCC and associated with poor prognosis of patients. A) An illustration showing the RNA‐binding proteins (RBPs) that are up‐regulated in ESCC of the TCGA and GEO (GSE164158 and GSE45670) cohorts. B) Kaplan–Meier curves showed that high levels of TDP‐43 were significantly associated with poor survival in the TCGA ESCA cohort. C–E) There were elevated TDP‐43 expression in ESCC tissues compared to normal specimens in the other three cohorts (GSE38129, GSE77861, and GSE32424). F) Expression of TDP‐43 in ESCC and normal tissue was determined by IHC (proteinatlas.org). G–I) Differential TDP‐43 expression in cancerous tissues of patients with esophageal carcinoma of different grades (G), clinical stages (H), and lymph nodal metastasis status (I) in the TCGA ESCA cohort. Data represent mean ± S.D., the p‐value was determined by a two‐tailed paired t‐test or unpaired Student's t‐test or one‐way ANOVA. Log‐rank test was used for survival comparison. *
p < 0.05, ***
p < 0.001.
In line with the TCGA, GSE164158, GSE45670, and the Cancer Cell Line Encyclopedia (CCLE) data (Figure S1, Supporting Information), TDP‐43 showed increased expression in cancerous tissues in comparation with normal tissues in other ESCC cohorts (GSE38129, GSE77861, and GSE32424) (all P < 0.05) (Figure 1C–E). Immunohistochemistry (IHC) data from the Human Protein Atlas (https://www.proteinatlas.org) demonstrated that the increased TDP‐43 protein expression exists in ESCC tissues, but not in normal esophageal tissues (Figure 1F). Furthermore, evidently elevated TDP‐43 expression in malignant tissues of ESCC patients with advanced diseases (higher cancer grades, higher AJCC stages, or more lymph node metastases) was observed compared to individuals with local diseases (lower cancer grades, lower AJCC stages, or less lymph node metastases) in TCGA (all P < 0.001) (Figure 1G–I). Collectively, these findings indicate that TDP‐43 might function as a novel oncogene in ESCC.
3.2
TDP‐43 Promotes ESCC Cell Proliferation In Vitro and In Vivo
To determine whether TDP‐43 is functionally involved in ESCC development, we employed lentiviral transduction strategies to either up‐ or down‐regulate TDP‐43 expression in KYSE‐450 and KYSE‐510 cells (Figure
2A,B; Figure S2A,B, Supporting Information). We found that the TDP‐43‐KD KYSE‐450 or KYSE‐510 cells showed inhibited proliferation ability in comparation with controls (all P < 0.001) (Figure 2C), whereas the TDP‐43‐OE ESCC cells exhibited increased proliferation capability compared to control cells (both P < 0.01) (Figure 2D). We also found that silencing of TDP‐43 significantly promoted apoptosis of ESCC cells (all P < 0.01) (Figure S2C,D, Supporting Information). Moreover, we found that depletion of TDP‐43 obviously increased the expression of the proapoptotic protein BAX and BID, and decreased the expression of antiapoptotic protein BCL2 in ESCC cells (Figure S2E, Supporting Information). That is, knockdown of TDP‐43 induced apoptosis of ESCC cells and, thus, cell death. We successfully validated oncogenic impacts of TDP‐43 on KYSE30, KYSE180 and KYSE520 ESCC cell lines (all P < 0.01) (Figure S2F–H, Supporting Information). Consistently, clone formation of ESCC cells was suppressed after knocking‐down TDP‐43 expression (all P < 0.001) (Figure 2E); while reinforced clonogenicity of ESCC cells was found after ectopic TDP‐43 expression (both P < 0.01) (Figure 2F). Next, we investigated the effects of TDP‐43 on ESCC cell motility and invasion. There were no evident impacts of TDP‐43 on motility and invasion of ESCC cells (Figure S3, Supporting Information). These data elucidate that TDP‐43 is essential for ESCC cell proliferation, but not for tumor migration and metastasis.
Figure 2
TDP‐43 promotes proliferation of ESCC cells in vivo and in vitro. A) Relative expression of TDP‐43 mRNA and protein levels in KYSE‐450 or KYSE‐510 cell lines that stabilized silenced TDP‐43 (by shRNAs). B) Relative expression of TDP‐43 mRNA and protein levels in the TDP‐43‐OE ESCC cells. C) Silencing of TDP‐43 inhibited proliferation of KYSE‐450 or KYSE‐510 cells. D) Overexpressed TDP‐43 promoted proliferation of ESCC cells. E,F) TDP‐43 promoted clonogenicity of KYSE‐450 or KYSE‐510 cells. G–J) In vivo growth of ESCC xenografts was accelerated by TDP‐43. Decreased or increased tumor growth were observed in nude mice via subcutaneous injection of ESCC cells after TDP‐43‐knockdown or TDP‐43‐overexpression (n = 5). Decreased or increased tumor weights were found in nude mice via subcutaneous injection of ESCC cells after TDP‐43‐knockdown or TDP‐43‐overexpression (n = 5). In (A–F) n = 3 biological replicates, G–J) n = 5 biological replicates. Data are the mean ± SD (A–F) and the mean ± SEM (G‐J). The p‐value was determined by a two‐tailed unpaired Student's t‐test. **
p < 0.01, ***
p < 0.001.
We next assessed the role of TDP‐43 in vivo using ESCC xenografts through injection of nude mice with the TDP‐43‐KD or TDP‐43‐OE KYSE‐450 cells. When comparing the TDP‐43‐KD xenografts to controls, there were clearly reduced tumor volumes and tumor weights (all P < 0.01) (Figure 2G,H). Conversely, the TDP‐43‐OE xenografts showed a significant increase in terms of tumor volumes and tumor weights compared to the control group (both P < 0.01) (Figure 2I,J). Together, these findings elucidate that TDP‐43 promotes malignant proliferation of ESCC cells in vitro and in vivo.
3.3
TP63 is a Crucial Target Gene of TDP‐43 in ESCC
We then investigate the mechanism that underlies the oncogenic functions of TDP‐43 in ESCC. Considering that TDP‐43 could function as a RBP and/or a TF, we performed RNA‐seq, TDP‐43 RIP‐seq, and TDP‐43 ChIP‐seq in ESCC cells (Figure
3A). For RNA‐seq, the TDP‐43‐KD KYSE‐450 cells (shT43‐1 and shT43‐2) and the control cells were utilized. To identify potential TDP‐43‐regulated genes, TDP‐43 RIP‐seq of KYSE‐450 cells as well as TDP‐43 ChIP‐seq in KYSE‐450 and KYSE‐510 cells were also carried out. After integration of the RNA‐seq, RIP‐seq, and ChIP‐seq data, we identified thirteen candidate TDP‐43 target genes in ESCC (Figure 3A). Among these thirteen genes, three genes (TP63, DHX9, and TBL1XR1) showed remarkably higher expression in ESCC tissues than that in normal tissues from the TCGA, GSE164158, or GSE45670 cohorts (all P < 0.05) (Figure 3A). To validate the potential regulation of TDP‐43 for these target genes, we measured expression levels of TP63, DHX9, and TBL1XR1 in the TDP‐43‐KD and wide‐type ESCC cells (Figure S4, Supporting Information). Considering that only TP63 levels could be evidently suppressed in ESCC cells with silenced TDP‐43 expression (Figure S4A,B, Supporting Information), we focused on how TDP‐43 regulates TP63 expression in ESCC.
Figure 3
TP63 is a target gene regulated by TDP‐43. A) A schematic view for identification of the candidate target genes of TDP‐43 in ESCC. B) In the TCGA and GEO cohorts, TP63 expression levels were higher in ESCC tissues than in normal specimens. C) The expression correlation between TDP‐43 and TP63 in ESCC tissues of the TCGA ESCA cohort. D) TP63 expression levels in different tumor histology of esophageal carcinoma of the TCGA ESCA cohort. E) There were elevated TP63 expression levels in LUSC, CESC, or HNSCC tissues compared to normal specimens in the TCGA cohorts. F) The expression correlations between TDP‐43 and TP63 in LUSC, CESC, or HNSCC tissues of TCGA. G–I) Differential TP63 expression in cancerous tissues of patients with esophageal carcinoma of different grades (G), clinical stages (H), and lymph nodal metastasis status I) in the TCGA ESCA cohort. Data represent mean ± S.D., the p‐value was determined by a two‐tailed paired t‐test or unpaired Student's t‐test or one‐way ANOVA. The expression correlation between genes were calculated with Pearson's correlation analyses. ns, not significant; *
P < 0.05, ***
p < 0.001.
There was an increase in TP63 expression in ESCC tissues relative to normal tissues in multiple cohorts (TCGA, GSE164158, or GSE45670, all P < 0.001) (Figure 3B). A significantly positive expression correlation between TDP‐43 and TP63 expression was observed in ESCC tissues (Figure 3C). Furthermore, TP63 was highly expressed only in ESCC tissues but not in EAC specimens in contrast to normal tissues (Figure 3D). We also analyzed TP63 and TDP‐43 expression levels in three other types of SCCs from TCGA, including lung squamous cell carcinoma (LUSC), cervical squamous cell carcinoma (CESC), and head and neck squamous cell carcinoma (HNSCC). Interestingly, there were higher levels of TDP‐43 and TP63 expression in tissues of LUSC, CESC, or HNSCC than those in normal tissues (all P < 0.05) (Figure 3E; Figure S5, Supporting Information). Moreover, positive correlations between TDP‐43 and TP63 expression were observed in TCGA LUSC, CESC or HNSCC tissues (all P < 0.05) (Figure 3F). Notably, there were strikingly up‐regulated TP63 levels in cancerous specimens of ESCC patients with advanced diseases (higher cancer grades, higher AJCC stages, or more lymph node metastases) compared with those of subjects with local diseases (lower cancer grades, lower AJCC stages, or less lymph node metastases) in TCGA (all P < 0.001) (Figure 3G–I). These data indicate that oncogenic TP63 might be a key target gene of TDP‐43 protein in ESCC cells.
3.4
TF TDP‐43 Promotes TP63 Transcription in ESCC
To examine the mechanism of how TDP‐43 transcriptionally up‐regulates TP63 expression, we first detected TP63 mRNA levels in either TDP‐43‐KD or TDP‐43‐OE ESCC cell lines (Figure
4A,B). Interestingly, silenced TDP‐43 evidently reduced TP63 mRNA levels in KYSE‐450 and KYSE‐510 cells (all P < 0.01) (Figure 4A). Conversely, enforced TDP‐43 expression increased TP63 mRNA levels in ESCC cells (both P<0.001) (Figure 4B). We then measured TP63 protein levels in either TDP‐43‐KD or TDP‐43‐OE ESCC cells (Figure 4C,D). Consistently, TDP‐43 led to significantly elevated TP63 protein levels in ESCC cells (all P<0.001) (Figure 4C,D; Figure S6A,B, Supporting Information).
Figure 4
TDP‐43 transcriptionally up‐regulates TP63 expression. A) Relative expression of TP63 mRNAs after knock‐down of TDP‐43 by shRNAs in KYSE‐450 and KYSE‐510 cells. B) Relative TP63 mRNA expression in the TDP‐43‐OE ESCC cells. C,D) Western blot assays showed TP63 protein levels in KYSE‐450 and KYSE‐510 cells that stabilized either silenced TDP‐43 or overexpressed TDP‐43. E) The ChIP assays showed that there was evident binding of TDP‐43 to the TP63 promoter in ESCC cells. F–H) The dual luciferase reporter assays indicated that the TDP‐43‐binding motif is essential for the TP63 promoter activities in KYSE‐450 and KYSE‐510 cells. Data represent mean ± S.D., the p‐value was determined by a two‐tailed unpaired Student's t‐test. ns, not significant; **
p < 0.01, ***
p < 0.001. Data shows one representative of three independent experiments with three biological replicates.
ChIP‐qPCR analyses showed evident TF TDP‐43 enrichments in the TP63 promoter of KYSE‐450 and KYSE0510 cell lines (both P < 0.01) (Figure 4E). We further investigated how TF TDP‐43 impacts TP63 promoter activities using reporter gene assays (Figure 4F). It has been shown that the promoter activities of the pGL3‐TP63‐WT construct with the TP63 promoter containing the TDP‐43 binding motif were significantly higher than those of the pGL3‐TP63‐del construct without the TF TDP‐43 binding motif in ESCC cells (Figure 4G–H). In particular, an obvious decrease of pGL3‐TP63‐WT luciferase activities in the TDP‐43‐KD cells was found compared to the control cells (all P < 0.01) (Figure 4G). Nevertheless, the TDP‐43‐KD cells transfected with pGL3‐TP63‐del did not show a similar decline in luciferase activities (Figure 4G). By contrast, enforced TDP‐43 expression markedly elevated luciferase activities of the pGL3‐TP63‐WT construct in ESCC cells (both P < 0.001). However, there was no such increased luciferase activities in the TDP‐43‐OE cells transfected with pGL3‐TP63‐del (Figure 4H). The rescue assays showed that overexpression of TDP‐43 in the ESCC cells with silenced TDP‐43 markedly restored luciferase activities of the pGL3‐TP63‐WT construct (Figure S6C,D, Supporting Information). Taken together, these results indicated that TF TDP‐43 binds to the TP63 promoter and activates TP63 expression in ESCC cells.
3.5
RBP TDP‐43 Stabilizes TP63 mRNAs in ESCC
Besides acting as a TF, TDP‐43 is also a multifunctional RBP controlling RNA metabolism. To further investigate whether TDP‐43 is involved in stabilizing TP63 mRNAs as an RBP, we performed RIP‐qPCR to verify the binding of TDP‐43 with TP63 mRNAs in ESCC cells. In KYSE‐450 and KYSE‐510 cells, there were significant enrichments of TP63 mRNAs in RNPs precipitated with antibody against TDP‐43 as compared with the IgG control (Figure
5A). RBP TDP‐43 contains two RNA recognition motif domains (RRM1 and RRM2), a nuclear localization signal (NLS) at N‐terminal domain (NTD), as well as two intrinsically disordered regions (IDR1 and IDR2) at the C‐terminal domain (CTD) (Figure 5B).[
31
] To explore the specific domains required for the interaction between TDP‐43 protein and TP63 mRNAs, we constructed various truncated TDP‐43 expression plasmids (Figure 5B,C). RIP‐qPCR assays revealed that the RRM‐1 domain (aa105‐169) and the RRM2 domain (aa193‐262) are required for binding of RBP TDP‐43 to TP63 mRNAs (Figure 5D). Next, we examined whether TDP‐43 affects the RNA stability of TP63 mRNAs in ESCC cells. Following actinomycin D treatments, the half‐life of TP63 mRNAs was markedly shortened in TDP‐43‐KD KYSE‐450 and KYSE‐510 cells in comparison with the control cells (both P<0.01) (Figure 5E).
Figure 5
RBP TDP‐43 stabilizes TP63 mRNAs via recruiting PABPC1 in cytoplasm or HuR in nucleus. A) The RIP assays showed relative enrichments of TP63 mRNAs associated with endogenous TDP‐43 relative to an input control in KYSE‐450 or KYSE‐510 cells. IgG served as the control. B,C) RBP TDP‐43 protein domains and various truncated TDP‐43 expression plasmids. Western blot assays showed expression of full length or truncated TDP‐43 proteins after cells transfected HA‐tagged plasmids encoding full length TDP‐43 or truncated TDP‐43. D) The RIP assays were performed using KYSE‐450 or KYSE‐510 cells transfected HA‐tagged plasmids encoding full length TDP‐43 or truncated TDP‐43. Relative enrichments of TP63 mRNAs associated with different HA‐tag‐TDP‐43 relative to an input control were shown. IgG served as the control. E) The TDP‐43‐KD KYSE‐450 or KYSE‐510 cells and the control cells were treated with actinomycin D. The TP63 mRNAs levels were determined by qRT‐PCR at different time points. F,G) The Co‐IP assays showed interactions between TDP‐43 and PABPC1 or TDP‐43 and HuR in KYSE‐450 and KYSE‐510 cells. H,I) The RIP‐qPCR analysis showed relative enrichments of TP63 mRNAs associated with endogenous PABPC1 or HuR relative to an input control in ESCC cells. IgG served as the control. J) Western blot assays showed expression of PABPC1 or TDP‐43 and HuR in in cytoplasm and the nucleus of KYSE‐450 or KYSE‐510 cells. K) The Co‐IP assays showed that TDP‐43 binds to HuR in the nucleus and binds to PABPC1 in the cytoplasm of KYSE‐450 or KYSE‐510 cells. L) The immunofluorescence assays showed co‐localization of TDP‐43 and PABPC1 or TDP‐43 and HuR proteins in ESCC cells. Data represent mean ± S.D., the p‐value was determined by a two‐tailed unpaired Student's t‐test. ns, not significant; **
p < 0.01, ***
p < 0.001. Data shows one representative of three independent experiments with three biological replicates.
Accumulated evidences indicated that multiple RBPs play essential part in maintaining RNA stability. Therefore, we hypothesized that TDP‐43 may affect TP63 mRNAs stability through recruitment of other RBPs to target RNAs. Using Co‐IP plus mass spectrometry (Co‐IP/MS), we identified PABPC1 and HuR, two well‐known RBPs involved in stabilizing mRNAs (Table S4, Supporting Information). Co‐IP assays consistently demonstrated that endogenous TDP‐43 in KYSE‐450 and KYSE‐510 cells could be precipitated with PABPC1 or HuR (Figure 5F). Additionally, endogenous PABPC1 or HuR could also be precipitated with TDP‐43 in ESCC cells (Figure 5G). RIP‐qPCR assays revealed significant enrichments of TP63 mRNAs in RNPs precipitated with antibody against PABPC1 or HuR in cells (Figure 5H,I). We then measured PABPC1 and HuR levels in cytoplasm and the nucleus of ESCC cells after separating the cytoplasm and the nucleus proteins of cells (Figure 5J). Interestingly, PABPC1 is mainly in the cytoplasm of cells, but HuR is dominantly in nucleus of cells (Figure 5J). Co‐IP assays showed that TDP‐43 interacts with HuR in the nucleus and interacts with PABPC1 in the cytoplasm (Figure 5K). In line with this, PABPC1 and TDP‐43 co‐localized mainly in the cytoplasm of KYSE450 and KYSE510 cells, in accordance with immunofluorescence assays (Figure 5L). However, HuR and TDP‐43 co‐localized mainly in nucleus of ESCC cells (Figure 5L). To investigate whether PABPC1 and HuR proteins are necessary for TDP‐43 to regulate TP63 mRNA stability, we examined half‐life of TP63 mRNAs in ESCC cells treated with actinomycin D (Figure S7, Supporting Information). The half‐life of TP63 mRNAs was significantly shortened in TDP‐43‐KD ESCC cells with silenced PABPC1 or HuR compared to the control cells (Figure S7, Supporting Information). Collectively, these data suggest that TDP‐43 recruits PABPC1 in the cytoplasm or HuR in the nucleus to TP63 mRNAs, facilitates TP63 mRNA stability in cells, and, thus, post‐transcriptionally elevates TP63 expression.
3.6
The Positive Feedback Regulation of TF TP63 on TDP‐43 Transcription
In light of the importance of TDP‐43 in ESCC, we investigated how TDP‐43 expression is regulated in cells. Using JASPAR (https://jaspar.genereg.net/) and hTFtarget (http://bioinfo.life.hust.edu.cn/hTFtarget#!/) algorithms, we firstly predicted the potential TFs binding to the 2500 bp TDP‐43 promoter region (−1 to −2500 bp from TSS) (Figure
6A). The expression levels of the one hundred and twenty‐two candidate TF genes were then profiled in ESCC tissues from the TCGA ESCC cohort and two ESCC cohorts (GSE164158 and GSE45670). Nine candidate TFs, including TP63, with significantly high expression in cancerous tissues were then identified (Figure 6A). It is worth noting that TP63 acts as a master TF during ESCC development. Therefore, we examined whether TF TP63 transcriptionally impacts TDP‐43 expression in ESCC cells. In fact, in KYSE‐450 and KYSE‐510 cells, silencing TP63 led to markedly decreased levels of TDP‐43 mRNA and protein (Figure 6B,C; Figure S8A, Supporting Information).
Figure 6
The master TF TP63 promotes transcription and expression of TDP‐43. A) Venn diagram analyses indicated potential TFs involved in transcriptional regulation of TDP‐43 by HTFtarget and JASPAR. B) Relative expression of TDP‐43 mRNAs after knock‐down of TP63 by siRNAs in KYSE‐450 and KYSE‐510 cells. C) Western blot assays showed expression of TDP‐43 protein after knock‐down of TP63 by siRNAs in ESCC cells. D,E) The dual luciferase reporter assays indicated that the TP63‐binding Motif a is essential for the TDP‐43 promoter activities. F) The ChIP‐qPCR analyses indicated evident enrichments of TF TP63 in the Motif a of the TDP‐43 promoter in ESCC cells. G) Graphical representation of the regulation and functions of TDP‐43 and TP63 in ESCC. TDP‐43 facilitated TP63 transcription and mRNA stability, which in turn markedly enhanced TP63 expression. The master TF TP63 in turn transcriptionally activated the TDP‐43 expression. The TDP‐43/TP63 positive feedback circuit promotes ESCC progression. Data represent mean ± S.D., the p‐value was determined by a two‐tailed unpaired Student's t‐test. ns, not significant; **
p < 0.01, ***
p < 0.001. Data shows one representative of three independent experiments with three biological replicates.
We next investigated whether TDP‐43 is a transcriptional target gene of TP63 in ESCC. There are two putative TF TP63 binding motifs (Motif a and Motif b) in the TDP‐43 promoter. To evaluate whether these motifs confer TP63‐dependent transcriptional activation of TDP‐43, we cloned the TDP‐43 promoter containing the wild‐type motifs into pGL3‐basic (pGL3‐TDP‐43‐WT). Three mutant constructs (pGL3‐TDP‐43‐del‐a, pGL3‐TDP‐43‐del‐b, and pGL3‐TDP‐43‐del‐a&b) were obtained after deletion of Motif a, Motif b, or both motifs from pGL3‐TDP‐43‐WT (Figure 6D). Dual luciferase reporter gene assays elucidated that silencing of TP63 expression led to reduced TDP‐43 promoter activities of KYSE‐450 and KYSE‐510 cells transfected with pGL3‐TDP‐43‐WT (both P < 0.001) (Figure 6E). Similar results were observed in cells transfected with pGL3‐TDP‐43‐del‐b (both P < 0.001) (Figure 6E). However, no such decrease in luciferase activities was observed in ESCC cells transfected with pGL3‐TDP‐43‐del‐a or pGL3‐TDP‐43‐del‐a&b (all P > 0.05) (Figure 6E). These findings revealed that the TP63 binding Motif a is essential for the TDP‐43 promoter. To further validate this, ChIP‐qPCR assays were performed using antibodies against TP63 or IgG. As shown in Figure 6F, evident enrichments of TF TP63 in the Motif a of the TDP‐43 promoter were observed, whereas no such enrichments existed at Motif b. The rescue assays indicated that the crosstalk between two TFs, TDP‐43, and TP‐63, are necessary for ESCC cell proliferation (Figure S8B–E, Supporting Information). Taken together, these data demonstrated that TF TP63 transcriptionally activates TDP‐43 expression through its binding of the Motif a in ESCC.
4
Discussion
RBPs are a class of highly conserved proteins that regulates RNA stability, RNA splicing, RNA alternative polyadenylation, and RNA transport during tumorigenesis.[
7
,
32
,
33
,
34
] In the current study, we found that RBP TDP‐43 was highly expressed in ESCC and associated with poor prognosis of patients. In ESCC cells, the master TF TP63 of SCCs induced high levels of TDP‐43 transcription and elevated TDP‐43 expression. TDP‐43 promoted proliferation of ESCC cells in vitro and in vivo. Mechanistically, TDP‐43 interacted with PABPC1 and HuR proteins in nucleus, recruited them to TP63 mRNAs, facilitated TP63 mRNA stability, and elevated TP63 expression at post‐transcriptional level. More importantly, we found that TDP‐43 also acted as a crucial TF binding to the TP63 promoter and transcriptionally activated TP63 expression in ESCC cells. Notably, there was a significantly positive correlations between TDP‐43 and TP63 expression in ESCC tissues from multiple patient cohorts. These findings indicate that the TDP‐43/TP63 positive feedback circuit plays a part in ESCC progression (Figure 6G).
Multiple RBPs are involved in gastrointestinal cancer progression.[
35
,
36
,
37
] For instance, we found that DDX5, as a multifunctional RBP, can interact with lncPSCA to regulate the transcription of the P53 signaling genes, thereby promoting gastric cancer cell growth and metastasis.[
28
] It has been reported that TDP‐43 functions as a DNA/RNA‐binding protein.[
14
] In our previous study, we found that TDP‐43, which was markedly up‐regulated in NSCLC tissues, interacted with lncRNA LCETRL3 to cause NSCLC cells to be resistant to EGFR‐TKIs.[
38
] LncRNA LCETRL3 could inhibit TDP‐43 protein ubiquitination, avoid TDP‐43 proteasome‐degradation, and increase NOTCH1 levels, resulting in activation of the downstream AKT signaling.[
38
] However, it is still unclear how TDP‐43 contributes to ESCC development. Indeed, we disclosed that TDP‐43 acts as a novel oncogene via boosting the master TF TP63 expression transcriptionally and post‐transcriptionally in ESCC, which developed the knowledge about the importance of TDP‐43‐controlled TP63 expression.
As a TF of the P53 family, TP63 plays an oncogenic role in SCCs.[
17
,
39
,
40
,
41
] Consistently, markedly elevated levels of TP63 were observed in LUSC, CESC, and HNSCC specimens compared to normal tissues according to TCGA. For ESCC, the genomic analyses revealed that TP63 is frequently amplified in cancerous tissues and is significantly up‐regulated in comparison with non‐tumor and adenocarcinoma tissues.[
42
] For example, TP63 can bind to promoters of DKK3, LINC01503 and/or RSK4 to drive high expression of these oncogenes in ESCC.[
23
,
43
,
44
] In line with these, we found that TP63 could transcriptionally enhance TDP‐43 expression and, thus, promote ESCC progression, which broadens the understanding of mechanisms by which the master TF TP63 functions.
PABPC1 and HuR are two highly conserved RBPs in eukaryotes and have elevated expression in multiple cancers.[
33
,
45
,
46
] PABPC1 is a member of the poly(A)‐binding proteins (PABPs) protein family, which binds to the poly(A) tail of eukaryotic mRNAs. PABPC1, a highly conserved RBP in species ranging from yeast to human, locates dominantly in cytoplasm, coats the 3′ end of cytoplasmic mRNAs with very high affinity, packages the mRNAs into poly(A) RNPs, physically protects the transcripts from unspecific degradation.[
47
,
48
] It has been reported that PABPC1 could promote ESCC progression through stabilizing IFI27 mRNAs.[
49
] Importantly, we found that TDP‐43 and PABPC1 interacted in ESCC cells, which improved PABPC1 binding to TP63 mRNAs, increased TP63 expression, and contributed to esophageal tumorigenesis. The idea that TDP‐43 interactions with PABPC1 in cytoplasm helps to explain why TDP‐43 might favor stabilization of mRNAs in ESCC. HuR, also known as ELAVL1, binds transcripts with adenyl‐uridine‐rich elements (AREs) within the 3′‐UTRs or introns.[
50
] HuR is predominantly found in the nucleus and plays an essential role in maintaining RNA stability, such as P53,[
46
]
VEGF,[
51
] and NEAT1.[
52
] Consistently, we identified multiple AREs within the TP63 mRNA 3′‐UTR (Figure S9, Supporting Information) and found evident enrichments of TP63 mRNAs‐precipitated by HuR in ESCC. Our findings revealed a novel TDP‐43‐mediated architecture that can protect transcripts from degradation via binding and recruiting different RBPs in cytoplasm or nucleus, with TP63 as an example.
In conclusion, we found that TDP‐43 is a novel oncogene in ESCC, showing markedly elevated expression in malignant tissues and associated with poor prognosis of patients. On one hand, TDP‐43 functions as an RBP by reducing degradation of TP63 mRNAs and as a TF by prompting TP63 transcription. Notably, TDP‐43 binds PABPC1 in cytoplasm or HuR in nucleus to stabilize TP63 mRNAs, though no such role of RBPs has been reported during tumorigenesis. On the other hand, the master TF TP63 of SCCs facilitates TDP‐43 transcription and expression, which creates a TDP‐43/TP63 positive feedback circuit in ESCC. Our findings extend the present understanding of RBPs and imply the clinical potential of TDP‐43 as novel therapeutic targets for ESCC.
Conflict of Interest
The authors declare no conflict of interest.
Author Contributions
W.L. and Y.Y. contributed equally to this work. M.Y. conceived, designed, and supervised this study. W.L., Y.Y., and M.Y. acquired, analyzed, and interpreted the data from experiments. L.H., X.Y., T.W., and N.Z. provided technique supports. W.L., Y.Y., and M.Y. were engaged in statistical and bioinformatics analyses. M.Y., Y.Y., and N.Z. drafted the manuscript. M.Y. and N.Z. critically revised the manuscript for important intellectual content.
Supporting information
Supporting Information |
Title: Vesicles: New Advances in the Treatment of Neurodegenerative Diseases | Body:
1. Introduction
Neurodegenerative diseases (NDs) (more than 600 pathologies), characterized by brain degeneration, lead to debilitating cognitive and motor deficits that limit normal activities. To date, NDs represent a major challenge to healthcare systems worldwide with a significant economic impact [1].
In addition to neurodegenerative diseases caused by brain injury, there are diseases caused by a variety of factors, genetic mutations, environmental factors, and metabolic problems. Several chronic neurodegenerative diseases manifest as deposits of misfolded or aggregated proteins that impair neuronal connectivity and plasticity and trigger cell death signaling pathways [2,3]. For example, the degenerating brain contains abnormal accumulations of misfolded, aggregated proteins such as α-synuclein and synphilin-1 in Parkinson’s disease (PD) and amyloid-β (Aβ) and tau in Alzheimer’s disease (AD). Other disorders in which protein aggregation is seen include Huntington’s disease (a polyQ disorder), amyotrophic lateral sclerosis (ALS), and prion diseases [2,3]. Mechanisms common to several neurodegenerative diseases may include a rare mutation in the disease-associated gene encoding the protein, inflammation, and excessive generation of reactive nitrogen species (RNS) and reactive oxygen species (ROS), which can contribute to neuronal cell injury and death [2].
SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) are a diverse family of proteins essential for synaptic vesicle exocytosis and synaptic trans-mission. These proteins are crucial for various fundamental neuronal functions, including neurite initiation and growth, axonal specification and extension, synaptogenesis, and synaptic transmission [4]. In fact, the inhibition of the SNARE complex formation, defects in SNARE-dependent exocytosis, and the impaired regulation of SNARE-mediated vesicle fusion have been linked to neurodegeneration [4].
Syntaxin-5 is a Qa SNARE that is ubiquitously expressed and plays an important function in the endoplasmic reticulum (ER), regulating membrane fusion in the ER and Golgi apparatus. Its overexpression has been linked to APP accumulation and reduced Aβ42 secretion and is associated with neurodegeneration. SNAP-25 is present in the cerebrospinal fluid (CSF) and has been linked to synaptic damage that precedes neuronal loss and impaired memory formation in AD [4]. Specifically, the SNAP-25/Aβ42 ratio was increased in patients with progressive mild cognitive impairment and AD compared to cognitively sound individuals [4].
Syntaxin-binding protein 6 (STXBP6), also known as amysin, is an essential component of the SNAP receptor (SNARE) complex. The truncated protein in the STXBP6 gene, resulting in a premature stop codon, could negatively modulate the exocytosis of synaptic vesicles. A plausible, robust correlation between the deletion of the STXBP6 gene and the manifestation of developmental epileptic encephalopathy has been observed [5].
Activation of the proteasomal degradation of misfolded proteins has been proposed as a therapy for neurodegenerative diseases, but its role is uncertain. Surprisingly, proteasome inhibitors alleviated neurodegeneration in CSPa-deficient mice, improved SNARE complex assembly, and extended their lifespan. These findings suggest that proteasome inhibition may be a therapeutic strategy for some NDs [6]. The SNARE complex is therefore a promising therapeutic target for several neurodegenerative and neurological diseases.
Previous therapeutic strategies based on pharmaceutical drugs targeting inflammation, oxidative stress, and neurodegeneration mechanisms faced challenges primarily due to difficulties in effective drug delivery. The main obstacle to the treatment of NDs is not only the intrinsic complexity of the nervous system but also the presence of biological barriers (the blood–brain barrier (BBB) and the blood–cerebrospinal fluid barrier). The BBB in the brain serves as a crucial protective shield for maintaining normal brain function and effectively protects the brain from harmful substances. The blood–brain barrier (BBB) is made up of several key components: endothelial cells, which form the walls of the cerebral capillaries and are tightly connected to each other; a basement membrane, which supports the endothelial cells and provides an additional layer of protection; astrocytes, which provide biochemical support to the endothelial cells; and pericytes, which surround the endothelial cells. Most macromolecules are unable to penetrate the cerebral endothelium; only small molecules with a molecular weight of less than 400 Da can cross the BBB. Therefore, these barriers reduce the accessibility and efficacy of therapies (BBB stops 95% of potential drugs), with a number of side effects resulting from the distribution of drugs in the body that cannot penetrate the central nervous system (CNS) [7,8]. It is also important that therapeutic agents reach the brain lesion without affecting other areas of the CNS to avoid adverse effects. Finally, it is very important that formulations selectively target specific brain cells (neurons, microglia, oligodendrocytes, or astrocytes) involved in different brain pathologies (Figure 1).
Steps towards enhancing the future of medicine are leading to the development of new therapeutic approaches, such as micro- and/or nanovesicles, which fall within the fields of nanomedicine and bioengineering [9,10].
Extracellular vesicles (EVs) and artificial vesicles (AVs) are emerging tools in nanomedicine. In fact, due to their ability to load and deliver drugs and regulatory gene sequences, they have been extensively studied for the treatment of brain diseases, demonstrating biological effects in counteracting neurodegenerative processes in various animal models [11]. EVs are cell-derived membrane structures released by mammalian, plant, and bacterial cells that form a cellular communication system (Figure 2). It has been shown that all types of brain cells (neurons, oligodendrocytes, astrocytes, and microglia) produce extracellular vesicles. EVs can transport different types of molecules (nucleic acids and proteins), which can influence the functional biological properties of recipient cells; indeed, in the CNS, EVs can influence neurodevelopment, myelination, regeneration, and synaptic activity [12]. Thanks to their content, EVs could represent powerful biomarkers for neurodegenerative diseases [13], or, since they constitute an intercellular communication system and containing regulatory molecules, they could also be candidates for therapeutic use or could represent a brain drug delivery system [14].
AVs that mimic natural vesicles can consist of an aqueous compartment physically separated from its environment by an artificial membrane, (liposomes and polymersomes) [11], or artificial vesicles can be derived from the destruction of cells whose membranes reassemble to form nano- or microvesicular structures [10]. Specifically, the biofabrication methods currently available for the production of cell-derived AVs are mainly based on several techniques such as sonication, extrusion, homogenization, and cavitation (Figure 2).
The approach of nanobiotechnologies, in particular EVs and AVs, could offer unique opportunities to (i) deliver molecules (RNA, such as miRNA or siRNA and/or proteins) or load drugs by increasing the local drug concentration in diseased tissues while reducing accumulation in other tissues and organs (e.g., the liver or kidneys); (ii) improve drug bioavailability, stability (the protection of a drug from degradation), and solubility; (iii) overcome physiological barriers; (iv) enable targeted delivery and controlled release; (v) prolong circulation times; and (vi) reduce doses and frequency of administration by limiting potential adverse effects. All of these advantages would increase the efficacy of therapies for the treatment of brain disorders [9,15].
2. Extracellular Vesicles in Neurodegenerative Diseases
Extracellular vesicles (EVs), including exosomes and microvesicles, have emerged as promising tools in the treatment and diagnosis of neurodegenerative diseases due to their role in intercellular communication and their ability to traverse the BBB. In recent years, research on EVs has expanded greatly, in part due to recent innovations in analytical imaging techniques that have greatly increased our understanding of how the chemical structure and function of vesicles contribute to biological roles such as compartmentalization, storage, and molecular trafficking. EVs are naturally secreted by neurons, glial cells, and other cell types, playing a crucial role in the regulation of cellular functions. Importantly, they can carry bioactive molecules such as proteins, lipids, and RNA (miRNA or siRNA), making them suitable for the targeted delivery of therapeutic agents to the brain. Studies have demonstrated that EVs can deliver siRNA and other small molecules across the blood–brain barrier to modify disease pathways in conditions such as Alzheimer’s (AD) and Parkinson’s diseases (PD) and multiple sclerosis (MS) [15,16]. For instance, Alvarez-Erviti et al. successfully used exosomes to deliver α-synuclein-targeting siRNA in a mouse model of Parkinson’s disease, reducing α-synuclein aggregates and ameliorating disease symptoms. Additionally, EVs are involved in the clearance of pathological proteins, such as amyloid-β (Aβ), which accumulate in the brain during AD. Research by Yuyama et al. [17] has shown that neuronal exosomes may sequester and aid in the clearance of Aβ, suggesting a potential neuroprotective role for EVs in mitigating Alzheimer’s pathology. Furthermore, exosomes may not only clear toxic proteins but also propagate neurodegenerative pathology. Sardar Sinha et al. [18] demonstrated that exosomes containing toxic amyloid-β oligomers can spread the pathological hallmark of AD, thereby accelerating disease progression. A particularly promising aspect of EVs is their role in promoting remyelination, the process of restoring myelin sheaths damaged by neurodegenerative diseases such as MS. Several studies have highlighted the potential of EVs, particularly those derived from mesenchymal stem cells (MSCs) and oligodendrocytes, to facilitate myelin regeneration. EVs have been shown to carry key molecules such as microRNAs and growth factors that enhance oligodendrocyte precursor cell (OPC) maturation and differentiation, leading to the restoration of myelin. A study by Pusic and Kraig [19] demonstrated that exosomes derived from MSCs contain specific miRNAs that promote remyelination in mouse models of demyelination, thereby indicating a novel therapeutic strategy for diseases like MS. Additionally, research by Lopez-Verrilli and Court [20] has shown that EVs derived from Schwann cells can support axonal regeneration and potentially enhance remyelination, providing further evidence of the regenerative potential of EVs in the nervous system. Beyond their role in disease modulation, EVs also hold potential as delivery vehicles for therapeutic interventions. The capacity of EVs to encapsulate and deliver anti-inflammatory drugs directly to the brain, offering a novel route for treating neuroinflammatory diseases. Given that neurodegenerative diseases, such as MS, AD, and amyotrophic lateral sclerosis (ALS), are often accompanied by inflammation, the anti-inflammatory properties of EVs could be harnessed to alleviate symptoms and slow disease progression. Moreover, the ability of EVs to facilitate communication between cells in the central nervous system (CNS) presents further opportunities for therapeutic interventions. Rajendran and Bali [21] emphasized how EVs mediate the transfer of proteins, lipids, and nucleic acids between neurons and glial cells, underscoring their role in the progression of neurodegenerative diseases. Similarly, Budnik et al. [22] underscored the importance of EVs in maintaining homeostasis in the CNS, suggesting that their dysregulation could contribute to neurodegenerative disorders. Lombardi and coworkers showed that proinflammatory microglia release EVs that block the remyelination process, whereas microglia in co-culture with immunosuppressive mesenchymal stem cells release EVs that induce myelin repair by promoting the recruitment of oligodendrocyte progenitor cells (OPCs) [23]. Furthermore, EVs derived from IFN-stimulated dendritic cells have been shown to contain microRNAs that promote remyelination after lysolecithin injury. In particular, EVs stimulated OPCs in myelin-forming cells and reduced oxidative stress [24]. BV-2 microglial cells were engineered to release EVs containing lactadherin (Mfg-e8) and cytokine IL-4 as an anti-inflammatory agent. These EVs modulated inflammation by significantly reducing the clinical signs of the experimental autoimmune encephalomyelitis (EAE) model of MS [25]. Curcumin-loaded exosomes (Exo-Cur) administered intranasally enabled the rapid delivery of the encapsulated drug to the brain, preventing lipopolysaccharide (LPS)-induced inflammation and reducing the severity of the MOG-induced EAE model [26,27].
A study by Upadhya R. [28] revealed the presence of several key proteins (hemopexin, agrin, PTX3, Gal-3BP, and nidogen-1) recognized for their neuroprotective effect in extracellular vesicles (EVs) released by neural stem cells (hiPSC-NSCs). In particular, hemopexin (Hpx), which is expressed in all regions of the CNS, plays a role in maintaining the integrity of the BBB, promoting angiogenesis, synaptic plasticity, and synaptogenesis [29,30]. Furthermore, it inhibits neuroinflammation by regulating the polarization of proinflammatory M1 microglia into anti-inflammatory M2 microglia via the low-density lipoprotein receptor-related protein 1 (LRP-1) [31]. EVs derived from hiPSC-NSCs have been observed to exert a rapid and pronounced effect on neurons, microglia, and a subset of astrocytes across all regions of the brain in an animal model of chronic neuroinflammation. Moreover, the same study identified the presence of eight miRNAs in hiPSC-NSC-EVs (miR-320a, 320b, 103a-3p, 21-5p, 26a-5p, 30a-3p, 181a-5p, 191-5p) that have a protective effect against various pathogenetic mechanisms in AD and PD models [28].
Mesenchymal stem-cell-derived extracellular vesicles (MSC-EVs) have been applied in several studies involving neurodegenerative disorders due to their ability to cross the BBB by exerting therapeutic effects in the CNS mediated by their content [32]. In this regard, Sha S. [33] showed that bone marrow MSC-EVs (BM-MSC-EVs) transport microRNAs such as miR-29c-3p in hippocampal neurons in a mouse model of AD. It has been shown that deregulation of the Wnt/β-catenin pathway and elevated levels of Aβ1-42 are involved in the onset and progression of AD. miR-29c-3p is able to downregulate the BACE1 (beta-secretase 1) gene by activating the Wnt/β-catenin pathway, thereby reducing the levels of Aβ1-42 and certain inflammatory cytokines (IL-1β, IL-6, and TNF-α), playing a therapeutic role in the treatment of AD [33].
The growing body of evidence on EVs highlights their dual potential as both biomarkers and therapeutic tools for neurodegenerative diseases. As outlined by Ravichandran and Prasad [27], the ability to detect specific EV signatures in bodily fluids, such as cerebrospinal fluid or blood, provides an exciting avenue for the early diagnosis and monitoring of diseases like Alzheimer’s and amyotrophic lateral sclerosis (ALS). This capability, combined with their ability to deliver therapeutics across the BBB, positions EVs as powerful assets in personalized medicine approaches for treating neurodegenerative conditions. However, for biomedical applications of EVs, there is an urgent need to establish their safety, source (patient cell culture), isolation protocols, reproducibility, and characterization. Indeed, the composition of EVs depends on the physiological state of the cells. Therefore, new approaches for the efficient and reproducible production of EVs will be needed in the coming years. To date, yield and heterogeneity issues have hindered the clinical use of EVs [7].
Each stage of the process, from isolation to administration, presents a range of challenges and limitations. The high structural complexity of these vesicles, coupled with their extreme heterogeneity, renders the establishment of a single isolation protocol that is equally adequate and productive an impossibility. The isolation methods described in the literature include differential and density gradient centrifugation, filtration, size-exclusion chromatography, and precipitation. Differential centrifugation, which employs a succession of centrifugations to remove debris and dead cells, is one of the most popular techniques [34]. Nevertheless, despite the advantages of this methodology in terms of speed, cost-effectiveness, and yield compared to density gradient centrifugation, it is important to note that it also has the disadvantage of requiring highly expensive machinery and achieving lower purity [35]. One technique used to obtain a purer product is size exclusion chromatography (SEC), which separates particles according to their size [36]. Another widely used alternative involves the use of membranes which, based on different pore sizes, allow particles with specific sizes or molecular weights to be selected [37]. The choice of one specific separation method over another will therefore depend on the known properties of the specific EV sources and the desired EV yield and specificity. The subsequent phase of this study entails the characterization of EVs, with the objective of acquiring data regarding their quantity, size, component composition, and biochemical properties. The combination of electron microscopy techniques and mass spectrometry enables the structure, morphology, and composition of vesicles to be studied [38,39]. Once more, nanoparticle tracking analysis (NTA) or flow cytometry evaluate the size and concentration of vesicles [40,41]. Unfortunately, the small and heterogeneous particle size, the lack of universal identification methods, and the lack of specificity of the techniques used act as obstacles to adequate characterization. It is evident that no single technique is capable of meeting the comprehensive requirements for a complete characterization of vesicles. Conversely, the utilization of measurements employing orthogonal methodologies for the same parameter could circumvent the inherent biases intrinsic to the techniques themselves. Once the vesicles have been properly characterized, they can be conjugated with therapeutic agents through the use of techniques such as electroporation. This involves the application of electrical impulses to temporarily permeate the vesicle membrane, thereby allowing the drug to enter and be absorbed [42]. However, this method carries the risk of damaging the vesicle membrane and reducing its stability. An alternative approach could be passive incubation of the drug, although this is often associated with low loading efficiency. The final stage in the development of EVs-based therapies is the administration of the vesicles. This can be achieved via three main routes: intravenous, intranasal, or intrathecal [43]. To date, intranasal administration has demonstrated considerable promise, offering a direct route to the central nervous system via the olfactory nerve, thereby circumventing the BBB.
The potential of EVs as a therapeutic agent has been acknowledged by the International Society for Extracellular Vesicles (ISEV), which has collated comprehensive manuscripts entitled Minimal Information for Studies of Extracellular Vesicles. These manuscripts outline the advantages, limitations, production methods, separation techniques, and characterization of EVs in a clear, comprehensive, and up-to-date manner [44,45].
3. Plant-Derived Extracellular Vesicles
Plant-derived extracellular vesicles (PEVs) are nanoscale vesicles that can be isolated from various plant tissues, including fruits, roots, seeds, and leaves, using standardized isolation and purification techniques. Of these methods, ultracentrifugation is the most commonly used, although other approaches such as differential centrifugation, sucrose density gradient centrifugation, and filtration are also widely used [46]. Fruits, vegetables, and spices have emerged as prime sources for the production of plant-based EVs. For instance, species such as grapes, grapefruit (Citrus × paradisi) [47,48], ginger (Zingiber officinale) [49], orange (Citrus sinensis), lemon (Citrus × limon) [50], tomato (Solanum lycopersicum) [51], prickly pear (Opuntia ficus-indica) [52] coconut, sunflower seeds, and cactus [53] have shown great potential for PEVs’ isolation, with promising applications in drug delivery and therapeutic development. The bioactive molecules carried by these vesicles, such as proteins, lipids, and RNA (miRNA or siRNA), allow them to modulate cellular functions and provide health benefits, including anti-inflammatory, anti-cancer, and regenerative effects [54].
PEVs are rich in microRNAs (miRNAs), a group of small non-coding RNAs that play critical roles in the regulation of physiological and pathological processes, including cell proliferation, apoptosis, metabolism, and immune responses. Teng et al. analyzed PEVs isolated from carrot, garlic, grape, and ginger, focusing on the presence of RNA. They found that these PEVs contain small RNAs, including miRNAs, that can influence the composition of the microbiome and suppress inflammatory conditions such as colitis. This suggests that EVs of plant origin ingested with food may play a role in modulating the gut microbiota [55]. Several studies have further investigated the effects of specific plant miRNAs, such as mi168a and miR159, found in edible plants and demonstrated their ability to regulate gene expression in mammals after ingestion. In addition, a comprehensive investigation by Xiao et al. examined the miRNA profiles of eleven different fruits and vegetables and identified several miRNAs that were shown to influence the expression of genes related to cancer and inflammatory cytokines in vitro. These findings highlight the potential of PEV miRNAs to influence human health through dietary intake [51].
PEVs have shown significant therapeutic powers in relation to their biological cargo [56] and, as a matter of fact, it is possible to use their nucleic acids, bioactive lipids, or cell surface proteins, or they can be applied as delivery systems for other active ingredients, due to their biocompatibility and adjustable nature [46]. As a consequence, plenty of PEV applications have been studied and, among them, plant-derived extracellular vesicles may be very efficient nutritional carriers if fruits and/or vegetables are included in someone’s diet and used as oral drug delivery systems. Furthermore, PEVs have been proved in oral administration for the treatment of various diseases, such as colitis [48], bowel disease [57], liver disease [58], brain tumor progression [59], and encephalitis [60]; in this regard, it is relevant to underline that this pathogenesis was strongly related to the dysfunction of the gut barrier and the dysbiosis of the gut microbiota. Furthermore, the gut–brain axis is necessary for the transduction of detrimental signals from the gut to the brain, and for instance, inflammatory factors resulting from leaky gut penetrate the BBB, causing the consequent destruction of its integrity. As a result, the activated gut immune cells may be translocated to the brain to amplify neuroinflammation and inflammatory factors transmitted through the vagal nerve which connects the gut and the brain, resulting in increased neuroinflammation. It is so evident that a tight relation exists between the gut and the brain, so any kinds of PEVs that are effective for inflammatory gut diseases might also be useful for neurodegenerative diseases targeted by neuroinflammation hallmarks. The possible mechanism of a therapeutic role of PEVs in the treatment of neurodegenerative diseases is obtained thanks to the balance of gut microorganisms’ composition, preventing, in this way, the peripheral inflammatory factors entering the brain and diminishing neuroinflammation. In fact, it is widely accepted that microglia play a pivotal role in the progression of Alzheimer’s disease (AD) [61]. In particular, physiological proliferation, chemotaxis, and phagocytosis processes are usually required to remove excessive Aβ protein deposition; on the other hand, over-activated microglia would release inflammatory cytokines to induce neuronal death [62].
Oxidative stress can also damage the BBB, contributing to neurological diseases such as Parkinson’s disease, ischemic stroke, and inflammatory brain disorders. Oxidative stress can be induced by ROS proteins, lipids, and DNA, ultimately compromising the integrity of the BBB. Recent evidence has shown that PEVs, such as those from lemon, strawberry, and carrot, offer protection against oxidative stress [63,64,65].
Drug delivery across the BBB is challenging due to the tight structure of the barrier and limited tumor penetration. Nanoparticle-based drug delivery systems have been developed to address this issue, offering advantages such as high drug loading, functionalization, and controlled release. However, synthetic nanoparticles often struggle with prolonged circulation and effective BBB penetration. In contrast, PEVs possess natural membrane proteins that facilitate cellular uptake and crossing physiological barriers such as the BBB. Novel approaches using PEVs have shown promise in bypassing the BBB [66]. For example, grapefruit-derived EVs have been used to deliver drugs to glioma tissue via receptor-mediated transport and membrane fusion, enhancing cellular internalization and antiproliferative effects. Additionally, researchers have explored non-invasive methods such as intranasal delivery using folic acid (FA)-coated grapefruit-derived nanocarriers to deliver therapeutic miRNAs across the BBB. This method improves drug absorption and reduces toxicity, offering potential for the treatment of brain diseases [59,67].
One study showed that EVs obtained from Drynariae Rhizoma roots were enriched in the enzyme NAD(P)H-quinone oxidoreductase, widely distributed in eukaryotic cells and recognized for its protective mechanism against quinone-induced oxidative damage in the oxidative phosphorylation pathway [68], suggesting a protective role in oxidative stress-associated disorders such as AD, PD, and in Huntington’s disease (HD) [69]. Furthermore, Vestuto V. [70] highlighted the presence in EVs of Salvia sclarea and Salvia dominica of eight proteins homologous to their human counterparts with >50% sequence identity. These include GRP75 (a 75 kDA glucose-regulated protein), which plays a key role in mitochondrial homeostasis [71]. Furthermore, plants naturally carry EVs containing small RNAs that play a key role in plants’ natural defense system against infection [72]. Interesting work has described certain miRNAs present in EVs isolated from apple, in particular miR-125, miR-146a, and miR-146b, which are recognized for their anti-inflammatory effect both in vivo and in vitro [73].
4. Artificial Vesicles
As our understanding of the biological properties of extracellular vesicles and their physiological functions deepens, increasingly elegant artificial vesicles with excellent physiological properties and functions are being developed for a wide range of applications. In particular, artificial vesicles (AVs) have emerged as an innovation in the fields of biotechnology and nanomedicine. These synthetic vesicles are inspired by natural extracellular vesicles, such as exosomes and microvesicles. AVs offer the possibility of encapsulating bioactive molecules, such as proteins, nucleic acids, or drugs, for targeted delivery to specific tissues. These synthetic or bioengineered vesicles are designed to mimic the functional and structural properties of natural vesicles, with the added advantage of greater control over size, composition, and surface modifications [74]. This unique flexibility makes AVs a powerful tool to address otherwise difficult-to-treat diseases, particularly neurodegenerative diseases, for which targeted delivery to the central nervous system is crucial.
AVs are obtained through a variety of techniques that allow for precise control over their size, composition, and functional properties. The three primary methods for generating AVs are bottom-up synthesis, top-down synthesis, and hybrid vesicle formation. In the bottom-up synthesis approach, synthetic vesicles are constructed from basic molecular components, such as lipids, polymers, and proteins. The most common method involves the self-assembly of phospholipids into liposomes, which resemble natural cell membranes (Figure 2). By manipulating the lipid composition of these vesicles, properties such as their size, surface charge, and encapsulation efficiency can be modified [75,76,77]. In contrast, the top-down methods start with whole cells, which are mechanically or chemically disrupted to form vesicles. For example, plasma membranes can be extracted or sonicated to form membrane-derived vesicles that retain functional surface proteins and receptors. These vesicles can mimic the behavior of natural extracellular vesicles and can be functionalized to carry therapeutic agents [78]. The hybrid vesicle formation method combines the best features of natural and synthetic systems. Hybrid vesicles are formed by fusing synthetic liposomes or polymersomes with natural extracellular vesicles. These vesicles can possess the targeting capabilities of natural EVs while being engineered for stability and cargo capacity. Hybrid vesicles allow for modifying the surface with targeting ligands or therapeutic molecules, enhancing their application in neurodegenerative disease treatment [79].
AVs offer potential solutions by facilitating targeted drug delivery through the BBB, improving bioavailability and reducing off-target effects [80,81]. Indeed, after formation, AVs are functionalized with targeting ligands, such as peptides or antibodies, to facilitate their uptake by specific cell types. These ligands can be designed to target receptors on the surface of neuronal cells or endothelial cells of the BBB, improving the specificity and efficiency of delivery. All of these aspects have made artificial vesicles a promising resource for the treatment of neurodegenerative brain diseases as they can bypass the BBB and deliver therapeutic agents directly to the affected neurons.
Liposomes were the first drug delivery system to be investigated and are approved by the Food and Drug Administration (FDA) as a therapeutic drug delivery system [82]. Such systems protect loaded molecules from degradation and allow for specific targeting [83]. Indeed, liposomes have been modified with cell-penetrating peptides (trans-acting activator of transcription—TAT) to cross the BBB and increase drug concentration in the CNS [84] (Figure 3). In addition, several data have demonstrated the therapeutic potential of liposomes encapsulated with myelin antigenic peptides in MS to induce immune tolerance (Figure 2).
Alzheimer’s disease is characterized by the accumulation of amyloid-beta plaques and tau tangles, which disrupt synaptic function and lead to cognitive decline. AVs can be loaded with small interfering RNA (siRNA)-targeting beta-secretase (BACE1), an enzyme involved in amyloid-beta production, to reduce plaque formation. Studies have shown that AVs can successfully deliver siRNA across the BBB and into neurons, reducing amyloid-beta levels and slowing down disease progression in animal models [85,86].
Moreover, oxidative stress and mitochondrial dysfunction are among the early events of AD, which trigger neurodegeneration. The use of natural antioxidants constitutes a neuroprotective strategy. To this end, solid lipid nanoparticles containing ferulic acid (FA-SNL), a natural molecule with important antioxidant properties, have been developed. Treatment with FA-SNL reduced ROS generation and restored neuronal mitochondrial functionality [87].
In addition to gene silencing, AVs can encapsulate anti-inflammatory drugs or neurotrophic factors like BDNF (brain-derived neurotrophic factor), providing neuroprotection and promoting neuronal survival [88]. Furthermore, in Parkinson’s disease, AVs loaded with siRNA- or miRNA-targeting alpha-synuclein expression have been shown to reduce its aggregation and protect dopaminergic neurons from degeneration [89]. This targeted approach could slow down the progression of PD and mitigate motor symptoms in patients.
Nanovesicles derived from neutrophil membranes were used to deliver Resolvin D2 (a molecule derived from docosahexaenoic acid) to mice with ischemic stroke. In particular, using nitrogen cavitation and inspired by the binding of neutrophils to the endothelium during stroke, the authors generated nanovesicles derived from HL-60 cells differentiated into neutrophils. By interacting with the inflamed brain endothelium, the nanovesicles attenuated neuroinflammation [90].
Nanovesicles derived from macrophage membranes loaded with nerve growth factor were generated by extrusion through the polycarbonate membrane [91]. These nanovesicles efficiently delivered nerve growth factor into the spinal cord lesion, exerting a neuroprotective effect [91].
We have recently bio-fabricated nanovesicles from brain myelin as a new potential brain-to-brain delivery system [92]. In particular, myelin nanovesicles produced with a simple, efficient, inexpensive, and reproducible production protocol exhibit high stability and cytocompatibility and are able to load drugs and reach the CNS after nasal administration [93]. They are also able to interact preferentially with microglial cells by modulating the inflammatory process [93].
Nanovesicles derived from brain endothelial cells by serial extrusion are a good nanocarrier alternative to exosomes [94]. In particular, loaded doxorubicin has been shown to cross the BBB with an ability to target glioblastoma and have anti-tumor effects. The yield in nanovesicle production is much higher (500-fold) than with exosomes.
Liposomes rich in phosphatidylserine (PS) (PS–liposomes), a component present in apoptotic cells that modulates immune responses, were also prepared. PS–liposomes, which are recognized by dendritic cell receptors, were loaded with the peptide MOG40-55 (an MS autoantigen) and tested in a mouse model of MS, where they induced immune tolerance [95].
Synaptosomes, a subcellular fraction isolated from synaptic terminals, have been pro-posed as vesicles for the delivery of mitochondria (mitochondrial transfer) into neuronal cells [96]. Synaptosomes could be used for the treatment of many brain diseases characterized by mitochondrial dysfunction, such as Alzheimer’s disease, Parkinson’s disease, etc. [96].
In addition to artificial vesicles derived from synthetic materials or mammalian cells, artificial plant-derived vesicles (APDVs) are emerging as a novel, natural alternative for therapeutic delivery. These vesicles, isolated from edible plants such as ginger, grapes, and grapefruit following the destruction of plant tissue, have properties similar to those of mammalian extracellular vesicles and can be used to treat neurodegenerative diseases [97]. Like AVs, APDVs have the potential to cross the BBB. Studies have shown that vesicles derived from grapefruit and ginger can traverse the BBB in animal models, making them suitable candidates for delivering therapeutics to the brain [98]. Moreover, APDVs can be functionalized with targeting molecules to enhance their delivery efficiency to neuronal cells. Furthermore, APDVs can be loaded with natural compounds that have anti-inflammatory and neuroprotective effects. For instance, ginger-derived vesicles loaded with curcumin have been shown to protect neurons from oxidative stress and inflammation, which are key contributors to neurodegeneration in diseases like AD and PD [99]. Similarly, grape-derived vesicles can be used to deliver miRNA that modulates the gene expression involved in neuroinflammation and protein aggregation, providing a novel approach to treating these conditions [59]. APDVs from bitter gourd, specifically miRNA5266, were found to reduce BBB damage by inhibiting the expression of matrix metalloproteinase-9 (MMP-9), thereby preventing ischemia–reperfusion injury and neuronal apoptosis [100].
The therapeutic mechanisms of EVs, PEVs and AVs in neurodegenerative diseases are summarized in Table 1.
5. Conclusions
To conclude, extracellular vesicles and artificial vesicles hold great promise as therapeutic agents in the context of neurodegenerative diseases.
Their ability to cross the BBB and carry diverse molecular cargoes positions them as versatile tools in the treatment of neurodegenerative diseases such as Alzheimer’s, Parkinson’s, ALS, and multiple sclerosis. Extracellular vesicles are naturally occurring and have advantages such as biocompatibility and the ability to normally cross biological barriers, but for biomedical applications, there is an urgent need to establish their safety, source (patient cell culture), isolation protocols, reproducibility, and characterization.
Thus, due to the inherently complex biogenesis of EVs and their wide heterogeneity in size, composition, and origin, the study of EVs has remained challenging to date. Artificial vesicles, on the other hand, are man-made and can be engineered with the advantage of greater control over size, composition, and surface modifications, but the safety profile needs to be addressed. However, for both EVs and AVs, further studies are needed to fully understand their mechanisms of action, to optimize their isolation and/or production methods, and to ensure their efficacy in clinical applications. |
Title: Types of cell death and their relations to host immunological pathways | Body:
INTRODUCTION
Cell death is an essential cellular process that plays crucial roles in development and immune responses. Apoptosis is a well-known example of cell death. Cytotoxic T cells and natural killer cells can utilize the apoptosis mechanism to kill virus-infected host cells. During apoptosis, DNA fragmentation can destroy the viral genome, eliminating viral pathogens. After the discovery of apoptosis, other cell death pathways have been discovered, including autophagy, ferroptosis, pyroptosis, necroptosis, and NETosis [1]. We have proposed a framework encompassing all discovered host immunological pathways, such as TH1, TH2a, TH2b, TH3, TH9, TH17, TH22, TH1-like, and THαβ immune reactions [2–4]. These immune responses combat different types of pathogens and are linked to four types of hypersensitivities. TH1 and TH1-like immune responses fight against intracellular microorganisms, including intracellular bacteria, protozoa, and fungi. TH1 immunity is an eradicable immune reaction, while TH1-like is a tolerable immune reaction. TH1 and TH1-like immune responses are associated with type 4 delayed-type hypersensitivities. TH2 and TH9 immune responses combat parasites, including ectoparasites (insects) and endoparasites (helminths). TH2 immunity is an eradicable immune reaction, and TH9 is a tolerable immune reaction. TH2 and TH9 immune responses are associated with type 1 allergic hypersensitivities. TH22 and TH17 immune responses fight against extracellular microorganisms, including extracellular bacteria, protozoa, and fungi. TH22 immunity is an eradicable immune reaction, and TH17 is a tolerable immune reaction. TH22 and TH17 immune responses are associated with type 3 immune complex-related hypersensitivities. THαβ and TH3 immune responses combat infectious particles, including viruses and prions. THαβ immunity is an eradicable immune reaction, and TH3 is a tolerable immune reaction. THαβ and TH3 immune responses are associated with type 2 antibody-dependent cytotoxic hypersensitivities. Programmed cell death is a crucial component of the host defense mechanism. Thus, different types of host immunological reactions can be related to different types of programmed cell death to defend against different pathogens. Here, we will review these cell death pathways associated with the host immunological pathways.
Overview of cell death pathways
Apoptosis
Apoptosis, the earliest discovered cell death pathway, stands in contrast to necrosis, an unprogrammed form of cell death induced by pathogens or various external factors. Unlike necrosis, apoptosis is a tightly regulated and programmed cell death pathway governed by genetic machinery. During embryonic development, apoptosis plays a crucial role as embryonic cells utilize this mechanism to eliminate unwanted cells. Moreover, the apoptosis mechanism is employed in host immune responses to combat pathogenic infections. For instance, the natural killer cell antibody-dependent cellular cytotoxic reaction uses apoptosis to eliminate virus-infected cells. Consequently, apoptosis emerges as a vital component in the body’s self-defense reactions.
Apoptosis can be categorized into two main pathways: the extrinsic pathway and the intrinsic pathway. The extrinsic pathway is activated by external signal molecules, initiating the apoptosis machinery. A classic example of the extrinsic apoptosis pathway involves the interaction between Fas and Fas ligand. On the other hand, the intrinsic pathway is activated by internal cellular signal molecules, with the release of cytochrome c from mitochondria being a characteristic event. Cytosolic cytochrome c serves as a trigger for cell apoptosis. Notably, there is an interconnection between the extrinsic and intrinsic pathways, converging into a common cell death pathway. The apoptosis machinery comprises initiator and executor caspases responsible for breaking down intracellular DNA and proteins. Initiator caspases include caspase 2, 8, 9, and 10, while executor caspases include caspase 3, 6, and 7. This intricate system underscores the convergence of both extrinsic and intrinsic apoptosis pathways into a unified mechanism of cell death.
Autophagy
Autophagy is a natural, conserved cellular process that digests unwanted, damaged, or old organelles through a lysosome-dependent regulated mechanism. It is referred to as type 2 cell death. Autophagy can be initiated during starvation or other cellular stress situations [5]. It is a process that recycles cell contents to maintain the required metabolism of cells. Special organelles involved in autophagy include mitophagy, the autophagy of mitochondria, and others. Autophagy is an essential cellular process. Autophagic death is a cellular process involving autophagy-induced programmed cell death. This machinery also plays a crucial role in the cell’s defense mechanism [6]. Although initially recognized as a principal degradation pathway to protect against starvation, it is now evident that autophagy also plays a vital role in the homeostasis of non-starved cells. Defects in autophagy are associated with various human diseases, especially neurodegenerative disorders, and modulating autophagy becomes a potential treatment for these detrimental illnesses.
Four types of autophagy have been classified: macroautophagy, microautophagy, chaperone-mediated autophagy (CMA), and crinophagy. In macroautophagy, cytoplasmic components such as mitochondria are targeted and isolated from the main part of the cell within a double-membrane autophagosome. Then, it fuses with a lysosome to become an autolysosome, and eventually, the contents of the vesicle are degraded and recycled. Compared to crinophagy, unnecessary secretory granules are degraded and recycled. In disease, autophagy has been seen as an adaptive response to stress, promoting cell survival; but in other conditions, it could promote cell death and morbidity. In extreme hunger, the breakdown of cellular components promotes cell survival by maintaining cellular energy.
Pyroptosis
Pyroptosis is another form of programmed cell death [7]. It is related to interleukin-1 and interleukin-18. It is associated with programmed cell death of macrophages. This process can help rapidly clear intracellular pathogens. Pyroptosis usually occurs in immune cells, keratinocytes, and sometimes epithelial cells. This process is triggered by the formation of an inflammasome complex (pyroptosome complex) via the stimulation of intracellular danger signals. The pyroptosome complex is related to the activation of caspases 1/4/5 in humans, which are different caspase sets compared to apoptosis. Caspases 1/4/5 cause the maturation of pro-inflammatory cytokines interleukin-1β and interleukin-18. These caspases also activate the pore-forming protein gasdermin D (GSDMD). Gasdermin D is the key effector molecule of pyroptosis. The inflammasome pathway can be canonical or noncanonical. The canonical pathway involves the activation of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) recognized by several endogenous pattern recognition receptors (PRRs). For example, NLRP3 or NLRC4 protein is activated by different PAMPs and DAMPs. These receptors can upregulate pro-inflammatory cytokines, including interleukin-12, via the NFkB and MAPK signaling mechanisms. Then, pro-IL-1β and pro-IL-18 are released to be activated via the action of cysteine-regulated caspase-1. Both NLRC4 and procaspase-1 contain a caspase activation and recruitment domain (CARD). After NLRC4 recruits pro-caspase-1, the homotypic CARD-CARD interaction will induce an autocatalytic reaction, allowing pro-caspase-1 to become active caspase-1. Activated caspase-1 cleaves pro-IL-1β and pro-IL-18, enabling these two cytokines to become activated forms. Besides, caspase-1 also cleaves the intracellular gasdermin D. GSDMD will be cleaved into two fragments: the N-terminal GSDMD-N and the C-terminal GSDMD-C. GSDMD-N can form transmembrane pores. These transmembrane pores allow the secretion of IL-1β and IL-18 into extracellular spaces. These pores also impair the extracellular-intracellular ion gradients, causing an increase in osmotic pressure with the influx of water, leading to cell swelling and bursting, resulting in pyroptosis. It is worth noting that GSDMD-N can only insert itself into the inner membrane of specific lipid compositions. And, without cleavage, GSDMD-N is autoinhibited by GSDMD-C. The noncanonical pathway involves the interaction of bacterial lipopolysaccharide and human caspase 4/5. Binding LPS to these caspases induces oligomerization and activation. These caspases also cleave GSDMD to become GSDMD-N, promoting pyroptosis.
Ferroptosis
Ferroptosis is a type of programmed cell death triggered by excess iron intracellularly. It is characterized by the accumulation of lipid peroxides. Its term is oxytosis. Ferroptosis is triggered by the failure of glutathione-mediated antioxidant defenses. The overall pattern of ferroptosis is the iron-mediated accumulation of oxidatively damaged phospholipids, especially lipid peroxides. When free radicals abstract electrons from a phospholipid, oxidation of phospholipids will occur. Typically, it affects polyunsaturated fatty acids. The main cellular defense mechanism against ferroptosis is mediated by glutathione peroxidase 4 (GPX4). GPX4 can convert lipid peroxides into non-toxic lipid alcohol molecules. Iron is vital and necessary to generate reactive oxygen species to initiate ferroptosis. Thus, treating cells with iron chelators can stop the occurrence of ferroptosis. Additionally, intracellular glutathione (GSH) levels are key to the function of GPX4, so depletion of GSH will lead to ferroptotic cell death. Besides, ferroptosis causes phenotypic changes in mitochondria.
Necroptosis
Necroptosis is a programmed form of cell death compared to necrosis. The key cytokine mediating necroptosis is TNFα. Binding of TNFα leads to the activation of its receptor TNFR1. TNFR1 receptor binds to TNFR-associated death protein (TRADD) and TNF receptor-associated factor 2 (TRAF2) to activate RIPK1, which recruits RIPK3 to form the necrosome (ripoptosome). During the necroptosis process, the anti-apoptotic protein cFLIP can inactivate caspase 8, facilitating necroptosis. In the absence of caspase 8, RIPK1 and RIPK3 can autophosphorylate and transphosphorylate each other to form a microfilament-like complex named the necrosome. The necrosome phosphorylates the pro-necroptotic protein MLKL, which causes MLKL oligomerization. The oligomerized MLKL will insert into plasma and organelle membranes to induce permeability. Besides, MLKL insertion will induce the leakage of cellular contents of the damage-associated molecular patterns (DAMPs) to trigger inflammation. The necrosome also inhibits the adenine nucleotide translocase in mitochondria, lowering intracellular ATP concentrations. Furthermore, uncoupling of the mitochondrial electron transport chain will lead to mitochondrial damage and open the mitochondrial permeability transition pores, allowing mitochondrial proteins to move into the cytoplasm. The necrosome can additionally cause leaks of lysosomal enzymes into the cytosol via the induction of reactive oxygen radicals by JNK, calpain activation by calcium release, and sphingosine formation. In contrast to apoptosis, the process of necroptosis does not relate to caspase activation. No apoptotic body formation is seen in necroptosis. Cells undergo necroptotic rupture, leaking cellular contents into intercellular spaces.
NETosis
Neutrophil extracellular traps (NETs) are networks of neutrophil-derived extracellular fibers binding to extracellular pathogens [8]. NETs allow neutrophils to kill extracellular microorganisms with minimal damage to the body [9]. NETs consist of DNA stretches and proteins, including azurophilic granules (neutrophil elastase, cathepsin G, and myeloperoxidase), tertiary granules (gelatinase), and specific granules (lactoferrin). NETs can also form intravascularly via the regulation of platelets. Platelet TLR4 can bind to extracellular microorganisms and activate neutrophils to initiate NETs. Thus, NETs can capture bacteria in blood vessels, stopping their migration via blood circulation. NETs activation and release are usually associated with neutrophil programmed cell death, suicidal NETosis. The NETosis pathway typically begins with NADPH oxidase activation of arginine deiminase 4 (PAD4) via reactive oxygen radicals. PAD4 will induce the citrullination of histones in the neutrophil cell nuclei, resulting in chromatin decondensation. Azurophilic granules (neutrophil elastase, cathepsin G, and myeloperoxidase) enter the neutrophil nucleus and cause the rupture of the nuclear envelope. Then, the decondensed chromatin enters the cytoplasm, where it combines with other cellular granules to form the early-stage NET. NETosis is a double-edged sword, which may cause complications. There is a report suggesting a relationship between NETosis and organ injury [10].
Apoptosis
Apoptosis, the earliest discovered cell death pathway, stands in contrast to necrosis, an unprogrammed form of cell death induced by pathogens or various external factors. Unlike necrosis, apoptosis is a tightly regulated and programmed cell death pathway governed by genetic machinery. During embryonic development, apoptosis plays a crucial role as embryonic cells utilize this mechanism to eliminate unwanted cells. Moreover, the apoptosis mechanism is employed in host immune responses to combat pathogenic infections. For instance, the natural killer cell antibody-dependent cellular cytotoxic reaction uses apoptosis to eliminate virus-infected cells. Consequently, apoptosis emerges as a vital component in the body’s self-defense reactions.
Apoptosis can be categorized into two main pathways: the extrinsic pathway and the intrinsic pathway. The extrinsic pathway is activated by external signal molecules, initiating the apoptosis machinery. A classic example of the extrinsic apoptosis pathway involves the interaction between Fas and Fas ligand. On the other hand, the intrinsic pathway is activated by internal cellular signal molecules, with the release of cytochrome c from mitochondria being a characteristic event. Cytosolic cytochrome c serves as a trigger for cell apoptosis. Notably, there is an interconnection between the extrinsic and intrinsic pathways, converging into a common cell death pathway. The apoptosis machinery comprises initiator and executor caspases responsible for breaking down intracellular DNA and proteins. Initiator caspases include caspase 2, 8, 9, and 10, while executor caspases include caspase 3, 6, and 7. This intricate system underscores the convergence of both extrinsic and intrinsic apoptosis pathways into a unified mechanism of cell death.
Autophagy
Autophagy is a natural, conserved cellular process that digests unwanted, damaged, or old organelles through a lysosome-dependent regulated mechanism. It is referred to as type 2 cell death. Autophagy can be initiated during starvation or other cellular stress situations [5]. It is a process that recycles cell contents to maintain the required metabolism of cells. Special organelles involved in autophagy include mitophagy, the autophagy of mitochondria, and others. Autophagy is an essential cellular process. Autophagic death is a cellular process involving autophagy-induced programmed cell death. This machinery also plays a crucial role in the cell’s defense mechanism [6]. Although initially recognized as a principal degradation pathway to protect against starvation, it is now evident that autophagy also plays a vital role in the homeostasis of non-starved cells. Defects in autophagy are associated with various human diseases, especially neurodegenerative disorders, and modulating autophagy becomes a potential treatment for these detrimental illnesses.
Four types of autophagy have been classified: macroautophagy, microautophagy, chaperone-mediated autophagy (CMA), and crinophagy. In macroautophagy, cytoplasmic components such as mitochondria are targeted and isolated from the main part of the cell within a double-membrane autophagosome. Then, it fuses with a lysosome to become an autolysosome, and eventually, the contents of the vesicle are degraded and recycled. Compared to crinophagy, unnecessary secretory granules are degraded and recycled. In disease, autophagy has been seen as an adaptive response to stress, promoting cell survival; but in other conditions, it could promote cell death and morbidity. In extreme hunger, the breakdown of cellular components promotes cell survival by maintaining cellular energy.
Pyroptosis
Pyroptosis is another form of programmed cell death [7]. It is related to interleukin-1 and interleukin-18. It is associated with programmed cell death of macrophages. This process can help rapidly clear intracellular pathogens. Pyroptosis usually occurs in immune cells, keratinocytes, and sometimes epithelial cells. This process is triggered by the formation of an inflammasome complex (pyroptosome complex) via the stimulation of intracellular danger signals. The pyroptosome complex is related to the activation of caspases 1/4/5 in humans, which are different caspase sets compared to apoptosis. Caspases 1/4/5 cause the maturation of pro-inflammatory cytokines interleukin-1β and interleukin-18. These caspases also activate the pore-forming protein gasdermin D (GSDMD). Gasdermin D is the key effector molecule of pyroptosis. The inflammasome pathway can be canonical or noncanonical. The canonical pathway involves the activation of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) recognized by several endogenous pattern recognition receptors (PRRs). For example, NLRP3 or NLRC4 protein is activated by different PAMPs and DAMPs. These receptors can upregulate pro-inflammatory cytokines, including interleukin-12, via the NFkB and MAPK signaling mechanisms. Then, pro-IL-1β and pro-IL-18 are released to be activated via the action of cysteine-regulated caspase-1. Both NLRC4 and procaspase-1 contain a caspase activation and recruitment domain (CARD). After NLRC4 recruits pro-caspase-1, the homotypic CARD-CARD interaction will induce an autocatalytic reaction, allowing pro-caspase-1 to become active caspase-1. Activated caspase-1 cleaves pro-IL-1β and pro-IL-18, enabling these two cytokines to become activated forms. Besides, caspase-1 also cleaves the intracellular gasdermin D. GSDMD will be cleaved into two fragments: the N-terminal GSDMD-N and the C-terminal GSDMD-C. GSDMD-N can form transmembrane pores. These transmembrane pores allow the secretion of IL-1β and IL-18 into extracellular spaces. These pores also impair the extracellular-intracellular ion gradients, causing an increase in osmotic pressure with the influx of water, leading to cell swelling and bursting, resulting in pyroptosis. It is worth noting that GSDMD-N can only insert itself into the inner membrane of specific lipid compositions. And, without cleavage, GSDMD-N is autoinhibited by GSDMD-C. The noncanonical pathway involves the interaction of bacterial lipopolysaccharide and human caspase 4/5. Binding LPS to these caspases induces oligomerization and activation. These caspases also cleave GSDMD to become GSDMD-N, promoting pyroptosis.
Ferroptosis
Ferroptosis is a type of programmed cell death triggered by excess iron intracellularly. It is characterized by the accumulation of lipid peroxides. Its term is oxytosis. Ferroptosis is triggered by the failure of glutathione-mediated antioxidant defenses. The overall pattern of ferroptosis is the iron-mediated accumulation of oxidatively damaged phospholipids, especially lipid peroxides. When free radicals abstract electrons from a phospholipid, oxidation of phospholipids will occur. Typically, it affects polyunsaturated fatty acids. The main cellular defense mechanism against ferroptosis is mediated by glutathione peroxidase 4 (GPX4). GPX4 can convert lipid peroxides into non-toxic lipid alcohol molecules. Iron is vital and necessary to generate reactive oxygen species to initiate ferroptosis. Thus, treating cells with iron chelators can stop the occurrence of ferroptosis. Additionally, intracellular glutathione (GSH) levels are key to the function of GPX4, so depletion of GSH will lead to ferroptotic cell death. Besides, ferroptosis causes phenotypic changes in mitochondria.
Necroptosis
Necroptosis is a programmed form of cell death compared to necrosis. The key cytokine mediating necroptosis is TNFα. Binding of TNFα leads to the activation of its receptor TNFR1. TNFR1 receptor binds to TNFR-associated death protein (TRADD) and TNF receptor-associated factor 2 (TRAF2) to activate RIPK1, which recruits RIPK3 to form the necrosome (ripoptosome). During the necroptosis process, the anti-apoptotic protein cFLIP can inactivate caspase 8, facilitating necroptosis. In the absence of caspase 8, RIPK1 and RIPK3 can autophosphorylate and transphosphorylate each other to form a microfilament-like complex named the necrosome. The necrosome phosphorylates the pro-necroptotic protein MLKL, which causes MLKL oligomerization. The oligomerized MLKL will insert into plasma and organelle membranes to induce permeability. Besides, MLKL insertion will induce the leakage of cellular contents of the damage-associated molecular patterns (DAMPs) to trigger inflammation. The necrosome also inhibits the adenine nucleotide translocase in mitochondria, lowering intracellular ATP concentrations. Furthermore, uncoupling of the mitochondrial electron transport chain will lead to mitochondrial damage and open the mitochondrial permeability transition pores, allowing mitochondrial proteins to move into the cytoplasm. The necrosome can additionally cause leaks of lysosomal enzymes into the cytosol via the induction of reactive oxygen radicals by JNK, calpain activation by calcium release, and sphingosine formation. In contrast to apoptosis, the process of necroptosis does not relate to caspase activation. No apoptotic body formation is seen in necroptosis. Cells undergo necroptotic rupture, leaking cellular contents into intercellular spaces.
NETosis
Neutrophil extracellular traps (NETs) are networks of neutrophil-derived extracellular fibers binding to extracellular pathogens [8]. NETs allow neutrophils to kill extracellular microorganisms with minimal damage to the body [9]. NETs consist of DNA stretches and proteins, including azurophilic granules (neutrophil elastase, cathepsin G, and myeloperoxidase), tertiary granules (gelatinase), and specific granules (lactoferrin). NETs can also form intravascularly via the regulation of platelets. Platelet TLR4 can bind to extracellular microorganisms and activate neutrophils to initiate NETs. Thus, NETs can capture bacteria in blood vessels, stopping their migration via blood circulation. NETs activation and release are usually associated with neutrophil programmed cell death, suicidal NETosis. The NETosis pathway typically begins with NADPH oxidase activation of arginine deiminase 4 (PAD4) via reactive oxygen radicals. PAD4 will induce the citrullination of histones in the neutrophil cell nuclei, resulting in chromatin decondensation. Azurophilic granules (neutrophil elastase, cathepsin G, and myeloperoxidase) enter the neutrophil nucleus and cause the rupture of the nuclear envelope. Then, the decondensed chromatin enters the cytoplasm, where it combines with other cellular granules to form the early-stage NET. NETosis is a double-edged sword, which may cause complications. There is a report suggesting a relationship between NETosis and organ injury [10].
Overview of host immunological pathways
The immune system is a marvelously complex network, where host immunological pathways play a pivotal role in defending against diverse pathogens. These pathways are categorized based on the dominance of certain immunoglobulins, predominantly into IgG-dominant eradicable immune responses and IgA-dominant tolerable immune responses [2–4, 11]. Eradicable immune responses are initiated by follicular helper T cells (Tfh) via interleukin-21, and tolerable immune responses are initiated by regulatory T cells (Treg) via TGF-β. Understanding the intricacies of these pathways is crucial in comprehending how the immune system combats various threats.
In the realm of eradicable immune responses, the action primarily revolves around combating different types of pathogens through specialized immune mechanisms. The TH1 immunity, for instance, stands guard against intracellular microorganisms such as bacteria, protozoa, and fungi. This branch mobilizes an array of defenders including M1 macrophages, IFNγ-producing CD4 T cells, iNKT1 cells, CD8 T cells (Tc1, EM4), and IgG3 B cells, forming a formidable defense line against these intruders. TH1 immunity is also intricately linked to type 4 delayed type hypersensitivity reactions, highlighting its role in specific immune responses.
In contrast, TH2 immunity gears up against parasites, presenting two distinct subtypes: TH2a and TH2b. TH2a tackles endoparasites (helminths) with its lineup of inflammatory eosinophils (iEOS), interleukin-4/interleukin-5 producing CD4 T cells, mast cells-tryptase (MCt), iNKT2 cells, and IgG4 B cells. On the other hand, TH2b focuses on combating ectoparasites (insects), marshaling basophils, interleukin-13/interleukin-4 producing CD4 T cells, mast cells-tryptase/chymase (MCtc), iNKT2 cells, and IgE B cells. These branches of TH2 immunity are instrumental in addressing parasitic threats and are associated with type 1 allergic hypersensitivity responses.
Expanding further, TH22 immunity is dedicated to countering extracellular microorganisms such as bacteria, protozoa, and fungi. Neutrophils (N1), interleukin-22 producing CD4 T cells, iNKT17 cells, and IgG2 B cells collaboratively orchestrate the defense in this domain. TH22 immunity plays a significant role in type 3 immune complex mediated hypersensitivity reactions, showcasing its specialized function in immune responses.
Moreover, THαβ immunity is specifically tailored to combat infectious particles like viruses and prions [12–15]. This immune pathway employs NK cells (NK1), interleukin-10 producing CD4 T cells, iNKT10 cells, CD8 T cells (Tc2, EM1), and IgG1 B cells to combat these minute yet potent adversaries. Its connection to type 2 antibody-dependent cytotoxic hypersensitivity underscores its significance in addressing infectious threats.
Transitioning to tolerable immune responses dominated by IgA, these pathways exemplify the system’s ability to mount defenses without causing excessive damage to the host. Regulatory T cells play a crucial role in steering these responses, facilitating the switch to IgA, thereby establishing a more tolerable immune milieu.
TH1-like immunity within the tolerable response framework mirrors TH1 immunity but in a more regulated manner. It safeguards against intracellular microorganisms through M2 macrophages, TGFβ/IFNγ-producing CD4 T cells, iNKT1 cells, CD8 T cells (EM3), and IgA1 B cells, while maintaining a balance to prevent hyperactive responses that might harm the host.
TH9 immunity, targeting parasites such as insects and helminths, relies on regulatory eosinophils (rEOS), basophils, interleukin-9 producing CD4 T cells, iNKT2 cells, mast cells (MMC9), and IgA2 B cells to ensure a measured and controlled defense. This pathway, associated with type 1 allergic hypersensitivity, showcases the immune system’s ability to mount responses without tipping the balance toward excessive reactions.
Continuing within the tolerable responses, TH17 immunity is specialized in combating extracellular microorganisms. Neutrophils (N2), interleukin-17 producing CD4 T cells, iNKT17 cells, and IgA2 B cells are the primary players in this pathway, illustrating a fine-tuned defense against extracellular threats while limiting immune-mediated damage through type 3 immune complex mediated hypersensitivity.
Lastly, TH3 immunity within tolerable responses gears up against infectious particles employing NK cells (NK2), interleukin-10/TGFβ-producing CD4 T cells, iNKT10 cells, CD8 T cells (EM2), and IgA1 B cells. This pathway showcases the immune system’s adaptability, mounting responses against infectious particles while maintaining a balanced immune environment to prevent excessive host damage, closely linked to type 2 antibody-dependent cytotoxic hypersensitivity.
The intricate network of host immunological pathways, categorized into eradicable and tolerable immune responses, showcases the remarkable adaptability and specificity of the immune system in combating diverse pathogens. These pathways not only defend against various threats but also highlight the delicate balance between mounting effective responses and preventing immune-mediated damage to the host. The framework of host immunological pathways and their relations to different types of cell death is shown in Figure 1.
Figure 1
The framework of host immunological pathways and their relation to programmed cell death. Apoptosis is related to host anti-virus eradicable THαβ immunity. Autophagy is related to host anti-virus tolerable TH3 immunity. Pyroptosis is related to host anti-intracellular micro-organism eradicable TH1 immunity. Ferroptosis is related to host anti-intracellular micro-organism tolerable TH1-like immunity. Necroptosis is related to host anti-extracellular micro-organism eradicable TH22 immunity. NETosis is related to host anti-extracellular micro-organism tolerable TH17 immunity.
THαβ immune response and its relation to apoptosis
The host immunological THαβ pathway is the host’s immune reaction against infectious particles, including viruses and prions. Viruses and prions must live intracellularly to replicate and produce more transmissible particles. Apoptosis, the most well-studied programmed cell death pathway, is a key mediator regulating the death of virus-infected cells. During apoptosis, cell death leads to DNA or RNA fragmentation, allowing the intracellular viral DNA or RNA to be destroyed. Thus, virus particles can be eliminated by sacrificing the infected cells. Additionally, activated caspases degrade all intracellular proteins, leading to the destruction of prions, which are protein-based infectious particles, during apoptosis.
THαβ-related immune cells include natural killer (NK) cells, cytotoxic T cells, and IgG1-producing B lymphocytes. NK cells can induce antibody-dependent cellular cytotoxicity (ADCC) of virus-infected cells by binding to IgG1 antibodies [16]. ADCC is an apoptosis mechanism involving DNA and RNA fragmentation. Cytotoxic T cells can also cause apoptosis of virus-infected cells through DNA or RNA fragmentation, thereby killing the viral genomes. This process is mediated by the recognition of viral peptides presented on major histocompatibility complex (MHC) molecules by specific T cell receptors on cytotoxic T cells, which induces the apoptosis machinery. A similar mechanism can be observed in other infectious particles, like prions.
The immunosuppressive cytokine TGFβ has been found to inhibit the apoptosis process [17, 18]. Inhibition of TGFβ signaling can promote NK cell ADCC and cause target cell apoptosis [19]. Conversely, TGFβ can suppress NK cell ADCC. TGFβ-activated kinase 1 (TAK1) can antagonize apoptosis [20]. TGFβ can also inhibit Fas and caspase 8-related apoptosis [21, 22] and induce anti-apoptotic transcription factors to prevent apoptosis. Apoptosis-related protein degradation can lead to the destruction of infectious prion protein pathogens. Additionally, type 1 interferons can induce caspase cascades to trigger apoptosis in malignant cell lines [23–25]. Thus, apoptosis is a THαβ-related host defense mechanism against infectious particles, including viruses and prions.
Furthermore, the THαβ immune response is the host’s eradicable immune reaction induced by follicular helper T cells via the production of interleukin-21. Reports suggest that interleukin-21 is associated with apoptosis, including lymphocyte or cancer cell apoptosis [26–29].
TH3 immune response and its relation to autophagy
Autophagy is the type 2 programmed cell death pathway and is a milder control mechanism for virus infection of host cells [30–32]. Since type 1 interferons can help control virus infection, research has found a correlation between type 1 interferons and autophagy [24, 33]. Type 1 interferon is an inducer of autophagy [34–36]. Interferon regulatory factor 1 (IRF1), which can activate interferon beta, is also related to autophagy [37].
Autophagy is involved in the presentation of cytosolic antigens to MHC class II molecules and the digestion of intracellularly produced viral protein antigens. Autophagy is a protective mechanism against virus infection by degrading viral particles in autolysosomes. For example, autophagy has been found in liver cells to protect against hepatic virus infection [38]. Hepatitis C virus induces autophagy and interferes with the anti-viral innate eradicable immunity [39–41]. In contrast to apoptosis, autophagy with organelle degradation induces mild host inflammation.
Compared to the THαβ eradicable host immune reaction, the TH3 immunological pathway is the host’s tolerable immune response against viruses and prions. During autophagy, organelles containing virus particles are degraded. Autophagy is often observed in chronic viral infections. The key cytokines in the TH3 immunological pathway are interleukin-10 and TGF-β. However, interleukin-10 is more important for the eradicable THαβ immunity. Research has reported that interleukin-10 can prevent autophagy, and neutralization of interleukin-10 can recover the cellular machinery of autophagy [42–44].
Previous studies have found that TGF-β can promote autophagy [45]. TGF-β can prevent caspase 8-induced apoptosis and induce cell autophagy. TGF-β is mainly produced by regulatory T cells (Treg cells), and impaired Treg activity also impairs autophagy activity [46]. Follicular helper T cells (Tfh cells), which produce interleukin-21, have the opposite function of Treg cells. Previous literature reported that interleukin-21 can suppress autophagy [47].
The TH3 immune response is an IgA-dominant immune reaction, and autophagy has been found to be associated with the pathogenesis of IgA nephropathy [48]. This implies that the TH3 immune response could be related to the autophagy pathway. Interleukin-1, a key cytokine of the TH22/TH17 immunity, increases after the TH3-associated autophagy is blocked. Another THαβ/TH3 cytokine, interleukin-27, can also promote autophagy [49, 50].
TH1 immune response and its relation to pyroptosis
The TH1 immunological pathway is the host’s eradicable immunity against intracellular microorganisms, including intracellular bacteria, protozoa, and fungi. Pyroptosis, a programmed cell death mechanism, defends against intracellular pathogens [51, 52]. The major effector cells of the TH1 immune reaction are macrophages. Pyroptosis is related to the programmed cell death of macrophages. The key TH1 cytokine, interferon-gamma, is related to the activation of pyroptosis. The inflammasome complex in pyroptosis induces the activation of interleukin-1β and interleukin-18, both of which are pro-inflammatory cytokines against microorganisms. Additionally, interleukin-18 can augment the potency of interferon-gamma, which is the key immune mediator of the TH1 immunological pathway. The activation of interleukin-1β and interleukin-18, triggered by the inflammasome, further induces the production of interferon-gamma.
M1 macrophages are the key effector immune cells of TH1 immunity, and a correlation between M1 macrophage polarization and pyroptosis has been noted in previous studies [53, 54]. Interleukin-23, a vital cytokine in triggering TH1 and TH17 immune reactions, is also associated with macrophage pyroptosis [55]. The activation of the inflammasome also causes the upregulation of NF-κB, the master gene for immune activation signaling. Furthermore, the activation of the inflammasome inactivates interleukin-33, a mediator of the TH2 immunological pathway. Pyroptosis can also trigger pore-induced intracellular traps to capture intracellular bacteria, protozoa, and fungi, leading to their clearance [56]. Caspase-1-induced pyroptosis is an innate immune effector machinery fighting against intracellular microorganisms [52]. The immunosuppressive mediator TGF-β can suppress pyroptosis [57, 58].
Moreover, TH1 immunity is the host’s IgG-dominant eradicable immunity induced by follicular helper T cells via interleukin-21. Previous literature reported that interleukin-21 can cause pyroptosis of certain cells like regulatory T cells (Treg cells) [59]. Additionally, IgG immune complexes can induce macrophage pyroptosis by upregulating the expression of GSDMD [60].
TH1-like immune response and its relation to ferroptosis
Ferroptosis is a programmed cell death process triggered by intracellular iron overload. Iron is a key chemical element that helps the survival of microorganisms. According to increasing evidence, the occurrence of ferroptosis is always accompanied by inflammation. During the infection of microorganisms, including bacteria, protozoa, or fungi, higher concentrations of iron elements lead to worse infection control by the host. High intracellular iron concentrations help the survival of intracellular microorganisms. To reduce the availability of iron for intracellular microorganisms, iron-triggered cell death can sacrifice the infected cells and eliminate the microorganisms. This is the underlying logical principle of ferroptosis.
Chronic iron overload can drive macrophages to polarize into M2 macrophages, the effector cells of the TH1-like immune reaction [61, 62]. During ferroptosis, iron triggers the accumulation of lipid peroxides, causing membrane peroxidation and damage. Thus, the cell membranes of intracellular bacteria, protozoa, or fungi are damaged, leading to their death. This is why ferroptosis is a mechanism to kill and control intracellular microorganism infections. Glutathione peroxidase 4 (GPX4), which can prevent lipid peroxidation, is a protective mechanism against ferroptosis.
Regulatory T cells (Treg cells) with their key effector cytokine TGF-β can induce a tolerable immune response and tissue fibrosis. TGF-β could enhance ferroptosis via further GPX4 inhibition [63]. Additionally, GPX4 can enhance follicular helper T cells to inhibit ferroptosis [64]. There is a linkage between ferroptosis and fibrosis [65]. Chronic inflammation can be related to ferroptosis-associated tissue destruction and subsequent tissue fibrosis. TGF-β inhibitors can inhibit both ferroptosis and fibrosis [66]. Previous literature suggested an association between ferroptosis and tissue fibrosis, including renal fibrosis, pulmonary fibrosis, and liver cirrhosis [67]. For example, SARS-CoV-2 infection of lung epithelial cells can induce ferroptosis and subsequently lead to pulmonary fibrosis.
The TH1 key cytokine interferon-gamma can enhance ferroptosis in cancer cell lines and epithelial cells [68–70]. TH1-like immunity is an IgA-dominant tolerable immune reaction, and ferroptosis has been found to be related to the pathogenesis of IgA nephropathy [71].
TH22 immune response and its relation to necroptosis
The TH22 immune response is the host’s eradicable immune reaction against extracellular microorganisms, including extracellular bacteria, protozoa, and fungi. The TH22 immune response is associated with pro-inflammatory cytokines, including TNF-α. TNF-α is the key immune cytokine of the TH22 immune reaction that activates neutrophils to kill extracellular microorganisms. TNF-α is also the major mediator that induces necroptosis. The reason for triggering necroptosis, a programmed cell death pathway, can be to initiate a potent pro-inflammatory immune reaction to kill these invading extracellular microorganisms [72]. Macrophage necroptosis is observed in acute bacterial pneumonia caused by Serratia marcescens, Staphylococcus aureus, Streptococcus pneumoniae, Listeria monocytogenes, or uropathogenic Escherichia coli (UPEC) [73]. Necroptosis is a key function of neutrophils [74].
Another reason for necroptosis is to destroy potential nutrients from host cells to prevent the growth of extracellular microorganisms. TNF-α activates RIP kinases to form the necrosome. Interferon-gamma, which belongs to the TH1 immune response (different from the TH22/TH17 immunity), can downregulate necroptosis. Type 3 innate lymphoid cells, which can help trigger the TH22/TH17 immunity, are associated with necroptosis [75]. Necroptosis can also stimulate the secretion of TH22/TH17-related pro-inflammatory cytokines. A research study found that TGF-β–activated kinase 1 binding protein 2 (TAB2) deficiency causes dilated cardiomyopathy by enhancing RIPK1-dependent apoptosis and necroptosis [76]. TGF-β is the mediator of tolerable immunological pathways. Thus, eradicable immune mechanisms like apoptosis or necroptosis can be enhanced without TGF-β signaling. Another study pointed out that TGF-β-activated kinase 1 (TAK1) serves as a key survival factor in cardiac organs by directly antagonizing necroptosis [20].
The TH22 immune response is the host’s IgG-mediated eradicable immunity, which is initiated by follicular helper T cells via interleukin-21. There is no direct evidence suggesting that interleukin-21 can induce necroptosis. However, previous literature reported that interleukin-21 can cooperate with TNF-α, the key factor of necroptosis, to induce autoimmune disorders [77]. Additionally, IgG immune complexes have been found to trigger necroptosis in a previous study [78]. Interleukin-22 is the central cytokine of the TH22 immune reaction, and previous research pointed out that interleukin-22-producing type 3 innate lymphoid cells are related to necroptosis [75].
TH17 immune response and its relation to NETosis
The TH17 host immune reaction is the tolerable immune response against extracellular bacteria, fungi, or protozoa. Neutrophils play dominant roles in the TH17 tolerable immune response. In this situation, neutrophils cannot successfully kill and eradicate these extracellular microorganisms. Thus, these neutrophils sacrifice themselves to stop the progression of these extracellular microorganisms. These polymorphonuclear neutrophils (PMNs) have condensed DNA contents in their cell nuclei, and they trigger the NETosis cell programmed death pathway. Then, these extracellular microorganisms can be entrapped in the neutrophil extracellular traps (NETs), and other alive neutrophils will digest these extracellular bacteria, protozoa, or fungi. NETs can also induce TH17 immune cells [79].
The tolerable antibody IgA is found to activate NETosis. NETosis is also found to be correlated with chronic inflammation and delayed wound healing [80]. The TH17 immune reaction-related IgA immune complex formation is also associated with NETosis via the activation of the Fc-α receptor [81, 82]. IgA vasculitis has also been reported to be associated with NETs [83]. The central cytokine of the TH17 immune response, interleukin-17, can also induce NETosis [84]. Neutrophils can release IL-17 through extracellular trap formation during psoriasis [85]. IL-17A is expressed on NETs in ankylosing spondylitis [86]. The TH22/TH17 key cytokine TNF signaling can induce NETosis of CCR5+ neutrophils [87]. Thus, the TH17 host tolerable immunological pathway is associated with NETosis.
TAK1 is also required for neutrophil extracellular trap formation [88], pointing out the significance of TGF-β in NETosis. TGF-β itself can also induce NETs [89]. Regulatory T cells, characterized by the secretion of TGF-β, suggest that Treg cells are mediators of NETosis. NETs can also directly trigger epithelial and endothelial cell death [90].
CONCLUSIONS
Programmed cell death pathways are related to different host immunological pathways. Apoptosis is related to the host’s anti-viral eradicable THαβ immunity. Autophagy is related to the host’s anti-viral tolerable TH3 immunity. Pyroptosis is related to the host’s anti-intracellular microorganism eradicable TH1 immunity. Ferroptosis is related to the host’s anti-intracellular microorganism tolerable TH1-like immunity. Necroptosis is related to the host’s anti-extracellular microorganism eradicable TH22 immunity. NETosis is related to the host’s anti-extracellular microorganism tolerable TH17 immunity. These relationships can help us understand the host defense mechanisms against invading pathogens and provide new insights for developing better therapeutic strategies against infections or autoimmune disorders.
Data availability statement
This review article was performed via literature search without conducting experiments. Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Data availability statement
This review article was performed via literature search without conducting experiments. Data sharing is not applicable to this article as no new data were created or analyzed in this study. |
Title: Making Sense of the Relationship Between Ultra-Processed Foods, Obesity, and Other Chronic Diseases | Body:
1. Introduction
An enormous body of research has emerged in recent decades that demonstrates that diet has a major impact on disease risk. In particular, the diet commonly eaten in the Western world (the “Western diet”) is closely associated with the risk of chronic diseases of lifestyle (CDL). Conversely, a healthy diet lowers the risk of these diseases. Based on these findings, many sets of recommendations have been made, with the goal of helping to prevent CDL [1,2,3,4].
The NOVA system represents a radically different approach to the classification of foods than nearly all other systems. In this system, foods are classified based on the degree of processing involved in their production. Within that system, one group of foods is classified as ultra-processed foods (UPFs). The system is based on the assumption that all UPFs are unhealthy. Accordingly, dietary recommendations based on the NOVA system recommend the reduction or elimination of UPFs. The NOVA system has steadily gained significant support from many nutrition experts. For example, Lawrence [5] stated, “UPF is a fit-for-purpose concept for guiding nutrition policy activities to tackle unhealthy and unsustainable diets”. Brazil has incorporated recommendations into their dietary advice for the general population that the intake of UPFs should be reduced [6].
This viewpoint has received much criticism in recent years. A debate is now underway in the pages of nutrition journals, with one side attacking the scientific rationale for classifying a wide variety of foods as UPFs [7,8]. This paper critically evaluates the evidence.
2. The NOVA System
The NOVA system was developed in Brazil by Monteiro and colleagues [9]. The system was later modified [10,11]. NOVA classifies foods into one of four food groups based on the extent and purpose of the processing they have undergone. The four food groups are as follows:Unprocessed or minimally processed foods. These include fresh, dry, or frozen fruits or vegetables, grains, legumes, meat, fish, and milk.Processed culinary ingredients, including sugar, oils, fats, salt, and other constituents extracted from foods and used in kitchens to make culinary preparations.Processed foods. These include such foods as canned fish and vegetables, simple breads, and cheeses, which are manufactured by only adding salt, sugar, oil, or other processed culinary ingredients to unprocessed or minimally processed foods.Ultra-processed foods (UPFs). These are foods that are made from mixtures of ingredients that are the products of industrial processes. UPFs have products extracted from actual foods combined with various additives.
In the original system, which was proposed in 2010, foods in group 3 were classed as UPFs and were therefore included together with all other UPFs [9]. By 2018, that single food group was divided into groups 3 and 4 [10,11]. The obvious explanation for this is that the researchers who developed the NOVA system realized that their definition of UPFs was seriously flawed. The evolution of the definition of UPFs was discussed by Gibney [12].
This classification system is altogether different from all conventional systems; it disregards the nutrient composition of foods. With other systems, the aim is to persuade people to eat a diet that consists mainly of foods that are strongly associated with a reduced risk of CDL while also providing appropriate amounts of all macronutrients and micronutrients. This approach is the guiding principle in the Dietary Guidelines for Americans [13]. We also see this with the design of food guides; recent versions used in different countries place a strong emphasis on the consumption of a diet rich in fruits, vegetables, and whole grains.
Front-of-package food labels use the same strategy. For example, Nutri-Score is a front-of-package food label that was developed in France [14,15]. Foods are placed in one of five categories based on their content, which includes a range of components. Foods are considered less healthy as the quantities of saturated fat, sugar, sodium, and energy increase but healthier with higher contents of protein, dietary fiber, fruits, vegetables, pulses, nuts, and healthy oils, such as olive and canola [16]. This is based on solid supporting evidence [17]. Nutri-Score was first implemented in France in 2017 and is now used in several other countries, including Belgium, Switzerland, Germany, and the Netherlands.
A variation in the conventional system for making dietary recommendations that has emerged in recent years is to focus on dietary patterns [18]. The best-known example of this is the recommendation to follow a Mediterranean diet [19]. These various sets of dietary recommendations share the common feature of being based on foods or dietary patterns where there is strong evidence that the recommendations, if followed, will lead to a reduced risk of CDL.
Forde [7] and Visioli et al. [8] made detailed critiques of the numerous flaws in the NOVA system. One anomaly in the system that should be highlighted is that foods are categorized in ways that ignore their likely impact on health. For example, many foods contain significant amounts of whole grains and are therefore likely to be healthy, but such foods can still be included in UPFs if the grain has been processed and they also contain several additives. This can be the case with some brands of bread. Conversely, red meat is often eaten as a steak and is therefore an unprocessed or minimally processed food. However, intake of red meat is associated with an increased risk of cardiovascular mortality [20] and of several types of cancer [21]. Similarly, many foods with added salt can be classified as a “processed food”, but if the food also contains several additives, it will probably be classified as a “UPF”. This ignores the fact that salt is arguably the most toxic of all food additives. The same inconsistency is seen with sugar. It is another substance added to many foods and is strongly linked to several types of CDL. In some cases, food with added sugar is classed as a “processed food” (e.g., coffee with sugar), but in other cases, it is a UPF (e.g., cola drinks).
An American study demonstrated that it is quite possible to design a diet based mainly on UPFs but that is relatively healthy [22]. In this study, a diet was designed based on the recommendations stated in the Dietary Guidelines for Americans [13]. It comprised foods commonly eaten in the USA. The large majority of food energy (91%) came from UPFs. While the diet had an excess of salt and a poor content of whole grains, it had adequate contents of all micronutrients, except vitamins D and E.
3. The Relationship Between Ultra-Processed Foods and Disease
Many epidemiological studies, mainly prospective cohort studies, have been carried out, where researchers investigated the possible association between the intake of UPFs and risk of CDL. The strongest and most consistent associations were reported for obesity, cardiovascular disease (CVD), type 2 diabetes, common mental disorders (especially depression), and all-cause mortality [23,24,25]. The evidence is weaker for cancer and hypertension. One notable feature of the findings is their lack of consistency. One factor that may help explain this is the quantity of UPFs consumed in the study population. This amount is at least twice as high in Northern Europe than in Southern Europe [26]. A major factor that is probably responsible for the inconsistencies between studies is the variation in which foods are classified as being UPFs.
In the above studies, UPFs were treated as a single food group, irrespective of the main types of foods included with the UPFs. However, other studies investigated the relationship between different types of UPFs and the risk of CDL. As pointed out earlier, UPF is an umbrella term that includes many unhealthy foods, as well as many healthy foods. It is therefore unsurprising that the relationship between UPFs and disease risk shows major differences between different types of UPFs. This was shown in the following cohort studies.
In the combined results from two cohort studies carried out in the USA, the association between UPFs and all-cause mortality was largely accounted for by ready-to-eat products based on meat (e.g., processed meat), poultry, and seafood, sugar-sweetened and artificially sweetened beverages, dairy-based desserts, and ultra-processed breakfast cereals (excluding whole grain products) [27]. A cohort study was carried out on renal transplant patients in the Netherlands [28]. The association between the intake of UPFs and all-cause mortality was entirely accounted for by sugar-sweetened beverages, processed meat, and desserts. A cohort study carried out in Europe studied the relationship between the intake of UPFs and the risk of a person developing both cancer and CVD [26]. A clear association was seen for sugar-sweetened and artificially sweetened beverages and for animal-based products but not for UPFs, such as breads and cereals, sweets and desserts, and plant-based alternatives.
Unfortunately, the weight of evidence is insufficient for definitive conclusions. However, the types of UPFs that seem to be most closely associated with an increased risk of CDL are processed meat and sugar-sweetened and artificially sweetened beverages. The findings on desserts are not consistent, possibly due to the different types of desserts commonly eaten in different countries. By contrast, UPFs with high contents of whole grains, fruits, or vegetables seem unrelated to the risk of CDL.
4. Ultra-Processed Foods and Obesity
The evidence linking UPFs with CDL is considerably stronger in the area of excess weight gain and obesity than appears to be the case with other types of CDL. The evidence that UPFs as a whole are a major factor in the causation of obesity was presented in a previous paper [29]. Additional evidence is presented here.
An epidemic of obesity emerged in the USA around 1976–80 [30]. It then steadily spread across the Western world. By the 1990s, the epidemic was clearly emerging in less affluent countries [31]. There has been a great deal of debate as to the cause of this epidemic. The growth in the intake of UPFs provides the best explanation [29]. In brief, there is solid evidence that UPF plays a causal role in obesity.
In order to more fully understand how some types of food tend to lead to obesity, it is essential to better understand the etiology of excessive food intake. One essential feature of eating is that people have very limited ability to control how much they eat. This was argued by Cohen and Farley [32], who concluded that “…eating [is] an automatic behavior, as opposed to one that humans can self-regulate”. The compositions of many types of UPFs bypass the appetite control system and induce an excessive energy intake. Gearhardt and Schulte [33] argued that UPFs act in a similar way to drugs in being addictive. UPFs that are most likely to trigger this addictive behavior are foods that have high contents of both fat and carbohydrates.
Dicken et al. [34] made a similar argument. In their view, the key feature of UPFs, with respect to excessive food intake, is their high content of fat, sugar, carbohydrates, and sodium. This composition results in the majority of UPFs being characterized as hyperpalatable. They demonstrated this based on an analysis of foods eaten in the UK. They divided foods according to the NOVA system and then calculated the proportion of foods in each group that met their definition of being hyperpalatable. Of those foods classified as being minimally processed, 13.4% were hyperpalatable, but this increased to 49.8% for processed food and 68.0% for UPFs.
Another key feature of UPFs that results in an excessive intake of food energy is a high energy density. In the above study of foods eaten in the UK, the researchers concluded that the median energy contents of minimally processed food, processed food, and UPFs are 94, 178, and 243 kcal/100 g, respectively [34]. The importance of this was demonstrated in a study by Klos et al. [35]. They conducted a systematic review and meta-analysis of 38 randomized controlled trials. They compared meals with higher and lower median energy densities (150 vs. 110 kcal/100 g). The higher energy density led to a higher energy intake of 223 kcal/day. This increased amount of food energy, if repeated at a single meal each day, was many times greater than that needed to account for the obesity epidemic.
That UPFs do indeed lead to an increase in energy intake was demonstrated in a randomized controlled trial [36]. Groups of subjects were fed either UPFs or a diet based on minimally processed foods. The subjects were free to eat as much food as they wished. Over the course of 2 weeks, subjects eating the UPFs diet consumed 508 kcal/day more food energy. This led to an average weight gain of 0.9 kg. This result of eating UPFs is not an accident. Food technologists have applied great skill to the design and manufacture of UPFs in order to make them as appealing and as enjoyable to eat as possible.
The evidence presented above demonstrates that UPFs, in general, are both hyperpalatable and have a high energy density in comparison with minimally processed foods. Foods classified as “processed”, according to the NOVA scheme, are in between but are closer to UPFs than to minimally processed foods.
5. Discussion
5.1. Ultra-Processed Foods and Chronic Diseases
This examination of the NOVA system compels the conclusion that it is based on badly flawed science. All conventional systems that make recommendations regarding which foods should be eaten are based on whether a food is likely to enhance health and prevent disease, as well as on the nutrient composition of the food. The NOVA system, by contrast, classifies foods into food groups based on the extent and purpose of the processing they have undergone. The key feature of the system is the claim that foods classified as UPFs (NOVA 4) are inherently unhealthy.
Multiple cohort studies have reported that the intake of UPFs is associated with an increased risk of several types of CDL. The strongest and most consistent findings have been seen for obesity, cardiovascular disease, type 2 diabetes, common mental disorders (especially depression), and all-cause mortality. The evidence is weaker for cancer and hypertension. It must be borne in mind that UPFs have much overlap with unhealthy foods. Accordingly, the association between UPFs and CDL does not imply that UPFs, as a broad food group, are causally related to the risk of CDL. Rather, UPFs may be acting merely as a crude indicator for the consumption of unhealthy foods. This does indeed appear to be the case. In the handful of cohort studies where the investigators reported on the relationship between different types of UPFs and the risk of CDL, the types of UPFs that seem to be most closely associated with an increased risk of CDL are processed meat, sugar-sweetened and artificially sweetened beverages, and possibly desserts. By contrast, there is no evidence that UPFs with high contents of whole grains, fruits, or vegetables are related to the risk of CDL.
5.2. Ultra-Processed Foods and Obesity
An impressive body of evidence has accumulated in recent years that strongly suggests that UPFs play a major causal role in obesity. Much of the evidence was presented in a previous paper [29]. The additional evidence presented here adds weight to the hypothesis. The high contents of fat, sugar, carbohydrates, and sodium commonly present in UPFs make these foods hyperpalatable. In addition, UPFs typically have a high energy density. These two features of UPFs probably act synergistically, with the result being that when given UPFs, most people consume an excessive energy intake. This behavior can be viewed as a form of hedonism, which the Cambridge Dictionary defines as “living and behaving in ways that mean you have as much pleasure as possible”. Such foods can be called hedonic (from H [hyperpalatable] ED [energy dense] onism).
However, it must again be stressed that UPFs include a wide range of foods, many of which are quite healthy. It is therefore likely that while many UPFs are obesogenic, many others are not. For example, some brands of bread can be categorized as UPFs but are relatively high in fiber and have a relatively low energy density. Such foods are unlikely to be either hedonic or obesogenic.
Table 1 presents a comparison between Nutri-Score (a front-of-package food label) and UPFs. This illustrates the key differences between the conventional approach to the evaluation of foods and the NOVA system. While Nutri-Score focuses on the risk of CDL and the nutritional value of food, the NOVA system focuses on the degree to which a food has been processed. Overall, the conventional approach to the evaluation of foods is clearly superior to the NOVA system. However, the NOVA system may have the advantage of highlighting the high probability that most UPFs are obesogenic.
5.3. Priority Areas for Research
The research findings discussed in this paper point to several areas where more research is needed. The relationship between UPFs and various types of CDL still has many gaps in the evidence. Further clarification is needed regarding the association between the intake of UPFs and different types of CDL. Of particular importance, more research is needed in order to determine which types of UPFs are most closely linked to the different types of CDLs.
These areas where more research is needed are especially relevant to the relationship between UPFs, energy intake, weight control, and obesity. The following are some of the key questions that require further investigation:Reference was made to a randomized controlled trial where subjects ate more food energy and gained weight when given a diet based on UPFs, compared with one based on minimally processed foods [36]. However, the duration was only 2 weeks. What will be the outcome with a longer duration, say 6 to 12 months? How will the outcomes be affected by changes in other variables (such as age and weight of subjects and types of UPFs)?What types of UPFs (and other foods) are most likely to be hyperpalatable and result in an excessive intake of food energy?Going one step further, what foods are most likely to be obesogenic based on being hyperpalatable, having a high energy density, and other characteristics (such as a soft texture and a low fiber content)?
5.4. Ultra-Processed Foods and Nutrition Advice
The evidence examined here has important implications for the nutrition advice that is delivered to the general population. It must be stressed that there are serious weaknesses in the claim that UPFs are a distinct type of food and are inherently unhealthy. For that reason, the inclusion of UPFs in food guides, front-of-package food labels, and similar documents adds little value and would therefore add more noise than signal. Let us now suppose that UPFs do indeed play an important role in the causation of obesity. This does not change the previous conclusion. A person who follows a typical food guide would eat a healthy diet and almost certainly cut down on his or her intake of obesogenic types of UPFs. Such a diet would be expected to greatly reduce the risk of obesity. However, as in many aspects of providing nutrition advice to the public, more research is a high priority. We must be open to the possibility that future research will reveal that specific advice to avoid UPFs, especially hedonic foods, will need to be included in nutrition advice for the population.
When it comes to formulating nutrition advice for health professionals, a different approach may be warranted. This is illustrated by a detailed statement published by the American Heart Association in 2021 that provides dietary guidance for promoting cardiovascular health and preventing CVD [37]. It was authored by leading American experts in the area of diet and health. The document presents ten sets of recommendations. Of these, nine are conventional sets of dietary recommendations, but one states the following: “Choose minimally processed foods instead of ultra-processed foods”. This document achieves an eminently sensible approach to dietary recommendations for health professionals.
5.1. Ultra-Processed Foods and Chronic Diseases
This examination of the NOVA system compels the conclusion that it is based on badly flawed science. All conventional systems that make recommendations regarding which foods should be eaten are based on whether a food is likely to enhance health and prevent disease, as well as on the nutrient composition of the food. The NOVA system, by contrast, classifies foods into food groups based on the extent and purpose of the processing they have undergone. The key feature of the system is the claim that foods classified as UPFs (NOVA 4) are inherently unhealthy.
Multiple cohort studies have reported that the intake of UPFs is associated with an increased risk of several types of CDL. The strongest and most consistent findings have been seen for obesity, cardiovascular disease, type 2 diabetes, common mental disorders (especially depression), and all-cause mortality. The evidence is weaker for cancer and hypertension. It must be borne in mind that UPFs have much overlap with unhealthy foods. Accordingly, the association between UPFs and CDL does not imply that UPFs, as a broad food group, are causally related to the risk of CDL. Rather, UPFs may be acting merely as a crude indicator for the consumption of unhealthy foods. This does indeed appear to be the case. In the handful of cohort studies where the investigators reported on the relationship between different types of UPFs and the risk of CDL, the types of UPFs that seem to be most closely associated with an increased risk of CDL are processed meat, sugar-sweetened and artificially sweetened beverages, and possibly desserts. By contrast, there is no evidence that UPFs with high contents of whole grains, fruits, or vegetables are related to the risk of CDL.
5.2. Ultra-Processed Foods and Obesity
An impressive body of evidence has accumulated in recent years that strongly suggests that UPFs play a major causal role in obesity. Much of the evidence was presented in a previous paper [29]. The additional evidence presented here adds weight to the hypothesis. The high contents of fat, sugar, carbohydrates, and sodium commonly present in UPFs make these foods hyperpalatable. In addition, UPFs typically have a high energy density. These two features of UPFs probably act synergistically, with the result being that when given UPFs, most people consume an excessive energy intake. This behavior can be viewed as a form of hedonism, which the Cambridge Dictionary defines as “living and behaving in ways that mean you have as much pleasure as possible”. Such foods can be called hedonic (from H [hyperpalatable] ED [energy dense] onism).
However, it must again be stressed that UPFs include a wide range of foods, many of which are quite healthy. It is therefore likely that while many UPFs are obesogenic, many others are not. For example, some brands of bread can be categorized as UPFs but are relatively high in fiber and have a relatively low energy density. Such foods are unlikely to be either hedonic or obesogenic.
Table 1 presents a comparison between Nutri-Score (a front-of-package food label) and UPFs. This illustrates the key differences between the conventional approach to the evaluation of foods and the NOVA system. While Nutri-Score focuses on the risk of CDL and the nutritional value of food, the NOVA system focuses on the degree to which a food has been processed. Overall, the conventional approach to the evaluation of foods is clearly superior to the NOVA system. However, the NOVA system may have the advantage of highlighting the high probability that most UPFs are obesogenic.
5.3. Priority Areas for Research
The research findings discussed in this paper point to several areas where more research is needed. The relationship between UPFs and various types of CDL still has many gaps in the evidence. Further clarification is needed regarding the association between the intake of UPFs and different types of CDL. Of particular importance, more research is needed in order to determine which types of UPFs are most closely linked to the different types of CDLs.
These areas where more research is needed are especially relevant to the relationship between UPFs, energy intake, weight control, and obesity. The following are some of the key questions that require further investigation:Reference was made to a randomized controlled trial where subjects ate more food energy and gained weight when given a diet based on UPFs, compared with one based on minimally processed foods [36]. However, the duration was only 2 weeks. What will be the outcome with a longer duration, say 6 to 12 months? How will the outcomes be affected by changes in other variables (such as age and weight of subjects and types of UPFs)?What types of UPFs (and other foods) are most likely to be hyperpalatable and result in an excessive intake of food energy?Going one step further, what foods are most likely to be obesogenic based on being hyperpalatable, having a high energy density, and other characteristics (such as a soft texture and a low fiber content)?
5.4. Ultra-Processed Foods and Nutrition Advice
The evidence examined here has important implications for the nutrition advice that is delivered to the general population. It must be stressed that there are serious weaknesses in the claim that UPFs are a distinct type of food and are inherently unhealthy. For that reason, the inclusion of UPFs in food guides, front-of-package food labels, and similar documents adds little value and would therefore add more noise than signal. Let us now suppose that UPFs do indeed play an important role in the causation of obesity. This does not change the previous conclusion. A person who follows a typical food guide would eat a healthy diet and almost certainly cut down on his or her intake of obesogenic types of UPFs. Such a diet would be expected to greatly reduce the risk of obesity. However, as in many aspects of providing nutrition advice to the public, more research is a high priority. We must be open to the possibility that future research will reveal that specific advice to avoid UPFs, especially hedonic foods, will need to be included in nutrition advice for the population.
When it comes to formulating nutrition advice for health professionals, a different approach may be warranted. This is illustrated by a detailed statement published by the American Heart Association in 2021 that provides dietary guidance for promoting cardiovascular health and preventing CVD [37]. It was authored by leading American experts in the area of diet and health. The document presents ten sets of recommendations. Of these, nine are conventional sets of dietary recommendations, but one states the following: “Choose minimally processed foods instead of ultra-processed foods”. This document achieves an eminently sensible approach to dietary recommendations for health professionals.
6. Conclusions
Conventional systems for food classification are based on the nutritional values of foods and whether a food is strongly associated (positively or negatively) with the risk of CDL. By contrast, the key feature of UPFs is that they are highly processed and contain various additives, especially those that are industrially produced. Many UPFs are unhealthy, and others are reasonably healthy, while some unhealthy foods are not classified as UPFs. The case has been presented here that the NOVA system (including classifying many foods as UPF) is not consistent with the evidence from nutrition science. However, many cohort studies have reported an association between the intake of UPFs and risk of obesity, CVD, type 2 diabetes, common mental disorders (especially depression), cancer, hypertension, and all-cause mortality. These associations are best explained as being largely due to a limited number of unhealthy foods, such as processed meat and sugar-sweetened beverages. The relationship between UPFs and obesity presents a different picture. Here, UPFs do appear to play a major causal role. This is because many UPFs (but not all) are hyperpalatable and have a high energy density. As a result, most people consume an excessive energy intake when given UPFs. |
Title: Unveiling the Interplay—Vitamin D and ACE-2 Molecular Interactions in Mitigating Complications and Deaths from SARS-CoV-2 | Body:
1. Introduction
The COVID-19 pandemic, triggered by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), caused havoc worldwide, leading to socio-economic crises and widespread negative repercussions on the global populace [1,2]. Vitamin D is a secosteroid molecule that undergoes two steps of hydroxylation to generate its most active form, calcitriol [1,25-dihydroxyvitamin D; 1,25(OH)2D] [3]. Beyond its pivotal role in calcium regulation, vitamin D engages in various biological functions affecting all tissues, especially the modulation of innate and adaptive immunity [4]. Upon binding to the vitamin (calcitriol) D receptor (VDR, CTR), it forms a complex with cofactors, translocates into the nucleus, and attaches to relevant portions of DNA [5]. While vitamin D and its receptor polymorphisms affect disease vulnerability and responses [6,7], they regulate over 1700 human genes, up- or down-regulating target genes [8].
The human immune system is operated by a complex network of mechanisms that respond to signals generated by membrane-bound signaling molecules, including toll-like receptors (TLR) [9,10,11]. In humans, many overlapping mechanisms effectively regulate the innate and adaptive immune systems [12]. Over 75% of the immune system functions rely on having sufficient calcitriol synthesized within immune cells. Consequently, vitamin D modulates immune cell functions and helps maintain a robust immune system [5,13]. Moreover, immune cells themselves express both vitamin D/calcitriol receptor (VDR/CTR) [14] and the 1α-hydroxylase enzyme (from the CYP27B1 gene) responsible for converting 25(OH)D into 1,25(OH)2D intracellularly [11,15,16]. Beyond its classical genomic functions, vitamin D exerts membrane-based and non-genomic actions, swiftly modulating various physiological pathways. These rapid actions and their autocrine and paracrine functions are particularly prominent within immune cells [17].
Vitamin D modulates the immune response, potentially reducing inflammation, and is associated with cytokine storm in severe COVID-19 [18]. Vitamin D up-regulates angiotensin-converting enzyme-2 (ACE-2) expression and generates angiotensin(1–7) (Ang(1–7)), a potent vasodilatory peptide that counteracts angiotensin-II (Ang-II). Ang(1–7) protects the cardiovascular system and mitigates lung injury caused by coronaviruses. Understanding the molecular interactions of these actions will provide insights into novel therapeutic strategies and public health interventions to reduce the burden of COVID-19. This review explores the interactions of vitamin D and ACE-2 on the backdrop of COVID-19 that affect clinical outcomes by critically evaluating ongoing research and recently published studies in this critical area.
This review article highlights the complex relationship between vitamin D and the ACE-2 receptor, focusing on their roles in mitigating the severity and mortality of COVID-19. We hypothesized that ACE-2 has a vital beneficial role in mitigating complications and infections of COVID-19 despite its role as the primary entry site for coronaviruses into epithelial cells. This study also investigated the vitamin D-dependent mechanisms of ACE-2 in inhibiting SARS-CoV-2 replication and subduing inflammation and oxidative stress reduction. This review highlights scientific data on the interactions of vitamin D on the expression and function of ACE-2 related to SARS-CoV-2 entering human cells and mitigating its harmful effects [19].
1.1. Benefits of Vitamin D
Research has shown that vitamin D is essential for more than just bone health. It plays a role in (A) the musculoskeletal system—it prevents rickets in children and osteomalacia in adults) [20,21,22], and (B) immune functions—vitamin D is crucial for maintaining a robust immune system [23,24] and reducing the risk of infections and autoimmune diseases [4,25], modulates metabolism [26] and energy generation [27,28], prevents acute infections [4,22], and minimizes chronic diseases [29]. Adequate vitamin D levels are linked to a lower risk of several chronic conditions, such as cardiovascular disease [30,31] and certain cancers [32,33], but others disagree [34,35]. In addition, it minimizes pregnancy-related complications and disorders [36,37,38,39] and mental health conditions [40,41], potentially including bipolar disorder [42] and the prevention of depression [43,44].
Meta-analyses and other clinical studies have consistently shown that sufficient vitamin D supplementation protects against acute respiratory tract infections, particularly in individuals with significant vitamin D deficiencies [45,46]. Further clinical research, including randomized controlled trials (RCTs), has revealed that vitamin D supplementation plays a crucial role in achieving positive health outcomes in cases of infectious diseases [13,47]. There are more than 310 peer-reviewed publications on the beneficial effects of prophylactic vitamin D in preventing SARS-CoV-2 [48] and as an adjunct in treatment [43,49,50]. Figure 1 illustrates the broader benefits of vitamin D in human health related to the immune system.
Vitamin D is a threshold nutrient. Consequently, there will be little additional benefit from over-supplementation or intake by those with sufficient vitamin D [5]. However, supplementing deficient individuals provides a significant benefit [52]. In parallel, over-exposure to sunlight does not lead to excess vitamin D entering the blood [53]. Notably, for those infected with SARS-CoV-2 (and other viruses), vitamin D and its metabolites are consumed rapidly during infections and conditions associated with immune system activation [51,54]. Therefore, unless supplemented, even those with reasonable serum 25(OH)D levels (e.g., on hospitalization) are likely to develop a deficiency from an acute severe infection like SARS-CoV-2 [5].
Notably, over half of the world’s population is vitamin D deficient at any given time [5]. Therefore, with little or no adverse effects, it is beneficial to consume vitamin D supplements or be exposed to safe levels of daily sunlight [55,56,57]. However, even in the sunniest regions in the world, the majority spend time indoors. Thus, most people need supplemental vitamin D to prevent deficiency to stay healthy.
1.2. The Entry of SARS-CoV-2 into Human Cells
SARS-CoV-2, the virus responsible for COVID-19, enters human cells primarily by its spike (S) protein interaction with the angiotensin-converting enzyme 2 (ACE-2) receptor on the surface of human cells [57]. The spike protein has two subunits: S1, which contains the receptor-binding domain (RBD) that specifically binds to ACE-2, and S2, which facilitates membrane fusion [58,59]. Upon binding to ACE-2, the spike protein undergoes a conformational change, enabling the virus to attach more firmly to the host cell. Transmembrane protease serine 2 (TMPRSS2) in the host cell cleaves the S-protein at specific sites, triggering the fusion of the viral and cellular membranes and allowing the viral RNA to enter the cytoplasm [60].
Once inside the host cell, the host’s ribosomes translate the viral RNA into viral proteins. The viral RNA genome serves as a template for replication and transcription to produce more viral RNA genomes and sub-genomic RNAs [61], which encode structural and accessory proteins [60,62]. These newly synthesized viral components are assembled into new virions in the host cell’s endoplasmic reticulum-Golgi intermediate compartment [63]. The mature virions are transported to the cell surface in vesicles and then released into the extra-cellular space via exocytosis, including the tissue fluid and circulation. These virions are primed to infect new local and distant host cells. This viral entry, replication, and release cycle leads to the spread of SARS-CoV-2 within the host and contributes to the pathology of COVID-19 [64].
Meanwhile, the expression of ACE-2 is regulated by vitamin D metabolites [65]. Vitamin D sufficiency increases the expression of ACE-2 (including the soluble ACE-2 in the circulation) and dampens the renin-angiotensin system/axis (RAS) [59]. Despite cell membrane-bound ACE-2 receptors serving as the primary entry point for the SARS-CoV-2 virus, numerous benefits of vitamin D interaction have been reported, initiating ACE-2-related anti-viral effects against SARS-CoV-2 [19]. This review investigates the impact of vitamin D on the immune system, focusing on its effects on the renin-angiotensin system (RAS) and ACE-2 [66].
1.3. Functions of the Renin-Angiotensin System (RAS)
The renin-angiotensin-aldosterone axis (RAS) is a crucial endocrine axis that controls critical physiological parameters, such as blood pressure homeostasis, immune functions, and metabolism [66]. It is also known as the renin-angiotensin-aldosterone system (RAAS). The RAS is an organized complex hormonal cascade that plays a vital role in controlling critical functions in many organs. Traditionally, RAS plays a crucial role in the cardiovascular system, metabolism, cell growth, and homeostasis.
However, over the past 15 years, additional functions of the RAS, such as ACE-1 and ACE-2) were identified. These include inflammatory processes that lead to lung and other epithelial cell injury, even causing acute respiratory distress syndrome (ARDS), sepsis, cardiac hypertrophy, pulmonary hypertension, acute pancreatitis, and glomerulonephritis [67]. The RAS is crucial in regulating several physiological parameters related to cardiovascular and renal function [66]. Details of these are illustrated in Table 1.
The primary active peptide of the RAS is Ang-II. However, there are other minor components, like Ang-III and Ang-IV, as well as a counter-regulatory peptide, Ang(1–7) [66]. From a physiological point of view, the most important and widely studied two peptides are Ang-II and Ang(1–7) [68], which are involved in human health maintenance and disease statuses. Investigations are ongoing to understand its role in inflammation [18]. The RAAS is integral to the homeostatic regulation of cardiovascular, renal, and fluid balances (homeostasis) and is essential in physiological stability [67]. A sustained imbalance of the RAS can lead to a lowering of ACE-2 (with loss of its protective effects) [59], causing acute lung injury, like acute respiratory distress syndrome (ARDS), with high mortality.
1.4. Vitamin D on the Renin-Angiotensin-Aldosterone Hormonal (RAS) Axis
Activating the RAS, including coronaviruses, leads to the excess production of the enzyme renin, which cleaves angiotensinogen into Ang-I. ACE -1 then cleaves Ang-1 to form Ang-II [69]. This over-activation of the RAS leads to the excess production of Ang-II, which enhances the production of inflammatory cytokines [70], worsening the morbidity and mortality associated with infections/sepsis and leading to a cytokine storm that causes lung damage [71]. Excessive production of inflammatory cytokines leads to uncontrolled inflammation and is linked to immune dysfunction, as seen with excess Ang-II, which causes enhanced inflammation and oxidative stress [70]. In contrast, sufficient vitamin D suppresses the expression of renin, thus damping the RAS while increasing ACE-2 and reducing Ang-II. Consequently, it reduces inflammation and oxidative stress, improving clinical outcomes from infection and septic consequences [51,72,73].
As aforementioned, calcitriol is a crucial regulator of the RAS axis and suppresses the expression of the renin gene—a rate-limiting step of the synthesis of Ang-II via cyclic AMP (cAMP)-dependent PKA signaling [74]. Calcitriol independently increases the expression of ACE-2, reducing Ang-II and increasing the generation of angiotensin(1–7), suppressing the formation of the CRE-CREB-CBP complex [70], and keeping RAS activity under control. Meanwhile, certain viral infections like SARS-CoV-2 stimulate RAS activity, aggravating the situation in those with vitamin D deficiency and making them more vulnerable to developing cytokine storms [19,70].
1.5. Functions of the ACE-2/Ang(1–7)/MasR Axis (Counter-Regulatory Pathway)
Vitamin D inhibits renin, induces ACE-2/Ang(1–7)/MasR axis activity, and modulates the ACE/Ang II/AT1R axis. Through these mechanisms, vitamin D increases the expression of ACE-2, MasR, and Ang(1–7) and elicits a protective role against acute lung injury/ARDS and abnormal clotting activities [75]. Vitamin D can stimulate these beneficial targets like ACE-2 [65,76]. The RAS is regulated mainly by vitamin D (calcitriol) suppressing renin and increasing the expression of ACE-2 [65]—the latter also acts as a primary receptor for SARS-CoV-2 entry into the cells [76].
In health, there is a balance between the RAS, the ACE-Ang–II-AT1R regulatory axis, and the counter-regulatory axis (a pathway) of ACE-2-Ang–1–7-MasR. They are crucial in maintaining human cardiovascular, inflammatory, and immune homeostasis [66]. The wide distribution of ACE-2 in the heart, kidneys, lungs, colon, testis, etc., reflects this. The activation of ACE-2 antagonizes the over-active RAS system, protecting against organ damage and mitigating hypertension and cardiovascular diseases [65]. Once infected, SARS-CoV-2 causes a major imbalance in the RAS, including the down-regulation of ACE-2 receptors when they invade human epithelial cells [75,77].
1.6. Molecular Aspects of the RAS and ACE
Angiotensin-converting enzyme-2 (ACE-2) is a peptidase expressed on epithelial cell membranes and plays a crucial role in catabolizing Ang-II, thereby regulating the RAS [78]. Furthermore, in humans, ACE-2 polymorphisms can influence susceptibility to diseases such as hypertension and coronaviruses, including SARS-CoV-2 [78,79]. ACE-2 counterbalances the enzymatic actions of ACE-1 and Ang-II synthesis by converting Ang-II into the peptide Ang(1–7), reducing Ang-II molecular concentration. Furthermore, Ang(1–7) acts via the G-protein-coupled Mas receptor (MasR) to induce vasodilation [80] and attenuate the expression of pro-inflammatory cytokines [68], like TNF-α and IL-6 in LPS-induced macrophages, thereby promoting anti-inflammatory effects [81]. Despite these benefits, functional membrane ACE-2 receptors have been identified as the primary entry site of coronavirus into human cells [82,83].
In addition, Ang II interacts with the adrenogenic axis—endothelin and neuro-adrenergic systems—enhancing the local expression of noradrenaline and endothelin. This pathway leads to trophic as well as adverse effects on the cardiac myocyte [84,85]. Ang II also stimulates aldosterone secretion from the adrenal glands, contributing to salt balances and cardiac and vascular remodeling [84]; these compensatory mechanisms readjust vascular homeostasis. However, continuously raised Ang-II could lead to a loss of compensatory capacity and negatively affect the vascular system. In these situations, ACE inhibitors (ACEi) and higher doses of angiotensin receptor blockers (ARBs) are vital in stabilizing the condition and preventing cardiac failure [85,86].
Additionally, Ang(1–7) has direct anti-inflammatory effects on microglia [87] and contributes to reducing inflammation in adipose tissue [88], as demonstrated in arthritis models [89]. Ang(1–7) also elicits anti-thrombotic actions through the Mas-receptor-mediated release of nitric oxide (NO) from platelets [90]. Therefore, Ang(1–7) also effectively counters and neutralizes the detrimental effects of Ang-II [91]. Ang(1–7) also provides benefits to several major organs. Ang(1–7) attenuates myocyte hypertrophy and cardiac interstitial fibrosis in cardiac tissue [92,93], leading to the higher expression of the ACE-2 gene [85]. In individuals with type-2 diabetes mellitus, Ang(1–7) improves insulin sensitivity, reverses hyperglycemia, and reduces diabetic nephropathy [94].
In the kidney, Ang(1–7) facilitates vasodilation, enhancing renal blood flow and mitigating renal hypertension, preventing further production of the vasoconstrictive Ang-II [95]. Additionally, Ang(1–7) increases the glomerular filtration rate (GFR) and water and electrolyte molecular excretion in a dose-dependent manner [68,96]. Following closed traumatic brain injury, both Ang(1–7) and ACE-2 have improved cognitive and neurological functions [97]. Furthermore, they exhibit protective effects in cerebral ischemia [98] and hemorrhagic stroke in animal models [99,100,101,102].
Moreover, the modulation of the ACE-2/MAS pathway holds promise in preventing pulmonary injuries and represents a potential target for drug development to reduce viral entry [103]. ACE-2 exists in membrane-bound and soluble forms [59]. It is a double-edged sword, as SARS-CoV-2 utilizes membrane-bound ACE-2 to invade epithelial cells [67]. Meanwhile, soluble ACE-2 in extra-cellular fluid, particularly in circulation, binds to coronaviruses like SARS-CoV-2, facilitating their neutralization [104].
1.1. Benefits of Vitamin D
Research has shown that vitamin D is essential for more than just bone health. It plays a role in (A) the musculoskeletal system—it prevents rickets in children and osteomalacia in adults) [20,21,22], and (B) immune functions—vitamin D is crucial for maintaining a robust immune system [23,24] and reducing the risk of infections and autoimmune diseases [4,25], modulates metabolism [26] and energy generation [27,28], prevents acute infections [4,22], and minimizes chronic diseases [29]. Adequate vitamin D levels are linked to a lower risk of several chronic conditions, such as cardiovascular disease [30,31] and certain cancers [32,33], but others disagree [34,35]. In addition, it minimizes pregnancy-related complications and disorders [36,37,38,39] and mental health conditions [40,41], potentially including bipolar disorder [42] and the prevention of depression [43,44].
Meta-analyses and other clinical studies have consistently shown that sufficient vitamin D supplementation protects against acute respiratory tract infections, particularly in individuals with significant vitamin D deficiencies [45,46]. Further clinical research, including randomized controlled trials (RCTs), has revealed that vitamin D supplementation plays a crucial role in achieving positive health outcomes in cases of infectious diseases [13,47]. There are more than 310 peer-reviewed publications on the beneficial effects of prophylactic vitamin D in preventing SARS-CoV-2 [48] and as an adjunct in treatment [43,49,50]. Figure 1 illustrates the broader benefits of vitamin D in human health related to the immune system.
Vitamin D is a threshold nutrient. Consequently, there will be little additional benefit from over-supplementation or intake by those with sufficient vitamin D [5]. However, supplementing deficient individuals provides a significant benefit [52]. In parallel, over-exposure to sunlight does not lead to excess vitamin D entering the blood [53]. Notably, for those infected with SARS-CoV-2 (and other viruses), vitamin D and its metabolites are consumed rapidly during infections and conditions associated with immune system activation [51,54]. Therefore, unless supplemented, even those with reasonable serum 25(OH)D levels (e.g., on hospitalization) are likely to develop a deficiency from an acute severe infection like SARS-CoV-2 [5].
Notably, over half of the world’s population is vitamin D deficient at any given time [5]. Therefore, with little or no adverse effects, it is beneficial to consume vitamin D supplements or be exposed to safe levels of daily sunlight [55,56,57]. However, even in the sunniest regions in the world, the majority spend time indoors. Thus, most people need supplemental vitamin D to prevent deficiency to stay healthy.
1.2. The Entry of SARS-CoV-2 into Human Cells
SARS-CoV-2, the virus responsible for COVID-19, enters human cells primarily by its spike (S) protein interaction with the angiotensin-converting enzyme 2 (ACE-2) receptor on the surface of human cells [57]. The spike protein has two subunits: S1, which contains the receptor-binding domain (RBD) that specifically binds to ACE-2, and S2, which facilitates membrane fusion [58,59]. Upon binding to ACE-2, the spike protein undergoes a conformational change, enabling the virus to attach more firmly to the host cell. Transmembrane protease serine 2 (TMPRSS2) in the host cell cleaves the S-protein at specific sites, triggering the fusion of the viral and cellular membranes and allowing the viral RNA to enter the cytoplasm [60].
Once inside the host cell, the host’s ribosomes translate the viral RNA into viral proteins. The viral RNA genome serves as a template for replication and transcription to produce more viral RNA genomes and sub-genomic RNAs [61], which encode structural and accessory proteins [60,62]. These newly synthesized viral components are assembled into new virions in the host cell’s endoplasmic reticulum-Golgi intermediate compartment [63]. The mature virions are transported to the cell surface in vesicles and then released into the extra-cellular space via exocytosis, including the tissue fluid and circulation. These virions are primed to infect new local and distant host cells. This viral entry, replication, and release cycle leads to the spread of SARS-CoV-2 within the host and contributes to the pathology of COVID-19 [64].
Meanwhile, the expression of ACE-2 is regulated by vitamin D metabolites [65]. Vitamin D sufficiency increases the expression of ACE-2 (including the soluble ACE-2 in the circulation) and dampens the renin-angiotensin system/axis (RAS) [59]. Despite cell membrane-bound ACE-2 receptors serving as the primary entry point for the SARS-CoV-2 virus, numerous benefits of vitamin D interaction have been reported, initiating ACE-2-related anti-viral effects against SARS-CoV-2 [19]. This review investigates the impact of vitamin D on the immune system, focusing on its effects on the renin-angiotensin system (RAS) and ACE-2 [66].
1.3. Functions of the Renin-Angiotensin System (RAS)
The renin-angiotensin-aldosterone axis (RAS) is a crucial endocrine axis that controls critical physiological parameters, such as blood pressure homeostasis, immune functions, and metabolism [66]. It is also known as the renin-angiotensin-aldosterone system (RAAS). The RAS is an organized complex hormonal cascade that plays a vital role in controlling critical functions in many organs. Traditionally, RAS plays a crucial role in the cardiovascular system, metabolism, cell growth, and homeostasis.
However, over the past 15 years, additional functions of the RAS, such as ACE-1 and ACE-2) were identified. These include inflammatory processes that lead to lung and other epithelial cell injury, even causing acute respiratory distress syndrome (ARDS), sepsis, cardiac hypertrophy, pulmonary hypertension, acute pancreatitis, and glomerulonephritis [67]. The RAS is crucial in regulating several physiological parameters related to cardiovascular and renal function [66]. Details of these are illustrated in Table 1.
The primary active peptide of the RAS is Ang-II. However, there are other minor components, like Ang-III and Ang-IV, as well as a counter-regulatory peptide, Ang(1–7) [66]. From a physiological point of view, the most important and widely studied two peptides are Ang-II and Ang(1–7) [68], which are involved in human health maintenance and disease statuses. Investigations are ongoing to understand its role in inflammation [18]. The RAAS is integral to the homeostatic regulation of cardiovascular, renal, and fluid balances (homeostasis) and is essential in physiological stability [67]. A sustained imbalance of the RAS can lead to a lowering of ACE-2 (with loss of its protective effects) [59], causing acute lung injury, like acute respiratory distress syndrome (ARDS), with high mortality.
1.4. Vitamin D on the Renin-Angiotensin-Aldosterone Hormonal (RAS) Axis
Activating the RAS, including coronaviruses, leads to the excess production of the enzyme renin, which cleaves angiotensinogen into Ang-I. ACE -1 then cleaves Ang-1 to form Ang-II [69]. This over-activation of the RAS leads to the excess production of Ang-II, which enhances the production of inflammatory cytokines [70], worsening the morbidity and mortality associated with infections/sepsis and leading to a cytokine storm that causes lung damage [71]. Excessive production of inflammatory cytokines leads to uncontrolled inflammation and is linked to immune dysfunction, as seen with excess Ang-II, which causes enhanced inflammation and oxidative stress [70]. In contrast, sufficient vitamin D suppresses the expression of renin, thus damping the RAS while increasing ACE-2 and reducing Ang-II. Consequently, it reduces inflammation and oxidative stress, improving clinical outcomes from infection and septic consequences [51,72,73].
As aforementioned, calcitriol is a crucial regulator of the RAS axis and suppresses the expression of the renin gene—a rate-limiting step of the synthesis of Ang-II via cyclic AMP (cAMP)-dependent PKA signaling [74]. Calcitriol independently increases the expression of ACE-2, reducing Ang-II and increasing the generation of angiotensin(1–7), suppressing the formation of the CRE-CREB-CBP complex [70], and keeping RAS activity under control. Meanwhile, certain viral infections like SARS-CoV-2 stimulate RAS activity, aggravating the situation in those with vitamin D deficiency and making them more vulnerable to developing cytokine storms [19,70].
1.5. Functions of the ACE-2/Ang(1–7)/MasR Axis (Counter-Regulatory Pathway)
Vitamin D inhibits renin, induces ACE-2/Ang(1–7)/MasR axis activity, and modulates the ACE/Ang II/AT1R axis. Through these mechanisms, vitamin D increases the expression of ACE-2, MasR, and Ang(1–7) and elicits a protective role against acute lung injury/ARDS and abnormal clotting activities [75]. Vitamin D can stimulate these beneficial targets like ACE-2 [65,76]. The RAS is regulated mainly by vitamin D (calcitriol) suppressing renin and increasing the expression of ACE-2 [65]—the latter also acts as a primary receptor for SARS-CoV-2 entry into the cells [76].
In health, there is a balance between the RAS, the ACE-Ang–II-AT1R regulatory axis, and the counter-regulatory axis (a pathway) of ACE-2-Ang–1–7-MasR. They are crucial in maintaining human cardiovascular, inflammatory, and immune homeostasis [66]. The wide distribution of ACE-2 in the heart, kidneys, lungs, colon, testis, etc., reflects this. The activation of ACE-2 antagonizes the over-active RAS system, protecting against organ damage and mitigating hypertension and cardiovascular diseases [65]. Once infected, SARS-CoV-2 causes a major imbalance in the RAS, including the down-regulation of ACE-2 receptors when they invade human epithelial cells [75,77].
1.6. Molecular Aspects of the RAS and ACE
Angiotensin-converting enzyme-2 (ACE-2) is a peptidase expressed on epithelial cell membranes and plays a crucial role in catabolizing Ang-II, thereby regulating the RAS [78]. Furthermore, in humans, ACE-2 polymorphisms can influence susceptibility to diseases such as hypertension and coronaviruses, including SARS-CoV-2 [78,79]. ACE-2 counterbalances the enzymatic actions of ACE-1 and Ang-II synthesis by converting Ang-II into the peptide Ang(1–7), reducing Ang-II molecular concentration. Furthermore, Ang(1–7) acts via the G-protein-coupled Mas receptor (MasR) to induce vasodilation [80] and attenuate the expression of pro-inflammatory cytokines [68], like TNF-α and IL-6 in LPS-induced macrophages, thereby promoting anti-inflammatory effects [81]. Despite these benefits, functional membrane ACE-2 receptors have been identified as the primary entry site of coronavirus into human cells [82,83].
In addition, Ang II interacts with the adrenogenic axis—endothelin and neuro-adrenergic systems—enhancing the local expression of noradrenaline and endothelin. This pathway leads to trophic as well as adverse effects on the cardiac myocyte [84,85]. Ang II also stimulates aldosterone secretion from the adrenal glands, contributing to salt balances and cardiac and vascular remodeling [84]; these compensatory mechanisms readjust vascular homeostasis. However, continuously raised Ang-II could lead to a loss of compensatory capacity and negatively affect the vascular system. In these situations, ACE inhibitors (ACEi) and higher doses of angiotensin receptor blockers (ARBs) are vital in stabilizing the condition and preventing cardiac failure [85,86].
Additionally, Ang(1–7) has direct anti-inflammatory effects on microglia [87] and contributes to reducing inflammation in adipose tissue [88], as demonstrated in arthritis models [89]. Ang(1–7) also elicits anti-thrombotic actions through the Mas-receptor-mediated release of nitric oxide (NO) from platelets [90]. Therefore, Ang(1–7) also effectively counters and neutralizes the detrimental effects of Ang-II [91]. Ang(1–7) also provides benefits to several major organs. Ang(1–7) attenuates myocyte hypertrophy and cardiac interstitial fibrosis in cardiac tissue [92,93], leading to the higher expression of the ACE-2 gene [85]. In individuals with type-2 diabetes mellitus, Ang(1–7) improves insulin sensitivity, reverses hyperglycemia, and reduces diabetic nephropathy [94].
In the kidney, Ang(1–7) facilitates vasodilation, enhancing renal blood flow and mitigating renal hypertension, preventing further production of the vasoconstrictive Ang-II [95]. Additionally, Ang(1–7) increases the glomerular filtration rate (GFR) and water and electrolyte molecular excretion in a dose-dependent manner [68,96]. Following closed traumatic brain injury, both Ang(1–7) and ACE-2 have improved cognitive and neurological functions [97]. Furthermore, they exhibit protective effects in cerebral ischemia [98] and hemorrhagic stroke in animal models [99,100,101,102].
Moreover, the modulation of the ACE-2/MAS pathway holds promise in preventing pulmonary injuries and represents a potential target for drug development to reduce viral entry [103]. ACE-2 exists in membrane-bound and soluble forms [59]. It is a double-edged sword, as SARS-CoV-2 utilizes membrane-bound ACE-2 to invade epithelial cells [67]. Meanwhile, soluble ACE-2 in extra-cellular fluid, particularly in circulation, binds to coronaviruses like SARS-CoV-2, facilitating their neutralization [104].
2. Regulation of the Immune System
Calcitriol, 1,25-dihydroxycholecalciferol, the most active vitamin D metabolite, is crucial for immune cell functions. It is a potent immune modulator. It has been estimated that two-thirds of immune cells’ physiological activation and functions rely on generating sufficient calcitriol within them [13,105]. Because circulatory concentrations are too low, the capacity of calcitriol to diffuse into immune cells is minimal [106]. In contrast, vitamin D and calcifediol 25(OH)D (in nmol) circulate approximately 900 times higher than the hormonal form of calcitriol (pmol). This allows them to diffuse into peripheral target cells and is utilized for the intracellular generation of calcitriol [5,13]. This intracellular calcitriol is essential for maintaining immune cell activities, including preventing autoimmunity and combating invading pathogens [5,107,108,109]. Once generated adequately within the immune cells, calcitriol activates the cytosol’s vitamin D (calcitriol) receptors (VDR/CTR) and provides autocrine and paracrine signaling as well as genomic modulations [5].
Calcitriol concentrations in the circulation are controlled by parathyroid hormone (PTH) and ionized calcium in the blood but not by tissue 24-hydroxylase. In contrast, in the target tissues, the production of calcitriol is mainly regulated by a combination of the serum 25(OH)D concentration (and D3) in a concentration-dependent manner (for diffusion). It is subjected to the feedback catabolic activity of tissue 24-hydroxylases and not by PTH. Except for membrane effects, vitamin D has minimal actions; therefore, the “physiological concentrations of vitamin D status” level refers to D3 and 25(OH)D concentrations in the circulation, of which only the latter is measured routinely to assess vitamin D status. Peripheral target cells, like immune cells, primarily depend on the diffusion of vitamin D and 25(OH)D from the circulation to generate higher concentrations of non-hormonal calcitriol, intracellularly [13,51].
2.1. Mechanisms of Adequate Vitamin D Supplementation in Infections
The crucial role of vitamin D adequacy in combating acute infections was confirmed a decade ago [47,110,111]. Serum 25(OH)D concentration thresholds needed for robust immune systems to overcome infections [112,113,114] and to reduce health risks were clarified in recent years in adults [13,115,116,117] and children [113,118,119,120]. Studies have affirmed the mechanisms of action of how ultraviolet-B (UVB) rays and vitamin D supplements help individuals with infections recover faster [121,122]. Subsequently, many studies have consolidated these findings [47,123,124,125].
The converging data indicate that the minimum effective serum 25(OH)D concentration to reduce infections and their severity is 40 ng/mL (100 nmol/L) [4,108,117,126], with the optimum being above 50 ng/mL [13,47,51,111,125,127,128]. Regarding SARS-CoV-2 infection, numerous studies reported that effective use of vitamin D3 and calcifediol significantly improves clinical outcomes from those with hypovitaminosis D, including reduced hospitalizations and deaths [129,130,131,132,133,134,135]. The primary mechanism is stimulating the immune system, supported by other mechanisms discussed previously, including subduing the RAS and increasing the expression of ACE-2 [66].
Meta-analyses encompassing a variety of heterogeneous studies concluded that vitamin D reduces the incidence of acute respiratory illnesses [45,114,136,137] and significantly reduces the severity and mortality from COVID-19 [45,46,125,130,138,139,140,141,142]. In total, ten clinical trials out of 321 peer-reviewed clinical studies that used vitamin D as the primary intervention to investigate the effects on clinical outcomes in COVID-19 (from May 2020 to June 2024) reported a significant reduction in hospitalizations, ICU admissions, or deaths [117,131,132,135,143,144,145,146,147,148,149,150,151,152,153,154,155,156] [early therapies for COVID-19 (https://c19early.org, accessed on 25 January 2024) and publications specifically related to vitamin D (https://c19early.org/d, accessed on 25 January 2024) [48].
2.2. Mechanisms Lowering the Severity of Infections
Vitamin D supplementation has been shown to reduce the severity and complications of COVID-19 [157]. Hundreds of peer-reviewed publications have confirmed that serum 25(OH)D concentrations below 12 ng/mL (indicating severe vitamin D deficiency) pose a significant risk for vulnerability to SARS-CoV-2 infection [158,159,160,161], its complications, and mortality [135,162,163,164]. Supplementation with cholecalciferol (vitamin D3) or calcifediol [25(OH)D] rapidly elevates serum 25(OH)D concentrations and decreases the risk of complications and deaths from SARS-CoV-2 infection [52,165,166,167,168,169,170,171].
Moreover, adequate vitamin D supplementation in COVID-19 patients with co-morbidities has been observed to reduce complications, length of hospital stays, and disease severity, leading to lower mortality rates [47,150,151,152,155,172,173,174,175,176]. Given the evidence, vitamin D should be considered a crucial component of the physician’s arsenal in the fight against COVID-19 [135].
2.3. Vitamin D Is Essential for Activating Immune Cells
Calcitriol is the most active vitamin D metabolite, crucial for combating invading pathogens and preventing autoimmunity and chronic diseases [29,107,108]. Through multiple mechanisms, calcitriol modulates the immune system [12]. When secreted from renal tubular cells into the bloodstream, calcitriol functions as a hormone [5]. Circulatory calcitriol alters the behavior of cells involved in calcium–phosphate–bone metabolism and intestinal, bone, and parathyroid cells.
The average circulatory concentration of calcitriol in the circulation is about 0.045 ng/mL, while the concentration of its free, diffusible form is far below the threshold needed to diffuse into immune cells and initiate intracellular signaling [4,29]. Moreover, vitamin D and calcifediol [25(OH)D] concentrations in the circulation are about 900-fold higher than circulating calcitriol (ng vs. pg/L in the blood); thus, only these two compounds serve as the substrate for intracellular calcitriol generation. Consequently, circulating calcitriol has no evident impact outside the muscular-skeletal, parathyroid, and fat cells.
However, the higher nmol-range concentrations of calcitriol generated intracellularly in response to TLR signaling provide (physiological) intracellular autocrine/intracrine signaling crucial for immune functions to overcome threats like infections [177]. Consequently, a holding mechanism increases serum levels, i.e., beyond a threat like detecting unfamiliar proteins or antigens in the circulation or local tissues [178,179]. The sporadic increases in the synthesis of calcitriol and VDR in response to TLR-4 signaling ensure the formation of sufficient calcitriol–VDR complexes to modulate transcriptions and intra-cellular autocrine signaling and genomic modulation, as and when needed [178].
The mechanisms mentioned above regulate inflammation and oxidative stresses through the abovementioned mechanisms, primarily by suppressing inflammatory cytokines and enhancing the synthesis of anti-inflammatory cytokines [177]. The immunomodulatory effects of vitamin D include the activation of immune cells such as T and B cells, macrophage and dendritic cells, and the enhanced production of several antimicrobial peptides and neutralizing antibodies [108,180].
2.1. Mechanisms of Adequate Vitamin D Supplementation in Infections
The crucial role of vitamin D adequacy in combating acute infections was confirmed a decade ago [47,110,111]. Serum 25(OH)D concentration thresholds needed for robust immune systems to overcome infections [112,113,114] and to reduce health risks were clarified in recent years in adults [13,115,116,117] and children [113,118,119,120]. Studies have affirmed the mechanisms of action of how ultraviolet-B (UVB) rays and vitamin D supplements help individuals with infections recover faster [121,122]. Subsequently, many studies have consolidated these findings [47,123,124,125].
The converging data indicate that the minimum effective serum 25(OH)D concentration to reduce infections and their severity is 40 ng/mL (100 nmol/L) [4,108,117,126], with the optimum being above 50 ng/mL [13,47,51,111,125,127,128]. Regarding SARS-CoV-2 infection, numerous studies reported that effective use of vitamin D3 and calcifediol significantly improves clinical outcomes from those with hypovitaminosis D, including reduced hospitalizations and deaths [129,130,131,132,133,134,135]. The primary mechanism is stimulating the immune system, supported by other mechanisms discussed previously, including subduing the RAS and increasing the expression of ACE-2 [66].
Meta-analyses encompassing a variety of heterogeneous studies concluded that vitamin D reduces the incidence of acute respiratory illnesses [45,114,136,137] and significantly reduces the severity and mortality from COVID-19 [45,46,125,130,138,139,140,141,142]. In total, ten clinical trials out of 321 peer-reviewed clinical studies that used vitamin D as the primary intervention to investigate the effects on clinical outcomes in COVID-19 (from May 2020 to June 2024) reported a significant reduction in hospitalizations, ICU admissions, or deaths [117,131,132,135,143,144,145,146,147,148,149,150,151,152,153,154,155,156] [early therapies for COVID-19 (https://c19early.org, accessed on 25 January 2024) and publications specifically related to vitamin D (https://c19early.org/d, accessed on 25 January 2024) [48].
2.2. Mechanisms Lowering the Severity of Infections
Vitamin D supplementation has been shown to reduce the severity and complications of COVID-19 [157]. Hundreds of peer-reviewed publications have confirmed that serum 25(OH)D concentrations below 12 ng/mL (indicating severe vitamin D deficiency) pose a significant risk for vulnerability to SARS-CoV-2 infection [158,159,160,161], its complications, and mortality [135,162,163,164]. Supplementation with cholecalciferol (vitamin D3) or calcifediol [25(OH)D] rapidly elevates serum 25(OH)D concentrations and decreases the risk of complications and deaths from SARS-CoV-2 infection [52,165,166,167,168,169,170,171].
Moreover, adequate vitamin D supplementation in COVID-19 patients with co-morbidities has been observed to reduce complications, length of hospital stays, and disease severity, leading to lower mortality rates [47,150,151,152,155,172,173,174,175,176]. Given the evidence, vitamin D should be considered a crucial component of the physician’s arsenal in the fight against COVID-19 [135].
2.3. Vitamin D Is Essential for Activating Immune Cells
Calcitriol is the most active vitamin D metabolite, crucial for combating invading pathogens and preventing autoimmunity and chronic diseases [29,107,108]. Through multiple mechanisms, calcitriol modulates the immune system [12]. When secreted from renal tubular cells into the bloodstream, calcitriol functions as a hormone [5]. Circulatory calcitriol alters the behavior of cells involved in calcium–phosphate–bone metabolism and intestinal, bone, and parathyroid cells.
The average circulatory concentration of calcitriol in the circulation is about 0.045 ng/mL, while the concentration of its free, diffusible form is far below the threshold needed to diffuse into immune cells and initiate intracellular signaling [4,29]. Moreover, vitamin D and calcifediol [25(OH)D] concentrations in the circulation are about 900-fold higher than circulating calcitriol (ng vs. pg/L in the blood); thus, only these two compounds serve as the substrate for intracellular calcitriol generation. Consequently, circulating calcitriol has no evident impact outside the muscular-skeletal, parathyroid, and fat cells.
However, the higher nmol-range concentrations of calcitriol generated intracellularly in response to TLR signaling provide (physiological) intracellular autocrine/intracrine signaling crucial for immune functions to overcome threats like infections [177]. Consequently, a holding mechanism increases serum levels, i.e., beyond a threat like detecting unfamiliar proteins or antigens in the circulation or local tissues [178,179]. The sporadic increases in the synthesis of calcitriol and VDR in response to TLR-4 signaling ensure the formation of sufficient calcitriol–VDR complexes to modulate transcriptions and intra-cellular autocrine signaling and genomic modulation, as and when needed [178].
The mechanisms mentioned above regulate inflammation and oxidative stresses through the abovementioned mechanisms, primarily by suppressing inflammatory cytokines and enhancing the synthesis of anti-inflammatory cytokines [177]. The immunomodulatory effects of vitamin D include the activation of immune cells such as T and B cells, macrophage and dendritic cells, and the enhanced production of several antimicrobial peptides and neutralizing antibodies [108,180].
3. Vitamin D Deficiency and Vulnerability to Infections
Vitamin D modulates the innate and adaptive immune systems [178,181]. It enhances innate immunity via complex mechanisms [182,183], including the expression of antimicrobial peptides like cathelicidin in various cells, including keratinocytes, epithelial cells, and monocytes [184]. Vitamin D also down-regulates inflammatory responses through multiple mechanisms, including dampening the RAS [183], switching Th1 cells to Th2 and Th17 to Treg cells [185,186], and increasing the expression of MAPK phosphatase-1 (MKP-1). Consequently, the latter inhibits p38 activation and reduces pro-inflammatory cytokine production in human monocytes/macrophages, as demonstrated when stimulated with lipopolysaccharides (LPS) [187].
3.1. Vitamin D Deficiency Increases Infection Vulnerability
Individuals with hypovitaminosis D are at a heightened risk of developing COVID-19 (and other infections) and experiencing unfavorable clinical outcomes [157,158,188,189]. In addition to vitamin D’s robust anti-inflammatory, anti-oxidant, and antimicrobial properties [148,176], intracellularly-generated calcitriol offers other beneficial effects. These include enhanced cell repair, reduced apoptosis [190], and the protection of epithelial and vascular endothelial cells [191]. In addition, it mitigates pregnancy-associated complications [179,180] and reduces all-cause mortality [192]. As a result, maintaining vitamin D sufficiency helps in rapid recovery and reduces complications and death from SARS-CoV-2 infection [157,193].
Studies reported that calcitriol inhibits the production of pro-inflammatory cytokines via T cells. Moreover, when combined with IL-2, 1,25(OH)2D3, it promotes the regulatory function of T cells, thereby contributing to immune regulation and homeostasis [194]. Numerous studies, including meta-analyses, have shown an inverse correlation between serum vitamin D levels and the severity of COVID-19 [125,129,157,158,189,195]. Therefore, it is unsurprising that vitamin D deficiency aggravates complications from COVID-19 [157,158,188,189].
Hypovitaminosis D and SARS-CoV-2 infections weaken epithelial cell gap junctions, potentially facilitating the passage of substances, fluids, and microbes across membranes and allowing viral dissemination [196]; when combined with other micronutrients like selenium and zinc, vitamin D provides a protective effect and enhances the physiological functions of tight cell gap junctions in epithelial cells [197]. This synergy improves the effectiveness of the tissue barrier, particularly in preventing the entry and propagation of microbes, particularly viruses [198,199].
3.2. Hypovitaminosis D Causes Immune Cell Dysfunction
Vitamin D deficiency leads to immune dysfunction, heightening susceptibility to infections and autoimmune disorders [4]. Low vitamin D reduces ACE-2 expression and increases viral loads, replication, and dissemination [200]. It also increases the potential of mutations (also after COVID-19 vaccines) [201,202,203] and could promote the naturally evolving gain of functions in viruses. For instance, dominant mutations enhance the affinity between the spike protein receptor-binding domain (RBD) and the ACE-2 receptor molecules [203], as seen in variants like Delta and Omicron, resulting in heightened infectivity (R0) [204,205], as illustrated in recent dominant mutants such as Omicron BA.2 [204].
Natural and vaccine-mediated neutralizing antibodies bind either to or near the ACE-2 binding region of the RBD [206]. Consequently, critical mutations in the viral RBD interfere with their recognition by neutralizing antibodies, leading to immune evasion. Moreover, Omicron variants BA.4 and BA.5 and other newly described dominant mutations notably occur in the L452R and F486V regions of the RBD, impairing the immune neutralization of viruses [197]. These mechanisms have been documented using sera from individuals who have received triple vaccination [205].
3.3. Vitamin D Insufficiency and Chronic Diseases
Epidemiological and case-control studies reported strong associations between vitamin D deficiency and several immune-related chronic diseases, such as type-1 diabetes [207,208], type-2 diabetes [209], connective tissue disorders [210], inflammatory bowel disorders [211], chronic hepatitis [212], asthma [213], respiratory infections [45], and cancer [214,215]. However, interventional studies have yielded weak data primarily due to poor study designs [5,216,217,218]. With a dysfunctional immune system and low ACE-2 expression, this is unsurprising.
Aging and chronic diseases [219,220] have a higher prevalence of hypovitaminosis D, and most of these conditions have reduced ACE-2 expression [29,221]. Since ACE-2 has protective functions, it is predictable that its lower expression in aging and co-morbidities increases SARS-CoV-2-associated complications and deaths [220]. Additionally, the lower expression of ACE-2 is reported in chronic conditions such as hypertension, obesity, and diabetes [197], which are also associated with an increased risk of complications from SARS-CoV-2 infection [157,158,188,189,222,223].
Therefore, the fundamental vulnerability in aging and co-morbidities may lie in hypovitaminosis and the low expression of ACE-2, illustrating the interlink between the mentioned chronic conditions and increased infection vulnerability. These data support that the synergistic adverse effects of hypovitaminosis D and reduced ACE-2 expression in patients with chronic diseases heighten risks for symptomatic COVID-19, complications, and deaths [220,221].
3.4. Co-Morbidities and Disease Vulnerability
Over the last 20 years, over 8000 clinical research articles have been published illustrating a robust inverse association between serum 25(OH)D concentrations and disease vulnerability, severity, and deaths from various diseases [224,225], especially from infections [4,226,227]. Despite the country’s location, gross national products, healthcare expenditure, or access to healthcare, older people with co-morbidities [18] and institutionalized people have shown to have the highest prevalence of severe vitamin D deficiency (e.g., serum 25(OH)D concentrations less than 12 ng/mL) [162,228,229].
Co-morbidities are strongly associated with low serum 25(OH)D and ACE-2 levels [230]. These groups include nursing home residents, developmental disability centers, group homes, incarcerated people, and routine night shift workers [29]. They have the highest rates of hypovitaminosis D and experience complications [231], ICU admissions, and deaths from SARS-CoV-2—this was observed vividly during the early part of the COVID-19 pandemic [125,232,233,234,235,236,237]. One common factor in the groups mentioned above is the low expression of ACE-2 [220,231]. Therefore, supplementing them with sufficient vitamin D daily (or once a week) dampens the RAS system (thus subduing inflammation) and enhances the ACE-2 expression, reducing disease severity and co-morbidity [220,231].
Such an approach has significantly reduced symptomatic SARS-CoV-2 infections, complications, and deaths [13,234,236,238,239]. However, this expectation failed to materialize during the pandemic because health agencies neglected this important natural defense mechanism [240]. This failure to boost the immune systems with simple remedies like vitamin D, the denial of natural immunity, disallowing the use of early repurposed therapies, and reliance on vaccination alone to control the pandemic [241,242] have increased hospitalizations and deaths from COVID-19 [193,231,243].
3.1. Vitamin D Deficiency Increases Infection Vulnerability
Individuals with hypovitaminosis D are at a heightened risk of developing COVID-19 (and other infections) and experiencing unfavorable clinical outcomes [157,158,188,189]. In addition to vitamin D’s robust anti-inflammatory, anti-oxidant, and antimicrobial properties [148,176], intracellularly-generated calcitriol offers other beneficial effects. These include enhanced cell repair, reduced apoptosis [190], and the protection of epithelial and vascular endothelial cells [191]. In addition, it mitigates pregnancy-associated complications [179,180] and reduces all-cause mortality [192]. As a result, maintaining vitamin D sufficiency helps in rapid recovery and reduces complications and death from SARS-CoV-2 infection [157,193].
Studies reported that calcitriol inhibits the production of pro-inflammatory cytokines via T cells. Moreover, when combined with IL-2, 1,25(OH)2D3, it promotes the regulatory function of T cells, thereby contributing to immune regulation and homeostasis [194]. Numerous studies, including meta-analyses, have shown an inverse correlation between serum vitamin D levels and the severity of COVID-19 [125,129,157,158,189,195]. Therefore, it is unsurprising that vitamin D deficiency aggravates complications from COVID-19 [157,158,188,189].
Hypovitaminosis D and SARS-CoV-2 infections weaken epithelial cell gap junctions, potentially facilitating the passage of substances, fluids, and microbes across membranes and allowing viral dissemination [196]; when combined with other micronutrients like selenium and zinc, vitamin D provides a protective effect and enhances the physiological functions of tight cell gap junctions in epithelial cells [197]. This synergy improves the effectiveness of the tissue barrier, particularly in preventing the entry and propagation of microbes, particularly viruses [198,199].
3.2. Hypovitaminosis D Causes Immune Cell Dysfunction
Vitamin D deficiency leads to immune dysfunction, heightening susceptibility to infections and autoimmune disorders [4]. Low vitamin D reduces ACE-2 expression and increases viral loads, replication, and dissemination [200]. It also increases the potential of mutations (also after COVID-19 vaccines) [201,202,203] and could promote the naturally evolving gain of functions in viruses. For instance, dominant mutations enhance the affinity between the spike protein receptor-binding domain (RBD) and the ACE-2 receptor molecules [203], as seen in variants like Delta and Omicron, resulting in heightened infectivity (R0) [204,205], as illustrated in recent dominant mutants such as Omicron BA.2 [204].
Natural and vaccine-mediated neutralizing antibodies bind either to or near the ACE-2 binding region of the RBD [206]. Consequently, critical mutations in the viral RBD interfere with their recognition by neutralizing antibodies, leading to immune evasion. Moreover, Omicron variants BA.4 and BA.5 and other newly described dominant mutations notably occur in the L452R and F486V regions of the RBD, impairing the immune neutralization of viruses [197]. These mechanisms have been documented using sera from individuals who have received triple vaccination [205].
3.3. Vitamin D Insufficiency and Chronic Diseases
Epidemiological and case-control studies reported strong associations between vitamin D deficiency and several immune-related chronic diseases, such as type-1 diabetes [207,208], type-2 diabetes [209], connective tissue disorders [210], inflammatory bowel disorders [211], chronic hepatitis [212], asthma [213], respiratory infections [45], and cancer [214,215]. However, interventional studies have yielded weak data primarily due to poor study designs [5,216,217,218]. With a dysfunctional immune system and low ACE-2 expression, this is unsurprising.
Aging and chronic diseases [219,220] have a higher prevalence of hypovitaminosis D, and most of these conditions have reduced ACE-2 expression [29,221]. Since ACE-2 has protective functions, it is predictable that its lower expression in aging and co-morbidities increases SARS-CoV-2-associated complications and deaths [220]. Additionally, the lower expression of ACE-2 is reported in chronic conditions such as hypertension, obesity, and diabetes [197], which are also associated with an increased risk of complications from SARS-CoV-2 infection [157,158,188,189,222,223].
Therefore, the fundamental vulnerability in aging and co-morbidities may lie in hypovitaminosis and the low expression of ACE-2, illustrating the interlink between the mentioned chronic conditions and increased infection vulnerability. These data support that the synergistic adverse effects of hypovitaminosis D and reduced ACE-2 expression in patients with chronic diseases heighten risks for symptomatic COVID-19, complications, and deaths [220,221].
3.4. Co-Morbidities and Disease Vulnerability
Over the last 20 years, over 8000 clinical research articles have been published illustrating a robust inverse association between serum 25(OH)D concentrations and disease vulnerability, severity, and deaths from various diseases [224,225], especially from infections [4,226,227]. Despite the country’s location, gross national products, healthcare expenditure, or access to healthcare, older people with co-morbidities [18] and institutionalized people have shown to have the highest prevalence of severe vitamin D deficiency (e.g., serum 25(OH)D concentrations less than 12 ng/mL) [162,228,229].
Co-morbidities are strongly associated with low serum 25(OH)D and ACE-2 levels [230]. These groups include nursing home residents, developmental disability centers, group homes, incarcerated people, and routine night shift workers [29]. They have the highest rates of hypovitaminosis D and experience complications [231], ICU admissions, and deaths from SARS-CoV-2—this was observed vividly during the early part of the COVID-19 pandemic [125,232,233,234,235,236,237]. One common factor in the groups mentioned above is the low expression of ACE-2 [220,231]. Therefore, supplementing them with sufficient vitamin D daily (or once a week) dampens the RAS system (thus subduing inflammation) and enhances the ACE-2 expression, reducing disease severity and co-morbidity [220,231].
Such an approach has significantly reduced symptomatic SARS-CoV-2 infections, complications, and deaths [13,234,236,238,239]. However, this expectation failed to materialize during the pandemic because health agencies neglected this important natural defense mechanism [240]. This failure to boost the immune systems with simple remedies like vitamin D, the denial of natural immunity, disallowing the use of early repurposed therapies, and reliance on vaccination alone to control the pandemic [241,242] have increased hospitalizations and deaths from COVID-19 [193,231,243].
4. Effects of SARS-CoV-2 on the Immune System
Having hypovitaminosis D pre-pandemic or pre-infection increases the risks and vulnerability of SARS-CoV-2 [130]. Moreover, vitamin D deficiency at the time of diagnosis of SARS-CoV-2 infection significantly increased disease severity and mortality [13,43,55,56]. In contrast, vitamin D sufficiency is protective against severe COVID-19 disease and death [13,46,57,58]. These data support the satisfaction of Bradford Hill’s criteria for establishing that hypovitaminosis D increases the risks of complications from SARS-CoV-2 [18,59,244,245]. Evidence also supports that vitamin D deficiency is a cause of vulnerability, severity, and mortality from the SARS-CoV-2 virus [13,43,46,47,48,49,50].
The RBD of the SARS-CoV-2 spike protein interacts with membrane-bound ACE-2 receptors [246,247,248]. Subsequently, the serine protease TMPRSS2 cleaves the spike protein into two functional subunits: S1 and S2. Following this cleavage, the S2 subunit undergoes a conformational change, enabling fusion with the host cell membrane and facilitating endocytotic internalization [103,249,250]. The Two-Pore Segment Channel 2 (TPC2) molecules in the endo-lysosomal system also facilitate the entry of SARS-CoV-2 into human cells [249]. This process is activated by ionized calcium (Ca2+), subsequently activating nicotinic acid adenine-dinucleotide phosphate (NAADP) intracellular messengers [251].
4.1. Physiology and Pathological Pathways of the RAS Axis
In the RAAS system, the enzyme renin (the rate-limiting step of RAS) activates angiotensinogen into angiotensin-I, and ACE-1 generates Ang-II (Figure 2). Renin catalyzes the formation of pro-peptide Ang-I and promotes the expression of ACE-1. Increased Ang-1 (relatively inactive molecules) leads to the increased synthesis of Ang-II via ACE-1—a potent vasoconstrictor peptide (Figure 2). Ang-II directly elevates peripheral vascular resistance and hypertension (especially pulmonary). Moreover, it also activates pro-coagulatory pathways, inflammatory cytokines, and interstitial fibrosis via the Ang-II receptor type 1 (AT1) receptors [93,100]. Figure 2 illustrates the normal, physiological, and alternative pathological pathways of the RAS.
The risk of the dysregulation of the RAS is high in hypovitaminosis D, which has less control over the enzyme renin. Severe coronaviral infections can lead to a cytokine storm, causing lung injury with ARDS and initiating coagulatory abnormalities [77]. SARS-CoV-2 primarily affects lung tissues, pneumocytes, alveolar interstitium, and capillaries. The alveolar epithelial cells, the port of entry of SARS-CoV-2, have high concentrations of ACE-2 receptors on their membranes [252]. ACE-2 expression is down-regulated with the viral infection, and the ACE-2/Ang(1–7)/Mas receptor (MasR) axis is suppressed [77], which augments the classic RAS, leading to diffuse inflammations and associated adverse effects. This cascade of events can cause severe inflammation, oxidative stress, and lung damage, leading to fluid extravasation into soft tissues, causing pulmonary edema and fibrosis [93,252].
4.2. Renin-Angiotensin System Related to SARS-CoV-2
As mentioned above, calcitriol is a crucial regulator of the RAS axis, mainly by suppressing renin gene expression, a rate-limiting step of synthesis of Ang-II via cyclic AMP (cAMP)-dependent PKA signaling [74]. Calcitriol independently increases the expression of ACE-2 [253], reducing Ang-II and increasing the generation of Ang(1–7), suppressing the formation of the CRE-CREB-CBP complex [70] and keeping RAS activity under control [197]. Meanwhile, certain viral infections like SARS-CoV-2 stimulate RAS activity, aggravating the situation in those with vitamin D deficiency and making them more vulnerable to developing cytokine storms [19,70].
SARS-CoV-2 infection in individuals with severe vitamin D deficiency markedly increases the vulnerability to severe complications, including ARDS and death [254,255,256]. Low ACE-2 concentrations in such persons [256] significantly increase the susceptibility to contract viruses, especially coronaviruses, and develop complications [117,146]. Once infected, coronaviruses indirectly increase the renin and, reduce ACE-2 and soluble ACE-2 concentrations. The latter is partly caused by the consumption (i.e., internalization and destruction) of membrane-bound ACE-2 receptors [59]. Low ACE-2, in conjunction with the over-activation of the RAS [55], leads to the excess and uncontrolled production of Ang-II, leading to severe adverse effects, increasing complications, the risk for cytokine storms, and death [19,197].
Also, widespread inflammation and oxidative stress injure the pulmonary epithelial and vascular endothelial cells and their basement membranes [257], impairing tight junction, with virus dissemination and fluid leakage into soft tissues [257], including the lungs, pericardium, intestine, and brain [29,196,258]. Pulmonary epithelial damage causes hypoxia and increases the risk of pneumonia and ARDS [69,70]. The endothelial abnormalities lead to micro-thrombosis, embolization, and intravascular thrombosis [259,260]. Figure 3 illustrates the RAS axis in a normal physiological state, in an activated state with hypovitaminosis D, and in the presence of SARS-CoV-2 infection [19].
4.3. Regulation of Inflammation by Vitamin D via the RAS
Vitamin D is a potent negative endocrine regulator in the RAS through a canonical pathway [197]. It achieves this by inhibiting the cAMP response element-binding protein (CREB), a key transcription factor for renin gene regulation [74]. Vitamin D-driven suppression of renin [261] reduces RAS activity, such as lowering the molecular expression of ACE-1 and Ang-II while increasing the expression of ACE-2 [74,253]. These effects have been observed both in vitro and in vivo [253]. Notably, low serum levels of 25(OH)D concentrations are inversely correlated with higher RAS activity, elevated plasma renin activity, and Ang-II levels, contributing to increased blood pressure [262,263,264].
In studies using LPS-exposed rat lung tissue, calcitriol increased ACE-2 expression while reducing renin, angiotensin II, ACE, and AT1 receptor expression. Additionally, calcitriol exhibited a dose-dependent reduction in the permeability of blood vessels in rat lungs, mitigating damage induced by LPS [253]. Activating the ACE-2/Ang(1–7)/MAS axis by vitamin D further enhances the production of ACE-2 and Ang(1–7) concentrations. Ang(1–7)’s vasodilatory, anti-inflammatory, and anti-thrombotic effects help counteract hypertension, inflammation, and coagulatory abnormalities [265,266,267] (Figure 2), mitigating the adverse effects caused by SARS-CoV-2 and its spike protein.
As previously mentioned, vitamin D also blocks viral entry, strengthens immunity by tightening epithelial gap junctions, and inhibits SARS-CoV-2 transcription enzymes [60], thereby combating the viral infection. Vitamin D deficiency over-activates the RAS with the inefficient counter-regulatory activation of ACE-2/angiotensin(1–7)/Mas axis [100,268], increasing the risks for ARDS [197]. Consequently, vitamin D deficiency is a critical component that exacerbates COVID-19 via the over-activation of RAS with an excess generation of Ang-II [197].
Vitamin D and inhibitors of ACE-1 and Ang receptor blockers work together indirectly to decrease renin synthesis, thereby reducing the activity of the RAS and the synthesis of Ang-I and Ang-II [66]. Angiotensin receptor blockers (ARBs) also inhibit the generation of Ang-II and decrease the stimulation of the angiotensin-1 (AT1) receptor. It has also been proposed that CD147/Basigin receptors bind to epitopes on the spike protein-S of the SARS-CoV virus, facilitating viral entry through endocytosis [269]. Therefore, blocking CD147/Basigin could be a target for drug development against COVID-19 [270]. Moreover, vitamin D indirectly regulates SARS-CoV-2 by modulating ACE-2 and CD147 receptor molecules [271,272]. The involvement of vitamin D and ACE-2 in modulating the SARS-CoV-2 virus [197] and its effects on the RAS are illustrated in Figure 4.
Figure 3 and Figure 4 illustrate multiple mechanisms of SARS-CoV-2 that dysregulated the RAS, reducing ACE-2 and increasing Ang-II levels. Vitamin D, ARBs, and ACEi can intervene positively in these cycles initiated by the virus, particularly in individuals with severe vitamin D deficiency. Vitamin D plays a crucial role by modulating the RAS, inhibiting Ang-II synthesis, and up-regulating ACE-2 expression, which mitigates the harmful effects of SARS-CoV-2 [19].
Ang-II type 1 receptor blockers (ARBs) and ACEi medications influence RAS, potentially mitigating the dysregulation caused by SARS-CoV-2 [19]. Together, these interventions aim to dampen the vicious cycles initiated by the virus, potentially reducing the morbidity and mortality associated with COVID-19, especially in individuals with severe vitamin D deficiency. ACEi and ARBs are indicated clinically in hypertension and other cardiovascular diseases [197]. These agents have been shown to mitigate acute lung injury by restoring the balance between two regulatory processes. Preclinical and clinical studies support the evidence of RAS disequilibrium in COVID-19 and the beneficial role of RAS modulation [252].
4.1. Physiology and Pathological Pathways of the RAS Axis
In the RAAS system, the enzyme renin (the rate-limiting step of RAS) activates angiotensinogen into angiotensin-I, and ACE-1 generates Ang-II (Figure 2). Renin catalyzes the formation of pro-peptide Ang-I and promotes the expression of ACE-1. Increased Ang-1 (relatively inactive molecules) leads to the increased synthesis of Ang-II via ACE-1—a potent vasoconstrictor peptide (Figure 2). Ang-II directly elevates peripheral vascular resistance and hypertension (especially pulmonary). Moreover, it also activates pro-coagulatory pathways, inflammatory cytokines, and interstitial fibrosis via the Ang-II receptor type 1 (AT1) receptors [93,100]. Figure 2 illustrates the normal, physiological, and alternative pathological pathways of the RAS.
The risk of the dysregulation of the RAS is high in hypovitaminosis D, which has less control over the enzyme renin. Severe coronaviral infections can lead to a cytokine storm, causing lung injury with ARDS and initiating coagulatory abnormalities [77]. SARS-CoV-2 primarily affects lung tissues, pneumocytes, alveolar interstitium, and capillaries. The alveolar epithelial cells, the port of entry of SARS-CoV-2, have high concentrations of ACE-2 receptors on their membranes [252]. ACE-2 expression is down-regulated with the viral infection, and the ACE-2/Ang(1–7)/Mas receptor (MasR) axis is suppressed [77], which augments the classic RAS, leading to diffuse inflammations and associated adverse effects. This cascade of events can cause severe inflammation, oxidative stress, and lung damage, leading to fluid extravasation into soft tissues, causing pulmonary edema and fibrosis [93,252].
4.2. Renin-Angiotensin System Related to SARS-CoV-2
As mentioned above, calcitriol is a crucial regulator of the RAS axis, mainly by suppressing renin gene expression, a rate-limiting step of synthesis of Ang-II via cyclic AMP (cAMP)-dependent PKA signaling [74]. Calcitriol independently increases the expression of ACE-2 [253], reducing Ang-II and increasing the generation of Ang(1–7), suppressing the formation of the CRE-CREB-CBP complex [70] and keeping RAS activity under control [197]. Meanwhile, certain viral infections like SARS-CoV-2 stimulate RAS activity, aggravating the situation in those with vitamin D deficiency and making them more vulnerable to developing cytokine storms [19,70].
SARS-CoV-2 infection in individuals with severe vitamin D deficiency markedly increases the vulnerability to severe complications, including ARDS and death [254,255,256]. Low ACE-2 concentrations in such persons [256] significantly increase the susceptibility to contract viruses, especially coronaviruses, and develop complications [117,146]. Once infected, coronaviruses indirectly increase the renin and, reduce ACE-2 and soluble ACE-2 concentrations. The latter is partly caused by the consumption (i.e., internalization and destruction) of membrane-bound ACE-2 receptors [59]. Low ACE-2, in conjunction with the over-activation of the RAS [55], leads to the excess and uncontrolled production of Ang-II, leading to severe adverse effects, increasing complications, the risk for cytokine storms, and death [19,197].
Also, widespread inflammation and oxidative stress injure the pulmonary epithelial and vascular endothelial cells and their basement membranes [257], impairing tight junction, with virus dissemination and fluid leakage into soft tissues [257], including the lungs, pericardium, intestine, and brain [29,196,258]. Pulmonary epithelial damage causes hypoxia and increases the risk of pneumonia and ARDS [69,70]. The endothelial abnormalities lead to micro-thrombosis, embolization, and intravascular thrombosis [259,260]. Figure 3 illustrates the RAS axis in a normal physiological state, in an activated state with hypovitaminosis D, and in the presence of SARS-CoV-2 infection [19].
4.3. Regulation of Inflammation by Vitamin D via the RAS
Vitamin D is a potent negative endocrine regulator in the RAS through a canonical pathway [197]. It achieves this by inhibiting the cAMP response element-binding protein (CREB), a key transcription factor for renin gene regulation [74]. Vitamin D-driven suppression of renin [261] reduces RAS activity, such as lowering the molecular expression of ACE-1 and Ang-II while increasing the expression of ACE-2 [74,253]. These effects have been observed both in vitro and in vivo [253]. Notably, low serum levels of 25(OH)D concentrations are inversely correlated with higher RAS activity, elevated plasma renin activity, and Ang-II levels, contributing to increased blood pressure [262,263,264].
In studies using LPS-exposed rat lung tissue, calcitriol increased ACE-2 expression while reducing renin, angiotensin II, ACE, and AT1 receptor expression. Additionally, calcitriol exhibited a dose-dependent reduction in the permeability of blood vessels in rat lungs, mitigating damage induced by LPS [253]. Activating the ACE-2/Ang(1–7)/MAS axis by vitamin D further enhances the production of ACE-2 and Ang(1–7) concentrations. Ang(1–7)’s vasodilatory, anti-inflammatory, and anti-thrombotic effects help counteract hypertension, inflammation, and coagulatory abnormalities [265,266,267] (Figure 2), mitigating the adverse effects caused by SARS-CoV-2 and its spike protein.
As previously mentioned, vitamin D also blocks viral entry, strengthens immunity by tightening epithelial gap junctions, and inhibits SARS-CoV-2 transcription enzymes [60], thereby combating the viral infection. Vitamin D deficiency over-activates the RAS with the inefficient counter-regulatory activation of ACE-2/angiotensin(1–7)/Mas axis [100,268], increasing the risks for ARDS [197]. Consequently, vitamin D deficiency is a critical component that exacerbates COVID-19 via the over-activation of RAS with an excess generation of Ang-II [197].
Vitamin D and inhibitors of ACE-1 and Ang receptor blockers work together indirectly to decrease renin synthesis, thereby reducing the activity of the RAS and the synthesis of Ang-I and Ang-II [66]. Angiotensin receptor blockers (ARBs) also inhibit the generation of Ang-II and decrease the stimulation of the angiotensin-1 (AT1) receptor. It has also been proposed that CD147/Basigin receptors bind to epitopes on the spike protein-S of the SARS-CoV virus, facilitating viral entry through endocytosis [269]. Therefore, blocking CD147/Basigin could be a target for drug development against COVID-19 [270]. Moreover, vitamin D indirectly regulates SARS-CoV-2 by modulating ACE-2 and CD147 receptor molecules [271,272]. The involvement of vitamin D and ACE-2 in modulating the SARS-CoV-2 virus [197] and its effects on the RAS are illustrated in Figure 4.
Figure 3 and Figure 4 illustrate multiple mechanisms of SARS-CoV-2 that dysregulated the RAS, reducing ACE-2 and increasing Ang-II levels. Vitamin D, ARBs, and ACEi can intervene positively in these cycles initiated by the virus, particularly in individuals with severe vitamin D deficiency. Vitamin D plays a crucial role by modulating the RAS, inhibiting Ang-II synthesis, and up-regulating ACE-2 expression, which mitigates the harmful effects of SARS-CoV-2 [19].
Ang-II type 1 receptor blockers (ARBs) and ACEi medications influence RAS, potentially mitigating the dysregulation caused by SARS-CoV-2 [19]. Together, these interventions aim to dampen the vicious cycles initiated by the virus, potentially reducing the morbidity and mortality associated with COVID-19, especially in individuals with severe vitamin D deficiency. ACEi and ARBs are indicated clinically in hypertension and other cardiovascular diseases [197]. These agents have been shown to mitigate acute lung injury by restoring the balance between two regulatory processes. Preclinical and clinical studies support the evidence of RAS disequilibrium in COVID-19 and the beneficial role of RAS modulation [252].
5. ACE-2 Receptor, Vitamin D, and SARS-CoV-2
Following the internalization of the ACE-2 receptor complex, the SARS-CoV-2 virus replicates using the host’s cell machinery [273]. The increased infectivity (R0) of SARS-CoV-2 and its mutants is attributed partly to the heightened affinity of their spike proteins to cell surface ACE-2 receptors [204,274]. Therefore, ACE-2 receptors are potential targets for drug development to prevent SARS-CoV-2 from entering human cells [249,251,274]. Respiratory tract epithelial cells co-expressing ACE-2 and TMPRSS2 molecules are the primary targets for SARS-CoV-2 entry [275]. The blockage of TMPRSS2 could serve as a molecular target for new drug development to prevent cellular access by COVID-19 [276].
5.1. Reduction in Viral Load through Soluble ACE-2
In the circulation, soluble ACE-2 molecules bind with SARS-CoV-2, facilitating the transport of these complexes to natural killer (NK) cells and macrophages for subsequent destruction [59]. This neutralization of viral particles prevents them from reaching membrane-bound ACE-2 receptors in lung and vascular epithelial cells. Since viruses cannot replicate outside of host cells, the binding of SARS-CoV-2 to soluble ACE-2 in the extra-cellular fluid could inhibit the replication of SARS-CoV-2 [37]. Contrary to earlier publications, the in vivo up-regulation of ACE-2 does not exacerbate but mitigates the effects of SARS-CoV-2 [277,278].
Studies have demonstrated that soluble ACE-2 can reduce viral load in vitro and protect against infection in preclinical models. For instance, Monteil et al. (2020) showed that human recombinant soluble ACE-2 (hrsACE-2) significantly blocked the initial stages of SARS-CoV-2 infections in engineered human blood vessels and kidney organoids, highlighting its potential as a therapeutic agent [104]. In contrast, some research indicated pharmacological strategies to reduce ACE-2 expression, such as using ursodeoxycholic acid (UDCA) to reduce the infection rate of SARS-CoV-2 by targeting the farnesoid X receptor (FXR) [279]. Further research is ongoing to evaluate the efficacy and safety of soluble ACE-2 in clinical settings, aiming to offer a novel approach to mitigate the impact of COVID-19 [59].
Nevertheless, it is notable that the levels of soluble ACE-2 required to inhibit SARS-CoV-2 infection may be above physiological levels. Soluble ACE-2 could enhance SARS-CoV-2 [19] infection at physiological levels by forming a complex with the virus that enters cells via endocytosis through the AT-1 surface receptor [280]. Overall evidence suggests that while increased ACE-1 activity may be harmful, the increased expression of ACE-2 is beneficial in controlling coronaviruses [281,282]. Therefore, discontinuing ACE inhibitors (ACEi) and angiotensin receptor blockers (ARBs) solely due to COVID-19 infection is not recommended [19].
5.2. Reduction Consequences of the Lower Expression of ACE-2
SARS-CoV-2 also reduces ACE-2 expression, further hampering protective physiological functions of the ACE-2/angiotensin(1–7)/Mas axis [100,268] and increasing the risk for pulmonary and cardiac complications [278]. Despite using the ACE-2 receptor by SARS-CoV-2 S-protein to enter human cells, up-regulation does not increase infection vulnerability [19,278]. Meanwhile, hypovitaminosis D not only reduces ACE-2 synthesis but also exacerbates the over-production of Ang-II [19,283]. These increase the risks for abnormal coagulation and interstitial fibrosis [93]. The increased Ang-II creates pathologic vasoconstriction, causing pulmonary hypertension (Figure 4), and liberates excessive amounts of harmful cytokines, causing systemic inflammation that could lead to cytokine storms [18].
Young children have been observed to have lower expression levels of ACE-2 in their nasal respiratory tract epithelia than adults [197]. Except for those who are immuno-suppressed or have hypovitaminosis D, children have stronger innate immunity. Consequently, they are less susceptible to severe COVID-19 complications [284]. The binding process of SARS-CoV-2 also involves cell-membrane-bound heparan sulfate proteoglycans (HSPG). Lactoferrin, a nutrient found in mammalian milk, interferes with SARS-CoV-2 binding to HSPG and membrane-bound ACE-2 receptors, potentially reducing coronaviruses’ entry into human host cells [285].
SARS-CoV-2 infection significantly reduces ACE-2 concentrations [197] and disrupts the balance of ACE-2/ACE-1 ratio. It, initiating a pathological cycle, worsens in those with hypovitaminosis D. Besides, SARS-CoV-2 up-regulates metallopeptidase ADAM17 molecules, enhancing the RAS activity and increasing the production of the inflammatogenic Ang-II [286,287,288,289,290].
Unlike epithelial and immune cells, erythrocytes and platelets lack membrane-bound ACE-2 receptors [291]. However, the interaction of the spike protein with CD147 receptors on platelets and erythrocyte membranes can lead to platelet aggregation and erythrocyte abnormalities [271,292]. SARS-CoV-2-induced damage to gap junctions and epithelial barriers results in the loss of epithelial cell integrity, which is aggravated in the presence of hypovitaminosis D. This damage also affects endothelin function, impairing gas exchange in pulmonary epithelia [100,248,276,277,278,284,285,293].
5.3. Restriction of Generic Medication Use and Conflicts of Interest
Despite the availability of over 200 independent RCTs and extensive data on vitamin D related to preventing and treating COVID-19 [200,294], regulatory agencies withheld approvals for the use of vitamin D [240,295]. Preventing access and use of widely available, cost-effective, generic agents, such as vitamin D and ivermectin [296,297], may have proven detrimental to patient welfare [193,242,295], increasing hospitalizations and deaths [48,132,240].
The actions mentioned above may have been driven by the desire to maintain Emergency Use Authorization (EUA) for vaccines and anti-viral agents [298]. However, this created a scenario for the approval and use of COVID-19 vaccines and anti-viral agents under (EUA), but not widely available, cost-effective generic agents like vitamin D and ivermectin [48,193,242,298]. The lack of approvals for repurposed early therapies may have harmed people [48,132,240,294,296,297,299].
5.4. ACE-2—A Double-Edged Sword in SARS-CoV-2 Infection
Whether ACEi and ARBs play a harmful or helpful role in COVID-19 remains controversial [300,301]. Theoretically, it has been suggested that increased ACE-2 membrane receptors on epithelial cells could potentially increase the cellular entry of SARS-CoV-2 [274,302]. Consequently, it has been hypothesized that the increased ACE-2 receptors resulting from ARBs, ACEi, or vitamin D up-regulation might enhance SARS-CoV-2 entry via epithelial cells [278], thereby increasing cellular infection [248]. However, subsequent data did not support the view that ACEi and ARBs increase the risk of SARS-CoV-2 infection or worsen COVID-19 outcomes [19,277,281].
Soluble ACE-2 can effectively mimic the membrane-bound ACE-2, to which the SARS-CoV-2 spike protein binds, sequestering the virus and inhibiting its ability to infect host cells. The excess synthesis of ACE-2 soluble receptor spills into the bloodstream, potentially decoying and removing the SARS-CoV-2 virus and reducing viral load. Because of the mechanisms of action, soluble ACE-2 has emerged as a potential therapeutic strategy to neutralize SARS-CoV-2 (and other coronaviruses) in the bloodstream by acting as a decoy that binds the virus and prevents it from entering human cells [19]. These findings collectively highlight the potential of soluble ACE-2 and other ACE-2-targeted therapies in mitigating the impact of COVID-19 [197].
5.5. Importance of Strengthening the Immune System to Overcome Infections
The body’s initial defense against invading pathogens is the innate immune system that relies on adequate vitamin D levels to function effectively [13,51]. Ensuring vitamin D sufficiency in the population is a quick and cost-effective approach to promoting a robust immune system. Additionally, optimal immune function requires other micronutrients, such as magnesium, zinc, omega-3 fatty acids, vitamin K2, resveratrol, and quercetin [67], and comprehensive mental and physical health support. By addressing these nutritional and lifestyle factors, individuals can enhance their immune response and overall well-being.
The mentioned approach would ensure the maintenance of a robust innate immune system by preserving sufficient vitamin D, calcitriol receptors (CTR/VDR), and CYP27B1 expression through the production of calcitriol within immune cells [5,105]. The combined deficiencies of vitamins D and K2 could elevate the risk of cardiovascular events, adverse cardiac remodeling [303], and mortality and add to all-cause mortality compared to individuals with adequate levels of both vitamins. Therefore, maintaining sufficient levels of both vitamins D and K is crucial for overall health and reducing the risk of adverse outcomes [304].
Strengthening the immune system is crucial for protecting against SARS-CoV-2 infections and reducing the risk of high viral loads, which can lead to viral mutations with greater infectivity and immune evasion capabilities [193,242]. Numerous RCTs and meta-analyses have concluded that vitamin D supplementation protects against acute respiratory tract infections, particularly in individuals with profound hypovitaminosis D [305]. Maintaining adequate physiological levels of 25(OH)D supports a robust immune function that reduces the risk of respiratory infections, including those caused by SARS-CoV-2 [189].
5.1. Reduction in Viral Load through Soluble ACE-2
In the circulation, soluble ACE-2 molecules bind with SARS-CoV-2, facilitating the transport of these complexes to natural killer (NK) cells and macrophages for subsequent destruction [59]. This neutralization of viral particles prevents them from reaching membrane-bound ACE-2 receptors in lung and vascular epithelial cells. Since viruses cannot replicate outside of host cells, the binding of SARS-CoV-2 to soluble ACE-2 in the extra-cellular fluid could inhibit the replication of SARS-CoV-2 [37]. Contrary to earlier publications, the in vivo up-regulation of ACE-2 does not exacerbate but mitigates the effects of SARS-CoV-2 [277,278].
Studies have demonstrated that soluble ACE-2 can reduce viral load in vitro and protect against infection in preclinical models. For instance, Monteil et al. (2020) showed that human recombinant soluble ACE-2 (hrsACE-2) significantly blocked the initial stages of SARS-CoV-2 infections in engineered human blood vessels and kidney organoids, highlighting its potential as a therapeutic agent [104]. In contrast, some research indicated pharmacological strategies to reduce ACE-2 expression, such as using ursodeoxycholic acid (UDCA) to reduce the infection rate of SARS-CoV-2 by targeting the farnesoid X receptor (FXR) [279]. Further research is ongoing to evaluate the efficacy and safety of soluble ACE-2 in clinical settings, aiming to offer a novel approach to mitigate the impact of COVID-19 [59].
Nevertheless, it is notable that the levels of soluble ACE-2 required to inhibit SARS-CoV-2 infection may be above physiological levels. Soluble ACE-2 could enhance SARS-CoV-2 [19] infection at physiological levels by forming a complex with the virus that enters cells via endocytosis through the AT-1 surface receptor [280]. Overall evidence suggests that while increased ACE-1 activity may be harmful, the increased expression of ACE-2 is beneficial in controlling coronaviruses [281,282]. Therefore, discontinuing ACE inhibitors (ACEi) and angiotensin receptor blockers (ARBs) solely due to COVID-19 infection is not recommended [19].
5.2. Reduction Consequences of the Lower Expression of ACE-2
SARS-CoV-2 also reduces ACE-2 expression, further hampering protective physiological functions of the ACE-2/angiotensin(1–7)/Mas axis [100,268] and increasing the risk for pulmonary and cardiac complications [278]. Despite using the ACE-2 receptor by SARS-CoV-2 S-protein to enter human cells, up-regulation does not increase infection vulnerability [19,278]. Meanwhile, hypovitaminosis D not only reduces ACE-2 synthesis but also exacerbates the over-production of Ang-II [19,283]. These increase the risks for abnormal coagulation and interstitial fibrosis [93]. The increased Ang-II creates pathologic vasoconstriction, causing pulmonary hypertension (Figure 4), and liberates excessive amounts of harmful cytokines, causing systemic inflammation that could lead to cytokine storms [18].
Young children have been observed to have lower expression levels of ACE-2 in their nasal respiratory tract epithelia than adults [197]. Except for those who are immuno-suppressed or have hypovitaminosis D, children have stronger innate immunity. Consequently, they are less susceptible to severe COVID-19 complications [284]. The binding process of SARS-CoV-2 also involves cell-membrane-bound heparan sulfate proteoglycans (HSPG). Lactoferrin, a nutrient found in mammalian milk, interferes with SARS-CoV-2 binding to HSPG and membrane-bound ACE-2 receptors, potentially reducing coronaviruses’ entry into human host cells [285].
SARS-CoV-2 infection significantly reduces ACE-2 concentrations [197] and disrupts the balance of ACE-2/ACE-1 ratio. It, initiating a pathological cycle, worsens in those with hypovitaminosis D. Besides, SARS-CoV-2 up-regulates metallopeptidase ADAM17 molecules, enhancing the RAS activity and increasing the production of the inflammatogenic Ang-II [286,287,288,289,290].
Unlike epithelial and immune cells, erythrocytes and platelets lack membrane-bound ACE-2 receptors [291]. However, the interaction of the spike protein with CD147 receptors on platelets and erythrocyte membranes can lead to platelet aggregation and erythrocyte abnormalities [271,292]. SARS-CoV-2-induced damage to gap junctions and epithelial barriers results in the loss of epithelial cell integrity, which is aggravated in the presence of hypovitaminosis D. This damage also affects endothelin function, impairing gas exchange in pulmonary epithelia [100,248,276,277,278,284,285,293].
5.3. Restriction of Generic Medication Use and Conflicts of Interest
Despite the availability of over 200 independent RCTs and extensive data on vitamin D related to preventing and treating COVID-19 [200,294], regulatory agencies withheld approvals for the use of vitamin D [240,295]. Preventing access and use of widely available, cost-effective, generic agents, such as vitamin D and ivermectin [296,297], may have proven detrimental to patient welfare [193,242,295], increasing hospitalizations and deaths [48,132,240].
The actions mentioned above may have been driven by the desire to maintain Emergency Use Authorization (EUA) for vaccines and anti-viral agents [298]. However, this created a scenario for the approval and use of COVID-19 vaccines and anti-viral agents under (EUA), but not widely available, cost-effective generic agents like vitamin D and ivermectin [48,193,242,298]. The lack of approvals for repurposed early therapies may have harmed people [48,132,240,294,296,297,299].
5.4. ACE-2—A Double-Edged Sword in SARS-CoV-2 Infection
Whether ACEi and ARBs play a harmful or helpful role in COVID-19 remains controversial [300,301]. Theoretically, it has been suggested that increased ACE-2 membrane receptors on epithelial cells could potentially increase the cellular entry of SARS-CoV-2 [274,302]. Consequently, it has been hypothesized that the increased ACE-2 receptors resulting from ARBs, ACEi, or vitamin D up-regulation might enhance SARS-CoV-2 entry via epithelial cells [278], thereby increasing cellular infection [248]. However, subsequent data did not support the view that ACEi and ARBs increase the risk of SARS-CoV-2 infection or worsen COVID-19 outcomes [19,277,281].
Soluble ACE-2 can effectively mimic the membrane-bound ACE-2, to which the SARS-CoV-2 spike protein binds, sequestering the virus and inhibiting its ability to infect host cells. The excess synthesis of ACE-2 soluble receptor spills into the bloodstream, potentially decoying and removing the SARS-CoV-2 virus and reducing viral load. Because of the mechanisms of action, soluble ACE-2 has emerged as a potential therapeutic strategy to neutralize SARS-CoV-2 (and other coronaviruses) in the bloodstream by acting as a decoy that binds the virus and prevents it from entering human cells [19]. These findings collectively highlight the potential of soluble ACE-2 and other ACE-2-targeted therapies in mitigating the impact of COVID-19 [197].
5.5. Importance of Strengthening the Immune System to Overcome Infections
The body’s initial defense against invading pathogens is the innate immune system that relies on adequate vitamin D levels to function effectively [13,51]. Ensuring vitamin D sufficiency in the population is a quick and cost-effective approach to promoting a robust immune system. Additionally, optimal immune function requires other micronutrients, such as magnesium, zinc, omega-3 fatty acids, vitamin K2, resveratrol, and quercetin [67], and comprehensive mental and physical health support. By addressing these nutritional and lifestyle factors, individuals can enhance their immune response and overall well-being.
The mentioned approach would ensure the maintenance of a robust innate immune system by preserving sufficient vitamin D, calcitriol receptors (CTR/VDR), and CYP27B1 expression through the production of calcitriol within immune cells [5,105]. The combined deficiencies of vitamins D and K2 could elevate the risk of cardiovascular events, adverse cardiac remodeling [303], and mortality and add to all-cause mortality compared to individuals with adequate levels of both vitamins. Therefore, maintaining sufficient levels of both vitamins D and K is crucial for overall health and reducing the risk of adverse outcomes [304].
Strengthening the immune system is crucial for protecting against SARS-CoV-2 infections and reducing the risk of high viral loads, which can lead to viral mutations with greater infectivity and immune evasion capabilities [193,242]. Numerous RCTs and meta-analyses have concluded that vitamin D supplementation protects against acute respiratory tract infections, particularly in individuals with profound hypovitaminosis D [305]. Maintaining adequate physiological levels of 25(OH)D supports a robust immune function that reduces the risk of respiratory infections, including those caused by SARS-CoV-2 [189].
6. Discussion
Numerous studies have explored the complex interactions between vitamin D and the immune system. However, it is essential to note that this article has focused on highlighting selected critically relevant studies. As previously mentioned, vitamin D is crucial in modulating and strengthening innate and adaptive immune systems. It achieves this by up-regulating or down-regulating the transcription of target genes [12] through the calcitriol receptor (CTR). Additionally, vitamin D prevents the weakening of epithelial cell barriers [190], induces the expression of antimicrobial peptides such as cathelicidin, and up-regulates MKP-1 to inhibit inflammatory cytokines [306]. Furthermore, in conjunction with interleukin-2, vitamin D promotes T cell regulation, further contributing to immune system modulation [194].
Strengthening the immune system protects against SARS-CoV-2 infections and greater viral loads, preventing the survival of viral mutations with greater infectivity and immune evasion capabilities. In addition to these general immune benefits, vitamin D and lumisterol inhibit SARS-CoV-2 transcription enzymes, reducing viral replication and infection [307]. Studies, including meta-analyses, have concluded that vitamin D supplementation protects from acute respiratory tract infection, especially in those with profound hypovitaminosis D [307].
Vitamin D counteracts the vasoconstriction, pulmonary hypertension, coagulation, and interstitial fibrosis caused by SARS-CoV-2 by inhibiting the transcription factor CREB to down-regulate renin [93], the rate-controlling step of the RAS system, resulting in reduced Ang-II production [66]. Secondly, vitamin D up-regulates ACE-2 [253], which counteracts Ang II. Converting Ang II into Ang(1–7) by ACE-2 makes less Ang-II available. This reduces Ang-II levels and causes the production of Ang(1–7), which, via its MAS receptor, exhibits vasodilatory, anti-inflammatory, and anti-thrombotic effects to prevent pulmonary injuries.
Vitamin D enhances the expression of ACE-2, but some have raised concerns that this could exacerbate the disease caused by SARS-CoV-2, as the virus exploits membrane-bound ACE-2 to invade epithelial cells [274,308,309,310]. However, as described above, this potential risk is mitigated by the excess synthesis of ACE-2 molecules, which spill over into the bloodstream as ACE-2-soluble decoy receptors. These soluble ACE-2 receptors bind to SARS-CoV-2 viruses and escort them to natural killer cells and macrophages for destruction [256]. This process has been confirmed in in vitro systems [104]. If this also occurs in vivo, this process will help reduce infection and viral load, minimizing complications and deaths from SARS-CoV-2 [19,286,311,312].
Therefore, rather than the associated risk of facilitating an increased entry of SARS-CoV-2 viruses, the up-regulation of ACE-2 by vitamin D sufficiency may reduce viral load, thus mitigating the effects of SARS-CoV-2 and decreasing the risks of complications from SARS-CoV-2 [259,313,314]. However, further investigation is required to gain an in-depth understanding and confirm if soluble ACE-2 is inhibitory at physiological levels [280]. These anti-viral processes are enhanced by having sufficient cofactors like magnesium, iodine, and selenium, which help maintain tight cell junctions in epithelial cells [198]. Vitamin D-mediated tight gap-junctions help to preserve cellular function and prevent excessive fluid diffusion and viral spread across cell membranes [59].
ACE-inhibiting enzymes and ARBs can prove beneficial by reducing Ang-II production and AT1 receptor activation and up-regulating soluble ACE-2 [277,281]. However, we note limited studies on the direct relationship between vitamin D and SARS-CoV-2 replication. In conclusion, overall data support that vitamin D sufficiency, ACE inhibitors, and ARBs reduce the risk of COVID-19, associated complications, and deaths [19,259,286,313].
7. Conclusions
The authors recommend ensuring the vitamin D sufficiency of patients with supplementation to rapidly raise and maintain serum 25(OH)D concentrations above 50 ng/mL for a robust immune system to protect against coronavirus [51], especially COVID-19, and other viral diseases (e.g., dengue) to mitigate morbidity/complications and mortality. Vitamin D is required for effective innate and adaptive immune system function and counteracts the pathological effects of over-stimulated RAS by SARS-CoV-2. Its beneficial actions include lowering renin and up-regulating ACE-2, which lowers Ang II and increases vasodilator Ang(1–7). The latter also has anti-inflammatory and anti-coagulant properties to mitigate the harmful effects of SARS-CoV-2, especially in the lungs and vascular system—vitamin D’s upregulation of soluble ACE-2 assists in the elimination of SARS-CoV-2, thus reducing viremia. ACEi and ARBs appear to contribute to partly relieving the adverse effects of SARS-CoV-2. |
Title: Establishment and Characterization of Three Human Ocular Adnexal Sebaceous Carcinoma Cell Lines | Body:
1. Introduction
Sebaceous carcinomas (SebCA) of the ocular adnexa most frequently arise from the Meibomian glands, commonly exhibit aggressive phenotypes, and complicate efforts for complete surgical resection, resulting in local invasion, tumor recurrence, and distant metastases [1,2,3,4,5,6]. SebCAs are rare, yet comprise approximately 5% of malignant epithelial eyelid tumors, with an incidence of 5 per 100,000 persons [7,8,9,10]. Several studies have identified mutations in tumor suppressor complexes and have correlated tumorigenesis with malignant transformation by high-risk human papillomavirus (HPV) [11,12,13,14,15,16,17,18,19]. Other investigators have associated ocular adnexal SebCA with concurrent visceral malignancies in patients with Muir–Torre Syndrome, characterized by microsatellite instability and autosomal dominant loss-of-function mutations in genes encoding mismatch repair proteins, although this is more common in extraocular SebCA [20,21,22,23,24,25,26]. Further, we have demonstrated an increased copy number at the MYC locus as well as high MYC protein levels in a subset of ocular adnexal SebCA [27]. However, the low incidence of ocular adnexal SebCA in the US and the generally fragmented nature of most biopsy specimens represent obstacles to accumulating large cohorts of tumors with excess tissue sufficient for molecular analyses, and, as a result, the drivers of SebCA remain incompletely understood [28].
While increased surveillance has led to improved patient survival, therapeutic advancement has not exhibited the same progress, and effective medical interventions for locally invasive, recurrent, and metastatic disease are lacking [29]. Orbital exenteration is ultimately performed in 13–23% of cases to achieve local control, with regional (nodal) or distant metastasis occurring in 8% of cases and disease-associated mortality occurring in 6–10% of cases [1,30,31,32]. The primary factor limiting our understanding of SebCA biology and the development of precision therapies is the lack of reliable models in which to evaluate tumorigenesis and the response to therapeutic intervention. Research on sebaceous malignancies has historically relied upon immortalized human sebocyte cultures with limited success, but, recently, two cell lines derived from human ocular adnexal SebCA were established at another institution and used for in vitro testing [29,33,34,35,36]. Another group has used short-term primary cultures of eyelid SebCA for expression analysis and testing [37]. Here, we describe the molecular and genomic features, cellular behavior, and therapeutic response of three new additional primary ocular adnexal SebCA cell lines with a correlation to the patient tumors from which they were derived. We use these models to begin testing the potential of MYC, and the neoplastic changes it promotes, as a target for therapy in SebCA.
2. Results
2.1. Clinical and Microscopic Characterization of Sebaceous Carcinoma Specimens
The SebCA tumoral tissues used to generate the JHH-SebCA01, JHH-SebCA02, and JHH-SebCA03 cell lines were derived from patients undergoing the initial surgical resection of their primary tumors. None had chemo- or radiation therapy to the carcinoma prior to this resection. JHH-SebCA01 was derived from a 58-year-old female with a left upper eyelid poorly differentiated SebCA that was surgically resected using frozen sections with clear margins and negative MAP biopsies. Two years after surgery, she was diagnosed with a glioblastoma multiforme and is currently on chemotherapy, immunotherapy, and high-dose steroids. JHH-SebCA02 was derived from a 67-year-old male with a recurrent right upper eyelid SebCA that was resected with clear margins on frozen sections; yet, it ultimately, demonstrated growth within the orbit and metastasis to the lymph nodes within the neck requiring exenteration and neck lymph node dissection. Following radiation and six weeks of cisplatin therapy, there was no evidence of recurrence at the last follow-up, 10 months after surgery. JHH-SebCA03 was derived from a 69-year-old female with a right upper eyelid poorly differentiated SebCA that was resected with clear margins on frozen sections and negative MAP biopsies. At the most recent recheck, 13 months postoperatively, there was no evidence of recurrence.
All three surgical specimens were analyzed microscopically and immunohistochemically. Hematoxylin and eosin staining revealed classical morphological characteristics of SebCA in all three cases (Figure 1A). Large nodules of a somewhat basophilic tumor were present in the dermis, and a subset of larger nodules showed comedo-necrosis. With respect to the intraepithelial extension of the carcinoma, the SebCA03 specimen revealed regions with tumor spread within the palpebral conjunctiva. The peripheral palisading and clefting characteristics of basal cell carcinoma were absent. As shown in the lower-magnification SebCA02 image, stromal inflammation was present around some tumor nodules. At higher magnification, the tumor cells showed large, pleomorphic nuclei and abundant mitotic figures and apoptotic bodies. At least focally in each case, the tumor cell cytoplasm contained small- to medium-sized vacuoles characteristic of sebaceous differentiation. On immunohistochemical stains, adipophilin staining around these vacuoles aided in confirming sebaceous differentiation in all three tumors (Figure 1A) [29]. Additional immunohistochemical stains included Ki67, showing a markedly increased proliferation, and all three cases were positive for p53 and p16 (Figure 1A). High-risk HPV testing was not performed in any of the three cases.
2.2. Establishment and Molecular Characterization of Sebaceous Carcinoma Cell Lines
Resection specimens were taken fresh from the operating room to pathology, where they were dissected under sterile conditions, and abnormal tissue, grossly consistent with carcinoma, was placed in media. Cells were then dissociated and serially passaged in conditional reprogramming cell (CRC) media. After a few (2 or 3) passages, all three cell lines began to exhibit a uniform appearance and stable growth characteristics, which were maintained throughout the subsequent passages. All three lines grew in an adherent fashion, with a morphology similar to that of the SebCA cell line BP50 previously described by another group (Figure 1B) [29]. The cells were spindled with short processes, and variable numbers of vacuoles were noted in their cytoplasm (Figure 1B insets). In order to confirm that the established lines came from their corresponding primary tumors, and that no contamination had taken place, short tandem repeat (STR) analysis was performed after 12 passages. As shown in Table 1, the STR profiles of the cell lines all matched those of the patient samples from which they were derived. The highest passage number to date for each cell line is as follows: SebCA01 (passage 24), SebCA02 (passage 25), and SebCA03 (passage 28). At the last passage, the cells were all still growing well in CRC media.
DNA alterations were also characterized in the cell lines using next-generation sequencing (NGS). In SebCA01 and SebCA02, we were able to compare mutations and other changes in the cell lines to those in the surgical specimen from which it was derived. However, for SebCA03, an insufficient amount of the tumor remained after the initial clinical work-up for NGS analysis. In SebCA01, cells’ alterations in the oncogenes ECTR2L, BCORL1, BRD4, and ERBB4 were shared with the surgical specimen, although the variant allele frequencies (VAF) tended to be higher in the cultured cells (Table 2). In contrast, a TP53 deletion detected in the primary tumor was not present in the SebCA01 cells, while they contained a BRCA alteration not identified in the primary tumor. In SebCA02, POLD1, DNM2, EPHA3, HISTH1D, HISTH2BO, RUNX1, ZNF703, and RAD51D alterations were also present in the primary tumor, while two ZFHX4 alterations were only identified in the cell line, and TP53 and MKI67 changes present in the primary tumors were not detected in the corresponding cultures. Finally, CBLB, CHD1, MAP3K14, MST1R, SPEN, and SYNE1 alterations were identified in the SebCA03 line. Paired, non-neoplastic tissue was not sequenced in the three patients; thus, it is not clear whether the molecular alterations, particularly those with a VAF near 50%, are germline. No copy number variations were identified at any loci evaluated with our NGS panel.
2.3. Characterization of Sebaceous Carcinoma Cell Line Growth and Differentiation
To evaluate the expression of differentiation and stem cell markers in cultured cells, protein extracts were analyzed on Western blots. All three SebCA lines expressed adipophilin protein, which is expressed in association with lipid vacuoles in more differentiated tumor cells, while two established medulloblastoma lines (D283 and D425) lacking sebaceous differentiation were negative (Figure 2A,B). Previous research showed that the MYC locus is amplified in a subset of SebCA [27]. Ectopic MYC expression in the skin can also drive the proliferation of adnexal epithelial cells and the enlargement of sebaceous glands [38]. MYC protein expression was, therefore, also assessed in our SebCA cell lines. Assessing the MYC expression in cultured non-neoplastic sebaceous tissue was not possible, and we, therefore, focused on comparing the expression in our SebCA cells to established cancer lines in which MYC was known to be highly expressed and functionally significant. A subset of Group 3 medulloblastoma highly express MYC protein, and two medulloblastoma lines (D283 and D425), reported to show an elevated MYC expression, were used as positive controls [39,40].
Our SebCA01, 02, and 03 cells all showed MYC protein expression (after normalization to loading using actin) similar to the expression in D425 medulloblastoma, and approximately twice that in D283 cells (Figure 2A,B). The observed differences in the molecular weight of MYC between the medulloblastoma and SebCA lines is likely a consequence of post-translational modifications and cleavages [41].
Next, we examined the overall growth in vitro, as well as the capacity for colony formation of the SebCA lines. All three lines showed robust growth at passage 15 in CRC media, with a doubling time of two to three days, although SebCA03 grew slightly faster than the other two lines (Figure 2C). We also assessed the growth in high serum media, but, while the three lines initially proliferated well under these conditions, over several weeks, they could no longer be passaged. Finally, we examined the clonogenic capacity of the three new SebCA lines. After seeding at a single cell density, anchorage-dependent colonies formed in all three lines, although these were somewhat irregular in shape, and some had a slightly dispersed morphology (Figure 2D). No colonies formed in the anchorage-independent assay using soft agar, and we did not attempt to grow organoids.
2.4. Response of Sebaceous Carcinoma Lines to Chemotherapy
Mitomycin-C (MMC) and 5-fluorurocil (5-FU) are two chemotherapeutic agents used for the topical treatment of SebCA [36]. Therefore, we investigated whether the growth in viable cells of the three carcinoma lines was inhibited over several days in vitro when these compounds were added to the media. MMC and 5-FU treatments both significantly inhibited cell growth in a dose-dependent manner compared to controls (Figure 3A,C). Mitomycin showed 60% or greater decreases in cell mass growth at concentrations of 1 μM or higher, while 5 μM 5-FU inhibited the growth of SebCA02 and SebCA03 by almost 50%. In SebCA01, 5-FU was slightly less effective, but still significantly slowed growth. The EC50 values for MMC and 5-FU are presented in Table 3.
We also examined if the inhibition of growth was at least in part due to the increased apoptotic death of tumor cells by measuring the cleaved PARP protein expression. The increased expression of cleaved PARP was seen in all lines treated with either MMC or 5-FU as compared to controls (Figure 3B,D). Because chemotherapies can target tumor growth by multiple mechanisms, we also measured the adipophilin expression in treated cultures. Marked increases in adipophilin were seen in all three lines after MMC treatment (Figure 4A). In contrast, 5-FU increased adipophilin expression only in SebCA01 and SebCA03, while a decrease was seen in SebCA02 (Figure 4B). Overall, this suggests that both MMC and 5-FU treatment can promote the sebaceous differentiation of these carcinoma cells, leading to the increased expression of adipophilin.
2.5. Ongoing Dependence of Sebaceous Carcinoma on Metabolic Changes Associated with MYC
Previous research has shown that MYC regulates glutamine metabolism in various types of cancers, and drugs targeting these metabolic alterations have shown clinical potential [42,43]. As shown in Figure 2A,B, all our SebCA cell lines exhibited a high expression of the MYC protein. Consequently, we assessed growth in these cell lines following treatment with the glutamine analog 6-Diazo-5-oxo-L-norleucine (DON), which binds to glutamine active sites on enzymes and selectively inactivates reactions utilizing glutamine. The viable cell mass was significantly inhibited by DON treatment in our three SebCA cell lines in a dose-dependent manner compared to the control (Figure 5).
2.1. Clinical and Microscopic Characterization of Sebaceous Carcinoma Specimens
The SebCA tumoral tissues used to generate the JHH-SebCA01, JHH-SebCA02, and JHH-SebCA03 cell lines were derived from patients undergoing the initial surgical resection of their primary tumors. None had chemo- or radiation therapy to the carcinoma prior to this resection. JHH-SebCA01 was derived from a 58-year-old female with a left upper eyelid poorly differentiated SebCA that was surgically resected using frozen sections with clear margins and negative MAP biopsies. Two years after surgery, she was diagnosed with a glioblastoma multiforme and is currently on chemotherapy, immunotherapy, and high-dose steroids. JHH-SebCA02 was derived from a 67-year-old male with a recurrent right upper eyelid SebCA that was resected with clear margins on frozen sections; yet, it ultimately, demonstrated growth within the orbit and metastasis to the lymph nodes within the neck requiring exenteration and neck lymph node dissection. Following radiation and six weeks of cisplatin therapy, there was no evidence of recurrence at the last follow-up, 10 months after surgery. JHH-SebCA03 was derived from a 69-year-old female with a right upper eyelid poorly differentiated SebCA that was resected with clear margins on frozen sections and negative MAP biopsies. At the most recent recheck, 13 months postoperatively, there was no evidence of recurrence.
All three surgical specimens were analyzed microscopically and immunohistochemically. Hematoxylin and eosin staining revealed classical morphological characteristics of SebCA in all three cases (Figure 1A). Large nodules of a somewhat basophilic tumor were present in the dermis, and a subset of larger nodules showed comedo-necrosis. With respect to the intraepithelial extension of the carcinoma, the SebCA03 specimen revealed regions with tumor spread within the palpebral conjunctiva. The peripheral palisading and clefting characteristics of basal cell carcinoma were absent. As shown in the lower-magnification SebCA02 image, stromal inflammation was present around some tumor nodules. At higher magnification, the tumor cells showed large, pleomorphic nuclei and abundant mitotic figures and apoptotic bodies. At least focally in each case, the tumor cell cytoplasm contained small- to medium-sized vacuoles characteristic of sebaceous differentiation. On immunohistochemical stains, adipophilin staining around these vacuoles aided in confirming sebaceous differentiation in all three tumors (Figure 1A) [29]. Additional immunohistochemical stains included Ki67, showing a markedly increased proliferation, and all three cases were positive for p53 and p16 (Figure 1A). High-risk HPV testing was not performed in any of the three cases.
2.2. Establishment and Molecular Characterization of Sebaceous Carcinoma Cell Lines
Resection specimens were taken fresh from the operating room to pathology, where they were dissected under sterile conditions, and abnormal tissue, grossly consistent with carcinoma, was placed in media. Cells were then dissociated and serially passaged in conditional reprogramming cell (CRC) media. After a few (2 or 3) passages, all three cell lines began to exhibit a uniform appearance and stable growth characteristics, which were maintained throughout the subsequent passages. All three lines grew in an adherent fashion, with a morphology similar to that of the SebCA cell line BP50 previously described by another group (Figure 1B) [29]. The cells were spindled with short processes, and variable numbers of vacuoles were noted in their cytoplasm (Figure 1B insets). In order to confirm that the established lines came from their corresponding primary tumors, and that no contamination had taken place, short tandem repeat (STR) analysis was performed after 12 passages. As shown in Table 1, the STR profiles of the cell lines all matched those of the patient samples from which they were derived. The highest passage number to date for each cell line is as follows: SebCA01 (passage 24), SebCA02 (passage 25), and SebCA03 (passage 28). At the last passage, the cells were all still growing well in CRC media.
DNA alterations were also characterized in the cell lines using next-generation sequencing (NGS). In SebCA01 and SebCA02, we were able to compare mutations and other changes in the cell lines to those in the surgical specimen from which it was derived. However, for SebCA03, an insufficient amount of the tumor remained after the initial clinical work-up for NGS analysis. In SebCA01, cells’ alterations in the oncogenes ECTR2L, BCORL1, BRD4, and ERBB4 were shared with the surgical specimen, although the variant allele frequencies (VAF) tended to be higher in the cultured cells (Table 2). In contrast, a TP53 deletion detected in the primary tumor was not present in the SebCA01 cells, while they contained a BRCA alteration not identified in the primary tumor. In SebCA02, POLD1, DNM2, EPHA3, HISTH1D, HISTH2BO, RUNX1, ZNF703, and RAD51D alterations were also present in the primary tumor, while two ZFHX4 alterations were only identified in the cell line, and TP53 and MKI67 changes present in the primary tumors were not detected in the corresponding cultures. Finally, CBLB, CHD1, MAP3K14, MST1R, SPEN, and SYNE1 alterations were identified in the SebCA03 line. Paired, non-neoplastic tissue was not sequenced in the three patients; thus, it is not clear whether the molecular alterations, particularly those with a VAF near 50%, are germline. No copy number variations were identified at any loci evaluated with our NGS panel.
2.3. Characterization of Sebaceous Carcinoma Cell Line Growth and Differentiation
To evaluate the expression of differentiation and stem cell markers in cultured cells, protein extracts were analyzed on Western blots. All three SebCA lines expressed adipophilin protein, which is expressed in association with lipid vacuoles in more differentiated tumor cells, while two established medulloblastoma lines (D283 and D425) lacking sebaceous differentiation were negative (Figure 2A,B). Previous research showed that the MYC locus is amplified in a subset of SebCA [27]. Ectopic MYC expression in the skin can also drive the proliferation of adnexal epithelial cells and the enlargement of sebaceous glands [38]. MYC protein expression was, therefore, also assessed in our SebCA cell lines. Assessing the MYC expression in cultured non-neoplastic sebaceous tissue was not possible, and we, therefore, focused on comparing the expression in our SebCA cells to established cancer lines in which MYC was known to be highly expressed and functionally significant. A subset of Group 3 medulloblastoma highly express MYC protein, and two medulloblastoma lines (D283 and D425), reported to show an elevated MYC expression, were used as positive controls [39,40].
Our SebCA01, 02, and 03 cells all showed MYC protein expression (after normalization to loading using actin) similar to the expression in D425 medulloblastoma, and approximately twice that in D283 cells (Figure 2A,B). The observed differences in the molecular weight of MYC between the medulloblastoma and SebCA lines is likely a consequence of post-translational modifications and cleavages [41].
Next, we examined the overall growth in vitro, as well as the capacity for colony formation of the SebCA lines. All three lines showed robust growth at passage 15 in CRC media, with a doubling time of two to three days, although SebCA03 grew slightly faster than the other two lines (Figure 2C). We also assessed the growth in high serum media, but, while the three lines initially proliferated well under these conditions, over several weeks, they could no longer be passaged. Finally, we examined the clonogenic capacity of the three new SebCA lines. After seeding at a single cell density, anchorage-dependent colonies formed in all three lines, although these were somewhat irregular in shape, and some had a slightly dispersed morphology (Figure 2D). No colonies formed in the anchorage-independent assay using soft agar, and we did not attempt to grow organoids.
2.4. Response of Sebaceous Carcinoma Lines to Chemotherapy
Mitomycin-C (MMC) and 5-fluorurocil (5-FU) are two chemotherapeutic agents used for the topical treatment of SebCA [36]. Therefore, we investigated whether the growth in viable cells of the three carcinoma lines was inhibited over several days in vitro when these compounds were added to the media. MMC and 5-FU treatments both significantly inhibited cell growth in a dose-dependent manner compared to controls (Figure 3A,C). Mitomycin showed 60% or greater decreases in cell mass growth at concentrations of 1 μM or higher, while 5 μM 5-FU inhibited the growth of SebCA02 and SebCA03 by almost 50%. In SebCA01, 5-FU was slightly less effective, but still significantly slowed growth. The EC50 values for MMC and 5-FU are presented in Table 3.
We also examined if the inhibition of growth was at least in part due to the increased apoptotic death of tumor cells by measuring the cleaved PARP protein expression. The increased expression of cleaved PARP was seen in all lines treated with either MMC or 5-FU as compared to controls (Figure 3B,D). Because chemotherapies can target tumor growth by multiple mechanisms, we also measured the adipophilin expression in treated cultures. Marked increases in adipophilin were seen in all three lines after MMC treatment (Figure 4A). In contrast, 5-FU increased adipophilin expression only in SebCA01 and SebCA03, while a decrease was seen in SebCA02 (Figure 4B). Overall, this suggests that both MMC and 5-FU treatment can promote the sebaceous differentiation of these carcinoma cells, leading to the increased expression of adipophilin.
2.5. Ongoing Dependence of Sebaceous Carcinoma on Metabolic Changes Associated with MYC
Previous research has shown that MYC regulates glutamine metabolism in various types of cancers, and drugs targeting these metabolic alterations have shown clinical potential [42,43]. As shown in Figure 2A,B, all our SebCA cell lines exhibited a high expression of the MYC protein. Consequently, we assessed growth in these cell lines following treatment with the glutamine analog 6-Diazo-5-oxo-L-norleucine (DON), which binds to glutamine active sites on enzymes and selectively inactivates reactions utilizing glutamine. The viable cell mass was significantly inhibited by DON treatment in our three SebCA cell lines in a dose-dependent manner compared to the control (Figure 5).
3. Discussion
Sebaceous carcinoma represents a rare, yet aggressive, tumor of the ocular adnexa that is clinically problematic given its propensity to spread intraepithelially and metastasize, leading to highly morbid surgery and, in rare cases, death [44]. Current therapies for ocular adnexal SebCA that can minimize the need for exenteration, including cryotherapy, radiation, and topical chemotherapy, such as MMC and interferon-alpha, show only limited efficacy [8,45]. In an effort to improve therapies for this patient population, we have sought to better understand the molecular drivers of this tumor, focusing on potentially targetable alterations.
In this study, we were able to establish three cell lines from ocular adnexal SebCA resected at the Wilmer Eye Institute using conditional reprogramming cell techniques, designated JHH-SebCA01, JHH-SebCA02, and JHH-SebCA03. These represent relatively novel cell lines, as only two other institutions have reported the establishment of cell lines for these tumors [29,36,46]. While the three previously reported cell lines were grown in high serum media, in our study, we used specialized “conditional reprogramming” culture techniques with media which are optimized for senescence-prone epithelial cells and tumors, first described by Liu et al. [47]. The conditional reprogramming cell technique has been modified in our laboratory, and used to generate other cell lines including low-grade gliomas of the optic pathways and brain [48]. In brief, this involves adding conditioned media from irradiated fibroblasts as well as a ROCK inhibitor to media lacking serum.
Molecular testing was performed using STR analysis and NGS to confirm that each cell line genetically reflected the tumor from which it was derived. These results show a good correlation overall between genetic alterations in the original tumors and the corresponding cell lines. Interestingly, TP53 alterations were identified in the tumors giving rise to both SebCA01 (VAF 17.6%) and SebCA02 (VAF 73.6%), but not in the paired cell cultures; thus, based on our NGS data, none of our three lines are TP53 mutants. This is similar to the findings of Rong et al., where TP53 alterations were also lost in their cell line as compared to the surgical specimen [29]. They believed that this was likely secondary to intratumoral genetic heterogeneity, and it is possible the TP53 mutant tumor subclone in the primary tumors was poorly represented in the portions of the surgical specimens used to generate our cell lines. Interestingly, the eyelid SebCA line reported by Gu and colleagues maintained a TP53 mutation in culture [46]. It is not clear why this group succeeded in maintaining TP53 alterations in their cells while ours and Rong et al. could not. The Gu and Rong studies both used similar high-serum conditions, making it less likely that serum played an important role in the ability to maintain TP53 alterations in culture. Either dominant negative constructs or deletion using CRISPR could be used in the future to assess the effects of p53 loss in our SebCA lines.
In terms of how the mutations present in the cell lines correlate with those previously reported in SebCA, several alterations reported here have been documented in tumoral tissue with the NGS and whole-exome sequencing approaches. Alterations at the BRCA1 and BRCA2 locus have been previously reported in 2/13 (15%) of ocular adnexal SebCAs evaluated by NGS with the mutations observed here, in the SebCA01 cell line [27]. This same study reported a missense mutation at the POLD1 locus in 1/13 tumors sequenced, and our current investigation similarly confirmed a POLD1 alteration in SebCA02 [27]. Mutations affecting members of the ERBB receptor tyrosine kinase family, including ERBB2, have been demonstrated in a cohort of SebCA, with an alteration at the ERBB4 locus occurring in SebCA01 in the current study [49]. A deletion at the SPEN locus was observed in SebCA03, and missense mutations at this same locus have been previously observed in 2/29 ocular adnexal SebCA [12]. Interestingly, mutations in SPEN, a transcriptional suppressor, have been previously reported in epithelial neoplasms of the breast, colon, ovary, and nasopharynx [50,51,52]. However, the clinical impact of these and the other alterations detected in our SebCA lines has not yet been elucidated, and many are worth studying further in order to determine their functional importance. Further, it is not entirely clear what sort of gain-of-function, loss-of-function, or neomorphic effects the alterations we have identified here will have. Finally, high-risk HPV can play a role in both SebCA and head and neck carcinomas, but the presence of this virus has not been determined in our cells [19,53].
Current topical therapies that are used for ocular adnexal SebCA, including MMC and 5-FU, demonstrated a dose-dependent response in all three cell lines. Two prior studies evaluating the SebCA cell line response to MMC demonstrated similar low micromolar dose-dependent reductions in viability to our three lines; however, 5-FU was effective at a somewhat lower micromolar concentration in our lines as compared to previous reports [36,46]. It is not clear if these differences in sensitivity to 5-FU were due to intrinsic properties of these tumors, or if the CRC media used played a role. Interestingly, adipophilin protein, which we used as a surrogate marker for sebaceous differentiation, showed an increased signal intensity on the Western blot after treatment, suggesting that at least a subset of cells surviving chemotherapy are better differentiated [54].
One of the most interesting findings of possible clinical importance from our prior study was the amplification of the MYC locus in a subset of cases [27]. Increased MYC activity has been shown in proliferating keratinocytes, and its role in physiologic epithelial differentiation is well-documented [55,56]. Additionally, diffuse MYC immunolabeling has been demonstrated in eyelid SebCA [57]. In this study, we, therefore, further investigated the pathogenic role and ongoing requirement of MYC using our new lines. While we focus here on the role of MYC, other stem cell markers including ALDH1A1, CD44, and CD133 could be examined in the future, along with other signaling pathways such as WNT which have been reported to affect sebaceous gland growth and differentiation [58].
MYC protein has been detected in each of our three cell lines by Western blot, at similar or greater levels as in two established Group 3 medulloblastoma cell lines that are known to be MYC-driven [59,60]. Several groups have shown that high MYC levels can alter glutamine metabolism in cancer, and sensitize tumor cells to therapies targeting glutamine [42,61,62,63]. Therefore, we treated all three cell lines with DON, a glutamine analogue shown to inhibit other MYC-amplified tumors [42]. DON inhibited the growth of all three lines in a dose-dependent fashion, providing support for an ongoing requirement for MYC in SebCA, and suggesting that it represents a potentially important therapeutic target. Additional studies targeting MYC in vivo, either directly or via the suppression of metabolic changes, are warranted.
In summary, we have generated three new SebCA cell lines, confirmed the ongoing expression of the sebaceous marker adipophilin in vitro, and shown that the cultures are useful for preclinical testing. One limitation of the lines is that they require maintenance in CRC media, as shifting them to high-serum conditions results in slower growth, which progresses to growth arrest. This growth arrest in serum may be due to cellular senescence; however, this was not directly evaluated. A final potential issue is that these cell lines have only been passaged 24 to 28 times, and, while they are still growing robustly at the last passage without signs of senescence, it remains undetermined whether they represent fully immortalized lines.
4. Materials and Methods
4.1. Immunohistochemistry of Sebaceous Carcinomas
Immunolabeling of formalin-fixed, paraffin-embedded (FFPE) tumor specimens was performed by the Johns Hopkins Department of Pathology clinical laboratory using 5 μm sections on a Ventana benchmark Ultra platform and ultraView kit reagents (Tucson, AZ, USA). Normal Meibomian gland was used as a positive control for adipophilin staining, while primary antibody was removed in negative controls. Slides were assessed by a board-certified pathologist (C.G.E).
4.2. Cell Line Establishment and Reagents
Three SebCA tumors were obtained from patients after their agreement to participate in research. The study was reviewed and received ethical approval by the Johns Hopkins Medicine Human Research Protection Program, with a consent protocol and form approved by the Institutional Review Board. Informed consent was obtained from all subjects involved in the study.
Fresh tumor was washed in phosphate-buffered saline solution (PBS) three times and minced, and then incubated with collagenase A solution (200 U/mL) in Hank’s balanced salt solution (HBSS) for 3 h. The dissociated tumor cells were centrifuged at 93× g for 5 min at room temperature (RT), and then supernatant was removed and cells were transferred to 60 mm cell culture dished and cultured in CRC medium at 37 °C and 5% CO2 conditions. The cells were sub-cultured routinely when cell density exceeded 80%. To prepare CRC conditioned medium, 3T3 fibroblast cells were cultured in F medium (25% F-12, 75% DMEM, 10% FBS, 1× GlutaMAX, and 5 µg/mL insulin) for 48–72 h. The cultured media were then collected and filtered. The filtered medium was mixed with F medium (1:1 ratio) containing 25 ng/mL hydrocortisone, 8.4 nmol Cholera toxin, 10 ng/mL human recombinant epidermal growth factor (EGF), and 5 µM ROCK inhibitor for use with SebCA cells.
Adipophilin antibody (#393A-14) and 5-Fluorouracil were purchased from Millipore Sigma (St. Louis, MO, USA); MYC (c-Myc) antibody (#5605), cleaved PARP antibody (#9541), and mitomycin C were obtained from Cell Signaling Technology (Danvers, MA, USA); and Beta-Actin antibody (SC-4778) was sourced from Santa Cruz Biotechnology (Dallas, TX, USA). 6-Diazo-5-oxo-L-norleucine (DON; #S8620) was acquired from Selleckchem (Houston, TX, USA). The Cell-Titer blue cell viability assay kit was purchased by Promega (Madison, IA, USA), and Thiazolyl Blue Tetrazolium Bromide (MTT) powder was obtained from Millipore Sigma (St. Lois, MO, USA).
4.3. Short Tandem Repeat (STR) Analysis
STR analysis of 17 independent genetic sites was conducted by the Genetic Resources Core Facility (Johns Hopkins, Baltimore, MD, USA) using a PowerPlex 18D short tandem repeat DNA analysis kit (Promega Corporation, Madison, WI, USA). Data were analyzed using Genemapper v5.0 software (Applied Biosystems; Waltham, MA, USA). Each PCR setup included both positive and negative controls. Verification of allele designation for each locus within the positive control kit was conducted for accuracy, and the negative water control was ensured to exhibit no amplification products.
4.4. Next-Generation Sequencing (NGS)
Manual microdissection of FFPE tissue sections followed by DNA extraction and next-generation sequencing (NGS) was performed in the Johns Hopkins Molecular Diagnostic Laboratory using standard clinical protocols and the UCSC version hg19 (NCBI build GRCh37) human reference sequence genome assembly. The Solid Tumor Panel used 435 cancer-related genes, and a complete list can be found at https://pathology.jhu.edu/jhml-services/assets/test-directory/SolidTumorPanel-II_GeneList_v9.0.pdf. URL last accessed on 21 September 2024. Sequences were examined for point mutations and small insertion/deletion mutations in all 435 loci, while, for 64 of these, copy number variations (CNVs) were also reported.
4.5. Cell-Titer Blue and MTT Assays
Cells were seeded onto 96-cell plates at a density of 500 cells per well and then incubated for 24 h, with six wells examined per experimental condition. For the evaluation of cell growth, cells were incubated with Cell-Titer blue solution (overnight incubation at 37 °C) on the indicated date. The fluorescence was measured at 560/590 nm for the Cell-Titer-blue-solution-added plates. To assess change in viable cell mass in therapeutic studies, cultures were treated with vehicle or the indicated concentrations of mitomycin, 5-FU, and DON diluted in complete medium for 3–5 days. The products formed by addition of the MTT solutions were dissolved in dimethyl sulfoxide (DMSO) and the absorbance was measured at 570 nm. The data are presented as a percentage of the control group.
4.6. Anchorage-Dependent Colony Formation Assay
Five hundred SebCA cells were seeded onto 6-well plate per well, and then incubated for two weeks until colonies were visualized. Colonies were fixed with 100% methanol for 10 min, and stained with 0.002% crystal violet solutions (Sigma, St. Louis, MO, USA), and washed with deionized water two times. The colonies were imaged and counted using the ImageJ program (v. 1.54k; National Institutes of Health, Bethesda, MD, USA).
4.7. Western Blot Analysis
Established SebCA cells were lysed with RIPA buffer (Sigma, St. Louis, MO, USA) with protease and phosphatase inhibitor cocktails (Roche, Indianapolis, IN, USA). Normalized proteins were subjected to SDS-PAGE and electrically transferred to polyvinylidene difluoride (PVDF) membranes (Bio-Rad, Hercules, CA, USA). Membranes were blocked with 3% BSA in tris-buffered saline (TBS) containing 0.01% of Tween-20 for 1 h. The membranes were incubated with primary antibodies diluted 1:1000 in 3% BSA in TBST overnight at 4 °C with the antibodies c-MYC, adipophilin, cleaved PARP, and β-actin, and then secondary antibodies were incubated at 1:5000 dilution for 1 h at RT. Membranes were washed three times with TBST and visualized by using enhanced chemiluminescence (ECL).
4.8. Statistical Analysis
For all in vitro experiments, the number of replicates is described in Results, and data are displayed as means ± standard deviation (SD). The data were calculated and analyzed with GraphPad Prism (v. 10.1.2; San Diego, CA, USA). Statistical significance of in vitro or in vivo data was determined using a one-way ANOVA, followed by Dunnett’s multiple comparisons test. A p-value of less than 0.05 was considered significant.
4.1. Immunohistochemistry of Sebaceous Carcinomas
Immunolabeling of formalin-fixed, paraffin-embedded (FFPE) tumor specimens was performed by the Johns Hopkins Department of Pathology clinical laboratory using 5 μm sections on a Ventana benchmark Ultra platform and ultraView kit reagents (Tucson, AZ, USA). Normal Meibomian gland was used as a positive control for adipophilin staining, while primary antibody was removed in negative controls. Slides were assessed by a board-certified pathologist (C.G.E).
4.2. Cell Line Establishment and Reagents
Three SebCA tumors were obtained from patients after their agreement to participate in research. The study was reviewed and received ethical approval by the Johns Hopkins Medicine Human Research Protection Program, with a consent protocol and form approved by the Institutional Review Board. Informed consent was obtained from all subjects involved in the study.
Fresh tumor was washed in phosphate-buffered saline solution (PBS) three times and minced, and then incubated with collagenase A solution (200 U/mL) in Hank’s balanced salt solution (HBSS) for 3 h. The dissociated tumor cells were centrifuged at 93× g for 5 min at room temperature (RT), and then supernatant was removed and cells were transferred to 60 mm cell culture dished and cultured in CRC medium at 37 °C and 5% CO2 conditions. The cells were sub-cultured routinely when cell density exceeded 80%. To prepare CRC conditioned medium, 3T3 fibroblast cells were cultured in F medium (25% F-12, 75% DMEM, 10% FBS, 1× GlutaMAX, and 5 µg/mL insulin) for 48–72 h. The cultured media were then collected and filtered. The filtered medium was mixed with F medium (1:1 ratio) containing 25 ng/mL hydrocortisone, 8.4 nmol Cholera toxin, 10 ng/mL human recombinant epidermal growth factor (EGF), and 5 µM ROCK inhibitor for use with SebCA cells.
Adipophilin antibody (#393A-14) and 5-Fluorouracil were purchased from Millipore Sigma (St. Louis, MO, USA); MYC (c-Myc) antibody (#5605), cleaved PARP antibody (#9541), and mitomycin C were obtained from Cell Signaling Technology (Danvers, MA, USA); and Beta-Actin antibody (SC-4778) was sourced from Santa Cruz Biotechnology (Dallas, TX, USA). 6-Diazo-5-oxo-L-norleucine (DON; #S8620) was acquired from Selleckchem (Houston, TX, USA). The Cell-Titer blue cell viability assay kit was purchased by Promega (Madison, IA, USA), and Thiazolyl Blue Tetrazolium Bromide (MTT) powder was obtained from Millipore Sigma (St. Lois, MO, USA).
4.3. Short Tandem Repeat (STR) Analysis
STR analysis of 17 independent genetic sites was conducted by the Genetic Resources Core Facility (Johns Hopkins, Baltimore, MD, USA) using a PowerPlex 18D short tandem repeat DNA analysis kit (Promega Corporation, Madison, WI, USA). Data were analyzed using Genemapper v5.0 software (Applied Biosystems; Waltham, MA, USA). Each PCR setup included both positive and negative controls. Verification of allele designation for each locus within the positive control kit was conducted for accuracy, and the negative water control was ensured to exhibit no amplification products.
4.4. Next-Generation Sequencing (NGS)
Manual microdissection of FFPE tissue sections followed by DNA extraction and next-generation sequencing (NGS) was performed in the Johns Hopkins Molecular Diagnostic Laboratory using standard clinical protocols and the UCSC version hg19 (NCBI build GRCh37) human reference sequence genome assembly. The Solid Tumor Panel used 435 cancer-related genes, and a complete list can be found at https://pathology.jhu.edu/jhml-services/assets/test-directory/SolidTumorPanel-II_GeneList_v9.0.pdf. URL last accessed on 21 September 2024. Sequences were examined for point mutations and small insertion/deletion mutations in all 435 loci, while, for 64 of these, copy number variations (CNVs) were also reported.
4.5. Cell-Titer Blue and MTT Assays
Cells were seeded onto 96-cell plates at a density of 500 cells per well and then incubated for 24 h, with six wells examined per experimental condition. For the evaluation of cell growth, cells were incubated with Cell-Titer blue solution (overnight incubation at 37 °C) on the indicated date. The fluorescence was measured at 560/590 nm for the Cell-Titer-blue-solution-added plates. To assess change in viable cell mass in therapeutic studies, cultures were treated with vehicle or the indicated concentrations of mitomycin, 5-FU, and DON diluted in complete medium for 3–5 days. The products formed by addition of the MTT solutions were dissolved in dimethyl sulfoxide (DMSO) and the absorbance was measured at 570 nm. The data are presented as a percentage of the control group.
4.6. Anchorage-Dependent Colony Formation Assay
Five hundred SebCA cells were seeded onto 6-well plate per well, and then incubated for two weeks until colonies were visualized. Colonies were fixed with 100% methanol for 10 min, and stained with 0.002% crystal violet solutions (Sigma, St. Louis, MO, USA), and washed with deionized water two times. The colonies were imaged and counted using the ImageJ program (v. 1.54k; National Institutes of Health, Bethesda, MD, USA).
4.7. Western Blot Analysis
Established SebCA cells were lysed with RIPA buffer (Sigma, St. Louis, MO, USA) with protease and phosphatase inhibitor cocktails (Roche, Indianapolis, IN, USA). Normalized proteins were subjected to SDS-PAGE and electrically transferred to polyvinylidene difluoride (PVDF) membranes (Bio-Rad, Hercules, CA, USA). Membranes were blocked with 3% BSA in tris-buffered saline (TBS) containing 0.01% of Tween-20 for 1 h. The membranes were incubated with primary antibodies diluted 1:1000 in 3% BSA in TBST overnight at 4 °C with the antibodies c-MYC, adipophilin, cleaved PARP, and β-actin, and then secondary antibodies were incubated at 1:5000 dilution for 1 h at RT. Membranes were washed three times with TBST and visualized by using enhanced chemiluminescence (ECL).
4.8. Statistical Analysis
For all in vitro experiments, the number of replicates is described in Results, and data are displayed as means ± standard deviation (SD). The data were calculated and analyzed with GraphPad Prism (v. 10.1.2; San Diego, CA, USA). Statistical significance of in vitro or in vivo data was determined using a one-way ANOVA, followed by Dunnett’s multiple comparisons test. A p-value of less than 0.05 was considered significant.
5. Conclusions
These new ocular adnexal SebCA cell lines should prove useful for interrogating the functional role of MYC and other possible molecular drivers, and for preclinical testing in vitro. Ultimately, this will serve to help develop targeted therapy for SebCA to minimize the need for aggressive surgical resection and allow a better treatment for metastatic disease. |
Title: Nebulized alteplase in coronavirus disease 2019 pneumonia: a case series | Body:
Background
At the time when the coronavirus disease 2019 (COVID-19) pandemic first emerged, there was no established causal pharmacological treatment for the virus [1]. Most infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifested with mild to moderate symptoms. However, in up to 5% of patients, the disease severity ranged from severe pneumonia and acute respiratory distress syndrome (ARDS) to shock, multiple organ failure, and death [2].
Severe COVID-19 cases are primarily driven by a cytokine storm, along with excessive endothelial activation and coagulopathies, which elevate the risk of thrombosis, including pulmonary embolism and microthrombosis[3–7].
The tissue plasminogen activator (tPA) alteplase is a systemically applied fibrinolytic agent frequently applied in patients with acute pulmonary embolism or stroke. The intravenous application has also been reviewed in small number of patients suffering from ARDS with or without severe COVID-19 [8–15]. Fibrinolytic agents are sometimes applied “off-label” through inhalation to reduce the systemic drug effects, including in patients with acute respiratory distress syndrome (ARDS) [16–18]. Therefore, inhalation with tPA poses a theoretically sound countermeasure to prevent or treat thromboembolic events in patients with COVID-19.
Through analyzing this retrospective case series at hand descriptively, we aim to draw attention to this subject matter and discuss the possible application of nebulized inhaled fibrinolytics in patients with severe COVID-19 infection.
Case presentation
All patients were white males which tested positive for SARS-CoV-2 in polymerase chain reaction (PCR) tests and exhibited radiological signs of ground-glass opacities (GGOs) with or without consolidations in computed tomography scans (CT scans) of the chest. Pulmonary embolism was excluded in all patients by pulmonary contrast-enhanced angiograms. All cases received optimal supportive therapy as recommended by the World Health Organization (WHO) and international guidelines in 2020 [1]. Accordingly, we administered dexamethasone 6 mg intravenously, as well as antipyretics, analgetics, and low-molecular heparin in prophylactic doses. All patients presented with elevated fibrinogen, d-dimers, and inflammatory markers c-reactive protein (CRP) and interleukin (IL)-6 and received treatment for respiratory failure, including high-flow oxygen and prolonged non-invasive ventilation (Fig. 1). The laboratory results for coagulation and inflammatory parameters over time are detailed in Supplementary Table 1.Fig. 1Levels of CRP, platelet count, d-dimer, and fibrinogen prior to and following inhalation of alteplase. All patients received inhaled alteplase at day 0. Patient 5 received an additional inhalation on days 6–10
Case 1
A 68-year-old male with a history of coronary and peripheral artery disease, chronic kidney failure [estimated glomerular filtration rate(eGFR) of 39.21 mL/min/1.73 m2], hypertension, multiple myeloma (active treatment with velcade/bendamustine/dexamethasone), hypothyroidism, hyperlipidemia, and obstructive sleep apnea, smoking [60 packs per year (PY)] presented to the hospital with three days of shortness of breath and chest pain. Oxygen therapy was started with 4 L/min. Due to progressive respiratory failure, a high-flow nasal cannula (HFNC) was administered, as the patient did not tolerate non-invasive ventilation (NIV). Antiplatelet therapy included acetyl salicylic acid (ASA) and ticagrelor. Alteplase was inhaled at 5 mg three times a day (TID) on days 8–12. Inflammatory markers, including CRP and IL-6, improved, similarly to fibrinogen and d-dimer values. HFNC was discontinued on day 13 and replaced with oxygen therapy at 3–4 L/min until day 21. The patient was then discharged to a rehabilitation clinic.
Case 2
A 51-year-old male with a history of non-Hodgkin's lymphoma (in remission) presented with a week of shortness of breath and loss of smell and taste. Extensive NIV therapy was established with 8 mbar positive end-expiratory pressure (PEEP), 2 mbar pressure support (PS), and 60% oxygen flow. During short therapy breaks, oxygen therapy was administered with 8 L/min. Due to an increase in d-dimer to 12.2 μg/mL on day 6, a follow-up CT scan was performed, revealing no signs of pulmonary embolism but showing bilateral ground-glass opacities and consolidations. Because of the significant increase in d-dimer, the dose of low-molecular weight heparin (Enoxaparin) was increased to 8000 IU twice a day (BID) until discharge. Additionally, alteplase 10 mg was inhaled TID on days 5 and 6, and 5 mg TID on days 7–9. NIV therapy was discontinued on day 10 and switched to oxygen insufflation via nasal cannula at 4 L/min. This was terminated on day 13, and the patient was subsequently discharged home.
Case 3
A 66-year-old male with a history of hypertension, prostate cancer, and active urinary cancer was admitted after 5 days of fever, shortness of breath, and slight confusion. Following initial oxygen insufflation, NIV therapy was started on day 3, along with alteplase 5 mg TID for a total of 5 days. On day 8, NIV was switched to HFNC and, after 8 days, substituted by oxygen insufflation per nasal cannula. The patient was discharged home on day 21.
Case 4
A 63-year-old male was admitted with 5 days of shortness of breath. He had a history of atrial fibrillation, hypertension, type 2 diabetes, severe chronic obstructive pulmonary disease with long-term oxygen therapy (LTOT: 2 L/min), and pulmonary hypertension. CT scan of the chest showed GGOs, paraseptal emphysema, a pulmonary mass in the left upper lung, and enlargement of the pulmonary trunk and arteries. The patient did not tolerate NIV; therefore, HFNC was established. Inhalative therapy with alteplase 5 mg was given TID for a total of 5 days. Laboratory findings showed an improvement of the inflammatory markers in the following days after admission, but later the laboratory findings and the general condition of the patient worsened gradually. On day 8 of hospitalization, the patient passed away due to respiratory failure.
Case 5
A 59-year-old male was admitted with a history of hypertension and obesity. He reported shortness of breath and reduced general condition. CT scan of the chest showed extensive GGOs and slight pleural effusion on the left side. HFNC was initiated, followed by NIV. On day 3, the patient inhaled alteplase 5 mg TID for the following 3 days. The therapy was repeated 4 days later due to a marked increase in d-dimer for 5 more days. NIV was discontinued on day 11 and switched to oxygen insufflation until day 31. Finally, the patient was discharged 34 days after admission.
Case 1
A 68-year-old male with a history of coronary and peripheral artery disease, chronic kidney failure [estimated glomerular filtration rate(eGFR) of 39.21 mL/min/1.73 m2], hypertension, multiple myeloma (active treatment with velcade/bendamustine/dexamethasone), hypothyroidism, hyperlipidemia, and obstructive sleep apnea, smoking [60 packs per year (PY)] presented to the hospital with three days of shortness of breath and chest pain. Oxygen therapy was started with 4 L/min. Due to progressive respiratory failure, a high-flow nasal cannula (HFNC) was administered, as the patient did not tolerate non-invasive ventilation (NIV). Antiplatelet therapy included acetyl salicylic acid (ASA) and ticagrelor. Alteplase was inhaled at 5 mg three times a day (TID) on days 8–12. Inflammatory markers, including CRP and IL-6, improved, similarly to fibrinogen and d-dimer values. HFNC was discontinued on day 13 and replaced with oxygen therapy at 3–4 L/min until day 21. The patient was then discharged to a rehabilitation clinic.
Case 2
A 51-year-old male with a history of non-Hodgkin's lymphoma (in remission) presented with a week of shortness of breath and loss of smell and taste. Extensive NIV therapy was established with 8 mbar positive end-expiratory pressure (PEEP), 2 mbar pressure support (PS), and 60% oxygen flow. During short therapy breaks, oxygen therapy was administered with 8 L/min. Due to an increase in d-dimer to 12.2 μg/mL on day 6, a follow-up CT scan was performed, revealing no signs of pulmonary embolism but showing bilateral ground-glass opacities and consolidations. Because of the significant increase in d-dimer, the dose of low-molecular weight heparin (Enoxaparin) was increased to 8000 IU twice a day (BID) until discharge. Additionally, alteplase 10 mg was inhaled TID on days 5 and 6, and 5 mg TID on days 7–9. NIV therapy was discontinued on day 10 and switched to oxygen insufflation via nasal cannula at 4 L/min. This was terminated on day 13, and the patient was subsequently discharged home.
Case 3
A 66-year-old male with a history of hypertension, prostate cancer, and active urinary cancer was admitted after 5 days of fever, shortness of breath, and slight confusion. Following initial oxygen insufflation, NIV therapy was started on day 3, along with alteplase 5 mg TID for a total of 5 days. On day 8, NIV was switched to HFNC and, after 8 days, substituted by oxygen insufflation per nasal cannula. The patient was discharged home on day 21.
Case 4
A 63-year-old male was admitted with 5 days of shortness of breath. He had a history of atrial fibrillation, hypertension, type 2 diabetes, severe chronic obstructive pulmonary disease with long-term oxygen therapy (LTOT: 2 L/min), and pulmonary hypertension. CT scan of the chest showed GGOs, paraseptal emphysema, a pulmonary mass in the left upper lung, and enlargement of the pulmonary trunk and arteries. The patient did not tolerate NIV; therefore, HFNC was established. Inhalative therapy with alteplase 5 mg was given TID for a total of 5 days. Laboratory findings showed an improvement of the inflammatory markers in the following days after admission, but later the laboratory findings and the general condition of the patient worsened gradually. On day 8 of hospitalization, the patient passed away due to respiratory failure.
Case 5
A 59-year-old male was admitted with a history of hypertension and obesity. He reported shortness of breath and reduced general condition. CT scan of the chest showed extensive GGOs and slight pleural effusion on the left side. HFNC was initiated, followed by NIV. On day 3, the patient inhaled alteplase 5 mg TID for the following 3 days. The therapy was repeated 4 days later due to a marked increase in d-dimer for 5 more days. NIV was discontinued on day 11 and switched to oxygen insufflation until day 31. Finally, the patient was discharged 34 days after admission.
Discussion
We report a case series of five patients with severe COVID-19 infection suffering from respiratory failure treated with off-label inhalation of tPA (alteplase). Four patients improved clinically, while one died due to multiple organ failure. No acute adverse effects were observed. Respiratory failure parameters, d-dimer and fibrinogen improved in all surviving patients following off-label treatment (Fig. 1).
Fibrinolytics are commonly used in patients suffering from extensive pulmonary embolism. Intravenous application is associated with significant complications: in the largest randomized study, involving 506 patients undergoing tenecteplase treatment for intermediate-risk pulmonary embolism, 32 (6.3%) suffered major extracranial bleeding and 10 (2%) hemorrhagic stroke [19]. Severe COVID-19 infection is often associated with a state of hyper-coagulopathy; resulting in microthrombosis of pulmonary arterioles not detected by contrast CT scans [4–7]. Some reports suggest that intravenous fibrinolytics may be beneficial for patients with COVID-19, both with and without ARDS [8–15]. However, the risk of significant bleeding appears elevated, particularly in the presence of enhanced fibrinolytic-type disseminated intravascular coagulation (DIC) [20]. As bleeding complications pose a life-threatening condition, inhalative application of fibrinolytics may be a safer alternative for patients with severe COVID-19 pneumonia [16, 20, 21]. Indeed, Wu et al. demonstrated in a small case series of 13 patients with moderate to severe COVID-19 infection that inhalation of 5 mg plasminogen twice-daily often improved oxygen saturation and heart rate without reporting major bleeding complications [11].
Off-label use of recombinant tPA is frequently used in children with plastic bronchitis in different regimens with single dosages ranging from 5 to 12 mg per inhalation without major bleeding complications [18, 22]. In this case series, dosages of 5–10 mg three times daily were used without any bleeding complications. The application route may not only have a safer complications profile but also result in a higher concentration of tPA in the lung. The effect may be through direct fibrinolytic effects on microthrombi in arterioles and on exceeding fibrin detritus in the alveoli resulting in increased pulmonary circulation and ventilation. Furthermore, previous studies demonstrated anti-inflammatory effects of tPA, which may be beneficial in patients with COVID-19 [23, 24].
On the other hand, the inhalative application may have several drawbacks. First, the application of inhaled therapeutics may cause an increase in virus aerosol in the patients' room, posing an additional risk for healthcare workers. Therefore, an exhalation filter was used during application. Second, the amount of deposition remains unknown, as particles may impact in the upper and small airways.
Limitations
We acknowledge several limitations in our study. Firstly, this is a case report series, which inherently limits the generalizability of the findings due to the small sample size and lack of control groups. The concurrent use of low-molecular weight heparin (LMWH) and glucocorticoids, such as dexamethasone, alongside inhaled alteplase, presents a potential bias in our results. This combination therapy complicates the isolation of the specific effects of inhaled alteplase. Therefore, the observed improvements in clinical outcomes cannot be solely attributed to alteplase.
Conclusion
Our findings primarily underscore the possible safety of inhaled alteplase in patients with severe COVID-19 pneumonia. While clinical improvements were observed, these cannot be definitively attributed to alteplase due to the concurrent use of other therapies. Given the encouraging safety profile of inhaled alteplase observed in this study, future research should focus on randomized controlled trials to evaluate its efficacy. These studies should aim to isolate the effects of inhaled alteplase by minimizing the use of concurrent therapies. Additionally, larger-scale studies could help establish standardized dosing regimens and identify specific patient populations that may benefit the most from this treatment. The insights gained from our study can guide the design and implementation of these future investigations.
Supplementary Information
Supplementary Material 1. |
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